Depending on Bonuses- Why the news on a $1 billion payout creates dependency for bonus recipients, residential brokers, luxury retailers, policymakers, the taxman….

For many employees in the District this is an important time of the year when decisions regarding their bonuses are made.  Employees typically receive their bonuses in their paychecks during the months of December through February.

Bonus pay (also called variable compensation or incentive pay) is a significant part of overall pay, particularly for certain sectors like finance, legal and management consulting.  Proponents of bonus pay argue that it is an effective management tool to tie pay to performance and one that affords firms greater flexibility during downturns to trim pay rather than payroll. Others argue that incentive pay is the reason for rising income inequality and that it is often determined in a subjective way by a supervisor or a friendly board, rather than tied to concrete measures of performance. There is evidence to support both sides of the debate.

The focus here is on measuring this important economic indicator and why it matters to so many people.

The large sums of money that are often involved are closely watched by high-end retailers and residential brokers as leading indicators for their industries.  Decisions on whether to sell, upgrade or renovate a home, or to purchase a luxury car or other high ticket items are often tied to bonuses.

For tax revenue forecasting purposes, bonuses can be a significant, yet highly volatile, source for income taxes and other taxes (the impact on DC sales taxes depends on whether bonus recipients purchase a new vehicle or decide to spend their bonus on a winter getaway).

Here’s what the data show for bonuses in DC for the lucky 50,000 employees in finance, legal services and consulting.

Bonus and Base Pay, 2006-2014.

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Source: U.S. Census Bureau, DISTRICTMEASURED.COM

As shown above, bonus compensation in 2014 averaged about $21,000 for employees in these industries, or nearly 15 percent of their base pay.

Using a 15 percent ratio, and assuming a two-earner bonus household making $500,000, this would imply a bonus check of $75,000.

For the very highest earners, (above $ 1 million) bonus payouts are likely to take on a more complicated structure than just wage compensation and include stock options and other deferred compensation forms which have a favorable carried interest tax treatment.

To get the total bonus payout in 2014 we multiplied the average bonus for all employee, $21,000, by the total number of employees in these sectors, about 50,000, to get $1.1 billion.

We also looked to see how bonuses vary by age groups.

Bonus Payout by Age Group, 2014

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Source: U.S. Census Bureau, DISTRICTMEASURED.COM

As expected, bonus pay generally grows with work experience. On average, senior employees (ages 45+) earned about $25,000 compared to $ 18,000 for junior employees (ages 25 to 44).

Finally we looked to see how bonus payments fluctuated from year to year, an issue that is clearly of importance to bonus recipients, but is also a complicating factor for tax revenue forecasters.

Annual Growth in Bonus Pay vs. Base Pay

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Source: U.S. Census Bureau, DISTRICTMEASURED.COM

 

Base pay grew on average by 4 percent over the 2006 to 2014 period, with growth varying from a low of zero in 2012 to a high of 5.8 percent in 2008. In contrast, bonus pay fluctuated considerably ranging from -28.5 percent in 2008 to +20.0 percent in 2014.

Other considerations:

In addition to studies that have linked bonus pay to increasing income inequality, the reliance on incentive pay as a part of overall compensation also helps to explain other changes in the economy.  A fascinating study by Peter Kuhn and Fernando Lozano,  “The Expanding Workweek? Understanding Trends in Long Work Hours Among U.S. Men, 1979-2004” explains how work hours, in certain industries where pay is linked to job performance, have increased.

One possible behavioral implication of overall pay being linked so much to incentive pay is that with so much at stake bonus recipients “cannot afford” to take time off or work less.

Betty Alleyne and Bob Zuraski contributed to this post

What exactly is the data?

Data on bonuses is imputed by computing the difference in pay during bonus quarters ( Q4, Q1) compared to base pay ( Q2,Q3). As noted, wage and employment data is for the following 4-digit NAICS sectors (legal services, securities and commodity brokers and management, scientific, and technical consulting services).

A Possible Key Reason for the Disparity in Income Statistics: ACS Income Estimates vs DC Individual Income Tax Data

In the previous post on this subject it was found that 1) 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; 2) some ACS and tax data reveal considerably different trends for the same phenomena; and  3) ACS Gini coefficients for the city show very little appreciable change over the 2006 to 2012 time period whereas tax data shows a 7.6 percent decline in the coefficients during that same time period but also a subsequent 3.6 percent increase between years 2011 and 2012.

The biggest distinction between the ACS and the tax data used in this analysis, full report here, is the basic socioeconomic unit of analysis. 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, and all income earned by household residents, regardless of their relation to the homeowner, is counted as income belonging to that household. On the other hand, the individual income tax return tends to represent an individual income earning resident (and their spouse and/or dependents, if applicable).

City income measures in terms of households (ACS) versus tax data returns (OTR) appears to be the most significant explanation in explaining the substantial and systematic difference in income statistics from the two data sources. The city’s mean and median income levels using the tax data are below the comparable ACS statistics.

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In terms of the means, there was an average of 323,253 annual tax returns in the city for the years 2009-2013. But, ACS data tells us that there was an annual average of 263,649 occupied housing units in the city for the same time period. Dividing the city’s total income by the number of tax returns automatically yields a higher mean number than dividing the total income by the number of households because there were 22.6 percent more tax returns than households. In terms of the medians, the ACS tells us that the median income for households in the city was $65,830, but the tax data tells us that the median income for tax filers was $44,794.  But according to the ACS, the average household size in the city ranged between 2.11 and 2.31.  With 60.9 percent of the city’s tax filers being single residents, the tax data median of $44,794 most likely represents a single filer’s income. And indeed, the panel of tax data for this analysis reveals that there were 22 tax records (tax filers) with an exact income of $44,794 for the 2009-2013 time period, 59 percent of them were single filers, 23 percent were head of households, and 14 percent were married filers. Consequently, the ACS median household income, which represents a household with an average of slightly more than two residents (and possibly two income earners), is largely being compared to an income of an single individual tax filer.

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Given that the economic units being compared (households versus tax returns), may be a major cause for the discrepancy in the income statistics using the ACS and tax data, it is necessary to compare the overall income level of the city from the two data sources. The inflation–adjusted total income for the city for the five year period of 2009-2013 was $131.0 billion.  The comparable income amount for the same time period from the ACS was $150.8 billion. Notwithstanding the estimated $19.8 billion (13.1 percent) discrepancy between the two data sources, which may be an estimate primarily of the income earned by city residents that was not reported to tax authorities over the five year period, total income numbers for the time period under investigation from the two sources ($131 billion versus $151 billion) are fairly similar. Thus, controlling for the inability to classify income tax data by households, it appears that the tax data generally supports ACS income numbers for the District of Columbia in the aggregate.

 Differing Trends

The below table shows that while the mean income grew at an annual average rate of 3.27 percent over the study period, the tax data show mean income declined at an annual rate of 0.04 percent with the income in the final time period being less than the prior time period.  This may stem from the facts that over 60 percent of the city’s tax filers are single filers, and single filers have been the fastest growing cohort of individual income tax filers over the study period. According to the ACS, the number of occupied households grew at an annual average rate of 0.3 percent between years 2006 and 2013. According to the tax data, on the other hand, the number tax returns grew only at an annual average rate of 2.3 percent over the same time period. And more strikingly, single filers grew at an annual average rate of 2.4 percent, signifying that the number of single filers grew faster than all other tax filer types. Furthermore, 61 percent of the total growth in tax filers from 2006 to 2013 was accounted for by single filers, and there were 9,332 (4.7 percent) more single filers in 2012 than in 2011. All of this suggests that the robust net in-migration of single filers from 2006 to 2013 may have also been a major reason for the decline in average income per the tax data.

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In sum, there has been a relatively large and continuous increase in new single filers migrating to the city.  But, with single filers having a mean income 69.4 percent lower than married filers (married filers and single filers account for over 90 percent of all income), the interplay of these dynamics may be the cause of the Gini coefficient per the tax data for 2012 beginning to trend upward while the ACS comparable statistic remained practically unchanged for years 2011 and 2012. For the study period, all of this suggests that while the out-migration of residents in the District of Columbia has been notable, the in-migration of new single residents has been slightly larger helping to cause the growth in the number of households to remain relatively unchanging. These important dynamics appear to have a significant bearing on the comparison of income statistics for the city when described in terms of tax filers and households.

This analysis finds great similarity in the citywide income level statistics as described by the ACS and DC tax data.  But means and median statistics from the two data sources differ substantially and systematically likely because the tax data is often explained in terms of tax filers/tax returns and the ACS often explains income, more appropriately, in terms of households. It also appears that the city’s demographic changes, and consequent changing tax filer profile, contributed to some of the differences in the levels and trends in income statistics from the two data sources.

What exactly is this data?

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

For the ACS household income comprises wage 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 “income”, and tax data “wages, salaries, and tips” is deemed the comparable income measure for the ACS’s “earnings”.

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”.

The Rise of Home Prices in D.C.’s Central Corridor

Home prices throughout D.C. have increased tremendously since the mid-1990s, but the ascent in home prices has not been steady; in much of the city, home prices peaked in 2006 near the height of the housing boom, fell, and then started to rise again several years later. But not all neighborhoods had the same experience. The exact trajectory of home prices and the level of home prices today versus at the peak of the housing boom, varies quite a bit by neighborhood.

We found that the neighborhoods in the city’s central corridor between Rock Creek Park and the Anacostia River have seen the biggest percent increase in home prices since 2001 (the earliest year for which we have reliable data). In Trinidad, LeDroit Park, and Columbia Heights, the median sales price for a single family home has more than doubled tripled since 2001, and the median sales price in these neighborhoods is above what it was during the peak of the housing boom. The story’s different in the city’s outlying areas. In all neighborhoods east of the Anacostia River, and in some neighborhoods west of Rock Creek Park, home prices have risen from 2001 levels but have not yet recovered from the housing bust in the mid- to late-2000s. For example, in Deanwood, a neighborhood in the eastern corner of the city, the median sales price for a single family home grew 74 percent between 2001 and 2015, but the median home price in 2015 is 24 percent lower than it was in 2006. (Note that in our analysis we adjusted for inflation by converting all home prices into 2015 dollars.)

Below are some interactive graphics we created that illustrate the trajectories of home prices from 2001 to 2015 in different D.C. neighborhoods.

In the first map, below, you can see that home prices in all of D.C.’s neighborhoods have increased since 2001, but the largest increases have occurred in city’s central corridor.

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The map below shows that neighborhoods in the city’s central corridor also better weathered the housing boom and bust than neighborhoods closer to the city’s borders. The median sales price of a single family home has decreased since 2006 in all neighborhoods east of the Anacostia River, in neighborhoods along the city’s northern border, and in some neighborhoods west of Rock Creek Park.

06to15

Home prices declined after the housing boom, but in 2009 prices started rising in many D.C. neighborhoods, though not in all. The neighborhoods that saw the biggest price increases since 2009 are those to the northeast of downtown: Trinidad, Eckington, Brookland, LeDroit Park, and Petworth, as you can see in the map below.

09to15

In the interactive graph below you can see the inflation-adjusted price trajectories of the neighborhoods that have grown the fastest since 2009. Prices in Trinidad, Petworth, and Brookland show signs of slowing, though we can’t be certain of how prices will change in the future. You can use the graph below to compare the home price trajectories of neighborhoods throughout the city

(click to interact)nhood trajectories

Even though home prices in the city’s central corridor have increased the most over the past 15 years, median prices for single family homes west of Rock Creek Park still tend to be higher than in other parts of the city. You can use the map below to see how home prices in each D.C. neighborhood have changed since 2001.

(click to interact)movie

What exactly is this data?

You can download the data for this analysis here (click the download button in the bottom right corner of the Tableau graphic).

Home price data is the median sales price for single family homes from 2001 to 2015. The 2015 data includes homes sold through 11/30/2015. This data is from the D.C. Recorder of Deeds.

We adjusted for inflation and converted all home sale prices into 2015 dollars using the Bureau of Labor Statistics inflation calculator.

We did not control for house size in our analysis.

The neighborhoods we use in our analysis are the city’s assessment neighborhoods for property taxes. We got the neighborhood boundaries from D.C.’s Office of the Chief Technology Officer.

District’s labor market and workforce are intertwined with Maryland and Virginia

In 2014, nearly 774,000 workers reported working in the District of Columbia and they collectively earned $63.5 billion in wages and salaries. Of these workers, only 251,000 or 32 percent were District residents. The remainder were commuters from Virginia or Maryland, accounting for 68 percent of people employed in our city. The District’s share in total wages earned was even lower: District residents accounted for $18 billion of salaries and wages earned in the District. This is about 28 percent of all wages and salaries earned in the city.

Wage and Salary.jpg

In addition 89,000 District residents reverse-commuted to Virginia and Maryland, mostly working for private entities (76 percent including non-profits) and the federal government. This group collectively earned $6 billion in wages, compared to the $45 billion Maryland and Virginia commuters earned in the District.

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The data reveal other trends. District residents who work in the District hold a disproportionate share of the lower-paying jobs: 44 percent of jobs that pay a wage of $30K or less are held by DC residents, compared to 32 percent of all jobs in the District. Virginia residents, on the other hand, tend to hold a larger proportion of higher paying jobs: 28 percent of jobs in the District and nearly 40 percent of all jobs that pay $100K or more.

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The data also show that District residents dominate employment in the non-profit sector, one of the lowest paying sectors in the District.  Commuters from Virginia and Maryland, on the other hand, typically come to the District to work in the private sector and the federal government.

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District’s labor market and workforce are tied deeply with those of Maryland and Virginia. If salaries are any indicators, the most educated and productive residents of our neighboring jurisdictions work in the District. In 2014, District residents who worked in the District reported wage earnings of $63,700 compared to $69,400 for commuters from Maryland, and nearly $95,000 for commuters from Virginia. But even within the same sector, District resident’s wages could be low: In the non-profit sector, District residents earned, on average, $68,500 in wages—13 percent less than Maryland workers and 20 percent less than VA workers.

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Here are the data, in greater detail, for you to explore:

Resident employment and workforce.jpg

What exactly is this data?

The data is from the single-years PUMS release from American Community Survey for 2014. The analysis was done in SAS, and SAS files are available from the author.

It pays to work at a DC law firm

On average legal services employees in DC make about $166,000 a year, earning even more than their counterparts in New York and Boston – and employees at DC law firms are more likely to be married to each other than in any other state.

In this post we look at average annual pay for employees in the legal services industry in the District compared to other parts of the nation.  As the seat of the Federal Government and the Supreme Court, the importance of the legal sector in the District’s economy is not surprising, but other places like New York, Boston, Chicago, and San Francisco would seemingly offer stiff competition to the District’s white shoe firms in terms of average pay.

Here’s what the data show:

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Key Highlights

Law firm employees in the District earn more on average than their counterparts in other comparable cities and considerably more than the national average.

An employee in legal services in DC earned about $166,000 in 2014- nearly $15,000 higher than in New York and almost double the average pay for the nation as a whole.

Interestingly the pay gap between DC and the comparable cities has widened since 2001. New York had closed in on the gap at the height of the stock market boom in 2007, likely due to a larger share of M&A related work but since then the gap has widened.

Legal services employees in San Francisco and in Chicago have seen the gap widen from less than 5 percent in 2001 to over ten percent in 2014.

In all these cities average annual pay is both far higher than the overall average and has grown at a far faster rate.

For DC workers the overall average annual pay was $85,877 in 2014.

Given the high pay that workers in legal services earn, as a follow up question, we wondered how many lawyers are married to other lawyers.

DC again topped the list with 16 percent compared to a national average of about 8 percent for all States.

Considering that lawyers account for about 3 percent of all occupations in DC, this suggests a non-random matching pattern.

There is an interesting body of research on whether and why people marry within their same economic background or occupation and whether this leads to greater income inequality. If high income earners have a higher tendency to partner with similarly high paying earners then income disparity can be exacerbated compared to the opposite situation where high income earners partner with low income earners. The former is referred to as assortative mating while the later is referred to as disassortative mating.

A a study by Jeremy Greenwod, Nezih Guner, Georgi Kocharkov and Cezar Santos on assortative mating confirms, at least nationally, an increase in assortative mating and a resulting increase in income inequality. The study can be found here. A recent study by Hani Mansour and Terra McKinnish, focuses on the role that preferences and “search costs” (the notion that meeting opportunities play a substantial role) have in dating decisions. Here is a link to the study.

Lawyers Married to Other Lawyers, Percent of Overall by State

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STATE Percent Married With-in Same Occupation Number Married Within Same Occupation Margin of Error
DC 16% 3,232 1,266
Ohio 15% 1,852 1,023
West Virginia 15% 878 565
South Carolina 14% 1,624 959
Michigan 14% 3,678 1,668
Louisiana 13% 2,114 1,090
Wyoming 12% 226 377
Alabama 12% 1,351 715
Montana 12% 2,404 1,521
Iowa 11% 761 809
Maryland 11% 4,088 1,539
Florida 11% 7,676 1,746
Missouri 11% 2,208 1,366
New Jersey 11% 4,429 1,417
Indiana 10% 1,684 835
Washington 10% 2,382 1,167
Massachusetts 10% 3,951 1,446
New Mexico 10% 566 694
Virginia 9% 4,536 1,464
New York 9% 11,472 3,378
California 9% 14,812 2,972

What exactly is the data?

Data on annual pay is from the Bureau of Labor Statistics (BLS) Quarterly Census of Employment and Wages. New York=New York County/Manhattan, Boston=Suffolk County, Chicago=Cook County. Data on married couples is from the 2013 American Community Survey (ACS). The ACS is based on sample data and data are therefore estimates. Margin of errors are shown below:

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