Income statistics for the District of Columbia: ACS Income Estimates vs. DC Individual Income Tax Data

The U.S. Census Bureau American Community Survey (ACS) is an important source of annual data on the economic, employment, housing, and demographic conditions of the nation and its subnational jurisdictions. Local jurisdictions also maintain a vast store of administrative data that captures some of the same socioeconomic measures included in the ACS. This raises the issue of how these measures from alternative data sources compare. In an attempt to address this issue, this analysis compares selected city income measures provided by both the ACS and the District of Columbia income tax returns database. This analysis also compares the Gini coefficient measure in the ACS to one derived from income measures in the District of Columbia income tax returns database.  See full report here.

Income Levels

For all time periods under investigation, median earnings for District workers in the ACS was at least $8,000 (20 percent) higher than the median wages and salaries in the DC tax returns database. The responses to the ACS survey indicated that the citywide median earnings in the 2009-2013 time period was $45,231, while the actual median wages and salaries of all tax filers was $36,288 for the same period.

2006-2010 2007-2011 2008-2012 2009-2013
Median Earnings for Workers (ACS) $41,171 $43,137 $44,423 $45,231
Median Wages, Salaries & Tips (DC tax data) $33,432 $34,679 $35,510 $36,288
Amount Difference $7,739 $8,458 $8,913 $8,943
% Difference 20.8% 21.7% 22.3% 21.9%

Examining household income, a slightly broader definition of income, the ACS data states that median household income for the city tended to be more than $16,000 (30 percent) higher than the median FAGI for all tax filers in the city.  The responses to the ACS survey indicated that the citywide estimated median household income in the 2009-2013 time period was $65,830 compared to the $44,794 actual median income of all tax filers.

  2006-2010 2007-2011 2008-2012 2009-2013
Median Household Income (ACS)  $58,526  $61,825  $64,267  $65,830
Median FAGI (DC tax data)  $42,017  $43,317  $44,124  $44,794
Amount Difference  $16,509  $18,508  $20,143  $21,036
% Difference 32.8% 35.2% 37.2% 38.0%

The above figures show persistent and fairly constant differences between the median measure of earnings in the ACS and the median measure of wages in the tax data.  But upon closer inspection, the above figure shows a mildly increasing difference between the median measures of income in the ACS versus the administrative data.  The figure and table below shows a more noticeable growing difference between measures of mean income in the two data sources. The ACS mean income in the 2006 – 2010 time period was $8,189 (9.3 percent) higher than the comparable statistic obtained from local income tax data.  By the 2009 – 2013 time period the ACS mean income was $17,589 (19.2 percent) higher than the mean income measure obtained from the tax data.  Over the period, the mean FAGI decreased by an estimated annual rate of 0.04 percent compared to a 3.27 percent estimated annual growth rate for mean household income in the ACS.

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  2006-2010 2007-2011 2008-2012 2009-2013
Mean Household Income (ACS)  $91,778  $96,183  $99,511  $101,076
Mean FAGI (DC tax data)  $83,589  $84,161  $84,448  $83,487
Amount Difference  $8,189  $12,022  $15,063  $17,589
% Difference 9.3% 13.3% 16.4% 19.2%
  2006-2010 2009-2013 Estimated Annual Average Growth
Mean Household Income (ACS)  $91,778  $101,076 3.27%
Mean FAGI (DC tax data)  $83,589  $83,487 -0.04%


The figure displaying the trends in mean income also displays the 90 percent confidence interval for the ACS means.  The ACS sample estimates and their statistical standard errors (the basis of the upper and lower bounds of the confidence interval) allow for the construction of confidence intervals.  The figure suggests that the process and method by which OTR obtains and processes its administrative data is considerably different from that of the ACS.

Income Inequality Levels

Gini coefficients reported by the two sources share a similar profile but differ in three important ways.  First, the Gini coefficients produced by the ACS are 12 to 18 percent lower than that produced using income tax data. This could significantly affect one’s view of inequality in the city. Second, the ACS coefficients stay in the very tight range of 0.53-0.54 whereas the tax data coefficients range from 0.61 to 0.66. These ACS coefficients suggest that the level of income inequality has been relatively unchanging whereas the tax data coefficients show an appreciable decline as the great recession began to take its toll on the city’s economy. Third, the tax data show income inequality began to increase in 2012 while the 2012 ACS coefficient appears practically indistinguishable from the coefficients in 2009 to 2011.

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In sum, this analysis presents two main findings. First, compared to comparable statistics derived from local tax data, ACS income statistics are systematically and consistently higher and ACS Gini coefficients are systematically and consistently lower than comparable statistics derived from the tax data. And second, and maybe more importantly, some ACS and tax data reveal considerably different trends for the same phenomena.

Possible Explanations

There are a few possible explanations for the large systematic differences in income statistics produced from ACS data and from the DC tax data.  First, ACS data is based on self-reported survey responses that may be less objective than income information provided on tax returns. Tax returns must be accompanied and supported by income documentation, and all tax returns are eligible for audit by OTR. Thus, data submitted on tax returns are subject to a greater degree of accountability and responsibility, making the quality of information in the ACS and tax data a possible issue.

Second, there may be a significant number of income earning residents that do not file tax returns or do not report all of their income. This pool of non-reported income by residents may represent a nontrivial number of households for the city and tax database. Consequently, the means, methods and processes of the ACS may better deal with this issue of capturing information from all households in the city regardless if they earn income, file tax returns, or report all of their income. ACS survey respondents are selected via a statistical methodology that makes them, in total, highly representative of the jurisdiction’s total population regardless of circumstances. The sum estimation of income characteristics of the city may be more comprehensively described by the ACS.

And third, for this analysis households are the basic socioeconomic unit for the ACS, and individual income tax returns are the basic unit of analysis for the tax data.  In the ACS, a household includes all the people who occupy a housing unit. The individual income tax return tends to represent an individual income earning resident (and their spouse and/or dependents, if applicable). It is not exceptional that different measures of the same variable that is quantified by different organizations using different data collection processes would be slightly different. But means and median statistics from the two data sources used in this analysis differ substantially and systematically. These differences may stem from the fact that the tax data is often explained in terms of tax filers/tax returns and the ACS often explains income in terms of households.

What exactly is this data?

In the case of the District of Columbia, the ACS is designed to annually collect household data from about 4,000-5,000 city household respondents on their prevailing demographic and economic circumstances.

For the ACS household income comprises wages and salaries, military pay, commissions, tips, cash bonuses, social security payments, pensions, child support, public assistance, annuities, money derived from rental properties, interest and dividends. Earnings, as defined by the ACS, a narrower measure of income, are simply wage and salaries from employment and self-employment income.

The District of Columbia’s individual income tax data is collected and administered by the District of Columbia’s Office of Tax and Revenue. The analysis uses the Federal Adjusted Gross Income (FAGI) and the Wages, Salaries, and Tips variables from the individual income tax database as its primary measures of income variables. The FAGI of all tax filers is deemed the comparable income measure for the ACS’s “household income”. Tax data “wages, salaries, and tips” is deemed the comparable income measure for the ACS’s “earnings”.

2 thoughts on “Income statistics for the District of Columbia: ACS Income Estimates vs. DC Individual Income Tax Data

  1. This statement in the conclusion says a lot to me:

    “There may be a significant number of income earning residents that do not file tax returns or do not report all of their income.”

    For me, watching this play out over decades here in DC, that is exactly what the disjunction shown here, between self-reported earned income and tax return income, represents.

    To me, tracking who files income taxes here has great ramifications. For one, public school enrollments have been bedeviled by people claiming to live in DC when they do not. For another, in all the decades I have lived here, I have watched as people use loopholes to never claim residency here while they actually DO live here (and no, I am not talking about members of Congress, either). Both represent a burden in terms of not paying for the services that the city provides and taking up parking and other privileges that really ought to be reserved for taxpaying residents.

    To me, this issue could be solved by having one database to determine residency for school and other purposes and make it the *same* database as the one that tracks who files income taxes here. No matter how little or much you earn, everyone has to file income tax somewhere. If you don’t file income taxes here, then you are not a resident, with allowance for extenuating circumstances.

    Like

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