Gridlock: The Impact of Income on Commutes to Work
December 2022
In this paper, I examine the relationship between incomes and commute times
to work in the United States. Using data from the 2018 American Community
Survey, an increase in wages by 1% led to an increase in expected commutes
by 2.26 minutes. These results imply that wealthier individuals live further
away from their place of work, likely in nonmetropolitan areas.
In 2021, Americans spent 91 billion hours on the road (American Driving Survey, 2022).
A steady increase since 2015, commute times to work were also at an all-time high. With traffic
being more prevalent than ever in today’s world, this paper examines whether income affects the
length of an individual’s commute to work.
Some studies have already attempted to convey the relationship between commuting and
wages. However, few have collected data from the United States, and of those studies, many
failed to account for the geographical effects of commuting behaviors. Urbanization factors and
traffic conditions are much more likely to affect commuters in metropolitan cities than in rural
areas. Past studies have either neglected to acknowledge these differences or confined their
sample to a few large cities.
My findings contribute relevant results on commute times while considering the effects
of location and income. I used cross-sectional data from the 2018 American Community Survey
(ACS) to examine a large sample of individuals in the labor force between the ages of 18 and 65
that reported their wages and commute times. The ACS divides respondents into sectors called
Public Use Microdata Area (PUMA) codes. I used a PUMA-code fixed effect and multiple
regression, controlling for race, gender, marital status, age, class of worker, and education. The
need for a fixed effect comes from the idea that the US is geographically-diverse, and
commuting terrains and behaviors vary within each sector across the country. Further,
controlling for these demographic variables correlated with individuals’ wages will isolate the
effect of income on commute length.
Because the income distribution was heavily skewed, I employed a logarithmic
transformation to explain how wages affect commutes. The estimates of my regression suggest
that a 1% increase in income is associated with an expected 2.26–minute increase in an
individual’s commute to work. Given that most of the US population lives in urbanized areas
(US Census, 2010), these results imply that wealthier individuals choose to live further away
from their jobs and in more suburban and rural locations. Conversely, lower-class households
likely live within large cities. In a country with one of the highest median incomes and vehicles
per capita, my findings provide insights into housing demographics that could help improve
public transit systems and traffic reduction policies.
The remainder of the paper is structured as follows: Section I provides context for the
paper and the economic theory of change. Section II details the data and provides the economic
specification. Section III describes the empirical analysis. Section IV concludes the paper.
I. Context, Theory of Change
I examine this relationship in response to the growing number of individuals beginning to
commute to work post-pandemic and the historically high traffic density in large cities across the
country. In a given area, the traffic conditions are likely the same for each commuter. Thus, the
main difference in commute times is how far individuals live from their jobs. Many factors can
dictate where one lives, but one of the main housing preferences for those in the labor force is
proximity to work. Most individuals prefer to live near their job or search for employment close
to where they live. Thus, when considering two jobs of different commute lengths, most would
have to be compensated with higher incomes if it meant having a longer commute. One’s
willingness to travel further to work is greater when wages are higher. Therefore, higher wages
can reasonably be associated with longer commutes.
Socioeconomic status may also play a role in housing locations. Lower-class individuals
have fewer affordable living options, which often results in purchasing smaller housing
commonly found in urban areas, such as condos or apartments. Wealthier individuals likely
prefer more rural areas with larger and more secluded homes. In a country where most jobs are in
larger cities, I hypothesize that higher incomes are associated with longer commutes to work
based on the income effect on commuting and housing by socioeconomic factors.
We can group related literature on income and commutes into two strategies. French et al.
(2020) and Dargay and Van Ommeren (2005) test this relationship while controlling for
demographic characteristics that affect income. French et al. (2020) use a multiple regression
that includes race, age, marital status, education, and household occupancy as explanatory
variables. However, their study aims to find the effect of commuting times on earnings, the
opposite of this paper. Their data is also extremely limited in that all the respondents own cars
and are similar in age, an unrealistic representation of the US population. Dargay and Van
Ommeren (2005) use 11 years of panel data to employ a fixed effects regression to control for
gender, race, and education, though their data comes from the British Panel Housing Survey.
They also failed to acknowledge the potential effects of geography on commute times, which is
not controlled for by their fixed effect. This paper improves on the works of French et al. (2020)
and Dargay and Van Ommeren (2005) because I account for the effects of geographical
commuting conditions in addition to using a dataset that is representative and applicable to the
actual US population. It is worth noting that both studies similarly found a positive relationship
between incomes and commutes, though their coefficient estimates are less than mine.
Bogomolov et al. (2019) and Johnston (2019) convey the relationship between income
and commutes based on the effect of living in metropolitan and micropolitan areas. Bogomolov
et al. (2019) used a gravity model to measure this relationship in 12 highly populated US cities.
Their findings failed to provide any information on commuting occurring outside urban areas or
even identify a pattern that applies to all large cities. Moreover, they group incomes into three
large ranges, reducing the precision of their results. Johnston (2019) uses the 2012–2016 ACS 5year estimates to regress mean commute times as a function of income and population density in
the respondent’s city. I conduct a similar study with more updated data while also considering
the demographic effects on income that could also explain commute lengths. This paper
combines the efforts from these income and geography–related strategies by using both as
controls to explain wages and commute times.
II. Data and Economic Specification
A. Data
The data used in this paper comes from the American Community Survey, an annual
survey conducted by the US Census Bureau covering a wide range of social, economic,
occupational, and demographic variables. Specifically, I used the 2018 cross-sectional data from
the ACS that collected information from 3.3 million households. The ACS selects a random
sample of addresses representative of the geographical population. The dataset contains all the
necessary variables relating to income and commute times.
From the data, the average commute time to work is 27.29 minutes. The adjusted mean
income of the respondents is $52,481 (see table I). The income distribution has a wide range and
is heavily right-skewed (see figure II). Thus, after conducting a skew test, I determined it would
be appropriate to use a logarithmic transformation for income to explain their effect on commute
times. My estimations based on this data may present potential issues in that survey respondents
likely round their income and commute times to even multiples. Rounding is likely why the
commuting distribution appears to be multimodal (see figure III). Thus, the precision of my
estimates may be affected because most respondents do not know or do not report their exact
values for income and commutes.
Many demographic variables in the data exhibit mean wages that differ among each
subgroup (see table IV for values). The average income for males is $20,735 more than for
females, a reflection of the gender pay gap in the United States. The mean wages for White and
Asian individuals are higher than the averages for Blacks and Hispanics, and the average income
for married individuals is $30,096 more than for unmarried individuals. One’s class of work also
appears to influence mean wages. Individuals who work for the federal government or are selfemployed have the highest average incomes. There is a positive relationship between the amount
of education one achieves and their mean wage, as more educated individuals are more likely to
obtain higher-paying jobs. There is also a roughly positive relationship between one’s age and
average income. Higher-status positions are commonly held by individuals with more work
experience and are, therefore, older than entry-level workers. From these differences in mean
wages, I determined that these variables are all factors that affect one’s income. I examined the
extent of their effects on wages and commute times in my analysis.
The data also provides variables on commuting behaviors (see table V). The average
vehicle occupancy to work is 1.158 people. Individuals who do not carpool to work have a mean
commute time that’s 5.5 minutes less than those who do carpool. Of the respondents who
reported their mode of transportation, 4.43% take public transport to work (bus, train, ferry, or
subway), which on average takes 26.4 minutes longer. The data also reports one’s typical
departure time for work, which assigns values of departure time in hour intervals (24-hour time).
The average person left for work around 9:00 am with most (83 percent) departing before 10:00
am.
The ACS also precisely classifies where its respondents are from using Public Use
Microdata Area (PUMA) codes. These areas group the US into statistical, non-overlapping
regions of roughly 100,000 people (US Census, 2010). Like zip codes, PUMA codes can classify
respondents by geography, which allows me to control incomes and commute times by location.
The ACS is a mandatory survey that all selected households must answer. The US
Census Bureau mails its surveys to select addresses but will conduct over-the-phone or in-person
follow-ups if surveys are not adequately completed or taken promptly. However, not all
questions of the ACS are mandatory. Thus, the variables for commute times and income both
have missing values. 203,000 respondents did not report their commute times to work but did
report their wages. Of these respondents, 146,000 reported earning income even when they listed
themselves as not in the labor force or unemployed. The other missing observations may be from
individuals who do not commute because they work from home.
53,000 individuals did not report their wages but did report their commute times. Because
not all the questions asked in the survey are mandatory, many respondents likely declined to
answer various questions. I acknowledge that these missing values may slightly affect my
results. However, I did not identify a source of sampling bias caused by a systematic or
nonrandom explanation for the missing data. I proceeded to discard these missing values by
restricting the sample: I observed the results from respondents between ages 18 and 65 who are
in the labor force (the ages of all respondents ranged from ages 0 to 96) who also answered both
questions on their commute times and incomes. 1.2 million individuals in the dataset fit this
restriction.
Overall, the American Community Survey presents an ideal dataset to discuss income
and commutes because of its large sample size, which will closely converge to the actual
relationship with a small standard error. Given the model for the US population, the data will
likely accurately account for the differences in traffic by location. Panel data may be preferred to
account for unobserved, time-invariant variables, but the data used still presents accurate results
for my research.
B. Economic Specification
I assess the relationship between income and commute times to work. My results
represent the US population, and I used a fixed effect, multiple regression. I restricted my
regression data to individuals between 18 and 65 in the labor force who reported both their
wages and commute times to work. My economic specification is:
Ci = ß0 + ß1ln(Ii) + Sd PUMA Effect + ß2Fi + ß3Esi + ß4Rmi + ß5Wni + ß6Mi + ß7Ai + ß8Ai2 + εi
where Ci represents the outcome of commute time to work, and ln(Ii) represents the logarithmic
transformation for incomes. Sd is the value of PUMA code-specific intercepts generated by the
fixed effect. The variable for PUMA codes was originally separated by state, meaning that some
codes overlapped across different states. Before inserting the fixed effect, I assigned a new value
for each PUMA-designated area that grouped states and PUMA codes so I could distinguish
between every identification number. Fi is the dummy variable for gender (female = 1), Mi is the
dummy variable for marital status (married = 1), and Ai represents the respondent’s age. Esi
represents the categorical variable of the highest level of education achieved, Rmi is the
respondent’s race (White, Black, Hispanic, Asian, other), and Wni is the categorical variable for
the class of worker (see table IV). εi is the error term.
I use Fi, Esi, Rmi, Wni, and Mi as explanatory variables after identifying that their
subgroups influence mean wages (table IV). These variables are a slightly modified version of
the regression from French et al. (2020) while adding a fixed effect. The PUMA fixed effect
controls for the different commuting conditions likely associated with population density such as
traffic, fuel prices, or other road conditions. I did not include explanatory variables for
commuting behaviors because they are likely influenced by geography. Those who carpool or
take public transit to work likely live in metropolitan areas or densely populated cities, which is
controlled by the fixed effect.
I determined that age more closely fits a nonlinear relationship with wages according to
the data (table VI). Previous empirical work also exhibited the nonlinear relationship between
age and income (Rosenzweig, 1976). Therefore, I used the variables age and age2 to explain
wages in my framework. From this specification, I expect to find a positive ß1 value for ln(Ii)
given the included controlled factors.
III. Empirical Analysis
I controlled for the relationship between income and commutes in two ways. Firstly, I
examined the effects of geography and determined that mean commute times vary greatly across
different PUMA codes. I then regressed commute times on incomes using these PUMA codes as
a fixed effect (see Table VII). This caused a change in the coefficient for ln(Ii) and an increase in
the adjusted R2 value from .02 to .08. With the fixed effect, a 1 percent increase in wages is
associated with an expected 2.73–minute increase in commute time to work. This value is
equivalent to 0.12 standard deviations in commute times.
Secondly, I identified demographic characteristics that could affect income by examining
the difference in mean wages among subgroups of variables (see Table IV). I ran simple
regressions of these demographics on wages to determine the extent of their effects. After
producing significant coefficients for each, I combined these variables with the fixed effect to
produce the multiple regression outlined in my framework (see Table VII). Upon adding each
variable, my results for the coefficient for ln(Ii) suggest that all the chosen demographic variables
are correlated with income. A 1 percent increase in wages is associated with a 2.26-minute
increase in commutes to work. The standard deviation for the distribution of the logarithmic
transformation of wages is 1.3. Therefore, an increase in 1 standard deviation, roughly, is
associated with about a 0.10 standard deviation increase in commute time. The coefficient for
ln(Ii) is statistically significant at the one percent level, and the adjusted R2 value is 0.088. The
positive correlation between income and commutes matches my hypothesis and theory outlined
in Section I. Individuals prefer higher-paying jobs and are willing to broaden the radius of their
job search to earn a higher income. I conducted F-tests for all the included coefficients and
determined that all values were significant at the 5 percent significance level except for the
variables “some college” and “advanced degree.” However, there is a strong positive correlation
between wages and education and commute times and education that justifies its inclusion in my
regression analysis.
My study consisted of individuals in the labor force between 18 and 65 that answered
both questions in the survey about their income and commutes. I believe that the selection on
observables is a sufficient estimator for the true population. Between the fixed effect and
demographic variables, there is reason to believe that the Ordinary Least Squares estimator is
unbiased. The use of the absorbed fixed effect based on PUMA codes controls for many
commuting behaviors and unobserved variables. Decisions such as the use of public
transportation, carpooling, or leaving earlier in the morning are all factors that are influenced by
where one lives. Large cities or more highly populated areas likely cause more individuals to
carpool, leave earlier, or take public transit due to traffic flows, all of which are covered by my
fixed effect. Therefore, any omitted variables surrounding commuting behaviors or tendencies do
not directly affect the relationship of interest.
The sample selection was also conducted in an unbiased process, eliminating the
possibility of sampling bias. The ACS selected a random sample of citizens to complete a
mandatory survey sent through the mail and ensured it was completed to satisfaction. Therefore,
non-response and interviewer bias is eliminated because of the credibility of the US Census
Bureau and the lack of in-person surveying.
The positive correlation between income and commute times to work suggests significant
economic information about the housing demographic. The results signify that higher-income
individuals generally commute further to work. Given that most jobs are in metropolitan areas,
this means that wealthier individuals who commute further to work often live outside of the city.
This relation is commonly observed in the US, as wealthier individuals seem to prefer quieter
lifestyles with more open, rural homes where plots are larger. In a society where neighborhoods
are largely clustered based on economic status, the estimated results confirm the fact that
commutes are divided by social classes, with wealthier individuals all tending to commute to
work from outside metropolitan areas.
The main underlying threat to the internal validity of my results is the possibility of
reverse causality. There have been studies that claim that commute times to work have a causal
effect on incomes. It is also plausible that simultaneity exists in the true relationship. It seems
more plausible that individuals decide how far they would like to commute between two
distances based on the job with the higher income. However, only the use of an instrumental
variable would be able to eliminate this threat.
IV. Conclusion
Few studies specifically examine the impact of income on commute times to work in
great depth. This paper provides more detailed information using comprehensive data from the
American Community Survey. I found that a 1 percent increase in wages causes an expected
2.26-minute increase in commute time to work. This positive correlation represents the housing
demographic in the US. Wealthier individuals live further away from their place of work, and
individuals appear more willing to commute further to work if it means earning a higher income.
My findings echo the same positive relationship as past studies while definitively determining
the magnitude of the income effect on housing and commuting using the necessary control
variables.
These results offer relevant applications to individuals’ health and city commuting plans.
Many individuals prefer to drive further for higher wages, which may deteriorate their mental
and physical health due to extended periods in cars daily (Ding et al., 2014). Firms that offer
higher wages have a larger radius of potential labor recruits. Ding et al. (2014) found that
individuals who spend over 120 minutes driving each day experienced higher rates of obesity,
sleep deprivation, and poor mental health. My results indicate that there may be implicit costs to
travelling further to work for a higher income.
Poorer individuals tend to live closer to their place of work. With most jobs being in
metropolitan areas, this suggests that lower-class workers also live in these sectors. This
information could help improve public transportation and route plans in cities where traffic is
often the heaviest. Understanding the housing demographic of where workers are commuting
from could provide relief for busy city streets.
This study does have some limitations that I was not able to address. Because many
respondents tend to round their responses for income and commutes, the precision and
magnitude of my estimates may be slightly off. However, I believe the positive trend from my
results sustains given that commutes are likely only rounded to the nearest five minutes, and
incomes are likely only rounded to the nearest thousand dollars. The ACS is the closest dataset
that details the entire US population. The large sample size and relatively low nonresponse rate
present close estimates to the true relationship. This study does not account for unmeasurable
variables, such as personality traits, which may explain why individuals choose where to live, or
innate ability, which directly influences income. An instrumental variable would be useful to
account for this and other possible confounding variables. In the end, despite these slight caveats
and limitations in the data, this paper solidifies claims about the general trend of housing
demographics and commuting behaviors based on income.
References
Bogomolov, Yuri, Mingyi He, Devashish Khulbe, and Stanislav Sobolevsky. (2021). Impact
of income on urban commute across major cities in the US. Procedia Computer Science 193:
325-332.
Ding, Ding, et al. (2014). Driving: a road to unhealthy lifestyles and poor health outcomes.
Public Library of Science One 9 (6).
French, Michael T., Ioana Popovici, and Andrew R. Timming. (2020). Analysing the effect
of commuting time on earnings among young adults. Applied Econometrics 52 (48): 5282-5297.
Johnston, Ahren. (2019). A note on commute times and average income levels. The Open
Transportation Journal 13: 151-153.
Dargay, Joyce M., and Jos Van Ommeren. (2005). The effect of income on commuting time –
an analysis based on panel data. 45th Conference of the European Regional Science Association:
“Land Use and Water Management in a Sustainable Network Society.
Rosenzweig, Mark R. (1976). Nonlinear Earnings Functions, Age, and Experience: A
Nondogmatic Reply and Some Additional Evidence. The Journal of Human Resources 11 (1):
23-27.
Tefft, Brian C. (2018). American Driving Survey, 2015-2016 (Research Brief). AAA
Foundation for Traffic Safety
Tefft, Brian C. (2022). American Driving Survey, 2020-2021 (Research Brief). AAA
Foundation for Traffic Safety.
US Census. (2021). Urban Area Facts.
TABLE I – Baseline Summary Statistics for Incomes and Commutes
(1)
N
(2)
Mean
(3)
Standard
Deviation
(4)
25th
Percentile
(5)
75th
Percentile
Commute Time to Work, minutes
1.395e+06
27.29
23.29
12
35
Adjusted Income, USD
1.574e+06
52,481
66,325
16,210
65,851
VARIABLES
Notes:
This table presents the summary statistics for the two main variables of interest: commute times to work and
adjusted incomes. All statistics are calculated from the 2018 American Community Survey. Column 1 presents the
total number of observations for each variable from the dataset. The total number of observations for commute time
to work is 1,395,191. The total number of observations for adjusted income is 1,575,313. Commute times are
measured in minutes, ranging from a 1-minute commute to 188 minutes. Wages are measured in US dollars, ranging
from $4 to $727,404.
Notes:
This graph presents the kernel density distribution for incomes from the 2018 American Community Survey. The
horizontal axis represents incomes of respondents measured in USD (2018 adjusted). The vertical axis represents the
probability density function at each income level smoothed out using the kernel densities. The right-skewed nature
of the distribution motivates our use of the logarithmic transformation to explain how income affects commute times
to work.
Notes:
This graph presents the kernel density distribution for commute times from the 2018 American Community Survey.
The horizontal axis represents the values for commute times to work measured in minutes. The vertical axis
represents the densities for the probability density function for commutes, smoothed out using kernel densities. The
distribution is clearly multimodal, likely because of responses being rounded to the nearest five or ten minutes.
TABLE IV – Summary Demographic Statistics and Relationship with Wages
(1)
Frequency
(2)
Percentage
(3)
Mean Wage
(USD)
(4)
SD Wage
(USD)
Male
1,639,921
1,574,618
51.02
49.98
41,778
62,513
49,733
77,433
White
Black
Hispanic
AAPI
Other
2,160,997
327,955
466,261
206,707
52,619
67.23
10.20
14.50
6.43
1.64
56,555
37,874
37,776
64,648
70,695
45,060
43,688
78,302
1,375,888
1,838,651
42.80
57.20
66,412
36,315
77,102
46,029
Class of Worker (if in LF)
Private, for profit
Federal Government
State Government
Local Government
Self-Employed, incorporated
Self-Employed, unincorporated
Without pay
Private, not for profit
992,986
36,756
72,003
107,396
60,587
94,793
3,314
130,532
66.27
2.45
4.81
7.17
4.04
6.33
0.22
8.71
51,171
66,505
50,175
47,379
81,283
40,752
66,915
49,955
45,546
39,820
108,995
70,722
51,710
63,695
Education Level
Less Than High School
High School
Some College
College
Advanced Degree
834,548
705,274
785,220
486,531
307,819
26.75
22.61
25.17
15.60
9.87
23,861
34,608
40,104
70,131
101,245
33,297
36,750
42,860
75,557
105,803
VARIABLES
Female
Race
Married
Not married
Notes:
This table presents the summary statistics for demographic characteristics. The data comes from the 2018 American
Community Survey. Column 1 presents the number of observations in each demographic’s subgroup. Column 2
presents each subgroup’s relative frequency, separated by each demographic. Columns 3 and 4 represent the twoway summary statistics between each subgroup and their relative income. The two-way summary statistics are
rounded to the nearest whole number. Key patterns and differences in mean wages within each category are
presented in Section II. Not all respondents included in the survey answered these demographic questions, missing
observations exist.
TABLE V – Descriptive Statistics for Commuting Behaviors
(1)
N
VARIABLES
Vehicle Occupancy to Work
(2)
Frequency
1.263e+06
Takes Public Transportation
No Pub. Trans.
Hour of Departure for Work,
24-hours
(3)
(4)
Relative Mean
Frequency
65,474
1,413,447
(5)
Standard
Deviation
(6)
25th
Percentile
(7)
75th
Percentile
1.158
0.594
1
1
9.027
3.544
7
9
4.43
95.57
1.395e+06
Notes:
This table presents the summary statistics for variables associated with commuting behaviors. Vehicle occupancy
and Time of Departure are continuous variables, and Public Transportation is a binary variable. Columns 1, 3, 4, 5,
6, and 7 present the summary statistics for the continuous variables. Columns 2 and 3 represent the frequency and
relative frequency distribution for the public transportation variable. A total of 1,478,921 individuals answered
whether they used public transportation to work. The ACS defines public transit as any of the following modes of
transportation: bus, trolley bus, streetcar/trolley car, subway, railroad, or ferryboat.
TABLE VI – Comparing Linear and Nonlinear Regressions Between Wages and Age
VARIABLES
Age
(1)
Linear Regression
(2)
Quadratic Regression
Wages (USD)
Wages (USD)
836.2***
(3.387)
Constant
16,739***
(153.8)
5,843***
(18.66)
-57.17***
(0.210)
-79,394***
(383.3)
Observations
R-squared
1,574,313
0.037
1,574,313
0.081
Age2
*** significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level
Notes:
This table presents two types of regression to determine if age has a linear or non-linear relationship with wages.
Standard errors for the coefficients are given in parentheses. All coefficients are significant at the 1 percent level.
Based on previous studies and these results, wages can be more closely explained by age using a quadratic
regression. The R-squared value for the quadratic regression is .081, greater than that for the linear regression (.037).
TABLE VII –Income and Commutes Including PUMA Fixed Effect and Demographics
(2)
With Fixed
Effects
(3)
Final Regression
Commute Time
Commute Time
2.732***
(0.0195)
2.259***
(.0238)
Female
-2.529***
(0.0413)
High School
Some College
College
Advanced Degree
-0.409***
-0.121
0.684***
0.0427
(0.0939)
(0.0922)
(0.0985)
(0.108)
Black
Hispanic
AAPI
Other
1.851***
0.328***
0.662***
1.193***
(0.0807)
(0.0707)
(0.0872)
(0.196)
Federal Government
State Government
Local Government
Self-Employed, Inc.
Self-Employed, not Inc.
Without Pay
Non-profit
1.473***
-1.799***
-4.647***
-4.706***
-0.649**
-1.740***
-1.771***
(0.134)
(0.0900)
(0.0720)
(0.119)
(0.302)
(0.526)
(0.0684)
0.175***
0.320***
-0.00351***
(0.0461)
(0.0117)
(0.000137)
(0.273)
VARIABLES
(1)
Single Regression
(4)
Standard Errors
for final regression
Commute Time
log(income)
3.078***
(0.0195)
Married
Age
Age2
Constant
4.612***
(0.205)
-0.978***
(0.203)
-1.096***
Observations
R-squared
PUMA Fixed Effect?
1,233,67
0.020
NO
1,233,670
0.080
YES
1,233,670
0.088
YES
*** significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level
Notes:
This table presents the changes in the coefficient for log(income) when fixed effects and explanatory variables are
added given the restricted dataset outlined in Section II. Standard errors are given in parentheses below columns 1
and 2, and standard errors for column 3 are given in parentheses in column 4. Column 1 presents the single
regression without controls. Column 2 presents a regression using the PUMA fixed effect. Column 3 presents the
final regression used for analysis with PUMA fixed effects and demographic explanatory variables.