European Union Data


Guten Tag!

You may not know this, but IMPLAN is working on other projects besides the beloved economic impact software for the US. In fact, IMPLAN economists have been researching European Union (EU) data behind the scenes. Just as IMPLAN was a pioneer in I-O modeling in the US, we are doing the same in the EU. That’s right, IMPLAN is crossing the pond!



IMPLAN has produced tables for 2010-2016 for all 28 EU member countries and subregions within them. The data sources are Eurostat and the World Input-Output Database (WIOD).  

Just like the creation of our longstanding US I-O tables, IMPLAN economists take publicly available data and assemble it into fully disclosed, entirely balanced tables. You can go pull some EU data, but IMPLAN has taken the hard work out of organizing it.  No need to worry about imputing missing values or ensuring your entire 28 country, 64 Industry I-O matrix is fully balanced.  

The data is NUTS; Nomenclature of territorial units for statistics.  There are four levels of NUTS.

  • NUTS0: 28 countries
  • NUTS1: 98 major socio-economic regions
  • NUTS2: 276 basic regions for the application of regional policies
  • NUTS3: 1,342 small regions for specific diagnosis

EU Countries 1 


There are 64 Industries and Commodities in the dataset. And guess what! It is even ready for MRIO with inter-regional commodity trade flows, inter-regional commuting flows, and of course a balanced Social Accounting Matrix (SAM).

You don’t have to learn any foreign languages to use the data, either.  All of the resources are in English. The monetary figures are all reported in Million Euros (€ Million).

The kicker is that the data isn’t available in Yet. Therefore, in order to use it, you must be familiar with manipulating I-O tables in Excel.  



Eurostat is the official statistical office of the EU. It is located in Luxembourg with a mission to “provide high quality statistics for Europe.” 2 Eurostat doesn’t collect data, but instead creates the standards by which EU members are to collect their data. They then compile it for use across the EU and have compilations through 2018 for some countries.

The World Input-Output Database (WIOD) was launched in 2009 with funding from the European Commission. 3 They are aimed at examining global integration and inequality across nations. The most recent released WIOD data in 2016 covers 43 countries’ data from 2000 to 2014. They have 64 industries and corresponding commodities in the supply and use tables, and 56 industries in the social economic accounts.



There are a few differences between the IMPLAN US data you are familiar with and the new EU data other than the fact that it isn’t in so far. The US data uses a gravity model, while the EU data uses a radiation model. This is due to limitations with the EU dataset that don’t have enough raw data details for a full gravity model to be constructed.  

Employment figures are a little different, too. While the US data counts jobs, the EU data counts people. So if a person has multiple jobs, they will only be classified under their primary one. They will not show up as an employee in their second (or third or fourth) jobs.

In terms of differences in the SAM structure, The EU data also does not have sufficient granularity to break out household income classes. Therefore, you will only find one household income group. The EU data does distinguish households from nonprofit institutions serving households (NPISH), while the US data distributes these over household groups. Also, taxes are reported as gross values, with subsidies reported separately; US data reports net taxes less subsidies.

The EU dataset is reported in Basic Prices. Basic Prices are the amount realized by the producer after taxes and subsidies.  The U.S. data is reported in Producer Prices, which are the amount realized by the producer before taxes and subsidies. 4 In the EU data, Taxes less Subsidies on Products are included in the TOPI for the U.S. data, but not for the EU data.



In detail, this means the intermediate use and final demand values are net of Margins and net of Taxes less Subsidies on Products. In the U.S. using the Producers’ Prices system, those values are only net of Margins. While the Employee Compensation, Proprietor Income, Other Property Income, and Taxes on Production and Imports Less Subsidies (TOPI) are included in the U.S. Value Added at Producers’ Prices, the difference from EU Value Added at Basic Prices lies in TOPI. TOPI in the U.S. includes two pieces: Other taxes less Subsidies on Production and Taxes less Subsidies on Products. Each industry’s value of Other Taxes less Subsidies on Products is equal between the two price systems.  The difference is in the remaining portion, which is known as Taxes less Subsidies on Products. While the sum total of this part of TOPI is the same in either price system, each industry’s value is different. Technically, in a Basic Prices framework, Taxes less Subsidies on Products are not part of Value Added. IMPLAN has estimated TOPI values for converting Basic Prices Value Added / Industry Output to Producers’ Prices Value Added / Industry Output. IMPLAN has also estimated Taxes less Subsidies on Products and Margins, which make the data available to convert from Basic Prices to Purchasers’ Prices as well.

Finally, in terms of support, users will have unlimited access to IMPLAN Community Forum at for any data sources and methodology questions you may have that are not addressed by the provided support document.  IMPLAN economists will gladly respond to all data sources and methodology questions on IMPLAN’s Community forum within 5 business days at no additional charge. The sample data is provided for the user to fully understand the data they are receiving. IMPLAN does not support data-application or related questions via email, phone, project consultation, or community forum.



Access to the IMPLAN EU data is protected with a custom license agreement.  The license agreement will be presented when you are ready to purchase data. Sorry, the lawyers make us do it.



To learn more about our new European Union data or to see a sample dataset, please contact IMPLAN at 800-507-9426 or [email protected]




Read Me File

Data Dictionary

Study Area Sample Data

Trade Flow Sample Data

Commuting Flows Sample Data

Transfers Sample Data

SAM Sample Data

Type 1 Multipliers Sample Data

Type SAM Multipliers Sample Data




World Bank

World Input-Output Database (WIOD)

How Commuter Employee Compensation is Estimated


While payroll taxes are paid in the county of employment, personal income taxes on that same income are paid in the county of residence, and these two places differ for commuters. Additionally, household demand is generated at the location of the household (that is, at the employee’s place of residence). Therefore, a proper measure of regional and inter-regional induced effects requires accounting for these inter-regional flows of employment-based income. IMPLAN derives region specific commuting flows as described below.  

It should be noted that while IMPLAN accounts for commuting precisely so that more spending is kicked off in the place of residence than in the place of work, where that spending ultimately occurs is based largely on IMPLAN’s trade flow model. For example, suppose that an employee in Mecklenburg County, NC lives in neighboring Rowan County and therefore takes his Employee Compensation less payroll taxes (unofficially termed “Commuter EC” for the purposes of this article) home with him to Rowan County, where he then pays personal income taxes on that income. Now suppose that this individual likes to go bowling, but there is no bowling alley in Rowan County.  These bowling expenditures occur back in Mecklenburg County. Thus, while the commuting data ensure that the employee’s demand originates in the place of residence, the fulfillment of that demand may occur in the place of work (or any number of other counties).



Initial estimates of Commuter EC between counties is derived from U.S. Census data. The Census Bureau’s Journey-To-Work (JTW) data provide information on commuting flows of people from county-of-residence to county-of-employment (including commuting to the same county as residence, or intra-commuting). IMPLAN combines the Census county-to-county commuting data with IMPLAN’s own annual estimates of county-level Commuter EC to estimate flows of compensation from the county in which compensation is earned back to the county of residence. Commuter EC is the remaining portion of Employee Compensation (EC) once payroll taxes and foreign commuting are removed. This adjustment needs to be made as payroll taxes are paid in the region in which compensation is earned and foreign commuting is treated as a leakage from the model. On the question of foreign commuting, IMPLAN uses U.S. level data on worker earnings that flow out of and into the country, from NIPA, distributed to states and counties based on EC and household income, respectively.

Unfortunately, the Census’s JTW data are lagged compared to IMPLAN’s annual Commuter EC estimates. Therefore, IMPLAN turns to the BEA REA data on earnings flows, which while only providing in- and out-flow data for a region and not its flow partner, are more up-to-date. As the JTW data include intra-flows and the REA gross flows data do not, IMPLAN also utilizes REA data on earnings by place of work to derive intra flows. These data (the gross in- and out-flows and the intra-flows) are used as controls in a matrix RAS of the Commuter EC previously derived from the JTW data. As the REA data are also lagged, IMPLAN does not control strictly to their values. Once completed, updated coefficients of commuting flows are derived, which are applied to annual county-level Commuter EC values. 

The resulting county level commuting flows are utilized in the generation of regional SAMs. Their inclusion allows for the calculation of the share of regionally generated compensation that leaks from the region (i.e., the region’s in-commuting rate). A region’s in-commuting rate is calculated as:

Commuter EC outflows / total EC generated in the region
    Commuter EC outflows are displayed in the IxC SAM as transfers of the EC column to
    Domestic Trade and Foreign Trade

The in-commuting rate is then used to determine leakages of EC in a region. For example, a region with a 25% in-commuting rate will see 25% of earned EC stemming from an impact analysis treated as leakage from the local economy.

The Commuter EC data are also utilized in Multi-Regional Input-Output analysis (MRIO); Commuter EC outflows from a region to linked model regions generate induced effects in the linked model regions. To continue with the prior example, if the entirety of the 25% in-commuting rate in the direct effect region represented commuters from the linked region, then the entirety of the 25% leaked Commuter EC in the direct effect region would be treated as additional household income in the linked region.

Note, in-commuting rates are region specific but not industry specific. If your analysis of an industry requires that you adjust the in-commuting rate, please see our article on adjusting compensation to account for a known in-commuting rate.



Flows of Commuter EC (EC less payroll taxes) between zip codes are calculated by distributing the flows of Commuter EC between the Counties to which those zip codes belong among those zip codes. The shares for out-flows of Commuter EC are based on total EC generated by each zip code in the county, while the shares for in-flows of Commuter EC are based on total receipts of EC less payroll taxes by households in each of the zip codes. Commuter EC flows for zip code-based regions like Congressional Districts or custom city models are simply sums of the component zip codes’ Commuter EC flows. 

Why is Personal Income for My Region so High?

Alternative Measures of Household Income

Robert Brown and Ann Dunbar of BEA also contributed to this paper.

This paper has been prepared for presentation to the Federal Economic Statistics Advisory Committee (FESAC) on December 14, 2004. It represents work in progress and does not represent any agency’s final positions on issues addressed. The FESAC is a Federal Advisory Committee sponsored jointly by the Bureau of Labor Statistics of the U.S. Department of Labor and by the Bureau of Economic Analysis (BEA) and the Bureau of the Census of the U.S. Department of Commerce.


Two of the most widely used measures of household income are BEA’s personal income and the Census Bureau’s money income. These two statistics spring from different traditions of measurement—personal income from national income accounting and money income from income distribution analysis. Yet, many of the conceptual difficulties in developing guidelines for income distribution statistics are the same or similar to the problems encountered in specifying guidelines for national income accounting.

This paper first considers briefly what is meant by the concept “income” and how the debate about the boundaries defining income has been framed. Then, personal income and money income are compared conceptually and empirically. This comparison highlights certain ways that the two measures differ—in the inclusion or exclusion of lump sum payments, of income of non-profit institutions serving households, and of in-kind payments; in the treatment of pension accruals versus disbursements; and in adjustments for underreporting.

Both personal income and money income are more limited concepts than the Haig-Simons-Hicks (HSH) theoretical concept of income as the maximum amount that can be consumed in a given time period while keeping real wealth unchanged. Both personal and money income, for example, do not capture income from capital gains. The Census Bureau has developed a set of alternative measures of money income designed to better measure economic well-being. These are briefly reviewed in the paper.

The final section of the paper discusses possible further extensions of the two income measures. Alternative measures of personal income are proposed that move away from an accrual and toward a disbursement approach to accounting for retirement income and that incorporate disbursements from a variety of tax-preferred assets. The alternative personal income measures address user needs to better measure the tax base or the capacity to spend.

Some of the principal points of the paper are the following:

  • BEA personal income is the income received by persons from participation in production, from government and business transfer payments, and from government interest.1 BEA estimates personal income largely from administrative data sources.
  • The Current Population Survey (CPS) Annual Social and Economic Supplement is the source of the Census Bureau’s official national estimates of poverty. CPS money income is defined as total pre-tax cash income earned by persons, excluding certain lump sum payments and excluding capital gains.
  • BEA estimates that personal income for the US was $8.678 trillion in 2001, as compared to a CPS money income estimate of $6.446 trillion.2 Over 64 percent of this $2.232 trillion gap—$1.429 trillion—can be accounted for by differences in the income types that are included in the two measures, including the $982 billion of property income that is counted in personal income but not in CPS money income.
  • Half of the remaining $804 billion money income gap can be accounted for by BEA adjustments to proprietors’ income and wages and salaries for underreporting in BEA source data.
  • The Census Bureau has developed a number of alternative measures of money income that may measure economic well-being better than CPS money income. These measures remove taxes, add in-kind transfers, add realized capital gains or losses, and add the imputed return on equity in own home. The Census Bureau has found that a broadened definition of income results in a more equal distribution of income and tends to reduce the gaps between the incomes of traditionally high-and low-income groups.
  • An important issue in measuring income is whether certain income types should be captured when accrued or when disbursed. BEA personal income includes employers’ contributions into pension plans, while CPS money income includes pension disbursements. The BEA approach measures payments to factors of production, but the CPS approach better measures current capacity to spend.
  • Alternative measures of personal income and disposable personal income are considered at the end of the paper. These alternative measures might better serve users who need measures of the current capacity to spend or of the tax base. These proposed definitions also move toward the theoretical HSH concept of income, capturing incomes when disbursed from all types of retirement schemes and capturing realized capital gains.

1 – “Persons” in BEA’s state personal income consist of individuals and quasi–individuals who serve or act on behalf of individuals. Quasi–individuals consist of nonprofit institutions that primarily serve individuals, private noninsured welfare funds, and private trust funds.
2 – As discussed in footnote 5, the BEA estimate reported here differs from the estimate in the National Income and Product Accounts because it is a national total of State Personal Income.

What is income?

A variety of definitions of household income have been advanced in the literature. Many of these spring from the Haig-Simons-Hicks (HSH) concept of income as the maximum amount that can be consumed in a given period while keeping real wealth unchanged (Eisner, 1989). This very general concept, cited in the System of National Accounts at section 8.15, has been applied differently by macro-analysts interested in measuring the income of the macro economy and by micro analysts interested in the distribution of income.

Income measures produced by different government agencies can in part be distinguished by definitional boundaries of income. The debate about these boundaries is well summarized in the report of the Canberra Expert Group on Household Income Statistics (The Canberra Group, 2001). According to the Group, the debate has centered on the following three questions:

  1. Should income include only receipts that are recurrent (i.e., exclude large and unexpected, typically one-time, receipts)?
  2. Should income only include those components that contribute to current economic well-being or extend also to those which contribute to future well-being?
    Components of income that contribute to future well-being include employer contributions to pension funds and social insurance, interest and dividends earned on retirement-based assets and capital gains.
  3. Should income allow for the maintenance of the value of net worth?

The Canberra group recognized that there are two traditions of measurement that have influenced the estimation of income. The macro approach has its roots in national income accounting and in particular in the System of National Accounts (SNA). This approach aims at estimating income for the macro economy as a whole or for other geographic aggregates. The BEA measure of personal income comes from this tradition. In contrast, the micro approach to income measurement has its roots in micro economics and in particular the study of poverty and income distribution. The Census Bureau’s estimates of money income arise from this approach. Not with standing the different traditions, the Canberra Group notes that many of the conceptual difficulties in developing guidelines for income distribution statistics are the same or similar to the problems encountered in specifying guidelines for national income accounting.

The micro and macro approaches differ in whether they stress the type of income or the means of payment (Harrison, 1999). The macro approach categorizes income according to the type of transaction giving rise to an income flow without regard to the means of payment. The types of transactions identified in the macro approach include income generated in the course of production, from the distribution of property income, or from current transfers.

The micro approach focuses on the means of payment, without regard to the how the income flow is generated. According to the Canberra Group, the definition of income in the micro approach is driven mainly by what the individual perceives to be an income receipt of direct benefit. Such an approach implies that it is current economic well-being, as opposed to future well-being, which is of interest to the microanalyst. The recipient may be scarcely aware of income components that contribute to future economic well-being (such as contributions to pension plans). Therefore, in addition to there being an issue as to whether these should be included in income, there is the practical difficulty of collecting such information from survey respondents.


Two of the most widely used measures of household income are BEA’s personal income and the Census Bureau’s money income. These two measures differ in the scope of individuals covered, in the income items included, in the sources of the data and in the extent of disaggregation of the estimates. This section will discuss the general definitions, sources and uses of these two measures, while the next section presents a reconciliation of aggregate income estimates as a means of indicating the nature and size of differences.3

Personal income and disposable personal income

Personal income is the income received by persons from participation in production, from government and business transfer payments, and from government interest. Personal income includes income received by non-profit institutions serving households, by private non-insured welfare funds, and by private trust funds.4 Income from production is generated both by the labor of individuals and by the capital that they own. Private income not earned in production, such as from capital gains or the sale of assets, is excluded. Personal income is calculated as the sum of wage and salary disbursements, employer contributions for employee pension and insurance funds, proprietors’ income, property income (personal interest, dividend and rental income), and transfer payments to individuals, less personal contributions for social insurance.

Disposable personal income is personal income less personal tax payments. While personal income does not include capital gains realized through the sale of assets, personal income taxes do include the taxes paid for these capital gains.

Personal income and disposable personal income are released by the BEA both as aggregate and as per capita estimates for differing geographic areas and time periods. Estimates are not available according to demographic characteristics of individuals.

Estimates of personal income are based primarily on data from administrative records and from censuses and similar surveys. The data from administrative records may originate either from the recipients of the income or from the source of the income. The most important sources of these data include the state unemployment insurance programs, the social insurance programs of the Center for Medicare and Medicaid Services and the Social Security Administration; the Federal income tax program of the Internal Revenue Service, veterans benefits programs, and military payroll systems of the U.S. Department of Defense.

The data from censuses are mainly collected from the recipients of the income. The most important sources of census data are the Census of Agriculture, which is now conducted by the U.S. Department of Agriculture (USDA), and the Census of Population and Housing, which is conducted by the Census Bureau. Some estimates are based on data from other sources. For example, the USDA’s national and state estimates of the income of all farms constitute the principal basis for BEA’s national and state estimates of farm proprietors’ income. The USDA uses sample surveys, along with census data and administrative–records data, to derive its estimates.

State personal income estimates are used widely in the public and private sectors to study economic trends for States and regions and to measure and track the levels and types of income that are received by the people who live or work in a State. Federal Government agencies use the estimates as a basis for allocating $167 billion and for determining matching grants. Federal agencies also use the estimates in econometric models, such as those used to project energy and water use. State governments use the estimates in econometric models to project tax revenues and the need for public services. Many states have set constitutional or statutory limits on State government revenues and spending that are tied to State personal income or to one of its components. The estimates are also used in market and economic research.

Census money income

The Census Bureau collects income data on several major surveys, including the Annual Social and Economic Supplement (ASEC) of the Current Population Survey (CPS), the Survey of Income and Program Participation (SIPP), the decennial Census and the American Community Survey (ACS). The CPS is the source of official national estimates of poverty and the most widely used source of annual national income estimates.

The CPS measure of money income is defined as total pre-tax cash income earned by persons, excluding certain lump sum payments and excluding capital gains. It includes money wages and salaries, self-employment income, property income (dividends, interest and rents), money transfer payments from a variety of government and private welfare and social insurance schemes (such as social security, unemployment and workers’ compensation, and public assistance), private and government retirement income, interpersonal transfers (such as alimony and child support) and other periodic income.

Unlike BEA’s measure of personal income, CPS money income excludes employer contributions to government employee retirement plans and to private health and pension funds, lumps-sum payments except those received as part of earnings, certain in-kind transfer payments—such as Medicare, Medicaid, and food stamps—and imputed income.5 Money income includes, but personal income excludes, personal contributions for social insurance, income from government employee retirement plans and from private pensions and annuities, and income from interpersonal transfers, such as child support.

The Census Bureau releases estimates of household money income as medians, percent distributions by income categories and on a per capita basis. Estimates are available by demographic characteristics of householders and by the composition of households.

Census money income estimates are based on the Annual Social and Economic Supplement (ASEC) of the CPS. Data are collected from a sample of households by means of a structured questionnaire. For each person in the sample age 15 years or older, the ASEC asks questions about the amount of money income received in the previous year from up to 50 different income sources. In 2002, survey responses were obtained for approximately 78,000 households. While data collectors attempt to collect data directly from each eligible household member, proxy reporting by other household members is approximately 50 percent. According to the Census Bureau, this may introduce non-sampling error because respondents may provide less accurate information on other members of the household than about themselves.

As mentioned previously, the CPS is the source of official U.S. poverty estimates and the income and poverty measures are widely used as barometers of economic well-being for the Nation. In addition to their importance to researchers and policymakers, income and poverty data from the CPS are also used in federal funding formulas that allocate billions of dollars annually to localities based on differences in economic well-being. For example, the State Children’s Health Insurance Program (SCHIP) allocates approximately $4 billion annually based on CPS-derived figures on the number of low-income uninsured children in each state. Also, the Title I Program uses CPS poverty data to allocate $14 billion dollars annually to school districts.

3 – A third widely used measure of income is IRS adjusted gross income (AGI). For a comparison of BEA personal income and IRS AGI, see Ledbetter (2004).
4 – Mead, McCully, and Reinsdorf (2003) identify the following 5 categories of nonprofit institutions serving households:

    1. Religious and welfare, including social services, grant-making foundations, political organizations, museums and libraries, and some civic and fraternal organizations
    2. Medical care;
    3. Education and research;
    4. Recreation, including cultural, athletic, and some civic and fraternal organizations; and,
    5. Personal business, including labor unions, legal aid, and professional associations.

5 – Imputed income is the market value of certain transactions that do not occur in the market economy or that are not observable in BEA data. BEA’s measure of imputed income includes pay-in-kind in the form of meals and lodging, the rental value of owner-occupied housing, the value of farm products consumed at home by the producers, the value of investment income earned on life insurance, and the value of services provided to persons by depository institutions without an explicit charge.


This section presents a reconciliation of aggregate estimates of BEA personal income and CPS money income. BEA’s national estimate of personal income derived from state personal income (SPI) estimates is converted to an “SPI-derived money income” estimate by adding and subtracting income types to bring personal income to the same scope as CPS money income.6

BEA estimates that state personal income for the US was $8.678 trillion in 2001, as compared to a CPS money income estimate of $6.446 trillion. Sixty-four percent of this $2.232 trillion gap—$1.429 trillion—can be accounted for by differences in the income types that are included in the two measures (see Table 1).

Personal income contained $2.241 trillion in 2001 that was not in CPS money income. Personal income exceeds money income in part because the former includes not only income received by individuals but also income received on behalf of individuals. In 2001, $982 billion in property income (dividends, interest and rents) was received on behalf of individuals by pension plans, nonprofit institutions serving households, and fiduciaries. Personal income also contains other income categories not in CPS money income. Most notably, personal income included $563 billion in employer contributions for employee pension and insurance funds and $593 billion in transfer payments, mostly non-cash, like Medicaid, food stamps, and energy assistance.

SPI-derived money income in 2001 included $813 billion not in personal income. Almost half (44 percent) of that—$360 billion—came from disbursements of retirement income benefits.7 Money income also included $372 billion in personal contributions to social insurance (largely social security) that was deducted from personal income.

While not affecting the total gap between income estimates, BEA and the Census Bureau categorize some types of income differently. The principal difference is the treatment of S corporation profits. Shareholders of S corporations report their share of company profits (whether distributed or not) on their individual tax returns. BEA classifies as dividends all S corporation profits distributed to shareholders, regardless of whether the shareholders are employees of the corporation. Census money income treats these profits as dividends when they are received by non-employee shareholders, but treats them as wage and salary income to shareholder-employees. $189 billion was reallocated from dividends to wages and salaries to make the personal and money income estimates comparable. Another difference occurs in the treatment of distributed earnings from money market accounts. These are classified as interest by BEA and dividends by the Census Bureau; therefore, $52 billion was reallocated from interest to dividends in this reconciliation.

The Money Income Gap by Type of Income for 2001

After adjusting for differences in income types included in the two measures, SPI-derived money income still exceeds CPS money income by $804 billion. What accounts for this “money income gap?” Some insights can be gleaned by comparing the gap by type of income as shown in Table 1, line 38. The gap occurs primarily in wages and salaries, proprietors’ income, personal dividends, personal interest, social security, and other retirement and disability income.

The income category experiencing the largest money income gap is proprietors’ income. BEA’s estimate of SPI-derived proprietors’ money income (that is, BEA’s estimate of proprietors’ income adjusted to include CPS money income categories) is $630 billion in 2001, as compared to a reported CPS money income estimate of $329 billion. The nearly $302 billion gap in these estimates can be fully accounted for by BEA misreporting adjustments.

BEA uses Internal Revenue Service (IRS) tabulations of sole proprietorship and partnership income tax returns as the primary source for nonfarm proprietors’ income estimates. IRS tax return data do not include the income of “nonfilers,” that is, those who are not required to file tax returns or those who illegally evade filing. Further, some filers underreport income. While the IRS can verify certain types of income reported on individual returns, such as wages, interest, and dividends, by matching tax return information with corresponding third party reports, document matching is ineffective for verifying business income.

BEA adjusts for income earned, but not reported on tax returns, by adding an estimate of “misreporting”. The adjustment is an extrapolation based primarily on the 1988 Taxpayer Compliance Measurement Program (TCMP) audit, 1999 exact match study, and current activity indicators, such as the Census Bureau’s value of new construction. Proprietors’ income has been consistently underreported to the IRS. The last TCMP audit estimated that proprietors’ actual income was more than double levels reported on tax returns (Landefeld and Fraumeni, p. 33). The 2001 proprietors’ income misreporting adjustment accounts for 42 percent of proprietors’ state personal income and 49 percent of SPI-derived proprietors’ money income in 2001.

Although the Census Bureau does not make a similar adjustment to money income estimates, BEA includes the misreporting adjustment in its derivation of SPI-derived money income in the belief that it is the best available approximation of actual unreported proprietors’ money income. However, respondents who underreport to the IRS may also underreport in a voluntary survey such as the CPS. At $308 billion in 2001, the proprietors’ income misreporting adjustment fully accounts for the $302 billion proprietors’ money income gap that year.8

The “other retirement and disability income” category constitutes another major source of the total money income gap. This income category consists primarily of retirement benefits from private, government, military, railroad, and individual funds. It also includes payments to beneficiaries of state temporary and disability insurance, black lung, pension benefit guarantee, and private accident insurance disability funds. It does not include either Social Security or workers’ compensation. Large both in percentage and dollar terms, at $360 billion SPI-derived money income in this category exceeds the CPS level of $253 billion by 42 percent.

SPI-derived money income significantly exceeds CPS money income in every government retirement income category. SPI-derived pension benefit figures are 49 percent higher than CPS money income for federal retirement and 91 percent higher for state and local government. BEA estimates in these categories are based on data from the Monthly Treasury Statement and the Census Bureau. Estimates of individual annuity benefits also vary widely. The BEA figure, based on data from the National Association of Insurance Commissioners, exceeds the CPS estimate by 481 percent.

CPS and SPI-derived wage and salary money income differ by only 3 percent, but this small percentage represents $158 billion. BEA includes a $104 billion adjustment for wage and salary income earned in the underground economy, which estimates cash wages from legal activities that are earned “off the books.”9 Although the CPS is designed to include these wages, as with proprietors’ income, individuals who don’t report or underrepresent income to the IRS or other agencies may be unlikely to fully report these wages on a voluntary survey such as the CPS, despite assurances of confidentiality.

Census Bureau research by Roemer (2002) comparing CPS wage data with administrative earnings records from the Social Security Administration’s Master Earnings File has shown that the CPS underestimates wages of part-year, part-time workers. Because the CPS does not survey military personnel living on a U.S. post without family, wages earned by military personnel from secondary jobs in the civilian sector would not be included. Underreporting by proxy reporters especially of secondary jobs may also be a factor. Finally, since the reference period for the CPS ASEC is the past calendar year, respondents may fail to recall small amounts and payments that are received infrequently. This might affect not only the reporting of wages for short duration jobs, but also the reporting of other small income components.

Within property income, CPS and SPI-derived money income differ substantially in the personal interest and dividend income categories. At $259 billion, SPI-derived personal monetary interest exceeds the CPS level of $188 billion by 38 percent. In 2001, taxable and tax-exempt interest reported on individual tax returns totaled $243 billion.10 Given the similarity between the BEA estimate and level of personal interest income reported to the IRS, the interest money income gap appears due to underreporting on the CPS survey. This may result in part from incomplete information provided by proxy reporters.

SPI-derived dividend income is $148 billion, 69 percent higher than the CPS dividend income level of $88 billion. Dividend income reported on individual tax returns for 2001 totaled $116 billion. The dividend money income gap occurs at least in part due to CPS underreporting, since the CPS level falls $28 billion below the IRS reported level. SPI-derived interest may be expected to exceed the IRS level since individual tax return data do not include the income of nonfilers, but it is unclear whether this fully explains the $32 billion by which the SPI derived dividend figure exceeds the data from individual income tax returns.

Within transfer payments, the major gap occurs in Social Security. CPS money income reports Social Security as $376 billion. At $425 billion, SPI-derived Social Security (based on data from the Social Security Administration) exceeds the CPS level by $49 billion and 13 percent.

6 – The reconciliation uses BEA’s national estimate constructed from state personal income (SPI) rather than the national estimate from the National Income and Product Accounts (NIPA’s). The main differences between the NIPA and SPI estimates of personal income stem from the treatment of the income of U.S. residents who are working abroad and the treatment of the income of foreign residents who are working in the United States. The national total of the state estimates of personal income consists of only the income earned by persons who live within the United States, including foreign residents working in the United States. This is closer to the scope of the CPS, though the CPS excludes certain individuals residing in the US, including military on US posts without family, the institutionalized, decedents in the reference year, and child workers under 15 (agricultural workers can legally be as young as 10).

7 – To produce SPI-derived retirement money income, estimates of lump-sum payments were removed from BEA’s national retirement benefit estimates. While lump sum payments (including withdrawals) constitute a negligible portion of public retirement payments, they appear to comprise over half of private retirement payments. BEA national private pension benefits are based primarily on Department of Labor (DOL) tabulations of Form 5500 reports filed by employers and data compiled by the American Council of Life Insurance (ACLI). BEA estimated private pension lump sum payments using the 1998 Form 5500 ratio of benefits from defined contribution plans to total private retirement benefits applied to the 2001 BEA national private pension benefit estimate. Although the unadjusted BEA national estimate of private pension benefits was substantially greater than the CPS figure, after the removal of lump sum payments the SPI-derived money income measure exceeded the CPS figure by only $6 billion or 6 percent.

8 – Given that the two primary studies on which the misreporting adjustment is based have not been conducted in recent years, the reliability of the 2001 misreporting adjustment may be questioned. The IRS has replaced the TCMP with the National Research Program (NRP), which has as part of its mandate the measurement of filing and reporting compliance. NRP audits were begun in 2002 and will provide a more accurate picture of current filing and reporting gaps when results become available. (U.S. Internal Revenue Service, 2002).

9 – For a fuller discussion of the underground economy see Carson (May and July 1984) and Parker (1984).

10 – See “Individual Income Tax Returns, Preliminary Data, 2001,” SOI Bulletin, Winter 2002-2003, p. 137.



The traditional money income concept is limited and does not provide a completely satisfactory measure of economic well-being. For example, money income (unlike BEA’s disposable income concept) does not include the effects of taxes and, therefore, does not reflect the effect of tax law changes on economic well-being. Similarly, the official measure of money income excludes the effect of noncash benefits (such as employment-related group health insurance and food stamps), which enhance economic well-being and are also included in BEA’s personal income. The Census Bureau has a fairly long history of producing estimates that address these shortcomings.

Since the early 1980s, the Census Bureau has published analysis showing the effect of using a broadened income definition on measures of economic well-being. Currently, annual Census Bureau reports on income and poverty show the effect of using an income measure that includes the effect of noncash benefits and taxes on the distribution of income, prevalence of poverty, and level of income inequality based on the 17 income definitions as summarized below:

Definition 1: official money income

Definition 1b: definition 1 plus capital gains/losses less taxes

Definition 2: definition 1 less government cash transfers

Definition 3: definition 2 plus capital gains/less capital losses

Definition 4: definition 3 plus the value of employment-related health benefits

Definition 5: definition 4 less Social Security payroll taxes

Definition 6: definition 5 less federal income taxes (excluding the Earned Income Tax Credit)

Definition 7: definition 6 plus the Earned Income Tax Credit

Definition 8: definition 7 less state income taxes

Definition 9: definition 8 plus non-means-tested government cash transfers

Definition 10: definition 9 plus the value of Medicare

Definition 11: definition 10 plus the value of regular-price school lunches

Definition 12: definition 11 plus means-tested cash transfers

Definition 13: definition 12 plus the value of Medicaid

Definition 14a: definition 13 plus the value of other means-tested government noncash transfers less Medicare and Medicaid

Definition 14: definition 13 plus the value of other means-tested government noncash transfers

Definition 15: definition 14 plus net imputed return on equity in own home

Obviously, the construction of 17 definitions of income was not based on the premise that each of these definitions represented a viable income concept. Rather, the construction of so many income definitions was to facilitate the analysis that examines which components of a broadened income measure are most responsible for the significant changes in income summary measures as one transitions from the money income concept to an expanded definition of well-being. That said, there are several expanded income definitions that the Census Bureau has found useful to track trends and differences between groups. For example, the 2002 CPS income report (U.S. Bureau of the Census, 2003) highlighted four definitions of income in addition to the traditional money income definition. These were definitions 1b, 14a, 14, and 15. It should be noted that in the 2002 income report for the first time these alternative income measures were featured in the main body of the report and presented along with the money income measures (in previous reports these figures were examined in supplemental report sections).


Clearly, an expanded definition of income has a significant effect on income and poverty summary measures. Looking at 2002 data for definition 15, for example, we see that while the median income is somewhat higher under the most comprehensive definition of income ($43,760 based on definition 15 vs. $42,409 based on money income), mean income under the most comprehensive definition is lower than money income and the distribution of income is substantially more equal under the expanded definition (see Table 1). The Gini index, for example was .400 under definition 15, 11 percent lower than the Gini index for money income.11 The percentage of aggregate income received by the top 20 percent of the income distribution was also lower (45.6 percent for definition 15 vs. 49.6 percent for money income). Census Bureau figures have consistently shown that government transfers have a much greater impact on lowering income inequality than the tax system. In 2002, for example, subtracting taxes and including the Earned Income Tax Credit lowered the Gini index by about 4 percent, while including transfers lowered the Gini index by around 17 percent.

As would be expected, the use of alternative income measures also has a significant effect on poverty measures. Using the same poverty thresholds as the official measure, the poverty rate based on the most broadened definition of income (definition 15) was 8.6 percent in 2002, 3.5 percentage points lower than the official poverty rate of 12.1 percent. Poverty rates increased between 2001 and 2002 based on both definitions of income.

It is also instructive to look at the effect of the use of alternative income definitions on the relationship of incomes between population subgroups. For example, under the money income definition, the median income in 2002 of households with householders that reported the single race of Black ($29,026) was 62 percent of the median of non- Hispanic White households in which the householder reported no other race. The comparable percentage under the broadest definition of income was 67 percent. Similarly, the use of a broadened definition of income reduces the gap between the median incomes of married-couple family households with children and households with a female householder, no husband present, with children (from 39 percent to 48 percent). Comparisons such as these show that the use of a broadened definition of income not only results in a more equal distribution of income, as might be expected it also tends to narrow the income differences between groups of households with traditionally high incomes and groups with lower incomes.

The Census Bureau plans on continuing to highlight alternative definitions of income because they offer a more comprehensive picture of economic well-being and are more sensitive to the effect of government tax and transfer policies than a money income concept. It should be noted that expanded definitions of income bring many complications, as noncash/tax values are not directly collected in the CPS and are therefore calculated. Thus, they are more prone to methodological changes that could conceivably make time series comparisons more problematic. For example, the Census Bureau’s goal is that the next release of after-tax income estimates (this fall) should incorporate a revised and improved tax model. But these improved estimates would be for calendar years 2002 and 2003 only. As the Census Bureau continues down the road of highlighting broadened income measures, the tradeoffs between the desire to continually improve methods and preserve the time series must be understood and factored into implementation decisions.

11 – The Gini index measures dispersion of income across an entire range and expresses it as a single statistic. At the extremes, 0 indicates perfect equality (everyone receives an equal share) and 1 indicates perfect inequality (one recipient or group receives all income).


As discussed previously, two important uses of BEA’s estimates of personal income, particularly at the state and local level, are to track spending capacity and to measure the tax base. There are alternatives to BEA’s data that can be used to address these needs. The IRS provides estimates of adjusted gross income and its major components at the state and county level, but these are available with a lag due in part to the need to wait for returns to be filed. The Census Bureau’s estimates of money income, and the alternative income measures in produces, are more timely, but the limited sample size of the CPS means that the Census Bureau only publishes two year moving averages by state.12 Thus, state and local users of income data often rely on BEA’s estimates of personal income, which are the most timely and comprehensive income estimates available at a detailed level of geography.

Alternative BEA measures of income may better meet user needs than does personal income. Personal income differs from a measure of the tax base, since it includes some nontaxable forms of income (e.g., employer contributions for pensions and health insurance) but excludes others (e.g., pension distributions and realized capital gains). Disposable personal income does not fully measure the capacity to spend, since it does not reflect either all money income flows available for spending or the accumulation of wealth that might be drawn down to support consumption. The following discusses how alternative measures of personal income and disposable personal income might be constructed so as to better meet user needs. This discussion is preliminary and will benefit from the input of the FESAC committee.

One alternative approach recognizes that the present scope of personal income is broader than households, since it also includes non-profit institutions serving households (NPISH’s). State and local area estimates of household income could be generated separately from NPISH’s, paralleling estimates that have been generated at the national level as the result of the latest comprehensive revision of the National Income and Product Accounts (NIPAs).13 The rationales for excluding NPISH’s are that their consumption patterns are different for those of households and that they are tax-exempt. Thus, state and local area estimates of BEA household income (and disposable household income) might better proxy for consumer spending capacity and the tax base.

In order to estimate household income at the state and local level, income and transfer payments from outside the personal sector would need to be split between households and NPISH’s. Further, income would need to reflect transfers between the household and NPISH sectors. Currently, transfers that NPISH’s receive from households – or make to them – are excluded from personal income because they are intrasector transfers in the consolidated accounts of households and NPISH’s. Estimates of household income would need to reflect transfers from NPISH’s.14

How different are estimates of personal income and household income? Data at the national level suggest that they are quite similar: $8.685 trillion for US personal income and $8.647 trillion for US household income in 2001 or a difference of less than 0.5 percent (Mead, McCully, and Reinsdorf, 2003). The similarity of the estimates stems from the fact that personal income in the form of property income and transfers that is attributable to NPISH’s is both relatively small and is largely offset by transfers from NPISH’s to households in the calculation of household income. However, it is conceivable that differences between personal and household income could be greater at the state and local area level, to the extent that donors to and recipients from NPISH’s are different individuals and live in different areas.

While the impact on estimates of income from excluding NPISH’s may be small, the previous reconciliation between BEA personal income and the Census Bureau’s money income indicates that there are other, more sizeable components of personal income that are not received directly by households. As previously noted, personal income includes employers’ payments into employee pension plans, but does not measure pension disbursements. Another alternative measure of personal income would remove the contribution items associated with pension plans and add back pension disbursements. Here “pension plans” refer to both defined benefit and defined contribution plans.

Constructing personal income based on pension disbursements rather than contributions can be rationalized in a variety of ways. In their paper on alternative measures of personal savings, Perozek and Reinsdorf (2002) note that pension funds are assigned to the personal sector as opposed to the business sector. Contributions from business into retirement accounts is compensation across sectors that is included in personal income, while disbursements from these accounts are transfers wholly within the personal sector that don’t increase personal income. Perozek and Reinsdorf argue that placing defined contribution plans in the personal sector is appropriate because they belong to employees. However, inclusion of defined benefit plans in the personal sector is more controversial, since employees are not entitled to all of the funds that accrue in defined benefit plans, but rather are entitled only to pensions based on a formula. If defined benefit plans were assigned to the business and government sectors, then personal income would be generated when pensions are disbursed, not when contributions are made into pension plans.

The Perozek and Reinsdorf argument applies only to defined benefit plans. A rationale for treating defined contribution plans in the same fashion must be sought elsewhere. There are two additional rationales that may apply to both defined benefit and defined contribution plans. First, if personal income (or, better, disposable personal income) is being used to measure current spending capacity and if there are liquidity constraints that limit borrowing against these plans, then pension plan disbursements measure increased capacity to spend better than pension plan contributions.15 Second, if personal income is being used to proxy for the tax base, then an income measure that includes taxable pension disbursements is preferred to a measure that includes non-taxed contributions.

BEA’s regional economics staff estimated personal income on a pension disbursement basis for the mid-1990s. This involved removing from personal income several pension related items and adding back an estimate of pension disbursements. The items that were removed from personal income included employee contributions to pension plans (such as 401(k) contributions) that are now included in wages and salaries, employer contributions that are now included in the category “employer contributions for employee pension and insurance funds,” and investment earnings on pension accounts (dividends, interest, and rent) that are currently included in property income.

Using pension disbursements in place of pension contributions and earnings lowers the estimate of US personal income for 1997 by $154 billion or 2.2 percent. Adjusted personal income was lower for all states except Florida. The states that experienced the largest gain in personal income shares were Florida, Arizona, Delaware and Michigan. Relative losers were DC, Maryland, Virginia, Alaska and Hawaii. All of these losing states have a large federal government presence, with large federal government contributions to pensions. Of course, these estimates reflect the current relationship between the number of retirees and working people in the US and in the states, a relationship that is expected to change with the retirement of the baby-boom generation. The impact of replacing pension accruals with disbursements in any given year will also depend on the strength of the stock market, as required employer contributions into pension plans decline with the appreciation of pension plan assets.

There are several source data problems with estimating personal income on a pension disbursement basis. These problems are an issue at the national level and they are even more acute at the subnational level where less source data are available or where, owing to small sample issues, the source data might be less reliable. One problem is that the unemployment insurance data used to generate wage and salary estimates do not break out the portion that employees contribute to pension plans. BEA’s preliminary research used data from the Office of Personnel Management (OPM) and the Census Bureau for the government sector and IRS Form 5500 data for the private sector to break out employee contributions at the national level. Wages and salaries were used to distribute the national estimates to the state level. The drawback of this approach at the national level is the timeliness of the IRS data from Form 5500. At the state level, employee contributions in different industries have different geographic coverage that the use of private wages will not capture. In addition, employees can contribute a variable amount up to a certain limit, and the state wages will not reflect that option.

Another important issue with regard to the source data available for estimating pension disbursements concerns pension rollovers. Lump sum distributions frequently occur for cash balance and defined contribution plans when employees leave their firms. Whether these distributions are rolled over into a new retirement plan or whether they are retained for spending or paying off debt is a crucial distinction for measuring personal income on a pension disbursement basis. According to data from the Survey of Income and Program Participation, Moore and Muller (2002) determined that, while “historically most distributions have not been rolled over, the majority of the dollar value of all distributions has been rolled over. For example, 73 percent of distributed dollars were rolled over in 1993, and 79 percent were rolled over in 1996” (page 33). Similarly, Sabelhaus and Weiner (1999) estimate from IRS micro data that 70 to 77 percent of distributions were rolled over in 1995 (pages 600-601).

BEA reviewed state and national information from IRS’s Compliance Research Information System (CRIS), a sample of seven percent of all 1040 filers. BEA estimated that total rollovers in 1999 were approximately $227 billion. One of the problems with the CRIS database is that it has not been edited. The quality of the database must be reviewed over time. Large errors were noticed for some categories at the state level. In addition, the database excludes nonfilers of IRS Form 1040.

BEA has recently been working with the IRS Statistics of Income Division to provide BEA with state level sample data on information returns that will allow the Bureau to adjust for nonfilers and to determine type of distributions from the 1099R. Data on distributions from pension and IRA accounts, and their disposition (whether rolled-over or paid to the individual), may be gleaned from taxpayer information returns (Forms 1099R and 5498). Administrative records and Census Bureau survey data are available for lump sum payments, refunds, and transfers from government retirement plans, but these government payments constitute only a small portion of all lump sum retirement payments.

The foregoing discussion considers including pension disbursements in an alternative definition of personal income, where pensions are limited to defined benefit and defined contribution plans. However, there are other tax-preferred schemes, such as, IRAs and annuities that facilitate savings to provide income during retirement. BEA currently treats contributions to these other schemes as personal savings. One can envision an alternative definition of personal income that includes not only pension distributions, but also distributions from IRAs and other tax-preferred schemes, that is, that reflects total retirement payments.16 Constructing this alternative measure would involve removing contributions to these other tax-preferred schemes, removing the property income earned on the schemes, and adding total retirement payments. As with pension rollovers, care would need to be taken to net out distributions that are rolled over into other similar tax preferred accounts.

The rationales for including disbursements from other tax-preferred retirement schemes parallel those given for including pension disbursements—an income measure capturing these disbursements may better measure current spending capacity and the tax base.

It is important to note that the alternative measures of personal income proposed above do not strictly fit the HSH concept of income. Specifically, when retirement assets are disbursed and used for consumption, they may lead to a decline in retirement asset balances. Thus, consumption may be supported by a decline in net worth, in contrast to the HSH concept of maintaining net worth.

The foregoing has discussed the inclusion of distributions of pensions and other retirement schemes into alternative measures of personal income. All of these schemes have the attribute that they are tax-preferred and were designed to provide income in retirement. But, private savings more generally may be viewed as generating assets that provide income during retirement or more generally that provide income for consumption smoothing over the life cycle. A more expansive definition of income might include the money flows from realized capital gains or the increase in wealth associated with unrealized capital gains. These more expansive personal income definitions come closer to measuring changes in net wealth and hence closer to the HSH concept of income.

Data on changes in the net worth of households and nonprofit organizations are published quarterly by the Federal Reserve Board in the Flow of Funds (see Table R 100). An examination of this table makes clear some drawbacks of a very broad measure of income that includes unrealized capital gains. Specifically, such a measure would be extremely volatile and would not be predictive of spending patterns. For example, the net worth of households and nonprofit organizations increased by over $4.9 trillion, or 63 percent of US personal income, in 1999, largely through holding gains on assets. In contrast, net worth declined by about $1.4 trillion in the second quarter of 2002 and it declined by $1.6 trillion in the third quarter of 2002. Such increases and decreases were not accompanied by corresponding changes in consumption. Thus, a very broad measure of income defined as the change in net worth does not seem useful for measuring spending capacity. Further, since unrealized capital gains are not taxed, such a broad measure does not serve as a good proxy for the tax base. A more useful expanded definition of income might include only realized capital gains.

12 – The Census Bureau also produces annual estimates of median household income for states and counties, based on models using data from the ASEC, the decennial census, administrative records, and BEA’s personal income. The estimate are available with greater lag than the state household income tabulations from the CPS.

13 – See Mead, McCully, and Reinsdorf (2003).

14 – Household transfer payments to non-profits are treated as household outlays in the NIPAs.

15 – In fact, however, defined contribution plans frequently contain provisions that allow employees to borrow on these accounts, so that the liquidity constraint assumption is weak.

16 – It should be noted that while IRAs were created to provide retirement income, the funds may be withdrawn before retirement for a variety of purposes. Thus, it is not strictly true that an alternative measure of personal income that incorporates IRA disbursements is reflecting only retirement payments.


This paper has compared two of the more widely used measures of household income—BEA’s personal income and the Census Bureau’s CPS money income. It has also presented alternative estimates of money income developed by the Census Bureau to better measure economic well-being and it has discussed how alternative BEA measures of personal income might be developed that better measure the capacity to spend or the tax base. It is clear that there is not one single definition of household income that can serve all purposes. Instead the BEA and the Census Bureau have and will continue to provide an array of measures that address different user needs.


After adjusting BEA personal income to the same scope as CPS money income, the BEA estimate remains $806 billion higher. The paper surmises that at least half of this might be due to CPS underreporting. Is this a reasonable surmise or could the BEA estimate be too high? Does possible underreporting in the CPS have any implications for income distribution measures?

Should BEA pursue generating alternative income estimates along the lines discussed in the paper? That is, should BEA generate alternative income measures that include retirement income disbursements and possibly realized capital gains? How well would these alternative measures address user needs to better measure the capacity to spend or the tax base?

Are there other user needs not discussed in the paper that could be met by alternative BEA measures of income? If so, what alternative definition of income might be proposed to meet these needs?

The Census Bureau has been producing estimates of income and poverty based on alternative definitions of income for a long time and, within the latest income report release, has begun to highlight some of these measures much more than previously. Are the measures that the Census Bureau has begun to highlight appropriate? Are there others they should highlight as well?

For future Census Bureau income reports, are there other components of well-being that should be added to Census Bureau research into broadened income measures? For example, alternative measures now examine the effect of one type of mandatory expense (taxes) on income. Should future research include the effect of other “mandatory” expenses, such as work-related or health-related expenses?

Should the Census Bureau consider using model-based techniques based on the relationships between administrative and survey data to produce improved experimental estimates of household income?

Is the reconciliation of BEA’s personal income and the Census Bureau’s money income of sufficient value that it should be updated and published on a regular basis? Is there value in extending the reconciliation to the state level?

Farm Data Procedures


The BEA Benchmark I-O tables provide us with Output, Employee Compensation (EC), and Gross Operating Surplus (GOS) by IMPLAN Sector. They also provide us with production functions for each IMPLAN Sector. The data is available for all 14 farm Sectors. However, the data does not separate GOS into its component parts (Proprietor Income (PI) and Other Property Income (OPI)), nor do they provide Employment estimates. We use the latest BEA Benchmark data with other data sources from the same year to create ratios that are used in the annual data creation process:

  1. Output-per-EC by farm Sector: The BEA Benchmark provides EC and Output by farm Sector.
  2. EC-per-W&S Worker by farm Sector: The BEA Benchmark does not provide any employment estimates. Thus, to get ‘Benchmark’ W&S Employment by farm Sector, we distribute benchmark-year BEA REA “All Farm” W&S Employment based on benchmark-year BLS CEW W&S Employment by farm sector and then calculate ratios from these estimates. While CEW has more sector detail than REA, we use the REA total W&S Employment figure since the CEW data only cover roughly 90% of W&S employment for the farm sectors. We only use the CEW data for distribution of the REA W&S Employment value. We then combine these W&S Employment estimates with the BEA Benchmark EC data to calculate EC-per-W&S Worker ratios by farm Sector.
  3. PI-per-Output by farm Sector: The BEA Benchmark does not directly give us PI by farm Sector, but rather gives us GOS by farm Sector. We use an agriculture-wide average ratio between PI and GOS (using PI from the 2007 BEA REA data, which has just one “All Farm” Sector) to estimate Benchmark PI by farm Sector. These ratios are then used to distribute the latest BEA REA “All Farm” PI data amongst the 14 farm Sectors. We then combine these PI estimates with the BEA Benchmark Output data to calculate PI-per-Output ratios by farm Sector.
  4. Output-per-Proprietor by farm Sector: Again, the BEA Benchmark does not provide any employment estimates. We estimate Benchmark Proprietor Employment by distributing benchmark-year BEA REA farm proprietor employment based on the farm count per farm sector from the latest Census of Agriculture. We then combine these Benchmark Proprietor Employment estimates with the BEA Benchmark Output data to calculate output-per-proprietor ratios by farm Sector.


  1. Each year, we obtain estimates of agricultural Output by commodity by state from USDA’s Economic Research Service (ERS) and National Agricultural Statistics Service (NASS). The data generally are current and empirical (i.e. based on observation and survey, rather than trend extrapolations), so we begin with Output data.
  2. ERS and NASS do not cover some states with low production, and can omit certain crops with low production. ERS often will report some of those low-production crops in its “miscellaneous” category. We attempt to reallocate those low-production crops into the appropriate Sector. We also use the Census of Agriculture to estimate crop production for states whose production is not reported by ERS or NASS. For both of these reasons, our estimates for a particular crop Sector often are somewhat larger than those reported by ERS or NASS.
  3. As of IMPLAN’s 2015 data set, we no longer control agricultural estimates to BEA for several reasons. The BEA release of farm cash receipts was released after we produced agricultural estimates. Additional discussions with BEA revealed that they primarily use ERS data, which is one of IMPLAN’s primary sources, so controlling to BEA estimates adds relatively little value. Furthermore, BEA’s commodity-level estimates are for cash receipts, which excludes crops put into inventory and home consumption, both of which drive intermediate expenditures. However, one benefit of controlling to BEA that we wanted to keep is that it theoretically corrects for ERS’ tendency to overestimate the Output of the Miscellaneous Crops Sector; therefore, we implemented an adjustment factor based on the ratio of BEA Benchmark Output to benchmark-year ERS output. We do, however, compare our Output estimates to estimates from NIPA, BEA REA, ERS, and the BEA Industry Series to ensure that they are reasonable.
  4. These state values are distributed to the counties by using the ratio of county physical production to state physical production from the latest Census of Agriculture (which comes out every 5 years). For example, if County A has 10% of the state’s corn sales (or acres if sales is not available, and farms if acreages is not available), then it gets 10% of the state’s Annual Cash Receipts value for corn.
    • For non-disclosed counties in the Census of Agriculture, we multiply the average value of a particular commodity per unit of volume (or per farm in the absence of volume data) in the state that produces that commodity by the county-level data that produce that commodity (number of farms is always disclosed), giving us an estimate of total value of that commodity at the county level. We adjust estimates based on these ratios to control totals when possible.
    • Annual Cash Receipts data are not available for all crops at the state level. In these cases, the average ratio of state production to national production from the Census of Agriculture is used, and controlled to the state total for a higher-level aggregation, if that aggregation accounts for non-disclosed crop values.1

1 ERS and NASS occasionally change their reporting for aggregate categories between a) including the sum of non-disclosed values for subcategories in their aggregate category values and b) making the aggregate value simply the sum of disclosed children. We adjust our processes accordingly.


There is no data source for employment by agricultural commodity, even at the U.S. level.

  1. We make our first estimates of employment and income by IMPLAN Sector applying the Benchmark ratios to annual Output by commodity (both described above).
  2. We control those estimates of employment and income to annual BEA REA estimates of “All Farm” employment and income (both for W&S workers and proprietors).
  3. Each state’s output, employment, and income are forced to sum to the U.S. totals, after which the counties’ values are forced to sum to the state values.


The BEA releases state-level GDP data, at approximately the 3-digit NAICS level, that includes the break-out of GDP into EC, TOPI, and GOS. These data collapse NAICS 111, Crop Production, and 112, Animal Production, into a single “Farms” Sector. GOS consists of Proprietor Income (PI) and Other Property Income (OPI), so OPI for 3-digit NAICS is derived by subtracting our estimates of PI (described above) from GOS. These 3-digit control values are distributed to the IMPLAN Sectors based on the latest BEA Benchmark’s characteristics for GOS and TOPI and by using data from the ERS Agriculture Resource Management Survey (ARMS) data as described below.


Updated source data and method for forecasting lagged state GDP data for farm sectors (IMPLAN Sectors 1-14):

  • At the national level, we now use growth rates from NIPA table 7.3.5, which reports national components of value added in the current Implan reference year. We apply the growth rates to BEA REA data, to maintain consistency with REA definitions and concepts.
  • At the state level, we use growth in total farm output rates to project value added growth. We have empirical sources for current-year agricultural output by state. This has the result of better approximating future BEA estimates of farm value added. Previously, we used only EC, which was extrapolated from REA total farm EC and current-year output estimates.
  • These state-level projections are then controlled to the national projections.

In Data Year 2015, we also incorporated USDA ERS Agriculture Resource Management Survey (ARMS) data to estimate components of value added by commodity at the national and state levels:

  • ARMS data report sub-components of value added components for certain commodities and certain states.
    • Only major agricultural states are included in ARMS. For non-covered states, we form initial estimates of OPI and TOPI by farm Sector according to national ratios of OPI and TOPI to state-level farm data. The national ratios are based on ARMS data, as described below.
    • ARMS reports government payments, i.e., subsidies, real estate and other property taxes (a large component of gross TOPI for farm sectors), interest (a component of OPI), and depreciation (another component of OPI).
    • Certain ARMS commodities line up well with IMPLAN commodities, including soybeans (over 90% of IMPLAN’s Oilseeds sector), wheat and corn (collectively over 90% of IMPLAN’s Grains sector), dairy, cattle, poultry, and other livestock. We make minor adjustments to oilseeds and grains to scale the ARMS values to account for non-covered commodities, e.g., canola in the Oilseeds sector and rice in the Grains sector. We do not use ARMS data in cases where the commodity classifications do not align well with IMPLAN Sectors.
  • Since ARMS data covers only certain parts of value added components, we don’t use the ARMS data outright but rather to distribute REA “Farm Total” value added components to IMPLAN farm Sectors.
    • For example:
      • First, we estimate, according to ARMS, the share of all real estate taxes in a state that go to dairy. Let’s say it is 5%.
      • If the ARMS data is lagged with respect to the IMPLAN reference year, we then use state-level output by commodity data from the lagged year and the current year to estimate the change in dairy’s share of the state’s total farm output. If dairy increased from 10% to 20% of the state’s agricultural Output, we multiply the 5% share of real estate taxes by 2, for a 10% share. We check for and downwardly adjust abnormally high or low changes in shares.
      • We then take 10% of our projected REA Gross TOPI value as the Gross TOPI that belongs to dairy. We perform an analogous calculation for subsidies, and then calculate net TOPI from that.
  • After using ARMS data where available, we use our pre-existing method of making initial estimates of value added components based on 5-year BEA benchmark data for farm commodities where ARMS coverage does not align with IMPLAN Sectoring.
  • The final estimate for farm GDP for a state is controlled to our projection of REA farm value added, which is consistent with past practices.



Generally, we prefer our own estimates of employment to QCEW, BEA, and the Census of Agriculture for a variety of reasons. Among those reasons: QCEW does not cover proprietors, which compose a significant share of farm employment; QCEW misses some W&S employment; BEA REA has employment data only at the “farm” level of detail, though it includes proprietor employment; the Census of Agriculture releases employment data only every 5 years and measures employment differently than our other data sources: it measures the number of unique human beings who worked on a farm as opposed to the “jobs” those humans filled.  For example, if a farm had 6 humans who worked 2 months each, sequentially in a year, the Census of Agriculture would report that as 6 jobs, whereas in other data sources (and in IMPLAN), this is considered just one job – one job that happens to be filled by 6 different temporary workers.  Our data attempts to correct for these omissions and inconsistencies. Since the agriculture data are, to a large extent, derived, analysts who have local agriculture data that also correct for these omissions and inconsistencies (e.g., from a survey) are encouraged to use their data when building their IMPLAN models. However, in the absence of such data, we encourage people to use IMPLAN’s estimates.

Special Sectors for Employment Data

There are several sectors that are not covered by the CEW data that require different data points and additional techniques to capture.

These special Sectors include the following:

  • Farm sectors
  • Construction sectors
  • Government sectors




The primary data sources for state-level agricultural output by sector are the NASS Value of Production and ERS Annual Cash Receipts data sets, both from the USDA.  The county-level data from these sources are not consistent enough for our use; thus, we use data from the latest Census of Agriculture to estimate county-level farm sector output.  Census of Agriculture data is also used to estimate state-level values not disclosed by NASS or ERS.

More details can be found in this article.

Employment and Income

BLS CEW does have estimates of wage and salary employment for farms with about 90% coverage. However, the CEW data for farm sectors are particularly difficult to integrate because BLS CEW data establishments are not reclassiffied year to year, while farmer commodity production is. For example, a given farmer will plant either corn or soybeans (2 separate IMPLAN sectors) based on that year’s prices and/or how late in the year they are able to plant their crops. Also, the CEW data does not include propietors.

The BEA’s Regional Economic Accounts (REA) program estimates county-level employment and income data, including proprietors, but these are farm totals that are not broken down by type of agricultural commodity. Therefore, we have developed procedures to generate first estimates of employment and income by commodity and county based on annual output values (described above) and various ratios from the Census of Agriculture and state-level data. These first estimates are controlled to the total farm employment value given by the REA data, as well as to higher geographic levels. 

Some of the state and county farm sectors are subject to large adjustments when controlled to the national totals. This is a result of inconsistencies between sources. Our benchmark dataset is the published ERS data for output. Since the agriculture data is, to a large extent, derived, analysts with local agriculture data are encouraged to use it when building their IMPLAN models.

More details can be found in this article.


IMPLAN construction sectors are not NAICS-based, but rather are defined by the Census Bureau’s types of construction.  There are 10 new construction and 3 maintenance and repair construction sectors in IMPLAN’s current sectoring scheme.


Employment and Income

The BLS’ CEW data and the BEA’s REA data provide construction employment and income values at the national, state, and county level for a single aggregate construction sector.  These values are split amongst IMPLAN’s 13 construction sectors using various ratios from the latest BEA Benchmark I-O tables.  “In-house” construction activity performed by non-construction industries is “re-defined” into the appropriate construction sectors, following BEA Benchmark conventions. All estimates are then controlled to higher geographic levels.



National output values for the new construction sectors come from BEA NIPA tables 5.4.5-Private Fixed Investment in Structures by Type and 5.9.5. Gross Government Fixed Investment by Type. National output for the maintenance and repair construction sectors are based on the ratio of maintenance and repair to new construction from the BEA’s Annual Gross Domestic Product (GDP) by Industry series.  These values already contain “redefined” construction activity and thus do not require any redefinition procedures on our part. These national values are distributed to states and counties based on employment and income. 





IMPLAN data includes several types of government activity. “Administrative” or “general” government is considered an Institution in IMPLAN. There are payroll-only sectors that are purchased exclusively by administrative government spending patterns. In contrast, government enterprises are sectors that have profiles similar to those of industries. The Bureau of Economic Analysis (BEA) defines government enterprises as “Government agencies that cover a substantial portion of their operating costs by selling goods and services to the public and that maintain their own separate accounts.” The BEA Benchmark I-O tables treat government enterprises as industries and IMPLAN follows this convention. IMPLAN has 8 government enterprise sectors. All remaining government agencies are part of “general” or “administrative” government.


Most data sources other than the BEA Benchmark table do not distinguish government enterprises from administrative government establishments. The BLS’ CEW program, for example, reports employment and wages for government establishments by NAICS code and by ownership type (federal, state, or local). We assign government establishments to enterprise or administrative payroll sectors according to the BEA Benchmark table. If a government enterprise makes a particular commodity, then any government establishment that fits the NAICS code of that commodity is classified as a government enterprise. For example, in the latest (2007) BEA Benchmark table, “Electric power generation, transmission, and distribution,” which corresponds to NAICS 2211, is made by private industries in the electrical sectors but also by “State and local government electric utilities.” Accordingly, any government establishment owned by state or local government under NAICS 2211 is classified in IMPLAN sector 522 or 525, respectively. If the BEA Benchmark table does not show a government enterprise sector making a commodity, then any government establishment in that commodity’s corresponding NAICS code is classified as administrative government. Tables 1 through 3 show IMPLAN sector assignments for establishments by ownership type and NAICS code. Note that military employment is not covered by any of our NAICS-based sources and is estimated from military employment data reported by the BEA


Sector Description NAICS Code
518 Postal Service 491 Postal Service
519 Federal Electric Utilities 2211 Power Generation and Supply
520 Other Federal Government Enterprises

44-45 Retail Trade

481 Air Transportation

531 Real Estate

51213 Motion Picture and Video Exhibition

5241 Insurance Carriers

722 Food Services and Drinking Places

8121 Personal Care Services

535 *Employment and Payroll Federal Government Non-Military All other NAICS codes
536 *Employment and Payroll Federal Government Military Not reported on a NAICS basis


Sector Description NAICS Code
521 State Government Passenger Transport 485 Transit and Ground Passenger Transportation
522 State Government Electric Utilities 2211 Power Generation and Supply
523 Other State Government Enterprises

2212 Natural Gas Distribution

2213 Water, Sewage and Other Systems

7112 Spectator Sports

7132 Gambling Industries

72112 Casino Hotels

5241 Insurance Carriers

521 Monetary Authorities – Central Bank

5221 Depository Credit Intermediation

531 Real Estate

562 Waste Management and Remediation Services

4453 Beer, Wine and Liquor Stores

481 Air Transportation

482 Rail Transportation

483 Water Transportation

487 Scenic and Sightseeing Transporation

531 *Employment and Payroll State Government Non-Education All other NAICS codes
532 *Employment and Payroll State Government Education 61


Sector Description NAICS Code
524 Local Government Passenger Transport 485 Transit and Ground Passenger Transportation
525 Local Government Electric Utilities 2211 Power Generation and Supply
526 Other Local Government Enterprises

2212 Natural Gas Distribution

2213 Water, Sewage and Other Systems

4453 Beer, Wine and Liquor Stores

5241 Insurance Carriers

531 Real Estate

562 Waste Management and Remediation Services

7112 Spectator Sports

481 Air Transportation

482 Rail Transportation

483 Water Transportation

487 Scenic and Sightseeing Transportation

521 Monetary Authorities – Central Bank

5221 Depository Credit Intermediation

72112 Casino Hotels

7132 Gambling Industries

533 *Employment and Payroll Local Government Non-Education All other NAICS codes
534 *Employment and Payroll Local Government Education 61


Primary data sources for state-level agricultural output by sector are the NASS Value of Production and ERS Annual Cash Receipts data sets, both from the USDA. The county-level data are not consistent enough for our use. Data used to estimate county level farm sector output come from the Census of Agriculture. We use estimates from the census of Agriculture to estimate values not disclosed by NASS or ERS.

IMPLAN Data Release Notes

Release notes from IMPLAN’s historical data releases. Data years 2009 & 2011 did not have noteworthy changes.

Time Series

Time Series Release Notes

IMPLAN’s new Time Series Data product was produced using our latest methodologies, which have been honed over the past 20 years of data development. A few special tactics were required for some data elements/years/places, which are noted later in this document. Benefits of this new data product include the following:


  • Use of revised raw data (many government data sources are later revised after the annual IMPLAN data creation process – this product takes advantage of the improved raw data!)
  • Use of current raw data (many of our annual data sources come to us a year lagged – this is of course not the case when going back and estimating past years, so no projections needed!)
  • Consistent estimation methodologies (incorporates all of our best practices and improved data sources learned throughout the years)
  • Consistent and more-detailed sectoring scheme (this is the only way to see 2001-2012 data in the current 536 sectoring scheme – the most sectors we’ve ever had!)
  • Statistical Analysis with IMPLAN data is now possible and easy!


Major Methodology Improvements and Changes Incorporated over Time

All years from 2001 – 2014 were based on the latest (2007) BEA Benchmark Make and Use tables.



On November 15, 2001, Broomfield County (State FIPS 08, County FIPS 014) separated from Boulder County to become the newest and smallest county of Colorado.



Four existing Alaskan boroughs underwent transformation from mid-2007 to mid-2008 creating five re-named and re-coded FIPs codes and a net gain of two boroughs, as shown in the table below.


2007 Boroughs

2008 Boroughs

130 Ketchikan Gateway Borough

201 Prince of Wales-Outer Ketchikan Census Area

130 Ketchikan Gateway Borough

198 Prince of Wales-Hyder Census Area

232 Skagway-Angoon

105 Hoonah-Angoon Census Division

230 Skagway Borough

280 Wrangell-Petersburg Census Area

195 Petersburg Census Area

275 Wrangell Borough 



Bedford City, Virginia (State FIPS 51, County FIPS 515) changed from independent city status to town status and was added to Bedford County (State FIPS 51, County FIPS 019), effective July 1, 2013.




The BEA provides data on TOPI by GSP sector (81 of them), by state. Previous to the original 2012 data year, we were only making use of the U.S.-level data, using U.S. ratios to estimate state-level data. In the 2012 and later IMPLAN Data, as well as the Time Series Data, we improved our process of incorporating the state-level BEA TOPI data.




Our source for Output for sectors 20 (Extraction of natural gas and crude petroleum) and 21 (Extraction of natural gas liquids) had been the U.S. Energy Information Administration (EIA). However, upon investigating some sizable differences between EIA values and BEA values, we discovered that the EIA data represent commodity output, while the BEA figures capture industry output.  However, we cannot use BEA figures directly because they are lagged a year and they do not have the same level of industry detail as IMPLAN (in this case, the two extraction sectors are combined as one in the BEA data). Thus, our new methodology involves using the ratio of “Extraction of natural gas and crude petroleum” output to “Extraction of natural gas liquids” output from the latest Economic Census to split out the lagged BEA value into the two IMPLAN sectors, and then project the two BEA figures using the EIA data.




We inquired with the Bureau of Economic Analysis (BEA) about the difference between their Regional Economic Accounts (REA) state-level wage and salary employment (SA27) and the Bureau of Labor Statistic (BLS)’s Census of Employment and Wages (CEW) wage and salary employment counts for the few industries where there is a significant difference but which the BLS does not acknowledge any coverage gap – Fishing/Hunting/Trapping, Membership Organizations, and Private Education (the BLS does acknowledge a coverage gap with military, private households, farms, and railroads). We were informed that BEA upwardly adjusts the employment and income estimates for these sectors due to coverage gaps.


  • The adjustment for Membership Organizations is for religious organizations, so we now adjust this IMPLAN sector according to state-specific REA/CEW ratios.
  • The Small Business Job Protection Act of 1996 exempted a lot of employees in shellfishing and finfishing from unemployment insurance coverage. This adjustment affects GA, RI, LA, TX, OR, and MA.  Thus, we now adjust this IMPLAN sector according to state-specific REA/CEW ratios as well.
  • There is an adjustment for Private Education, which applies primarily to student workers at universities.   Thus, we now adjust this IMPLAN sector according to state-specific REA/CEW ratios as well.
  • There is an adjustment for Private Households. Thus, we now adjust this IMPLAN sector according to state-specific REA/CEW ratios as well.




We added a control of the sum of our state-level estimates to BEA’s national estimates for the value of crop sales.   The Economic Research Service (ERS), which is BEA’s primary initial source of cash receipts by commodity, estimates include adjustments for Commodity Credit Corporation (CCC) loans, and do not account for home consumption or inventory, all of which need to be addressed when estimating output based on cash receipts.   We obtain estimates for value of production for certain agricultural products from the Department of Agriculture’s National Agricultural Statistics Service (NASS); these values don’t require adjustments for CCC or inventory. BEA adds the value of intra-state livestock sales to its estimates, which should be included in output, so this tends to increase our estimates. We do not control individual state values to BEA values since we generally can obtain and process more current ERS and NASS data before they are incorporated into BEA’s data. Although BEA’s “other crops” category includes sugar cane, BEA does not produce any detailed estimate of sugarcane output, which is well-measured by NASS and ERS, so we do not apply the control to that IMPLAN sector. The NASS, ERS, and the Census of Agriculture continue to be our primary data sources for estimating state- and county-level agricultural output.




The NIPA control totals for Government Gross Investment in structures (from table 3.9.5) and Private Fixed Investment in structures (from table 5.3.5) have already been redefined – that is, they include all activity related to the construction of structures, regardless of which industry performed that construction. Thus, when redefining the Output of each sector, while we still need to take construction activity out of the other sectors, we do not need to add that activity to the construction sectors (since their output figures for the construction sectors presumably already includes that activity).   Thus, in the Time Series Data set and all annual IMPLAN Data sets beginning with 2012 R2, we no longer add the non-construction-sector construction output to the construction sectors. However, the other sectors’ Employment, EC, PI, OPI, and IBT will continue to be moved into the construction sectors because the data for these factors is not redefined.




Starting in the 2010 data year, indirect business taxes (IBT) have been converted to taxes on production and imports net of government subsidy (TOPI). This removes business transfers to government from GDP. It also subtracts government subsidy to business from IBT. Thus, it is possible for TOPI to be negative for some industries, meaning that government subsidy exceeds taxes paid by the industry. This change has been incorporated into all annual IMPLAN Data sets since 2010, as well as the Time Series Data sets.




For agricultural sector Output, in the 2012 data year we shifted from using sales data to production data multiplied by the average price for that commodity for that year. The reason for this change is that agricultural commodities are not always sold in the same year that they are produced, making revenues an imprecise measure of Output. The same can be said for other manufacturing sectors; however, we get the Output data for those sectors from the Anuual Survey of Manufactures, which includes data on net inventory changes, which allows us to separate sales from production for those sectors. This improvement has been incorporated into all annual IMPLAN Data sets since 2012, as well as the Time Series Data sets.

Inconsistencies between County Business Patterns and Bureau of Labor Statistics Coverage

When creating our data, we use Bureau of Labor Statistics (BLS) Covered Employment and Wages data (also known as CEW, QCEW and ES202) as our benchmark as it is widely accepted and provides a broad spectrum of coverage for all wage and salary employment. It is also a means of capturing the most current data available for our employment and wage and salary income estimations. However, the data are inherently incomplete and must be supplemented in order to get a “full picture” of an economic region.

Like most government data, CEW data are subject to non-disclosure rules. To estimate non-disclosured values, we use County Business Patterns (CBP) data from the U.S. Census Bureau to get an idea of the overall size of the industry (based on the CBP’s firm count by size class information).

Unfortunately, the data between the two are not always consistent, as demonstrated by Ralls County, Missouri. This county has come into question in the past and provides an excellent demonstration of the problem. The issues are illustrated in the table below.

Note that CBP reports 11 establishments, 483 jobs, and $19.1 million in wage and salary income in manufacturing in 2006 for this county. In contrast, the BLS reports 19 establishments, 1,242 jobs, and $42.6 million in wages and salary income in manufacturing in 2006 for the same county. IMPLAN datasets will only have those industries that exist in BLS data sets; thus, all the blank cells in the BLS column will be missing from the IMPLAN datasets.


Ralls County, Missouri 2006

Census Bureau CBP

Bureau of Labor Statistics CEW

Industry Code

Industry Code Description








Soybean processing




Animal (except Poultry) Slaughtering








Truss Manufacturing




All Other Miscellaneous Wood Product Manufacturing




Paint and Coating Manufacturing




Rubber and plastics hose and belting mfg.




Rubber product mfg. for mechanical use




Cement Manufacturing




Ready-mix concrete manufacturing




Other concrete product manufacturing




Aluminum Die-Casting Foundries




Cutlery and Flatware (except Precious) Manufacturing




Machine Shops




Metal Heat Treating




Electroplating, anodizing, and coloring metal




Industrial mold manufacturing




Special Die and Tool, Die Set, Jig, and Fixture Manufacturing




Industrial process furnace and oven mfg.




Other communication and energy wire mfg.




Sign Manufacturing




 Annual Survey of Manufactures Data

CBP and the Annual Survey of Manufacturing (ASM), for concurrent years, have a high degree of correspondence (about 90%). However, ASM is also inconsistent with BLS. The following two NAICS codes provide the most stark examples of these discrepancies at the US level.

  • NAICS code 334113: BLS reports 15,376 jobs and $1.6 billion in wages. ASM reports 1,138 jobs and $61 million in wages. (CBP reported 926 jobs). This is approximately 6-7% of BLS.

At the other end of the spectrum.

  • NAICS 322114: BLS reports 6,826 jobs and $321 million in wages, ASM reports 18,182 jobs and $790 million in wages. (CBP reported 18,401 jobs). This is approximately 2.6 times BLS.

In these instances, IMPLAN data will reflect BLS values.

Estimating Employee Compensation Adjustments for Known Commuting Rates

To view the commuting data for your region, go to Explore > Social Accounts > IxC Social Accounting Matrix. If the Employee Compensation column makes a payment to the Domestic Trade row, then there is net in-commuting into the region (more income earned by workers who work in the area and live outside the area than vice-versa). The ratio of this value divided by the Employee Compensation column total gives you an estimate of the net in-commuting rate. If the cell is empty, then the region has net out-commuting, which will show up as a payment from the Domestic Trade column to the household rows.  To get a net out-commuting rate, divide the sum of these payments (across the 9 HH types) by the sum of the household column totals.

The equation below allows you to adjust IMPLAN’s estimated regional commuting rate to your known regional commuting rate.


newEC = EC*[(1-userCR)/(1-samCR)]


EC = original, unmodified employee compensation

userCR = your known commuting rate

samCR = commuting rate reported in the SAM

newEC = the EC value you want to use when running the analysis



For example, if the SAM shows that the average in-commuting rate in your region is 10% but you know that for your industry it is 20%, then: new EC = $1,000,000*(0.8/0.9) = $1,000,000*(0.88888) = $888,888.

After the analysis has been run, add the difference (EC – newEC) back to your direct EC effect since by definition EC occurs at the site of employment. Since EC is a component of Value Added, you should update the calculation of Value Added to include the difference. Because payroll taxes (social insurance taxes) are paid at the site of employment, the direct effect payroll taxes in your tax results will also need to be revised to their pre-EC adjustment levels. Create a separate activity, duplicate your event but use your EC value that has not been adjusted for commuting, and perform the analysis in a new scenario. Use the resulting direct effect EC payments to social insurance as replacement values for your adjusted EC analysis and re-sum the direct effect and total effect tax impact tables. This way you correctly account for the in-commuters’ direct effect, but you have also made sure that they did not generate any further local impact.

Balancing the Accounts

After the data are collected and compiled, it is necessary to balance the SAM table.

Balancing is accomplished by making adjustments in the foriegn trade and capital accounts. This is done by the software during the model building process.

Balancing of households can serve as an example. Households receive income from industries and institutions. With this income, households buy goods and services, pay taxes, and save for the future. We have information about income, consumption, and tax payments. Savings is the difference between income and spending and serves as a balancing element. Savings can be either positive or negative. Negative savings implies that the household category spent more than it earned that year – by withdrawing from household capital stocks or borrowing from financial Institutions.

Other balancing elements work similarly. The difference between government income and spending is a surplus or deficit.