Office of Tax Analysis

Working Paper 108

August 2016

Joint Filing by Same-Sex Couples after Windsor:

Characteristics of Married Tax Filers in 2013 and 2014

Robin Fisher, Geof Gee, and Adam Looney

The OTA Working Papers Series presents original research by the staff of the Office of Tax

Analysis. These papers are intended to generate discussion and critical comment while

informing and improving the quality of the analysis conducted by the Office. The papers are

works in progress and subject to revision. Views and opinions expressed are those of the authors

and do not necessarily represent official Treasury positions or policy. Comments are welcome,

as are suggestions for improvements, and should be directed to the authors. OTA Working

Papers may be quoted without additional permission. 
1

JOINT FILING BY SAME-SEX COUPLES AFTER WINDSOR:

CHARACTERISTICS OF MARRIED TAX FILERS IN 2013 AND 2014

August 2016

Robin Fisher, Geof Gee, and Adam Looney1

In June 2013, the Supreme Court invalidated a key provision of the 1996 Defense of Marriage 

Act (Windsor v. United States), allowing same-sex spouses to be treated as married for all 

federal tax purposes. Treasury and the Internal Revenue Service (IRS) subsequently ruled that 

same-sex spouses legally married in jurisdictions that recognize their marriages will be treated 

as married for federal tax purposes. This paper provides estimates of the population of same-sex 

tax filers in the first two years affected by the decision drawn from the population of returns filed 

and using methods developed by the Census to address measurement error in gender 

classification. In 2014, we estimate that about 0.35 percent of all joint filers were same-sex 

couples or about 183,280 couples. 

Keywords: Tax Law, Marriage, Same-Sex

JEL Codes: J1, J12, H2, K34, C13 

 

1 We thank Gary Gates, Rose Kreider, Zach Liscow, and Gui Woolston for helpful comments and conversations.

Robin Fisher: Office of Tax Analysis, U.S. Department of the Treasury, Robin.Fisher@treasury.gov. 

Geof Gee: Office of Tax Analysis, U.S. Department of the Treasury, Geoffrey.Gee@treasury.gov. 

Adam Looney: Office of Tax Analysis, U.S. Department of the Treasury, Adam.Looney@treasury.gov. 
2

Introduction

In June 2013, the Supreme Court invalidated a key provision of the 1996 Defense of 

Marriage Act (Windsor v. United States) in a case concerning whether a same-sex partner was 

eligible to claim the estate tax exemption for surviving spouses. The ruling allowed same-sex 

couples to be treated as married for all federal tax purposes, including income and gift and estate 

taxes. Treasury and the Internal Revenue Service (IRS) subsequently ruled that same-sex couples 

legally married in jurisdictions that recognize their marriages will be treated as married for 

federal tax purposes.2The 2013 ruling applied regardless of whether the couple lives in a 

jurisdiction that recognizes same-sex marriage or a jurisdiction that does not recognize same-sex 

marriage. As a result, legally-married same-sex couples generally were required to file their 2013 

federal income tax return using either married filing jointly or married filing separately filing 

status. In 2015, the Supreme Court, in Obergefell v. Hodges subsequently established the right to 

same-sex marriage in 2015 in all states, including those whose state governments had not 

permitted same-sex marriage. 

This paper provides the first estimates of the U.S. domestic population of married samesex tax filers from the first two tax years affected by the decision.3

In 2013, we estimate that 

about 0.25 percent of all joint filers were same-sex couples, or about 131,080 couples (out of 

52.6 million total joint filers). In 2014, the number of same-sex joint filers increased by 

40 percent to about 183,280 (0.35 percent of all joint filers). Same-sex joint filers are generally 

younger, higher income, less likely to claim dependent children (especially for male couples), 

and disproportionately located in metropolitan areas and costal states. Tabulations by state and 

 

2

IRS Revenue Ruling 2013-17.

3

2015 data is not yet available at time of writing.
3

finer geographic areas reveal large differences in the rate of same-sex marriage across the 

country, with the highest rates in states which had legalized same-sex marriage prior to 2013. 

These data also provide new insights into the demographics of same-sex couples that 

differ in important ways from information available from survey data. Because these estimates 

are drawn from the universe of returns filed and because most married couples file joint returns, 

these estimates also provide new and more accurate information on the distribution and 

frequency of same-sex marriage. Measuring the rate of same-sex marriage and how it changes 

over time is difficult in survey-based data because of the relatively small share of the population 

in same-sex marriages and because of serious mismeasurement problems arising from 

misclassification of gender.4 Building on methods developed by the Census to address such 

errors, these data provide greater detail on the geographic distribution of the same-sex married 

population and, in some cases, reveal substantial differences between Census- and tax-derived 

estimates. For instance, the number of same-sex filers is roughly 55 percent of the Census 

estimate of same-sex spouses. 

One source of this difference may be the influence of state policies and state tax systems 

on whether same-sex couples filed joint returns, at least in the years prior to the 2015 Obergefell

ruling. In particular, the rate of joint filing among couples—both relative to same-sex joint filers, 

and relative to Census-estimated same-sex couples—is highest in those states that both recognize 

same-sex marriage and, correspondingly, have state income tax systems that accommodated 

filing same-sex joint returns. Filing rates are generally lowest in states which barred same-sex 

 

4

For a discussion of these issues as they pertain to measurement of same-sex couples see Black, D, Gates, GJ, 

Sanders, SG, Taylor, L. The Measurement of Same-Sex Unmarried Partner Couples in the 2000 U.S. Census. 

California Center for Population Research Working Paper, 2007.; O’Connell, M, Gooding, G. Editing Unmarried 

Couples in Census Bureau Data. Housing and Household Economic Statistics Division, U.S. Bureau of the Census, 

2007.; or Kreider, R and D. Lofquist. Matching Survey Data with Administrative Records to Evaluate Reports of 

Same-sex Married Couple Households. U.S. Census, SEHSD Working Paper 2015.
4

marriage and whose income tax systems required same-sex couples to file separate state returns, 

which imposed substantial additional compliance burdens.

5

Data and Methodology

The data are tabulated from individual returns of married-filing-jointly (MFJ) taxpayers 

to which information on the gender of the primary and secondary taxpayer listed is linked from 

Social Security Administration (SSA) records. The vast majority of married tax filers (roughly 

97.5 percent) file joint tax returns. The remaining 2.5 percent of couples file married-filingseparately on different returns, and we do not examine those returns in this paper. The data were 

extracted in late 2015 for tax years 2013 and 2014. While most returns are filed and available in 

the year they are due, roughly 1 percent are filed late. Hence, a small number of returns for those 

years have not yet been filed and processed. Nevertheless, the data includes information on about 

52.5 million couples per year.

6

A central empirical challenge for providing estimates for small populations, such as the 

population of same-sex marriages, is that small measurement errors may lead to large biases. For 

example, if same-sex marriages make up roughly 0.2 percent of all filers filing joint returns, a 1-

in-1000 error in the reported gender of either spouse would lead to measured estimates of the 

same-sex filing population that was roughly double the actual rate. 

 

5 According to Henchman and Stephens (2014) in a Tax Foundation report, for tax year 2013, 22 states did not 

recognize same-sex marriage while requiring taxpayers to reference their federal return when filing state income tax. 

In 18 of those states, same-sex filers were either required to complete pro forma single federal tax returns, to 

apportion income according to single state returns, or advised to file federal returns as single. The twelve states 

requiring the additional burden of pro forma single returns were Georgia, Idaho, Indiana, Kentucky, Louisiana, 

Michigan, Nebraska, North Carolina, Oklahoma, South Carolina, Virginia, and West Virginia. Alabama, Arizona, 

Kansas, North Dakota, Ohio, and Wisconsin require apportionment. Montana’s rules were unclear. 

6 The IRS Statistics of Income Publication 1304 (2014) reported about 53.9 million joint filers in Tax Year 2013. 

The population examined in this study is slightly smaller because we exclude returns filed in 2013 for calendar years 

earlier than 2013, taxpayers whose address indicates that they live abroad (including in a U.S. Territory or on a 

military base outside of the U.S.) and a very small number of returns with missing or erroneous geographic 

information. Further, while the vast majority of 2013 and 2014 returns have been processed, a small percentage from 

2014 (about 1 percent) have yet to be processed at the time of writing.
5

While administrative records appear to have much lower classification errors than survey 

estimates, classification errors still appear to result in large biases. Indeed, an initial tabulation of 

the data showed that approximately 0.8 percent of Married Filing Jointly (MJF) returns appeared 

to be same-sex couples, roughly double the rate estimated by the Census. Moreover, the 

correlation of tax- and Census-estimated rates across geography and demographic characteristics 

was weak, which is consistent with attenuation bias from measurement error. In short, the key 

methodological challenge to estimating accurately rates of same-sex filing rates is addressing the 

very small, but economically significant, measurement error in the SSA administrative data. 

The estimates in this paper adapt Census-developed methods for reducing 

misclassification error using indices based on the gender specificity of first names. The Census 

method relies on an internally-developed name directory for each state identifying the ratio of the 

number of times each name was associated with a male respondent to the total number of times 

the name was recorded. If this index is inconsistent with the respondent-reported gender of a 

member of an apparent same-sex couple (at an index level of 95 percent or more), the gender is 

edited to match the gender indicated by the name (e.g. they are re-classified as different sex) 

(O’Connell and Feliz 2011). 

Similarly, we construct an index indicating the likelihood an individual is male (female) 

based on first name, birth year, state, and whether the individual is listed as the primary or 

secondary filer among different-sex filers.

7

The index is constructed from the 2013 and 2014 

return data of different-sex couples and the Social Security Administration’s database of names, 

which includes all first names of Social Security Card applicants that occur at least 5 times since 

 

7

In different-sex joint filers, the primary taxpayer is male in about 93 percent of cases. Errors in classification that 

result in misidentification of same-sex filers therefore disproportionately take on a specific form (primary taxpayer 

misclassified “F” instead of “M” or secondary filer misclassified as “M” instead of “F”), which can be used to 

improve the accuracy of the correction. 
6

the 1880 birth cohort. For individuals whose first name appears in the SSA name database, the 

index indicates the fraction of individuals with a given first name that are male (or female). 

(Details of the construction of the index are provided in the appendix.)

We use the index to provide an independent estimate of whether a couple is likely to be 

in a Male-Female (MF), Male-Male (MM), or Female-Female (FF) relationship. Specifically, we 

assume that an individual’s gender is reported accurately (‘validated’) if their name index is 

greater than 95 percent specific to their SSA-reported gender. We classify couples as MF, MM, 

or FF based on the validated gender. (As described below, couples for whom the index and SSAreported gender disagree or are non-informative are imputed the rate of marriage based on their 

characteristics and state of residence.)

For extremely rare names (less than 5 occurrences in the history of SSA records), or 

individuals whose name is not recorded in the tax data the name index is missing. The name 

index may be missing in the tax data because the first name is recorded only by the first initial, 

there is a typographical error in the name so it cannot be recognized as a proper name, or only 

the last name is included. In about 9.5 percent of couples either the primary or the secondary’s 

name index is missing.8

For cases where the index is available, in 85 percent of couples the information from the 

name index matches the SSA-reported gender of both individuals. (This means, for example, that 

85 percent of the time when we observe M-F in the SSA-reported gender, the name index 

indicates ‘male’ in more than 95 percent primary individuals and ‘female’ among more than 

95 percent of secondary taxpayers.) (For observed MM and FF couples, however, the 

correspondence rate is 33 percent—in two thirds of cases the name and reported gender of at 

least one individual does not match.) The name index is highly concentrated close to 1—primary

 

8

In addition, for 0.1 percent of couples, the SSA has no record of gender for one of the taxpayers.
7

filers whose name index is greater than 95 percent male are reported to be male in the SSA data 

99.65 percent of the time. Excluding couples missing one or both name indices, and those 

couples where the name index fails to confirm the SSA-reported gender leaves 77 percent of the 

original population with name-validated gender information. 

This method substantially reduces the extent of misclassification error. Intuitively, the 

likelihood of misclassification of gender in the administrative data is very small, on the order of 

1-in-1000. By construction, the likelihood that an individual’s gender does not match their name 

index is less than 5 percent (and closer to 0.4 percent, on average). As a result, the likelihood that 

an individual is both misclassified in the SSA data and according to the name index is roughly 

two orders of magnitude smaller (proportionate to the product of the two probabilities). 

Alternatively, this method can be viewed as examining the rates of same-sex marriage 

within the population of individuals with highly gender specific names, like James, John, or 

Robert (all more than 99.5 percent male) and Mary, Elizabeth, and Patricia (all more than 

99.5 percent female). In effect, we estimate rates of same sex marriage by comparing the ratio of 

James-Robert (and male-male, according to SSA) and Mary-Elizabeth (female-female) 

marriages, to the number of John-Mary and Elizabeth-James marriages. Of the 90,025 individual 

first names included in the SSA database, 89,199 names are more than 95 percent male or 

female, which means that the index includes not just Roberts and Elizabeths, but names ranging 

from Aaditya, Brazos, and Candarius to Xana, Yasmeen, and Zayne. Hence, this method 

includes individuals from a very wide range of geographic, ethnic, national, and religious naming 

conventions. 

To arrive at national estimates, and estimates by state, AGI class, age, and presence of 

children, the subsample of name-validated couples was raked to match population totals. 
8

Specifically, the data are weighted by the ratio of the population total to the name-validated 

population within cells formed by tax year, state of residence, an indicator for presence of 

children, age of primary taxpayer, and AGI income class. In effect, this method estimates the rate 

of same-sex marriage within these detailed demographic groups with the name-validated sample 

and weights the rates by the share of the population of each group to arrive at national totals. 

Under the assumption that the sample of name-validated filers is representative of the 

population within each demographic group, we believe the estimates of the relative frequency of 

same-sex marriage in this population provides an accurate estimate of the rate of filing in the 

population.9

Put another way, we have chosen a subset of the population where the classification error 

is relatively small, by testing for consistency between the name and assigned sex. If we assume 

that most of the covariance between the demographic variables and the classification error is 

explained by names, and the relationship between names and gender is consistent among the 

demographic groups, then this method should work well. These assumptions are probably not 

strictly kept, but we can still expect to reduce the classification error by reducing the component 

that varies with name. The approximation is less exact when the gender-specificity has a large 

variability by demographic variable.

Overall, the method is an attempt to find a good compromise between classification error 

and model error. We can show that, as the classification error gets small, the effect of the model 

on the expectation also gets smaller, while the effect of dropping part of the sample and of the 

simplifying assumptions become important. It’s an open question what the best value of the 

 

9 Qualitatively, the name index appears to identify misclassified couples well, in the sense that a large fraction of 

reported MM couples include apparently misclassified secondary taxpayers (and vice versa for FF couples). 

Simulations in generated data suggest that this method provides an accurate correction for misclassification under 

the assumption that misclassification in the SSA data and using the name index is independent. 
9

threshold is, though we keep the tradition established by previous authors by setting it at 0.95. In 

the appendix to this paper we present a table produced with several alternative values of the 

threshold. The estimates appear not to be very sensitive to the exact value, which is reassuring.

Nevertheless, one concern with this approach is that naming conventions may vary across 

groups because of factors like changes in naming conventions across birth cohorts, regional 

differences, or differences across among immigrant or ethnic groups. For instance, a name which 

is highly gender-specific in some parts of the country or age groups may not be in others, even 

though the name still meets the 0.95 threshold overall. The resulting reduction in classification 

error might therefore not be as great in those areas where the name index is less specific, leading 

us, for instance, to identify more individuals in those areas as misclassified even though they 

were not.

Similarly, classification error may vary by region, birth cohort, demographic, or filing 

characteristics. If that error is correlated with likelihood of being in a same-sex couple, that could 

result in bias (either up or down) toward the rate of same-sex marriage in the population less 

likely to be misclassified. In effect, our method diminishes the contribution of misclassified 

groups, which matters for the average reported to the extent the same-sex marriage rate of the 

group differs from the overall population. 

The adjusted data were then tabulated by state, 3-digit zip code, AGI class, age 

categories, and the presence of children. Totals were rounded to the nearest 5 filers and the 

number (and rate) of same-sex filers was bottom coded at “less than 10” by assigning them a 

value of 5 (and rate of 5/(number of observations)) in small-population geographic areas. 

Because the data represent population tabulations where any error arises from misclassification 

and our model-based correction, no standard errors are computed. 
10

Estimates of the population and characteristics of same-sex joint filers

In 2013, we estimate that about 0.25 percent of all joint filers were same-sex filers, or 

about 131,080 couples (out of 52.6 million joint filers). Table 1A provides estimates of the 

number and share of joint filers that are same-sex male, same-sex female, and different sex 

couples by state in 2013. According to these estimates, the proportion of same-sex couples varied 

substantially across the country, from about 3.0 percent of couples in Washington DC, 0.8

percent in Massachusetts and Vermont, and close to 0.5 percent in Delaware, California, 

Washington, Maine, New Hampshire, New York, and Connecticut to less than 0.08 percent in

North Dakota, Montana, Mississippi, Wisconsin, Kentucky, Idaho, and Arkansas. 

Table 1B provides the corresponding estimates for 2014. In almost every state, rates of 

same-sex filing appeared to increase. Rates were little changed in Alabama, Mississippi, 

Missouri, and South Dakota. Rates of same sex marriage more than doubled in Indiana, Illinois, 

Montana, Wisconsin, Idaho, Pennsylvania, Oklahoma, North Carolina, and Colorado. Between 

2013 and 2013, for the country as a whole, the number of same-sex joint filers increased by 

about 52,200, an increase of about 40 percent. 

To examine one source of differences in the rate of same-sex joint filing across states, 

figure 1 relates the proportion of same-sex filers by state to the year in which same-sex marriage 

was recognized or legalized. In general, rates of same-sex filing are highest in states that had 

legalized same-sex marriage prior to 2013 or in 2013. While rates were relatively lower in 2013 

and 2014 in states that had not legalized same-sex marriage until 2014, the percentage increase in 

filing rates between 2013 and 2014 were relatively high in those states. 
11

Tables 2A and 2b provide estimates of the share of joint filers type of couple, income 

class, age of primary taxpayer, and presence of dependents. According to Table 2B, in 2014

same-sex couples were slightly younger (based on the age of the primary taxpayer) relative to 

different-sex couples, and substantially less likely to be over age 65. While 49 percent of 

different-sex couples claimed children as dependents, only about 7 percent of male-male couples 

claimed children, and about 28 percent of female-female couples. Same-sex couples generally 

appeared to be higher income than different-sex couples. For instance, male-male couples were 

almost twice as likely to earn more than $150,000 than different sex filers and female-female 

filers somewhat more likely. The average adjusted gross income (AGI) of male-male filers was 

about $176,000, versus $124,000 for female-female couples and $113,000 for different-sex 

couples. 

These differences in income partly reflect the fact that same-sex couples are more likely 

to be of working age and, as described more below, to live in major metropolitan areas and 

coastal states where incomes (and costs of living) are high. Table 3 provides more detailed 

analysis of the economic characteristics of different-sex and same-sex filers in 2014 and 

examines the relationship between these and other factors and income. For each group of 

different-sex couples, FF couples, and MM couples, the table provides information on the 

average income and distribution of income for each group and by subsample. For instance, the 

table shows that the average AGI of different-sex couples is about $113,115 and about 

18 percent had income over $150,000. Different-sex couples with dependent were slightly higher 

income ($122,150) and only slightly more likely (20 percent) to earn more than $150,000. 
12

This pattern in which families with dependent children are higher income is also true of 

FF and MM couples, but is particularly striking for MM couples where the average income of 

couples with children is almost $275,000; more than half of MM couples with children earn 

more than $150,000. 

Geographic differences in where same-sex couples live are an important contributor to 

differences in incomes across groups. Table 3 presents two measures intended to illustrate how 

geographic differences in where same-sex couples live affect their relative economic status. The 

first measure takes the population of working age (25-55) different-sex couples and weighting 

the sample according to the geographic residence (measured by 3-digit zip code) of MM and FF 

couples. In effect, this adjustment is intended to reflect what the distribution of income of MF is 

among MF couples whose geographic residence is the same as for MM or FF couples. This 

analysis, presented as “reweighted to MM (and FF) geographic distribution,” shows that the 

average income of MF couples weighted to correspond to FF places of residence is about 

$132,360. In contrast, the average income of FF couples in the same age range is about 

$121,220. In other words, while FF couples appear to be higher income than different-sex 

couples nationwide, relative to MF couples in their local neighborhoods their income is 

somewhat lower. A similar analysis, which provides the mean income of different-sex couples 

living in each FF couples three-digit zipcode, also suggests that the income of local MF couples 

is more than $9,000 greater. 

Reweighting MF couples to approximate the geographic distribution of MM couples 

shows that the average incomes of MF couples is higher than in the nation as a whole 

($155,425), but MM couples remain much higher incomes. The average income of MM couples 

in the same age range is about $180,525. Likewise, the average income of MF couples living in 
13

the same 3-digit zipcode as MM couples is about $154,265, showing that MM couples are 

relatively higher income even relative to other couples in their own neighborhoods. 

Table 4 compares the number of same-sex joint filers to the estimated number of samesex marriages estimated in the same year (2013 or 2014) by the U.S. Census Bureau using the 

American Community Survey (ACS). The first two columns for each year provide the Census 

estimates of the number of same-sex householders and the number of same-sex spouses. The 

Third column provides the relevant estimates from Table 1 of the number of same-sex filers by 

state. The fourth column is the ratio of same-sex filers to same-sex spouses. The final column 

shows the percent change in the number of same-sex filers between 2013 and 2014.

Overall, the estimated number of same-sex filers is just over half the estimated number of 

same-sex spouses in the ACS in both 2013 and 2014. One potential source for this difference is 

measurement error and/or estimation error arising from the application of the name index in our 

sample, or sampling error or measurement error in the Census based estimates. Because the 

population of tax filers changes little from year to year, and because the methodology applied to 

the tax data is unchanged between years, there is effectively no sampling error in the tax 

estimates. In contrast, the ACS-based estimates are derived from samples and sampling error 

may be especially pronounced in the state-by-state estimates. (For instance, the variance of 

changes in same-sex marriage rates from year to year is greater in the ACS data and eight states 

are reported to have declining rates of same-sex marriage, which seems improbable in the first 

years when it became legally recognized at the federal level.) Moreover, misclassification, nonresponse, or missing information appears to occur much less frequently in the administrative 

data, suggesting that errors from imputation of marital status or gender in the ACS may be larger. 
14

Another source of difference is that not all households file tax returns. For instance, the 

2014 ACS estimate of the number of married-couple households is 56.1 million compared to the 

52.6 million married-filing jointly couples in the 2014 domestic filing population. This 

difference—about 6.7 percent—could explain part of the gap. However, non-filers tend to be 

older and lower income, which are both associated with lower rates of same-sex marriage in 

Table 2. Hence, non-filers are unlikely to account for a large share of the difference. 

Nevertheless, measurement-related errors seem unlikely to account for all of the 

differences in estimates. For instance, in several states (Alaska, Delaware, DC, Hawaii, Maine, 

New Mexico, Washington, and Oregon) the estimated populations are consistently relatively 

close, and sampling error in large states like California, Texas, or New York should be relatively 

smaller. 

An alternative explanation for relatively low rates of same-sex filing is that legal, 

administrative, or other economic barriers made it difficult for same-sex couples to file in the 

first years after Windsor. The Windsor decision occurred mid-year in 2013 and the official 

Treasury and IRS guidance was released somewhat later. Hence, considerable uncertainty 

existed regarding the legal status, filing requirements, and other tax-related issues until late in the 

year. 

In more than a dozen states, same-sex couples were prohibited from filing joint state 

returns even if they filed joint returns federally, imposing considerable uncertainty and 

compliance costs on would-be joint filers. For instance, in 2014 in 10 states taxpayers were faced 

with state tax systems that required them to file a joint state return if they filed a joint federal 

return, while simultaneously prohibiting same-sex couples from filing joint state returns. Some 

couples may have filed married-filing separate returns or continued filing separate single or 
15

head-of-household returns pending the resolution of these differences. While these states (and 

several others that did not recognize same-sex marriage) provided guidance to same-sex 

taxpayers on how to file, the procedures often involved substantial compliance burdens, such as 

providing duplicative pro forma single federal returns to accompany their state returns. 

Figure 2 provides some evidence that the rate of joint filing among same-sex spouses was 

relatively low in states that delayed legalizing same-sex marriage. The figure presents the ratio of 

joint filers to Census-estimated counts of same-sex spouses by state in 2014 (from Table 4) 

according to the year in which same-sex marriage was recognized in each state. It is clear that the 

propensity to file a joint return is lower in states where same-sex marriage is not legally 

recognized or was recognized only in 2014. 

Indeed, in 2014 the states with the lowest apparent joint filing rate among ACS-estimated 

same-sex spouses were almost uniformly those that prohibited same-sex couples from filing joint 

state returns. For example, 10 of the 11 states with the lowest rate of joint filing among ACSestimated same-sex spouses were those that prohibited same-sex couples from filing joint state 

returns (according to the Tax Foundation 2014): Mississippi, Louisiana, Alabama, Arkansas, 

Ohio, Michigan, Tennessee, Kansas, North Dakota, and Kentucky; South Carolina is the 

exception. Missouri, Georgia, and Nebraska, which also prohibited joint filing on state returns 

also fell into the bottom 20 states. 

Table 5 provides additional information on geographic differences in the rate of same-sex 

marriage and presents the range in rates among the top 100 largest commuting zones in the U.S. 

(Commuting Zones (CZs) provide a local labor market geography that covers the entire land area 

of the United States (Autor and Dorn 2013).

10) Even within the most populous labor markets in 

 

10 David Autor and David Dorn. "The Growth of Low Skill Service Jobs and the Polarization of the U.S. Labor 

Market." American Economic Review, 103(5), 1553-1597, 2013.
16

the country, the rate of same-sex marriage differs widely. In the San Francisco area, the rate is 

1.4 percent of married couples more than 22 times the rate in Brownsville, TX (0.06 percent).

Figure 3 provides an expanded illustration of the geographic distribution of same-sex 

couples by 3-digit zip code. Same-sex filers are highly concentrated in certain regions: the North 

East, Mid-Atlantic states, the West Coast, and New Mexico. In between, same-sex filers are 

concentrated in very small geographic areas, particularly urban areas of otherwise rural states, or 

cities and towns hosting colleges and universities. 

To examine some of these differences, tables 6 and 7 list the 20 3-digit zip code areas 

with the highest rates of male and female same-sex marriage among the 500 most populous 3-

digit zip code areas (those with more than about 31,000 married couples). For example, table 6

shows that more than 3 percent of married couples in downtown San Francisco are male samesex couples. The highest rates of male same-sex marriage exist in the central areas of San 

Francisco, Washington DC, New York, and in other major cities like Seattle, Boston, Atlanta, 

Chicago, Portland, and Minneapolis. While many of the same major cities also appear in table 7, 

which provides a similar analysis for female same-sex couples, relatively small cities and towns 

like Springfield, MA, Madison, WI, Santa Fe, NM, Durham, NC, Burlington, VT, and those on 

the coast of Delaware. 

Conclusion

This paper provides new, detailed statistics on the characteristics of same-sex married 

couples filing joint tax returns in 2013 and 2014 drawn from administrative data sources. The use 

of administrative data has strong advantages over survey-based measures for studying small 
17

populations like the married same-sex couples, providing more precise information regarding 

their economic and demographic characteristics, and geographic distribution.

The data show striking differences between same-sex and different-sex couples in terms 

of income, presence of children, and place of residence. While we explore some sources of 

differences and speculate as to others, many interesting and important questions related to 

employment, income, family structure, living arrangements of children, the relationship between 

family responsibilities and economic outcomes, or the role of state and federal policies fall

beyond the scope of this analysis. 
18

Appendix: 

Name Index Methodology

The name index is constructed from two sources. First, the Social Security 

Administration provides tabulations of applicants for Social Security cards by first name and 

gender dating back to 1880 by year and by state for all names with at least 5 occurrences. From 

these tabulations, we form an index of the fraction of individuals who are male for each name

first for all names and all individuals observed in the SSA name database and secondly by 

weighting the SSA names by the empirical year of birth of individuals in the tax data (to adjust 

for the fact that the gender specificity of some names may have changed over time). The index 

includes 95,025 first names. 

Second, we construct a similar index directly from tax returns using the first name of 

each taxpayer and their status as the primary or secondary filer using the population of differentsex couples in 2013 and 2015. We focus on different-sex couples when forming the index 

because misclassification of gender is less frequent. For each name we calculate the fraction of 

all filers, primary filers, and secondary filers that are male. For MF couples, on about 93 percent 

of returns the male is listed as the primary filer; in many states and among older taxpayers, the 

rate is above 97 percent. Only in certain states and among younger married couples does the 

fraction of primary taxpayers that are male fall close to 75 percent. Because of this behavior, 

misclassification errors are more likely to take a certain form with misclassifications resulting in 

FF couples most likely to occur with the primary filer and MM with the secondary file. (In 

alternative specifications, we experimented with constructing more detailed indices using 
19

information on year of birth and state of birth and found that the estimates changed little from 

these relatively marginal changes.)11

The two indices are merged by first name and any non-matched names are excluded. This 

effectively purges the tax records of erroneous or extremely unusual first names and of last 

names or abbreviations that were reported in lieu of the actual first name. The final index we 

used is the simple average of non-missing values of the SSA name index and the index derived 

from the tax data on primary (secondary) filers. Because the indices are highly correlated and 

because values for the fraction of individuals with a given name are concentrated close to zero 

and one, alternative specifications result in nearly identical results. 

Sensitivity to alternative thresholds for name index

In the primary analysis, we assume that an individual’s gender is corroborated or 

validated if their SSA-reported gender matches the gender indicated by the name among names 

with greater than 95 percent gender specificity. Appendix Table 1 presents estimates of the 

number of male and female same-sex couples and their demographic and economic 

characteristics in 2014 using alternative thresholds for the name index. In these alternatives, we 

use indexes of 0.99, 0.9, 0.75, and 0.5 to ‘validate’ the SSA gender classification, and then use 

the same raking method on those data to construct national population estimates and the 

demographic and economic characteristics of male and female same-sex couples provided in 

Table 2B. We also present estimates without any adjustment.

 

11 In other specifications, we also examined whether comparisons between observed rates of same-sex marriage pre2013 could be used to establish a baseline rate with which to compare to 2013 and 2014. However, we abandoned 

that approach because we could not reject the possibility that some same-sex couples filed joint returns prior to 

2013. 
20

Changing the threshold in the narrow range around 0.95 has little effect on the estimates, 

both because relatively few individuals fall into those ranges and because the odds of 

misclassification are small. Increasing the threshold to 0.99, however, reduces the estimated 

number of male same-sex couples by more than 10 percent because it screens out a relatively 

sizable number of couples with names just under the threshold. Reducing the threshold to 0.5 

increases the reported number of both male and female couples by about 10 percent while also 

shifting the reported characteristics of those couples toward the distribution of characteristics of 

male-female couples. For instance, the proportion with children rises substantially for male 

couples. 
21

References

Black, D., Gates, G.J., Sanders, S.G., and Taylor, L. (2007). “The Measurement of Same-Sex 

Unmarried Partner Couples in the 2000 U.S. Census.” California Center for Population Research 

Working Paper. http://papers.ccpr.ucla.edu/papers/PWP-CCPR-2007-023/PWP-CCPR-2007-

023.pdf

David Autor and David Dorn. (2013). "The Growth of Low Skill Service Jobs and the 

Polarization of the U.S. Labor Market." American Economic Review, 103(5), 1553-1597.

Henchman, J. (2015). “10 Remaining States Provide Tax Filing Guidance to Same-Sex Married 

Taxpayers.” The Tax Foundation. http://taxfoundation.org/blog/10-remaining-states-provide-taxfiling-guidance-same-sex-married-taxpayers

Henchman, J and C. Stephens (2014). “States Provide Income Tax Filing Guidance to Same-Sex 

Couples.” The Tax Foundation. http://taxfoundation.org/article/states-provide-income-tax-filingguidance-same-sex-couples

Internal Revenue Service (2013). Revenue Ruling 2013-17. https://www.irs.gov/pub/irs-drop/rr13-17.pdf

Internal Revenue Service (2013). “SOI Tax Stats —Individual Income Tax Returns Publication 

1304 (Complete Report).” https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-returnspublication-1304-complete-report

Kreider, R. and D. Lofquist. (2015). Matching Survey Data with Administrative Records to 

Evaluate Reports of Same-sex Married Couple Households. U.S. Census, SEHSD Working 

Paper. http://www.census.gov/hhes/samesex/files/Kreider-Lofquist-Working-Paper.pdf

Obergefell v. Hodges 135 S. Ct. 2071, 576 US __, 191 L. Ed. 2d 953 - Supreme Court, 2015.

O’Connell, M., Gooding, G. (2007). “Editing Unmarried Couples in Census Bureau Data.” 

Housing and Household Economic Statistics Division, U.S. Bureau of the Census. 

http://www.census.gov/population/www/documentation/twps07/twps07.pdf

O’Connell, Martin and Sarah Feliz. 2011. “Same-Sex Couple Household Statistics from the 2010 

Census.” SEHSD Working Paper, 2011-26. U.S. Census Bureau: Washington, DC.

http://www.census.gov/hhes/samesex/files/ss-report.doc

Social Security Administration (2015). “Beyond the Top 1000 Names.” National data. 

https://www.ssa.gov/oact/babynames/names.zip

United States Census Bureau, American Community Survey (2013 and 2014). “Characteristics 

of Same-Sex Couple Households.” Tables 2 and 3. 

http://www.census.gov/hhes/samesex/data/acs.html

United States v. Windsor 133 S. Ct. 2675, 570 US 12, 186 L. Ed. 2d 808 - Supreme Court, 2013.
22

Table 1A: Same-Sex Couple Households by State 2013

Married Married Married Married Married Married Married Married

different-sex same-sex male-male female-female different-sex

samesex

male-male female-female

couples couples couples couples couples couples couples couples

Area (number) (number) (number) (number) (percent) (percent) (percent) (percent)

United States 52,515,785 131,080 61,735 69,325 99.75% 0.25% 0.12% 0.13%

Alabama 758,680 595 285 310 99.92% 0.08% 0.04% 0.04%

Alaska 125,810 235 65 170 99.81% 0.19% 0.05% 0.13%

Arizona 1,028,985 2,005 995 1,010 99.81% 0.19% 0.10% 0.10%

Arkansas 488,510 365 145 220 99.93% 0.07% 0.03% 0.04%

California 6,021,905 32,785 17,155 15,630 99.46% 0.54% 0.28% 0.26%

Colorado 960,465 1,575 675 900 99.84% 0.16% 0.07% 0.09%

Connecticut 619,790 2,775 1,125 1,650 99.55% 0.45% 0.18% 0.27%

Delaware 153,565 860 385 475 99.44% 0.56% 0.25% 0.31%

District of Columbia 51,365 1,525 1,140 385 97.12% 2.89% 2.16% 0.73%

Florida 3,024,480 6,645 3,730 2,915 99.78% 0.22% 0.12% 0.10%

Georgia 1,482,640 2,110 1,055 1,055 99.86% 0.14% 0.07% 0.07%

Hawaii 243,985 660 335 325 99.73% 0.27% 0.14% 0.13%

Idaho 316,550 240 85 150 99.92% 0.08% 0.03% 0.05%

Illinois 2,157,390 2,430 1,225 1,205 99.89% 0.11% 0.06% 0.06%

Indiana 1,181,390 955 420 535 99.92% 0.08% 0.04% 0.05%

Iowa 603,885 1,500 525 975 99.75% 0.25% 0.09% 0.16%

Kansas 548,235 460 160 300 99.92% 0.08% 0.03% 0.05%

Kentucky 758,340 560 245 310 99.93% 0.07% 0.03% 0.04%

Louisiana 646,885 545 275 265 99.92% 0.08% 0.04% 0.04%

Maine 250,505 1,260 410 850 99.50% 0.50% 0.16% 0.34%

Maryland 951,385 3,780 1,535 2,245 99.60% 0.40% 0.16% 0.24%

Massachusetts 1,138,195 8,970 3,320 5,650 99.22% 0.78% 0.29% 0.49%

Michigan 1,747,610 1,370 660 710 99.92% 0.08% 0.04% 0.04%

Minnesota 1,044,115 2,735 1,095 1,640 99.74% 0.26% 0.10% 0.16%

Mississippi 405,020 265 110 150 99.93% 0.06% 0.03% 0.04%

Missouri 1,063,415 2,115 805 1,310 99.80% 0.20% 0.08% 0.12%

Montana 196,390 110 30 80 99.94% 0.05% 0.01% 0.04%

Nebraska 359,655 325 105 215 99.91% 0.09% 0.03% 0.06%
23

Nevada 421,390 1,355 890 465 99.68% 0.32% 0.21% 0.11%

New Hampshire 265,475 1,255 360 895 99.53% 0.47% 0.13% 0.34%

New Jersey 1,536,950 3,485 1,655 1,830 99.77% 0.23% 0.11% 0.12%

New Mexico 312,220 1,265 450 810 99.60% 0.40% 0.14% 0.26%

New York 2,907,775 13,385 6,790 6,595 99.54% 0.46% 0.23% 0.23%

North Carolina 1,624,020 1,990 770 1,220 99.88% 0.12% 0.05% 0.08%

North Dakota 141,850 75 30 45 99.95% 0.05% 0.02% 0.03%

Ohio 1,885,655 1,940 960 980 99.90% 0.10% 0.05% 0.05%

Oklahoma 658,430 615 275 345 99.91% 0.09% 0.04% 0.05%

Oregon 705,660 2,655 1,255 1,400 99.63% 0.37% 0.18% 0.20%

Pennsylvania 2,284,255 2,590 1,095 1,495 99.89% 0.11% 0.05% 0.07%

Rhode Island 169,050 700 300 400 99.59% 0.41% 0.18% 0.24%

South Carolina 756,965 590 280 310 99.92% 0.08% 0.04% 0.04%

South Dakota 165,745 130 50 80 99.92% 0.08% 0.03% 0.05%

Tennessee 1,096,925 990 440 550 99.91% 0.09% 0.04% 0.05%

Texas 4,260,385 5,910 2,940 2,970 99.86% 0.14% 0.07% 0.07%

Utah 548,890 1,070 470 600 99.81% 0.19% 0.09% 0.11%

Vermont 121,060 930 305 630 99.24% 0.76% 0.25% 0.51%

Virginia 1,463,895 2,195 940 1,255 99.85% 0.15% 0.06% 0.09%

Washington 1,305,495 6,985 2,905 4,080 99.47% 0.53% 0.22% 0.31%

West Virginia 333,905 260 100 160 99.92% 0.08% 0.03% 0.05%

Wisconsin 1,106,395 810 310 500 99.93% 0.07% 0.03% 0.05%

Wyoming 114,250 145 70 75 99.87% 0.12% 0.06% 0.06%

Office of Tax Analysis 2016
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Table 1B: Same-Sex Couple Households by State 2014

Married Married Married Married Married Married Married Married

different-sex same-sex male-male female-female different-sex

samesex male-male female-female

couples couples couples couples couples couples couples couples

Area (number) (number) (number) (number) (percent) (percent) (percent) (percent)

United States 52,444,330 183,280 82,765 100,535 99.65% 0.35% 0.16% 0.19%

Alabama 751,610 620 250 370 99.92% 0.08% 0.03% 0.05%

Alaska 125,920 355 100 255 99.72% 0.28% 0.08% 0.20%

Arizona 1,026,185 3,775 1,640 2,135 99.63% 0.37% 0.16% 0.21%

Arkansas 483,495 560 210 350 99.88% 0.12% 0.04% 0.07%

California 6,033,840 41,305 21,430 19,875 99.32% 0.68% 0.35% 0.33%

Colorado 965,690 3,170 1,160 2,010 99.67% 0.33% 0.12% 0.21%

Connecticut 614,990 3,160 1,245 1,915 99.49% 0.51% 0.20% 0.31%

Delaware 153,345 1,100 475 625 99.29% 0.71% 0.31% 0.40%

District of Columbia 52,425 1,985 1,485 500 96.35% 3.65% 2.73% 0.92%

Florida 3,040,635 8,835 5,055 3,780 99.71% 0.29% 0.17% 0.12%

Georgia 1,478,555 2,900 1,420 1,480 99.80% 0.20% 0.10% 0.10%

Hawaii 244,795 1,140 590 555 99.54% 0.46% 0.24% 0.23%

Idaho 317,765 545 160 385 99.83% 0.17% 0.05% 0.12%

Illinois 2,146,900 6,705 3,205 3,505 99.69% 0.31% 0.15% 0.16%

Indiana 1,174,570 2,660 975 1,690 99.77% 0.23% 0.08% 0.14%

Iowa 601,220 1,700 560 1,140 99.72% 0.28% 0.09% 0.19%

Kansas 545,525 750 250 495 99.86% 0.14% 0.05% 0.09%

Kentucky 751,500 950 380 570 99.87% 0.13% 0.05% 0.08%

Louisiana 643,435 655 270 385 99.90% 0.10% 0.04% 0.06%

Maine 247,740 1,575 490 1,085 99.37% 0.63% 0.20% 0.43%

Maryland 946,400 4,850 1,940 2,910 99.49% 0.51% 0.20% 0.31%

Massachusetts 1,136,885 10,235 3,860 6,375 99.11% 0.89% 0.34% 0.56%

Michigan 1,735,280 1,730 660 1,070 99.90% 0.10% 0.04% 0.06%

Minnesota 1,043,940 4,000 1,550 2,450 99.62% 0.38% 0.15% 0.23%

Mississippi 400,860 255 85 170 99.94% 0.06% 0.02% 0.04%

Missouri 1,055,195 1,910 715 1,195 99.82% 0.18% 0.07% 0.11%

Montana 195,915 245 55 190 99.88% 0.13% 0.03% 0.10%

Nebraska 359,335 430 140 290 99.88% 0.12% 0.04% 0.08%

Nevada 421,805 1,610 845 765 99.62% 0.38% 0.20% 0.18%
25

New Hampshire 264,110 1,515 455 1,065 99.43% 0.57% 0.17% 0.40%

New Jersey 1,532,115 5,195 2,330 2,865 99.66% 0.34% 0.15% 0.19%

New Mexico 308,825 1,795 615 1,185 99.42% 0.58% 0.20% 0.38%

New York 2,905,445 16,810 8,745 8,065 99.42% 0.58% 0.30% 0.28%

North Carolina 1,624,160 4,290 1,575 2,715 99.74% 0.26% 0.10% 0.17%

North Dakota 143,475 115 35 80 99.92% 0.08% 0.02% 0.05%

Ohio 1,872,470 2,245 910 1,335 99.88% 0.12% 0.05% 0.07%

Oklahoma 653,475 1,290 445 845 99.80% 0.20% 0.07% 0.13%

Oregon 704,990 3,775 1,265 2,505 99.47% 0.53% 0.18% 0.35%

Pennsylvania 2,270,380 5,945 2,505 3,440 99.74% 0.26% 0.11% 0.15%

Rhode Island 167,590 950 385 565 99.44% 0.56% 0.23% 0.34%

South Carolina 756,170 1,110 425 685 99.85% 0.15% 0.06% 0.09%

South Dakota 165,950 130 40 90 99.92% 0.08% 0.03% 0.05%

Tennessee 1,093,805 1,435 600 835 99.87% 0.13% 0.05% 0.08%

Texas 4,291,525 8,040 3,755 4,285 99.81% 0.19% 0.09% 0.10%

Utah 554,065 1,520 610 910 99.73% 0.27% 0.11% 0.16%

Vermont 119,965 1,080 360 720 99.11% 0.89% 0.30% 0.59%

Virginia 1,463,835 4,020 1,630 2,390 99.73% 0.27% 0.11% 0.16%

Washington 1,310,855 9,635 3,960 5,675 99.27% 0.73% 0.30% 0.43%

West Virginia 329,085 500 155 345 99.85% 0.15% 0.05% 0.10%

Wisconsin 1,102,170 2,015 705 1,310 99.82% 0.18% 0.06% 0.12%

Wyoming 114,115 160 60 105 99.86% 0.14% 0.05% 0.09%

Office of Tax Analysis 2016
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Table 2A. Characteristics of Couples Filing Married-Filing-Jointly 2013

(In percent.)

Married Married Married Married

different-sex same-sex male-male female-female

couples couples couples couples

Household Characteristics Percent Percent Percent Percent

Total households (number) 52,515,785 131,080 61,735 69,325

Age of householder

 15 to 24 years 1% 2% 2% 3%

 25 to 34 years 13% 16% 13% 18%

 35 to 44 years 20% 22% 20% 23%

 45 to 54 years 22% 29% 32% 27%

 55 to 64 years 21% 21% 22% 20%

 65 years and over 22% 10% 12% 9%

 Average age of primary taxpayer (years) 51.9 47.9 49.3 46.7

 Average age of secondary taxpayer (years) 49.9 47.1 47.8 46.5

Children in the household 49% 17% 7% 26%

Household Adjusted Gross Income

 Less than $35,000 21% 12% 10% 13%

 $35,000 to $49,999 11% 7% 6% 7%

 $50,000 to $74,999 17% 13% 12% 14%

 $75,000 to $99,999 16% 16% 15% 17%

 $100,000 to $150,000 18% 24% 23% 25%

 $150,000 or more 17% 28% 33% 24%

Average AGI (dollars) 107,970 143,970 165,540 124,760 

Office of Tax Analysis 2016
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Table 2B. Characteristics of Couples Filing Married-Filing-Jointly 2014

(In percent.)

Married Married Married Married

different-sex same-sex male-male female-female

couples couples couples couples

Household Characteristics Percent Percent Percent Percent

Total households (number) 52,444,330 183,280 82,765 100,535

Age of householder

 15 to 24 years 1% 2% 1% 3%

 25 to 34 years 13% 17% 13% 20%

 35 to 44 years 19% 21% 20% 23%

 45 to 54 years 22% 28% 31% 26%

 55 to 64 years 22% 21% 22% 19%

 65 years and over 22% 10% 12% 8%

 Average age of primary taxpayer (years) 52.1 47.5 49.5 45.8

 Average age of secondary taxpayer (years) 50.2 46.5 47.6 45.5

Children in the household 49% 18% 7% 28%

Household Adjusted Gross Income

 Less than $35,000 21% 13% 12% 14%

 $35,000 to $49,999 10% 7% 6% 8%

 $50,000 to $74,999 17% 14% 12% 15%

 $75,000 to $99,999 16% 15% 13% 16%

 $100,000 to $150,000 19% 22% 21% 23%

 $150,000 or more 18% 29% 35% 24%

Average AGI (dollars) 113,115 147,290 175,590 123,995 

Median AGI (dollars) 77,790 102,020 113,465 94,570 

Office of Tax Analysis 2016
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Table 3. Economic Characteristics of Couples Filing Married-Filing-Jointly 2014

Average 

AGI

 Less 

than 

$35,000

 $35,000 

to 

$49,999

 $50,000 

to 

$74,999

 $75,000 

to 

$99,999

 $100,000 

to 

$150,000

 

$150,000 

or more

Household Characteristics Dollars Percent Percent Percent Percent Percent Percent

All married joint filers

 

113,235 21% 10% 17% 16% 19% 18%

Different-sex couples 113,115 21% 10% 17% 16% 19% 18%

 without dependent children 122,150 18% 10% 17% 15% 19% 20%

 with dependent children 104,475 23% 10% 17% 16% 18% 15%

 primary taxpayer age 25-55 116,845 17% 11% 18% 17% 20% 18%

 Reweighted to FF geographic distribution 132,360 16% 10% 16% 15% 20% 22%

 Reweighted to MM geographic distribution 155,425 17% 10% 15% 14% 19% 26%

Female same-sex couples 123,995 14% 8% 15% 16% 23% 24%

 with dependent children 130,865 17% 8% 14% 14% 22% 25%

 without dependent children 121,360 13% 8% 15% 16% 24% 23%

 primary taxpayer age 25-55 121,220 14% 9% 16% 16% 23% 22%

 mean different-sex income in own zip-3 130,620 

Male same-sex couples 175,590 12% 6% 12% 13% 21% 35%

 with dependent children 274,855 8% 4% 8% 10% 19% 52%

 without dependent children 168,025 12% 7% 13% 13% 21% 34%

 primary taxpayer age 25-55 180,525 10% 6% 12% 13% 21% 37%

 mean different-sex income in own zip-3 154,265 

Office of Tax Analysis 2016
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Table 4: Comparison of Same-Sex Data by State 2013 and 2014

2013 2014 Change

Census Tax Ratio Census Tax Ratio

Same-Sex 

Couples

Same-Sex 

Spouses

Same-Sex 

Filers Tax/Census Same-Sex Couples

Same-Sex 

Spouses

Same-Sex 

Filers Tax/Census

Tax 

2014/ 

Tax 2013

United States 726,600 251,695 131,080 52% 783,100 334,854 183,280 55% 40%

Alabama 7,157 2,907 595 20% 6,797 2,936 620 21% 4%

Alaska 982 172 235 137% 1,816 497 355 71% 51%

Arizona 17,352 3,862 2,005 52% 17,515 5,935 3,775 64% 88%

Arkansas 4,928 1,026 365 36% 5,399 2,461 560 23% 53%

California 107,991 40,334 32,785 81% 109,296 53,285 41,305 78% 26%

Colorado 15,276 3,681 1,575 43% 15,402 5,581 3,170 57% 101%

Connecticut 8,859 4,732 2,775 59% 9,701 5,908 3,160 53% 14%

Delaware 2,690 859 860 100% 3,850 2,012 1,100 55% 28%

District of Columbia 5,776 1,646 1,525 93% 5,224 2,387 1,985 83% 30%

Florida 47,973 13,402 6,645 50% 55,372 18,365 8,835 48% 33%

Georgia 20,780 5,976 2,110 35% 24,707 7,225 2,900 40% 37%

Hawaii 3,128 669 660 99% 3,831 1,771 1,140 64% 73%

Idaho 1,757 811 240 30% 2,599 732 545 74% 127%

Illinois 26,003 10,627 2,430 23% 29,115 12,849 6,705 52% 176%

Indiana 12,722 3,604 955 26% 15,431 5,687 2,660 47% 179%

Iowa 4,971 2,620 1,500 57% 7,080 4,653 1,700 37% 13%

Kansas 4,270 1,547 460 30% 5,674 2,541 750 30% 63%

Kentucky 9,827 3,233 560 17% 8,310 2,839 950 33% 70%

Louisiana 7,320 2,291 545 24% 8,906 3,184 655 21% 20%

Maine 4,805 1,665 1,260 76% 5,442 2,477 1,575 64% 25%

Maryland 15,732 7,589 3,780 50% 14,977 8,515 4,850 57% 28%

Massachusetts 24,182 14,267 8,970 63% 24,461 13,817 10,235 74% 14%
30

Michigan 18,821 5,513 1,370 25% 18,742 6,514 1,730 27% 26%

Minnesota 13,922 4,647 2,735 59% 14,539 7,562 4,000 53% 46%

Mississippi 3,796 1,869 265 14% 3,628 1,599 255 16% -4%

Missouri 13,403 4,182 2,115 51% 12,371 4,930 1,910 39% -10%

Montana 1,126 538 110 20% 1,235 540 245 45% 123%

Nebraska 3,331 1,375 325 24% 3,349 960 430 45% 32%

Nevada 6,906 2,120 1,355 64% 7,365 2,268 1,610 71% 19%

New Hampshire 4,201 2,417 1,255 52% 4,166 2,538 1,515 60% 21%

New Jersey 20,043 6,518 3,485 53% 20,061 9,600 5,195 54% 49%

New Mexico 5,180 1,159 1,265 109% 6,838 2,415 1,795 74% 42%

New York 54,515 24,802 13,385 54% 59,405 29,248 16,810 57% 26%

North Carolina 19,327 6,469 1,990 31% 23,127 8,316 4,290 52% 116%

North Dakota 1,137 467 75 16% 774 349 115 33% 53%

Ohio 23,894 5,839 1,940 33% 26,021 8,973 2,245 25% 16%

Oklahoma 5,228 2,051 615 30% 7,283 3,508 1,290 37% 110%

Oregon 11,903 2,493 2,655 106% 13,380 6,150 3,775 61% 42%

Pennsylvania 26,294 7,679 2,590 34% 28,654 11,675 5,945 51% 130%

Rhode Island 4,481 1,484 700 47% 2,917 1,088 950 87% 36%

South Carolina 8,347 2,583 590 23% 9,908 4,160 1,110 27% 88%

South Dakota 1,183 643 130 20% 1,094 332 130 39% 0%

Tennessee 11,255 2,661 990 37% 13,140 4,864 1,435 30% 45%

Texas 52,150 15,007 5,910 39% 58,654 20,280 8,040 40% 36%

Utah 5,331 2,001 1,070 53% 5,099 3,355 1,520 45% 42%

Vermont 2,881 1,412 930 66% 2,433 1,625 1,080 66% 16%

Virginia 16,328 5,167 2,195 42% 19,027 7,778 4,020 52% 83%

Washington 20,483 7,538 6,985 93% 22,981 12,529 9,635 77% 38%

West Virginia 3,164 943 260 28% 2,353 1,004 500 50% 92%

Wisconsin 12,306 3,963 810 20% 12,706 4,640 2,015 43% 149%

Wyoming 1183 635 145 23% 945 372 160 43% 10%

Office of Tax Analysis 2016
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Table 5: Top 10 and Bottom 10 among the 100 Largest Commuting Zones

Rate of Same-Sex Couples in Married Filing Jointly Returns 2014

Rank Commuting Zone

Fraction 

Same-Sex Rank Commuting Zone

Fraction 

Same-Sex

1 San Francisco, CA 1.36% 91 Detroit, MI 0.12%

2 Santa Rosa, CA 1.08% 92 Greenville, SC 0.11%

3 Seattle, WA 0.95% 93 Dayton, OH 0.11%

4 Boston, MA 0.93% 94 Johnson City, TN 0.10%

5 San Diego, CA 0.74% 95 Huntsville, AL 0.09%

6 Albuquerque, NM 0.73% 96 Baton Rouge, LA 0.09%

7 Portland, OR 0.73% 97 Toledo, OH 0.09%

8 New York, NY 0.70% 98 Grand Rapids, MI 0.09%

9 Portland, ME 0.69% 99 Youngstown, OH 0.07%

10 Washington, DC 0.65% 100 Brownsville, TX 0.06%

Office of Tax Analysis 2016
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Table 6: Top 20 among the 500 Largest 3-Digit Zip Codes

Rate of Male Same-Sex Couples Filing Joint Returns 2014

Rank 3-Digit Zip Code

Fraction 

Same-Sex 

Male Rank 3-Digit Zip Code

Fraction 

Same-Sex 

Male

1 San Francisco, CA 941 3.2% 11 Jersey City, NJ 073 0.79%

2 Washington, DC 200 2.7% 12 San Diego, CA 921 0.75%

3 New York, NY 100 2.4% 13 Atlanta, GA 303 0.75%

4 California 922 1.7% 14 Van Nuys, CA 914 0.70%

5 Seattle, WA 981 1.4% 15 Chicago, IL 606 0.68%

6 Oakland, CA 946 1.4% 16 Arlington, VA 222 0.61%

7 Los Angeles, CA 900 1.1% 17 Sacramento, CA 958 0.58%

8 Long Beach, CA 908 1.0% 18 Portland, OR 972 0.54%

9 Ft. Lauderdale, FL 333 1.0% 19 Minneapolis, MN 554 0.53%

10 Boston, MA 021 1.0% 20 North Bay, CA 954 0.51%

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Table 7: Top 20 among the 500 Largest 3-Digit Zip Codes

Rate of Female Same-Sex Couples Filing Joint Returns 2014

Rank 3-Digit Zip Code

Fraction 

Same-Sex 

Female Rank 3-Digit Zip Code

Fraction 

Same-Sex 

Female

1 Oakland, CA 946 2.1% 11 Sacramento, CA 958 0.76%

2 Seattle, WA 981 1.3% 12 Silver Spring, MD 209 0.74%

3 San Francisco, CA 941 1.1% 13 Santa Fe, NM 875 0.72%

4 Springfield, MA 010 1.1% 14 North Bay, CA 954 0.69%

5 Long Beach, CA 908 1.0% 15 Durham, NC 277 0.68%

6 Washington, DC 200 0.9% 16 Burlington, VT 054 0.66%

7 Boston, MA 021 0.9% 17 Tacoma, WA 984 0.65%

8 Portland, OR 972 0.9% 18 Minneapolis, MN 554 0.64%

9 Boston, MA 024 0.8% 19 New York, NY 100 0.64%

10 Madison, WI 537 0.8% 20 Delaware, 199 0.56%

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Appendix Table 1. Estimates using alternative name-index thresholds

(In percent.)

Index Threshold No Adjustment 0.5 0.75 0.9 0.95 0.99

M-M F-F M-M F-F M-M F-F M-M F-F M-M F-F M-M F-F

couples couples couples couples couples couples couples couples couples couples couples couples

Household Characteristics Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent

Total Filers (number) 226,496 256,240 92,160 110,298 85,800 104,211 84,183 101,203 82,763 100,532 73,173 98,241

Age of householder

 15 to 24 years 2% 3% 2% 3% 1% 3% 1% 3% 1% 3% 1% 4%

 25 to 34 years 17% 22% 14% 20% 14% 21% 13% 21% 13% 20% 13% 21%

 35 to 44 years 25% 27% 21% 23% 20% 23% 20% 23% 20% 23% 20% 23%

 45 to 54 years 28% 25% 30% 26% 31% 26% 31% 26% 31% 26% 31% 26%

 55 to 64 years 20% 17% 22% 19% 22% 19% 22% 19% 22% 19% 23% 19%

 65 years and over 9% 7% 12% 8% 12% 8% 12% 8% 12% 8% 12% 7%

 Age of primary (years) 47.3 44.9 49.0 45.7 49.3 45.8 49.4 45.8 49.5 45.8 49.5 45.4

 Age of secondary 

(years) 45.0 43.7 46.9 45.3 47.3 45.4 47.5 45.5 47.6 45.5 47.9 45.2

Children in the household 44% 50% 12% 30% 8% 28% 7% 28% 7% 28% 7% 27%

Adjusted Gross Income

 Less than $35,000 20% 18% 14% 15% 12% 14% 12% 14% 12% 14% 11% 15%

 $35,000 to $49,999 11% 10% 7% 8% 7% 8% 6% 8% 6% 8% 6% 8%

 $50,000 to $74,999 16% 17% 13% 15% 12% 15% 12% 15% 12% 15% 12% 15%

 $75,000 to $99,999 14% 16% 13% 16% 13% 16% 13% 16% 13% 16% 13% 16%

 $100,000 to $150,000 17% 20% 20% 23% 21% 23% 21% 23% 21% 23% 21% 23%

 $150,000 or more 22% 18% 33% 23% 35% 23% 35% 24% 35% 24% 36% 23%

Average AGI (dollars)

 

127,080 

 

112,391 

 

166,580 

 

123,412 

 

172,826 

 

123,828 

 

174,382 

 

124,096 

 

175,590 

 

123,993 

 

178,323 

 

122,479 

Office of Tax Analysis 2016
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0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

Figure 1: Same-Sex Filers as a Percent of Joint Filers 

by State and Year of Same-sex Marriage Legalization

2013 2014

Legalized before 2013

2013

2014

2015
36

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Figure 2: Same-Sex Filers as a percent of Census-estimated Same-sex Spouses 2014

by State and Year Same-Sex Marriage Legalized

Legalized 

before 2013

2013

2014

2015
37 

Figure 3: Rate of Same-Sex Filing by Zip-3 (2014) 

(.0095,.0386](.0037,.0095](.002,.0037](.0011,.002](.0004,.0011][0,.0004]