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Expanding the family of US Consumer Price Indexes

Read more about Expanding the family of US Consumer Price Indexes

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English

1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Expanding the family of US Consumer Price Indexes

Anya Stockburger, Bill Johnson, Joshua Klick, Paul Liegey, Robert Martin,

Bureau of Labor Statistics

Meeting of the Group of Experts on CPIs

June 8, 2023

2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

CPI Family of Indexes - Concepts

CPI-U

Chained CPI-U

CPI-W, R-CPI-E

R-CPI-Income

• Best for escalationHousehold Cost Indexes

Measure change in purchasing power of the average dollar of expenditure

Measure tied to outlays explicitly related to household purchasing

3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Outline

Motivation

Income-based indexes

Household Cost Indexes

Next steps

4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Motivation – Increased need for data granularity

 Committee on National Statistics recommendation  Federal Reserve Bank interest  Office of Management and Budget, Bureau of

Economic Analysis, and other government interest  General user interest (major media)  Publications: Initial working paper, Spotlight on

Statistics

https://www.bls.gov/osmr/research-papers/2021/ec210030.htm

https://www.bls.gov/spotlight/2022/inflation-experiences-for-lower-and-higher-income-households/home.htm

5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

CPI by Income Methodology

$12,000

$118,000

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Q1 Q5

Median Equivalized Income (Interview Survey - 2021)

Expenditure weights Group CE respondents into weighted ranking of equivalized income quintiles

Prices/rents All lower-level data the same (prices, outlets, rents)

Index aggregation Lowe, Tornqvist aggregation from lowest-level basic indexes

6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Snapshot of spending weights by population, 2019-2020 biennial expenditure weight share, equivalized income

0% 5% 10% 15% 20% 25% 30%

Rent

Food at home

Motor fuel

Owner's equivalent rent

Vehicles and maintenance

Food away from home

Recreation

Q1 U Q5

7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Annualized Inflation Gap Annualized inflation rate, CPI by income quintile, Lowe Formula, December 2005 -

December 2022

2.6

2.5

2.4

2.3

2.4

2.1

2.2

2.3

2.4

2.5

2.6

2.7

Q1 Q2 Q3 Q4 Q5

Income Quintiles Urban

8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Inflation Gap Variation Lowest income quintile – Highest income quintile

Annual 12-month percent change December 2006 – December 2022

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

Equivalized income Unadjusted income

9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

2022 Year over year inflation gap (Q1-Q5) CPI contributions to All-Items (percentage)

-5 0 5 10 15

Rent primary residence Gasoline (all types)

Electricity Utility (piped) gas service

Cigarettes Motor vehicle insurance

Limited service meals/snacks Nonfrozen noncarbonated juices…

Cable & satellite tv/radio Chicken

Club membership Child care & nursery school

Owners' rent secondary residence Leased cars and trucks

Full service meals and snacks Commercial Health Insurance

Owners' rent primary residence Lodging away from home

Airline fare New vehicles

10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Limitations and Future Improvements

 Lower-level price heterogeneity Re-weighting housing prices shows little impact (Malloy,

Larson 2021) 2/3rd price change in grocery items missed (Jaravel 2019)

 BLS future research Further investigate housing adjustments Re-weighting alternative data (gasoline, new vehicles) Interested in a scanner data program (CNSTAT

recommendation), but funding…

11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Household Cost Index

 Inspired by Office for National Statistics and Statistics New Zealand

 Definition: Measure the change in cash outflows required, on average, for households to access the goods and services they consume

 Methodology:  Household (democratic) aggregation,  Payments-approach to owner-occupied housing  Urban population

12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Household (Democratic) Aggregation

 Create household-level expenditure shares using Consumer Expenditure Survey data  Eligible expenditures from the Diary survey imputed to the Interview sample

using a matching procedure based on Hobijn, et. al. (2009)

 Aggregation across households  Lowe formula with lagged expenditure weights

 Limitations  Infrequent purchases (particularly vehicles) pose a challenge  Limit to household with 4 quarters of expenditures (limits use of data to about

a third)

13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Payments Approach – Mortgage Interest Payment

 Weights Consumer Expenditure Survey

 Prices Mortgage interest payment index =

Debt index * Interest rate index Data sources: • Federal Housing Finance Agency’s All Transactions House Price

Index • Freddie Mac Primary Mortgage Market Survey

14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Payments Approach – Property Tax Payments

 Weights Consumer Expenditure Survey

 Prices Property Tax Payment Index =

Total property tax payments * Constant quality Total housing stock value home price index

Data source: CE

15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

HCI – Relative Importance Major Group CPI-U HCI-U

Food and Beverages 15.2 20.1 Housing 42.4 34.3 Apparel 2.7 3.1 Transportation 15.2 14.3 Medical 8.9 11.1 Recreation 5.8 6.6 Education and Comm. 6.8 6.7 Other 3.2 3.7 Reference Period 2017-18 2019 CE Sample Full 4-quarter

16 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

HCI – Index Results

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

lowe-u (ew, req) lowe-u (ew, pay) cpi-u hci-u

Average 12-month % change CPI-U 1.86% HCI (Payments Approach + Household Aggregation)

1.52%

HCI-U (Payments Approach Only) 1.48%

17 — U.S. BUREAU OF LABOR STATISTICS • bls.gov17 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Limitations - HCI

 Household aggregation Infrequent purchases (challenge especially with

Tornqvist) Include in HCI given small impact?

 Payments approach Investigate a microdata approach for mortgage

interest index Investigate including mortgage principal

18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

What’s next?

Improve methodology

Income-group specific lower- level indexes

Next step for HCI research?

Stakeholder outreach

Group of Experts BLS advisory committees Federal Committee on Statistical Methodology

Publish regular updates

R-CPI-Income C-CPI-Income

HCI?

Contact Information

19 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Anya Stockburger Chief, Branch of Revision Methodology

Division of Consumer Price Indexes www.bls.gov/cpi

[email protected]

Expanding the family of US Consumer Price Indexes
CPI Family of Indexes - Concepts
Outline
Motivation – Increased need for data granularity
CPI by Income Methodology
Snapshot of spending weights by population, 2019-2020 biennial expenditure weight share, equivalized income
Annualized Inflation Gap�Annualized inflation rate, CPI by income quintile, Lowe Formula, December 2005 - December 2022
Inflation Gap Variation�Lowest income quintile – Highest income quintile�Annual 12-month percent change�December 2006 – December 2022
2022 Year over year inflation gap (Q1-Q5) CPI contributions to All-Items (percentage)�
Limitations and Future Improvements
Household Cost Index
Household (Democratic) Aggregation
Payments Approach – Mortgage Interest Payment
Payments Approach – Property Tax Payments
HCI – Relative Importance
HCI – Index Results
Limitations - HCI
What’s next?
Contact Information

Expanding the family of US Consumer Price Indexes

Read more about Expanding the family of US Consumer Price Indexes

Languages and translations

English

1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Expanding the family of US Consumer Price Indexes

Anya Stockburger, Bill Johnson, Joshua Klick, Paul Liegey, Robert Martin,

Bureau of Labor Statistics

Meeting of the Group of Experts on CPIs

June 8, 2023

2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

CPI Family of Indexes - Concepts

CPI-U

Chained CPI-U

CPI-W, R-CPI-E

R-CPI-Income

• Best for escalationHousehold Cost Indexes

Measure change in purchasing power of the average dollar of expenditure

Measure tied to outlays explicitly related to household purchasing

3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Outline

Motivation

Income-based indexes

Household Cost Indexes

Next steps

4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Motivation – Increased need for data granularity

 Committee on National Statistics recommendation  Federal Reserve Bank interest  Office of Management and Budget, Bureau of

Economic Analysis, and other government interest  General user interest (major media)  Publications: Initial working paper, Spotlight on

Statistics

https://www.bls.gov/osmr/research-papers/2021/ec210030.htm

https://www.bls.gov/spotlight/2022/inflation-experiences-for-lower-and-higher-income-households/home.htm

5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

CPI by Income Methodology

$12,000

$118,000

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Q1 Q5

Median Equivalized Income (Interview Survey - 2021)

Expenditure weights Group CE respondents into weighted ranking of equivalized income quintiles

Prices/rents All lower-level data the same (prices, outlets, rents)

Index aggregation Lowe, Tornqvist aggregation from lowest-level basic indexes

6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Snapshot of spending weights by population, 2019-2020 biennial expenditure weight share, equivalized income

0% 5% 10% 15% 20% 25% 30%

Rent

Food at home

Motor fuel

Owner's equivalent rent

Vehicles and maintenance

Food away from home

Recreation

Q1 U Q5

7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Annualized Inflation Gap Annualized inflation rate, CPI by income quintile, Lowe Formula, December 2005 -

December 2022

2.6

2.5

2.4

2.3

2.4

2.1

2.2

2.3

2.4

2.5

2.6

2.7

Q1 Q2 Q3 Q4 Q5

Income Quintiles Urban

8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Inflation Gap Variation Lowest income quintile – Highest income quintile

Annual 12-month percent change December 2006 – December 2022

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

Equivalized income Unadjusted income

9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

2022 Year over year inflation gap (Q1-Q5) CPI contributions to All-Items (percentage)

-5 0 5 10 15

Rent primary residence Gasoline (all types)

Electricity Utility (piped) gas service

Cigarettes Motor vehicle insurance

Limited service meals/snacks Nonfrozen noncarbonated juices…

Cable & satellite tv/radio Chicken

Club membership Child care & nursery school

Owners' rent secondary residence Leased cars and trucks

Full service meals and snacks Commercial Health Insurance

Owners' rent primary residence Lodging away from home

Airline fare New vehicles

10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Limitations and Future Improvements

 Lower-level price heterogeneity Re-weighting housing prices shows little impact (Malloy,

Larson 2021) 2/3rd price change in grocery items missed (Jaravel 2019)

 BLS future research Further investigate housing adjustments Re-weighting alternative data (gasoline, new vehicles) Interested in a scanner data program (CNSTAT

recommendation), but funding…

11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Household Cost Index

 Inspired by Office for National Statistics and Statistics New Zealand

 Definition: Measure the change in cash outflows required, on average, for households to access the goods and services they consume

 Methodology:  Household (democratic) aggregation,  Payments-approach to owner-occupied housing  Urban population

12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Household (Democratic) Aggregation

 Create household-level expenditure shares using Consumer Expenditure Survey data  Eligible expenditures from the Diary survey imputed to the Interview sample

using a matching procedure based on Hobijn, et. al. (2009)

 Aggregation across households  Lowe formula with lagged expenditure weights

 Limitations  Infrequent purchases (particularly vehicles) pose a challenge  Limit to household with 4 quarters of expenditures (limits use of data to about

a third)

13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Payments Approach – Mortgage Interest Payment

 Weights Consumer Expenditure Survey

 Prices Mortgage interest payment index =

Debt index * Interest rate index Data sources: • Federal Housing Finance Agency’s All Transactions House Price

Index • Freddie Mac Primary Mortgage Market Survey

14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Payments Approach – Property Tax Payments

 Weights Consumer Expenditure Survey

 Prices Property Tax Payment Index =

Total property tax payments * Constant quality Total housing stock value home price index

Data source: CE

15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

HCI – Relative Importance Major Group CPI-U HCI-U

16 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

HCI – Index Results

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

lowe-u (ew, req) lowe-u (ew, pay) cpi-u hci-u

Average 12-month % change CPI-U 1.86% HCI (Payments Approach + Household Aggregation)

1.52%

HCI-U (Payments Approach Only) 1.48%

17 — U.S. BUREAU OF LABOR STATISTICS • bls.gov17 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Limitations - HCI

 Household aggregation Infrequent purchases (challenge especially with

Tornqvist) Include in HCI given small impact?

 Payments approach Investigate a microdata approach for mortgage

interest index Investigate including mortgage principal

18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

What’s next?

Improve methodology

Income-group specific lower- level indexes

Next step for HCI research?

Stakeholder outreach

Group of Experts BLS advisory committees Federal Committee on Statistical Methodology

Publish regular updates

R-CPI-Income C-CPI-Income

HCI?

Contact Information

19 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Anya Stockburger Chief, Branch of Revision Methodology

Division of Consumer Price Indexes www.bls.gov/cpi

[email protected]

Expanding the family of US Consumer Price Indexes
CPI Family of Indexes - Concepts
Outline
Motivation – Increased need for data granularity
CPI by Income Methodology
Snapshot of spending weights by population, 2019-2020 biennial expenditure weight share, equivalized income
Annualized Inflation Gap�Annualized inflation rate, CPI by income quintile, Lowe Formula, December 2005 - December 2022
Inflation Gap Variation�Lowest income quintile – Highest income quintile�Annual 12-month percent change�December 2006 – December 2022
2022 Year over year inflation gap (Q1-Q5) CPI contributions to All-Items (percentage)�
Limitations and Future Improvements
Household Cost Index
Household (Democratic) Aggregation
Payments Approach – Mortgage Interest Payment
Payments Approach – Property Tax Payments
HCI – Relative Importance
HCI – Index Results
Limitations - HCI
What’s next?
Contact Information

Presentation

Read more about Presentation

Languages and translations

English

Measuring Sexual Orientation and Gender Identity on the American Community Survey UNECE Group of Experts on Gender Statistics

4. Measuring sex and gender

Andrew Roberts (he/him)

Chief, Sex and Age Statistics Branch

United States Census Bureau

10-12 May 2023

Sexual Orientation and Gender Identity (SOGI) Data Collection • The Census Bureau continues to engage with stakeholders on sexual orientation and

gender identity data, including international peers

• Quarterly meeting with SOGI advocacy and expert groups to share progress and solicit input on plans

• Staff from across the Census Bureau participate in the Federal Committee on Statistical Methodology SOGI Working Group

• Same-sex relationship categories added to American Community Survey, Current Population Survey, Survey of Income and Program Participation, and 2020 Census

• SOGI questions added to the Household Pulse Survey in July 2021

American Community Survey - Overview

• Replaced the Decennial Census “long form” between the 2000 and 2010 Censuses

• Largest demographic survey conducted by United States federal government

• Sample of ~3.5 million households per year divided into 12 monthly panels

• Data are collected via Internet, mailed paper questionnaires, and persona interviews; includes special enumerations for group quarters, Remote Alaska, and tribal lands

• Data are used to distribute more than $675 billion in federal funds annually

• Used by state and local governments, communities, and businesses to assess past and future demographic and economic trends

• Data are released in 1-year and 5-year products, with 5-year combined data going down to very small levels of geography

• Participation is required by law

• Content must have a statutory or regulatory justification

How a Question Becomes Part of the ACS

Proposal

•A federal agency proposes a new or changed question.

•Request specifies frequency, geographic precision needed, and consideration of other sources.

•OMB and Census Bureau decide whether the request has merit.

Testing

•Wording options are created and tested.

•Question performance is evaluated in a field test.

Evaluation

• Test results are reviewed by the Census Bureau and requesting federal agency.

• The Census Bureau solicits public comment through a Federal Register Notice.

Decision

•A final decision is made in consultation with the OMB and Interagency Council on Statistical Policy Subcommittee on the ACS.

• If approved, the Census Bureau implements the change.

Request for SOGI Content on the ACS

• U.S. Department of Justice (DOJ) submitted a request in December 2022 to add SOGI content to the ACS. This request was deemed to meet the strict requirements for adding content to the ACS.

• Working with DOJ to determine specific requirements of data needs, including:

• Level of reporting for sexual orientation/gender

• Level of geographic detail needed for data products

• Level of accuracy needed for survey estimates

• Specific concepts to be measured (e.g., identity vs expression)

• Degree to which data about gender can supplant sex data

Cognitive Testing • Cognitive testing is planned to begin sometime in 2023

• Goals of cognitive testing will include:

• Evaluation of Spanish translations

• Evaluation of impacts of proxy reporting

• Evaluation of differences between self-response modes and personal interview modes

• Comprehension of non-gendered relationship categories (e.g., “Child” vs “Son or daughter,” “Sibling” vs “Brother or sister,” “Child-in-law” vs “Son- or daughter-in-law”)

Field Test Design

• Field testing planned for 2024

• Self-Response test with personal interviews for non- respondents if funding allows

• Two treatments and potentially a control treatment

• Reinterview of respondents to determine response reliability

Field Testing Goals • Evaluation of question designs and placement

• Evaluation of question reliability

• Evaluation of impact of including a gender question on the sex distribution

• Evaluation of self-response modes vs personal interview modes

• Evaluation of proxy interviews

• Evaluation of write-in responses

• Evaluation of item missing data rates

• Evaluation of survey break off rates

Analysis and Results • Following the conclusion of field testing, the Census Bureau will analyze

data to answer our research questions

• Hope to have conclusions available by the end of 2024

• Results will be evaluated and decisions on implementation made

• After testing, must create technical documentation for processing, editing,

imputation, and tabulation

• Implementation not expected until 2027 at the earliest

Contact Info

Co-Leads for SOGI Testing

Andrew Roberts: [email protected] Amy Smith: [email protected]

mailto:[email protected]

Measuring Sexual Orientation and Gender Identity on the American Community Survey (United States Census Bureau)

Read more about Measuring Sexual Orientation and Gender Identity on the American Community Survey (United States Census Bureau)

This presentation will outline research underway at the United States Census Bureau to test sexual orientation and gender identity (SOGI) questions on the American Community Survey (ACS). Not having population-level data from a census is one of the major challenges in studying the characteristics of the LGBTQIA+ population. The ACS is the largest demographic survey conducted by the United States government, sampling 3.5 million housing units and group quarters each year, thus allowing for information for small populations.

Languages and translations

English

*Prepared by Andrew Roberts

NOTE: The designations employed in this document do not imply the expression of any opinion whatsoever on the part

of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its

authorities, or concerning the delimitation of its frontiers or boundaries.

Economic Commission for Europe

Conference of European Statisticians

Group of Experts on Gender Statistics Geneva, Switzerland, 10-12 May 2023

Item D of the provisional agenda

Measuring sex and gender

Measuring Sexual Orientation and Gender Identity on the American Community Survey

Note by the United States Census Bureau*

Abstract

This presentation will outline research underway at the United States Census Bureau

to test sexual orientation and gender identity (SOGI) questions on the American

Community Survey (ACS). Not having population-level data from a census is one of

the major challenges in studying the characteristics of the LGBTQIA+ population.

The ACS is the largest demographic survey conducted by the United States

government, sampling 3.5 million housing units and group quarters each year, thus

allowing for information for small populations. The current research will consist of

both cognitive and field testing. Cognitive testing will include testing of questions in

English and translation and testing of questions in Spanish. Field testing will include

self-response using both paper and internet modes and examine question wording,

response options and placement. One important area of research this testing will

illuminate is the quality of proxy reporting of SOGI information in demographic

surveys.

Working paper 13

Distr.: General

19 April 2023

English

Working paper 13

I. Introduction

1. The American Community Survey (ACS) is the largest demographic survey administered by the

United States government. Currently, the ACS does not collect data about sexual orientation or

gender identity. Recently, the United States Census Bureau has been charged with researching the

addition of these topics to the ACS. This is a unique and important challenge, and one which we

undertake carefully and with great excitement.

II. Background

A. State of Sexual Orientation and Gender Identity (SOGI) Data Collection at

the United States Census Bureau

2. The Census Bureau has made significant progress in recent years toward preparing to include

content about sexual orientation and gender identity on our surveys. This includes engaging with

LGBTQI+ stakeholders and our peers at national statistical offices in other countries. We have

recently conducted knowledge sharing sessions with Statistics Canada, which included a question

about gender identity on the 2021 Canadian Census. We hope to conduct similar sessions in the

future with other countries who have or who are researching the possibility of including SOGI and

intersex questions on their censuses or large national surveys.

3. The Census Bureau conducts quarterly meetings with SOGI advocacy and expert groups to share our

progress on collecting SOGI data and to get feedback about our plans. These meetings help to

ensure we are staying up-to-date on current trends and gaining buy-in from these communities about

our research.

4. Staff from the Census Bureau participate in the Federal Committee on Statistical Methodology SOGI

Working Group. This is an interagency group consisting of SOGI experts and interested parties

from across the United States federal government. The group facilitates sharing expertise,

conducting research, and discussing emerging issues related to collecting and publishing SOGI data.

5. Same-sex relationship categories have been added to the American Community Survey, the Current

Population Survey, and the Survey of Income and Program Participation and were collected on the

2020 United States Census.

6. In July 2021, SOGI questions were added to the Household Pulse Survey, a survey that was created

to measure the impact of the COVID-19 pandemic on different populations. This has provided

valuable data for SOGI populations and also yielded important lessons learned on the

implementation of this content.

B. About the American Community Survey

7. Prior to the 2010 United States Census, the United States used a two-prong approach for data

collection. While most households received a short questionnaire featuring only questions about the

basic demographics of each person living there and home ownership information (the “short form”),

a subset of the population—around 1 in 6—received a much longer questionnaire (“the long form”).

The Census long form asked more detailed questions each person, including questions about marital

status, educational attainment, nativity, migration, disabilities, occupational status, and income. It

also asked more detailed questions about the housing unit, such as when it was built, how many

rooms it has, and the cost of utilities. This information was used to inform legislators and

communities about the needs and characteristics of different communities and provided valuable

Working paper 13

planning data for state and local governments, residents, and businesses, especially in rural areas that

do not otherwise have access to such data through other means.

8. Over time, federal, state, and local governments, as well as many in the private sector, determined it

would be beneficial to have updated long-form data on a more frequent basis than every 10 years.

While many ideas were considered, following the 1990 Census, the Census Bureau began to develop

a new approach for collecting the more detailed long-form data. The new design consisted of

continuous monthly panels that would allow for data to be reported annually for larger geographic

areas, and aggregated over longer periods to provide data for smaller geographic areas or sparser

population groups.

9. In 2005, following the 2000 Census and after more than a decade of testing, this new effort became

the American Community Survey. The ACS replaced the Census long form; the 2010 Census was

the first Census in many years not to feature a long-form questionnaire. The ACS asked many of the

same questions as the Census long-form, but provided data down to the county level every year for

many topics, and also produced 3- and 5-year data products with increasingly more geographic and

population detail. The 3-year data products have since been discontinued, but 1-year and 5-year data

products are still produced annually.

10. Today, the ACS is the largest demographic survey administered by the federal government. It has an

annual sample of approximately 3.5 million households, divided into 12 monthly panels. The survey

is conducted via internet questionnaire, paper questionnaire, and in-person interviews. There are

special enumeration operations for group quarters, remote Alaska, and tribal lands.

11. Data from the ACS are used to distribute more than $675 billion in federal funds annually. State and

local governments, communities, and businesses also use ACS data to plan and distribute funds. For

many small communities, the ACS is the only detailed socioeconomic data source available. The

ACS is considered an extension of the U.S. Decennial Census and remains a part of the Decennial

program of operations.

C. The American Community Survey Change Process

12. There is also a very deliberate process for adding or changing content to the survey. The process

begins with a request from a federal agency proposing new or changed content. Included in the

request are the need for the data, the specific geographies for which the data are needed, other

sources available for the data, and how frequently new data are needed.

13. Participation in the ACS is required by law, and as such, any content on the survey must have a

statutory or regulatory justification. The data must also be unavailable from any other data source at

the required levels of geographic and accuracy. The data must also be expected to change over time,

necessitating the annual collection of the data.

14. Once a request for new or changed content is received, the Census Bureau, along with the Office of

Management and Budget (OMB), will evaluate the request. If the request is deemed to meet the

required criteria, it will move on the testing phase.

15. In the testing phase, all new or changed content goes through a lengthy process of qualitative and

quantitative testing. Content will be cognitively tested in both English and Spanish. A large field

test will be conducted for substantive changes to evaluate question performance and the effect on

High Inflation in BEA’s Statistics, United States

Read more about High Inflation in BEA’s Statistics, United States

Languages and translations

English

High Inflation in BEA’s Statistics Bob Kornfeld

Meeting of the Group of Experts on National Accounts

Geneva, Switzerland, 25-27 April 2023

BEA’s key price measures

• Prices for gross domestic product (GDP, expenditure approach) o final consumption (households, NPISH, government) + capital formation + exports – imports

• Prices for gross domestic purchases - equal to GDP minus net exports o goods and services purchased by U.S. residents, regardless of where produced

• Prices for personal consumption expenditures (PCE) – o actual final consumption of households and NPISH

o includes purchases financed by both cash and in-kind government transfers (eg, health insurance)

o often compared with CPI

o monthly PCE prices (released 30 after month) are important for “real time” updates

• “Core” prices (less food and energy) and prices for detailed components

• Prices for gross value added, output, intermediate consumption by industry

4/13/2023

Key quarterly price measures

4/13/2023

Key monthly price measures

4/13/2023

Prices: data sources and methods

• BEA deflates at the detailed commodity level, using appropriate price measures from several sources

• Seasonal adjustment occurs at the detailed commodity level

o Source data agencies often provide seasonally adjusted prices (for example, CPIs)

o BEA seasonally adjusts selected PPIs and other price measures

• Quality adjusted prices for several commodities

• Possibly less relevant for short-run price changes?

• Aggregation uses chain-type measures

• Chain-weighted, versus fixed-weighed, captures substitution effects

• Some key issues and challenges

• Seasonal adjustment (and associated revisions) can be challenging during and after the pandemic

• Aligning mid-month price indexes with full-month expenditures

• Survey response rates can be low

• Matching current-price expenditures with definitionally appropriate prices is important

• Contributions calculations are needed to remove the effects of select items (eg for core measures) 4/13/2023

Estimate review process, use of alternative indicators, and research

• During times of rapid changes and high inflation o We have paid close attention to the possible role of price changes in our current-price source data

o Additional time to review relationship between changes in prices and current-price measures

▪ Sales, shipments, receipts, expenses…

o One issue is that monthly CPIs and PPIs are “mid-month” measures

▪ They may not fully reflect rapid price changes within a month

▪ For example, we augment the PPI for petroleum with Department of Energy’s Refiners Acquisition Cost Index

• BEA obtained more alternative indicators during and after the pandemic: o Fiserv: real-time estimates of credit card transactions for several industries

▪ https://www.bea.gov/recovery/estimates-from-payment-card-transactions

o Health care and mass transit: private volume measures of service utilization

o Air travel: Transportation Safety Administration (TSA) passenger quantity data

o Numerous other indicators that help us understand changes in quantities and prices

• BEA staff also investigated price measurement when products are unavailable. • https://apps.bea.gov/fesac/

https://www.bea.gov/recovery/estimates-from-payment-card-transactions

https://apps.bea.gov/fesac/

The inventory valuation adjustment is both important and challenging with high inflation

Double deflation: Gross output, intermediate inputs, value added

• With double deflation, GO and II have separate price measures

• Recently….

o prices for GO and II can differ substantially

o leads to notable differences in current-price vs constant-price changes in VA

o A good example: petroleum refining

Manufacturing, petroleum and coal products: Percent changes in prices, current- price values, and constant-price values, for GO, II, VA, 2022Q3

4/13/2023

Intermediate Input Prices By Industry

4/13/2023

Intermediate input prices, private industries

4/13/2023

PCE price index vs CPI: Key differences

Line 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4

1 PCE Chain-type price index (percent change) 1.6 4.5 6.4 5.6 6.2 7.5 7.3 4.3 3.7

2 Less: Formula effect (percentage points) -0.23 -0.13 -0.25 -0.11 -0.16 0.06 -0.07 -0.21 -0.23

12 Equals: PCE fixed-weight price index (percent change) 1.88 4.63 6.69 5.70 6.35 7.41 7.36 4.53 3.97

13 Less: Weight effect (percentage points) -1.22 -0.91 -2.51 -2.37 -2.28 -1.94 -2.11 -1.39 -0.58

Less: Scope effect - PCE price index items out-of-scope

of the CPI (ppts) 0.87 2.02 1.66 1.12 1.10 0.76 0.29 0.64 1.06

Plus: Scope effect - CPI items out-of-scope of the PCE

price index (ppts) -0.07 0.32 0.09 0.14 0.32 0.42 0.43 0.51 -0.07

32 Less: Other effects (percentage points) -0.65 -0.35 0.11 0.49 -0.95 -0.16 -0.04 0.24 -0.74

39 Equals: CPI (percent change) 2.8 4.2 7.5 6.6 8.8 9.2 9.7 5.5 4.2

CPI: Consumer Price Index

PCE: Personal Consumption Expenditures

“Artisinal” inflation measures and other research

• Olivier Blanchard: “When shocks to relative prices come largely from other sectors than energy or food, core inflation can be a very bad measure of underlying inflation.”

• Economists would like to subtract chosen commodities from aggregate prices o PCE prices less food, energy, housing, used cars, financial services, portfolio management…

• Alternative inflation measures o “Supercore” inflation -- excludes food, energy, used cars, and housing

o Cleveland Federal Reserve’s trimmed means CPI

o Atlanta Federal Reserve- sticky price CPI

o New York Federal Reserve - Multivariate Core Trend (MCT) and Underlying Inflation Gauge

o Average hourly wages, BLS Employment Cost Index

• National Academies Panel on Improving Cost of Living Indexes and Consumer Inflation Statistics in the Digital Age

o Several suggestions for improving the CPI (also relevant for BEA)

o Some research suggests that inflation varies for lower- and higher- income households 13

https://www.clevelandfed.org/indicators-and-data/median-cpi

https://www.atlantafed.org/research/inflationproject/stickyprice/

https://libertystreeteconomics.newyorkfed.org/2023/01/the-layers-of-inflation-persistence/

https://www.newyorkfed.org/research/policy/underlying-inflation-gauge

https://nap.nationalacademies.org/initiative/panel-on-improving-cost-of-living-indexes-and-consumer-inflation-statistics-in-the-digital-age

Contributions tables for chain weighted aggregates

• Contributions tables are helpful o These tables show the contributions (in percentage points) to aggregate percent changes

o Analysts can easily subtract contributions to estimate “PCE prices excluding….”

o Without these tables, analysts need to estimate contributions

o Contributions = share of current-price levels in previous period X price change

• BEA currently publishes a limited set of price contributions tables o For GDP and gross domestic purchases

o BEA produces current expenditures and prices for detailed PCE categories, but not PCE contributions tables

o Some want contributions tables for year over year price changes in addition to m/m or q/q

High Inflation in BEA's Statistics, United States

Read more about High Inflation in BEA's Statistics, United States

Languages and translations

English

High Inflation in BEA’s Statistics Bob Kornfeld

Meeting of the Group of Experts on National Accounts Geneva, Switzerland, 25-27 April 2023

BEA’s key price measures

• Prices for gross domestic product (GDP, expenditure approach) o final consumption (households, NPISH, government) + capital formation + exports – imports

• Prices for gross domestic purchases - equal to GDP minus net exports o goods and services purchased by U.S. residents, regardless of where produced

• Prices for personal consumption expenditures (PCE) – o actual final consumption of households and NPISH o includes purchases financed by both cash and in-kind government transfers (eg, health insurance) o often compared with CPI o monthly PCE prices (released 30 after month) are important for “real time” updates

• “Core” prices (less food and energy) and prices for detailed components

• Prices for gross value added, output, intermediate consumption by industry

3/29/2023

Key quarterly price measures

3/29/2023

Percent change from preceding quarter, SAAR

Key monthly price measures

3/29/2023

Percent change from preceding month in PCE prices, seasonally adjusted at monthly rates

Prices: data sources and methods • BEA deflates at the detailed commodity level, using appropriate price measures from several sources

• PCE • Bureau of Labor Statistics (BLS) Consumer Price Indexes (CPIs) • BLS Producer Price Indexes (PPIs) for health care and financial services • Input costs indexes for NPISH, using CPIs, PPIs, BLS Employment Cost Index (ECI)

• Gross fixed capital formation • Equipment: Mostly BLS PPIs, also BLS import price indexes • Structures: Census Bureau price index for single-family houses under construction, Turner Construction Co. building-cost

index • Software: PPIs and BEA composite input cost index with productivity adjustment • R&D: BEA composite input cost index with productivity adjustment

• Imports and Exports • Mostly BLS import and export price indexes

• Government • BEA composite input cost indexes, BLS employment cost indexes, PPIs and CPIs

3/29/2023

Prices: data sources and methods (cont’d) • Seasonal adjustment occurs at the detailed commodity level

o Source data agencies often provide seasonally adjusted prices (for example, CPIs) o BEA seasonally adjusts selected PPIs and other price measures

• Quality adjusted prices for several commodities • Possibly less relevant for short-run price changes?

• Aggregation uses chain-type measures • Chain-weighted, versus fixed-weighed, captures substitution effects

• Some key issues and challenges • Seasonal adjustment (and associated revisions) can be challenging during and after the pandemic • Aligning mid-month price indexes with full-month expenditures • Survey response rates can be low • Matching current-price expenditures with definitionally appropriate prices is important • Contributions calculations are needed to remove the effects of select items (eg for core measures)

3/29/2023

BEA’s release schedule and revisions

• Revisions to source data outside the current “open” quarter are not fully incorporated until the next annual update o More important with larger revisions and a need to get the latest picture ASAP.

• Example: revised seasonal factors for BLS CPIs: o In February 2023, BLS revised CPI seasonal factors for the last 5 years o BEA’s open period of revision was limited to October-December o PCE prices do not fully reflect the latest CPI data until annual update

Estimate review process, use of alternative indicators, and research

• During times of rapid changes and high inflation o We have paid close attention to the possible role of price changes in our current-price source data o Additional time to review relationship between changes in prices and current-price measures

 Sales, shipments, receipts, expenses… o One issue is that monthly CPIs and PPIs are “mid-month” measures

 They may not fully reflect rapid price changes within a month  For example, we augment the PPI for petroleum with Department of Energy’s Refiners Acquisition Cost Index

• BEA obtained more alternative indicators during and after the pandemic: o Fiserv: real-time estimates of credit card transactions for several industries

 https://www.bea.gov/recovery/estimates-from-payment-card-transactions o Health care and mass transit: private volume measures of service utilization o Air travel: Transportation Safety Administration (TSA) passenger quantity data o Numerous other indicators that help us understand changes in quantities and prices

• BEA staff also investigated price measurement when products are unavailable. • https://apps.bea.gov/fesac/

https://www.bea.gov/recovery/estimates-from-payment-card-transactions

https://apps.bea.gov/fesac/

The inventory valuation adjustment is both important and challenging with high inflation (Billions of current dollars)

Double deflation: Gross output, intermediate inputs, value added

• With double deflation, GO and II have separate price measures

• Recently….

o prices for GO and II can differ substantially

o leads to notable differences in current-price vs constant-price changes in VA

o A good example: petroleum refining

Manufacturing, petroleum and coal products: Percent changes in prices, current- price values, and constant-price values, for GO, II, VA, 2022Q3, (SAAR)

3/29/2023

Intermediate Input Prices By Industry

3/29/2023

Intermediate input prices, private industries

3/29/2023

Percent change from preceding quarter, SAAR

PCE price index (PCE PI) vs CPI: Key differences • Formula effect

o CPI uses a modified Laspeyres formula; PCE PI uses a Fisher Ideal formula

• Scope effects o CPI: out-of-pocket expenditures of all urban households o The PCE PI: purchases by households and NPISHs financed by cash, third party payors (eg

insurance) and in-kind government transfers (eg, health insurance)

• Weight effect o CPI: relative weights based primarily on household surveys o PCE PI: relative weights based primarily on business surveys

o higher weights to health care and financial services o lower weights to housing and transportation

• Other effects • Seasonal adjustment, price differences, all other differences 14

Moving from the PCE price index to CPI: Recent history (percentage points from each effect)

Positive numbers indicate that CPI is higher than the PCE PI due to the effect. Weight effect frequently results from higher weight for housing in the CPI. Scope effect (in PCE not in CPI) frequently results from additional weight for health care services in PCE PI.

“Artisinal” inflation measures and other research • Olivier Blanchard: “When shocks to relative prices come largely from other sectors

than energy or food, core inflation can be a very bad measure of underlying inflation.”

• Economists would like to subtract chosen commodities from aggregate prices o PCE prices less food, energy, housing, used cars, financial services, portfolio management…

• Alternative inflation measures o “Supercore” inflation -- excludes food, energy, used cars, and housing o Cleveland Federal Reserve’s trimmed means CPI o Atlanta Federal Reserve- sticky price CPI o New York Federal Reserve - Multivariate Core Trend (MCT) and Underlying Inflation Gauge o Average hourly wages, BLS Employment Cost Index

• National Academies Panel on Improving Cost of Living Indexes and Consumer Inflation Statistics in the Digital Age o Several suggestions for improving the CPI (also relevant for BEA) o Some research suggests that inflation varies for lower- and higher- income households

https://www.clevelandfed.org/indicators-and-data/median-cpi

https://www.atlantafed.org/research/inflationproject/stickyprice/

https://libertystreeteconomics.newyorkfed.org/2023/01/the-layers-of-inflation-persistence/

https://www.newyorkfed.org/research/policy/underlying-inflation-gauge

https://nap.nationalacademies.org/initiative/panel-on-improving-cost-of-living-indexes-and-consumer-inflation-statistics-in-the-digital-age

Contributions tables for chain weighted aggregates • Contributions tables are helpful

o These tables show the contributions (in percentage points) to aggregate percent changes o Analysts can easily subtract contributions to estimate “PCE prices excluding….” o Without these tables, analysts need to estimate contributions

o Contributions = share of current-price levels in previous period X price change

• BEA currently publishes a limited set of price contributions tables o For GDP and gross domestic purchases o BEA produces current expenditures and prices for detailed PCE categories, but not PCE contributions tables o Some want contributions tables for year over year price changes in addition to m/m or q/q

External communication about prices and related issues

• BEA Web Page: COVID-19 and Recovery o Estimates of the expenditures of several government programs included in GDP and personal income o Research on estimates from payment card transactions o Technical notes and press releases from recent estimates  GDP, personal income, International Transactions Accounts

o Frequently asked questions (FAQs) on several topics o Paper summarizing the treatment of government programs

• NIPA Handbook: Concepts and Methods of the U.S. National Income and Product Accounts

• FAQs, press releases

• Subject matter experts, media and customer service representatives, contact information available on website

https://www.bea.gov/recovery

https://www.bea.gov/system/files/2020-06/COVID-19%20Pandemic-Federal%20Recovery%20Legislation%20and%20the%20NIPAs.pdf

High Inflation in BEA’s Statistics
BEA’s key price measures
Key quarterly price measures
Key monthly price measures
Prices: data sources and methods
Prices: data sources and methods (cont’d)
BEA’s release schedule and revisions
Estimate review process, use of alternative indicators, and research
The inventory valuation adjustment is both important and challenging with high inflation (Billions of current dollars)
Double deflation: Gross output, intermediate inputs, value added
Manufacturing, petroleum and coal products: Percent changes in prices, current- price values, and constant-price values, for GO, II, VA, 2022Q3, (SAAR)�
Intermediate Input Prices By Industry
Intermediate input prices, private industries
PCE price index (PCE PI) vs CPI: Key differences
Moving from the PCE price index to CPI: Recent history (percentage points from each effect)
“Artisinal” inflation measures and other research
Contributions tables for chain weighted aggregates
External communication about prices and related issues

Evaluating coverage of the US Census Bureau’s Integrated Database for International Migration (IDIM) (United States)

Read more about Evaluating coverage of the US Census Bureau’s Integrated Database for International Migration (IDIM) (United States)

Languages and translations

English

*Prepared by Jason Schachter, Esther Miller, and Angelica Menchaca. The U.S. Census Bureau reviewed this data product for unauthorized disclosure of confidential information and approved the disclosure avoidance practices applied to this release. CBDRB-FY23-POP001-0007 NOTE: The designations employed in this document do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

Economic Commission for Europe Conference of European Statisticians Group of Experts on Migration Statistics Geneva, Switzerland, 26−28 October 2022 Item A of the provisional agenda Improvements in use of administrative data for migration statistics

Evaluating Coverage of the US Census Bureau’s Integrated Database for International Migration (IDIM)

Note by U.S. Census Bureau* Abstract In recent years the US Census Bureau’s International Migration Branch has explored using administrative data to improve foreign-born international migration flow estimates, resulting in a linked database called the Integrated Database for International Migration (IDIM). A limitation of the IDIM is that it is restricted to persons covered by Federal administrative record data sets acquired by the Census Bureau resulting in coverage gaps for the foreign born. We assume the IDIM underestimates, or misses, specific groups of foreign born not covered by the linked data sources including working migrants who did not file tax returns, non-working dependents not claimed as exemptions on tax returns, international students, exchange visitors, and unauthorized migrants. The American Community Survey (ACS) is a large annual household survey conducted by the US Census Bureau that, in theory, should cover these populations missing from the IDIM. To examine IDIM coverage limitations, we match individual records from the ACS to the IDIM. This paper presents results of exploratory research to assess IDIM coverage and evaluates the magnitude and characteristics of ACS survey respondents who are not included in the IDIM, as opposed to those who are present in the IDIM. Further, this research serves to assess data quality of both the ACS and IDIM, as we can compare results between linked individuals and households within each data source. These findings provide us with information on the potential for using the ACS to help adjust for IDIM coverage limitations.

Working paper 3

Distr.: General 19 January 2023 English

Working paper 3

I. Introduction

1. The International Migration Branch at the US Census Bureau produces annual estimates of foreign-born

immigration flows to the United States with demographic detail (age, sex, race, and Hispanic origin) at national, state, and county-level geographies. These estimates are created using the American Community Survey (ACS) as a primary data source, though survey estimates come with limitations such as increased variance, particularly at subnational geographies, and lagged measurement of migration events. To help overcome these limitations, the Census Bureau has been developing an alternative data source called the Integrated Database on International Migration (IDIM), which incorporates available administrative data from social security and tax records to estimate international migration.

2. One limitation of the IDIM is that it is restricted to persons covered by Federal administrative record datasets,

resulting in coverage gaps for the foreign born who are not included in these records. This means the IDIM likely underestimates, or misses, specific groups of foreign born, such as working migrants who did not file tax returns, non-working dependents not claimed as exemptions on tax returns, international students, exchange visitors, and most unauthorized migrants. The ACS is a large annual household survey conducted by the US Census Bureau that is representative of the entire resident population, and thus theoretically should cover these missing IDIM populations. While the ACS likely underrepresents some of these missing foreign-born groups as well, we can still examine IDIM coverage limitations by matching individual records from the ACS to the IDIM. This allows us to compare individuals who are included in both the ACS and the IDIM (“ACS match” or “ACS-IDIM match”) to those in the ACS but not in the IDIM (“ACS only,” or “ACS-IDIM non-match”). In addition to this comparison, for individuals both in the ACS and IDIM, we can evaluate data quality for a number of variables shared across both data sets, including age, sex, citizenship status, year of entry, and current geography. Finally, based on these findings, this paper discusses the potential of using the ACS to adjust IDIM results to improve its estimates.

II. IDIM overview

3. The IDIM is created by linking administrative data sources which can be used to generate foreign-born immigration estimates. While there are many administrative data sources which could potentially be used in the IDIM, such as those maintained by the Department of Homeland Security (DHS), we are currently limited to data sources readily available at the Census Bureau, namely the Numident from the Social Security Administration (SSA) and tax filing information from the Internal Revenue Service (IRS). Data linking is done via matching of unique Personal Identification Keys (PIKs) which are assigned to individuals across data sets. PIKs are most easily created using directly matched encrypted Social Security Numbers (SSNs), but are also created by probabilistically matching name, sex, age, and address information.

A. Numident

4. The Numident is a micro-record dataset that combines SSA SSN records with Census Bureau death records. It includes data on demographic characteristics, place of birth, and citizenship status. It does not, however, include address data. SSNs can be easily anonymized using PIKs which allow for linking across datasets. Given that most documented immigrants to the United States apply for SSNs, the Numident was chosen to serve as IDIM’s spine for initial data integration and research.

5. Numident data are delivered on a quarterly basis and contain records for all persons who have ever received SSNs. In addition to native births, this includes applications for SSNs by the foreign born. Foreign-born individuals who are either authorized to work or have become naturalized citizens are eligible to receive SSNs. Using a combination of citizenship status, place of birth, and date of record creation, we can identify foreign- born migrants at the national level by demographic characteristics. Linking Numident data to other sources can give us additional information, such as place of residence, which would allow us to create sub-national

Working paper 3

estimates of the foreign born. It also provides information about “signs of life,” which give us additional confidence as to whether the social security holder has moved to the United States for the requisite period of time to establish residency.

B. Internal Revenue Service Tax Filings

6. The IRS provides tax form 1040 filing data to the Census Bureau every four weeks. While these data do not

include demographic characteristics, they do include address information and PIKs for the primary filer, spouses and dependents. Where possible, address data are linked to a Master Address File ID (MAFID). IRS data do not include information on foreign-born status, which must come from linked Numident files. They also do not include information for individuals who do not file taxes (either through not having sufficient income or for failing to claim income). SSNs included on tax filing data make for the possibility of directly matching individuals to the Numident.

7. It is also possible to identify tax filings that use Individual Tax Identification Numbers (ITINs), unique

identifiers used by individuals without SSNs to file taxes. ITINs are only issued to non-US citizens; thus it is not necessary to link these individuals to the Numident to assign foreign-born status. While this universe is assumed to include migrants unauthorized to work, it also includes non-working dependents of authorized migrant workers. Changes to tax laws in 2017 caused drastic decreases in reported ITINs, as spouses and dependents are no longer eligible for ITINs unless they qualify for specific deductions or file their own separate return.1 This significantly reduces the usefulness of ITINs for identifying migrants after 2017. Further complicating use of ITINs is that individuals are periodically required to reapply for ITINs, and thus someone can have multiple ITINs over the course of a lifetime. In addition, ITINs holders can apply for SSNs later in life, and thus could have both an ITIN and SSN on file. As ITINs are not registered with the Social Security System, they cannot be directly matched to the Numident, though probabilistic methods could be used to match ITIN records to other data sets. The current version of IDIM does not include ITIN holders, but additional research is being conducted to see how they can possibly be incorporated into future analyses.

C. IDIM Creation

8. As noted earlier, the Numident acts as our spine for identifying foreign-born immigrants. It is used in the first

phase of processing whereby the foreign born are identified using citizenship variables from the Numident (this includes non-citizens authorized to work and naturalized citizens). We then use record creation year as a proxy for year of entry into the US. Lastly, we remove individuals who died the same year they migrated. This step results in an estimate of foreign-born immigrants by year with demographic characteristics, albeit a clear overestimation. We expect an overestimation at this point, as this universe includes SSN applicants who received SSNs, but who either only came to the United States for a short period of time or never actually migrated to the United States. The native-born population is retained in the file to have a comparison group to the foreign born. Race and Hispanic origin data are incomplete or missing from the Numident,2 so it is necessary to use alternative methods to assign race and Hispanic origin by modeling decennial Census 2010 and ACS files. These methods to assign race to the Numident have not been incorporated into this paper, which limits analysis for these variables.

9. In the second phase of processing, we match Numident records to IRS tax form 1040 filings to confirm entry

into the United States. The Numident contains all applications for SSNs, including individuals who received SSNs, but never actually migrated to the United States or only stayed for a short period of time. To remove this group from our estimates, we match IRS data to restrict the universe to authorized migrants who worked and filed taxes in the United States, as well as both working and non-working naturalized citizens. This step also

1 See https://www.irs.gov/individuals/individual-taxpayer-identification-number. 2 Why Researchers Now Rely on Surveys for Race Data on OASDI and SSI Programs: A Comparison of Four Major

Surveys (ssa.gov)

https://www.irs.gov/individuals/individual-taxpayer-identification-number

https://www.ssa.gov/policy/docs/rsnotes/rsn2016-01.html

Working paper 3

assigns geocodes, giving us foreign-born immigrants with demographic characteristics at national, state, and county geographies. We expect an underestimation of the foreign-born non-citizen population at this point, as we are missing migrants who fail to file taxes, as well as authorized migrants who did not work.

10. At this stage the IDIM includes the following immigrant populations: naturalized citizens, non-citizens

authorized to work and who filed taxes, and their non-working dependents and spouses. Populations not included are: US citizens born abroad of American parents, unauthorized migrants, working migrants who did not file a tax return, and non-working dependents not claimed as an exemption. Given the ACS is designed to be representative of the entire US resident population, it should include information on many of the foreign- born groups currently missing on the IDIM.

D. ACS

11. This paper links 2019 ACS micro data to the IDIM to help evaluate IDIM’s coverage, as well as data quality of

both IDIM and the ACS. The ACS is a large annual continuous household survey of the US population that asks detailed information previously collected on the decennial census long form. Fully implemented in 2005, it currently surveys about 3.5 million addresses per year. Inclusion in the sample is based on having lived, or planning to live, for at least two months in the sampled address. The ACS asks detailed sociodemographic and economic questions, including immigration-pertinent variables such as country of birth, citizenship status, year of entry to the United States, and country of residence one year ago. While SSN information is not collected on the ACS, individuals on the ACS can be assigned PIKs using the Person Verification System (PVS), which assigns probability by matching name, sex, and address information.

12. Since the ACS includes all US residents in its sample universe, and does not distinguish by legal status, we feel

the ACS is a potentially good source of information on migrants missing from the IDIM. However, given the hard-to-count nature of recent and unauthorized migrants, it is likely the ACS underrepresents these populations to some degree (Jensen et al, 2015). While it is not our intent to evaluate ACS coverage in this paper, this issue should be kept in mind when interpreting some of our findings.

E. Person Identification Validation System (PVS)

13. The PVS is the Census Bureau’s process to identify and verify SSNs and PIKs for person records in surveys,

censuses, and administrative records. The Census Bureau attempts to assign PIKs to every administrative record via a probabilistic model known as the Person Verification Model (Wagner and Layne, 2014) that is composed of four modules. First, if the administrative data contain SSNs, the verification module checks for an exact SSN match to the Numident file and verifies that name and date of birth elements sufficiently agree. If they do agree, the SSN is considered verified and PVS assigns the corresponding PIK to the person record. If there is no SSN, such as in the case of the ACS, the PVS continues through three more probabilistic modules to attempt to assign an SSN to the administrative record using geography, name, and date of birth. Approximately 94% of all 2010 ACS records received a PIK, implying that only 6% of all records could not be linked to any administrative data.

14. Those not assigned PIKs can potentially introduce bias to linked data. A study of the 2009 and 2010 ACS concluded that PVS is less likely to validate young children, minorities, residents of group quarters, immigrants, recent movers, low-income individuals, and non-employed individuals (Bond, 2014). In addition to unassigned PIKs, there is the possibility of PIKs being erroneously assigned to individuals (also referred to as record linkage error), though the rate of these misassigned PIKs for the foreign born is not known (Abowd et al., 2020). This is one possible reason for mismatch between variables on the IDIM and ACS, when individuals are matched incorrectly.

Working paper 3

F. Linking the ACS to IDIM

15. For this paper, we linked 2019 ACS data to the 2019 IDIM universe. Since the IDIM universe is defined by linked Numident and IRS records, which have undercoverage of older populations, we restricted our universe to those under 65 years of age to make the ACS universe more comparable. For our ACS universe, 14% are identified as foreign born. To define foreign born on the ACS, we use responses to the ACS citizenship question. Those born in the US and born abroad of American parents are defined as “native born,” while US citizens by naturalization and non-US citizens are defined as “foreign born.” The proportion of foreign born in the IDIM is 12.6%. The foreign born are defined similarly to the ACS using a citizenship variable, due to data quality concerns with the country of birth and foreign-born indicator variables on the Numident file. In the IDIM’s case, we use a variable that identifies “US citizens” and “legal aliens,” in combination with a variable that denotes if a person was ever naturalized. Neither the ACS nor IDIM definitions of the foreign born disaggregate this group by citizenship status, which is important to note due to data quality concerns for the naturalization variable on both the ACS (Van Hook and Bachmeirb, 2013) and Numident.

16. For the total ACS universe, 89% of the sample can be assigned an individual PIK, and thus matched to the

Numident or assigned an ITIN by the IRS. Among the foreign-born identified on the ACS, 79% of the sample can be assigned a PIK. When further linked to IRS/SSN data, these match rates drop, with 80% of the total ACS universe matched via PIK and 71% of the foreign-born sample. These drops in match rates are expected, since the IDIM is limited to tax filers with SSNs. The lower match rate for the foreign-born universe was expected based on previous research discussed earlier, thus contributing to foreign-born undercoverage in the IDIM.

17. Conversely, the non-match rate for the ACS foreign-born universe is 29%, which provides us with a key

comparison group. This universe consists of three distinct groups: those in the ACS for which a PIK is not able to be assigned, those with ITINs who file taxes, and those linkable to the Numident, but who did not file taxes or appear as exemptions on IRS tax returns. Future research will attempt to disaggregate the ACS not matched to IDIM universe, but it is assumed to include a large proportion of unauthorized migrants, as well as groups like international students and dependents not claimed as exemptions on tax returns, hence those assumed to be missing from the IDIM universe.

III. Comparison of IDIM and ACS Foreign-Born Universes

18. There are several different universes that can be compared to evaluate IDIM coverage. Figure 1 is a conceptual diagram of how the different universes are created, linking the Numident, IRS and ACS. The IDIM consists of Numident and IRS matches, and currently excludes those with ITIN records (light blue, outside the Numident and IDIM). The figure also denotes the important comparison groups used in this analysis: (1) total IDIM (purple), (2) the ACS-match or ACS-IDIM match group (shaded yellow), and (3) the ACS-only or ACS- IDIM non-match groups. The ACS-only group consists of three sub-groups: non-PIKable ACS files (light yellow), ACS respondents on the Numident but not on the IDIM (orange, e.g., non-tax filers), and ACS respondents on the IRS but not on the Numident (green, e.g., ITIN holders).

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Figure 1. Numident, IRS, IDIM, and ACS Evaluation Universes

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey

19. For comparison purposes, Table 1 shows detailed demographic and socioeconomic characteristics for each of these foreign-born universes. All these characteristics can be derived from the ACS, while a limited number of variables are also available on the IDIM, including sex, age, citizenship status, year of entry, and state of current residence. Thus, for the ACS-IDIM match group, some variables can be derived independently from both the ACS and IDIM, which will allow us to evaluate data quality in the following section.

(See Table 1 in appendix)

20. As expected, we found clear differences between the IDIM/ACS-IDIM matched and the ACS-only universes.

The ACS-only universe was more male, younger (under 25), less Asian, more Hispanic (Mexican and Central American), less educated, and more not in labor force and in poverty. There were few differences in terms of year of entry between the IDIM and the ACS-only group. Comparisons between ACS-only and ACS-matched universes showed more extreme differences between groups than total IDIM comparisons. Of particular interest were differences in terms of citizenship status and year of entry.

21. Sex and age distributions can be gleaned from both the IDIM and ACS for the various groups. The ACS-only universe was more male than both the IDIM and the ACS-IDIM-matched universes. The IDIM had a generally older age distribution (50 and older), while the ACS-only group skewed younger, including more college aged and children in its universe.

22. As discussed earlier, race and Hispanic origin are currently not measurable on the IDIM, so this comparison was limited to the ACS-only and ACS-matched groups. Clear differences were found, with far more Asians in the ACS-matched universe, and far more Hispanics in the ACS-only universe. Among Hispanics, far more were of Mexican or Central American descent in the ACS-only universe compared to the ACS-matched universe.

23. Socioeconomic variables are only available on the ACS and clear differences were seen between the ACS-only and ACS-matched universes. In terms of education, the ACS-only universe was far more likely to have less than a high school degree, while the ACS-matched universe was more likely to have at least a college degree. Relatedly, the ACS-matched group was more likely to be employed and not in poverty than the ACS-only group.

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24. Some interesting findings were discovered looking at the year of entry and citizenship variables present on both the ACS and IDIM. Year of entry is defined on the IDIM as the year when an SSN was entered into the Numident, while the ACS asks respondents which year they came to live in the United States, so we would expect to see differences between datasets. This was not the case between the ACS-only and IDIM universes, as their year of entry distributions are quite similar. However, when comparing the ACS-only to ACS-matched universes, which use the same variable of measurement for year of entry, differences do appear. The ACS- only universe was more likely to have been recent migrants (since 2015) than the ACS-matched universe, who were more likely to have arrived before 1999. Given the similar year of entry distribution between the IDIM and ACS-only universes, there were some surprising differences between the ACS-IDIM matched IDIM-based year of entry distributions and the ACS-IDIM matched ACS-based year of entry distributions, which will be examined in greater detail in the following section.

25. Citizenship status also produces interesting results, which bring to question the quality of this variable on the

IDIM. The foreign born on the IDIM are far more likely to be non-citizens than the ACS-matched universe, which was an unexpected result. This suggests that IDIM records are not updated on a regular basis after an individual naturalizes or suggests that ACS reporting are of poor quality for this variable (or a combination of both reasons). More telling is the comparison between the ACS-only to ACS-matched universes, where large differences are found. The proportion of non-citizens is far higher for the ACS-only universe, while conversely the proportion of naturalized foreign born is far greater for the ACS-matched universe. This suggests that the ACS-only universe is more representative of persons ineligible for SSNs, including unauthorized migrants and dependents of legal migrants.

26. Specific citizenship status is not relevant from the perspective of how we use the IDIM to produce estimates of

the foreign born. It is not important if someone on the IDIM has accurate up-to-date naturalization status information, since what is important is whether they are foreign born or not. However, these findings could have important implications for other types of analysis using administrative data, and would likely require additional linkages to other data sources (e.g. from US Citizenship and Immigration Services (USCIS)) to accurately measure citizenship status.

27. Finally, Figure 2 shows the difference in state of residence for those on the ACS-only and IDIM files.

Differences were relatively small, with the IDIM having a bit more representation in states like Florida and New York and the ACS-only universe having a bit more representation in Texas.

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Figure 2. Percent Difference in State of Residence between IDIM and ACS-only Universes

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service

28. These findings suggest there is clear underrepresentation of specific foreign-born groups in the IDIM. Despite the ACS’s own potential coverage biases for the foreign born, it appears to better measure the hard-to-count foreign-born populations missing from the IDIM, and thus should be useful to adjust undercoverage of specific migrant groups in the IDIM. Some of the underrepresentation seen in the IDIM could be addressed by incorporating ITINs, but this has its own set of challenges that still need to be worked out. In lieu of linking additional administrative data sources to IDIM, integrating the ACS into IDIM to account for some of this undercoverage seems feasible. This is discussed in more detail later in the paper.

IV. Data Quality Analysis

29. The initial analysis brought up some questions about both IDIM and ACS data quality, specifically for the citizenship and year of entry variables. To provide insight into data quality, we compare shared ACS- and IDIM- derived variables for individuals who are matched to both data sets. As discussed previously, these shared variables include sex, age, citizenship status, year of entry, and state of current residence.

30. Results for ACS- and IDIM-derived variables for both sex and age are promising. There is very strong concurrence for individual sex variables derived from both the IDIM and ACS, with over 97% of male respondents and 98% of female respondents reporting the same sex. For age, we would expect to see differences based on how age is defined in each data source. The ACS asks for age at the time of survey (in addition to date of birth), while age on the Numident is based on the mid-point of the year. This appears to be the case, as only 48% of respondents have the same age on both files. However, when we expand the age to plus or minus one year, the concurrence rate increases to 95%. While we cannot determine which data source has more accurate values, age heaping is a known issue for survey-based age responses, and yet the overall age agreement between data sources seems to be high.

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31. Citizenship status is measured very similarly on both the ACS and IDIM, with foreign-born respondents being disaggregated as either naturalized or non-citizens. Earlier results suggested a disconnect between these variables as derived by the IDIM and ACS, which is confirmed by this analysis. For the foreign-born on the IDIM who are identified as non-citizens, only 56% of those matched to the ACS report the same non-citizen status with the ACS citizenship variable, while 35% are naturalized and 9% are native born per the ACS citizenship question. For naturalized foreign born on the IDIM matched to the ACS, there is better concurrence, with 84% being naturalized on the ACS question and only 6% being non-citizens, while 10% are native born. The large differences between ACS- and IDIM-derived citizenship status could be indicative of lack of updates to naturalized status on SSA records, but also could reflect inaccurate data reporting on the ACS. Also concerning is that close to 10% of the Numident foreign born are classified as native born on the IDIM. Looking at the foreign born on the ACS, similarly, over 10% are classified as natives on the ACS. Further cross-tabulations by country of birth could help elucidate some of these findings. One partial explanation for this misconnect could be miscategorization of the native born from the “Born Abroad of American Parents” ACS citizenship question, which likely includes those whose parents were not naturalized citizens at the time of respondent’s birth, due to confusing question wording on the survey questionnaire. High imputation associated with the citizenship variable and mismatched PIKs are other possible contributing factors, in addition to erroneous ACS and IDIM data responses. More research would be needed to thoroughly investigate this issue though.

32. As discussed earlier, we would expect incongruence between the IDIM and ACS year of entry values, given

the different ways this variable is measured on the two data sets, as well as data quality concerns with the ACS variable regarding accurate recall and year heaping in responses. Analysis shown in Figure 3 confirms incongruence between the year of entry variable on the IDIM and ACS for matched individuals. The year of entry values from these two data sets only match 38% of the time. If we expand this range to within one year of each other, this only improves to 54% of cases, while plus or minus two years improves this to 61%. Even with a range of plus or minus 9 years, the year of entry values match just 83% of the time between data sets. Again, to what extent this is mostly due to data reporting issues (for both the ACS or the Numident), high imputation, and/or PIK record linkage error is not known. More research on the year of entry and citizenship status variables is clearly warranted.

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Figure 3. Distribution of Differences in Responses to the Year of Entry Question Among Matched Individuals in the ACS and the IDIM

Source: US Census Bureau, Integrated Database for International Migration, 2019 American Community Survey; Social Security Administration; and Internal Revenue Service

33. Finally, for matched ACS-IDIM individuals we look at the reported state of residence on the IDIM and the ACS. The ACS geography variable comes from the location where the survey respondent resided at the time of inclusion in the survey, while IDIM geography comes from where the individual filed their tax return. It is possible that a person made an interstate move during the measurement period, so we would expect some differences on this variable between data sets. Evaluation of this variable shows relatively high congruence with 92% of ACS and IDIM geographies matching at the state level for linked individuals. Differences could easily be caused by interstate moves during the period, though less likely due to ACS imputation since this variable comes from the sampled address list.

34. In summary, differences between some IDIM- and ACS-derived variables for linked individuals were

unexpectantly large. As mentioned during the discussion of the citizenship status and year of entry variables, one possible explanation is that data are reported incorrectly on each data set. There is also the possibility that high imputation for foreign-born specific variables like citizenship, place of birth, and year of entry on the ACS further contributes to these differences. Additionally, there may be record linkage errors between IDIM and ACS, and thus they are not the same individuals, which is possible given the probabilistic method used to assign PIKs in the absence of SSN information. This is another area where future research is needed to allay possible concerns about data quality in the IDIM and the ACS.

V. Using the ACS to adjust IDIM

35. The purpose of this exploratory research was not only to evaluate IDIM coverage and data quality, but also to provide us with information about whether the ACS could be used to adjust the IDIM for its confirmed coverage limitations. Despite potential biases in foreign-born unit and item response, it appears the ACS does measure foreign-born populations missing from the IDIM, namely unauthorized migrants and informally employed migrants who do not file taxes, international students, and some dependents of IRS tax filers.

36. The US Census Bureau produces net international migration flow estimates for the nation, state, and county by

age, sex, race and Hispanic origin, primarily using ACS data. Development of the IDIM was not done with the

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intent of replacing the ACS, but rather is an effort to draw from the strengths of each dataset through data integration, thereby improving our estimates. The IDIM could be particularly useful for improving county- level estimates, for which our survey-based estimates are reliant on 5 years of pooled ACS foreign-born stock data and still have high levels of sampling variability, particularly for smaller counties. At the same time, there is still the potential of using the ACS to adjust IDIM undercoverage for both national and county-level estimates, as well as for national and subnational characteristics.

37. The Census Bureau has previously integrated administrative data at the macro-level to improve national

survey-based estimates of migration to and from Puerto Rico after Hurricane Maria, as well as to account for the impact of the COVID-19 pandemic on international migration flows to and from the United States. These methods used historical trends between ACS and administrative data to adjust ACS estimates based on levels seen with administrative data. Informing adjustments to the IDIM with the ACS would be an instance of using survey data to adjust administrative records and could potentially occur at both the macro- and micro-level, given the nature of linking procedures.

38. For example, from a macro-integration perspective, the ACS could be used to adjust for missing international

student populations, as well as age distributions at the subnational level, by adding a proportion of students to the national totals, or by applying ACS county-level age distributions for counties with large student populations. It could also be possible to use the levels and characteristics of the ACS-only population to account for missing IDIM populations, either through proportional or modeled estimate adjustments. These methods could help account for some missing unauthorized migrants, as well as other foreign-born populations missing from the IDIM. Further work to better disaggregate the ACS-only population into different categories would also improve the nuances of any adjustments made for this population.

39. From a micro-integration perspective, it could be possible to use linked householders on the IDIM and ACS,

for which information about family members is on the ACS but not the IDIM, to adjust for missing dependents who are not included on tax returns. Knowing the size of family on the ACS and to what extent this population is missing on the IDIM could inform some probabilistic estimation methods. All these macro- and micro-data integration methods would still need to be developed, but these initial findings suggest that the ACS could be a useful tool to improve migration estimates produced by the IDIM.

VI. Discussion

40. As this paper illustrates, there is still much work to be done to improve coverage of the IDIM and its estimates. Next steps include adding race and ethnicity data to the IDIM though an established method used by other areas of the Census Bureau—namely, using matched information from the 2010 and 2020 Census on race/ethnicity to assign values, as well as modeling missing information on new migrants from ACS country of origin race distributions. This would provide us with the ability to derive all characteristics needed to produce our migration estimates from the IDIM. The application of the IDIM to produce subnational county-level estimates needs to be further evaluated, even if questions about data coverage persist.

41. Further work on the potential for adding ITINs to the IDIM through a process that does not duplicate

individuals could be beneficial and greatly help improve coverage of the unauthorized migrant population. The IDIM also underestimates young children. This may be partially resolved by refining our imputation processes for non-matching dependents but will need further investigation. This paper provided additional insight into this underestimated population, and as discussed earlier, further disaggregation of the ACS-only population would improve our understanding the IDIM. Use of additional data sources could also help in this endeavor.

42. Linking the IDIM to other data sources, such as files provided by the United States Citizenship and Immigration Services or the Department of Health and Human Services, would be very helpful. Data sharing agreements are being developed with these agencies and could provide invaluable information, not just for missing populations, but for verifying and improving data quality on the IDIM.

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43. Similarly, though we do not currently have access to data from US Immigration and Customs Enforcement, the Student and Exchange Visitor Program would be ideal for estimating student and exchange visitor flows. Arrival and Departure Information System data from Customs and Border Protection could help us measure unauthorized flows. These are examples of potential data sources which could be incorporated into the IDIM at a future date.

44. In addition to improving IDIM coverage, further work should be conducted to better understand data quality, such as for the citizenship and year of entry questions. In any event, the US Census Bureau will continue to attempt to develop and integrate administrative sources with survey data to improve our net international migration estimates.

References

Abowd, J., William R. Bell, J. David Brown, et al. (2020). Determination of the 2020 US Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology. Center for Economic Studies Working Paper Series No. 20-23. Washington, DC: US Census Bureau. Bond, B., J.D, Brown, A. Luque, and A. O’Hara. (2014). The Nature of the Bias when Studying Only Linkable Person Records: Evidence from the American Community Survey. Center for Administrative Records Research and Applications Working Paper Series No. 2014-08. Washington, DC: US Census Bureau. Brown, J. David, Misty L. Heggeness, Suzanne M. Dorinski, Lawrence Warren and Moises Yi. (2019). Predicting the Effect of Adding a Citizenship Question to the 2020 Census. Demography 56:1173–1194. Jensen, Eric b., Renuka Bhaskar, and Melissa Scopilliti. (2015). Demographic Analysis 2010: Estimates of Coverage of the Foreign-Born Population in the American Community Survey. US Census Bureau Working Paper No. 103. Washington, DC: US Census Bureau. Luque, A., and R. Bhaskar. (2014). 2010 American Community Survey Match Study. Center for Administrative Records Research and Applications Series Working Paper No. 2014-03. Washington, DC: US Census Bureau. Rastogi, S., and A. O’Hara. (2012). 2010 Census Match Study Report. 2010 Census Planning Memoranda Series No. 247. Washington, DC: US Census Bureau. Van Hook, Jennifer and James D. Bachmeier. (2013). How Well Does the American Community Survey Count Naturalized Citizens? Demographic Research 29(1): 1–32. Wagner, D., and M. Layne. (2014). The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications’ (CARRA) Record Linkage Software. Center for Administrative Records Research and Applications Working Paper Series No. 2014-01. Washington, DC: US Census Bureau.

https://pubmed.ncbi.nlm.nih.gov/?term=Van%20Hook%20J%5BAuthor%5D

https://pubmed.ncbi.nlm.nih.gov/?term=Bachmeier%20JD%5BAuthor%5D

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Appendix Table 1. Demographic and Socioeconomic Characteristics for the IDIM and ACS Universes

Foreign-Born Population ACS as the Base IDIM as the Base

Demographic Characteristics

ACS Records Matched to IDIM

ACS Records not Matched to IDIM

IDIM Administrative

Records

IDIM-subset for Matched ACS

Records Sex Male 48% 52% 48% 47% Female 52% 48% 52% 53% Age 0-17 6% 10% 5% 5% 18-24 7% 10% 8% 7% 25-34 19% 21% 20% 18% 35-44 25% 25% 24% 24% 45-54 25% 20% 24% 26% 55-64 20% 15% 18% 20% Race White Alone 54% 67% X X Black Alone 12% 10% X X Asian Alone 31% 19% X X Other 3% 4% X X Non-Hispanic 60% 39% X X Hispanic Mexican 21% 37% X X Central American/ Dominican Republic 9% 16% X X Other 10% 8% X X Poverty Status Not In Poverty 91% 78% X X In poverty 9% 22% X X Employment Status Employed 78% 64% X X Unemployed 3% 3% X X Not in Labor Force 19% 33% X X Education Less than high school 20% 37% X X High School 21% 26% X X Some college / College graduate 42% 29% X X Post grad 17% 8% X X

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Citizenship Status Non-Citizen 46% 71% 72% 67% Naturalized 54% 29% 28% 33% Year of Entry Before 1990 23% 17% 17% 19% 1990 to 1999 24% 20% 22% 25% 2000-2009 27% 29% 25% 26% 2010 to 2014 12% 12% 16% 15% 2015 and later 13% 23% 20% 15%

N (in thousands) 26,450 11,040 29,690 283 Note: ACS values are weighted. Sources: US Census Bureau, Integrated Database for International Migration, 2019 American Community Survey; Social Security Administration; and Internal Revenue Service

I. Introduction
II. IDIM overview
III. Comparison of IDIM and ACS Foreign-Born Universes
IV. Data Quality Analysis
V. Using the ACS to adjust IDIM
VI. Discussion

Russian

*Подготовили Джейсон Шахтер, Эстер Миллер и Анджелика Менчака ПРИМЕЧАНИЕ: Обозначения в настоящем документе не подразумевают выражения какого-либо мнения Секретариата Организации Объединенных Наций в отношении юридического положения любой страны, территории, города или края или их властей или в отношении делимитации ее границ.

Европейская экономическая комиссия Конференция европейских статистиков Группа экспертов по статистике миграции Женева, Швейцария, 26-28 октября 2022 года Пункт A предварительной повестки дня Положительные изменения в использовании административных данных для статистики миграции

Оценка охвата для Интегрированной базы данных по международной миграции (ИБДММ) Бюро переписи США

Записка Бюро переписи США Аннотация В последние годы Отдел международной миграции Бюро переписи населения США изучал использование административных данных для улучшения оценок потоков международной миграции иностранцев, в результате чего была создана связанная база данных под названием Интегрированная база данных по международной миграции (ИБДММ). Недостаток ИБДММ заключается в том, что она ограничена лицами, присутствующими в наборах данных федеральных административных записей, полученных Бюро переписи населения, что приводит к пробелам в учете лиц иностранного происхождения. Мы предполагаем, что ИБДММ занижает показатели или упускает определенные группы иностранцев, не присутствующие в связанных источниках данных, в том числе работающих мигрантов, которые не подавали налоговые декларации, неработающих иждивенцев, не заявленных в качестве освобожденных от уплаты налогов, иностранных студентов, посетителей по обмену и нелегальных мигрантов. Обследование американского сообщества (ОАС) — это крупное ежегодное обследование домохозяйств, проводимое Бюро переписи населения США, которое теоретически должно охватывать группы населения, не учтенные в ИБДММ. Чтобы изучить ограничения охвата ИБДММ, мы сопоставляем отдельные записи ОАС с ИБДММ. В настоящем документе представлены результаты зондирующего исследования для оценки охвата ИБДММ. Здесь также оцениваются численность и характеристики респондентов ОАС, не включенных в ИБДММ, в отличие от тех, кто присутствует в ИБДММ. Кроме того, это исследование призвано оценить качество данных ОАС и ИБДММ, поскольку мы можем сравнить результаты для связанных лиц и домохозяйств в каждом источнике данных. Результаты сравнения дают нам информацию о потенциале использования ОАС для коррекции ограниченного охвата ИБДММ.

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I. Введение

1. Отделение международной миграции Бюро переписи населения США ежегодно производит оценки

иммиграционных потоков иностранцев в США с указанием демографических данных (возраст, пол, раса и латиноамериканское происхождение) в географическом разрезе страны, штата и округа. Эти оценки формируются с использованием Обследования американского сообщества (ОАС) в качестве основного источника данных, хотя оценки по итогам обследования имеют ограничения, такие как повышенная дисперсия, особенно в субнациональных регионах, и запаздывающее измерение миграционных событий. Чтобы помочь преодолеть эти ограничения, Бюро переписи населения разработало альтернативный источник данных под названием «Интегрированная база данных по международной миграции» (ИБДММ), который включает доступные административные данные из системы социального обеспечения и налоговой отчетности для оценки международной миграции.

2. Одним из недостатков ИБДММ является то, что она ограничивается лицами, присутствующими в

наборах данных федеральных административных записей, из-за чего лица, родившиеся за границей и не включенные в эти записи, не учитываются. Это означает, что ИБДММ, вероятно, занижает количество или упускает определенные группы иностранцев, например работающих мигрантов, которые не подавали налоговые декларации, неработающих иждивенцев, не заявленных в качестве освобожденных от уплаты налогов, иностранных студентов, посетителей по обмену и нелегальных мигрантов. ОАС представляет собой крупное ежегодное обследование домохозяйств, проводимое Бюро переписи населения США. Обследование репрезентативно для всего постоянного населения и, таким образом, теоретически должно охватывать эти недостающие в ИБДММ группы населения. Хотя вероятно, что в ОАС тоже недостаточно представлены некоторые из этих отсутствующих групп иностранного происхождения, мы все же можем проанализировать ограничения охвата ИБДММ, сопоставив отдельные записи ОАС с ИБДММ. Это позволяет нам сравнивать тех, кто присутствует и в ОАС, и в ИБДММ («совпадение ОАС» или «совпадение ОАС-ИБДММ»), с теми, кто присутствует в ОАС, но не в ИБДММ («только ОАС» или «несовпадение ОАС-ИБДММ»). В дополнение к этому сравнению для лиц, представленных как в ОАС, так и в ИБДММ, мы можем оценить качество данных по ряду переменных, общих для обоих наборов данных, включая возраст, пол, статус гражданства, год въезда и текущее место жительства. Наконец, на основе этих выводов в настоящем документе обсуждаются возможности использования ОАС для коррекции результатов ИБДММ для улучшения соответствующих оценок.

II. Обзор ИБДММ

3. ИБДММ создается путем установления связей между источниками административных данных,

которые можно использовать для оценки показателей иммиграции лиц иностранного происхождения. Хотя существует много административных источников данных, которые потенциально могут быть использованы в ИБДММ, например, те, которые ведет Министерство национальной безопасности (МНБ), в настоящее время мы ограничены источниками данных, уже доступными Бюро переписи населения: система Numident Службы социального обеспечения (ССО) и информация о налоговых декларациях от Налоговой службы (НС). Привязка данных осуществляется путем сопоставления

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уникальных персональных ключей идентификации (ПКИ), которые присваиваются каждому отдельному человеку в наборах данных. ПКИ проще всего создать с помощью прямого сопоставления зашифрованных номеров социального страхования (НСС), но они также создаются путем вероятностного сопоставления информации об имени, поле, возрасте и адресе.

А. Numident (Цифровая система опознавания)

4. Numident — это набор данных микрозаписей, который объединяет записи НСС ССО с записями о

смертях Бюро переписи населения. Он включает данные о демографических характеристиках, месте рождения и статусе гражданства. Однако он не включает адресных данных. НСС можно легко анонимизировать с помощью ПКИ, которые позволяют связывать наборы данных. Поскольку большинство зарегистрированных иммигрантов в США подают заявки на получение НСС, система Numident была выбрана в качестве основы ИБДММ для первоначальной интеграции данных и исследования

5. Данные Numident предоставляются ежеквартально и содержат записи обо всех лицах, которые когда-

либо получали НСС. Помимо родившихся в стране, сюда входят заявки на НСС от родившихся за границей. Лица, родившиеся за границей, которые либо имеют разрешение на работу, либо стали натурализованными гражданами, имеют право на получение НСС. Используя комбинацию статуса гражданства, места рождения и даты создания записи, мы можем идентифицировать мигрантов, родившихся за границей, на национальном уровне по демографическим характеристикам. Связывание данных Numident с другими источниками может дать нам дополнительную информацию, такую как место жительства, что позволит нам создать субнациональные оценки численности лиц, родившихся за границей. Так мы получаем информацию еще и о «признаках жизни», которые дают нам дополнительную уверенность в том, что лицо, имеющее номер социального обеспечения, переехало в США на период времени, необходимый для получения вида на жительство.

B. Налоговая информация от Налоговой службы

6. НС представляет в Бюро переписи населения информацию на основе формы 1040 каждые четыре

недели. Хотя эти данные не включают демографические характеристики, они включают информацию об адресах и ПКИ для основного заявителя, супругов и иждивенцев. Там, где это возможно, адресные данные связаны с Идентификатором главного адресного файла. Данные НС не включают информацию о статусе лиц иностранного происхождения, которая должна поступать из связанных файлов Numident. Кроме того, они не включают информацию о лицах, не подающих налоговые декларации (либо из-за отсутствия достаточного дохода, либо из-за того, что не заявляют о доходах). НСС, включенные в налоговые данные, обеспечивают возможность прямого сопоставления физических лиц с данными системы Numident.

7. Кроме того, можно идентифицировать налоговые данные, в которых используются индивидуальные

идентификационные номера налогоплательщика (ИИНН), – уникальные идентификаторы, используемые физическими лицами, не имеющими НСС, для подачи налоговых деклараций. ИИНН выдаются только лицам, не являющимся гражданами США; таким образом, чтобы присвоить статус родившихся за границей, нет необходимости связывать этих людей с системой Numident. Хотя предполагается, что эта совокупность включает мигрантов, не имеющих разрешения на работу, она также включает неработающих иждивенцев легальных рабочих-мигрантов. Изменения в налоговом законодательстве 2017 года привели к резкому сокращению сообщаемых ИИНН, поскольку супруги и иждивенцы с тех пор имеют право на ИИНН, только если они имеют право на определенные вычеты

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или подают свою собственную отдельную декларацию.1 Эта ситуация значительно снижает полезность ИННН для идентификации мигрантов после 2017 года. Дальнейшее усложнение использования ИИНН заключается в том, что от отдельных лиц периодически требуется повторно подавать заявку на получение ИИНН, и, таким образом, кто-то может иметь несколько ИИНН в течение жизни. Кроме того, держатели ИИНН могут позже подать заявку на получение НСС и, таким образом, для них в файле могут быть как ИИНН, так и НСС. Поскольку номера ИИНН не регистрируются в системе социального обеспечения, их нельзя напрямую сопоставить с данными системы Numident, хотя можно использовать вероятностные методы для сопоставления записей ИИНН с другими наборами данных. Текущая версия ИБДММ не включает держателей ИИНН, но в настоящее время проводятся дополнительные исследования, чтобы понять, как их можно включить в будущие анализы.

C. Создание ИБДММ

8. Как отмечалось ранее, система Numident является основой для выявления иммигрантов, родившихся за

границей. Она используется на первом этапе обработки, когда лица, родившиеся за границей, идентифицируются с помощью переменных гражданства из системы Numident (включая иностранцев, имеющих разрешение на работу, и натурализованных граждан). Затем мы используем год создания записи в качестве приблизительного показателя для года въезда в США. Наконец, мы удаляем лиц, умерших в том же году, когда они мигрировали. Этот шаг приводит к оценке численности иммигрантов иностранного происхождения по годам с демографическими характеристиками, хотя и явно завышенной. Мы ожидаем, что здесь оценка будет завышена, так как эта совокупность включает заявителей на получение НСС, которые получили НСС, но либо приехали в США только на короткий период времени, либо никогда фактически не мигрировали в США. Информация о лицах, родившихся в США, сохраняется в файле, чтобы иметь группу сравнения с родившимся за границей. Данные о расе и латиноамериканском происхождении являются неполными или отсутствуют в системе Numident2, поэтому необходимо использовать альтернативные методы для определения расы и латиноамериканского происхождения путем моделирования данных десятилетней переписи населения 2010 года и файлов ОАС. Такие методы определения расы в системе Numident не были включены в статью, что ограничивает анализ этих переменных.

9. На втором этапе обработки мы сопоставляем записи Numident с данными налоговых деклараций по

форме 1040 НС, чтобы подтвердить въезд в США. Numident содержит все заявки на получение НСС, в том числе от тех, кто получил НСС, но фактически никогда не въезжал в США или проживал там в течение короткого периода времени. Чтобы исключить эту группу из наших оценок, мы сопоставляем данные НС, чтобы ограничить совокупность законными мигрантами, которые работали и платили налоги в США, а также как работающими, так и неработающими натурализованными гражданами. На этом этапе также присваиваются геокоды, что дает нам информацию об иммигрантах иностранного происхождения с демографическими характеристиками в разрезе страны, штата или округа. Здесь мы ожидаем занижение оценки численности населения иностранного происхождения, поскольку мы не учитываем мигрантов, не подающих налоговую декларацию, а также легальных неработающих мигрантов.

10. На данном этапе ИБДММ включает следующие группы иммигрантов: натурализованные граждане,

иностранные граждане, имеющие разрешение на работу и подающие налоговые декларации, а также их неработающие иждивенцы и супруги. Не включены следующие группы населения: граждане США, родившиеся за границей от родителей-американцев, нелегальные мигранты, работающие мигранты, не подавшие налоговую декларацию, и неработающие иждивенцы, на которых не распространяется

1 См.ttps://www.irs.gov/individuals/individual-taxpayer-identification-number. 2 Почему исследователи теперь полагаются на опросы для получения данных о расе по программам OASDI и

SSI: Сравнение четырех основных обследований (ssa.gov)

https://www.irs.gov/individuals/individual-taxpayer-identification-number

https://www.ssa.gov/policy/docs/rsnotes/rsn2016-01.html

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освобождение от уплаты налогов. Учитывая, что ОАС призвано быть репрезентативным для всего постоянного населения США, оно должно включать информацию о многих группах иностранного происхождения, которые в настоящее время отсутствуют в ИБДММ.

D. ОАС

11. В настоящем документе микроданные ОАС за 2019 год связываются с ИБДММ, чтобы помочь оценить

охват ИБДММ, а также качество данных как ИБДММ, так и ОАС. ОАС представляет собой большое ежегодное непрерывное обследование домохозяйств США, в ходе которого запрашивается подробная информация, ранее собранная в развернутой форме десятилетней переписи. Полностью реализованное в 2005 году, оно в настоящее время охватывает около 3,5 миллионов адресов в год. Включение в выборку основано на проживании или планировании проживания не менее двух месяцев по адресу, включенному в выборку. В ходе ОАС респондентам задают вопросы социально-демографической и экономической направленности, которые включают переменные, относящиеся к иммиграции, такие как страна рождения, статус гражданства, год въезда в США и страна проживания год назад. Хотя информация о НСС не собирается в рамках ОАС, респондентам ОАС могут быть присвоены ПКИ с помощью системы проверки личности (СПЛ), которая определяет вероятность путем сопоставления информации об имени, поле и адресе.

12. Поскольку выборочная совокупность ОАС включает всех жителей США и не различает их по

правовому статусу, мы считаем, что ОАС является потенциально хорошим источником информации о мигрантах, не учтенных в ИБДММ. Однако, учитывая сложность подсчета недавно въехавших и нелегальных мигрантов, вполне вероятно, что эти группы населения недостаточно представлены в ОАС (Jensen et al, 2015). Хотя мы не собираемся оценивать охват ОАС в этой статье, эту проблему следует иметь в виду при интерпретации некоторых наших результатов.

E. Система проверки личности (СПЛ)

13. СПЛ применяется Бюро переписи населения для определения и проверки номеров НСС и ПКИ для

персональных записей в ходе обследований, переписей и административных записей. Бюро переписи пытается присвоить ПКИ каждой административной записи с помощью вероятностной модели, известной как Модель проверки личности (Wagner and Layne, 2014), и состоящей из четырех модулей. Во-первых, если административные данные содержат НСС, модуль верификации проверяет точное совпадение НСС с файлом Numident и проверяет достаточное соответствие элементов имени и даты рождения. Если они согласуются, НСС считается верифицированным и СПЛ присваивает соответствующий ПКИ записи о человеке. Если НСС отсутствует, как в случае с ОАС, СПЛ переходит к еще трем вероятностным модулям, пытаясь присвоить НСС административной записи, используя географические данные, имя и дату рождения. Приблизительно 94 % всех записей ОАС за 2010 год получили ПКИ, из чего следует, что только 6% всех записей не могут быть связаны с какими-либо административными данными.

14. Лица, которым не присвоены ПКИ, потенциально могут привести к смещению связанных данных. При

изучении ОАС 2009 и 2010 годов был сделан вывод о том, что СПЛ с меньшей вероятностью верифицирует маленьких детей, представителей меньшинств, проживающих в помещениях для группового проживания, иммигрантов, недавно переехавших, лиц с низким доходом и безработных (Bond, 2014). В дополнение к неприсвоению ПКИ существует возможность ошибочного присвоения ПКИ отдельным лицам (также называемая ошибкой привязки записей), хотя частота этих неправильно присвоенных ПКИ для иностранцев неизвестна (Abowd et al., 2020). Это одна из возможных причин

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несоответствия между переменными в ИБДММ и ОАС, когда сопоставление людей происходит неправильно.

F. Привязка ОАС к ИБДММ

15. В этой статье мы связали данные ОАС за 2019 год с совокупностью ИБДММ за 2019 год. Поскольку

совокупность ИБДММ определяется связанными записями из системы Numident и НС, которые характеризуются недостаточным охватом пожилых людей, мы ограничили нашу совокупность лицами моложе 65 лет, чтобы совокупность ОАС была более сопоставимой. Для нашей совокупности ОАС 14% идентифицированы как родившиеся за границей. Чтобы определить иностранца в ОАС, мы используем ответы на вопрос о гражданстве, полученные в рамках ОАС. Те, кто родился в США и за границей от родителей-американцев, определяются как «родившиеся в стране», в то время как натурализованные граждане США и лица, не являющиеся гражданами США, определяются как «родившиеся за границей». Доля родившихся за границей в ИБДММ составляет 12,6%. Родившиеся за границей определяются аналогично ОАС на основании переменной гражданства из-за опасений относительно качества данных о стране рождения и индикаторных переменных иностранного происхождения в системе Numident. В случае ИБДММ мы используем переменную, которая идентифицирует «граждан США» и «легальных иностранцев», в сочетании с переменной, которая указывает, был ли человек когда-либо натурализован. Ни определения ОАС, ни определения в ИБДММ для родившихся за границей не разбивают эту группу по статусу гражданства, что важно отметить из- за проблем с качеством данных для переменной натурализации как в ОАС (Van Hook and Bachmeirb, 2013), так и в Numident.

16. Для всей совокупности ОАС 91% выборки может быть присвоен индивидуальный ПКИ и, таким

образом происходит сопоставление с системой Numident или присвоение ИИНН НС. Среди лиц иностранного происхождения, выявленных в рамках ОАС, 79% выборки могут быть присвоены ПКИ. При дальнейшей привязке к данным НС/НСС эти коэффициенты совпадения снижаются: через ПКИ можно привязать 82% всей совокупности ОАС и 71% выборки лиц иностранного происхождения. Такое снижение коэффициентов совпадения ожидаемо, поскольку ИБДММ ограничивается налоговыми декларантами с НСС. На основании предыдущих исследований, рассмотренных выше, ожидался более низкий коэффициент совпадения для совокупности лиц иностранного происхождения, что приводит к недостаточному учету лиц иностранного происхождения в ИБДММ.

17. И наоборот, показатель несовпадения для совокупности родившихся за границей в ОАС составляет

29%, что дает нам ключевую группу сравнения. Эта совокупность состоит из трех отдельных групп: учтенные в ОАС, кому нельзя присвоить ПКИ, те, у кого есть ИИНН и кто подает налоговые декларации, и те, кто может быть связан с системой Numident, но не подавал налоговые декларации или не фигурировал в налоговых декларациях, подаваемых в НС, как освобожденный от уплаты налога. В будущих исследованиях будет предпринята попытка сделать разбивку совокупности ОАС, не сопоставленной с совокупностью ИБДММ, но предполагается, что она включает большую часть нелегальных мигрантов, а также такие группы, как иностранные студенты и иждивенцы, которые не заявлены как освобожденные от налогов в налоговых декларациях, следовательно, те, кто считается отсутствующими в совокупности ИБДММ.

III. Сравнение совокупностей родившихся за границей в ИБДММ и ОАС

18. Существует несколько различных совокупностей, которые можно сравнить для оценки охвата ИБДММ. Рисунок 1 представляет собой концептуальную схему того, как создаются различные совокупности, связывающие между собой Numident, НС и ОАС. ИБДММ состоит из совпадений Numident и НС и в настоящее время исключает тех, у кого есть записи ИИНН (светло-голубого цвета, за пределами Numident и ИБДММ). На рисунке также обозначены важные группы сравнения,

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использованные в этом анализе: (1) ИБДММ в целом (фиолетовый), (2) группа совпадения ОАС или ОАС-ИБДММ (желтый) и (3) группы только ОАС или группы несовпадения ОАС-ИБДММ. Группа только ОАС состоит из трех подгрупп: файлы ОАС, не поддерживающие ПКИ (светло-желтый), респонденты ОАС в Numident, но не в ИБДММ (оранжевый, например, не подающие налоговые декларации), и респонденты ОАС в НС, но не в Numident (зеленый, например, держатели ИИНН)

Рисунок 1. Оценочные совокупности Numident, НС, ИБДММ и ОАС

Источники: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey

19. В целях сравнения в Таблице 1 приведены подробные демографические и социально-экономические характеристики каждой из этих совокупностей родившихся за границей. Все эти характеристики могут быть получены из ОАС, хотя ограниченное количество переменных также доступно в ИБДММ, включая пол, возраст, статус гражданства, год въезда и государство текущего проживания. Таким образом, для группы совпадения ОАС-ИБДММ некоторые переменные могут быть получены независимо как из ОАС, так и из ИБДММ, что позволит нам оценить качество данных в следующем разделе.

(См. Таблицу 1 в приложении)

20. Как и ожидалось, мы обнаружили четкие различия между сопоставленными совокупностями

ИБДММ/ОАС-ИБДММ и совокупностями только ОАС. Совокупность только ОАС содержала больше мужчин, была более молодой (до 25 лет), менее азиатской, более латиноамериканской (мексиканской и центральноамериканской), менее образованной и в большей степени не работающей и бедной. Между группой ИБДММ и группой только ОАС были некоторые расхождения, касающиеся года въезда. Сравнение совокупностей только ОАС и сопоставленных с ОАС показало более резкие различия между группами, чем сравнения с ИБДММ в целом. Особый интерес представляли различия относительно статуса гражданства и года въезда.

21. Распределение по полу и возрасту можно получить как из ИБДММ, так и из ОАС для различных групп.

Совокупность только ОАС была более мужской, чем совокупность ИБДММ и сопоставленная совокупность ОАС-ИБДММ. Возраст в ИБДММ был в целом выше (50 лет и старше), в то время как совокупность группы только ОАС была более молодой, включая больше людей студенческого возраста и детей.

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22. Как обсуждалось ранее, раса и латиноамериканское происхождение в настоящее время не поддаются

измерению в ИБДММ, поэтому это сравнение было ограничено группами только ОАС и группами, сопоставленными с ОАС. Были обнаружены четкие различия: гораздо больше азиатов в совокупности, сопоставленной с ОАС, и гораздо больше латиноамериканцев в совокупности только ОАС. Среди латиноамериканцев в совокупности только ОАС было гораздо больше лиц мексиканского или центральноамериканского происхождения по сравнению с совокупностью, сопоставленной с ОАС.

23. Социально-экономические переменные доступны только в ОАС, и были видны четкие различия между

совокупностями, в которых используется только ОАС, и совокупностями, сопоставленными с ОАС. С точки зрения образования, совокупность только ОАС с гораздо большей вероятностью имела ниже среднего образование, в то время как совокупность, сопоставленная с ОАС, с большей вероятностью имела, по крайней мере, диплом колледжа. В связи с этим, группа сопоставления с ОАС с большей вероятностью работала и не жила в бедности, чем группа только ОАС.

24. Некоторые интересные результаты были обнаружены при рассмотрении переменных года въезда и

гражданства, представленных как в ОАС, так и в ИБДММ. Год въезда определяется в ИБДММ как год, когда НСС был введен в систему Numident, в то время как ОАС спрашивает респондентов, в каком году они переехали в США, поэтому мы ожидаем увидеть различия между этими наборами данных. Это не относится к совокупностям только ОАС и ИБДММ, поскольку в них распределение по годам въезда очень похоже. Однако при сравнении совокупностей данных только ОАС и сопоставления с ОАС, в которых используется одна и та же переменная измерения для года въезда, различия действительно появляются. Совокупность только ОАС с большей вероятностью включала недавних мигрантов (с 2015 года), чем совокупность сопоставления с ОАС, представители которой с большей вероятностью прибыли до 1999 года. Учитывая аналогичное распределение года въезда между совокупностями ИБДММ и только ОАС, были обнаружены некоторые неожиданные различия между распределениями года въезда на основе ИБДММ при сопоставлении ОАС-ИБДММ, и распределениями года въезда на основе ОАС при сопоставлении ОАС-ИБДММ, которые будут более подробно рассматриваться в следующем разделе.

25. Статус гражданства также дает интересные результаты, которые ставят под сомнение качество этой

переменной в ИБДММ. Лица иностранного происхождения в ИБДММ с гораздо большей вероятностью не являются гражданами США, чем в совокупности, сопоставленной с ОАС, что стало неожиданным результатом. Это говорит о том, что записи ИБДММ не обновляются регулярно после того, как человек натурализуется, или дает основания полагать, что данные ОАС для этой переменной низкого качества (или наблюдается сочетание обеих причин). Более показательным является сравнение совокупностей только ОАС и сопоставленных с ОАС, где обнаруживаются серьезные расхождения. Доля иностранцев и лиц без гражданства намного выше в совокупности только ОАС, в то время как, наоборот, доля натурализованных иностранцев, родившихся за границей, намного выше для совокупности сопоставления с ОАС. Это позволяет говорить о том, что совокупность только ОАС более репрезентативна для лиц, не имеющих права на НСС, включая нелегальных мигрантов и иждивенцев легальных мигрантов.

26. Тот или иной статус гражданства не имеет значения с точки зрения того, как мы используем ИБДММ

для оценок лиц, родившихся за границей. Неважно, есть ли у кого-то в ИБДММ точная и актуальная информация о статусе натурализации, поскольку важно, родился он за границей или нет. Однако эти результаты могут иметь важное значение для других типов анализа с использованием административных данных и, вероятно, потребуют дополнительных связей с другими источниками данных (например, из Службы гражданства и иммиграции США (USCIS)) для точного определения статуса гражданства.

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27. Наконец, на рисунке 2 показана разница в статусе проживания для тех, кто присутствует в файлах только ОАС и ИБДММ. Различия были относительно небольшими: в ИБДММ присутствовало немного больше представителей в таких штатах, как Флорида и Нью-Йорк, а в совокупности только ОАС был немного больше представлен Техас.

Рисунок 2. Процентная разница в статусе проживания между совокупностями ИБДММ и только ОАС

Источники: Бюро переписи населения США, Интегрированная база данных по международной миграции и Обследование американского сообщества 2019 года; Служба социального обеспечения; и Налоговая служба

28. Эти результаты свидетельствуют о том, что в ИБДММ очевидно недостаточно представлены определенные группы лиц иностранного происхождения. Несмотря на собственные потенциальные погрешности в охвате ОАС лиц, родившихся за границей, представляется, что оно более полно учитывает трудные для подсчета группы населения, родившегося за границей, отсутствующие в ИБДММ, и, следовательно, должно быть полезно для корректировки неполного охвата отдельных групп мигрантов в ИБДММ. Частично проблему недостаточной репрезентативности ИБДММ можно было бы решить путем включения ИИНН, но это сопряжено с рядом новых сложностей, которые еще предстоит решить. Вместо привязки дополнительных источников административных данных к

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ИБДММ представляется целесообразным интегрировать ОАС в ИБДММ для частичного решения проблемы недостаточного охвата. Более подробно это обсуждается далее в статье.

IV. Анализ качества данных

29. Первоначальный анализ поднял некоторые вопросы о качестве данных как ИБДММ, так и ОАС, особенно в отношении переменных гражданства и года въезда. Чтобы получить представление о качестве данных, мы сравниваем общие переменные, полученные с помощью ОАС и ИБДММ, для людей, которые сопоставляются по обоим наборам данных. Как обсуждалось ранее, эти общие переменные включают пол, возраст, статус гражданства, год въезда и штат текущего проживания.

30. Результаты для переменных, полученных на основе данных ОАС и ИБДММ, как для пола, так и для

возраста, являются многообещающими. Существует очень сильное совпадение индивидуальных половых переменных, полученных как из ИБДММ, так и из данных ОАС: более 97% респондентов- мужчин и 98% респондентов-женщин сообщили об одном и том же поле. Что касается возраста, мы ожидаем увидеть различия в зависимости от того, как возраст определяется в каждом источнике данных. В ходе ОАС запрашивается возраст на момент обследования (в дополнение к дате рождения), в то время как возраст в системе Numident привязан к середине года. По-видимому проблема именно в этом, поскольку только 48% респондентов имеют одинаковый возраст в обоих файлах. Однако, когда мы увеличиваем возраст до плюс-минус одного года, уровень совпадения увеличивается до 95%. Хотя мы не можем определить, какой источник данных имеет более точные значения, возрастная аккумуляция является известной проблемой для ответов о возрасте при опросах, и все же в целом соответствие возрастов между источниками данных кажется высоким.

31. Статус гражданства определяется очень похоже в ОАС и в ИБДММ, при этом респонденты, родившиеся за границей, делятся на натурализованных и иностранцев. Более ранние результаты предполагали отсутствие связи между этими переменными, полученными с помощью ИБДММ и ОАС, что подтверждается этим анализом. Что касается родившихся за границей по данным ИБДММ, идентифицированных как иностранцы, только 56% из группы сопоставления с данными ОАС сообщают о том же статусе иностранца в соответствии с переменной гражданства в ОАС, в то время как 35% являются натурализованными гражданами и 9% являются коренными жителями согласно вопросу ОАС о гражданстве. Для натурализованных граждан, родившихся за границей, в данных ИБДММ, сопоставленных с ОАС, наблюдается лучшее совпадение: 84% натурализованных согласно вопросу ОАС и только 6% являются иностранцами, а 10% являются коренными жителями. Большие различия между статусами гражданства, полученными на основе ОАС и ИБДММ, могут свидетельствовать об отсутствии обновлений статуса натурализованного гражданина в записях ССО. Однако это также может свидетельствовать о том, что в рамках ОАС сообщаются неточные данные. Кроме того, вызывает беспокойство тот факт, что около 10% родившихся за границей по данным

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системы Numident в ИБДММ классифицируются как коренные жители. Если рассмотреть иностранцев, представленных в данных ОАС, то более 10% также классифицируются как коренные жители в ОАС. Дальнейшие построение комбинационных таблиц по странам рождения может прояснить некоторые из этих выводов. Одним из частичных объяснений этого несоответствия может быть неправильная категоризация коренных жителей при ответе на вопрос ОАС о гражданстве «Родившийся за границей от родителей-американцев». По всей вероятности из-за неоднозначной формулировки вопроса о гражданстве в анкете сюда включены лица, чьи родители не были натурализованными гражданами на момент рождения респондента. В дополнение к ошибочным ответам в данных ОАС и ИБДММ причина кроется еще и в высокой импутации, связанной с переменной гражданства, и неверно подобранных ПКИ. Однако для тщательного изучения этого вопроса потребуются дополнительные исследования.

32. Как обсуждалось ранее, мы ожидаем несоответствия между значениями года въезда в ИБДММ и ОАС,

учитывая разные способы измерения этой переменной в двух наборах данных, а также проблемы качества данных с этой переменной ОАС в связи с точностью отклика модели и возрастной аккумуляцией в ответах. Анализ, показанный на рисунке 3, подтверждает несоответствие между переменной года въезда в ИБДММ и ОАС для сопоставленных лиц. Значения года въезда из этих двух наборов данных совпадают только в 38% случаев. Если мы расширим этот диапазон до плюс-минус одного года, наблюдается улучшение только до 54% случаев, а плюс-минус два года улучшит этот результат до 61%. Даже с диапазоном плюс-минус 9 лет значения года въезда совпадают только в 83% случаев между наборами данных. Опять же, неизвестно, в какой степени это преимущественно связано с проблемами предоставления данных (как для ОАС, так и для Numident), высокой импутацией и (или) ошибкой привязки записей ПКИ. Очевидно, что необходимо дополнительное изучение переменных года въезда и статуса гражданства.

Рисунок 3. Распределение различий в ответах на вопрос о годе въезда среди сопоставленных лиц в данных ОАС и ИБДММ

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Источник: Бюро переписи населения США, Интегрированная база данных по международной миграции и Обследование американского сообщества 2019 года; Служба социального обеспечения; и Налоговая служба

33. Наконец, для лиц, сопоставленных в ОАС-ИБДММ, мы видим сообщаемый статус проживания в ИБДММ и ОАС. Географическая переменная в ОАС зависит от места, где проживал респондент обследования на момент включения в обследование, а в ИБДММ - от того, где человек подавал свою налоговую декларацию. Вполне возможно, что человек переехал в другой штат в течение периода измерения, поэтому можно было бы ожидать некоторых различий по этой переменной между наборами данных. Оценка этой переменной показывает относительно высокое соответствие с 92% географических совпадений по данным ОАС и ИБДММ на уровне штата для связанных лиц. Различия вполне могут быть вызваны перемещениями между штатами в течение периода, хотя менее вероятно, что это связано с импутацией данных ОАС, поскольку эта переменная берется из выборочного списка адресов.

34. Если подытожить, то различия между некоторыми переменными, полученными из ИБДММ и ОАС, для

сопоставленных лиц были неожиданно большими. Как упоминалось при обсуждении переменных статуса гражданства и года въезда, одним из возможных объяснений является то, что данные сообщаются неправильно по каждому набору данных. Существует также вероятность того, что высокие импутации для определенных переменных для родившихся за границей, таких как гражданство, место рождения и год въезда в ОАС, еще больше усиливают эти различия. Кроме того, могут быть ошибки при установлении связей между записями ИБДММ и ОАС, и, следовательно, они будут присвоены разными лицами, что вполне возможно, учитывая вероятностный метод, используемый для присвоения ПКИ при отсутствии информации о НСС. Это еще одно направление, которое требует дальнейшего изучения, чтобы развеять возможные опасения по поводу качества данных ИБДММ и ОАС.

V. Использование ОАС для корректировки ИБДММ

35. Цель этого зондирующего исследования заключалась не только в оценке охвата и качества данных ИБДММ, но и в предоставлении нам информации о том, можно ли использовать ОАС для корректировки ИБДММ с учетом подтвержденных ограничений охвата. Несмотря на потенциальные погрешности в ответах для целых домохозяйств и для отдельных вопросов об иностранном происхождении, по всей видимости ОАС действительно учитывает население иностранного происхождения, отсутствующее в ИБДММ, а именно нелегальных мигрантов и неофициально трудоустроенных мигрантов, которые не подают налоговых деклараций, иностранных студентов и некоторых иждивенцев налоговых декларантов НС.

Рабочий документ 3

36. Бюро переписи населения США производит оценки чистых международных миграционных потоков для страны, штата и округа в разбивке по возрасту, полу, расе и латиноамериканскому происхождению, в основном с использованием данных ОАС. Разработка ИБДММ проводилась не с целью заменить ОАС, скорее это была попытка использовать сильные стороны каждого набора данных посредством интеграции данных, что позволило бы улучшить наши оценки. ИБДММ может быть особенно полезна для улучшения оценок на уровне округов, где наши оценки основываются на сводных данных ОАС о численности иностранцев за 5 лет и по-прежнему имеют высокий уровень изменчивости выборки, особенно для небольших округов. В то же время все еще существует возможность использования ОАС для корректировки недостаточного охвата ИБДММ для оценок на уровне страны и округа, а также для национальных и субнациональных характеристик.

37. Бюро переписи населения ранее интегрировало административные данные на макроуровне, чтобы

улучшить оценки миграции в Пуэрто-Рико и из Пуэрто-Рико после урагана «Мария» на основе национальных обследований, а также для учета воздействия пандемии COVID-19 на международные миграционные потоки в и из США. Эти методики использовали исторические тенденции между данными ОАС и административными данными для корректировки оценок ОАС на основе уровней, наблюдаемых на административных данных. Использование данных ОАС для корректировки ИБДММ будет примером использования данных обследования для корректировки административных записей и потенциально может происходить как на макро-, так и на микроуровне, учитывая характер процедур привязки данных.

38. Например, с точки зрения макроинтеграции, ОАС можно использовать для корректировки не

представленных групп иностранных студентов, а также распределения по возрасту на субнациональном уровне путем добавления доли учащихся к общей численности по стране или путем применения данных ОАС о возрастном распределении на уровне округа для округов с большим количеством студентов. Также можно было бы использовать уровни и характеристики групп населения только по данным ОАС для учета отсутствующих в ИБДММ групп населения посредством либо пропорциональных, либо смоделированных поправок оценок. Эти методы могут помочь учесть часть отсутствующих в ИБДММ нелегальных мигрантов, а также другие группы населения, родившиеся за границей. Дальнейшая работа по совершенствованию разбивки населения по данным только ОАС на различные категории также улучшит нюансы любых корректировок, сделанных для этого населения.

39. С точки зрения микроинтеграции можно было бы использовать связанных в ИБДММ и ОАС

домохозяев, информация о членах семьи которых присутствует в ОАС, но не в ИБДММ, для корректировки данных о неучтенных иждивенцах, не включенных в налоговые декларации. Информация о размере семьи в данных ОАС и о том, в какой степени это население отсутствует в ИБДММ, может дать информацию для некоторых вероятностных методов оценки. Все эти методы интеграции макро- и микроданных еще предстоит разработать, но первоначальные результаты показывают, что ОАС может быть полезным инструментом для улучшения оценок миграции, полученных с помощью ИБДММ.

VI. Обсуждение

40. Как показано в настоящем документе, предстоит еще многое сделать для улучшения охвата ИБДММ и соответствующих оценок. На следующих этапах необходимо добавить в ИБДММ данные о расе и этнической принадлежности с помощью хорошо зарекомендовавшего себя метода, используемого другими подразделениями Бюро переписи населения, а именно: использование сопоставленной информации из переписей 2010 и 2020 годов о расе/этнической принадлежности для присвоения значений, а также моделирование отсутствующей информации о новых мигрантах на основе расового распределения по стране происхождения согласно ОАС. Это дало бы нам возможность получить все характеристики, необходимые для получения оценок миграции, исходя из ИБДММ. Применение ИБДММ для получения субнациональных оценок на уровне округов нуждается в дальнейшей оценке, даже если сохраняются проблемы охвата.

Рабочий документ 3

41. Может быть полезной и значительно поможет улучшить охват нелегальных мигрантов дальнейшая работа над возможностью добавления ИИНН в ИБДММ без дублирования. Кроме того, ИБДММ недостаточно учитывает маленьких детей. Это может быть частично решено за счет доработки процессов импутации для несовпадающих иждивенцев, но это потребует дальнейшего изучения. Настоящая статья предоставила дополнительную информацию об этой недостаточно учтенной группе населения, и, как обсуждалось ранее, дальнейшая разбивка населения на группы по данным только ОАС улучшило бы наше понимание ИБДММ. Использование дополнительных источников данных также может помочь в этом.

42. Было бы очень полезно связать ИБДММ с другими источниками данных, например файлами Службы гражданства и иммиграции США или Министерства здравоохранения и социальных служб. С указанными агентствами разрабатываются соглашения об обмене данными. В результате можно получить бесценную информацию не только о неучтенных группах населения, но и для проверки и улучшения качества данных в ИБДММ.

43. Аналогично, хотя в настоящее время у нас нет доступа к данным Иммиграционной и таможенной

полиции США, данные Программы студентов и посетителей по обмену были бы идеальными для оценки потоков студентов и посетителей по обмену. Данные системы информации о въезде и выезде от Бюро таможенного и пограничного контроля могут помочь нам измерить потоки нелегальных мигрантов. Это примеры потенциальных источников данных, которые могут быть включены в ИБДММ в будущем.

44. Помимо улучшения охвата ИБДММ, необходимо провести дальнейшую работу, чтобы лучше понять качество данных, например относительно гражданства и года въезда. В любом случае Бюро переписи населения США будет продолжать попытки развивать и интегрировать административные источники с данными обследований, чтобы улучшить оценки чистой международной миграции.

Ссылки

Рабочий документ 3

Van Hook, Jennifer and James D. Bachmeier. (2013). How Well Does the American Community Survey Count Naturalized Citizens? Demographic Research 29(1): 1–32. Wagner, D., and M. Layne. (2014). The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications’ (CARRA) Record Linkage Software. Center for Administrative Records Research and Applications Working Paper Series No. 2014-01. Washington, DC: US Census Bureau. Приложение Таблица 1. Демографические и социально-экономические характеристики для совокупностей ИБДММ и ОАС

Родившиеся за границей ОАС в качестве основы ИБДММ в качестве основы

Демографические характеристики

Записи ОАС, сопоставленные с

ИБДММ

Записи ОАС, не сопоставленные с

ИБДММ

Административн ые записи ИБДММ

Подмножество ИБДММ для

сопоставленных записей ОАС

Пол Мужской 48% 52% 48% 47% Женский 52% 48% 52% 53% Возраст 0 -17 6% 10% 5% 5% 18 -24 7% 10% 8% 7% 25 -34 19% 21% 20% 18% 35 -44 25% 25% 24% 24% 45 -54 25% 20% 24% 26% 55 -64 20% 15% 18% 20% Раса Только белые 54% 67% X X Только черные 12% 10% X X Только азиаты 31% 19% X X Прочие 3% 4% X X Нелатиноамериканцы 60% 39% X X Латиноамериканцы Мексиканцы 21% 37% X X Центральная Америка/Доминиканск ая республика 9% 16% X X

https://pubmed.ncbi.nlm.nih.gov/?term=Van%20Hook%20J%5bAuthor%5d

https://pubmed.ncbi.nlm.nih.gov/?term=Bachmeier%20JD%5bAuthor%5d

Рабочий документ 3

Прочие 10% 8% X X Статус бедности Отсутствие бедности 91% 78% X X Бедность 9% 22% X X Статус занятости Занятость 78% 64% X X Незанятость 3% 3% X X Не входит в число работающих 19% 33% X X Образование Ниже средней школы 20% 37% X X Средняя школа 21% 26% X X Высшее неоконченное/высшее образование 42% 29% X X Последипломное образование 17% 8% X X Статус гражданства Иностранцы 46% 71% 72% 67% Натурализованные 54% 29% 28% 33% Год въезда До 1990 23% 17% 17% 19% с 1990 до 1999 24% 20% 22% 25% 2000 -2009 27% 29% 25% 26% с 2010 до 2014 12% 12% 16% 15% 2015 и позже 13% 23% 20% 15%

N (тыс.) 26 450 11 040 29 690 283 Примечание: Значения ОАС являются взвешенными. Источники: Бюро переписи населения США, Интегрированная база данных по международной миграции и Обследование американского сообщества 2019 года; Служба социального обеспечения; и Налоговая служба

I. Введение
II. Обзор ИБДММ
III. Сравнение совокупностей родившихся за границей в ИБДММ и ОАС
IV. Анализ качества данных
V. Использование ОАС для корректировки ИБДММ
VI. Обсуждение

Market Forecast Tables 2022

Read more about Market Forecast Tables 2022

These tables show forest products production and trade forecasts for 2022 and 2023. These cover roundwood (logs, pulpwood and fuel wood), sawnwood (coniferous and non-coniferous), wood-based panels (plywood, particle board, OSB and fibreboard), pulp, paper and wood pellets. The forecast data are provided by national correspondents and approved at the meeting of the Committee on Forests and the Forest Industry.

Languages and translations

English

List of tables

List of Tables and Notes

Table 1 - Sawn Softwood

Table 2 - Sawn Hardwood (total)

Table 2a - Sawn Hardwood (temperate)

Table 2b - Sawn Hardwood (tropical)

Table 3 - Veneer Sheets

Table 4 - Plywood

Table 5 - Particle Board (excluding OSB)

Table 5a - Oriented Strand Board

Table 6 - Fibreboard

Table 6a - Hardboard

Table 6b - MDF/HDF

Table 6c - Other Fibreboard

Table 7 - Wood Pulp

Table 8 - Paper and Paperboard

Table 9 - Removals of wood in the rough

Table 9a - Removals of wood in the rough (softwood)

Table 9b - Removals of wood in the rough (hardwood)

Table 10 - Softwood sawlogs

Table 11 - Hardwood sawlogs

Table 11a - Hardwood logs (temperate)

Table 11b - Hardwood logs (tropical)

Table 12 - Pulpwood

Table 12a - Pulpwood (softwood)

Table 12b - Pulpwood (hardwood)

Table 12c - Wood Residues, Chips and Particles

Table 13 - Wood Pellets

Table 14 - Europe: Summary table of market forecasts for 2022 and 2023

Table 15 - North America: Summary table of market forecasts for 2022 and 2023

Source: UNECE Committee on Forests and the Forest Industry , November 2022, http://www.unece.org/forests/fpm/timbercommittee.html

Notes: Data in italics are estimated by the secretariat. EECCA is Eastern Europe, Caucasus and Central Asia.

Data for the two latest years are forecasts.

In contrast to previous years, data are shown only for countries providing forecasts. Sub-regional totals are only for reporting countries.

Data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subregion. No sub-regional forecast is provided for "Eastern Europe, Caucasus and Central Asia" due to lack of information provided by countries in this sub-region.

Germany – Pellets consumption is an estimated consumption as reported by Pellet Federation. There is a difference between reported consumption and apparent consumption of 242,000 metric tonnes and 214,000 metric tonnes, respectively. For 2022 and 2023, this difference is additionally stored at newly installed plants, i.e. sold but not yet consumed.

Slovenia trade figures are lower than actual as they do not include estimates for non-recorded trade with other EU countries.

Polish trade data exclude non-reporters (estimated at 1-3% of total). Residues exclude recovered wood. Polish sawnwood data exclude shop lumber. Wood pulp production is in metric tonnes, not air-dried, and excludes recovered fibre pulp. Wood pellets production data includes briquettes and non-wood based material.

United Kingdom production figures for OSB is secretariat estimate.

Softwood = coniferous, hardwood = non-coniferous

For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year.

Countries with nil, missing or confidential data for all years on a table are not shown.

Table 1

5.C
		TABLE 1
			SAWN SOFTWOOD						SCIAGES CONIFERES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	6,547	6,469	6,400	10,582	10,370	10,300	1,911	2,045	2,000	5,947	5,947	5,900	Autriche
	Bulgaria	Bulgaria	528	...	...	638	...	...	25	...	...	135	...	...	Bulgarie
	Cyprus	Cyprus	32	33	32	2	1	1	31	32	31	0	0	0	Chypre
	Czech Republic	Czech Republic	3,250	3,249	3,272	5,015	5,144	5,279	526	555	560	2,291	2,450	2,567	République tchèque
	Estonia	Estonia	2,296	1,910	1,910	1,600	1,600	1,600	1,699	1,360	1,360	1,003	1,050	1,050	Estonie
	Finland	Finland	3,731	3,650	3,570	11,900	11,750	12,200	547	350	70	8,716	8,450	8,700	Finlande
	Germany	Germany	20,104	19,800	19,500	25,313	25,300	25,000	5,700	5,000	4,500	10,909	10,500	10,000	Allemagne
	Latvia	Latvia	1,968	1,500	1,300	3,641	3,500	3,300	1,463	900	700	3,136	2,900	2,700	Lettonie
	Luxembourg	Luxembourg	56	43	43	39	39	39	28	5	5	12	1	1	Luxembourg
	Malta	Malta	7	8	9	0	0	0	7	8	9	0	0	0	Malte
	Netherlands	Netherlands	3,036	2,905	2,850	110	100	100	3,408	3,276	3,226	481	470	475	Pays-Bas
	Poland	Poland	4,857	4,750	4,900	4,583	4,500	4,650	1,239	1,250	1,300	965	1,000	1,050	Pologne
	Portugal	Portugal	632	730	665	817	850	840	121	130	125	306	250	300	Portugal
	Serbia	Serbia	379	400	422	99	110	120	295	300	310	15	10	8	Serbie
	Slovakia	Slovakia	563	650	675	1,302	1,300	1,325	324	350	350	1,063	1,000	1,000	Slovaquie
	Slovenia	Slovenia	627	600	550	904	1,000	970	563	500	500	840	900	920	Slovénie
	Sweden	Sweden	6,954	6,450	5,300	19,000	18,500	17,500	514	450	300	12,560	12,500	12,500	Suède
	Switzerland	Switzerland	1,245	1,275	1,315	1,150	1,180	1,220	280	275	270	185	180	175	Suisse
	UK	United Kingdom	10,960	8,920	9,410	3,574	3,010	3,400	7,623	6,150	6,250	237	240	240	Royaume-Uni
		Total Europe	67,771	63,342	62,124	90,268	88,255	87,844	26,303	22,936	21,866	48,800	47,848	47,586	Total Europe
	Canada	Canada a	19,841	18,893	24,156	55,842	52,183	50,290	1,030	752	745	37,031	34,041	26,878	Canada a
	United States	United States a	88,263	88,484	89,272	63,417	64,178	64,820	26,931	26,270	26,533	2,085	1,963	2,081	Etats-Unis a
		Total North America	108,104	107,378	113,428	119,259	116,361	115,109	27,961	27,021	27,277	39,116	36,005	28,959	Total Amérique du Nord
			a converted from nominal to actual size using factor of 0.72						a convertis du dimension nominale au véritable avec une facteur du 0.72

Table 2

5.NC
		TABLE 2
			SAWN HARDWOOD (total)						SCIAGES NON-CONIFERES (total)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	186	215	220	182	178	170	177	210	220	173	173	170	Autriche
	Bulgaria	Bulgaria	71	...	...	79	...	...	22	...	...	30	...	...	Bulgarie
	Cyprus	Cyprus	6	7	6	0	0	0	6	7	6	0	0	0	Chypre
	Czech Republic	Czech Republic	416	427	430	145	147	151	338	340	344	67	60	65	République tchèque
	Estonia	Estonia	230	150	150	150	100	100	177	140	140	97	90	90	Estonie
	Finland	Finland	64	55	55	54	50	50	30	25	25	20	20	20	Finlande
	Germany	Germany	786	760	700	1,061	1,060	1,000	459	400	400	735	700	700	Allemagne
	Latvia	Latvia	234	160	150	797	850	750	75	60	50	638	750	650	Lettonie
	Luxembourg	Luxembourg	38	51	51	39	39	39	18	12	12	19	0	0	Luxembourg
	Malta	Malta	7	7	8	0	0	0	7	7	8	0	0	0	Malte
	Netherlands	Netherlands	309	301	283	38	40	40	343	331	308	72	70	65	Pays-Bas
	Poland	Poland	512	500	540	486	460	510	312	350	380	286	310	350	Pologne
	Portugal	Portugal	224	220	225	148	160	150	106	90	100	31	30	25	Portugal
	Serbia	Serbia	185	197	200	353	382	390	103	95	100	271	280	290	Serbie
	Slovakia	Slovakia	225	325	350	350	375	400	52	100	100	177	150	150	Slovaquie
	Slovenia	Slovenia	121	55	80	140	125	130	99	100	100	118	170	150	Slovénie
	Sweden	Sweden	111	110	110	100	100	100	50	45	45	39	35	35	Suède
	Switzerland	Switzerland	80	85	90	55	60	65	40	40	40	15	15	15	Suisse
	UK	United Kingdom	534	540	540	37	40	40	536	540	540	39	40	40	Royaume-Uni
		Total Europe	4,338	4,166	4,188	4,215	4,166	4,085	2,950	2,892	2,918	2,826	2,893	2,815	Total Europe
	Canada	Canada	1,208	1,229	1,116	880	813	714	798	894	779	470	478	377	Canada
	United States	United States	14,348	15,065	14,707	17,326	17,607	17,467	717	1,040	878	3,695	3,581	3,638	Etats-Unis
		Total North America	15,556	16,295	15,823	18,206	18,420	18,181	1,514	1,934	1,658	4,165	4,059	4,015	Total Amérique du Nord

Table 2a

	TABLE 2a
		SAWN HARDWOOD (temperate)						SCIAGES NON-CONIFERES (zone tempérée)
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
							1000 m3
		Apparent Consumption						Imports			Exports
	Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
		2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria	182	211	216	182	178	170	172	205	215	172	172	169	Autriche
Bulgaria	Bulgaria	70	...	...	79	...	...	21	...	...	30	...	...	Bulgarie
Cyprus	Cyprus	4	3	2	0	0	0	4	3	2	0	0	0	Chypre
Czech Republic	Czech Republic	409	421	424	145	147	151	329	331	335	65	57	62	République tchèque
Estonia	Estonia	227	149	149	150	100	100	172	136	136	94	87	87	Estonie
Finland	Finland	63	54	54	54	50	50	25	20	20	16	16	16	Finlande
Germany	Germany	747	718	658	1,059	1,058	998	385	325	325	698	665	665	Allemagne
Luxembourg	Luxembourg	27	49	49	39	39	39	6	10	10	19	0	0	Luxembourg
Malta	Malta	6	6	7	0	0	0	6	6	7	0	0	0	Malte
Netherlands	Netherlands	166	160	142	31	32	32	184	177	154	49	49	45	Pays-Bas
Poland	Poland	498	485	524	486	459	509	295	333	362	283	307	347	Pologne
Portugal	Portugal	222	190	197	136	150	137	74	50	70	-12	10	10	Portugal
Serbia	Serbia	184	196	199	352	381	389	103	95	100	271	280	290	Serbie
Slovenia	Slovenia	119	52	77	140	125	130	96	97	97	118	170	150	Slovénie
Sweden	Sweden	111	109	109	100	100	100	49	44	44	37	35	35	Suède
Switzerland	Switzerland	71	76	81	52	57	62	34	34	34	15	15	15	Suisse
UK	United Kingdom	458	460	460	37	40	40	456	460	460	36	40	40	Royaume-Uni
	Total Europe	3,563	3,340	3,349	3,042	2,917	2,908	2,411	2,326	2,372	1,890	1,903	1,931	Total Europe
Canada	Canada	1,202	1,214	1,107	880	813	714	781	865	753	459	464	360	Canada
United States	United States	14,162	14,835	14,498	17,326	17,607	17,467	491	773	632	3,656	3,545	3,600	Etats-Unis
	Total North America	15,364	16,049	15,605	18,206	18,420	18,181	1,272	1,638	1,385	4,115	4,009	3,960	Total Amérique du Nord

Table 2b

5.NC.T
		TABLE 2b
			SAWN HARDWOOD (tropical)						SCIAGES NON-CONIFERES (tropicale)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	4	4	4	0	0	0	5	5	5	1	1	1	Autriche
	Bulgaria	Bulgaria	1	...	...	0	...	...	1	...	...	0	...	...	Bulgarie
	Cyprus	Cyprus	2	4	4	0	0	0	2	4	4	0	0	0	Chypre
	Czech Republic	Czech Republic	7	6	6	0	0	0	9	9	9	2	3	3	République tchèque
	Estonia	Estonia	3	1	1	0	0	0	5	4	4	2	3	3	Estonie
	Finland	Finland	1	1	1	0	0	0	5	5	5	4	4	4	Finlande
	Germany	Germany	39	42	42	2	2	2	74	75	75	37	35	35	Allemagne
	Luxembourg	Luxembourg	12	2	2	0	0	0	12	2	2	0	0	0	Luxembourg
	Malta	Malta	1	1	1	0	0	0	1	1	1	0	0	0	Malte
	Netherlands	Netherlands	143	141	141	8	8	8	159	154	153	23	21	20	Pays-Bas
	Poland	Poland	14	15	16	0	1	1	17	17	18	3	3	3	Pologne
	Portugal	Portugal	2	30	28	12	10	13	32	40	30	43	20	15	Portugal
	Serbia	Serbia	1	1	1	1	1	1	0	0	0	0	0	0	Serbie
	Slovenia	Slovenia	2	3	3	0	0	0	3	3	3	0	0	0	Slovénie
	Sweden	Sweden	-0	1	1	0	0	0	1	1	1	1	0	0	Suède
	Switzerland	Switzerland	9	9	9	3	3	3	6	6	6	0	0	0	Suisse
	UK	United Kingdom	76	80	80	0	0	0	79	80	80	3	0	0	Royaume-Uni
		Total Europe	316	341	339	26	25	28	412	406	396	122	90	84	Total Europe
	Canada	Canada	6	16	9	0	0	0	16	29	26	11	14	17	Canada
	United States	United States	186	230	208	0	0	0	226	267	246	39	36	38	Etats-Unis
		Total North America	192	246	217	0	0	0	242	296	273	50	50	55	Total Amérique du Nord

Table 3

6.1x
		TABLE 3
			VENEER SHEETS						FEUILLES DE PLACAGE
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	59	56	56	8	7	7	70	65	65	18	16	16	Autriche
	Bulgaria	Bulgaria	31	...	...	18	...	...	24	...	...	10	...	...	Bulgarie
	Cyprus	Cyprus	1	1	1	0	0	0	1	1	1	0	0	0	Chypre
	Czech Republic	Czech Republic	11	20	21	30	32	33	41	50	54	60	62	66	République tchèque
	Estonia	Estonia	103	140	140	110	140	140	85	86	90	92	86	90	Estonie
	Finland	Finland	8	8	7	170	184	178	9	9	9	171	185	180	Finlande
	Germany	Germany	167	165	160	116	115	110	111	110	110	59	60	60	Allemagne
	Latvia	Latvia	155	85	55	42	45	45	154	110	50	41	70	40	Lettonie
	Luxembourg	Luxembourg	1	0	0	0	0	0	1	0	0	0	0	0	Luxembourg
	Malta	Malta	1	1	2	0	0	0	1	1	2	0	0	0	Malte
	Netherlands	Netherlands	34	34	34	0	0	0	41	41	41	7	7	7	Pays-Bas
	Poland	Poland	146	145	150	46	42	44	121	125	130	21	22	24	Pologne
	Portugal	Portugal	-71	12	3	21	22	23	37	30	30	130	40	50	Portugal
	Serbia	Serbia	19	18	23	27	22	25	13	14	15	21	18	17	Serbie
	Slovakia	Slovakia	16	25	25	29	30	30	21	20	20	34	25	25	Slovaquie
	Slovenia	Slovenia	8	4	5	23	24	21	14	14	14	29	34	30	Slovénie
	Sweden	Sweden	30	25	20	60	55	50	17	20	15	47	50	45	Suède
	Switzerland	Switzerland	3	3	3	0	0	0	4	4	4	1	1	1	Suisse
	UK	United Kingdom	14	10	10	0	0	0	14	10	10	0	0	0	Royaume-Uni
		Total Europe	737	752	715	700	718	706	779	710	660	742	676	651	Total Europe
	Canada	Canada	144	181	173	581	565	565	183	222	230	620	607	622	Canada
	United States	United States	2,675	2,784	2,730	2,284	2,284	2,284	671	759	715	281	258	269	Etats-Unis
		Total North America	2,819	2,965	2,903	2,866	2,849	2,849	854	981	945	901	865	891	Total Amérique du Nord
		Note: Definition of veneers now includes all production (including converted directly to plywood). However most replies here continue to exclude the part going to plywood.
		La définition des placages comprend maintenant toute la production (y compris la conversion directe en contreplaqué).
		Cependant, la plupart des réponses continuent d'exclure la partie destinée au contreplaqué.

Table 4

6.2x
		TABLE 4
			PLYWOOD						CONTREPLAQUES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	94	85	80	184	180	170	267	205	200	357	300	290	Autriche
	Bulgaria	Bulgaria	65	...	...	37	...	...	66	...	...	38	...	...	Bulgarie
	Cyprus	Cyprus	13	15	14	0	0	0	13	15	14	0	0	0	Chypre
	Czech Republic	Czech Republic	201	199	201	260	262	263	186	188	187	245	251	249	République tchèque
	Estonia	Estonia	102	100	100	190	180	180	118	110	110	206	190	190	Estonie
	Finland	Finland	296	280	285	1,130	1,120	1,120	121	110	100	955	950	935	Finlande
	Germany	Germany	1,185	1,170	1,170	103	100	100	1,464	1,450	1,450	382	380	380	Allemagne
	Latvia	Latvia	68	30	30	310	300	250	98	60	30	340	330	250	Lettonie
	Luxembourg	Luxembourg	11	2	2	0	0	0	12	2	2	1	0	0	Luxembourg
	Malta	Malta	10	11	11	0	0	0	10	11	11	0	0	0	Malte
	Netherlands	Netherlands	600	580	565	0	0	0	695	670	650	95	90	85	Pays-Bas
	Poland	Poland	773	770	790	543	540	550	604	620	650	374	390	410	Pologne
	Portugal	Portugal	215	215	200	126	110	100	116	130	120	27	25	20	Portugal
	Serbia	Serbia	41	43	48	15	14	17	30	32	34	4	3	3	Serbie
	Slovakia	Slovakia	232	320	345	307	375	400	65	70	70	140	125	125	Slovaquie
	Slovenia	Slovenia	79	66	68	102	96	98	57	50	50	80	80	80	Slovénie
	Sweden	Sweden	260	245	245	101	90	90	206	200	200	47	45	45	Suède
	Switzerland	Switzerland	209	214	220	7	7	8	205	210	215	3	3	3	Suisse
	UK	United Kingdom	1,486	1,490	1,490	0	0	0	1,541	1,540	1,540	55	50	50	Royaume-Uni
		Total Europe	5,940	5,835	5,864	3,415	3,374	3,346	5,874	5,673	5,633	3,349	3,212	3,115	Total Europe
	Canada	Canada	2,485	2,288	2,490	1,698	1,644	1,639	1,421	1,144	1,406	634	500	555	Canada
	United States	United States	17,031	17,295	17,163	9,705	9,895	9,800	8,086	8,163	8,124	759	762	761	Etats-Unis
		Total North America	19,516	19,583	19,653	11,403	11,539	11,439	9,507	9,306	9,530	1,393	1,263	1,316	Total Amérique du Nord

Table 5

6.3xPB
		TABLE 5
			PARTICLE BOARD (excluding OSB)						PANNEAUX DE PARTICULES (ne comprennent pas l'OSB)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	868	776	766	2,550	2,350	2,300	361	370	360	2,043	1,944	1,894	Autriche
	Bulgaria	Bulgaria	487	...	...	773	...	...	118	...	...	403	...	...	Bulgarie
	Cyprus	Cyprus	47	42	41	0	0	0	47	42	41	0	0	0	Chypre
	Czech Republic	Czech Republic	739	688	671	965	945	930	578	598	577	804	855	836	République tchèque
	Estonia	Estonia	182	155	155	210	130	130	76	85	85	103	60	60	Estonie
	Finland	Finland	107	119	119	54	50	50	83	93	93	30	24	24	Finlande
	Germany	Germany	6,015	5,970	5,870	6,036	6,020	5,920	2,142	2,100	2,050	2,162	2,150	2,100	Allemagne
	Latvia	Latvia	139	120	180	350	300	300	53	70	80	264	250	200	Lettonie
	Luxembourg	Luxembourg	15	3	3	0	0	0	16	4	4	2	1	1	Luxembourg
	Malta	Malta	10	10	11	0	0	0	10	10	11	0	0	0	Malte
	Netherlands	Netherlands	446	430	430	0	0	0	520	500	500	74	70	70	Pays-Bas
	Poland	Poland	7,601	7,700	7,740	6,333	6,370	6,370	2,093	2,150	2,220	824	820	850	Pologne
	Portugal	Portugal	451	527	427	743	730	720	313	295	304	605	498	597	Portugal
	Serbia	Serbia	420	417	422	272	230	235	208	235	240	60	48	53	Serbie
	Slovakia	Slovakia	182	220	215	608	625	625	143	140	135	568	545	545	Slovaquie
	Slovenia	Slovenia	155	155	147	0	0	0	162	163	154	6	8	7	Slovénie
	Sweden	Sweden	971	985	975	561	550	550	506	520	510	97	85	85	Suède
	Switzerland	Switzerland	280	300	320	380	390	400	125	130	135	225	220	215	Suisse
	UK	United Kingdom	2,664	2,242	2,242	2,090	1,722	1,722	638	600	600	65	80	80	Royaume-Uni
		Total Europe	21,780	20,859	20,734	21,926	20,412	20,252	8,189	8,105	8,099	8,336	7,658	7,617	Total Europe
	Canada	Canada	1,487	1,594	1,591	1,647	1,724	1,686	593	594	586	754	724	681	Canada
	United States	United States	5,111	7,189	5,725	4,136	4,220	3,874	1,462	3,144	2,159	488	175	309	Etats-Unis
		Total North America	6,597	8,783	7,316	5,783	5,944	5,560	2,056	3,738	2,745	1,241	899	989	Total Amérique du Nord
		Data are calculated by subtracting OSB from the particleboard/OSB total - les données sont calculées en soustrayant les OSB du total des panneaux de particules et OSB.

Table 5a

6.3.1
		TABLE 5a
			ORIENTED STRAND BOARD (OSB)						PANNEAUX STRUCTURAUX ORIENTES (OSB)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	186	224	214	0	0	0	192	230	220	6	6	6	Autriche
	Bulgaria	Bulgaria	175	...	...	252	...	...	8	...	...	85	...	...	Bulgarie
	Cyprus	Cyprus	15	18	17	0	0	0	15	18	17	0	0	0	Chypre
	Czech Republic	Czech Republic	355	360	366	745	770	795	127	132	135	517	542	564	République tchèque
	Estonia	Estonia	44	45	45	0	0	0	44	45	45	1	0	0	Estonie
	Finland	Finland	47	47	47	0	0	0	47	47	47	0	0	0	Finlande
	Germany	Germany	1,473	1,480	1,480	1,282	1,280	1,280	746	750	750	555	550	550	Allemagne
	Latvia	Latvia	211	160	100	700	600	600	73	60	50	562	500	550	Lettonie
	Luxembourg	Luxembourg	117	265	265	338	338	338	7	1	1	229	74	74	Luxembourg
	Netherlands	Netherlands	192	185	185	0	0	0	208	200	200	16	15	15	Pays-Bas
	Poland	Poland	802	800	860	827	830	880	316	350	380	341	380	400	Pologne
	Portugal	Portugal	31	33	33	0	0	0	34	35	36	3	2	3	Portugal
	Serbia	Serbia	44	53	58	0	0	0	46	55	60	2	2	2	Serbie
	Slovakia	Slovakia	91	90	95	0	0	0	94	95	100	3	5	5	Slovaquie
	Slovenia	Slovenia	33	35	34	0	0	0	36	37	36	2	2	2	Slovénie
	Sweden	Sweden	116	95	95	0	0	0	121	100	100	5	5	5	Suède
	Switzerland	Switzerland	90	90	90	0	0	0	90	90	90	0	0	0	Suisse
	UK	United Kingdom	925	868	868	598	598	598	461	440	440	133	170	170	Royaume-Uni
		Total Europe	4,948	4,848	4,852	4,741	4,416	4,491	2,665	2,685	2,707	2,459	2,253	2,346	Total Europe
	Canada	Canada	1,618	1,589	1,570	7,240	7,581	7,646	124	72	72	5,746	6,064	6,147	Canada
	United States	United States	19,804	20,091	20,381	13,839	14,040	14,243	6,147	6,236	6,326	182	185	188	Etats-Unis
		Total North America	21,422	21,680	21,951	21,079	21,621	21,889	6,271	6,308	6,398	5,928	6,249	6,335	Total Amérique du Nord

Table 6

	TABLE 6
		FIBREBOARD						PANNEAUX DE FIBRES
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
							1000 m3
		Apparent Consumption						Imports			Exports
	Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
		2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria	504	469	445	690	570	545	370	340	330	556	441	430	Autriche
Bulgaria	Bulgaria	106	...	...	75	...	...	118	...	...	87	...	...	Bulgarie
Cyprus	Cyprus	15	16	15	0	0	0	15	16	15	0	0	0	Chypre
Czech Republic	Czech Republic	462	470	481	45	46	47	574	590	609	157	166	175	République tchèque
Estonia	Estonia	74	68	68	80	70	70	77	69	69	83	71	71	Estonie
Finland	Finland	162	165	165	46	46	46	164	161	161	48	41	41	Finlande
Germany	Germany	4,401	4,425	4,335	6,105	6,100	6,000	1,944	1,940	1,865	3,648	3,615	3,530	Allemagne
Latvia	Latvia	19	11	6	37	35	20	69	57	47	87	81	61	Lettonie
Luxembourg	Luxembourg	13	128	128	147	147	147	19	5	5	153	24	24	Luxembourg
Malta	Malta	...	...	...	...	...	...	...	...	...	...	...	...	Malte
Netherlands	Netherlands	454	436	436	29	29	29	572	550	550	147	143	143	Pays-Bas
Poland	Poland	4,398	4,650	4,770	5,750	5,850	6,050	912	970	990	2,264	2,170	2,270	Pologne
Portugal	Portugal	488	510	500	555	550	540	336	335	340	404	375	380	Portugal
Serbia	Serbia	123	151	163	21	18	20	143	168	181	41	35	38	Serbie
Slovakia	Slovakia	248	239	239	0	0	0	275	265	265	27	26	26	Slovaquie
Slovenia	Slovenia	26	30	30	136	135	135	57	55	55	167	160	160	Slovénie
Sweden	Sweden	308	317	293	0	0	0	391	395	365	84	78	72	Suède
Switzerland	Switzerland	292	302	312	205	210	215	266	266	266	179	174	169	Suisse
UK	United Kingdom	1,807	1,780	1,710	798	900	850	1,080	950	930	72	70	70	Royaume-Uni
	Total Europe	13,901	14,168	14,097	14,719	14,706	14,714	7,385	7,132	7,043	8,203	7,670	7,660	Total Europe
Canada	Canada	1,492	1,348	1,355	1,349	1,395	1,395	1,009	889	885	866	936	924	Canada
United States	United States	9,727	10,134	9,985	7,560	7,691	7,663	3,008	3,190	3,123	841	747	801	Etats-Unis
	Total North America	11,219	11,482	11,340	8,909	9,086	9,058	4,017	4,079	4,007	1,707	1,683	1,725	Total Amérique du Nord

Table 6a

6.4.1
		TABLE 6a
			HARDBOARD						PANNEAUX DURS
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	32	36	34	75	48	45	17	20	19	60	32	30	Autriche
	Bulgaria	Bulgaria	45	...	...	51	...	...	40	...	...	46	...	...	Bulgarie
	Cyprus	Cyprus	1	1	1	0	0	0	1	1	1	0	0	0	Chypre
	Czech Republic	Czech Republic	146	146	147	0	0	0	167	168	170	21	22	23	République tchèque
	Estonia	Estonia	27	24	24	0	0	0	34	30	30	7	6	6	Estonie
	Finland	Finland	26	30	30	46	46	46	21	20	20	41	36	36	Finlande
	Germany	Germany	213	210	210	0	0	0	242	240	240	29	30	30	Allemagne
	Latvia	Latvia	11	10	5	0	0	0	23	20	15	12	10	10	Lettonie
	Luxembourg	Luxembourg	-71	-9	-9	0	0	0	2	1	1	73	10	10	Luxembourg
	Netherlands	Netherlands	44	35	35	0	0	0	66	55	55	22	20	20	Pays-Bas
	Poland	Poland	-212	10	10	76	100	100	139	180	180	427	270	270	Pologne
	Portugal	Portugal	44	20	30	12	0	0	42	30	40	10	10	10	Portugal
	Serbia	Serbia	33	38	41	21	18	20	31	35	37	19	15	16	Serbie
	Slovakia	Slovakia	17	20	20	0	0	0	22	25	25	5	5	5	Slovaquie
	Slovenia	Slovenia	1	0	0	0	0	0	8	6	6	7	6	6	Slovénie
	Sweden	Sweden	75	87	78	0	0	0	88	100	90	14	13	12	Suède
	Switzerland	Switzerland	13	13	13	0	0	0	21	21	21	8	8	8	Suisse
	UK	United Kingdom	101	100	100	0	0	0	111	110	110	11	10	10	Royaume-Uni
		Total Europe	544	771	769	281	212	211	1,075	1,062	1,060	812	503	502	Total Europe
	Canada	Canada	46	42	36	90	90	90	68	60	66	112	108	120	Canada
	United States	United States	503	509	514	499	504	509	252	255	258	248	250	253	Etats-Unis
		Total North America	549	551	550	589	594	599	320	315	324	360	358	373	Total Amérique du Nord

Table 6b

6.4.2
		TABLE 6b
			MDF/HDF
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	316	283	265	615	522	500	192	166	161	491	405	396	Autriche
	Bulgaria	Bulgaria	58	...	...	24	...	...	75	...	...	41	...	...	Bulgarie
	Cyprus	Cyprus	13	13	12	0	0	0	13	13	12	0	0	0	Chypre
	Czech Republic	Czech Republic	248	249	251	45	46	47	243	250	258	40	47	54	République tchèque
	Estonia	Estonia	21	20	20	0	0	0	38	35	35	17	15	15	Estonie
	Finland	Finland	117	116	116	0	0	0	124	121	121	7	5	5	Finlande
	Germany	Germany	2,385	2,425	2,400	4,693	4,700	4,650	625	625	600	2,932	2,900	2,850	Allemagne
	Latvia	Latvia	1	0	0	37	35	20	28	25	20	64	60	40	Lettonie
	Luxembourg	Luxembourg	80	136	136	147	147	147	13	3	3	80	14	14	Luxembourg
	Malta	Malta	5	5	5	0	0	0	5	5	5	0	0	0	Malte
	Netherlands	Netherlands	291	285	285	0	0	0	408	400	400	117	115	115	Pays-Bas
	Poland	Poland	3,533	3,560	3,630	3,542	3,600	3,700	743	760	780	752	800	850	Pologne
	Portugal	Portugal	456	485	465	535	550	540	280	285	280	359	350	355	Portugal
	Serbia	Serbia	88	110	118	0	0	0	110	130	140	22	20	22	Serbie
	Slovakia	Slovakia	162	150	150	0	0	0	183	170	170	22	20	20	Slovaquie
	Slovenia	Slovenia	20	29	29	136	135	135	39	43	43	155	149	149	Slovénie
	Sweden	Sweden	213	210	200	0	0	0	272	265	250	59	55	50	Suède
	Switzerland	Switzerland	105	110	115	205	210	215	70	65	60	170	165	160	Suisse
	UK	United Kingdom	1,622	1,600	1,530	798	900	850	878	750	730	54	50	50	Royaume-Uni
		Total Europe	9,734	9,786	9,727	10,776	10,845	10,804	4,341	4,111	4,068	5,383	5,170	5,145	Total Europe
	Canada	Canada	1,301	1,135	1,153	1,159	1,205	1,205	780	648	639	637	718	691	Canada
	United States	United States	6,012	6,042	6,073	3,882	3,901	3,921	2,552	2,565	2,578	422	424	426	Etats-Unis
		Total North America	7,313	7,177	7,226	5,041	5,106	5,126	3,332	3,213	3,217	1,059	1,142	1,117	Total Amérique du Nord

Table 6c

6.4.3
		TABLE 6c
			OTHER FIBREBOARD						AUTRES PANNEAUX DE FIBRES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	157	150	146	0	0	0	162	154	150	5	4	4	Autriche
	Bulgaria	Bulgaria	3	...	...	0	...	...	3	...	...	0	...	...	Bulgarie
	Cyprus	Cyprus	1	2	2	0	0	0	1	2	2	0	0	0	Chypre
	Czech Republic	Czech Republic	68	75	83	0	0	0	164	172	181	96	97	98	République tchèque
	Estonia	Estonia	26	24	24	80	70	70	5	4	4	59	50	50	Estonie
	Finland	Finland	19	19	19	0	0	0	20	20	20	0	0	0	Finlande
	Germany	Germany	1,803	1,790	1,725	1,412	1,400	1,350	1,078	1,075	1,025	686	685	650	Allemagne
	Latvia	Latvia	7	1	1	0	0	0	18	12	12	11	11	11	Lettonie
	Luxembourg	Luxembourg	4	1	1	0	0	0	4	1	1	0	0	0	Luxembourg
	Malta	Malta	1	1	2	0	0	0	1	1	2	0	0	0	Malte
	Netherlands	Netherlands	119	116	116	29	29	29	98	95	95	8	8	8	Pays-Bas
	Poland	Poland	1,077	1,080	1,130	2,132	2,150	2,250	30	30	30	1,085	1,100	1,150	Pologne
	Portugal	Portugal	-12	5	5	8	0	0	15	20	20	35	15	15	Portugal
	Serbia	Serbia	2	3	4	0	0	0	2	3	4	0	0	0	Serbie
	Slovakia	Slovakia	70	69	69	0	0	0	70	70	70	0	1	1	Slovaquie
	Slovenia	Slovenia	5	1	1	0	0	0	10	6	6	5	5	5	Slovénie
	Sweden	Sweden	20	20	15	0	0	0	31	30	25	11	10	10	Suède
	Switzerland	Switzerland	174	179	184	0	0	0	175	180	185	1	1	1	Suisse
	UK	United Kingdom	84	80	80	0	0	0	91	90	90	7	10	10	Royaume-Uni
		Total Europe	3,628	3,616	3,607	3,661	3,649	3,699	1,976	1,965	1,922	2,009	1,997	2,013	Total Europe
	Canada	Canada	144	171	166	100	100	100	162	181	180	117	110	114	Canada
	United States	United States	3,212	3,583	3,398	3,179	3,286	3,233	204	370	287	171	73	122	Etats-Unis
		Total North America	3,357	3,754	3,564	3,279	3,386	3,333	366	551	467	288	183	236	Total Amérique du Nord

Table 7

7.x
		TABLE 7
			WOOD PULP						PATE DE BOIS
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 mt
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	2,261	2,229	2,235	2,004	1,970	1,980	578	577	577	321	318	322	Autriche
	Bulgaria	Bulgaria	125	...	...	210	...	...	7	...	...	92	...	...	Bulgarie
	Czech Republic	Czech Republic	803	806	806	614	640	660	310	300	289	121	134	143	République tchèque
	Estonia	Estonia	84	140	140	260	200	200	49	50	50	225	110	110	Estonie
	Finland	Finland a	6,625	5,760	6,280	10,950	9,360	10,520	150	220	220	4,475	3,820	4,460	Finlande a
	Germany	Germany	5,622	5,685	5,715	2,327	2,390	2,420	4,451	4,400	4,400	1,156	1,105	1,105	Allemagne
	Latvia	Latvia	2	6	2	14	10	10	2	2	2	14	6	10	Lettonie
	Netherlands	Netherlands	929	987	987	37	37	37	2,167	2,150	2,150	1,274	1,200	1,200	Pays-Bas
	Poland	Poland	2,767	2,750	2,790	1,749	1,720	1,750	1,194	1,220	1,250	177	190	210	Pologne
	Portugal	Portugal	1,660	1,640	1,645	2,809	2,750	2,800	141	140	145	1,290	1,250	1,300	Portugal
	Serbia	Serbia	75	76	79	0	0	0	76	77	80	1	1	1	Serbie
	Slovakia	Slovakia	680	685	695	769	775	800	160	160	170	248	250	275	Slovaquie
	Slovenia	Slovenia	331	309	309	86	82	82	250	230	230	5	3	3	Slovénie
	Sweden	Sweden	8,146	8,250	8,400	11,701	11,950	12,150	602	600	600	4,157	4,300	4,350	Suède
	Switzerland	Switzerland	160	160	160	70	70	70	90	90	90	0	0	0	Suisse
	UK	United Kingdom	984	990	...	220	220	...	766	780	790	2	10	10	Royaume-Uni
		Total Europe	31,255	30,473	30,243	33,820	32,174	33,479	10,993	10,996	11,043	13,558	12,697	13,499	Total Europe
	Canada	Canada	7,265	6,097	6,156	14,886	13,861	13,468	1,095	950	1,185	8,717	8,714	8,497	Canada
	United States	United States	48,100	48,274	48,187	49,685	49,859	49,772	6,036	6,036	6,036	7,621	7,621	7,621	Etats-Unis
		Total North America	55,365	54,372	54,344	64,571	63,720	63,240	7,131	6,986	7,221	16,337	16,335	16,118	Total Amérique du Nord
			a imports exclude dissolving pulp						a les importations excluent pâte à dissoudre

Table 8

10.x
		TABLE 8
			PAPER AND PAPERBOARD						PAPIERS ET CARTONS
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 mt
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	2,334	2,340	2,315	5,065	5,100	5,080	1,296	1,310	1,300	4,028	4,070	4,065	Autriche
	Bulgaria	Bulgaria	573	...	...	394	...	...	358	...	...	180	...	...	Bulgarie
	Cyprus	Cyprus	44	48	46	0	0	0	44	48	46	0	0	0	Chypre
	Czech Republic	Czech Republic	1,618	1,602	1,604	901	906	909	1,623	1,604	1,600	906	908	905	République tchèque
	Estonia	Estonia	126	150	150	70	90	90	138	130	130	83	70	70	Estonie
	Finland	Finland	636	590	620	8,660	7,450	8,150	361	350	350	8,385	7,210	7,880	Finlande
	Germany	Germany	18,980	18,500	18,400	23,123	22,800	22,700	10,009	9,800	9,800	14,152	14,100	14,100	Allemagne
	Latvia	Latvia	174	182	182	28	30	30	186	200	200	40	48	48	Lettonie
	Luxembourg	Luxembourg	31	8	8	0	0	0	38	8	8	7	0	0	Luxembourg
	Malta	Malta	26	27	27	0	0	0	26	27	27	0	0	0	Malte
	Netherlands	Netherlands	2,869	2,890	2,890	2,942	2,950	2,950	2,268	2,260	2,260	2,341	2,320	2,320	Pays-Bas
	Poland	Poland	8,002	8,100	8,150	5,324	5,450	5,550	5,233	5,300	5,400	2,556	2,650	2,800	Pologne
	Portugal	Portugal	1,245	1,250	1,290	2,247	2,200	2,240	928	850	900	1,931	1,800	1,850	Portugal
	Serbia	Serbia	760	780	785	535	520	525	462	470	480	237	210	220	Serbie
	Slovakia	Slovakia	554	600	600	1,019	975	1,000	474	450	475	939	825	875	Slovaquie
	Slovenia	Slovenia	491	435	440	635	605	590	435	420	420	579	590	570	Slovénie
	Sweden	Sweden	704	950	950	8,924	8,700	8,850	897	750	750	9,117	8,500	8,650	Suède
	Switzerland	Switzerland	1,050	1,055	1,060	1,170	1,175	1,180	610	600	590	730	720	710	Suisse
	UK	United Kingdom	7,482	7,430	7,450	3,640	3,530	3,650	4,589	4,660	4,550	747	760	750	Royaume-Uni
		Total Europe	47,697	46,937	46,967	64,677	62,481	63,494	29,977	29,237	29,286	46,957	44,781	45,813	Total Europe
	Canada	Canada	4,940	4,796	4,930	8,787	8,436	8,436	2,424	2,567	2,538	6,272	6,207	6,045	Canada
	United States	United States	65,622	68,268	66,945	67,476	70,196	68,836	8,223	8,555	8,389	10,077	10,483	10,280	Etats-Unis
		Total North America	70,561	73,064	71,874	76,263	78,632	77,272	10,647	11,122	10,927	16,348	16,690	16,325	Total Amérique du Nord

Table 9

	TABLE 9
			REMOVALS OF WOOD IN THE ROUGH									QUANTITES ENLEVEES DE BOIS BRUT
			TOTAL									TOTAL
	1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
	Country		Industrial wood - Bois industriels												Wood fuel c Bois de chauffage c						Pays
			Total			Logs			Pulpwood a			Other b						Total
						Grumes			Bois de trituration a			Autre b
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria		13,521	13,873	13,650	10,420	10,607	10,300	3,101	3,266	3,350	0	0	0	4,900	5,263	5,400	18,420	19,136	19,050	Autriche
Bulgaria	Bulgaria		3,172	...	...	1,524	...	...	1,606	...	...	42	...	...	2,357	...	...	5,529	...	...	Bulgarie
Cyprus	Cyprus		...	...	...	...	...	...	...	...	...	0	0	0	7	7	8	...	...	...	Chypre
Czech Republic	Czech Republic		25,146	21,122	20,576	17,739	15,858	14,572	7,294	5,149	5,887	113	115	117	5,110	4,933	4,536	30,256	26,055	25,112	République tchèque
Estonia	Estonia		6,520	6,317	6,317	4,145	4,060	4,060	2,323	2,200	2,200	52	57	57	4,148	4,100	4,100	10,667	10,417	10,417	Estonie
Finland	Finland		58,036	55,847	58,540	26,292	24,618	24,713	31,744	31,229	33,827	0	0	0	8,868	8,868	8,868	66,904	64,715	67,408	Finlande
Germany	Germany		59,187	57,179	54,270	47,403	44,256	42,085	11,624	12,765	12,027	161	158	158	23,224	23,900	24,100	82,411	81,079	78,370	Allemagne
Latvia	Latvia		13,003	12,650	12,550	7,827	7,400	7,300	3,986	4,100	4,100	1,190	1,150	1,150	2,940	3,150	3,200	15,943	15,800	15,750	Lettonie
Luxembourg	Luxembourg		217	332	197	39	86	144	84	160	38	94	86	15	46	73	45	262	405	242	Luxembourg
Netherlands	Netherlands		648	653	653	210	214	214	394	395	395	43	44	44	2,362	2,350	2,350	3,010	3,003	3,003	Pays-Bas
Poland	Poland		38,587	40,300	41,530	18,508	19,300	19,950	19,471	20,410	21,000	608	590	580	4,519	4,450	4,350	43,106	44,750	45,880	Pologne
Portugal	Portugal		12,136	12,240	12,155	2,147	2,190	2,220	9,659	9,700	9,600	331	350	335	1,762	1,830	1,780	13,899	14,070	13,935	Portugal
Serbia	Serbia		1,646	1,586	1,630	1,176	1,166	1,185	307	280	295	163	140	150	6,251	6,950	7,010	7,897	8,536	8,640	Serbie
Slovakia	Slovakia		7,170	7,475	7,590	4,243	4,335	4,400	2,893	3,100	3,150	34	40	40	495	550	610	7,665	8,025	8,200	Slovaquie
Slovenia	Slovenia		2,673	3,078	2,995	1,977	2,210	2,130	648	825	825	48	43	40	1,043	1,200	1,260	3,716	4,278	4,255	Slovénie
Sweden	Sweden		71,400	71,400	70,400	39,300	37,800	36,000	31,800	33,300	34,100	300	300	300	5,400	5,400	5,400	76,800	76,800	75,800	Suède
Switzerland	Switzerland		3,003	3,088	3,163	2,450	2,550	2,610	550	535	550	3	3	3	1,980	2,030	2,100	4,983	5,118	5,263	Suisse
UK	United Kingdom		8,716	7,660	8,410	6,354	5,360	6,060	1,898	1,900	1,900	463	400	450	2,184	2,180	2,180	10,899	9,840	10,590	Royaume-Uni
	Total Europe		324,781	314,800	314,626	191,753	182,010	177,943	129,382	129,314	133,244	3,646	3,476	3,439	77,596	77,234	77,297	402,369	392,027	391,915	Total Europe
Canada	Canada		138,131	135,303	135,303	120,741	117,995	117,995	15,239	15,040	15,040	2,152	2,268	2,268	1,472	1,567	1,567	139,603	136,869	136,869	Canada
United States	United States		382,956	386,045	384,500	183,473	184,966	184,219	185,734	187,318	186,526	13,749	13,762	13,755	71,111	71,127	71,119	454,066	457,172	455,619	Etats-Unis
	Total North America		521,087	521,348	519,803	304,213	302,961	302,214	200,973	202,358	201,566	15,901	16,030	16,023	72,582	72,693	72,685	593,669	594,041	592,488	Total Amérique du Nord
		a	Pulpwood, round and split, as well as chips and particles produced directly								a	Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
			therefrom and used as pulpwood									directement à partir des rondins et quartiers et utilisées comme bois de trituration
		b	Pitprops, poles, piling, posts etc.								b	Bois de mine, poteaux, pilotis, piquets etc.
		c	Including chips and particles produced from wood in the rough and								c	Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
			used for energy purposes									à des fins energétiques

Table 9a

1.2.3.C
		TABLE 9a
				REMOVALS OF WOOD IN THE ROUGH									QUANTITES ENLEVEES DE BOIS BRUT
				SOFTWOOD									CONIFERES
		1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		Country		Industrial wood - Bois industriels												Wood fuel c Bois de chauffage c						Pays
				Total			Logs			Pulpwood a			Other b						Total
							Grumes			Bois de trituration a			Autre b
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		12,671	12,947	12,700	10,139	10,315	10,000	2,531	2,632	2,700	0	0	0	2,993	3,158	3,200	15,663	16,105	15,900	Autriche
	Bulgaria	Bulgaria		2,228	...	...	1,178	...	...	1,019	...	...	31	...	...	608	...	...	2,836	...	...	Bulgarie
	Cyprus	Cyprus		...	...	...	2	2	2	...	...	...	0	0	0	6	6	7	...	...	...	Chypre
	Czech Republic	Czech Republic		24,251	20,470	19,943	17,301	15,480	14,210	6,841	4,880	5,621	109	110	112	4,463	4,365	3,965	28,714	24,835	23,908	République tchèque
	Estonia	Estonia		4,447	4,330	4,330	3,268	3,200	3,200	1,152	1,100	1,100	27	30	30	1,431	1,400	1,400	5,878	5,730	5,730	Estonie
	Finland	Finland		48,840	46,602	48,616	25,247	23,457	23,590	23,593	23,145	25,026	0	0	0	4,279	4,279	4,279	53,119	50,881	52,895	Finlande
	Germany	Germany		55,270	53,354	50,415	44,611	41,447	39,283	10,505	11,757	10,982	153	150	150	9,265	9,600	9,800	64,534	62,954	60,215	Allemagne
	Latvia	Latvia		8,661	8,350	8,250	5,975	5,600	5,500	2,036	2,100	2,100	650	650	650	315	350	400	8,976	8,700	8,650	Lettonie
	Luxembourg	Luxembourg		156	169	143	27	51	122	35	32	6	94	86	15	24	30	11	180	199	154	Luxembourg
	Netherlands	Netherlands		452	449	449	154	154	154	263	260	260	35	35	35	451	450	450	903	899	899	Pays-Bas
	Poland	Poland		31,131	32,500	33,350	15,698	16,370	16,900	14,861	15,570	15,900	572	560	550	2,189	2,150	2,100	33,320	34,650	35,450	Pologne
	Portugal	Portugal		3,352	3,440	3,455	1,851	1,900	1,970	1,370	1,400	1,350	131	140	135	445	480	450	3,797	3,920	3,905	Portugal
	Serbia	Serbia		319	315	335	202	210	220	76	70	75	41	35	40	129	150	160	448	465	495	Serbie
	Slovakia	Slovakia		3,678	3,815	3,830	2,724	2,735	2,750	928	1,050	1,050	26	30	30	223	250	285	3,901	4,065	4,115	Slovaquie
	Slovenia	Slovenia		1,790	1,978	1,888	1,510	1,680	1,600	262	285	275	18	13	13	106	150	160	1,896	2,128	2,048	Slovénie
	Sweden	Sweden		64,850	64,650	63,450	39,100	37,600	35,800	25,600	26,900	27,500	150	150	150	2,700	2,700	2,700	67,550	67,350	66,150	Suède
	Switzerland	Switzerland		2,602	2,652	2,712	2,224	2,300	2,350	376	350	360	2	2	2	834	880	900	3,436	3,532	3,612	Suisse
	UK	United Kingdom		8,608	7,550	8,300	6,298	5,300	6,000	1,895	1,900	1,900	415	350	400	1,571	1,570	1,570	10,179	9,120	9,870	Royaume-Uni
		Total Europe		273,305	263,571	262,166	177,509	167,801	163,651	93,345	93,431	96,205	2,454	2,341	2,312	32,032	31,968	31,837	305,330	295,533	293,995	Total Europe
	Canada	Canada		113,236	110,975	110,975	108,690	106,633	106,633	4,232	3,975	3,975	314	367	367	659	724	724	113,895	111,700	111,700	Canada
	United States	United States		306,264	307,884	307,074	150,702	151,554	151,128	143,462	144,219	143,840	12,100	12,111	12,106	33,760	33,770	33,765	340,023	341,654	340,839	Etats-Unis
		Total North America		419,499	418,859	418,049	259,392	258,187	257,761	147,694	148,194	147,816	12,414	12,478	12,472	34,419	34,495	34,489	453,918	453,354	452,538	Total Amérique du Nord
			a	Pulpwood, round and split, as well as chips and particles produced directly								a	Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
				therefrom and used as pulpwood									directement à partir des rondins et quartiers et utilisées comme bois de trituration
			b	Pitprops, poles, piling, posts etc.								b	Bois de mine, poteaux, pilotis, piquets etc.
			c	Including chips and particles produced from wood in the rough and								c	Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
				used for energy purposes									à des fins energétiques

Table 9b

1.2.3.NC
		TABLE 9b
				REMOVALS OF WOOD IN THE ROUGH									QUANTITES ENLEVEES DE BOIS BRUT
				HARDWOOD									NON-CONIFERES
		1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		Country		Industrial wood - Bois industriels												Wood fuel c Bois de chauffage c						Pays
				Total			Logs			Pulpwood a			Other b						Total
							Grumes			Bois de trituration a			Autre b
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		850	926	950	280	292	300	570	634	650	0	0	0	1,907	2,105	2,200	2,757	3,031	3,150	Autriche
	Bulgaria	Bulgaria		944	...	...	346	...	...	587	...	...	11	...	...	1,749	...	...	2,693	...	...	Bulgarie
	Cyprus	Cyprus		...	...	...	...	...	...	...	...	...	0	0	0	1	1	1	...	...	...	Chypre
	Czech Republic	Czech Republic		895	652	633	438	378	362	453	269	266	4	5	5	647	568	571	1,542	1,220	1,204	République tchèque
	Estonia	Estonia		2,073	1,987	1,987	877	860	860	1,171	1,100	1,100	25	27	27	2,717	2,700	2,700	4,789	4,687	4,687	Estonie
	Finland	Finland		9,196	9,245	9,924	1,045	1,161	1,123	8,151	8,084	8,801	0	0	0	4,589	4,589	4,589	13,785	13,834	14,513	Finlande
	Germany	Germany		3,918	3,824	3,855	2,792	2,809	2,802	1,119	1,008	1,045	8	8	8	13,959	14,300	14,300	17,877	18,124	18,155	Allemagne
	Latvia	Latvia		4,342	4,300	4,300	1,852	1,800	1,800	1,950	2,000	2,000	540	500	500	2,625	2,800	2,800	6,967	7,100	7,100	Lettonie
	Luxembourg	Luxembourg		61	163	54	12	35	22	49	128	32	0	0	0	22	43	34	83	206	89	Luxembourg
	Netherlands	Netherlands		196	204	204	57	60	60	131	135	135	9	9	9	1,912	1,900	1,900	2,108	2,104	2,104	Pays-Bas
	Poland	Poland		7,456	7,800	8,180	2,810	2,930	3,050	4,610	4,840	5,100	36	30	30	2,330	2,300	2,250	9,787	10,100	10,430	Pologne
	Portugal	Portugal		8,784	8,800	8,700	296	290	250	8,289	8,300	8,250	200	210	200	1,318	1,350	1,330	10,102	10,150	10,030	Portugal
	Serbia	Serbia		1,327	1,271	1,295	974	956	965	231	210	220	122	105	110	6,122	6,800	6,850	7,449	8,071	8,145	Serbie
	Slovakia	Slovakia		3,492	3,660	3,760	1,519	1,600	1,650	1,965	2,050	2,100	8	10	10	272	300	325	3,764	3,960	4,085	Slovaquie
	Slovenia	Slovenia		883	1,100	1,107	467	530	530	386	540	550	30	30	27	937	1,050	1,100	1,820	2,150	2,207	Slovénie
	Sweden	Sweden		6,550	6,750	6,950	200	200	200	6,200	6,400	6,600	150	150	150	2,700	2,700	2,700	9,250	9,450	9,650	Suède
	Switzerland	Switzerland		401	436	451	226	250	260	174	185	190	1	1	1	1,146	1,150	1,200	1,547	1,586	1,651	Suisse
	UK	United Kingdom		108	110	110	56	60	60	3	0	0	48	50	50	613	610	610	720	720	720	Royaume-Uni
		Total Europe		51,476	51,228	52,460	14,247	14,211	14,294	36,038	35,883	37,039	1,191	1,135	1,127	45,564	45,266	45,460	97,039	96,493	97,919	Total Europe
	Canada	Canada		24,896	24,328	24,328	12,051	11,361	11,361	11,007	11,065	11,065	1,838	1,901	1,901	812	842	842	25,708	25,170	25,170	Canada
	United States	United States		76,692	78,161	77,427	32,771	33,412	33,091	42,272	43,099	42,685	1,649	1,651	1,650	37,351	37,356	37,354	114,043	115,517	114,780	Etats-Unis
		Total North America		101,588	102,489	101,754	44,822	44,773	44,453	53,279	54,164	53,750	3,487	3,552	3,551	38,163	38,199	38,196	139,751	140,687	139,950	Total Amérique du Nord
			a	Pulpwood, round and split, as well as chips and particles produced directly								a	Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
				therefrom and used as pulpwood									directement à partir des rondins et quartiers et utilisées comme bois de trituration
			b	Pitprops, poles, piling, posts etc.								b	Bois de mine, poteaux, pilotis, piquets etc.
			c	Including chips and particles produced from wood in the rough and								c	Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
				used for energy purposes									à des fins energétiques

Table 10

1.2.1.C
		TABLE 10
				SOFTWOOD SAWLOGS						GRUMES DE SCIAGES DES CONIFERES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
									1000 m3
				Apparent Consumption a						Imports			Exports
		Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		17,589	16,095	16,500	10,139	10,315	10,000	8,044	6,660	7,000	594	880	500	Autriche
	Bulgaria	Bulgaria		1,173	...	...	1,178	...	...	0	...	...	5	...	...	Bulgarie
	Cyprus	Cyprus		2	2	2	2	2	2	0	0	0	0	0	0	Chypre
	Czech Republic	Czech Republic		8,801	8,617	7,916	17,301	15,480	14,210	750	663	660	9,250	7,526	6,954	République tchèque
	Estonia	Estonia		3,640	3,730	3,730	3,268	3,200	3,200	455	600	600	83	70	70	Estonie
	Finland	Finland		25,080	23,224	23,365	25,247	23,457	23,590	165	78	86	332	311	311	Finlande
	Germany	Germany		39,795	39,077	38,613	44,611	41,447	39,283	3,190	3,300	3,600	8,006	5,670	4,270	Allemagne
	Latvia	Latvia		6,786	6,350	6,100	5,975	5,600	5,500	1,088	1,100	900	277	350	300	Lettonie
	Luxembourg	Luxembourg		393	90	161	27	51	122	609	164	164	243	125	125	Luxembourg
	Netherlands	Netherlands		176	169	169	154	154	154	87	80	80	65	65	65	Pays-Bas
	Poland	Poland		14,868	15,470	16,000	15,698	16,370	16,900	1,090	1,150	1,200	1,920	2,050	2,100	Pologne
	Portugal	Portugal		1,971	1,990	2,075	1,851	1,900	1,970	150	130	140	30	40	35	Portugal
	Serbia	Serbia		226	220	233	202	210	220	28	12	15	4	2	2	Serbie
	Slovakia	Slovakia		3,057	3,235	3,250	2,724	2,735	2,750	1,049	900	900	716	400	400	Slovaquie
	Slovenia	Slovenia		1,511	1,740	1,620	1,510	1,680	1,600	287	320	300	286	260	280	Slovénie
	Sweden	Sweden		39,240	37,680	35,880	39,100	37,600	35,800	880	1,010	1,010	740	930	930	Suède
	Switzerland	Switzerland		1,935	2,045	2,105	2,224	2,300	2,350	52	55	55	341	310	300	Suisse
	UK	United Kingdom		6,515	5,510	6,200	6,298	5,300	6,000	359	360	360	142	150	160	Royaume-Uni
		Total Europe		172,758	165,244	163,919	177,509	167,801	163,651	18,283	16,582	17,070	23,033	19,139	16,802	Total Europe
	Canada	Canada		104,025	102,730	102,894	108,690	106,633	106,633	2,221	1,489	1,245	6,887	5,392	4,984	Canada
	United States	United States		142,644	143,443	143,043	150,702	151,554	151,128	278	280	279	8,336	8,391	8,364	Etats-Unis
		Total North America		246,668	246,173	245,937	259,392	258,187	257,761	2,500	1,769	1,524	15,223	13,784	13,348	Total Amérique du Nord
			a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11

1.2.1.NC
		TABLE 11
				HARDWOOD SAWLOGS (total)						GRUMES DE SCIAGES DES NON-CONIFERES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
									1000 m3
				Apparent Consumption a						Imports			Exports
		Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023		Country	Consumption			Production			Imports			Exports			Country
	Austria	Austria		398	362	370	280	292	300	162	140	120	45	70	50	Autriche	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Bulgaria	Bulgaria		346	...	...	346	...	...	0	...	...	0	...	...	Bulgarie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Czech Republic	Czech Republic		305	239	218	438	378	362	132	135	138	265	274	282	République tchèque	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Estonia	Estonia		901	885	885	877	860	860	48	45	45	23	20	20	Estonie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Finland	Finland		1,218	1,193	1,123	1,045	1,161	1,123	173	32	0	0	0	0	Finlande	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Germany	Germany		2,174	2,346	2,348	2,792	2,809	2,802	110	111	120	727	574	574	Allemagne	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Latvia	Latvia		1,570	1,470	1,520	1,852	1,800	1,800	27	60	50	309	390	330	Lettonie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Luxembourg	Luxembourg		199	139	126	12	35	22	209	111	111	22	7	7	Luxembourg	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Netherlands	Netherlands		62	70	70	57	60	60	65	70	70	59	60	60	Pays-Bas	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Poland	Poland		2,740	2,860	3,050	2,810	2,930	3,050	80	80	...	150	150	...	Pologne	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Portugal	Portugal		406	380	350	296	290	250	140	120	130	30	30	30	Portugal	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Serbia	Serbia		954	951	955	974	956	965	30	15	20	50	20	30	Serbie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Slovakia	Slovakia		1,658	1,700	1,750	1,519	1,600	1,650	562	500	500	423	400	400	Slovaquie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Slovenia	Slovenia		263	242	260	467	530	530	43	42	40	248	330	310	Slovénie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Sweden	Sweden		226	226	226	200	200	200	26	26	26	0	0	0	Suède	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Switzerland	Switzerland		104	130	150	226	250	260	27	35	40	149	155	150	Suisse	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	UK	United Kingdom		67	80	80	56	60	60	15	20	20	4	0	0	Royaume-Uni	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
		Total Europe		13,592	13,273	13,481	14,247	14,211	14,294	1,849	1,542	1,430	2,504	2,480	2,243	Total Europe	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Canada	Canada		13,122	12,288	12,248	12,051	11,361	11,361	1,145	1,018	969	75	92	83	Canada	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	United States	United States		30,814	31,416	31,115	32,771	33,412	33,091	151	154	153	2,109	2,150	2,129	Etats-Unis	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
		Total North America		43,935	43,704	43,363	44,822	44,773	44,453	1,297	1,173	1,122	2,183	2,241	2,212	Total Amérique du Nord	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
			a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11a

	TABLE 11a
			HARDWOOD LOGS (temperate)						GRUMES DE NON-CONIFERES (zone tempérée)
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption a						Imports			Exports
	Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria		280	292	300	280	292	300	...	...	...	...	...	...	Autriche
Bulgaria	Bulgaria		346	...	...	346	...	...	...	...	...	...	...	...	Bulgarie
Czech Republic	Czech Republic		303	237	216	438	378	362	130	133	136	265	274	282	République tchèque
Estonia	Estonia		877	860	860	877	860	860	...	...	...	...	...	...	Estonie
Finland	Finland		1,045	1,161	1,123	1,045	1,161	1,123	...	...	...	...	...	...	Finlande
Germany	Germany		2,168	2,341	2,343	2,792	2,809	2,802	98	101	110	722	569	569	Allemagne
Latvia	Latvia		1,852	1,800	1,800	1,852	1,800	1,800	...	...	...	...	...	...	Lettonie
Luxembourg	Luxembourg		191	139	22	12	35	22	201	111	...	22	7	...	Luxembourg
Netherlands	Netherlands		53	62	62	57	60	60	50	55	55	53	53	53	Pays-Bas
Poland	Poland		2,739	2,858	3,050	2,810	2,930	3,050	78	78	...	150	150	...	Pologne
Portugal	Portugal		382	355	325	296	290	250	110	90	100	24	25	25	Portugal
Serbia	Serbia		953	950	954	974	956	965	29	14	19	50	20	30	Serbie
Slovakia	Slovakia		1,519	1,600	1,650	1,519	1,600	1,650	...	...	...	...	...	...	Slovaquie
Slovenia	Slovenia		262	241	259	467	530	530	42	41	39	248	330	310	Slovénie
Sweden	Sweden		200	200	200	200	200	200	...	...	...	...	...	...	Suède
Switzerland	Switzerland		226	250	260	226	250	260	...	...	...	...	...	...	Suisse
UK	United Kingdom		66	80	80	56	60	60	14	20	20	4	0	0	Royaume-Uni
	Total Europe		13,462	13,426	13,504	14,247	14,211	14,294	753	643	479	1,538	1,428	1,269	Total Europe
Canada	Canada		12,051	11,361	11,361	12,051	11,361	11,361	...	...	...	...	...	...	Canada
United States	United States		30,813	31,415	31,114	32,771	33,412	33,091	150	152	151	2,108	2,149	2,128	Etats-Unis
	Total North America		42,864	42,777	42,475	44,822	44,773	44,453	150	152	151	2,108	2,149	2,128	Total Amérique du Nord
		a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11b

1.2.1.NC.T
		TABLE 11b
			HARDWOOD LOGS (tropical)						GRUMES DE NON-CONIFERES (tropicale)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Net Trade						Imports			Exports
		Country	Commerce Net			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Czech Republic	Czech Republic	-2	-2	-2				2	2	2	0	0	0	République tchèque
	Germany	Germany	-7	-5	-5				11	10	10	5	5	5	Allemagne
	Luxembourg	Luxembourg	-8	0	...				8	0	...	0	0	...	Luxembourg
	Netherlands	Netherlands	-9	-8	-8				15	15	15	6	7	7	Pays-Bas
	Poland	Poland	-1	-2	-2				2	2	2	0	0	0	Pologne
	Portugal	Portugal	-24	-25	-25				30	30	30	6	5	5	Portugal
	Serbia	Serbia	-1	-1	-1				1	1	1	0	0	0	Serbie
	Slovenia	Slovenia	-1	-1	-1				1	1	1	0	0	0	Slovénie
	UK	United Kingdom	-1	0	0				1	0	0	0	0	0	Royaume-Uni
		Total Europe	-54	-44	-44				71	61	61	17	17	17	Total Europe
	United States	United States	-1	-1	-1				2	2	2	1	1	1	Etats-Unis
		Total North America	-1	-1	-1				2	2	2	1	1	1	Total Amérique du Nord

Table 12

	TABLE 12
			PULPWOOD (total)						BOIS DE TRITURATION (total)
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption a						Imports			Exports
	Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria		14,154	13,876	13,900	10,568	10,416	10,450	4,953	4,650	4,500	1,367	1,190	1,050	Autriche
Bulgaria	Bulgaria		1,639	...	...	1,699	...	...	27	...	...	87	...	...	Bulgarie
Cyprus	Cyprus		...	...	...	...	...	...	...	...	...	...	...	...	Chypre
Czech Republic	Czech Republic		6,155	4,306	4,709	9,027	6,877	7,621	475	469	475	3,347	3,040	3,387	République tchèque
Estonia	Estonia		3,575	3,600	3,600	6,323	6,100	6,100	268	240	240	3,015	2,740	2,740	Estonie
Finland	Finland		57,262	49,139	51,233	47,052	46,586	49,737	11,200	3,364	2,307	990	811	811	Finlande
Germany	Germany		27,289	28,098	28,031	28,327	29,465	28,527	3,819	3,509	3,680	4,858	4,876	4,176	Allemagne
Latvia	Latvia		4,879	5,025	5,180	8,296	8,600	8,800	1,417	875	780	4,834	4,450	4,400	Lettonie
Luxembourg	Luxembourg		605	681	559	605	681	559	...	...	...	...	...	...	Luxembourg
Malta	Malta		…	…	…	…	…	…	...	...	...	...	...	...	Malte
Netherlands	Netherlands		1,566	1,565	1,565	1,365	1,365	1,365	644	680	680	443	480	480	Pays-Bas
Poland	Poland		31,755	32,925	31,800	29,682	30,910	31,800	3,947	3,810	...	1,874	1,795	...	Pologne
Portugal	Portugal		13,642	12,870	12,780	11,483	11,620	11,500	2,833	1,815	1,850	674	565	570	Portugal
Serbia	Serbia		812	840	890	800	830	875	15	12	17	3	2	2	Serbie
Slovakia	Slovakia		3,849	4,000	4,100	4,043	4,250	4,350	1,043	1,000	1,000	1,237	1,250	1,250	Slovaquie
Slovenia	Slovenia		1,129	1,019	1,345	2,008	2,275	2,325	653	674	680	1,532	1,930	1,660	Slovénie
Sweden	Sweden		61,355	61,618	61,918	55,300	55,800	56,100	6,858	6,774	6,774	803	956	956	Suède
Switzerland	Switzerland		1,773	1,773	1,793	1,340	1,345	1,370	623	613	613	190	185	190	Suisse
UK	United Kingdom		5,304	5,040	5,280	5,020	4,770	5,010	384	380	380	101	110	110	Royaume-Uni
	Total Europe		236,743	226,375	228,683	222,939	221,890	226,489	39,158	28,865	23,976	25,354	24,380	21,782	Total Europe
Canada	Canada		40,927	37,948	37,855	38,095	36,525	36,525	3,722	2,250	2,204	890	827	873	Canada
United States	United States		240,634	243,316	241,975	246,219	249,015	247,617	264	268	266	5,849	5,966	5,908	Etats-Unis
	Total North America		281,560	281,264	279,830	284,314	285,540	284,142	3,986	2,517	2,469	6,740	6,794	6,781	Total Amérique du Nord
		Includes wood residues, chips and particles for all purposes							Comprend les dechets de bois, plaquettes et particules pour toute utilisation
		a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12a

1.2.2.C
		TABLE 12a
				PULPWOOD LOGS (ROUND AND SPLIT)						BOIS DE TRITURATION (RONDINS ET QUARTIERS)
				Softwood						Conifères
		1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
				Apparent Consumption a						Imports			Exports
		Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		4,145	4,212	4,300	2,531	2,632	2,700	1,973	1,850	1,800	360	270	200	Autriche
	Bulgaria	Bulgaria		1,000	...	...	1,019	...	...	15	...	...	34	...	...	Bulgarie
	Czech Republic	Czech Republic		4,111	2,444	2,838	6,841	4,880	5,621	270	263	265	3,000	2,699	3,048	République tchèque
	Estonia	Estonia		533	560	560	1,152	1,100	1,100	32	40	40	650	580	580	Estonie
	Finland	Finland		24,151	23,246	25,181	23,593	23,145	25,026	1,294	754	808	736	653	653	Finlande
	Germany	Germany		10,697	11,527	11,552	10,505	11,757	10,982	2,523	2,200	2,400	2,331	2,430	1,830	Allemagne
	Latvia	Latvia		1,714	1,850	1,750	2,036	2,100	2,100	473	400	350	795	650	700	Lettonie
	Luxembourg	Luxembourg		35	32	6	35	32	6	...	...	...	...	...	...	Luxembourg
	Netherlands	Netherlands		195	180	180	263	260	260	113	110	110	182	190	190	Pays-Bas
	Poland	Poland		14,706	15,470	15,900	14,861	15,570	15,900	1,174	1,200	1,250	1,329	1,300	1,250	Pologne
	Portugal	Portugal		1,402	1,420	1,380	1,370	1,400	1,350	75	65	70	43	45	40	Portugal
	Serbia	Serbia		76	70	75	76	70	75	0	0	0	0	0	0	Serbie
	Slovakia	Slovakia		843	900	900	928	1,050	1,050	645	600	600	730	750	750	Slovaquie
	Slovenia	Slovenia		288	325	315	262	285	275	264	270	270	239	230	230	Slovénie
	Sweden	Sweden		28,302	29,632	30,232	25,600	26,900	27,500	3,110	3,255	3,255	408	523	523	Suède
	Switzerland	Switzerland		306	280	290	376	350	360	20	20	20	90	90	90	Suisse
	UK	United Kingdom		2,085	2,080	2,080	1,895	1,900	1,900	213	210	210	23	30	30	Royaume-Uni
		Total Europe		94,588	94,228	97,539	93,345	93,431	96,205	12,194	11,237	11,448	10,950	10,440	10,114	Total Europe
	Canada	Canada		5,139	4,236	4,204	4,232	3,975	3,975	961	297	273	54	36	45	Canada
	United States	United States		143,467	144,224	143,845	143,462	144,219	143,840	5	5	5	0	0	0	Etats-Unis
		Total North America		148,606	148,460	148,049	147,694	148,194	147,816	966	302	278	54	36	45	Total Amérique du Nord
			a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12b

1.2.2.NC
		TABLE 12b
				PULPWOOD LOGS (ROUND AND SPLIT)						BOIS DE TRITURATION (RONDINS ET QUARTIERS)
				Hardwood						Non-conifères
		1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
				Apparent Consumption a						Imports			Exports
		Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		1,182	1,214	1,200	570	634	650	688	700	650	75	120	100	Autriche
	Bulgaria	Bulgaria		583	...	...	587	...	...	5	...	...	9	...	...	Bulgarie
	Czech Republic	Czech Republic		365	187	190	453	269	266	2	9	8	90	91	84	République tchèque
	Estonia	Estonia		336	370	370	1,171	1,100	1,100	139	130	130	974	860	860	Estonie
	Finland	Finland		12,721	9,117	9,428	8,151	8,084	8,801	4,661	1,085	679	91	52	52	Finlande
	Germany	Germany		1,111	1,021	1,079	1,119	1,008	1,045	261	259	280	269	246	246	Allemagne
	Latvia	Latvia		432	375	480	1,950	2,000	2,000	166	175	180	1,684	1,800	1,700	Lettonie
	Luxembourg	Luxembourg		49	128	32	49	128	32	...	...	...	...	...	...	Luxembourg
	Netherlands	Netherlands		62	65	65	131	135	135	19	20	20	89	90	90	Pays-Bas
	Poland	Poland		5,095	5,325	5,100	4,610	4,840	5,100	560	560	...	75	75	...	Pologne
	Portugal	Portugal		8,939	8,930	8,890	8,289	8,300	8,250	1,000	950	970	350	320	330	Portugal
	Serbia	Serbia		230	210	220	231	210	220	0	0	0	1	0	0	Serbie
	Slovakia	Slovakia		1,909	2,000	2,050	1,965	2,050	2,100	91	100	100	147	150	150	Slovaquie
	Slovenia	Slovenia		131	154	160	386	540	550	117	114	110	372	500	500	Slovénie
	Sweden	Sweden		8,485	8,486	8,686	6,200	6,400	6,600	2,313	2,119	2,119	28	33	33	Suède
	Switzerland	Switzerland		137	148	153	174	185	190	3	3	3	40	40	40	Suisse
	UK	United Kingdom		54	50	50	3	0	0	52	50	50	1	0	0	Royaume-Uni
		Total Europe		41,820	37,780	38,153	36,038	35,883	37,039	10,077	6,274	5,299	4,294	4,377	4,185	Total Europe
	Canada	Canada		10,804	10,827	10,819	11,007	11,065	11,065	46	47	39	249	284	285	Canada
	United States	United States		42,259	43,085	42,672	42,272	43,099	42,685	42	42	42	55	56	56	Etats-Unis
		Total North America		53,063	53,912	53,491	53,279	54,164	53,750	88	89	81	304	340	340	Total Amérique du Nord
			a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12c

3
		TABLE 12c
			WOOD RESIDUES, CHIPS AND PARTICLES						DECHETS DE BOIS, PLAQUETTES ET PARTICULES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	8,827	8,450	8,400	7,467	7,150	7,100	2,292	2,100	2,050	931	800	750	Autriche
	Bulgaria	Bulgaria	56	...	...	93	...	...	7	...	...	44	...	...	Bulgarie
	Cyprus	Cyprus	8	10	10	8	9	9	1	1	1	0	0	0	Chypre
	Czech Republic	Czech Republic	1,679	1,675	1,681	1,733	1,728	1,734	203	197	202	257	250	255	République tchèque
	Estonia	Estonia	2,706	2,670	2,670	4,000	3,900	3,900	96	70	70	1,390	1,300	1,300	Estonie
	Finland	Finland	20,390	16,776	16,624	15,308	15,357	15,910	5,245	1,525	820	163	106	106	Finlande
	Germany	Germany	15,481	15,550	15,400	16,703	16,700	16,500	1,036	1,050	1,000	2,258	2,200	2,100	Allemagne
	Latvia	Latvia	2,733	2,800	2,950	4,310	4,500	4,700	778	300	250	2,355	2,000	2,000	Lettonie
	Luxembourg	Luxembourg	680	517	517	521	521	521	283	17	17	124	21	21	Luxembourg
	Malta	Malta	2	2	3	0	0	0	2	2	3	0	0	0	Malte
	Netherlands	Netherlands	1,310	1,320	1,320	971	970	970	512	550	550	173	200	200	Pays-Bas
	Poland	Poland	11,954	12,130	12,400	10,211	10,500	10,800	2,213	2,050	2,000	469	420	400	Pologne
	Portugal	Portugal	3,301	2,520	2,510	1,824	1,920	1,900	1,758	800	810	281	200	200	Portugal
	Serbia	Serbia	506	560	595	493	550	580	15	12	17	2	2	2	Serbie
	Slovakia	Slovakia	1,097	1,100	1,150	1,150	1,150	1,200	307	300	300	360	350	350	Slovaquie
	Slovenia	Slovenia	710	540	870	1,360	1,450	1,500	272	290	300	922	1,200	930	Slovénie
	Sweden	Sweden	24,568	23,500	23,000	23,500	22,500	22,000	1,435	1,400	1,400	367	400	400	Suède
	Switzerland	Switzerland	1,330	1,345	1,350	790	810	820	600	590	590	60	55	60	Suisse
	UK	United Kingdom	3,164	2,910	3,150	3,122	2,870	3,110	119	120	120	77	80	80	Royaume-Uni
		Total Europe	100,505	94,375	94,600	93,564	92,585	93,254	17,174	11,374	10,500	10,234	9,584	9,154	Total Europe
	Canada	Canada	24,984	22,884	22,832	22,856	21,485	21,485	2,716	1,906	1,891	587	507	544	Canada
	United States	United States	54,907	56,007	55,457	60,485	61,697	61,091	216	221	219	5,794	5,910	5,852	Etats-Unis
		Total North America	79,892	78,891	78,290	83,341	83,182	82,576	2,932	2,127	2,110	6,382	6,417	6,396	Total Amérique du Nord

Table 13

4.1x
		TABLE 13
			WOOD PELLETS						GRANULES DE BOIS
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 mt
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	1,144	1,270	1,410	1,607	1,720	2,060	413	328	400	875	778	1,050	Autriche
	Bulgaria	Bulgaria	202	...	...	216	...	...	132	...	...	146	...	...	Bulgarie
	Cyprus	Cyprus	4	3	3	0	0	0	4	3	3	0	0	0	Chypre
	Czech Republic	Czech Republic	126	199	201	490	503	517	39	35	37	403	339	353	République tchèque
	Estonia	Estonia	67	70	70	1,600	1,550	1,550	26	20	20	1,559	1,500	1,500	Estonie
	Finland	Finland	552	479	456	365	375	380	196	110	80	9	6	4	Finlande
	Germany	Germany	2,932	3,200	3,400	3,353	3,600	3,800	392	450	500	813	850	900	Allemagne
	Latvia	Latvia	221	280	100	2,138	2,200	2,000	592	380	400	2,509	2,300	2,300	Lettonie
	Luxembourg	Luxembourg	49	63	63	63	63	63	13	4	4	28	4	4	Luxembourg
	Malta	Malta	1	1	1	0	0	0	1	1	1	0	0	0	Malte
	Netherlands	Netherlands	2,449	2,457	2,457	307	315	315	2,297	2,297	2,297	155	155	155	Pays-Bas
	Poland	Poland	1,169	1,220	1,330	1,594	1,620	1,680	269	280	300	694	680	650	Pologne
	Portugal	Portugal	224	270	265	731	860	800	3	10	15	510	600	550	Portugal
	Serbia	Serbia	497	430	485	468	420	460	84	60	80	55	50	55	Serbie
	Slovakia	Slovakia	19	145	195	310	325	350	46	45	45	337	225	200	Slovaquie
	Slovenia	Slovenia	111	112	150	149	162	170	166	120	150	204	170	170	Slovénie
	Sweden	Sweden	1,771	1,985	1,985	1,900	2,100	2,100	154	235	235	282	350	350	Suède
	Switzerland	Switzerland	350	355	360	275	285	295	75	70	65	0	0	0	Suisse
	UK	United Kingdom	9,430	9,450	9,450	304	320	320	9,128	9,130	9,130	2	0	0	Royaume-Uni
		Total Europe	21,318	21,989	22,381	15,870	16,418	16,860	14,030	13,578	13,762	8,582	8,007	8,241	Total Europe
	Canada	Canada	706	761	548	3,830	4,131	4,131	29	33	35	3,153	3,402	3,618	Canada
	United States	United States	1,122	1,136	1,129	8,449	8,557	8,503	196	198	197	7,523	7,619	7,571	Etats-Unis
		Total North America	1,828	1,898	1,677	12,279	12,688	12,634	225	231	232	10,676	11,021	11,189	Total Amérique du Nord

Table 14

3+4
	TABLE 14
	Europe: Summary table of market forecasts for 2022 and 2023
	Europe: Tableau récapitulatif des prévisions du marché pour 2022 et 2023
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
		Apparent Consumption
		Consommation Apparente			Production			Imports - Importations			Exports - Exportations
		2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
		actual	forecasts		actual	forecasts		actual	forecasts		actual	forecasts
		réels	prévisions		réels	prévisions		réels	prévisions		réels	prévisions
	Sawn softwood	67.77	63.34	62.12	90.27	88.25	87.84	26.30	22.94	21.87	48.80	47.85	47.59	Sciages conifères
	Softwood logs a	172.76	165.24	163.92	177.51	167.80	163.65	18.28	16.58	17.07	23.03	19.14	16.80	Grumes de conifères a
	Sawn hardwood	4.34	4.17	4.19	4.22	4.17	4.09	2.95	2.89	2.92	2.83	2.89	2.82	Sciages non-conifères
	– temperate zone b	3.56	3.34	3.35	3.04	2.92	2.91	2.41	2.33	2.37	1.89	1.90	1.93	– zone tempérée b
	– tropical zone b	0.32	0.34	0.34	0.03	0.02	0.03	0.41	0.41	0.40	0.12	0.09	0.08	– zone tropicale b
	Hardwood logs a	13.59	13.27	13.48	14.25	14.21	14.29	1.85	1.54	1.43	2.50	2.48	2.24	Grumes de non-conifères a
	– temperate zone b	13.46	13.43	13.50	14.25	14.21	14.29	0.75	0.64	0.48	1.54	1.43	1.27	– zone tempérée b
	– tropical zone b	0.05	0.04	0.04				0.07	0.06	0.06	0.02	0.02	0.02	– zone tropicale b
	Veneer sheets	0.74	0.75	0.72	0.70	0.72	0.71	0.78	0.71	0.66	0.74	0.68	0.65	Feuilles de placage
	Plywood	5.94	5.84	5.86	3.41	3.37	3.35	5.87	5.67	5.63	3.35	3.21	3.12	Contreplaqués
	Particle board (excluding OSB)	21.78	20.86	20.73	21.93	20.41	20.25	8.19	8.10	8.10	8.34	7.66	7.62	Pann. de particules (sauf OSB)
	OSB	4.95	4.85	4.85	4.74	4.42	4.49	2.67	2.69	2.71	2.46	2.25	2.35	OSB
	Fibreboard	13.90	14.17	14.10	14.72	14.71	14.71	7.39	7.13	7.04	8.20	7.67	7.66	Panneaux de fibres
	– Hardboard	0.54	0.77	0.77	0.28	0.21	0.21	1.07	1.06	1.06	0.81	0.50	0.50	– Durs
	– MDF	9.73	9.79	9.73	10.78	10.85	10.80	4.34	4.11	4.07	5.38	5.17	5.15	– MDF
	– Other board	3.63	3.62	3.61	3.66	3.65	3.70	1.98	1.96	1.92	2.01	2.00	2.01	– Autres panneaux
	Pulpwood a	236.74	226.37	228.68	222.94	221.89	226.49	39.16	28.87	23.98	25.35	24.38	21.78	Bois de trituration a
	– Pulp logs	136.41	132.01	135.69	129.38	129.31	133.24	22.27	17.51	16.75	15.24	14.82	14.30	– Bois ronds de trituration
	– softwood	94.59	94.23	97.54	93.34	93.43	96.20	12.19	11.24	11.45	10.95	10.44	10.11	– conifères
	– hardwood	41.82	37.78	38.15	36.04	35.88	37.04	10.08	6.27	5.30	4.29	4.38	4.19	– non-conifères
	– Residues, chips and particles	100.50	94.37	94.60	93.56	92.59	93.25	17.17	11.37	10.50	10.23	9.58	9.15	– Déchets, plaquettes et part.
	Wood pulp	31.26	30.47	31.02	33.82	32.17	33.48	10.99	11.00	11.04	13.56	12.70	13.50	Pâte de bois
	Paper and paperboard	47.70	46.94	46.97	64.68	62.48	63.49	29.98	29.24	29.29	46.96	44.78	45.81	Papiers et cartons
	Wood Pellets	21.32	21.99	22.38	15.87	16.42	16.86	14.03	13.58	13.76	8.58	8.01	8.24	Granulés de bois
	a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fourni des données sur le commerce
	b Trade figures by zone do not equal the total as some countries cannot provide data for both zones							b Les chiffres du commerce par zone ne correspondent pas aux totaux
								en raison du fait que certains pays ne peuvent les différencier.

Table 15

3+4
	TABLE 15
	North America: Summary table of market forecasts for 2022 and 2023
	Amérique du Nord: Tableau récapitulatif des prévisions du marché pour 2022 et 2023
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
		Apparent Consumption
		Consommation Apparente			Production			Imports - Importations			Exports - Exportations
		2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
		actual	forecasts		actual	forecasts		actual	forecasts		actual	forecasts
		réels	prévisions		réels	prévisions		réels	prévisions		réels	prévisions
	Sawn softwood	108.10	107.38	113.43	119.26	116.36	115.11	27.96	27.02	27.28	39.12	36.00	28.96	Sciages conifères
	Softwood logs	246.67	246.17	245.94	259.39	258.19	257.76	2.50	1.77	1.52	15.22	13.78	13.35	Grumes de conifères
	Sawn hardwood	15.56	16.29	15.82	18.21	18.42	18.18	1.51	1.93	1.66	4.17	4.06	4.02	Sciages non-conifères
	– temperate zone	15.36	16.05	15.61	18.21	18.42	18.18	1.27	1.64	1.38	4.11	4.01	3.96	– zone tempérée
	– tropical zone	0.19	0.25	0.22	0.00	0.00	0.00	0.24	0.30	0.27	0.05	0.05	0.06	– zone tropicale
	Hardwood logs	43.94	43.70	43.36	44.82	44.77	44.45	1.30	1.17	1.12	2.18	2.24	2.21	Grumes de non-conifères
	– temperate zone	42.86	42.78	42.48	44.82	44.77	44.45	0.15	0.15	0.15	2.11	2.15	2.13	– zone tempérée
	– tropical zone	0.00	0.00	0.00				0.00	0.00	0.00	0.00	0.00	0.00	– zone tropicale
	Veneer sheets	2.82	2.97	2.90	2.87	2.85	2.85	0.85	0.98	0.95	0.90	0.86	0.89	Feuilles de placage
	Plywood	19.52	19.58	19.65	11.40	11.54	11.44	9.51	9.31	9.53	1.39	1.26	1.32	Contreplaqués
	Particle board (excluding OSB)	6.60	8.78	7.32	5.78	5.94	5.56	2.06	3.74	2.74	1.24	0.90	0.99	Pann. de particules (sauf OSB)
	OSB	21.42	21.68	21.95	21.08	21.62	21.89	6.27	6.31	6.40	5.93	6.25	6.34	OSB
	Fibreboard	11.22	11.48	11.34	8.91	9.09	9.06	4.02	4.08	4.01	1.71	1.68	1.73	Panneaux de fibres
	– Hardboard	0.55	0.55	0.55	0.59	0.59	0.60	0.32	0.32	0.32	0.36	0.36	0.37	– Durs
	– MDF	7.31	7.18	7.23	5.04	5.11	5.13	3.33	3.21	3.22	1.06	1.14	1.12	– MDF
	– Other board	3.36	3.75	3.56	3.28	3.39	3.33	0.37	0.55	0.47	0.29	0.18	0.24	– Autres panneaux
	Pulpwood	281.56	281.26	279.83	284.31	285.54	284.14	3.99	2.52	2.47	6.74	6.79	6.78	Bois de trituration
	– Pulp logs	201.67	202.37	201.54	200.97	202.36	201.57	1.05	0.39	0.36	0.36	0.38	0.39	– Bois ronds de trituration
	– softwood	148.61	148.46	148.05	147.69	148.19	147.82	0.97	0.30	0.28	0.05	0.04	0.04	– conifères
	– hardwood	53.06	53.91	53.49	53.28	54.16	53.75	0.09	0.09	0.08	0.30	0.34	0.34	– non-conifères
	– Residues, chips and particles	79.89	78.89	78.29	83.34	83.18	82.58	2.93	2.13	2.11	6.38	6.42	6.40	– Déchets, plaquettes et part.
	Wood pulp	55.37	54.37	54.34	64.57	63.72	63.24	7.13	6.99	7.22	16.34	16.33	16.12	Pâte de bois
	Paper and paperboard	70.56	73.06	71.87	76.26	78.63	77.27	10.65	11.12	10.93	16.35	16.69	16.32	Papiers et cartons
	Wood pellets	1.83	1.90	1.68	12.28	12.69	12.63	0.23	0.23	0.23	10.68	11.02	11.19	Granulés de bois
															printed on 16/12

Joint Forest Sector Questionnaire - 2020 - National Reply - United States of America

Read more about Joint Forest Sector Questionnaire - 2020 - National Reply - United States of America

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Manual

Changes from JQ2019 to JQ2020 Below is a complete list of all changes to JQ2020. Items in bold are significant changes. 1) Definitions a) Included additional products under definition of production b) Changed definition of veneer to exclude veneer used for plywood (item 7). This reverts to the pre-2017 definition. c) Removed reference to particle board as an aggregate (item 8.2). d) Added fine OSB to definition of OSB (item 8.2.1). 2) Questionnaires a) Changed representation of unit “mt” to “t” (metric tonnes). b) Cubic metre (m3) referenced as solid volume (in accordance with definitions). c) Included m3ub (underbark) for roundwood on ITTO 2. d) ECE-EU i) Removed the “ex” (partial) HS codes ii) Removed item 1.2.C.Other (3 rows) iii) Restored data checks between this questionnaire and JQ2

JQ1|Primary Products|Production

		Country:	United States	Date: May 27, 2021			Country:	United States
		Name of Official responsible for reply:

		Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ1		USDA Forest Service, SRS FIA
		4700 Old Kingston Pike, Knoxville, TN 37919								Industrial Roundwood Balance
PRIMARY PRODUCTS		Telephone:		Fax:		This table highlights discrepancies between items and sub-items. Please verify your data for any non-zero figure!		Discrepancies
Removals and Production		E-mail:								test for good numbers, missing number, bad number, negative number
			51	51
Product	Product	Unit	2019	2020	Product	Product	Unit	2019	2020			2019	2020	% change		Conversion factors
Code			Quantity	Quantity	Code			Quantity	Quantity	Roundwood	Industrial roundwood availability McCusker 14/6/07: McCusker 14/6/07: minus 1.2.3 (other ind. RW) production	367,097	355,491	-3%		m3 of wood in m3 or mt of product
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)						REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)					Recovered wood used in particle board	1521	1448	-5%		Solid wood equivalent
1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	459,129	429,700	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	0	0	Solid Wood Demand	agglomerate production	8,593	8,412	-2%		2.4
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	71,427	60,525	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	0	0		Sawnwood production	82,472	79,134	-4%		1
1.1.C	Coniferous	1000 m3ub	32,799	26,345	1.1.C	Coniferous	1000 m3ub				veneer production	14,024	2,284	-84%		1
1.1.NC	Non-Coniferous	1000 m3ub	38,628	34,180	1.1.NC	Non-Coniferous	1000 m3ub				plywood production	9,925	9,500	-4%		1
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	387,702	369,175	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	0	0		particle board production (incl OSB)	17,782	17,849	0%		1.58
1.2.C	Coniferous	1000 m3ub	293,642	293,023	1.2.C	Coniferous	1000 m3ub	0	0		fibreboard production	3,467	2,879	-17%		1.8
1.2.NC	Non-Coniferous	1000 m3ub	94,060	76,151	1.2.NC	Non-Coniferous	1000 m3ub	0	0		mechanical/semi-chemical pulp production	5,547	4,593	-17%		2.5
1.2.NC.T	of which: Tropical	1000 m3ub	0	0	1.2.NC.T	of which: Tropical	1000 m3ub				chemical pulp production	45,263	45,161	-0%		4.9
1.2.1	SAWLOGS AND VENEER LOGS	1000 m3ub	187,160	180,237	1.2.1	SAWLOGS AND VENEER LOGS	1000 m3ub	0	0		dissolving pulp production	1,252	1,117	-11%		5.7
1.2.1.C	Coniferous	1000 m3ub	142,587	147,988	1.2.1.C	Coniferous	1000 m3ub			Availability	Solid Wood Demand	404,172	383,630	-5%
1.2.1.NC	Non-Coniferous	1000 m3ub	44,573	32,250	1.2.1.NC	Non-Coniferous	1000 m3ub				Difference (roundwood-demand)	-402,651	-382,182	-5%	positive = surplus
1.2.2	PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)	1000 m3ub	186,918	175,722	1.2.2	PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)	1000 m3ub	0	0		gap (demand/availability)	-10%	-8%		Negative number means not enough roundwood available
1.2.2.C	Coniferous	1000 m3ub	139,454	133,458	1.2.2.C	Coniferous	1000 m3ub								Positive number means more roundwood available than demanded
1.2.2.NC	Non-Coniferous	1000 m3ub	47,464	42,264	1.2.2.NC	Non-Coniferous	1000 m3ub
1.2.3	OTHER INDUSTRIAL ROUNDWOOD	1000 m3ub	13,624	13,215	1.2.3	OTHER INDUSTRIAL ROUNDWOOD	1000 m3ub	0	0
1.2.3.C	Coniferous	1000 m3ub	11,601	11,578	1.2.3.C	Coniferous	1000 m3ub									% of particle board that is from recovered wood	35%
1.2.3.NC	Non-Coniferous	1000 m3ub	2,023	1,638	1.2.3.NC	Non-Coniferous	1000 m3ub									share of agglomerates produced from industrial roundwood residues	100%
PRODUCTION						PRODUCTION										usable industrial roundwood - amount of roundwood that is used, remainder leaves industry	98.5%
2	WOOD CHARCOAL	1000 t	…	…	2	WOOD CHARCOAL	1000 t
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	61,644	57,501	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	0	0
3.1	WOOD CHIPS AND PARTICLES	1000 m3	46,355	44,259	3.1	WOOD CHIPS AND PARTICLES	1000 m3
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	15,289	13,242	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3
4	RECOVERED POST-CONSUMER WOOD	1000 t	…	…	4	RECOVERED POST-CONSUMER WOOD	1000 t
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	8,593	8,412	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	ERROR:#VALUE!	ERROR:#VALUE!
5.1	WOOD PELLETS	1000 t	8,593	8,412	5.1	WOOD PELLETS	1000 t
5.2	OTHER AGGLOMERATES	1000 t	…	…	5.2	OTHER AGGLOMERATES	1000 t
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	82,472	79,134	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	0	0
6.C	Coniferous	1000 m3	60,042	62,446	6.C	Coniferous	1000 m3
6.NC	Non-Coniferous	1000 m3	22,429	16,688	6.NC	Non-Coniferous	1000 m3
6.NC.T	of which: Tropical	1000 m3	0	0	6.NC.T	of which: Tropical	1000 m3
7	VENEER SHEETS	1000 m3	14,024	2,284	7	VENEER SHEETS	1000 m3	0	0
7.C	Coniferous	1000 m3	12,462	1,803	7.C	Coniferous	1000 m3
7.NC	Non-Coniferous	1000 m3	1,562	481	7.NC	Non-Coniferous	1000 m3
7.NC.T	of which: Tropical	1000 m3	0	0	7.NC.T	of which: Tropical	1000 m3
8	WOOD-BASED PANELS	1000 m3	31,174	30,228	8	WOOD-BASED PANELS	1000 m3	0	0
8.1	PLYWOOD	1000 m3	9,925	9,500	8.1	PLYWOOD	1000 m3	0	0
8.1.C	Coniferous	1000 m3	9,691	9,321	8.1.C	Coniferous	1000 m3
8.1.NC	Non-Coniferous	1000 m3	234	178	8.1.NC	Non-Coniferous	1000 m3
8.1.NC.T	of which: Tropical	1000 m3	0	0	8.1.NC.T	of which: Tropical	1000 m3
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	17,782	17,849	8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	13,435	13,713	8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3
8.3	FIBREBOARD	1000 m3	3,467	2,879	8.3	FIBREBOARD	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!
8.3.1	HARDBOARD	1000 m3	282	212	8.3.1	HARDBOARD	1000 m3
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	3,185	2,667	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3
8.3.3	OTHER FIBREBOARD	1000 m3	…	…	8.3.3	OTHER FIBREBOARD	1000 m3
9	WOOD PULP	1000 t	52,062	50,871	9	WOOD PULP	1000 t	0	0
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	5,547	4,593	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t
9.2	CHEMICAL WOOD PULP	1000 t	45,263	45,161	9.2	CHEMICAL WOOD PULP	1000 t	0	0
9.2.1	SULPHATE PULP	1000 t	45,015	44,912	9.2.1	SULPHATE PULP	1000 t
9.2.1.1	of which: BLEACHED	1000 t	22,938	21,739	9.2.1.1	of which: BLEACHED	1000 t
9.2.2	SULPHITE PULP	1000 t	248	249	9.2.2	SULPHITE PULP	1000 t
9.3	DISSOLVING GRADES	1000 t	1,252	1,117	9.3	DISSOLVING GRADES	1000 t
10	OTHER PULP	1000 t	28,362	28,579	10	OTHER PULP	1000 t	0	0
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	146	149	10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t
10.2	RECOVERED FIBRE PULP	1000 t	28,216	28,430	10.2	RECOVERED FIBRE PULP	1000 t
11	RECOVERED PAPER	1000 t	44,661	42,248	11	RECOVERED PAPER	1000 t
12	PAPER AND PAPERBOARD	1000 t	68,157	66,239	12	PAPER AND PAPERBOARD	1000 t	0	0
12.1	GRAPHIC PAPERS	1000 t	11,635	8,513	12.1	GRAPHIC PAPERS	1000 t	0	0
12.1.1	NEWSPRINT	1000 t	840	440	12.1.1	NEWSPRINT	1000 t
12.1.2	UNCOATED MECHANICAL	1000 t	680	420	12.1.2	UNCOATED MECHANICAL	1000 t
12.1.3	UNCOATED WOODFREE	1000 t	6,056	4,789	12.1.3	UNCOATED WOODFREE	1000 t
12.1.4	COATED PAPERS	1000 t	4,058	2,864	12.1.4	COATED PAPERS	1000 t
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	7,003	7,281	12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t
12.3	PACKAGING MATERIALS	1000 t	48,045	48,163	12.3	PACKAGING MATERIALS	1000 t	0	0
12.3.1	CASE MATERIALS	1000 t	33,418	34,510	12.3.1	CASE MATERIALS	1000 t
12.3.2	CARTONBOARD	1000 t	6,570	6,311	12.3.2	CARTONBOARD	1000 t
12.3.3	WRAPPING PAPERS	1000 t	2,393	1,678	12.3.3	WRAPPING PAPERS	1000 t
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	5,664	5,664	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	1,474	2,282	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t
	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)
	t = metric tonnes

JQ2 | Primary Products | Trade

		FOREST SECTOR QUESTIONNAIRE JQ2				Country:	United States		Date: June 9, 2021
						Name of Official responsible for reply:
		PRIMARY PRODUCTS				Official Address (in full):																This table highlights discrepancies between production and trade. For any negative number, indicating greater net exports than production, please verify your data!
		Trade				Telephone:			Fax:			This table highlights discrepancies between items and sub-items. Please verify your data for any non-zero figure!
						E-mail:											Country:	United States							Country:	United States
	Specify Currency and Unit of Value (e.g.:1000 US $):			$1,000 US								Trade		Discrepancies
Product		Unit of	I M P O R T				E X P O R T				Product			I M P O R T				E X P O R T				Product			Apparent Consumption
code	Product	quantity	2019		2020		2019		2020		code			2019		2020		2019		2020		code			2019	2020
			Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value				Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	1,032	97,307	6,961	99,496	7,923	1,604,218	7,355	1,524,224	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	0	0	0	0	0	0	0	0	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	452,238	429,306
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	90	19,313	75	23,425	1	1,166	1	1,061	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	0	0	0	0	0	0	0	0	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	71,517	60,600
1.1.C	Coniferous	1000 m3ub	51	11,077	14	5,802	1	709	0	783	1.1.C	Coniferous	1000 m3ub									1.1.C	Coniferous	1000 m3ub	32,849	26,359
1.1.NC	Non-Coniferous	1000 m3ub	39	8,235	60	17,623	0	457	0	278	1.1.NC	Non-Coniferous	1000 m3ub									1.1.NC	Non-Coniferous	1000 m3ub	38,667	34,241
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	942	77,994	6,886	76,071	7,922	1,603,052	7,355	1,523,164	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	0	0	0	0	0	0	0	0	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	380,721	368,706
1.2.C	Coniferous	1000 m3ub	502	48,881	6,462	54,679	5,991	931,312	5,609	899,462	1.2.C	Coniferous	1000 m3ub									1.2.C	Coniferous	1000 m3ub	288,153	293,876
1.2.NC	Non-Coniferous	1000 m3ub	440	29,113	424	21,392	1,931	671,740	1,746	623,702	1.2.NC	Non-Coniferous	1000 m3ub									1.2.NC	Non-Coniferous	1000 m3ub	92,568	74,830
1.2.NC.T	of which: Tropical	1000 m3ub	2	2,000	0	235	7	2,442	9	3,229	1.2.NC.T	of which: Tropical	1000 m3ub									1.2.NC.T	of which: Tropical	1000 m3ub	-5	-9
2	WOOD CHARCOAL	1000 t	103	60,194	152	87,710	24	17,552	23	20,442	2	WOOD CHARCOAL	1000 t									2	WOOD CHARCOAL	1000 t	NT -79.092	NT -128.923
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	129	28,907	143	29,938	5,408	248,811	4,918	212,631	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	0	0	0	0	0	0	0	0	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	56,365	52,726
3.1	WOOD CHIPS AND PARTICLES	1000 m3	30	15,117	12	11,638	5,322	236,810	4,899	205,415	3.1	WOOD CHIPS AND PARTICLES	1000 m3									3.1	WOOD CHIPS AND PARTICLES	1000 m3	41,063	39,373
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	99	13,789	131	18,300	86	12,001	19	7,216	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3									3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	15,302	13,353
4	RECOVERED POST-CONSUMER WOOD	1000 t	0	0	0	0	0	0	0	0	4	RECOVERED POST-CONSUMER WOOD	1000 t									4	RECOVERED POST-CONSUMER WOOD	1000 t	NT 0	NT 0
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	370	82,201	348	80,646	6,882	955,258	7,272	991,553	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	0	0	0	0	0	0	0	0	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	2,081	1,489
5.1	WOOD PELLETS	1000 t	212	42,323	205	42,118	6,858	942,627	7,257	981,593	5.1	WOOD PELLETS	1000 t									5.1	WOOD PELLETS	1000 t	1,946	1,360
5.2	OTHER AGGLOMERATES	1000 t	158	39,879	143	38,529	23	12,630	15	9,960	5.2	OTHER AGGLOMERATES	1000 t									5.2	OTHER AGGLOMERATES	1000 t	NT -134.819671	NT -128.045528
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	34,839	5,794,412	36,228	8,021,488	6,916	2,978,928	6,232	2,720,999	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	0	0	0	0	0	0	0	0	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	110,394	109,130
6.C	Coniferous	1000 m3	34,066	5,290,192	35,598	7,597,367	3,206	923,264	2,721	806,376	6.C	Coniferous	1000 m3									6.C	Coniferous	1000 m3	90,902	95,324
6.NC	Non-Coniferous	1000 m3	773	504,220	630	424,121	3,710	2,055,664	3,511	1,914,622	6.NC	Non-Coniferous	1000 m3									6.NC	Non-Coniferous	1000 m3	19,492	13,806
6.NC.T	of which: Tropical	1000 m3	245	275,766	161	224,438	53	35,119	46	34,661	6.NC.T	of which: Tropical	1000 m3									6.NC.T	of which: Tropical	1000 m3	191	115
7	VENEER SHEETS	1000 m3	615	348,095	600	328,877	256	301,825	215	278,822	7	VENEER SHEETS	1000 m3	0	0	0	0	0	0	0	0	7	VENEER SHEETS	1000 m3	14,383	2,668
7.C	Coniferous	1000 m3	545	166,622	535	175,959	82	37,445	53	24,174	7.C	Coniferous	1000 m3									7.C	Coniferous	1000 m3	12,925	2,285
7.NC	Non-Coniferous	1000 m3	70	181,472	65	152,918	174	264,379	163	254,648	7.NC	Non-Coniferous	1000 m3									7.NC	Non-Coniferous	1000 m3	1,457	383
7.NC.T	of which: Tropical	1000 m3	10	30,745	10	22,092	9	17,176	8	15,068	7.NC.T	of which: Tropical	1000 m3									7.NC.T	of which: Tropical	1000 m3	1	1
8	WOOD-BASED PANELS	1000 m3	15,337	4,931,186	14,462	5,749,207	1,910	646,395	1,775	606,461	8	WOOD-BASED PANELS	1000 m3	0	0	0	0	0	0	0	0	8	WOOD-BASED PANELS	1000 m3	44,601	42,914
8.1	PLYWOOD	1000 m3	4,664	2,464,641	5,058	2,623,810	556	244,135	528	222,148	8.1	PLYWOOD	1000 m3	0	0	0	0	0	0	0	0	8.1	PLYWOOD	1000 m3	14,033	14,029
8.1.C	Coniferous	1000 m3	1,960	682,818	2,177	776,831	365	136,631	407	157,362	8.1.C	Coniferous	1000 m3									8.1.C	Coniferous	1000 m3	11,286	11,092
8.1.NC	Non-Coniferous	1000 m3	2,704	1,781,823	2,881	1,846,979	191	107,504	121	64,785	8.1.NC	Non-Coniferous	1000 m3									8.1.NC	Non-Coniferous	1000 m3	2,747	2,937
8.1.NC.T	of which: Tropical	1000 m3	519	308,575	533	284,995	21	8,920	23	9,190	8.1.NC.T	of which: Tropical	1000 m3									8.1.NC.T	of which: Tropical	1000 m3	498	510
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	7,844	1,410,194	6,528	2,018,766	569	182,534	556	187,104	8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3									8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	25,057	23,821
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	6,369	1,068,600	5,066	1,718,156	189	62,471	195	66,288	8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3									8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	19,615	18,584
8.3	FIBREBOARD	1000 m3	2,829	1,056,351	2,876	1,106,631	785	219,727	690	197,209	8.3	FIBREBOARD	1000 m3	0	0	0	0	0	0	0	0	8.3	FIBREBOARD	1000 m3	5,512	5,065
8.3.1	HARDBOARD	1000 m3	199	98,277	238	121,918	213	62,323	239	71,743	8.3.1	HARDBOARD	1000 m3									8.3.1	HARDBOARD	1000 m3	268	211
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	2,419	910,289	2,453	948,869	424	119,565	284	82,362	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3									8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	5,180	4,836
8.3.3	OTHER FIBREBOARD	1000 m3	211	47,785	185	35,844	148	37,839	167	43,104	8.3.3	OTHER FIBREBOARD	1000 m3									8.3.3	OTHER FIBREBOARD	1000 m3	NT -63.612	NT -17.717037
9	WOOD PULP	1000 t	5,300	3,317,631	5,661	2,851,004	7,850	5,596,711	7,806	4,930,067	9	WOOD PULP	1000 t	0	0	0	0	0	0	0	0	9	WOOD PULP	1000 t	49,512	48,726
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	125	52,311	123	45,796	88	43,685	126	57,952	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t									9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	5,583	4,590
9.2	CHEMICAL WOOD PULP	1000 t	5,041	3,086,642	5,356	2,594,787	6,834	4,641,468	6,961	4,188,170	9.2	CHEMICAL WOOD PULP	1000 t	0	0	0	0	0	0	0	0	9.2	CHEMICAL WOOD PULP	1000 t	43,470	43,555
9.2.1	SULPHATE PULP	1000 t	4,763	2,972,791	5,080	2,477,873	6,796	4,624,231	6,910	4,165,520	9.2.1	SULPHATE PULP	1000 t									9.2.1	SULPHATE PULP	1000 t	42,982	43,083
9.2.1.1	of which: BLEACHED	1000 t	4,594	2,892,343	4,957	2,410,430	6,531	4,475,938	6,593	4,017,731	9.2.1.1	of which: BLEACHED	1000 t									9.2.1.1	of which: BLEACHED	1000 t	21,000	20,104
9.2.2	SULPHITE PULP	1000 t	278	113,851	275	116,913	37	17,237	51	22,651	9.2.2	SULPHITE PULP	1000 t									9.2.2	SULPHITE PULP	1000 t	489	473
9.3	DISSOLVING GRADES	1000 t	134	178,678	183	210,422	928	911,557	719	683,945	9.3	DISSOLVING GRADES	1000 t									9.3	DISSOLVING GRADES	1000 t	458	581
10	OTHER PULP	1000 t	63	24,181	319	229,962	353	223,235	414	242,463	10	OTHER PULP	1000 t	0	0	0	0	0	0	0	0	10	OTHER PULP	1000 t	28,072	28,484
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	35	23,548	299	227,607	85	112,047	85	111,078	10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t									10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	96	364
10.2	RECOVERED FIBRE PULP	1000 t	28	633	20	2,355	268	111,188	329	131,386	10.2	RECOVERED FIBRE PULP	1000 t									10.2	RECOVERED FIBRE PULP	1000 t	27,977	28,121
11	RECOVERED PAPER	1000 t	513	82,225	611	80,781	16,467	2,698,175	14,384	2,454,434	11	RECOVERED PAPER	1000 t									11	RECOVERED PAPER	1000 t	28,707	28,475
12	PAPER AND PAPERBOARD	1000 t	8,724	8,439,773	7,642	7,048,640	10,569	9,169,646	10,314	8,265,097	12	PAPER AND PAPERBOARD	1000 t	0	0	0	0	0	0	0	0	12	PAPER AND PAPERBOARD	1000 t	66,311	63,567
12.1	GRAPHIC PAPERS	1000 t	5,383	4,451,902	4,210	3,209,920	1,551	1,499,398	1,200	1,132,828	12.1	GRAPHIC PAPERS	1000 t	0	0	0	0	0	0	0	0	12.1	GRAPHIC PAPERS	1000 t	15,467	11,524
12.1.1	NEWSPRINT	1000 t	1,277	745,358	1,046	510,639	212	120,003	69	37,064	12.1.1	NEWSPRINT	1000 t									12.1.1	NEWSPRINT	1000 t	1,906	1,417
12.1.2	UNCOATED MECHANICAL	1000 t	1,342	980,611	1,070	716,075	77	74,270	54	54,341	12.1.2	UNCOATED MECHANICAL	1000 t									12.1.2	UNCOATED MECHANICAL	1000 t	1,945	1,436
12.1.3	UNCOATED WOODFREE	1000 t	1,019	1,106,582	859	875,491	488	584,688	362	410,598	12.1.3	UNCOATED WOODFREE	1000 t									12.1.3	UNCOATED WOODFREE	1000 t	6,588	5,287
12.1.4	COATED PAPERS	1000 t	1,745	1,619,352	1,236	1,107,715	774	720,437	715	630,825	12.1.4	COATED PAPERS	1000 t									12.1.4	COATED PAPERS	1000 t	5,029	3,384
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	382	489,996	417	500,721	202	265,545	184	237,711	12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t									12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	7,184	7,513
12.3	PACKAGING MATERIALS	1000 t	2,918	3,232,345	2,973	3,076,755	8,435	7,063,109	8,642	6,637,062	12.3	PACKAGING MATERIALS	1000 t	0	0	0	0	0	0	0	0	12.3	PACKAGING MATERIALS	1000 t	42,529	42,494
12.3.1	CASE MATERIALS	1000 t	1,285	942,783	1,310	895,556	5,130	3,206,011	5,503	3,099,523	12.3.1	CASE MATERIALS	1000 t									12.3.1	CASE MATERIALS	1000 t	29,573	30,316
12.3.2	CARTONBOARD	1000 t	919	1,367,501	1,099	1,430,051	2,221	2,726,752	2,081	2,498,745	12.3.2	CARTONBOARD	1000 t									12.3.2	CARTONBOARD	1000 t	5,268	5,328
12.3.3	WRAPPING PAPERS	1000 t	627	852,671	484	694,425	1,002	1,074,223	974	987,570	12.3.3	WRAPPING PAPERS	1000 t									12.3.3	WRAPPING PAPERS	1000 t	2,018	1,188
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	87	69,390	80	56,723	81	56,123	83	51,223	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t									12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	5,670	5,661
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	40	265,530	42	261,244	383	341,594	288	257,496	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t									12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	1,132	2,036
	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)
	t = metric tonnes

JQ3 | Secondary Products| Trade

			62	91	91
			Country: United States		Date: May 27, 2021				Country: United States
			Name of Official responsible for reply:
			Official Address (in full):
	FOREST SECTOR QUESTIONNAIRE JQ3
	SECONDARY PROCESSED PRODUCTS		Telephone:		Fax:
	Trade		E-mail:
						This table highlights discrepancies between items and sub-items. Please verify your data for any non-zero figure!
		Specify Currency and Unit of Value (e.g.:1000 US $):	$1,000 US					Discrepancies

Product	Product	I M P O R T V A L U E		E X P O R T V A L U E		Product	Product	I M P O R T V A L U E		E X P O R T V A L U E
code		2019	2020	2019	2020	Code		2019	2020	2019	2020
13	SECONDARY WOOD PRODUCTS					13	SECONDARY WOOD PRODUCTS
13.1	FURTHER PROCESSED SAWNWOOD	1,244,537	1,252,251	289,201	196,356	13.1	FURTHER PROCESSED SAWNWOOD	0	0	0	0
13.1.C	Coniferous	947,608	996,108	60,230	51,865	13.1.C	Coniferous
13.1.NC	Non-coniferous	296,929	256,143	228,971	144,491	13.1.NC	Non-coniferous
13.1.NC.T	of which: Tropical	36,563	45,723	2,044	1,188	13.1.NC.T	of which: Tropical
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	417,378	385,151	418,353	334,040	13.2	WOODEN WRAPPING AND PACKAGING MATERIAL
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	1,140,652	1,034,487	74,644	65,083	13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD	2,098,519	2,264,693	394,135	390,278	13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD
13.5	WOODEN FURNITURE	19,953,632	20,245,200	1,743,989	1,582,853	13.5	WOODEN FURNITURE
13.6	PREFABRICATED BUILDINGS OF WOOD	141,252	111,477	36,768	21,664	13.6	PREFABRICATED BUILDINGS OF WOOD
13.7	OTHER MANUFACTURED WOOD PRODUCTS	1,219,000	1,337,741	231,683	164,705	13.7	OTHER MANUFACTURED WOOD PRODUCTS
14	SECONDARY PAPER PRODUCTS					14	SECONDARY PAPER PRODUCTS
14.1	COMPOSITE PAPER AND PAPERBOARD	66,977	66,807	69,970	47,556	14.1	COMPOSITE PAPER AND PAPERBOARD
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	951,705	866,635	1,057,996	959,989	14.2	SPECIAL COATED PAPER AND PULP PRODUCTS
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	1,292,044	1,537,947	898,877	904,437	14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4	PACKAGING CARTONS, BOXES ETC.	2,612,713	2,557,941	2,014,543	1,896,608	14.4	PACKAGING CARTONS, BOXES ETC.
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	2,967,665	2,144,878	1,885,627	1,163,336	14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	OK	OK	OK	OK
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	455	491	10,192	10,722	14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	327,181	324,194	66,143	70,963	14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	126,698	126,264	92,676	89,712	14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE

ECE-EU | Species | Trade


							Country:	USA		Date:	9-Jun-21
							Name of Official responsible for reply:

			FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade				Official Address (in full):									DISCREPANCIES - please note cells with notes and review data		Checks
																		- looks to see if JQ2 and this sheet the same
			Trade in Roundwood and Sawnwood by species				Telephone:			Fax:								- checks the sum when they should be equal
							E-mail:

			Specify Currency and Unit of Value (e.g.:1000 national currency):	$1,000 US

					I M P O R T				E X P O R T									I M P O R T				E X P O R T
Product	Classification	Classification		Unit of	2019		2020		2019		2020		Product	Classification	Classification		Unit of	2019		2020		2019		2020
Code	HS2017	CN2017	Product	Quantity	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Code	HS2017	CN2017	Product	Quantity	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	1000 m3ub	502	48,881	6,462	54,679	5,991	931,312	5,609	899,462	1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	1000 m3ub
	4403.23/24		Fir/Spruce (Abies spp., Picea spp.)	1000 m3ub	155	22,152	6,213	33,636	722	166,754	882	175,502		4403.23/24		Fir/Spruce (Abies spp., Picea spp.)	1000 m3ub	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data
		4403 23 10	sawlogs and veneer logs	1000 m3ub											4403 23 10	sawlogs and veneer logs	1000 m3ub
		4403 23 90 4403 24 00	pulpwood and other industrial roundwood	1000 m3ub											4403 23 90 4403 24 00	pulpwood and other industrial roundwood	1000 m3ub
	4403.21/22		Pine (Pinus spp.)	1000 m3ub	3	812	5	1,018	1,574	175,064	1,596	185,341		4403.21/22		Pine (Pinus spp.)	1000 m3ub	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data
		4403 21 10	sawlogs and veneer logs	1000 m3ub											4403 21 10	sawlogs and veneer logs	1000 m3ub
		4403 21 90 4403 22 00	pulpwood and other industrial roundwood	1000 m3ub											4403 21 90 4403 22 00	pulpwood and other industrial roundwood	1000 m3ub
1.2.NC	4403.12/41/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	1000 m3ub	440	29,113	424	21,392	1,931	671,740	1,746	623,702	1.2.NC	4403.12/41/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	1000 m3ub
	4403.91		of which: Oak (Quercus spp.)	1000 m3ub	3	1,390	2	990	513	213,363	513	206,996		4403.91		of which: Oak (Quercus spp.)	1000 m3ub
	4403.93/94		of which: Beech (Fagus spp.)	1000 m3ub	0	33	0	39	11	874	10	849		4403.93/94		of which: Beech (Fagus spp.)	1000 m3ub
	4403.95/96		of which: Birch (Betula spp.)	1000 m3ub	5	622	13	1,189	169	15,599	123	10,790		4403.95/96		of which: Birch (Betula spp.)	1000 m3ub	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data	incomplete data
		4403 95 10	sawlogs and veneer logs	1000 m3ub											4403 95 10	sawlogs and veneer logs	1000 m3ub
		4403 95 90 4403 96 00	pulpwood and other industrial roundwood	1000 m3ub											4403 95 90 4403 96 00	pulpwood and other industrial roundwood	1000 m3ub
	4403.97		of which: Poplar/Aspen (Populus spp.)	1000 m3ub	33	1,456	36	1,170	55	16,654	53	15,792		4403.97		of which: Poplar/Aspen (Populus spp.)	1000 m3ub
	4403.98		of which: Eucalyptus (Eucalyptus spp.)	1000 m3ub	1	221	2	688	0	86	0	136		4403.98		of which: Eucalyptus (Eucalyptus spp.)	1000 m3ub
6.C	4406.11/91 4407.11/12/19		Sawnwood, Coniferous	1000 m3	34,066	5,290,192	35,598	7,597,367	3,206	923,264	2,721	806,376	6.C	4406.11/91 4407.11/12/19		Sawnwood, Coniferous	1000 m3
	4407.12		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3	1,582	412,218	2,790	815,039	132	33,757	108	30,846		4407.12		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3
	4407.11		of which: Pine (Pinus spp.)	1000 m3	1,627	596,916	1,672	635,358	2,170	601,391	1,744	499,333		4407.11		of which: Pine (Pinus spp.)	1000 m3
6.NC	4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	1000 m3	773	504,220	630	424,121	3,710	2,055,664	3,511	1,914,622	6.NC	4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	1000 m3
	4407.91		of which: Oak (Quercus spp.)	1000 m3	29	22,076	26	18,117	1,419	849,679	1,321	786,045		4407.91		of which: Oak (Quercus spp.)	1000 m3
	4407.92		of which: Beech (Fagus spp.)	1000 m3	69	33,131	67	30,642	9	1,965	4	1,084		4407.92		of which: Beech (Fagus spp.)	1000 m3
	4407.93		of which: Maple (Acer spp.)	1000 m3	110	50,487	102	48,504	148	86,484	134	78,057		4407.93		of which: Maple (Acer spp.)	1000 m3
	4407.94		of which: Cherry (Prunus spp.)	1000 m3	1	708	2	930	148	105,131	134	95,762		4407.94		of which: Cherry (Prunus spp.)	1000 m3
	4407.95		of which: Ash (Fraxinus spp.)	1000 m3	1	602	1	595	273	177,762	265	160,167		4407.95		of which: Ash (Fraxinus spp.)	1000 m3
	4407.97		of which: Poplar/Aspen (Populus spp.)	1000 m3	124	29,808	101	23,914	555	222,989	569	217,453		4407.97		of which: Poplar/Aspen (Populus spp.)	1000 m3
	4407.96		of which: Birch (Betula spp.)	1000 m3	52	20,220	59	27,707	23	6,773	18	5,517		4407.96		of which: Birch (Betula spp.)	1000 m3
Light blue cells are requested only for EU members using the Combined Nomenclature to fill in - other countries are welcome to do so if their trade classification nomenclature permits
Please note that information on tropical species trade is requested in questionnaire ITTO2 for ITTO member countries
m3 = cubic metres solid volume
m3ub = cubic metres solid volume underbark (i.e. excluding bark)

ITTO1 | Estimates

				Country:		Date:
				Name of Official responsible for reply:

				Official Address (in full):
	FOREST SECTOR QUESTIONNAIRE ITTO1
				Telephone:		Fax:
	Production and Trade Estimates for 2021			E-mail:

	Specify Currency and Unit of Value (e.g.:1000 US $):			_____________________

Product		Unit of	Production	I M P O R T		E X P O R T
Code	Product	quantity	Quantity	Quantity	Value	Quantity	Value

1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub
1.2.C	Coniferous	1000 m3ub
1.2.NC	Non-Coniferous	1000 m3ub
1.2.NC.T	of which: Tropical	1000 m3ub
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3
6.C	Coniferous	1000 m3
6.NC	Non-Coniferous	1000 m3
6.NC.T	of which: Tropical	1000 m3
7	VENEER SHEETS	1000 m3
7.C	Coniferous	1000 m3
7.NC	Non-Coniferous	1000 m3
7.NC.T	of which: Tropical	1000 m3
8.1	PLYWOOD	1000 m3
8.1.C	Coniferous	1000 m3
8.1.NC	Non-Coniferous	1000 m3
8.1.NC.T	of which: Tropical	1000 m3
	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)

ITTO2 | Species | Trade


						Country:	USA		Date:	9-Jun-21
						Name of Official responsible for reply:

						Official Address (in full):
		FOREST SECTOR QUESTIONNAIRE ITTO2

		Trade in Tropical Species				Telephone:			Fax:
						E-mail:
			Specify Currency and Unit of Value (e.g.:1000 US $):	$1,000 US

				I M P O R T				E X P O R T
Product	Classifications			2019		2020		2019		2020
	HS2017/HS2012/HS2007	Scientific Name	Local/Trade Name	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
				1000 m3ub / 1000 m3		1000 m3ub / 1000 m3		1000 m3ub / 1000 m3		1000 m3ub / 1000 m3
1.2.NC.T	HS2017:			2	2,000	0	235	7	2,442	9	3,229
Industrial Roundwood, Tropical	ex4403.12 4403.41/49	Shorea spp.	Dark/light red meranti and meranti bakau	0	91	0	0	1	193	2	440
	HS2012/2007:	Other tropical	Other tropical	2	1,909	0	235	5	2,250	7	2,789
	ex4403.10 4403.41/49 ex4403.99
6.NC.T	HS2017:			245	275,766	161	224,438	53	35,119	46	34,661
Sawnwood (including sleepers), Tropical	ex4406.12/92 4407.21/22/25/26/27/28/29	Swietenia spp.	Mahogany	6	7,102	4	7,076	4	2,848	2	2,157
		Ocotea porosa & Ochroma pyramidale	Virola and Imbuia	7	4,804	6	3,736	22	16,257	34	25,527
	HS2012/2007:	Shorea spp.	Dark/light red, white and yellow meranty, white luan/seraya, and bakau	7	9,562	8	8,051	0	237	0	121
	ex4406.10/90 4407.21/22/25/26/27/28/30	Ochroma pyramidale	Balsa	43	28,437	14	19,377
		Entandrophragma cylindricum	Sapelli /Sapele	32	30,312	20	19,207	3	2,867	3	2,269
		Milicia excelsa, M. regia (syn. Chlorophora excelsa, C. regia)	Iroko	1	532	2	907	0	90	0	27
		Hymenaea courbaril	Jatoba/ Brazilian cherry	3	2,345	1	855
		Dipterocarpus spp.	Keruing	30	30,087	15	14,689
		Khaya spp.	Acajou d'afrique/ African mahogany	13	12,852	9	8,660
		Pouteria spp.	Aningre / Aniegre/ Anegre	0	23	0	14
		Tectona grandis	Teak	9	31,174	4	17,753
		Handroanthus spp.	Ipe	35	75,271	36	84,072
		Carapa spp.	Andiroba/ Padauk	1	1,368	0	401
		Cedrela odorata	Cedro/ Spanish cedar	6	5,125	5	4,032
		Other tropical	Other tropical	51	36,772	37	35,609	24	12,820	7	4,560
7.NC.T	HS2017:			10	30,745	10	22,092	9	17,176	8	15,068
Veneer Sheets, Tropical	4408.31/39	Shorea spp.	Dark/light red meranti and meranti bakau	0	1,418	0	323	2	3,596	4	6,124
	HS2012/2007:	Other tropical	Other tropical	10	29,327	10	21,769	7	13,580	4	8,944
	4408.31/39 ex4408.90
8.1.NC.T	HS2017:			519	308,575	533	284,995	21	8,920	23	9,190
Plywood, Tropical	4412.31 ex4412.94/99	Swietenia spp.	Mahogany	1	1,337	0	740
	HS2012/2007:	Cedrela odorata	Cedro/ Spanish cedar	3	1,982	1	1,143
	4412.31 ex4412.32/94/99	Other tropical	Other tropical	515	305,255	531	283,112	21	8,920	23	9,190
	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)
Note: List the major species traded in each category. Use additional sheet if more species to be explicitly reported. For tropical plywood, identify by face veneer if composed of more than one species.

ITTO3 | Miscellaneous

Country:

USA

Date:

9-Jun-21

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE ITTO3

Miscellaneous Items

Telephone:

Fax:

(use additional paper if necessary)

E-mail:

Please enter current import tariff rates applied to tropical and non-tropical timber products. If available, please provide tariffs by the relevant customs classification category. If tariff levels have been reported in previous years, enter changes only. (Logs = JQ code 1.2, Sawn = JQ code 6, Veneer = JQ code 7, and Plywood = JQ code 8.1)

Current import tariff

Logs

Tropical:

Sawn

Tropical:

Veneer

Tropical:

Plywood

Tropical:

Non-Tropical:

Comments (if any):

Please comment on any quotas, incentives, disincentives, tariff/non-tariff barriers or other related factors which now or in future will significantly affect your production and trade of tropical timber products.

No icentive program for trade of tropical timber products

Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.

None

Please indicate any trends or changes expected in the species composition of your trade. How important are lesser-used tropical timber species and/or minor tropical forest products?

No change in species composition expected

Please indicate trends in domestic building activity, housing starts, mortgage/interest rates, substitution of non-tropical wood and/or non-wood products for tropical timbers, and any other domestic factors having a significant impact on tropical timber consumption in your country.

Housing starts in 2020 were 7% higher than in 2019. Mortgage rates declined further in 2020, reaching an average 3.11% (30-year rate type)

Tropical timber (plywood) apparent consumption increased slightly in 2020.

Please indicate the extent of foreign involvement in your timber sector (e.g. number and nationalities of concessionaires/mill (joint) owners, area of forest allocated, scale of investment, etc.).

No change from previous report

Please provide details of any relevant forest law enforcement activities (e.g. legislation, fines, arrests, etc.) in your country in the past year.

No change from previous report

Please indicate the current extent of forest plantations in your country (ha), annual establishment rate (ha/yr) and proportion of industrial roundwood production from plantations.

Forest plantations represent 13% of all timberland acres, or 27 million ha. Annual establishment rate is approximately 926 thousand ha/year.

Approximately 43% of average annual harvest removals come from stands with clear evidence of artificial regeneration

conversion factors


			JFSQ
			FOREST SECTOR QUESTIONNAIRE
			Conversion Factors
NOTE THESE ARE ONLY GENERAL NUMBERS. IT WOULD BE PREFERABLE TO USE SPECIES- OR COUNTRY-SPECIFIC FACTORS
			Multiply the quantity expressed in units on the right side of "per" with the factor to get the value expressed in units on left side of "per".
			FAO and UNECE Statistical Publications			Results from UNECE/FAO 2009 Conversion Factors Questionnaire (median)
Product	JFSQ	Product	volume to weight	volume to area	volume/weight of finished product to volume of roundwood	volume to weight	volume/weight of finished product to volume of roundwood
Code	Quantity		m3 per MT	m3 per m2	Roundwood	m3 per MT	Roundwood
	Unit				equivalent		equivalent	Notes to results of UNECE/FAO Conversion Factor Questionnaire
1	1000 m3 ub	ROUNDWOOD (WOOD IN THE ROUGH)
1.1	1000 m3 ub	WOOD FUEL, INCLUDING WOOD FOR CHARCOAL	1.38
1.1.C	1000 m3 ub	Coniferous	1.60			Green = 1.12		Based on 891 kg/m3 green, basic density of .41, and 20% moisture seasoned
						Seasoned = 1.82		Based on 407 kg/m3 dry, assuming 20% moisture
1.1.NC	1000 m3 ub	Non-Coniferous	1.33			Green=1.05		Based on 1137 kg/m3 green, specific gravity of .55, and 20% moisture seasoned
						Seasoned=1.43
1.2	1000 m3 ub	INDUSTRIAL ROUNDWOOD
1.2.C	1000 m3 ub	Coniferous				1.10		Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood
1.2.C.Fir		Fir (and Spruce)				1.21		Austrian Energy Agency, 2009. weighted by share of standing inventory of European speices (57% spruce, 10% silver fir and remaining species)
1.2.C.Pine		Pine				1.08		Austrian Energy Agency, 2009, weighted 25% Scots Pine, 2% maritime pine, 2% black pine and remaining species
1.2.NC	1000 m3 ub	Non-Coniferous				0.91		Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood
1.2.NC.T	1000 m3 ub	of which:Tropical	1.37			AFRICA=1.31, ASIA=0.956, LA. AM= 0.847, World=1.12		Source: Fonseca "Measurement of Roundwood" 2005, ITTO Annual Review 2007, table 3-2-a Species weight averaged using m3/tonne from Fonseca 2005 and volume exported by species from each region as shown in ITTO 2007 (assumes that bark is removed)
1.2.1	1000 m3 ub	SAWLOGS AND VENEER LOGS				1.05		Based on 950 kg/m3 green. Bark is included in weight but not in volume.
1.2.1.C	1000 m3 ub	Coniferous	1.43			1.07		Based on 935 kg/m3 green. Bark is included in weight but not in volume.
1.2.1.NC	1000 m3 ub	Non-Coniferous	1.25			0.91		Based on 1093 kg/m3 green. Bark is included in weight but not in volume.
1.2.NC.Beech		Beech				0.92		Austrian Energy Agency, 2009
1.2.NC.Birch		Birch				0.88		Austrian Energy Agency, 2009
1.2.NC.Eucalyptus		Eucalyptus				0.77		ATIBT, 1982
1.2.NC.Oak		Oak				0.88		Austrian Energy Agency, 2009
1.2.NC.Poplar		Poplar				1.06		Austrian Energy Agency, 2009
1.2.2	1000 m3 ub	PULPWOOD (ROUND & SPLIT)	1.48			1.08		Based on 930 kg/m3 green. Bark is included in weight but not in volume.
1.2.2.C	1000 m3 ub	Coniferous	1.54			1.12		Based on 891 kg/m3 green. Bark is included in weight but not in volume.
1.2.2.NC	1000 m3 ub	Non-Coniferous	1.33			0.91		Based on 1095 kg/m3 green. Bark is included in weight but not in volume.
1.2.3	1000 m3 ub	OTHER INDUSTRIAL ROUNDWOOD	1.33			1.07
1.2.3.C	1000 m3 ub	Coniferous	1.43			1.12		same as 1.2.2.C
1.2.3.NC	1000 m3 ub	Non-Coniferous	1.25			0.91		same as 1.2.2.NC
2	1000 MT	WOOD CHARCOAL	6.00			5.35		Does not include the use of any of the wood fiber to generate the heat to make (add about 30% if inputted wood fiber used to provide heat)
3	1000 m3	WOOD CHIPS, PARTICLES AND RESIDUES
3.1	1000 m3	WOOD CHIPS AND PARTICLES	1.60			softwood=1.19	1.205	Based on swe/odmt of 2.41 and avg delivered mt / odmt of 2.0 in solid m3
						hardwood = 1.05	1.123	Based on swe/odmt of 2.01 and avg delivered mt / odmt of 1.79 in solid m3
						mix = 1.15
3.2	1000 m3	WOOD RESIDUES	1.50			Green=1.15		Based on wood chips
						Seasoned = 2.12	2.07	Assumption for seasoned is based on average basic density of .42 from questionnaire and assumes 15% moisture content
4	1000 mt	RECOVERED POST-CONSUMER WOOD						Delivered MT (12-20% atmospheric moisture). Convert to dry weight for energy purposes (multiply by 0.88 - 0.80)
5	1000 MT	WOOD PELLETS AND OTHER AGGLOMERATES
5.1	1000 MT	WOOD PELLETS				1.51	1.44	Bulk (loose) volume, 5-10% moisture
5.2	1000 MT	OTHER AGGLOMERATES				1.31	2.29	roundwood equivalent is m3rw/odmt, volume to weight is bulk (loose volume)
6	1000 m3	SAWNWOOD			1.6 / 1.82*
6.C	1000 m3	Coniferous	1.82			Green=1.202	RoughGreen=1.67	Green sawnwood based on basic density of .94, less bark (11%)
						Dry = 1.99	RoughDry=1.99	Dry sawnwood weight based on basic density of .42, 4% shrinkage and 15% moisture content
							PlanedDry=2.13
6.C.Fir		Fir and Spruce				2.16		Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.
6.C.Pine		Pine				1.72		Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.
6.NC	1000 m3	Non-Coniferous	1.43			Green=1.04	RoughGreen=1.86	Green sawnwood based on basic density of 1.09, less bark (12%)
						Seasoned=1.50	RoughDry=2.01	Dry sawnwood weight based on basic density of .55, 5% shrinkage and 15% moisture content
							PlanedDry=2.81
6.NC.Ash		Ash				1.47		Wood Database (wood-database.com). Air-dry.
6.NC.Beech		Beech				1.42		Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.Birch		Birch				1.47		Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.Cherry		Cherry				1.62		Giordano, 1976, Tecnologia del legno. Air-dry. Prunus avium.
6.NC.Maple		Maple				1.35		Giordano, 1976, Tecnologia del legno. Air-dry
6.NC.Oak		Oak				1.38		Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.Poplar		Poplar				2.29		Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.T	1000 m3	of which:Tropical				1.38		Based on FP Conversion Factors (2019), Asia (720 kg / m3)
7	1000 m3	VENEER SHEETS	1.33	0.0025	1.9*
7.C	1000 m3	Coniferous		0.003		Green=1.20	1.5***	Green veneer based on basic density of .94, less bark (11%)
						Seasoned=2.06	1.6***	Dry veneer weight based on basic density of .42, 9% shrinkage and 5% moisture content
7.NC	1000 m3	Non-Coniferous		0.001		Green=1.04	1.5***	Green veneer based on basic density of 1.09, less bark (11%)
						Seasoned=1.53	1.6***	Dry veneer weight based on basic density of .55, 11.5% shrinkage and 5% moisture content
7.NC.T	1000 m3	of which:Tropical
8	1000 m3	WOOD-BASED PANELS			1.6
8.1	1000 m3	PLYWOOD	1.54	0.105	2.3*
8,1.C	1000 m3	Coniferous		0.0165***		1.69	2.12	dried, sanded, peeled
8.1.NC	1000 m3	Non-Coniferous		0.0215***		1.54	1.92	dried, sanded, sliced
8.1.NC.T	1000 m3	of which:Tropical
8.2	1000 m3	PARTICLE BOARD (including OSB)	1.54
8.2x	1000 m3	PARTICLE BOARD (excluding OSB)		0.018***		1.53	1.50
8.2.1	1000 m3	of which: OSB		0.018***		1.67	1.63
8.3	1000 m3	FIBREBOARD
8.3.1	1000 m3	HARDBOARD	1.05	0.005 Alex McCusker: Alex McCusker: 0.003 per Conversion Factors Study		1.06	1.93	solid wood per m3 of product
8.3.2	1000 m3	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	2.00	0.016		1.37	1.70	solid wood per m3 of product
8.3.3	1000 m3	OTHER FIBREBOARD	4.00	0.025		3.44	0.71	solid wood per m3 of product, mostly insulating board
9	1000 MT	WOOD PULP			3.37		3.86
9.1	1000 MT	MECHANICAL AND SEMI-CHEMICAL					2.60	air-dried metric ton (mechanical 2.50, semi-chemical 2.70)
9..2	1000 MT	CHEMICAL					4.90
9.2.1	1000 MT	SULPHATE					4.57	air-dried metric ton (unbleached 4.63, bleached 4.50)
9.2.1.1	1000 MT	of which: bleached					4.50	air-dried metric ton
9.2.2	1000 MT	SULPHITE					4.83	air-dried metric ton (unbleached 4.64 and bleached 5.01)
9.3	1000 MT	DISSOLVING GRADES					5.65	air-dried metric ton
10	1000 MT	OTHER PULP
10.1	1000 MT	PULP FROM FIBRES OTHER THAN WOOD
10.2	1000 MT	RECOVERED FIBRE PULP
11	1000 MT	RECOVERED PAPER					1.28 MT in per MT out
12	1000 MT	PAPER AND PAPERBOARD			3.37		3.6
12.1	1000 MT	GRAPHIC PAPERS
12.1.1	1000 MT	NEWSPRINT					2.80	air-dried metric ton
12.1.2	1000 MT	UNCOATED MECHANICAL					3.50	air-dried metric ton
12.1.3	1000 MT	UNCOATED WOODFREE
12.1.4	1000 MT	COATED PAPERS					3.95	air-dried metric ton
12.2	1000 MT	SANITARY AND HOUSEHOLD PAPERS					4.90	air-dried metric ton
12.3	1000 MT	PACKAGING MATERIALS					3.25	air-dried metric ton
12.3.1	1000 MT	CASE MATERIALS					4.20	air-dried metric ton
12.3.2	1000 MT	CARTONBOARD					4.00	air-dried metric ton
12.3.3	1000 MT	WRAPPING PAPERS					4.10	air-dried metric ton
12.3.4	1000 MT	OTHER PAPERS MAINLY FOR PACKAGING					4.00	air-dried metric ton
12.4	1000 MT	OTHER PAPER AND PAPERBOARD N.E.S					3.48	air-dried metric ton
For inverse relationships divide 1 by the factor given, e.g. to convert m3 of wood charcoal to mt divide 1 by m3/mt factor of 6 = 0.167
Notes:				Forest Measures
MT = metric tonnes (1000 kg)				Unit	m3/unit	m3/unit
m3 = cubic meters (solid volume)				1000 board feet (sawlogs)	4.53**
m2 = square meters				1000 board feet (sawnwood - nominal)	2.36	1.69	nominal board feet to actual m3
(s) = solid volume				1000 square feet (1/8 inch thickness)	0.295
				cord	3.625	2.43
Unit Conversion				cord (pulpwood)	2.55	2.43
1 inch = 25.4 millimetres				cord (wood fuel)	2.12	2.43
1 square foot = 0.0929 square metre				cubic foot	0.02832
1 pound = 0.454 kilograms				cubic foot (stacked)	0.01841
1 short ton (2000 pounds) = 0.9072 metric ton				cunit	2.83
1 long ton (2240 pounds) = 1.016 metric ton				fathom	6.1164
Bold = FAO published figure				hoppus cubic foot	0.0222
				hoppus super(ficial) foot	0.00185
* = ITTO				hoppus ton (50 hoppus cubic feet)	1.11
** = obolete - more recent figures would be				Petrograd Standard	4.672
for OR, WA, AK (west of Cascades), SE US (Doyle region): 6.3				stere	1	0.67
Inland west US, Great Lakes US, E. Can.: 5.7				stere (pulpwood)	0.72	0.67
NE US Int 1/4": 5				stere (wood fuel)	0.65	0.67
*** = Conversion Factor Study, US figures, rotary for conifer and sliced for non-conifer
Fonseca *Measurement of Roundwood" 2005. Estimated by Matt Fonseca based on regional knowledge of the scaling methods and timber types
prepared February 2004
updated 2007 with RWE factors
updated 2009 with provisional results of forest products conversion factors study
updated 2011 with results of forest products conversion factors study (DP49)

Annex1 | JQ1-Corres.




		FOREST SECTOR QUESTIONNAIRE JQ1 (Supp. 1)
		PRIMARY PRODUCTS
		Removals and Production
		CORRESPONDENCES to CPC Ver.2.1
		Central Product Classification Version 2.1 (CPC Ver. 2.1)
Product	Product
Code

REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
1	ROUNDWOOD (WOOD IN THE ROUGH)	031
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	0313
1.1.C	Coniferous	03131
1.1.NC	Non-Coniferous	03132
1.2	INDUSTRIAL ROUNDWOOD	0311 0312
1.2.C	Coniferous	0311
1.2.NC	Non-Coniferous	0312
1.2.NC.T	of which: Tropical	ex0312
1.2.1	SAWLOGS AND VENEER LOGS	ex03110 ex03120
1.2.1.C	Coniferous	ex03110
1.2.1.NC	Non-Coniferous	ex03120
1.2.2	PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)	ex03110 ex03120
1.2.2.C	Coniferous	ex03110
1.2.2.NC	Non-Coniferous	ex03120
1.2.3	OTHER INDUSTRIAL ROUNDWOOD	ex03110 ex03120
1.2.3.C	Coniferous	ex03110
1.2.3.NC	Non-Coniferous	ex03120
PRODUCTION
2	WOOD CHARCOAL	ex34510
3	WOOD CHIPS, PARTICLES AND RESIDUES	ex31230 ex39283
3.1	WOOD CHIPS AND PARTICLES	ex31230
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	ex39283
4	RECOVERED POST-CONSUMER WOOD	ex39283
5	WOOD PELLETS AND OTHER AGGLOMERATES	39281 39282
5.1	WOOD PELLETS	39281
5.2	OTHER AGGLOMERATES	39282
6	SAWNWOOD (INCLUDING SLEEPERS)	311 3132
6.C	Coniferous	31101 ex31109 ex3132
6.NC	Non-Coniferous	31102 ex31109 ex3132
6.NC.T	of which: Tropical	ex31102 ex31109 ex3132
7	VENEER SHEETS	3151
7.C	Coniferous	31511
7.NC	Non-Coniferous	31512
7.NC.T	of which: Tropical	ex31512
8	WOOD-BASED PANELS	3141 3142 3143 3144
8.1	PLYWOOD	3141 3142
8.1.C	Coniferous	31411 31421
8.1.NC	Non-Coniferous	31412 31422
8.1.NC.T	of which: Tropical	ex31412 ex31422
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	3143
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	31432
8.3	FIBREBOARD	3144
8.3.1	HARDBOARD	31442
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	31441
8.3.3	OTHER FIBREBOARD	31449
9	WOOD PULP	32111 32112 ex32113
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	ex32113
9.2	CHEMICAL WOOD PULP	32112
9.2.1	SULPHATE PULP	ex32112
9.2.1.1	of which: BLEACHED	ex32112
9.2.2	SULPHITE PULP	ex32112
9.3	DISSOLVING GRADES	32111
10	OTHER PULP	ex32113
10.1	PULP FROM FIBRES OTHER THAN WOOD	ex32113
10.2	RECOVERED FIBRE PULP	ex32113
11	RECOVERED PAPER	3924
12	PAPER AND PAPERBOARD	3212 3213 32142 32143 ex32149 32151 32198 ex32199
12.1	GRAPHIC PAPERS	3212 ex32143 ex32149
12.1.1	NEWSPRINT	32121
12.1.2	UNCOATED MECHANICAL	ex32122 ex32129
12.1.3	UNCOATED WOODFREE	32122 ex32129
12.1.4	COATED PAPERS	ex32143 ex32149
12.2	HOUSEHOLD AND SANITARY PAPERS	32131
12.3	PACKAGING MATERIALS	32132 ex32133 32134 32135 ex32136 ex32137 32142 32151 ex32143 ex32149
12.3.1	CASE MATERIALS	32132 32134 32135 ex32136
12.3.2	CARTONBOARD	ex32133 ex32136 ex32143 ex32149
12.3.3	WRAPPING PAPERS	ex32133 ex32136 ex32137 32142 32151
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	ex32136
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	ex32149 ex32133 ex32136 ex32137 32198 ex32199
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the CPC Ver.2.1 code is applicable.
For instance "ex31512" under product 7.NC.T means that only a part of CPC Ver.2.1 code 31512 refers to non-coniferous tropical veneer sheets.
In CPC, if only 3 or 4 digits are shown, then all sub-codes at lower degrees of aggregation are included (for example, 0313 includes 03131 and 03132).

Annex2 | JQ2-Corres.




		FOREST SECTOR QUESTIONNAIRE JQ2 (Supp. 1)
		PRIMARY PRODUCTS
		Trade
		CORRESPONDENCES to HS2017, HS2012 and SITC Rev.4
		C l a s s i f i c a t i o n s
Product	Product
Code		HS2017	HS2012	SITC Rev.4

1	ROUNDWOOD (WOOD IN THE ROUGH)	4401.11/12 44.03	4401.10 44.03	245.01 247
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	4401.11/12	4401.10	245.01
1.1.C	Coniferous	4401.11	ex4401.10	ex245.01
1.1.NC	Non-Coniferous	4401.12	ex4401.10	ex245.01
1.2	INDUSTRIAL ROUNDWOOD	44.03	44.03	247
1.2.C	Coniferous	4403.11/21/22/23/24/25/26	ex4403.10 4403.20	ex247.3 247.4
1.2.NC	Non-Coniferous	4403.12/41/49/91/93/94/95/96/97/98/99	ex4403.10 4403.41/49/91/92/99	ex247.3 247.5 247.9
1.2.NC.T	of which: Tropical	ex4403.12 4403.41/49	ex4403.10 4403.41/49 ex4403.99	ex247.3 247.5 ex247.9
2	WOOD CHARCOAL	4402.90	4402.90	ex245.02
3	WOOD CHIPS, PARTICLES AND RESIDUES	4401.21/22 ex4401.40	4401.21/22 ex4401.39	246.1 ex246.2
3.1	WOOD CHIPS AND PARTICLES	4401.21/22	4401.21/22	246.1
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	ex4401.40	ex4401.39	ex246.2
4	RECOVERED POST-CONSUMER WOOD	ex4401.40	ex4401.39	ex246.2
5	WOOD PELLETS AND OTHER AGGLOMERATES	4401.31/39	4401.31 ex4401.39	ex246.2
5.1	WOOD PELLETS	4401.31	4401.31	ex246.2
5.2	OTHER AGGLOMERATES	4401.39	ex4401.39	ex246.2
6	SAWNWOOD (INCLUDING SLEEPERS)	44.06 44.07	44.06 44.07	248.1 248.2 248.4
6.C	Coniferous	4406.11/91 4407.11/12/19	ex4406.10/90 4407.10	ex248.11 ex248.19 248.2
6.NC	Non-Coniferous	4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99	ex4406.10/90 4407.21/22/25/26/27/28/29/91/92/93/94/95/99	ex248.11 ex248.19 248.4
6.NC.T	of which: Tropical	ex4406.12/92 4407.21/22/25/26/27/28/29	ex4406.10/90 4407.21/22/25/26/27/28/29 ex4407.99	ex248.11 ex248.19 ex248.4
7	VENEER SHEETS	44.08	44.08	634.1
7.C	Coniferous	4408.10	4408.10	634.11
7.NC	Non-Coniferous	4408.31/39/90	4408.31/39/90	634.12
7.NC.T	of which: Tropical	4408.31/39	4408.31/39 ex4408.90	ex634.12
8	WOOD-BASED PANELS	44.10 44.11 4412.31/33/34/39/94/99	44.10 44.11 4412.31/32/39/94/99	634.22/23/31/33/39 634.5
8.1	PLYWOOD	4412.31/33/34/39/94/99	4412.31/32/39/94/99	634.31/33/39
8.1.C	Coniferous	4412.39 ex4412.94 ex4412.99	4412.39 ex4412.94 ex.4412.99	ex634.31 ex634.33 ex634.39
8.1.NC	Non-Coniferous	4412.31/33/34 ex4412.94 ex4412.99	4412.31/32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.1.NC.T	of which: Tropical	4412.31 ex4412.94 ex4412.99	4412.31 ex4412.32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	44.10	44.10	634.22/23
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	4410.12	4410.12	ex634.22
8.3	FIBREBOARD	44.11	44.11	634.5
8.3.1	HARDBOARD	4411.92	4411.92	ex634.54 ex634.55
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	4411.12/13 ex4411.14*	4411.12/13 ex4411.14*	ex634.54 ex634.55
8.3.3	OTHER FIBREBOARD	ex4411.14 4411.93/94	ex4411.14 4411.93/94	ex634.54 ex634.55
9	WOOD PULP	47.01/02/03/04/05	47.01/02/03/04/05	251.2 251.3 251.4 251.5 251.6 251.91
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	47.01 47.05	47.01 47.05	251.2 251.91
9.2	CHEMICAL WOOD PULP	47.03 47.04	47.03 47.04	251.4 251.5 251.6
9.2.1	SULPHATE PULP	47.03	47.03	251.4 251.5
9.2.1.1	of which: BLEACHED	4703.21/29	4703.21/29	251.5
9.2.2	SULPHITE PULP	47.04	47.04	251.6
9.3	DISSOLVING GRADES	47.02	47.02	251.3
10	OTHER PULP	47.06	47.06	251.92
10.1	PULP FROM FIBRES OTHER THAN WOOD	4706.10/30/91/92/93	4706.10/30/91/92/93	ex251.92
10.2	RECOVERED FIBRE PULP	4706.20	4706.20	ex251.92
11	RECOVERED PAPER	47.07	47.07	251.1
12	PAPER AND PAPERBOARD	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	641.1 641.2 641.3 641.4 641.5 641.62/63/64/69/71/72/74/75/76/77/93 642.41
12.1	GRAPHIC PAPERS	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	641.1 641.21/22/26/29 641.3
12.1.1	NEWSPRINT	48.01	48.01	641.1
12.1.2	UNCOATED MECHANICAL	4802.61/62/69	4802.61/62/69	641.29
12.1.3	UNCOATED WOODFREE	4802.10/20/54/55/56/57/58	4802.10/20/54/55/56/57/58	641.21/22/26
12.1.4	COATED PAPERS	48.09 4810.13/14/19/22/29	48.09 4810.13/14/19/22/29	641.3
12.2	HOUSEHOLD AND SANITARY PAPERS	48.03	48.03	641.63
12.3	PACKAGING MATERIALS	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	641.41/42/46 ex641.47 641.48/51/52 ex641.53 641.54/59/62/64/69/71/72/74/75/76/77
12.3.1	CASE MATERIALS	4804.11/19 4805.11/12/19/24/25/91	4804.11/19 4805.11/12/19/24/25/91	641.41/51/54 ex641.59
12.3.2	CARTONBOARD	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	ex641.47 641.48 ex641.59 641.75/76 ex641.77 641.71/72
12.3.3	WRAPPING PAPERS	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	641.42/46/52 ex641.53 641.62/64/69/74 ex641.77
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	4805.93	4805.93	ex641.59
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	641.24 ex641.47 641.56 ex641.53 641.55/93 642.41
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017 or SITC Rev.4 code is applicable.
For instance "ex4401.40" under product 3.2 means that only a part of HS2017 code 4401.40 refers to wood residues coming from wood processing (the other part coded under 4401.40 is recovered post-consumer wood).
In SITC Rev.4, if only 4 digits are shown, then all sub-headings at lower degrees of aggregation are included (for example, 634.1 includes 634.11 and 634.12).
* - Please assign the trade data for HS code 4411.14 to product 8.3.2 (MDF/HDF) and 8.3.3 (other fibreboard) if it is possible to do this in national statistics. If not, please assign all the trade data to item 8.3.2 as in most cases MDF/HDF will represent the large majority of trade.

Annex3 | JQ3-Corres.



		FOREST SECTOR QUESTIONNAIRE JQ3 (Supp. 1)
		SECONDARY PROCESSED PRODUCTS
		Trade
		CORRESPONDENCES to HS2017, HS2012 and SITC Rev.4
		C l a s s i f i c a t i o n s
Product	Product
Code		HS2017	HS2012	SITC Rev.4

13	SECONDARY WOOD PRODUCTS
13.1	FURTHER PROCESSED SAWNWOOD	4409.10/22/29	4409.10/29	248.3 248.5
13.1.C	Coniferous	4409.10	4409.10	248.3
13.1.NC	Non-coniferous	4409.22/29	4409.29	248.5
13.1.NC.T	of which: Tropical	4409.22	ex4409.29	ex248.5
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	44.15/16	44.15/16	635.1 635.2
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	44.14 4419.90 44.20	44.14 ex4419.00 44.20	635.41 ex635.42 635.49
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD	4418.10/20/40/50/60/74/75/79/99	4418.10/20/40/50/60 ex4418.71 ex4418.72 ex4418.79 ex4418.90	635.31/32/33 ex635.34 ex635.39
13.5	WOODEN FURNITURE	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	821.16 ex821.19 821.51/53/55/59 ex821.8
13.6	PREFABRICATED BUILDINGS OF WOOD	9406.10	ex94.06	ex811.0
13.7	OTHER MANUFACTURED WOOD PRODUCTS	44.04/05/13/17 4421.10/99	44.04/05/13/17 4421.10 ex4421.90	634.21/91/93 635.91 ex635.99
14	SECONDARY PAPER PRODUCTS
14.1	COMPOSITE PAPER AND PAPERBOARD	48.07	48.07	641.92
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	4811.10/41/49/60/90	4811.10/41/49/60/90	641.73/78/79
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	48.18	48.18	642.43/94
14.4	PACKAGING CARTONS, BOXES ETC.	48.19	48.19	642.1
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	48.14/16/17/20/21/22/23	48.14/16/17/20/21/22/23	641.94 642.2 642.3 642.42/45/91/93/99 892.81
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	ex4823.90	ex4823.90	ex642.99
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	4823.70	4823.70	ex642.99
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	4823.20	4823.20	642.45
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017 or SITC Rev.4 code is applicable.
For instance "ex811.00" under "Prefabricated buildings of wood" means that only a part of SITC code 811.00 refers to buildings prefabricated from wood, as that code does not distinguish between the materials buildings were prefabricated from.
In SITC Rev.4, if only 4 digits are shown, then all subheadings at lower degrees of aggregation are included (for example, 892.2 includes 892.21 and 892.29).

Annex4 |JQ2-JQ3-Corres.

JQ Product code	Nomenclature	HS Code	Remarks on HS codes
1	HS2002	440110
1	HS2002	4403
1	HS2007	440110
1	HS2007	4403
1	HS2012	440110
1	HS2012	4403
1	HS2017	440111
1	HS2017	440112
1	HS2017	4403
1.1	HS2002	440110
1.1	HS2007	440110
1.1	HS2012	440110
1.1	HS2017	440111
1.1	HS2017	440112
1.1C	HS2002	440110	Only some part of it
1.1C	HS2007	440110	Only some part of it
1.1C	HS2012	440110	Only some part of it
1.1C	HS2017	440111
1.1NC	HS2002	440110	Only some part of it
1.1NC	HS2007	440110	Only some part of it
1.1NC	HS2012	440110	Only some part of it
1.1NC	HS2017	440112
1.2	HS2002	4403
1.2	HS2007	4403
1.2	HS2012	4403
1.2	HS2017	4403
1.2.C	HS2002	440310	Only some part of it
1.2.C	HS2002	440320
1.2.C	HS2007	440310	Only some part of it
1.2.C	HS2007	440320
1.2.C	HS2012	440310	Only some part of it
1.2.C	HS2012	440320
1.2.C	HS2017	440311
1.2.C	HS2017	440321
1.2.C	HS2017	440322
1.2.C	HS2017	440323
1.2.C	HS2017	440324
1.2.C	HS2017	440325
1.2.C	HS2017	440326
1.2.NC	HS2002	440310	Only some part of it
1.2.NC	HS2002	440341
1.2.NC	HS2002	440349
1.2.NC	HS2002	440391
1.2.NC	HS2002	440392
1.2.NC	HS2002	440399
1.2.NC	HS2007	440310	Only some part of it
1.2.NC	HS2007	440341
1.2.NC	HS2007	440349
1.2.NC	HS2007	440391
1.2.NC	HS2007	440392
1.2.NC	HS2007	440399
1.2.NC	HS2012	440310	Only some part of it
1.2.NC	HS2012	440341
1.2.NC	HS2012	440349
1.2.NC	HS2012	440391
1.2.NC	HS2012	440392
1.2.NC	HS2012	440399
1.2.NC	HS2017	440312
1.2.NC	HS2017	440341
1.2.NC	HS2017	440349
1.2.NC	HS2017	440391
1.2.NC	HS2017	440393
1.2.NC	HS2017	440394
1.2.NC	HS2017	440395
1.2.NC	HS2017	440396
1.2.NC	HS2017	440397
1.2.NC	HS2017	440398
1.2.NC	HS2017	440399
1.2.NC.T	HS2002	440310	Only some part of it
1.2.NC.T	HS2002	440341
1.2.NC.T	HS2002	440349
1.2.NC.T	HS2002	440399	Only some part of it
1.2.NC.T	HS2007	440310	Only some part of it
1.2.NC.T	HS2007	440341
1.2.NC.T	HS2007	440349
1.2.NC.T	HS2007	440399	Only some part of it
1.2.NC.T	HS2012	440310	Only some part of it
1.2.NC.T	HS2012	440341
1.2.NC.T	HS2012	440349
1.2.NC.T	HS2012	440399	Only some part of it
1.2.NC.T	HS2017	440312	Only some part of it
1.2.NC.T	HS2017	440341
1.2.NC.T	HS2017	440349
2	HS2002	440200	Only some part of it
2	HS2007	440290
2	HS2012	440290
2	HS2017	440290
3	HS2002	440121
3	HS2002	440122
3	HS2002	440130	Only some part of it
3	HS2007	440121
3	HS2007	440122
3	HS2007	440130	Only some part of it
3	HS2012	440121
3	HS2012	440122
3	HS2012	440139	Only some part of it
3	HS2017	440121
3	HS2017	440122
3	HS2017	440140
3.1	HS2002	440121
3.1	HS2002	440122
3.1	HS2007	440121
3.1	HS2007	440122
3.1	HS2012	440121
3.1	HS2012	440122
3.1	HS2017	440121
3.1	HS2017	440122
3.2	HS2002	440130	Only some part of it
3.2	HS2012	440130	Only some part of it
3.2	HS2012	440139	Only some part of it
3.2	HS2017	440140	Only some part of it
4	HS2002	440130	Only some part of it
4	HS2007	440130	Only some part of it
4	HS2012	440139	Only some part of it
4	HS2017	440140	Only some part of it
5	HS2002	440130	Only some part of it
5	HS2007	440130	Only some part of it
5	HS2012	440131
5	HS2012	440139	Only some part of it
5	HS2017	440131
5	HS2017	440139
5.1	HS2002	440130	Only some part of it
5.1	HS2007	440130	Only some part of it
5.1	HS2012	440131
5.1	HS2017	440131
5.2	HS2002	440130	Only some part of it
5.2	HS2007	440130	Only some part of it
5.2	HS2012	440139	Only some part of it
5.2	HS2017	440139
6	HS2002	4406
6	HS2002	4407
6	HS2007	4406
6	HS2007	4407
6	HS2012	4406
6	HS2012	4407
6	HS2017	4406
6	HS2017	4407
6.C	HS2002	440610	Only some part of it
6.C	HS2002	440690	Only some part of it
6.C	HS2002	440710
6.C	HS2007	440610	Only some part of it
6.C	HS2007	440690	Only some part of it
6.C	HS2007	440710
6.C	HS2012	440610	Only some part of it
6.C	HS2012	440690	Only some part of it
6.C	HS2012	440710
6.C	HS2017	440611
6.C	HS2017	440691
6.C	HS2017	440711
6.C	HS2017	440712
6.C	HS2017	440719
6.NC	HS2002	440610	Only some part of it
6.NC	HS2002	440690	Only some part of it
6.NC	HS2002	440724
6.NC	HS2002	440725
6.NC	HS2002	440726
6.NC	HS2002	440729
6.NC	HS2002	440791
6.NC	HS2002	440792
6.NC	HS2002	440799
6.NC	HS2007	440610	Only some part of it
6.NC	HS2007	440690	Only some part of it
6.NC	HS2007	440721
6.NC	HS2007	440722
6.NC	HS2007	440725
6.NC	HS2007	440726
6.NC	HS2007	440727
6.NC	HS2007	440728
6.NC	HS2007	440729
6.NC	HS2007	440791
6.NC	HS2007	440792
6.NC	HS2007	440793
6.NC	HS2007	440794
6.NC	HS2007	440795
6.NC	HS2007	440799
6.NC	HS2012	440610	Only some part of it
6.NC	HS2012	440690	Only some part of it
6.NC	HS2012	440721
6.NC	HS2012	440722
6.NC	HS2012	440725
6.NC	HS2012	440726
6.NC	HS2012	440727
6.NC	HS2012	440728
6.NC	HS2012	440729
6.NC	HS2012	440791
6.NC	HS2012	440792
6.NC	HS2012	440793
6.NC	HS2012	440794
6.NC	HS2012	440795
6.NC	HS2012	440799
6.NC	HS2017	4406.12
6.NC	HS2017	4406.92
6.NC	HS2017	4407.21
6.NC	HS2017	4407.22
6.NC	HS2017	4407.25
6.NC	HS2017	4407.26
6.NC	HS2017	4407.27
6.NC	HS2017	4407.28
6.NC	HS2017	4407.29
6.NC	HS2017	4407.91
6.NC	HS2017	4407.92
6.NC	HS2017	4407.93
6.NC	HS2017	4407.94
6.NC	HS2017	4407.95
6.NC	HS2017	4407.96
6.NC	HS2017	4407.97
6.NC	HS2017	4407.99
6.NC.T	HS2002	440610	Only some part of it
6.NC.T	HS2002	440690	Only some part of it
6.NC.T	HS2002	440724
6.NC.T	HS2002	440725
6.NC.T	HS2002	440726
6.NC.T	HS2002	440729
6.NC.T	HS2002	440799	Only some part of it
6.NC.T	HS2007	440610	Only some part of it
6.NC.T	HS2007	440690	Only some part of it
6.NC.T	HS2007	440721
6.NC.T	HS2007	440722
6.NC.T	HS2007	440725
6.NC.T	HS2007	440726
6.NC.T	HS2007	440727
6.NC.T	HS2007	440728
6.NC.T	HS2007	440729
6.NC.T	HS2007	440799	Only some part of it
6.NC.T	HS2012	440610	Only some part of it
6.NC.T	HS2012	440690	Only some part of it
6.NC.T	HS2012	440721
6.NC.T	HS2012	440722
6.NC.T	HS2012	440725
6.NC.T	HS2012	440726
6.NC.T	HS2012	440727
6.NC.T	HS2012	440728
6.NC.T	HS2012	440729
6.NC.T	HS2012	440799	Only some part of it
6.NC.T	HS2017	440612	Only some part of it
6.NC.T	HS2017	440692	Only some part of it
6.NC.T	HS2017	440721
6.NC.T	HS2017	440722
6.NC.T	HS2017	440725
6.NC.T	HS2017	440726
6.NC.T	HS2017	440727
6.NC.T	HS2017	440728
6.NC.T	HS2017	440729
7	HS2002	4408
7	HS2007	4408
7	HS2012	4408
7	HS2017	4408
7.C	HS2002	440810
7.C	HS2007	440810
7.C	HS2012	440810
7.C	HS2017	440810
7.NC	HS2002	440831
7.NC	HS2002	440839
7.NC	HS2002	440890
7.NC	HS2007	440831
7.NC	HS2007	440839
7.NC	HS2007	440890
7.NC	HS2012	440831
7.NC	HS2012	440839
7.NC	HS2012	440890
7.NC	HS2017	440831
7.NC	HS2017	440839
7.NC	HS2017	440890
7.NC.T	HS2002	440831
7.NC.T	HS2002	440839
7.NC.T	HS2002	440890	Only some part of it
7.NC.T	HS2007	440831
7.NC.T	HS2007	440839
7.NC.T	HS2007	440890	Only some part of it
7.NC.T	HS2012	440831
7.NC.T	HS2012	440839
7.NC.T	HS2012	440890	Only some part of it
7.NC.T	HS2017	440831
7.NC.T	HS2017	440839
8	HS2002	4410
8	HS2002	4411
8	HS2002	441213
8	HS2002	441214
8	HS2002	441219
8	HS2002	441299	Only some part of it
8	HS2007	4410
8	HS2007	4411
8	HS2007	441231
8	HS2007	441232
8	HS2007	441239
8	HS2007	441294
8	HS2007	441299
8	HS2012	4410
8	HS2012	4411
8	HS2012	441231
8	HS2012	441232
8	HS2012	441239
8	HS2012	441294
8	HS2012	441299
8	HS2017	4410
8	HS2017	4411
8	HS2017	441231
8	HS2017	441233
8	HS2017	441234
8	HS2017	441239
8	HS2017	441294
8	HS2017	441299
8.1	HS2002	441213
8.1	HS2002	441214
8.1	HS2002	441219
8.1	HS2002	441299	Only some part of it
8.1	HS2007	441231
8.1	HS2007	441232
8.1	HS2007	441239
8.1	HS2007	441294
8.1	HS2007	441299
8.1	HS2012	441231
8.1	HS2012	441232
8.1	HS2012	441239
8.1	HS2012	441294
8.1	HS2012	441299
8.1	HS2017	441231
8.1	HS2017	441233
8.1	HS2017	441234
8.1	HS2017	441239
8.1	HS2017	441294
8.1	HS2017	441299
8.1.C	HS2002	441219
8.1.C	HS2002	441299	Only some part of it
8.1.C	HS2007	441239
8.1.C	HS2007	441294	Only some part of it
8.1.C	HS2007	441299	Only some part of it
8.1.C	HS2012	441239
8.1.C	HS2012	441294	Only some part of it
8.1.C	HS2012	441299	Only some part of it
8.1.C	HS2017	441239
8.1.C	HS2017	441294	Only some part of it
8.1.C	HS2017	441299	Only some part of it
8.1.NC	HS2002	441213
8.1.NC	HS2002	441214
8.1.NC	HS2002	441299	Only some part of it
8.1.NC	HS2007	441231
8.1.NC	HS2007	441232
8.1.NC	HS2007	441294	Only some part of it
8.1.NC	HS2007	441299	Only some part of it
8.1.NC	HS2012	441231
8.1.NC	HS2012	441232
8.1.NC	HS2012	441294	Only some part of it
8.1.NC	HS2012	441299	Only some part of it
8.1.NC	HS2017	441231
8.1.NC	HS2017	441233
8.1.NC	HS2017	441234
8.1.NC	HS2017	441294	Only some part of it
8.1.NC	HS2017	441299	Only some part of it
8.1.NC.T	HS2002	441213
8.1.NC.T	HS2002	441214	Only some part of it
8.1.NC.T	HS2002	441299	Only some part of it
8.1.NC.T	HS2007	441231
8.1.NC.T	HS2007	441232	Only some part of it
8.1.NC.T	HS2007	441294	Only some part of it
8.1.NC.T	HS2007	441299	Only some part of it
8.1.NC.T	HS2012	441231
8.1.NC.T	HS2012	441232	Only some part of it
8.1.NC.T	HS2012	441294	Only some part of it
8.1.NC.T	HS2012	441299	Only some part of it
8.1.NC.T	HS2017	441231
8.1.NC.T	HS2017	441294	Only some part of it
8.1.NC.T	HS2017	441299	Only some part of it
8.2	HS2002	4410
8.2	HS2007	4410
8.2	HS2012	4410
8.2	HS2017	4410
8.2.1	HS2002	441021	Only some part of it
8.2.1	HS2002	441029	Only some part of it
8.2.1	HS2007	441012
8.2.1	HS2012	441012
8.2.1	HS2017	441012
8.3	HS2002	4411
8.3	HS2007	4411
8.3	HS2012	4411
8.3	HS2017	4411
8.3.1	HS2002	441111	Only some part of it
8.3.1	HS2002	441119	Only some part of it
8.3.1	HS2007	441192
8.3.1	HS2012	441192
8.3.1	HS2017	441192
8.3.2	HS2002	441111	Only some part of it
8.3.2	HS2002	441119	Only some part of it
8.3.2	HS2002	441121	Only some part of it
8.3.2	HS2002	441129	Only some part of it
8.3.2	HS2007	441112
8.3.2	HS2007	441113
8.3.2	HS2007	441114	Only some part of it
8.3.2	HS2012	441112
8.3.2	HS2012	441113
8.3.2	HS2012	441114	Only some part of it
8.3.2	HS2017	441112
8.3.2	HS2017	441113
8.3.2	HS2017	441114	Only some part of it
8.3.3	HS2002	441131
8.3.3	HS2002	441139
8.3.3	HS2002	441191
8.3.3	HS2002	441199
8.3.3	HS2007	441114	Only some part of it
8.3.3	HS2007	441193
8.3.3	HS2007	441194
8.3.3	HS2012	441114	Only some part of it
8.3.3	HS2012	441193
8.3.3	HS2012	441194
8.3.3	HS2017	441114	Only some part of it
8.3.3	HS2017	441193
8.3.3	HS2017	441194
9	HS2002	4701
9	HS2002	4702
9	HS2002	4703
9	HS2002	4704
9	HS2002	4705
9	HS2007	4701
9	HS2007	4702
9	HS2007	4703
9	HS2007	4704
9	HS2007	4705
9	HS2012	4701
9	HS2012	4702
9	HS2012	4703
9	HS2012	4704
9	HS2012	4705
9	HS2017	4701
9	HS2017	4702
9	HS2017	4703
9	HS2017	4704
9	HS2017	4705
9.1	HS2002	4701
9.1	HS2002	4705
9.1	HS2007	4701
9.1	HS2007	4705
9.1	HS2012	4701
9.1	HS2012	4705
9.1	HS2017	4701
9.1	HS2017	4705
9.2	HS2002	4703
9.2	HS2002	4704
9.2	HS2007	4703
9.2	HS2007	4704
9.2	HS2012	4703
9.2	HS2012	4704
9.2	HS2017	4703
9.2	HS2017	4704
9.2.1	HS2002	4703
9.2.1	HS2007	4703
9.2.1	HS2012	4703
9.2.1	HS2017	4703
9.2.1.1	HS2002	470321
9.2.1.1	HS2002	470329
9.2.1.1	HS2007	470321
9.2.1.1	HS2007	470329
9.2.1.1	HS2012	470321
9.2.1.1	HS2012	470329
9.2.1.1	HS2017	470321
9.2.1.1	HS2017	470329
9.2.2	HS2002	4704
9.2.2	HS2007	4704
9.2.2	HS2012	4704
9.2.2	HS2017	4704
9.3	HS2002	4702
9.3	HS2007	4702
9.3	HS2012	4702
9.3	HS2017	4702
10	HS2002	4706
10	HS2007	4706
10	HS2012	4706
10	HS2017	4706
10.1	HS2002	470610
10.1	HS2002	470691
10.1	HS2002	470692
10.1	HS2002	470693
10.1	HS2007	470610
10.1	HS2007	470630
10.1	HS2007	470691
10.1	HS2007	470692
10.1	HS2007	470693
10.1	HS2012	470610
10.1	HS2012	470630
10.1	HS2012	470691
10.1	HS2012	470692
10.1	HS2012	470693
10.1	HS2017	470610
10.1	HS2017	470630
10.1	HS2017	470691
10.1	HS2017	470692
10.1	HS2017	470693
10.2	HS2002	470620
10.2	HS2007	470620
10.2	HS2012	470620
10.2	HS2017	470620
11	HS2002	4707
11	HS2007	4707
11	HS2012	4707
11	HS2017	4707
12	HS2002	4801
12	HS2002	4802
12	HS2002	4803
12	HS2002	4804
12	HS2002	4805
12	HS2002	4806
12	HS2002	4808
12	HS2002	4809
12	HS2002	4810
12	HS2002	481151
12	HS2002	481159
12	HS2002	4812
12	HS2002	4813
12	HS2007	4801
12	HS2007	4802
12	HS2007	4803
12	HS2007	4804
12	HS2007	4805
12	HS2007	4806
12	HS2007	4808
12	HS2007	4809
12	HS2007	4810
12	HS2007	481151
12	HS2007	481159
12	HS2007	4812
12	HS2007	4813
12	HS2012	4801
12	HS2012	4802
12	HS2012	4803
12	HS2012	4804
12	HS2012	4805
12	HS2012	4806
12	HS2012	4808
12	HS2012	4809
12	HS2012	4810
12	HS2012	481151
12	HS2012	481159
12	HS2012	4812
12	HS2012	4813
12	HS2017	4801
12	HS2017	4802
12	HS2017	4803
12	HS2017	4804
12	HS2017	4805
12	HS2017	4806
12	HS2017	4808
12	HS2017	4809
12	HS2017	4810
12	HS2017	481151
12	HS2017	481159
12	HS2017	4812
12	HS2017	4813
12.1	HS2002	4801
12.1	HS2002	480210
12.1	HS2002	480220
12.1	HS2002	480254
12.1	HS2002	480255
12.1	HS2002	480256
12.1	HS2002	480257
12.1	HS2002	480258
12.1	HS2002	480261
12.1	HS2002	480262
12.1	HS2002	480269
12.1	HS2002	4809
12.1	HS2002	481013
12.1	HS2002	481014
12.1	HS2002	481019
12.1	HS2002	481022
12.1	HS2002	481029
12.1	HS2007	4801
12.1	HS2007	480210
12.1	HS2007	480220
12.1	HS2007	480254
12.1	HS2007	480255
12.1	HS2007	480256
12.1	HS2007	480257
12.1	HS2007	480258
12.1	HS2007	480261
12.1	HS2007	480262
12.1	HS2007	480269
12.1	HS2007	4809
12.1	HS2007	481013
12.1	HS2007	481014
12.1	HS2007	481019
12.1	HS2007	481022
12.1	HS2007	481029
12.1	HS2012	4801
12.1	HS2012	480210
12.1	HS2012	480220
12.1	HS2012	480254
12.1	HS2012	480255
12.1	HS2012	480256
12.1	HS2012	480257
12.1	HS2012	480258
12.1	HS2012	480261
12.1	HS2012	480262
12.1	HS2012	480269
12.1	HS2012	4809
12.1	HS2012	481013
12.1	HS2012	481014
12.1	HS2012	481019
12.1	HS2012	481022
12.1	HS2012	481029
12.1	HS2017	4801
12.1	HS2017	480210
12.1	HS2017	480220
12.1	HS2017	480254
12.1	HS2017	480255
12.1	HS2017	480256
12.1	HS2017	480257
12.1	HS2017	480258
12.1	HS2017	480261
12.1	HS2017	480262
12.1	HS2017	480269
12.1	HS2017	4809
12.1	HS2017	481013
12.1	HS2017	481014
12.1	HS2017	481019
12.1	HS2017	481022
12.1	HS2017	481029
12.1.1	HS2002	4801
12.1.1	HS2007	4801
12.1.1	HS2012	4801
12.1.1	HS2017	4801
12.1.2	HS2002	480261
12.1.2	HS2002	480262
12.1.2	HS2002	480269
12.1.2	HS2007	480261
12.1.2	HS2007	480262
12.1.2	HS2007	480269
12.1.2	HS2012	480261
12.1.2	HS2012	480262
12.1.2	HS2012	480269
12.1.2	HS2017	480261
12.1.2	HS2017	480262
12.1.2	HS2017	480269
12.1.3	HS2002	480210
12.1.3	HS2002	480220
12.1.3	HS2002	480254
12.1.3	HS2002	480255
12.1.3	HS2002	480256
12.1.3	HS2002	480257
12.1.3	HS2002	480258
12.1.3	HS2007	480210
12.1.3	HS2007	480220
12.1.3	HS2007	480254
12.1.3	HS2007	480255
12.1.3	HS2007	480256
12.1.3	HS2007	480257
12.1.3	HS2007	480258
12.1.3	HS2012	480210
12.1.3	HS2012	480220
12.1.3	HS2012	480254
12.1.3	HS2012	480255
12.1.3	HS2012	480256
12.1.3	HS2012	480257
12.1.3	HS2012	480258
12.1.3	HS2017	480210
12.1.3	HS2017	480220
12.1.3	HS2017	480254
12.1.3	HS2017	480255
12.1.3	HS2017	480256
12.1.3	HS2017	480257
12.1.3	HS2017	480258
12.1.4	HS2002	4809
12.1.4	HS2002	481013
12.1.4	HS2002	481014
12.1.4	HS2002	481019
12.1.4	HS2002	481022
12.1.4	HS2002	481029
12.1.4	HS2007	4809
12.1.4	HS2007	481013
12.1.4	HS2007	481014
12.1.4	HS2007	481019
12.1.4	HS2007	481022
12.1.4	HS2007	481029
12.1.4	HS2012	4809
12.1.4	HS2012	481013
12.1.4	HS2012	481014
12.1.4	HS2012	481019
12.1.4	HS2012	481022
12.1.4	HS2012	481029
12.1.4	HS2017	4809
12.1.4	HS2017	481013
12.1.4	HS2017	481014
12.1.4	HS2017	481019
12.1.4	HS2017	481022
12.1.4	HS2017	481029
12.2	HS2002	4803
12.2	HS2007	4803
12.2	HS2012	4803
12.2	HS2017	4803
12.3	HS2002	480411
12.3	HS2002	480419
12.3	HS2002	480421
12.3	HS2002	480429
12.3	HS2002	480431
12.3	HS2002	480439
12.3	HS2002	480442
12.3	HS2002	480449
12.3	HS2002	480451
12.3	HS2002	480452
12.3	HS2002	480459
12.3	HS2002	480511
12.3	HS2002	480512
12.3	HS2002	480519
12.3	HS2002	480524
12.3	HS2002	480525
12.3	HS2002	480530
12.3	HS2002	480591
12.3	HS2002	480592
12.3	HS2002	480593
12.3	HS2002	480610
12.3	HS2002	480620
12.3	HS2002	480640
12.3	HS2002	4808
12.3	HS2002	481031
12.3	HS2002	481032
12.3	HS2002	481039
12.3	HS2002	481092
12.3	HS2002	481099
12.3	HS2002	481151
12.3	HS2002	481159
12.3	HS2007	480411
12.3	HS2007	480419
12.3	HS2007	480421
12.3	HS2007	480429
12.3	HS2007	480431
12.3	HS2007	480439
12.3	HS2007	480442
12.3	HS2007	480449
12.3	HS2007	480451
12.3	HS2007	480452
12.3	HS2007	480459
12.3	HS2007	480511
12.3	HS2007	480512
12.3	HS2007	480519
12.3	HS2007	480524
12.3	HS2007	480525
12.3	HS2007	480530
12.3	HS2007	480591
12.3	HS2007	480592
12.3	HS2007	480593
12.3	HS2007	480610
12.3	HS2007	480620
12.3	HS2007	480640
12.3	HS2007	4808
12.3	HS2007	481031
12.3	HS2007	481032
12.3	HS2007	481039
12.3	HS2007	481092
12.3	HS2007	481099
12.3	HS2007	481151
12.3	HS2007	481159
12.3	HS2012	480411
12.3	HS2012	480419
12.3	HS2012	480421
12.3	HS2012	480429
12.3	HS2012	480431
12.3	HS2012	480439
12.3	HS2012	480442
12.3	HS2012	480449
12.3	HS2012	480451
12.3	HS2012	480452
12.3	HS2012	480459
12.3	HS2012	480511
12.3	HS2012	480512
12.3	HS2012	480519
12.3	HS2012	480524
12.3	HS2012	480525
12.3	HS2012	480530
12.3	HS2012	480591
12.3	HS2012	480592
12.3	HS2012	480593
12.3	HS2012	480610
12.3	HS2012	480620
12.3	HS2012	480640
12.3	HS2012	4808
12.3	HS2012	481031
12.3	HS2012	481032
12.3	HS2012	481039
12.3	HS2012	481092
12.3	HS2012	481099
12.3	HS2012	481151
12.3	HS2012	481159
12.3	HS2017	480411
12.3	HS2017	480419
12.3	HS2017	480421
12.3	HS2017	480429
12.3	HS2017	480431
12.3	HS2017	480439
12.3	HS2017	480442
12.3	HS2017	480449
12.3	HS2017	480451
12.3	HS2017	480452
12.3	HS2017	480459
12.3	HS2017	480511
12.3	HS2017	480512
12.3	HS2017	480519
12.3	HS2017	480524
12.3	HS2017	480525
12.3	HS2017	480530
12.3	HS2017	480591
12.3	HS2017	480592
12.3	HS2017	480593
12.3	HS2017	480610
12.3	HS2017	480620
12.3	HS2017	480640
12.3	HS2017	4808
12.3	HS2017	481031
12.3	HS2017	481032
12.3	HS2017	481039
12.3	HS2017	481092
12.3	HS2017	481099
12.3	HS2017	481151
12.3	HS2017	481159
12.3.1	HS2002	480411
12.3.1	HS2002	480419
12.3.1	HS2002	480511
12.3.1	HS2002	480512
12.3.1	HS2002	480519
12.3.1	HS2002	480524
12.3.1	HS2002	480525
12.3.1	HS2002	480591
12.3.1	HS2007	480411
12.3.1	HS2007	480419
12.3.1	HS2007	480511
12.3.1	HS2007	480512
12.3.1	HS2007	480519
12.3.1	HS2007	480524
12.3.1	HS2007	480525
12.3.1	HS2007	480591
12.3.1	HS2012	480411
12.3.1	HS2012	480419
12.3.1	HS2012	480511
12.3.1	HS2012	480512
12.3.1	HS2012	480519
12.3.1	HS2012	480524
12.3.1	HS2012	480525
12.3.1	HS2012	480591
12.3.2	HS2002	480442
12.3.2	HS2002	480449
12.3.2	HS2002	480451
12.3.2	HS2002	480452
12.3.2	HS2002	480459
12.3.2	HS2002	480592
12.3.2	HS2002	481032
12.3.2	HS2002	481039
12.3.2	HS2002	481092
12.3.2	HS2002	481151
12.3.2	HS2002	481159
12.3.2	HS2007	480442
12.3.2	HS2007	480449
12.3.2	HS2007	480451
12.3.2	HS2007	480452
12.3.2	HS2007	480459
12.3.2	HS2007	480592
12.3.2	HS2007	481032
12.3.2	HS2007	481039
12.3.2	HS2007	481092
12.3.2	HS2007	481151
12.3.2	HS2007	481159
12.3.2	HS2012	480442
12.3.2	HS2012	480449
12.3.2	HS2012	480451
12.3.2	HS2012	480452
12.3.2	HS2012	480459
12.3.2	HS2012	480592
12.3.2	HS2012	481032
12.3.2	HS2012	481039
12.3.2	HS2012	481092
12.3.2	HS2012	481151
12.3.2	HS2012	481159
12.3.2	HS2017	480442
12.3.2	HS2017	480449
12.3.2	HS2017	480451
12.3.2	HS2017	480452
12.3.2	HS2017	480459
12.3.2	HS2017	480592
12.3.2	HS2017	481032
12.3.2	HS2017	481039
12.3.2	HS2017	481092
12.3.2	HS2017	481151
12.3.2	HS2017	481159
12.3.3	HS2002	480421
12.3.3	HS2002	480429
12.3.3	HS2002	480431
12.3.3	HS2002	480439
12.3.3	HS2002	480530
12.3.3	HS2002	480610
12.3.3	HS2002	480620
12.3.3	HS2002	480640
12.3.3	HS2002	4808
12.3.3	HS2002	481031
12.3.3	HS2002	481099
12.3.3	HS2007	480421
12.3.3	HS2007	480429
12.3.3	HS2007	480431
12.3.3	HS2007	480439
12.3.3	HS2007	480530
12.3.3	HS2007	480610
12.3.3	HS2007	480620
12.3.3	HS2007	480640
12.3.3	HS2007	4808
12.3.3	HS2007	481031
12.3.3	HS2007	481099
12.3.3	HS2012	480421
12.3.3	HS2012	480429
12.3.3	HS2012	480431
12.3.3	HS2012	480439
12.3.3	HS2012	480530
12.3.3	HS2012	480610
12.3.3	HS2012	480620
12.3.3	HS2012	480640
12.3.3	HS2012	4808
12.3.3	HS2012	481031
12.3.3	HS2012	481099
12.3.3	HS2017	480421
12.3.3	HS2017	480429
12.3.3	HS2017	480431
12.3.3	HS2017	480439
12.3.3	HS2017	480530
12.3.3	HS2017	480610
12.3.3	HS2017	480620
12.3.3	HS2017	480640
12.3.3	HS2017	4808
12.3.3	HS2017	481031
12.3.3	HS2017	481099
12.3.4	HS2002	480593
12.3.4	HS2007	480593
12.3.4	HS2012	480593
12.3.4	HS2017	480593
12.4	HS2002	480240
12.4	HS2002	480441
12.4	HS2002	480540
12.4	HS2002	480550
12.4	HS2002	480630
12.4	HS2002	4812
12.4	HS2002	4813
12.4	HS2007	480240
12.4	HS2007	480441
12.4	HS2007	480540
12.4	HS2007	480550
12.4	HS2007	480630
12.4	HS2007	4812
12.4	HS2007	4813
12.4	HS2012	480240
12.4	HS2012	480441
12.4	HS2012	480540
12.4	HS2012	480550
12.4	HS2012	480630
12.4	HS2012	4812
12.4	HS2012	4813
12.4	HS2017	480240
12.4	HS2017	480441
12.4	HS2017	480540
12.4	HS2017	480550
12.4	HS2017	480630
12.4	HS2017	4812
12.4	HS2017	4813
13.1	HS2002	440910
13.1	HS2002	440920	Only some part of it
13.1	HS2007	440910
13.1	HS2007	440929
13.1	HS2012	440910
13.1	HS2012	440929
13.1	HS2017	440910
13.1	HS2017	440922
13.1	HS2017	440929
13.1.C	HS2002	440910
13.1.C	HS2007	440910
13.1.C	HS2012	440910
13.1.C	HS2017	440910
13.1.NC	HS2002	440920	Only some part of it
13.1.NC	HS2007	440929
13.1.NC	HS2012	440929
13.1.NC	HS2017	440922
13.1.NC	HS2017	440929
13.1.NC.T	HS2002	440920	Only some part of it
13.1.NC.T	HS2007	440929	Only some part of it
13.1.NC.T	HS2012	440929	Only some part of it
13.1.NC.T	HS2017	440922
13.2	HS2002	4415
13.2	HS2002	4416
13.2	HS2007	4415
13.2	HS2007	4416
13.2	HS2012	4415
13.2	HS2012	4416
13.2	HS2017	4415
13.2	HS2017	4416
13.3	HS2002	4414
13.3	HS2002	4419	Only some part of it
13.3	HS2002	4420
13.3	HS2007	4414
13.3	HS2007	4419	Only some part of it
13.3	HS2007	4420
13.3	HS2012	4414
13.3	HS2012	4419	Only some part of it
13.3	HS2012	4420
13.3	HS2017	4414
13.3	HS2017	441990
13.3	HS2017	4420
13.4	HS2002	441810
13.4	HS2002	441820
13.4	HS2002	441830
13.4	HS2002	441840
13.4	HS2002	441850
13.4	HS2002	441890	Only some part of it
13.4	HS2007	441810
13.4	HS2007	481820
13.4	HS2007	441840
13.4	HS2007	441850
13.4	HS2007	441860
13.4	HS2007	441871	Only some part of it
13.4	HS2007	441872	Only some part of it
13.4	HS2007	441879	Only some part of it
13.4	HS2007	441890	Only some part of it
13.4	HS2012	441810
13.4	HS2012	441820
13.4	HS2012	441840
13.4	HS2012	441850
13.4	HS2012	441860
13.4	HS2012	441871	Only some part of it
13.4	HS2012	441872	Only some part of it
13.4	HS2012	441879	Only some part of it
13.4	HS2012	441890	Only some part of it
13.4	HS2017	441810
13.4	HS2017	441820
13.4	HS2017	441840
13.4	HS2017	441850
13.4	HS2017	441860
13.4	HS2017	441874
13.4	HS2017	441875
13.4	HS2017	441879
13.4	HS2017	441899
13.5	HS2002	940161
13.5	HS2002	940169
13.5	HS2002	940190	Only some part of it
13.5	HS2002	940330
13.5	HS2002	940340
13.5	HS2002	940350
13.5	HS2002	940360
13.5	HS2002	940390	Only some part of it
13.5	HS2007	940161
13.5	HS2007	940169
13.5	HS2007	940190	Only some part of it
13.5	HS2007	940330
13.5	HS2007	940340
13.5	HS2007	940350
13.5	HS2007	940360
13.5	HS2007	940390	Only some part of it
13.5	HS2012	940161
13.5	HS2012	940169
13.5	HS2012	940190	Only some part of it
13.5	HS2012	940330
13.5	HS2012	940340
13.5	HS2012	940350
13.5	HS2012	940360
13.5	HS2012	940390	Only some part of it
13.5	HS2017	940161
13.5	HS2017	940169
13.5	HS2017	940190	Only some part of it
13.5	HS2017	940330
13.5	HS2017	940340
13.5	HS2017	940350
13.5	HS2017	940360
13.5	HS2017	940390	Only some part of it
13.6	HS2002	9406	Only some part of it
13.6	HS2007	9406	Only some part of it
13.6	HS2012	9406	Only some part of it
13.6	HS2017	940610
13.7	HS2002	4404
13.7	HS2002	4405
13.7	HS2002	4413
13.7	HS2002	4417
13.7	HS2002	442110
13.7	HS2002	442190	Only some part of it
13.7	HS2007	4404
13.7	HS2007	4405
13.7	HS2007	4413
13.7	HS2007	4417
13.7	HS2007	442110
13.7	HS2007	442190	Only some part of it
13.7	HS2012	4404
13.7	HS2012	4405
13.7	HS2012	4413
13.7	HS2012	4417
13.7	HS2012	442110
13.7	HS2012	442190	Only some part of it
13.7	HS2017	4404
13.7	HS2017	4405
13.7	HS2017	4413
13.7	HS2017	4417
13.7	HS2017	442110
13.7	HS2017	442199
14.1	HS2002	4807
14.1	HS2007	4807
14.1	HS2012	4807
14.1	HS2017	4807
14.2	HS2002	481110
14.2	HS2002	481141
14.2	HS2002	481149
14.2	HS2002	481160
14.2	HS2002	481190
14.2	HS2007	481110
14.2	HS2007	481141
14.2	HS2007	481149
14.2	HS2007	481160
14.2	HS2007	481190
14.2	HS2012	481110
14.2	HS2012	481141
14.2	HS2012	481149
14.2	HS2012	481160
14.2	HS2012	481190
14.2	HS2017	481110
14.2	HS2017	481141
14.2	HS2017	481149
14.2	HS2017	481160
14.2	HS2017	481190
14.3	HS2002	4818
14.3	HS2007	4818
14.3	HS2012	4818
14.3	HS2017	4818
14.4	HS2002	4819
14.4	HS2007	4819
14.4	HS2012	4819
14.4	HS2017	4819
14.5	HS2002	4814
14.5	HS2002	4816
14.5	HS2002	4817
14.5	HS2002	4820
14.5	HS2002	4821
14.5	HS2002	4822
14.5	HS2002	4823
14.5	HS2007	4814
14.5	HS2007	4816
14.5	HS2007	4817
14.5	HS2007	4820
14.5	HS2007	4821
14.5	HS2007	4822
14.5	HS2007	4823
14.5	HS2012	4814
14.5	HS2012	4816
14.5	HS2012	4817
14.5	HS2012	4820
14.5	HS2012	4821
14.5	HS2012	4822
14.5	HS2012	4823
14.5	HS2017	4814
14.5	HS2017	4816
14.5	HS2017	4817
14.5	HS2017	4820
14.5	HS2017	4821
14.5	HS2017	4822
14.5	HS2017	4823
14.5.1	HS2002	482390	Only some part of it
14.5.1	HS2007	482390	Only some part of it
14.5.1	HS2012	482390	Only some part of it
14.5.1	HS2017	482390	Only some part of it
14.5.2	HS2002	482370
14.5.2	HS2007	482370
14.5.2	HS2012	482370
14.5.2	HS2017	482370
14.5.3	HS2002	482320
14.5.3	HS2007	482320
14.5.3	HS2012	482320
14.5.3	HS2017	482320
12.6	HS2002	482110	Only some part of it
12.6	HS2002	482190	Only some part of it
12.6	HS2002	482210	Only some part of it
12.6	HS2002	482290	Only some part of it
12.6	HS2002	482312	Only some part of it
12.6	HS2002	482319	Only some part of it
12.6	HS2002	482320	Only some part of it
12.6	HS2002	482340	Only some part of it
12.6	HS2002	482360	Only some part of it
12.6	HS2002	482370	Only some part of it
12.6	HS2002	482390	Only some part of it
12.6	HS2002	480210	Only some part of it
12.6	HS2002	480220	Only some part of it
12.6	HS2002	480230	Only some part of it
12.6	HS2002	480240	Only some part of it
12.6	HS2002	480254	Only some part of it
12.6	HS2002	480255	Only some part of it
12.6	HS2002	480256	Only some part of it
12.6	HS2002	480257	Only some part of it
12.6	HS2002	480258	Only some part of it
12.6	HS2002	480261	Only some part of it
12.6	HS2002	480262	Only some part of it
12.6	HS2002	480269	Only some part of it
12.6	HS2002	481013	Only some part of it
12.6	HS2002	481014	Only some part of it
12.6	HS2002	481019	Only some part of it
12.6	HS2002	481022	Only some part of it
12.6	HS2002	481029	Only some part of it
12.6	HS2002	481031	Only some part of it
12.6	HS2002	481032	Only some part of it
12.6	HS2002	481039	Only some part of it
12.6	HS2002	481092	Only some part of it
12.6	HS2002	481099	Only some part of it
12.6	HS2007	481410
12.6	HS2007	481420
12.6	HS2007	481490
12.6	HS2007	481710
12.6	HS2007	481720
12.6	HS2007	481730
12.6	HS2007	482010
12.6	HS2007	482020
12.6	HS2007	482030
12.6	HS2007	482040
12.6	HS2007	482050
12.6	HS2007	482090
12.6	HS2007	482110
12.6	HS2007	482190
12.6	HS2007	482210
12.6	HS2007	482290
12.6	HS2007	482320
12.6	HS2007	482340
12.6	HS2007	482361
12.6	HS2007	482369
12.6	HS2007	482370
12.6	HS2007	482390
12.6	HS2012	481420
12.6	HS2012	481490
12.6	HS2012	481710
12.6	HS2012	481720
12.6	HS2012	481730
12.6	HS2012	482020
12.6	HS2012	482030
12.6	HS2012	482040
12.6	HS2012	482050
12.6	HS2012	482090
12.6	HS2012	482110
12.6	HS2012	482190
12.6	HS2012	482210
12.6	HS2012	482290
12.6	HS2012	482320
12.6	HS2012	482340
12.6	HS2012	482361
12.6	HS2012	482369
12.6	HS2012	482370
12.6	HS2012	482390
12.6.1	HS2002	480210	Only some part of it
12.6.1	HS2002	480220	Only some part of it
12.6.1	HS2002	480230	Only some part of it
12.6.1	HS2002	480240	Only some part of it
12.6.1	HS2002	480254	Only some part of it
12.6.1	HS2002	480255	Only some part of it
12.6.1	HS2002	480256	Only some part of it
12.6.1	HS2002	480257	Only some part of it
12.6.1	HS2002	480258	Only some part of it
12.6.1	HS2002	480261	Only some part of it
12.6.1	HS2002	480262	Only some part of it
12.6.1	HS2002	480269	Only some part of it
12.6.1	HS2002	481013	Only some part of it
12.6.1	HS2002	481014	Only some part of it
12.6.1	HS2002	481019	Only some part of it
12.6.1	HS2002	481022	Only some part of it
12.6.1	HS2002	481029	Only some part of it
12.6.1	HS2002	481031	Only some part of it
12.6.1	HS2002	481032	Only some part of it
12.6.1	HS2002	481039	Only some part of it
12.6.1	HS2002	481092	Only some part of it
12.6.1	HS2002	481099	Only some part of it
12.6.1	HS2002	482390	Only some part of it
12.6.1	HS2007	482390	Only some part of it
12.6.1	HS2012	482390	Only some part of it
12.6.2	HS2002	482370
12.6.2	HS2007	482370
12.6.2	HS2012	482370
12.6.3	HS2002	482320
12.6.3	HS2007	482320
12.6.3	HS2012	482320

JFSQ Item codes

Below are algebraic expressions of the relationships of items in the JFSQ. These are to help in understanding and filling out the JFSQ in a way to minimize inconsistencies.

1 = 1.1 + 1.2

1.1 = 1.1.C + 1.1.NC

1.2 = 1.2.1 + 1.2.2 + 1.2.3

= 1.2.C + 1.2.NC

= 1.2.1.C + 1.2.1.NC + 1.2.2.C + 1.2.2.NC + 1.2.3.C + 1.2.3.NC

1.2.C = 1.2.1.C + 1.2.2.C + 1.2.3.C

1.2.NC = 1.2.1.NC + 1.2.2.NC + 1.2.3.NC

1.2.NC ≥ 1.2.NC.T

1.2.1 = 1.2.1.C + 1.2.1.NC

1.2.2 = 1.2.2.C + 1.2.2.NC

1.2.3 = 1.2.3.C + 1.2.3.NC

3 = 3.1 + 3.2

5 = 5.1 + 5.2

6 = 6.C + 6.NC

6.NC ≥ 6.NC.T

7 = 7.C + 7.NC

7.NC ≥ 7.NC.T

8 = 8.1 + 8.2 + 8.3

8.1 = 8.1.C + 8.1.NC

8.1.NC ≥ 8.1.NC.T

8.2 ≥ 8.2.1

8.3 = 8.3.1 + 8.3.2 + 8.3.3

9 = 9.1 + 9.2 + 9.3

9.2 = 9.2.1 + 9.2.2

9.2.1 >= 9.2.1.1

10 = 10.1 + 10.2

12 = 12.1 + 12.2 + 12.3 + 12.4

12.1 = 12.1.1 + 12.1.2 + 12.1.3 + 12.1.4

12.3 = 12.3.1 + 12.3.2 + 12.3.3 + 12.3.4

13.1 = 13.1.C + 13.1.NC

13.1.NC >= 13.1.NC.T

14.5 >= 14.5.1 + 14.5.2 + 14.5.3

JFSQ Item codes

Below are algebraic expressions of the relationships of items in the JFSQ. These are to

help in understanding and filling out the JFSQ in a way to minimize inconsistencies.

1 = 1.1 + 1.2

1.1 = 1.1.C + 1.1.NC

1.2 = 1.2.1 + 1.2.2 + 1.2.3

= 1.2.C + 1.2.NC

= 1.2.1.C + 1.2.1.NC + 1.2.2.C + 1.2.2.NC + 1.2.3.C + 1.2.3.NC

1.2.C = 1.2.1.C + 1.2.2.C + 1.2.3.C

1.2.NC = 1.2.1.NC + 1.2.2.NC + 1.2.3.NC

1.2.NC ≥ 1.2.NC.T

1.2.1 = 1.2.1.C + 1.2.1.NC

1.2.2 = 1.2.2.C + 1.2.2.NC

1.2.3 = 1.2.3.C + 1.2.3.NC

3 = 3.1 + 3.2

5 = 5.1 + 5.2

6 = 6.C + 6.NC

6.NC ≥ 6.NC.T

7 = 7.C + 7.NC

7.NC ≥ 7.NC.T

8 = 8.1 + 8.2 + 8.3

8.1 = 8.1.C + 8.1.NC

8.1.NC ≥ 8.1.NC.T

8.2 ≥ 8.2.1

8.3 = 8.3.1 + 8.3.2 + 8.3.3

9 = 9.1 + 9.2 + 9.3

9.2 = 9.2.1 + 9.2.2

9.2.1 >= 9.2.1.1

10 = 10.1 + 10.2

12 = 12.1 + 12.2 + 12.3 + 12.4

12.1 = 12.1.1 + 12.1.2 + 12.1.3 + 12.1.4

12.3 = 12.3.1 + 12.3.2 + 12.3.3 + 12.3.4

13.1 = 13.1.C + 13.1.NC

13.1.NC >= 13.1.NC.T

14.5 >= 14.5.1 + 14.5.2 + 14.5.3

Symbol usage

We urge respondents to fill in the questionnaire completely. If, however, this is not

possible, please try to use the following symbols. Blank spaces leave us unsure whether

the data are not available or whether they are zero.

… = not available (please make an estimate!)

0 = nil or less than half the unit indicated

+++ = confidential

Presentation

Read more about Presentation

Languages and translations

English

Evaluating Coverage of the US Census Bureau’s Integrated Database for International Migration (IDIM)

UNECE Meeting Jason Schachter, Chief, International Migration Branch

Esther Miller and Angelica Menchaca, International Migration Branch October 26, 2022

This presentation is released to inform interested parties of ongoing research and to encourage discussion of work in progress. Any opinions and conclusions expressed herein are those of the author(s) and do not reflect the views of the US Census Bureau.

The U.S. Census Bureau reviewed this data product for unauthorized disclosure of confidential information and approved the disclosure avoidance practices applied to this release. CBDRB-FY23-POP001-0007

Overview

Background

Linking Administrative Record (AR) Data Sources, Censuses, and Surveys including the American Community Survey (ACS)

Integrated Database on International Migration (IDIM) Universes and Matching Rates between the ACS and the IDIM

Compare Distributions for the ACS-IDIM Matched and Unmatched Foreign-Born Respondents

Compare ACS Survey Responses to Linked IDIM Administrative Records

Next Steps

Background

• Increases Variance • Lag Measurement

Construct Foreign-born International Migration

Flows from American Community Survey (ACS)

Data Likely misses: • Irregular migrants • Working Migrants Did Not File Taxes • Non-working dependents • International students

Developed Integrated Database for International

Migration (IDIM) to estimate IN-Flows

• Do the distributions of ACS foreign-born records linked to the IDIM differ from distributions of ACS records that cannot be linked to the IDIM?

Can We Use ACS to Adjust IDIM?

Administrative Record (AR) and Survey Data Sources IRS 1040 Tax Data

• SSN (Social Security Number)

• Name • Address

• Domestic* • Foreign

SSA Numident

• SSN • Name • Date of Birth* • Sex* • Year of Entry* • Citizenship status*

• Native Born • Naturalized Citizen • Non-citizen

• Place of Birth • Death Flag

ACS

• Name • Date of Birth* • Sex* • Race and Hispanic

Origin • Year entered United

States* • Citizenship Status* • Socio-economic

Characteristics • Place of Birth • Address*

Linking AR, Census, and Survey Data Sources

Protected Identification Key (PIK)

• Unique identifier across ARs, censuses, and surveys

• Assigned to all records with an SSN or Individual Tax Identification Numbers (ITIN) via lookup table

Person Validation System (PVS) • Exact SSN match to

Numident • Geography • Name • Date of birth

Reliability, Validity, and Bias • SSNs most reliable (not

collected in surveys) • Less likely to validate young

children, minorities, residents of group quarters, immigrants, recent movers, low-income individuals

• PIK erroneously assigned

Numident, IRS, IDIM, and ACS Evaluation Universes

#3 ACS N=4 million (Unweighted)

#2 IRS 1040 N=243 Million

#1 Numident N=519 Million

IDIM (1040 matches

Numident)

ACS matches IDIM

No Match to IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey 6

Numident, IRS, IDIM, and ACS Evaluation Universes

#3 ACS N=4 million (Unweighted)

#2 IRS 1040 N=243 Million

#1 Numident N=519 Million

IDIM (1040 matches

Numident)

ACS matches IDIM

No Match to IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey 7

US population is 331 million people

Numident, IRS, IDIM, and ACS Evaluation Universes

#3 ACS N=4 million (Unweighted)

#2 IRS 1040 N=243 Million

# N=519 Million

ACS matches IDIM

No Match to IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey 6

Numident, IRS, IDIM, and ACS Evaluation Universes

#3 ACS N=4 million (Unweighted)

#2 IRS 1040 N=243 Million

#1 Numident N=519 Million

IDIM (1040 matches t)

No Match to IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey 6

Numident, IRS, IDIM, and ACS Evaluation Universes

#2 IRS 1040 N=243 Million

#1 Numident N=519 Million

IDIM (1040 matches

Numident)

No Match to IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey 6

Numident, IRS, IDIM, and ACS Evaluation Universes

#3 ACS N=4 million (Unweighted)

#2 IRS 1040 N=243 Million

#1 Numident N=519 Million

IDIM (1040 matches

Numident)

ACS matches IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey 6

Numident, IRS, IDIM, and ACS Evaluation Universes

#3 ACS N=4 million (Unweighted)

#2 IRS 1040 N=243 Million

#1 Numident N=519 Million

IDIM (1040 matches

Numident)

ACS matches IDIM

No Match to IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey 6

Percent Foreign Born in The American Community Survey and the IDIM

ACS 14%

IDIM 13%

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

AR Linkage Rates for All ACS Records and Foreign- Born ACS Records

91% of All ACS Records with PIK (SSN or ITIN)

% of all ACS Foreign Born Records with PIK

% of All ACS Records Matched to IDIM

% of All ACS Foreign Born Records Matched to IDIM

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

AR Linkage Rates for All ACS Records and Foreign Born ACS Records

91% of All ACS Records with PIK (SSN or ITIN)

% of all ACS Foreign Born Records with PIK (SSN or ITIN)

% of All ACS Records Matched to IDIM

% of All ACS Foreign Born Records Matched to IDIM

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Demographic Characteristic Distributions: Race and Ethnicity

White non- Hispanic

Black non- Hispanic

Asian non- Hispanic

Other non- Hispanic

Mexican Central American/ Dominican

Other Hispanic

Not Linked to IDIM Linked to IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey 9

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Demographic Characteristics Distributions: Age and Sex

52 48

21 25

48 52

6 7

25 25

Male Female 0-17 18-24 25-34 35-44 44-54 55-64

Not Linked to IDIM

Linked to IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Demographic Characteristics Distributions: Age and Sex

52 48

21 25

48 52

6 7

25 25

Male Female 0-17 18-24 25-34 35-44 44-54 55-64

Not Linked to IDIM

Linked to IDIM

Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Socio-Economic Characteristic Distributions

20 21

Less than High School

High School

Some College -

Post Grad

In Poverty Not in Poverty

Unemployed Not in Labor Force

Employed

Not Linked to IDIM

Linked to IDIM

11 Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community SurveySources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Socio-Economic Characteristic Distributions

20 21

Less than High School

High School

Some College -

Post Grad

In Poverty Not in Poverty

Unemployed Not in Labor Force

Employed

Not Linked to IDIM

Linked to IDIM

Socio-Economic Characteristic Distributions

20 21

Less than High School

High School

Some College -

Post Grad

In Poverty Not in Poverty

Unemployed Not in Labor Force

Employed

Not Linked to IDIM

Linked to IDIM

Distributions of Citizenship Status and Year of Entry

20 17

13 12

27 24 23

Non-Citizen Naturalized 2015 and later

2010 - 2014 2000 - 2009 1990 - 1999 Before 1990

Not Linked to IDIM

Linked to IDIM

12Sources: 2020 Census Numident, IRS 1040 TY19, 2019 American Community Survey Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Distributions of Citizenship Status and Year of Entry

20 17

13 12

27 24 23

Non-Citizen Naturalized 2015 and later

2010 - 2014 2000 - 2009 1990 - 1999 Before 1990

Not Linked to IDIM

Linked to IDIM

How Does the ACS Survey Response Compare to Administrative Records?

IDIM is the

standard

Sex 98%

Match

Age 95% Match within

one year

State of Current

Residence • 92%

currently reside in the same state

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Distribution of the Difference in Years for Responses to Year of Entry Question on the ACS Compared to the IDIM

6 4 3 2

5 6 9

-10 or more -5 to -9 -3 to -4 -2 -1 0 1 2 3 to 4 5 to 9 10 or more

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Difference in Years

% % %

Distribution of the Difference in Years for Responses to Year of Entry Question on the ACS Compared to the IDIM

6 4 3 2

5 6 9

-10 or more -5 to -9 -3 to -4 -2 -1 0 1 2 3 to 4 5 to 9 10 or more

Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Difference in Years

% % %

Comparison of Citizenship Status Reported on the IDIM and ACS for Matched Respondents

IDIM = Foreign-born Non-Citizen

IDIM = Naturalized

ACS = Naturalized

ACS = Non-Citizen

ACS = Native

ACS = Naturalized

ACS = Non-Citizen

ACS = Native

15 Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Pe rc

en t

Comparison of Citizenship Status Reported on the IDIM and ACS for Matched Respondents

0 10 20 30 40 50 60 70 80 90

IDIM = Non-Citizen

IDIM = Naturalized

ACS = Naturalized

ACS = Non-Citizen

ACS = Native

ACS = Naturalized

ACS = Non-Citizen

ACS = Native

15 Sources: US Census Bureau, Integrated Database for International Migration and 2019 American Community Survey; Social Security Administration; and Internal Revenue Service.

Percent

Summary: Foreign Born Residents Not Matched to IDIM (vs Matched to IDIM)

ACS measures foreign- born population missing from IDIM

• More Male • Younger • Less Asian • More Hispanic (Mexican and Central

American) • Less Educated • Lower Labor Force Participation • Higher Poverty

Next Steps

Add Race and Hispanic Origin to IDIM and

Compare Responses

Develop Strategies to Adjust IDIM for Missing

Groups, e.g. Irregular Migrants

with ACS Data

Add More Administrative Record

Data Sources

Work on Underestimate of

Foreign-Born Children

Contact information

[email protected] International Migration Branch

Population Division US Census Bureau

mailto:[email protected]

Evaluating Coverage of the US Census Bureau’s Integrated Database for International Migration (IDIM)�
Overview
Background
Administrative Record (AR) and Survey Data Sources
Linking AR, Census, and Survey Data Sources
Numident, IRS, IDIM, and ACS Evaluation Universes
Numident, IRS, IDIM, and ACS Evaluation Universes
Numident, IRS, IDIM, and ACS Evaluation Universes
Numident, IRS, IDIM, and ACS Evaluation Universes
Numident, IRS, IDIM, and ACS Evaluation Universes
Numident, IRS, IDIM, and ACS Evaluation Universes
Numident, IRS, IDIM, and ACS Evaluation Universes
Percent Foreign Born in The American Community Survey and the IDIM
AR Linkage Rates for All ACS Records and Foreign-Born ACS Records
AR Linkage Rates for All ACS Records and Foreign Born ACS Records
Demographic Characteristic Distributions: Race and Ethnicity
Demographic Characteristics Distributions: Age and Sex
Demographic Characteristics Distributions: Age and Sex
Socio-Economic Characteristic Distributions
Socio-Economic Characteristic Distributions
Socio-Economic Characteristic Distributions
Distributions of Citizenship Status and Year of Entry
Distributions of Citizenship Status and Year of Entry
How Does the ACS Survey Response Compare to Administrative Records?
Distribution of the Difference in Years for Responses to Year of Entry Question on the ACS Compared to the IDIM
Distribution of the Difference in Years for Responses to Year of Entry Question on the ACS Compared to the IDIM
Comparison of Citizenship Status Reported on the IDIM and ACS for Matched Respondents
Comparison of Citizenship Status Reported on the IDIM and ACS for Matched Respondents
Summary: Foreign Born Residents Not Matched to IDIM (vs Matched to IDIM)�
Next Steps
Contact information

United States of America

List of tables

Table 1

Table 2

Table 2a

Table 2b

Table 3

Table 4

Table 5

Table 5a

Table 6

Table 6a

Table 6b

Table 6c

Table 7

Table 8

Table 9

Table 9a

Table 9b

Table 10

Table 11

Table 11a

Table 11b

Table 12

Table 12a

Table 12b

Table 12c

Table 13

Table 14

Table 15

Manual

JQ1|Primary Products|Production

JQ2 | Primary Products | Trade

JQ3 | Secondary Products| Trade

ECE-EU | Species | Trade

ITTO1 | Estimates

ITTO2 | Species | Trade

ITTO3 | Miscellaneous

conversion factors

Annex1 | JQ1-Corres.

Annex2 | JQ2-Corres.

Annex3 | JQ3-Corres.

Annex4 |JQ2-JQ3-Corres.

Follow UNECE