# The Forest Behind the Tree:
Heterogeneity in How US Governor’s Party Affects Black Workers††thanks: The
authors thank Louis-Philippe Beland for making the data publicly available and
Thomas (Danny) Boston for comments on the early version of this manuscript.
All errors and omissions are ours.

Guy Tchuente School of Economics, University of Kent, NIESR, and GLO, UK;
Correspondence Email<EMAIL_ADDRESS>Johnson Kakeu  Department of
Economics, University of Prince Edward Island, Canada, Email<EMAIL_ADDRESS>John Nana Francois College of Business, West Texas A&M University, Canyon,
USA, Email<EMAIL_ADDRESS>
###### Abstract

Income inequality is a distributional phenomenon. This paper examines the
impact of U.S governor’s party allegiance (Republican vs Democrat) on ethnic
wage gap. A descriptive analysis of the distribution of yearly earnings of
Whites and Blacks reveals a divergence in their respective shapes over time
suggesting that aggregate analysis may mask important heterogeneous effects.
This motivates a granular estimation of the comparative causal effect of
governors’ party affiliation on labor market outcomes. We use a regression
discontinuity design (RDD) based on marginal electoral victories and samples
of quantiles groups by wage and hours worked. Overall, the distributional
causal estimations show that the vast majority of subgroups of black workers
earnings are not affected by democrat governors’ policies, suggesting the
possible existence of structural factors in the labor markets that contribute
to create and keep a wage trap and/or hour worked trap for most of the
subgroups of black workers. Democrat governors increase the number of hours
worked of black workers at the highest quartiles of earnings. A bivariate
quantiles groups analysis shows that democrats decrease the total hours worked
for black workers who have the largest number of hours worked and earn the
least. Black workers earning more and working fewer hours than half of the
sample see their number of hours worked increase under a democrat governor.

Keywords: U.S. State Policy, Black Workers, US Labor Market, RDD, Governors’
Effects Heterogeneity, Bivariate Quantile Causality.

JEL Classification: D72, J15, J22, J31, R23.

##  Introduction

Racial income inequality is a distributional phenomenon that is drawing a
growing concern on both sides of the political aisle in the U.S. In this
paper, we address one question related to the extent to which party allegiance
affects the labor market outcomes for blacks relative to other demographic
groups in the U.S. More specifically, we investigate the causal impact of the
gubernatorial party allegiance (i.e., Democratic or Republican) of US
governors by scrutinizing its effects on subgroups of the population of Black
workers in the United States. The effect of the governors’ party is assessed
by decomposing the sample into different quantiles based on the level of
earnings and number of hours worked— two important labor market outcomes that
matter for wealth accumulation. The motivation for this decomposition are
twofold: First, a descriptive analysis of the earning distribution of whites
and blacks in the U.S uncovers that the earning distribution of whites is bi-
modal while that of blacks stays uni-modal (see Figure 1 on page 1). Given the
difference in the shape of the two distributions, estimating the governor’s
party affiliation effect for the average representative black American may
lead to effects that are substantially different from the ones richer, or more
disadvantaged individuals are experiencing. This difference is consistent with
the existence of a subtended increased in wage inequality in the 80’s and 90’s
described by Autor et al. (2008). While the first motivation is driven by
statistical concerns, the possibility of facing a highly heterogeneous causal
effect when evaluating the policies is highlighted by several papers (see
Autor et al. (2008), (Bayer and Kerwin, 2018)). The second motivation of this
study stems from the growing literature showing that some policies have a
different impact for individuals having higher income and those having lower
income (Bayer and Kerwin, 2018).

Table 1: Causal effect of governor Party Affiliation on Number of Hours worked
by Blacks

|  $Q_{1}$ Earnings |  $Q_{2}$ Earnings |  $Q_{3}$ Earnings |  $Q_{4}$ Earnings | Total Hours
---|---|---|---|---|---
$Q_{1}$ Hours |  |  | + |  | +
$Q_{2}$ Hours |  |  |  | + |
$Q_{3}$ Hours | - |  |  |  |
Total Earnings |  |  |  | + | +

  * •

Notes: This table summarizes the qualitative results of the estimation of
governor’s party effects using RDD in different quartiles of earnings and
hours worked. The cells left blank are subgroups with non statically
significant effects at levels 1, 5, or 10%. The cells in green are subgroups
with positive and statistically significant effects, while red cells are
negative effects.

This paper brings new insights into the existing literature by showing that
governor party allegiance plays different role for different subgroups of the
population. Our analysis uses labor market outcome, the total yearly earnings
and number of hours worked, and creates quantiles subgroups along them.

Table 2: Causal effect of governor Party Affiliation on Blacks’ Earning

|  $Q_{1}$ Earnings |  $Q_{2}$ Earnings |  $Q_{3}$ Earnings |  $Q_{4}$ Earnings | Total Hours
---|---|---|---|---|---
$Q_{1}$ Hours |  |  |  |  |
$Q_{2}$ Hours |  |  |  |  |
$Q_{3}$ Hours |  |  |  |  |
Total Earnings |  |  |  |  | +

  * •

Notes: This table summarizes the qualitative results of the estimation of
governor’s party effects using RDD in different quartiles of earnings and
hours worked. The cells left blank represent subgroups with a non statically
significant effects at levels 1, 5, or 10%. The cells in blue are subgroups
with positive and statistically significant effects, while red cells are
negative effects. It should be noted that in none of the subgroups the party
affiliation of the governor has any effect on the outcome except for the full
sample.

We first document the growing, over-time, divergence in the distribution of
annual wage of blacks and whites workers. This difference in the distribution
is then tested using the non-parametric Kolmogorov-Smirnov test. The evidence
differences in the distribution motivate our estimation method that combines a
Regression Discontinuity Design (RDD) and decomposition of the sample by
quantiles approach. We exploit marginal victories at the state gubernatorial
elections to design an RDD to estimate the effect of the governor on black’s
labor market outcomes and use subgroups granular analysis to unveil a large
heterogeneity in the treatment effects.

As summarized in tables 1 and 2, the granular analysis of the causal effect of
governors suggests that there is no difference between Republican and Democrat
governors in most quantiles groups, especially for earning. As for hours
worked, Democrat governors increase the participation of black workers at the
highest quantiles of earning. Democrats governors decrease the total hours
worked for black workers who have the largest number of hours and earn the
least while increasing the number of hours of black workers earning more and
working fewer hours than half of the sample. We also find that in the US,
independent of the party affiliation of the governor, certain subgroups of
black workers see no change in their labor market condition, suggesting the
existence of a low-wage trap for lower-income quantiles individuals. The low-
wage trap may be viewed as a situation where those black people already in
low-paid work are not able to increase income no matter which party is in
charge of making gubernatorial policies. One work is closely related to Beland
(2015) who showed that party allegiance matters with Democratic governors
causing an increase in the annual hours worked by blacks relative to whites.
While insightful, the methodology used in that work is silent on the income
and worked hours distribution heterogeneity in investigating the question of
the causal effect of party allegiance. The analysis is based on the first
moments of the whole distribution of the black population, namely the average
value. Several studies have shown that the analysis based on the average value
or the median of the whole distribution may be misleading when the shape of
the distribution is not well-behaved (Banzhaf et al., 2019). For instance, in
surveying studies that look at race differences in analyzing the effect of
pollution, Banzhaf et al. (2019) conclude that the relationship estimated from
aggregated data is only equal to the relationship at the micro-level if there
are no group-level effects correlated with the variable of interest.
Therefore, it is increasingly suggested to go beyond the aggregate average
indicator analysis to a heterogeneous approach that explores finer sub-groups
when analyzing race inequality and labor market issues. For instance, Manduca
(2018) showed that the observed stable ratio between median black and median
white family incomes conceals two large and diametrically opposed trends
between 1968 to 2016. Along the same lines, the work by Bayer and Kerwin
(2018) discusses why the median black man’s relative position in the earnings
distribution has remained constant in the overall distribution over the past
70 years. They go beyond the standard mean or median to use the concept of
rank earning gap that provides a finer picture of heterogeneity issues in
several subgroups of the income distribution.

As mentioned earlier, empirically assessing the extent to which subgroups’
considerations influence causal inference can increase our understanding of
the link between global average effects and more micro effects. The average
effect may not be a meaningful statistic if the application of the analysis to
a subgroup in the analysis of the effects does not lead to the same conclusion
as the average effect on the whole distribution (Bertanha, 2020). One
important motivation to scrutinize in going beyond macro aggregate estimation
to less aggregate analysis is the increasing public demand for more robust
approaches to the evaluation of public policy programs directed toward
specific subgroups of the population (Heckman, 2001; Darity Jr. et al., 2021).
There is a small but growing strand of literature that is looking at these
issues in using Regression discontinuity design approaches. For instance,
Frandsen et al. (2012) use quantile treatment effects in the regression
discontinuity designs design for studying the distributional effects of a
children’s education program. Their data exhibits regular distributions,
excluding the possibility of dealing with isolated masses in the analysis,
unlike our income and hours worked data. Along the same lines, Huang and Zhan
(2020) provide a methodology for performing causal inferences that combine
both a local quantile regression analysis and a regression discontinuity
design.

The remainder of this paper unfolds as follows: In Section 2, describes some
background by explaining the crucial role of the governor in shaping the US
labor market. Section 3 presents some descriptive statistics of the data,
while section 4 discusses the results obtained from the causal inference.

##  The role of US governors in the shaping of states’ labor markets

The US political system grants governors with a high degree of power over the
management of the rules that shape the states’ economies, including labor
market conditions. The governor heads the executive branch in each state, sets
policies, prepares and administers a budget, recommends legislation, signs
laws, and appoints department heads. Governors can veto state bills. Unlike
U.S. presidents, many governors also have additional veto powers at their
disposal. For instance, most states provide governors the power of the line-
item veto, which gives governors the ability to strike out a line or
individual portions of a bill while letting the remainder pass into law.
However, party composition at the state legislature has consequences on how
governors will implement their own policy agendas. Alternatively, when the
governor is not from the same party as the one controlling the state, she/he
may have to work harder to build relationships and to reach a consensus. In
the situation known as unified government where the governor’s party
affiliation controls the legislature, governors are hamstrung in their ability
to implement their policy choices. Under the leadership of the governors,
states can set taxes, manage budgets, and regulate businesses. They can also
enforce worker protections by regulating issues related to minimum wage,
overtime pay coverage, anti-discrimination protections, workers’ compensation,
unemployment insurance, paid sick leave, and paid family leave. Other state
activities that are important in shaping the labor market include managing the
healthcare system, funding the public sector retirement plans, increasing
access to affordable home care, and providing childcare services. The way
these policies are implemented is of particular importance for people working
in low-paid jobs including janitorial, delivery, home care, agriculture,
landscaping, security, hospitality, trucking, transportation, and warehousing.
The substantial role governors play suggests that their gubernatorial party
affiliation could have a pivotal role in shaping the labor market allocations.

##  Data and Descriptive Analysis

The data used in this paper are obtained by matching gubernatorial elections
and the data from the Current Population Survey’s (CPS’s) March supplements
from 1977 to 2008 as constructed by Beland (2015).111For detail on the
construction of data set see Beland (2015) in Section III.

###  Comparing Income Distribution by Ethnicity

The large majority of work on the racial difference in labor outcomes has
focused on mean or median differences (Bayer and Kerwin (2018), Chetty et al.
(2020)). By focusing on measures of central tendency, there is a possibility
that the measure of the effect does not fully capture the reality in the
context of US racial wealth inequality. In a recent paper, Bayer and Kerwin
(2018) have identified some economics reasons to be interested in the full
distribution of income when analyzing the racial wage gap: the
disproportionate recent increase in non-work among U.S black men,222The over
representation of black men out of labor market exacerbated the need to
account for all men to avoid sample selection issues. and recent evidence
suggesting that economic changes such as raising general inequality
differently affected black outcomes at different points in the distribution.
For similar reasons, our analysis will take a holistic approach by examining
the causal effect at all parts of the distribution.

Figure 1: Distribution of Income by Race

Figure 2: Distribution of Income by Race 4-year split 1977–1992

The analysis of figures 1 and 3 as well as figures 5 to 9 [in Appendix A]
produce statistical evidence supporting fundamental difference (beyond mean or
median) in the income distribution by race. Each figure represents the
distribution of the natural logarithm of individual annual income by
ethnicity.

Figure 3: Distribution of Income by Race 4-year split 1993–2008

Figure 1 shows that the income distribution of whites is bi-modal while of
blacks is uni-modal. A Kolmogorov-Smirnov test for difference in the
distribution confirms that at both distributions are different with the white
individuals distribution more likely to have larger values.333The results of
the others Kolmogorov-Smirnov tests are similar.

Figures 3 and 3 present the dynamic of the distributions over time. While from
1977 to 1992 the distribution of whites income dominates that of blacks, it is
only from 1993 that the shapes of the distribution start to diverge. As we
move from 1993 to 2008, the whites’ income distribution becomes more bi-modal.
This means the level of income inequality become more pronounce for whites
than for blacks.444Heywood and Parent (2012) provide empirical evidence that
the racial wage gap is the largest at the very top of the distribution of pay
components related to bonuses, commissions, and so forth. The authors,
however, do not address the question as to whether bonuses and commissions can
be used to discriminate against an employee because of their sex, race, age,
disability, or religion. While there is suggestive evidence that non-black
managers hire more whites and fewer blacks than do black managers (Giuliano et
al., 2009), there is also no obligation on employers to make the criteria for
awarding bonuses transparent. Figures 3 to 5 [ in Appendix A ]suggest that the
observed divergence in income distribution by ethnicity is present
irrespective of the party allegiance of the ruling state’s Governor.

To the extent that the distribution of income exhibits substantial differences
and they are existing evidence of differential effects of the policy depending
on the position of a black worker in the distribution, we propose a holistic
approach to the causal analysis of political parties in labor market outcome
in the US.

###  Evidence of Discontinuity by Quantiles Subgroups

The application of the RDD methodology for each quantile implies that we need
to investigate the presence of discontinuity at 0 percent at each income
quantiles subgroup. The Figure 4, and figures 10, and 11 [in Appendix A] have
four panels, each of them representing a quantile of annual income. Each panel
shows the discontinuity at 0 percent, this represents a Democratic governor
barely marginal win over a Republican. Figure 4 represents the hours worked by
white and black workers. Figure 10 and 11 in Appendix A represent respectively
the proportion of employed and the natural logarithm of earning of whites and
blacks.

Figure 4: Margin of Democratic Victory and Total Hours Worked per Year for
black (left on each panel) and white (right on each panel). The analysis is by
Earning quartile.

Figure 4 suggests a positive effect of having a Democrat governor on the
number of hours worked by workers. The effect seems to be concentrated at the
lowest quartile. While the effect looks stronger for blacks it also exists for
white in the first quartile. The heterogeneity in the evidence of the presence
of discontinuity at the cut-off supports the need for an in-depth analysis of
the effect of political parties.

The other labor market outcomes of interest in this section’s analysis are the
probability of employment represented in Figure 10 and the the annual total
earning shown in Figure 11. The analysis of figures 10 and 11 suggests that a
Democrat victory has a positive effect on the probability of work and earning
when individuals in the first and second quartiles are considered. The effects
are larger for black workers and virtually no effect for white workers.
Moreover, as we move to higher quartiles, electing a Democrat governor does
not affect the extensive margin of working irrespective of the race.

The results suggest a certain level of heterogeneity in the effect of
governors’ party allegiance on labor market outcomes. Individuals with
earnings in the lower quartiles (first and second) seem the most affected by
the marginal election of a democrat vs a republican governor. It is worth
noting the fitted values obtained in Figures 4 and figures 10 and 11 [in
Appendix A] are estimated without controlling for any covariates. The next
section extends this analysis by introducing additional controls for better
precision and by dividing the sample into quartiles along two dimensions
(annual income and hours worked). This also enable us to understand more
precisely the extend of the heterogeneity of the governors effect on labor
market outcome.

##  Methodology for The Causal Estimation

The effects of interest are identified based on the application of the
regression discontinuity design (RDD) to different sub-groups in the sample.
The use of an RDD helps to account for potential endogeneity of the victory of
a specific party’s governor (see Beland (2015), Lee (2001)). The assumption
used for identification is that in case of marginal victory, conditional on
some observable factors, there are no other unobservables factors driving both
the outcome of interest and the victory of a governor of a specific party.

The descriptive analysis of earnings of black workers and white workers
summarized in figures 1, 2, and 3 suggests that the earning distribution of
White workers is bi-modal while that of black workers is uni-model. The nature
of the difference in the distribution makes the relevance of the average
treatment effect as the parameter of interest questionable. Especially in a
situation where the treatment effect has a very high likelihood of being
heterogeneous. This implies that the estimation of the conditional expectation
of difference estimators could be biased because of the non-symmetry of the
treatment effects distribution. Indeed, the RDD creates a quasi-experiment
around the cut-off of marginal victories, but the difference in the
distribution of earnings could lead to an asymmetric distribution of treatment
effects, which poses threats to significance testing as noted Deaton and
Cartwright (2018) for randomized control trials(RCT).555 Deaton and Cartwright
(2018) show that even in RCT, when the distribution of treatment effects are
not symmetric the classical inference strategy may not be appropriate. RDD is
usually considered as the quasi-experimental strategy closest to RTC, the
over-representation of whites at the top of the distribution means that if the
effect of governor depends on the level of income, the asymmetry is very
likely.

To alleviate the consequence of potential biases in the estimates of the
governors’ effects using the full sample, we propose an estimation of the
treatment effect on subgroups along earning and total hours-worked
quantiles.666 The presence of masses probabilities on the distribution of
earnings and total hours-worked prevents the used quantile regression for RDD
as in Frandsen et al. (2012) which would have been our preferred estimation
method.

For each quantile group, the following model is estimated

$Y_{ist}=\beta_{0}+\beta_{1,\tau}T_{st}+\beta_{2,\tau}T_{st}\times
Black_{i}+G(MV_{st},Black_{i},X_{ist})+\varepsilon_{ist}.$ (1)

The parameters of interest are $\beta_{2,\tau}$ with $\tau$ being a specific
quantile. For an individual $i$, in the state $s$ at time $t$, $Y_{ist}$ is
the labour market outcome. $T_{st}$ is a dummy variable that takes the value 1
if a Democrat is in office as a governor in the state $s$ at time $t$. The
variable $Black_{i}$ indicates the ethnicity of the individual $i$, it takes
the value 1 if the person is an African American and zero otherwise. $MV_{st}$
is the victory margin in the last gubernatorial election in state $s$ prior to
year $t$. $X_{ist}$ is the set of other observable characteristics that can
affect the labor market outcome $Y_{ist}$. $G(.,.,.)$ represented different
functional form specifications. The estimation of the parameters of Equation
(1) for each quantile group using an OLS estimator will deliver a consistent
estimator under the assumption that the distribution of the labor market
outcomes is similar in the subgroups.

##  Discussion of Empirical Findings

###  Estimation Results

Table 5 to Table 10 in Appendix B show the results of the estimation of the
causal effects of governors party affiliation decomposed in quantile sub-
groups along two dimensions: earning and hours worked. Table 5 shows that when
democrat governors’ policies are implemented, only black people in the high-
earning quantile increase their average number of hours worked. However,
blacks in the lower-earning quantile do not increase the average number of
hours worked. The stagnation of the average number of hours worked in the
lower-earning quantiles may be viewed as “an hour trap”, suggesting that
incentives for blacks to work more hours are not boosted by Democrat policies
in a complex economic and social environment which is shaped by the welfare
system, the childcare, and the Medicare. There are several ways for
understanding this finding. One possible interpretation is that all blacks in
that earning quantile do not increase at all their hours worked when Democrat
policies are implemented. Another interpretation is that even if we assume
some black in that low earning quantile increase the number of hours worked,
this would be counterbalanced by another black in the same lower earning group
that reduces the amount of time they used to work. The combined effect of
these two forces can contribute to keeping the number of hours worked
unchanged in the low earning quantiles. In that sense, the effects of the
policies may not be effective in increasing the number of hours worked for
black workers in the lower-earning quantiles. If a low-income black is already
working too much to pay his/her bills, a government policy that increases
childcare or paid sick leave may give incentive to that person to reduce
his/her hours of works and enjoy more leisure time to have a more balanced
life. In addition, the combination of income taxation combined with
restrictions for people in the welfare system may also contribute to reduce
work incentives and hold back hours worked for blacks. There is literature
that discusses how work fatigue accumulates over time as a consequence of work
effort and can lead workers to reduce the number of hours worked (Donsimoni,
2020; Cygan-Rehm and Wunder, 2018). In the same way, health issues may create
an incentive to work less, work fatigue may result in people who are already
working several jobs or too many hours to reduce the number of hours
worked.777Racial and ethnic minority populations have lower levels of access
to medical care in the United States (Williams and Rucker, 2000). One recent
shred of evidence on this issue is that Blacks are about half as likely as
Whites to receive treatment for depression (McGregor et al., 2020).

While it is commonly believed that democrats tend to put greater emphasis on
addressing poverty and unemployment issues (Beland, 2015; Wolf, 2015; Sieber
and Izraeli, 2018; Leigh, 2008; Wright, 2012), our findings contrast and
complement the study done by Beland (2015) who using aggregate data found that
under Democrat governors, blacks are more likely to work, participate in the
labor market, and work more intensively. As Beland (2015) mentioned, more work
is needed in this area to understand the full extent of the role of political
parties.

The findings in Table 5 also show that the democrat governors’ policies only
give incentive to black in the highest-earning quantile to increase their
average hours worked. State policies that subsidize childcare may have
different effects on workers depending on the classification in terms of
earning quantiles. It could be that childcare for instance allows black in the
highest-earning quantile to take time away from childrearing to increase their
hours worked.888The U.S. welfare system is composed of dozens of programs
whose management is also shared by states and local governments, which
actually deliver the services and contribute to funding. For instance, states
can use funds related to the Temporary Assistance for Needy Families (TANF) to
provide benefits to low-income families to help pay the costs of working, such
as childcare or transportation.

Table 6 shows that none of the earning quantile taken individually experienced
an increase in earnings after Democrat governors implemented their labor
policies. This may be because some workers who were already working too much
decide to reduce their hours worked if for instance the minimum wage,
childcare, or sick leave days become paid under democrat governors’ policies.
In that case, the reduction in time may be driven by the need for a more
balanced life. This may suggest the importance of empirically analyzing work
fatigue as a factor to be taken into account in labor supply decisions along
with democrat governors’ policies.999According to Ricci et al. (2007),
prevalence of fatigue is estimated at 37.9% in the U.S. workforce.

Table 7 shows that only workers with the lowest number of hours worked to
increase their number of hours worked after democrat governors implement their
labor policies. This represents a total of 34% of black workers who decide to
increase their number of hours worked.

Table 8 shows that in contrast to Beland (2015) results, at the more micro
subgroup level of hours worked, there has not been an increase in the earnings
at all. This may suggest that the increase in hours worked observed in the
lowest quantile of hours worked in Table 7 did not translate into a
substantial increase in earnings. This could be viewed as a wage trap for that
subgroup. It may be that the increase in hours worked is mainly driven by new
workers. New workers who enter that subgroup after democrat governors policies
are implemented may be new immigrants or people coming out of the welfare
system or from prison whose marginal earnings are equal to or below the
average wage of people who were already in that subgroup before the policy is
implemented. This situation may be connected to the comparing marginal wage of
new workers in a group with that of the average wage of worked already in the
group before the implementation of the policy. If the marginal wage of new
workers is less than the average wage of preexisting workers in that group,
the average wage in that subgroup will not increase.101010Even today, black
workers are over-represented in low-wage entry-level jobs. In 2018, black
workers made up 18% of minimum wage workers despite being only 12.7% of the
population (US Joint Economic Committee, 2020).

Table 8 shows that at the more micro levels in terms of subgroups related to
the number of hours worked and earnings quantiles, there has not been an
increase in the earnings at all. This also could be explained by the effect of
the marginal wage of new workers in a subgroup being less or equal than the
average wage of preexisting workers in that subgroup, which leads to the
average wage in that subgroup not increase after the democrat governor
policies are implemented.

Table 9 shows that there is no subgroup of black workers in terms of hours
worked and earning quantiles that we’re able to increase their earnings after
the democrat governors’ policies are implemented.

Table 10 brings additional light to Table 7 by showing that workers in the
subgroup composed of black workers in the first quantiles in hours worked and
third quantiles in earnings ($Q_{1}$ Hours and $Q_{3}$ Earnings) and those in
the second quantiles in terms of hours worked and third-fourth in earnings
($Q_{2}$ Hours and $Q_{4}$ Earnings) are the ones that increase their number
of hours after the democrat governors’ policies are implemented. Again, the
behavior of workers in $Q_{1}$ Hours and $Q_{3}$ Earnings shows that actually,
only the workers who work the least and have earnings in the third quartile
decide to increase their supply of labor. This raises the question of whether
there is a “catching-up with the Jones” phenomena at play as those who work
the least and are in the top highest-earning quantile do not increase their
hours worked. The workers in the subgroup $Q_{2}$ Hours and $Q_{4}$ Earnings
may be standard top earners who find it possible to increase the number of
hours worked, given their flexible preferences between labor and leisure after
the democrat governors’ policies are implemented.

Since the findings demonstrate that subgroup heterogeneity with respect to
hours worked and earnings should be of interest to policymakers. The
functioning of the American welfare system is a good one important element to
have in mind when analyzing the full picture from our empirical results.

###  Understanding why hours may increase or decrease while wage decrease or
remain fixed for certain subgroups

Let us start with explaining why wage may decrease or remain fixed. After the
policy, if in a subgroup, there are new entrants (welfare leavers) who decide
to work full time but have an average wage that is lower than the previous
average wage of that subgroup, then the average wage of that subgroup will
decrease while the number of hours is increasing. Another explanation for the
decrease in wage in a subgroup maybe that new entrants (welfare leavers) do
not work for four quarters in a row, signaling a potential problem with
employee retention and stability. Now let us provide a possible explanation
for a decrease in hours as it related to the welfare system. It may be after
that after the state policy, the number of people previously in the subgroup
but that are not able to keep stable jobs is more important quantitatively
than people (welfare leavers) entering that subgroup that are working full
time.111111On the period 1981-2017, Wrigley-Field and Seltzer (2019) found
that black/white displacement disparities have grown over time, with excess
Black displacement doubling for women and tripling for men since the 1990s.
They found that during the 1990s, being Black replaced lacking a college
degree as the better predictor of displacement.

###  Earning trap and hours worked trap

The results have shown that the average wage of several groups does not change
when the policies are implemented. This suggests several subgroups experience
a “wage trap” , especially the group with the lower average wage. The “low-
wage trap” shown in the estimations may be understood as a situation where
those already in low-paid work are not able to increase their income when
democrat governors implement their policies. The combination of income taxes,
social security contributions, and benefits withdrawal may “tax away” all or a
large part of any wage gain.121212The growing stratification economics
literature contends that social prejudices influence productivity in labor
markets (Darity et al., 2017). Historically, black workers are less likely to
work in jobs that provide retirement benefits. As far as back during the
interwar 1919-1943, Whatley and Sedo (1998) found that black workers had less
access to stable jobs (high-turnover jobs) and complained about several kinds
of discrimination in job assignments.

The estimations have also shown that the number of hours worked does not
change after the democrat governors implement their policies. This suggests
the existence of a “hours worked trap” referring to a situation where the
number of hours worked does not change for a certain subgroup of workers.
Income restrictions related to the welfare system may be an important factor
to be considered in explaining the hours’ trap observed for certain subgroups.
Some restrictions in the welfare system can entertain situations where
increasing the number of hours worked is judged by certain subgroups to be not
to not optimal from a well-being standpoint due to expected income-related
benefits which would be lost upon working a full-time paid job.

###  State policies are needed to improve the income gains of those who have
left welfare for work

The benefits and taxes can create a wage floor below which increasing the
number of hours worked for certain subgroups does not bring any financial gain
in the short term.131313Derenoncourt and Montialoux (2020) provide a piece of
causal evidence on how minimum wage policy can play a critical role in
reducing the income disadvantage suffered by black workers in the United
States. Taxes and benefits while affecting earnings shape work incentives. If
earnings gains are insufficient to counter reductions in benefits, the number
of hours worked may decrease or be stagnant for certain subgroups. Therefore,
there is a need for a comprehensive strategy to assist individuals in the
subgroups where both earnings and hours worked do not change after the
democrat governors’ policies are implemented. In assisting these subgroups,
developing new innovative ways to deal with issues related to low levels of
education and job skills,141414As shown by Carruthers and Wanamaker (2017),
public sector discrimination in denying black public schools the same quality
available to white public schools played a predominant in determining wage
differences for black and white workers during the Jim-crow era in the United
States. weak public transportation systems, health problems, substance abuse,
and domestic violence, and discrimination in labor markets should be at the
forefront of a strategy that pursues poverty reduction while maximizing social
well-being for black workers.151515Darity et al. (2018) highlight complexities
surrounding commonly held myths about economic issues faced by black
households in the United States.

##  Conclusion

Understanding how black workers in the United States are affected by US state
policies in labor markets can shed light on new ways to improve or implement
public policymaking. While there is substantial literature on the aggregate
impact of labor policies on black workers, little is known on the
distributional impact of state policies among black workers in the labor
market. In this paper, we have contributed to this debate by highlighting the
distributional impact of Democrat governors’ policies across twelve subgroups
of black workers sorted on two dimensions: earnings quantiles and hours
worked. With respect to wages, we found that none of these subgroups
experienced wage increase after Democrat governor’s policies are implemented.
With respect to hours worked, only two subgroups increase their hours worked.
These are workers in the subgroup composed of Black workers in the first
quantile in hours worked and the third quantile in earnings ($Q_{1}$ Hours and
$Q_{3}$ Earnings) and the ones in the subgroup composed of black workers in
the second quantile in terms of hours worked and third-fourth in terms of
earnings ($Q_{2}$ Hours and $Q_{4}$ Earnings). In contrast, there is a
subgroup that decreases their average number of hours worked. This subgroup is
composed of people who are both in the lowest-earning quantile and highest
hours worked quantile. The causal empirical estimations highlight that the
vast majority of subgroups of black workers are not affected at all by
Democrat governors’ policies, suggesting the possible existence of structural
factors in the labor markets that contribute to create and keep a wage trap
and/or hour worked trap for most of the subgroups of black workers. Our
results also suggest the importance of incorporating a wide range of
heterogeneity at the household level while designing economic theoretical
models aiming at understanding the impact of state policies on black workers
in the US.

There is currently a great deal of discussion on how to improve the labor
markets outcomes for minorities in the US. One important insight from our
paper is that the distribution of earnings and hours worked among black
workers can have considerable significance on how the Democrat governor’s
labor policies affect them. Our results suggest that even within minority
ethnic groups in the US, accounting for heterogeneity in the distribution
impacts of policies implemented should not be overlooked and can provide more
knowledge on how to improve the social well-being overall. In designing
policies at the state level, it is important to take into account some
characteristics of labor markets suggesting that jobs with more black workers
do tend to pay less than other jobs (Huffman and Cohen, 2004), and according
to Boston (1990) the hypothesis that workers in the primary sector engage in
less frequent job changes than workers in the secondary sector is supported
for white workers but not for black workers, which negatively affect their
work experience. Black Americans have substantially lower rates of upward
mobility and higher rates of downward mobility than whites (Chetty et al.,
2020).161616Historically, the origins of economic issues faced by black
households could be traced back to the failure to provide the formerly
enslaved with the promised 40-acre land grants in the immediate aftermath of
the Civil War (Darity Jr. et al., 2021). The dynamic of labor markets is not
immune to the historical context in which existing social, economic, and
economic forces have been shaped. One example is provided by America (1990)
who questions the functioning of the labor markets while accounting for the
spillover effects of past racial institutions. Along the same lines, only 10%
of black households could be viewed as “solidly” middle-class (Darity Jr. et
al., 2021) in the United States. Another unusual disadvantage faced by black
workers is related to the nexus between the penal system and the labor market
(Western and Sirois, 2018). One in three Black men will be incarcerated over
his lifetime (US Joint Economic Committee, 2020), and this will
disproportionally hinder their future earning prospects (Western and Pettit,
2005).171717The consequences of mass incarceration on black communities are
far reaching - including greater health disparities, the destruction of the
black family, greater obstacles to human capital investment, and food
insecurity (Cox, 2018; Cox and Wallace, 2016). Finally, the staggering job
losses sparked by the coronavirus pandemic have disproportionately affected
Black communities and there is a concern that the COVID-19 recession is
expected to be felt by Black workers even once it concludes (Hotchkiss, 2021).

## References

  * America (1990) America, R. (1990): _The Wealth of Races : the Present value of Benefits from Past Injustices_ , New York: Greenwood Press, 1990.
  * Autor et al. (2008) Autor, D. H., L. F. Katz, and M. S. Kearney (2008): “Trends in US wage inequality: Revising the revisionists,” _The Review of economics and statistics_ , 90, 300–323.
  * Banzhaf et al. (2019) Banzhaf, S., L. Ma, and C. Timmins (2019): “Environmental Justice: The Economics of Race, Place, and Pollution,” _Journal of Economic Perspectives_ , 33, 185–208.
  * Bayer and Kerwin (2018) Bayer, P. and C. Kerwin (2018): “Divergent Paths: A New Perspective on Earnings Differences Between Black and White Men Since 1940,” _The Quarterly Journal of Economics_ , 133, 1459–1501.
  * Beland (2015) Beland, L. (2015): “Political Parties and Labor-market Outcomes: Evidence from us States,” _American Economic Journal: Applied Economics_ , 7, 198–220.
  * Bertanha (2020) Bertanha, M. (2020): “Regression Discontinuity Design with many Thresholds,” _Journal of Econometrics_ , 218, 216–241.
  * Boston (1990) Boston, T. (1990): “Segmented Labor Markets: New Evidence from a Study of Four Race-Gender Groups,” _ILR Review_ , 44, 99–115.
  * Carruthers and Wanamaker (2017) Carruthers, C. K. and M. Wanamaker (2017): “Separate and Unequal in the Labor Market: Human Capital and the Jim Crow Wage Gap,” _Journal of Labor Economics_ , 35, 655–696.
  * Chetty et al. (2020) Chetty, R., N. Hendren, M. Jones, and S. Porter (2020): “Race and Economic Opportunity in the United States: an Intergenerational Perspective,” _The Quarterly Journal of Economics_ , 135, 711–783.
  * Cox (2018) Cox, R. (2018): “Mass Incarceration, Racial Disparities in Health, and Successful Aging,” _Generations: Journal of the American Society on Aging_ , 42, 48–55.
  * Cox and Wallace (2016) Cox, R. and S. Wallace (2016): “Identifying the Link Between Food Security and Incarceration,” _Southern Economic Journal_ , 82, 1062–1077.
  * Cygan-Rehm and Wunder (2018) Cygan-Rehm, K. and C. Wunder (2018): “Do Working Hours Affect Health? Evidence from Statutory Workweek Regulations in Germany,” _Labour Economics_ , 53, 162–171.
  * Darity et al. (2017) Darity, W., D. Hamilton, P. Mason, G. Price, A. Davila, M. Mora, S. Stockly, A. Flynn, D. Warren, F. Wong, and et al. (2017): _Stratification Economics: A General Theory of Intergroup Inequality_ , Cambridge University Press, 35–51, Cambridge Studies in Stratification Economics: Economics and Social Identity.
  * Darity et al. (2018) Darity, W., D. Hamilton, M. Paul, A. Aja, A. Price, A. Moore, and C. Chiopris (2018): “What We Get Wrong About Closing the Racial Wealth Gap,” Insight center for community economic development, Samuel DuBois Cook Center on Social Equity.
  * Darity Jr. et al. (2021) Darity Jr., W., F. Addo, and I. Smith (2021): “A Subaltern Middle Class: The Case of the Missing “Black Bourgeoisie” in America,” _Contemporary Economic Policy_ , 39, 494–502.
  * Deaton and Cartwright (2018) Deaton, A. and N. Cartwright (2018): “Understanding and Misunderstanding Randomized Controlled Trials,” _Social Science & Medicine_, 210, 2–21.
  * Derenoncourt and Montialoux (2020) Derenoncourt, E. and C. Montialoux (2020): “Minimum Wages and Racial Inequality,” _The Quarterly Journal of Economics_ , 136, 169–228.
  * Donsimoni (2020) Donsimoni, J. (2020): “Work Intensity and Labour Supply,” _medRxiv_.
  * Frandsen et al. (2012) Frandsen, B., M. Frölich, and B. Melly (2012): “Quantile Treatment Effects in the Regression Discontinuity Design,” _Journal of Econometrics_ , 168, 382–395.
  * Giuliano et al. (2009) Giuliano, L., D. Levine, and J. Leonard (2009): “Manager Race and the Race of New Hires,” _Journal of Labor Economics_ , 27, 589–631.
  * Heckman (2001) Heckman, J. (2001): “Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture,” _Journal of Political Economy_ , 109, 673–748.
  * Heywood and Parent (2012) Heywood, J. and D. Parent (2012): “Performance Pay and the White-Black Wage Gap,” _Journal of Labor Economics_ , 30, 249–290.
  * Hotchkiss (2021) Hotchkiss, J. (2021): “Will COVID-19 Erase Black Workers’ Labor Market Gains?” Policy Hub 2021-2, Federal Reserve Bank of Atlanta.
  * Huang and Zhan (2020) Huang, X. and Z. Zhan (2020): “Local Composite Quantile Regression for Regression Discontinuity,” .
  * Huffman and Cohen (2004) Huffman, M. and P. Cohen (2004): “Racial Wage Inequality: Job Segregation and Devaluation Across U.S. Labor Markets,” _American Journal of Sociology_ , 109, 902–36.
  * Lee (2001) Lee, D. S. (2001): “The Electoral Advantage to Incumbency and Voters’ Valuation of Politicians’ Experience: A Regression Discontinuity Analysis of Elections to the US..” Tech. rep., National Bureau of Economic Research.
  * Leigh (2008) Leigh, A. (2008): “Estimating the Impact of Gubernatorial Partisanship on Policy Settings and Economic Outcomes: A Regression Discontinuity Approach,” _European Journal of Political Economy_ , 24, 256–268.
  * Manduca (2018) Manduca, R. (2018): “Income Inequality and the Persistence of Racial Economic Disparities,” _Sociological Science_ , 5, 182–205.
  * McGregor et al. (2020) McGregor, B., C. Li, P. Baltrus, M. Douglas, J. Hopkins, G. Wrenn, K. Holden, E. Respress, and A. Gaglioti (2020): “Racial and Ethnic Disparities in Treatment and Treatment Type for Depression in a National Sample of Medicaid Recipients,” _. Psychiatr Serv._ , 71, 663–669.
  * Ricci et al. (2007) Ricci, J., E. Chee, A. Lorandeau, and J. Berger (2007): “Fatigue in the U.S. Workforce: Prevalence and Implications for Lost Productive Work Time,” _Journal of Occupational and Environmental Medicine_ , 49, 1–10.
  * Sieber and Izraeli (2018) Sieber, S. and O. Izraeli (2018): “U.S. State Government Economic and Social Performance: Uni ed vs. Divided and Democrat vs. Republican Controlled State Governments,” _International Social Science Review_ , 94.
  * US Joint Economic Committee (2020) US Joint Economic Committee (2020): “The Economic State of Black America in 2020,” .
  * Western and Pettit (2005) Western, B. and B. Pettit (2005): “Black‐White Wage Inequality, Employment Rates, and Incarceration,” _American Journal of Sociology_ , 111, 553–578.
  * Western and Sirois (2018) Western, B. and C. Sirois (2018): “Racialized Re-entry: Labor Market Inequality After Incarceration,” _Social Forces_ , 97, 1517–1542.
  * Whatley and Sedo (1998) Whatley, W. and S. Sedo (1998): “Quit Behavior as a Measure of Worker Opportunity: Black Workers in the Interwar Industrial North,” _American Economic Review_ , 88, 363–367.
  * Williams and Rucker (2000) Williams, D. and T. Rucker (2000): “Understanding and Addressing Racial Disparities in Health,” _Health Care Financing Review_ , 21, 75–90.
  * Wolf (2015) Wolf, J. (2015): “Changes in Income Inequality Under Democratic and Republican Governors,” Master’s thesis, Indiana University-Purdue University Indianapolis, USA.
  * Wright (2012) Wright, J. (2012): “Unemployment and the Democratic Electoral Advantage,” _American Political Science Review_ , 106, 685–702.
  * Wrigley-Field and Seltzer (2019) Wrigley-Field, E. and N. Seltzer (2019): “Unequally Insecure: Rising Black/White Disparities in Job Displacement, 1981-2017,” _Minnesota Population Center Working Paper Series_.

##  Appendix A Appendix: Figures 5 to 11

Figure 5: Distribution of income by race Under a Democrat Figure 6:
Distribution of income by race Under a Republican Figure 7: Distribution of
income of whites over time under Dems vs Reps. Figure 8: Distribution of
income of blacks over time under Dems vs Reps. Figure 9: Distribution of
income by race 4-year split 1993–2008 (guess Race=1 is White) Figure 10:
Margin of Democratic Victory and the proportion of black (left on each panel)
and white (right on each panel) who worked. Figure 11: Margin of Democratic
Victory and log wages for black (left on each panel) and white (right on each
panel). The analysis is by Earning quartile.

##  Appendix B Appendix: Tables 3 to 10

Table 3: Two-sample Kolmogorov-Smirnov test for equality of distribution functions Smaller group | D | P-value
---|---|---
Blacks | 0.0005 | 0.904
White | -0.1127 | 0.000
Combined | 0.1127 | 0.000
Table 4: Proportion of blacks by quartile of earning / hours worked

| Quartile (Total Earnings) |
---|---|---
Quartile (Hours) |  $Q_{1}$ Earning |  $Q_{2}$ Earning |  $Q_{3}$ Earning |  $Q_{4}$ Earning |
$Q_{1}$ Hours | 12.22% | 9.83% | 8.62% | 7.36% |
| (5.63%) | (13.92%) | (1.68%) | (0.47%) |
$Q_{2}$ Hours | 11.21% | 13.16% | 11.74% | 8.35% |
| (0.38%) | (21.72%) | (28.35%) | (15.43%) |
$Q_{3,4}$ Hours | 2.72% | 8.30% | 6.97% | 4.30% |
| (0.04%) | (1.79%) | (4.72%) | (5.87%) |

  * •

Notes: Percentages in parenthesis are computed as the number of blacks in a
given quartile combination divided by the total number black in the entire
sample. In contrast, percent without parenthesis is the with group proportion
of blacks.

Table 5: RD estimates for total hours worked: Earnings as Quartile

| (1) | (2) | (3) | (4) | (5)
---|---|---|---|---|---
| Beland (2015, Table 2) |  $Q_{1}$ Earnings |  $Q_{2}$ Earnings |  $Q_{3}$ Earnings |  $Q_{4}$ Earnings
Democrat | -0.006 | -0.025 | 0.004 | 0.005 | 0.002
| (-1.06) | (-0.74) | (0.53) | (1.38) | (1.05)
Black $\times$ Democrat |  0.038∗∗ | 0.035 | -0.011 | 0.005 |  0.013∗∗
| (2.09) | (0.35) | (-0.54) | (0.58) | (2.32)
Black |  -0.065∗∗∗ | -0.088 |  0.028∗∗ |  -0.009∗∗ |  -0.041∗∗∗
| (-5.61) | (-1.23) | (2.39) | (-2.59) | (-12.56)
Constant |  -4.244∗∗∗ |  3.420∗∗∗ | 0.292 |  5.852∗∗∗ |  5.782∗∗∗
| (-10.49) | (3.40) | (0.79) | (43.95) | (29.14)
$N$ | 1912653 | 94770 | 606923 | 605602 | 605358

  * •

Notes: Quartiles are governed by wages. Control variables include highest
level of education, marital status, age, age two, age three, age four, a
female dummy, state fixed effects, and year fixed effects. Outcome variables
are expressed in log form. Results are clustered at the state level. t
statistics in parentheses.

  * •

*** Significant at the 1 percent level.

  * •

** Significant at the 5 percent level.

  * •

* Significant at the 10 percent level.

Table 6: RD Estimate Effect for Earnings: Earnings as Quartile

| (1) | (2) | (3) | (4) | (5)
---|---|---|---|---|---
| Beland (2015, Table A2) |  $Q_{1}$ Earning |  $Q_{2}$ Earning |  $Q_{3}$ Earning |  $Q_{4}$ Earning
Democrat |  -0.024∗∗ | -0.038 | 0.004 | -0.002 | -0.005
| (-2.16) | (-1.60) | (0.78) | (-0.82) | (-0.92)
Black $\times$ Democrat |  0.050∗ | -0.011 | -0.001 | 0.004 | 0.009
| (1.99) | (-0.18) | (-0.04) | (0.66) | (0.68)
Black |  -0.164∗∗∗ | 0.022 | -0.007 |  -0.016∗∗∗ |  -0.073∗∗∗
| (-10.34) | (0.59) | (-0.66) | (-3.15) | (-9.24)
$N$ | 1921186 | 94873 | 608775 | 609851 | 607687

  * •

Notes: Quartiles are governed by wages. Control variables include highest
level of education, marital status, age, age two, age three, age four, a
female dummy, state fixed effects, and year fixed effects. Outcome variables
are expressed in log form. Results are clustered at the state level. t
statistics in parentheses.

  * •

*** Significant at the 1 percent level.

  * •

** Significant at the 5 percent level.

  * •

* Significant at the 10 percent level.

Table 7: RD estimates for total hours worked: Hours as Quartile

| (1) | (2) | (3) | (4) |
---|---|---|---|---|---
| Beland (2015, Table A2) |  $Q_{1}$ Hours |  $Q_{2}$ Hours |  $Q_{3}$ Hours |
Democrat | -0.006 | -0.006 | -0.000 | -0.000 |
| (-1.06) | (-0.52) | (-0.29) | (-0.32) |
Black $\times$ Democrat |  0.038∗∗ |  0.080∗∗ | 0.001 | 0.007 |
| (2.09) | (2.33) | (0.32) | (0.79) |
Black |  -0.065∗∗∗ |  -0.106∗∗∗ |  0.006∗∗∗ | 0.007 |
| (-5.61) | (-4.23) | (4.28) | (1.11) |
| (-10.49) | (3.59) | (213.90) | (93.24) |
$N$ | 1912653 | 387536 | 1103159 | 421958 |

  * •

Notes: Quartiles are governed by total hours worked. Control variables include
highest level of education, marital status, age, age two, age three, age four,
a female dummy, state fixed effects, and year fixed effects. Outcome variables
are expressed in log form. Results are clustered at the state level. t
statistics in parentheses.

  * •

*** Significant at the 1 percent level.

  * •

** Significant at the 5 percent level.

  * •

* Significant at the 10 percent level.

Table 8: RD Estimate Effect for Earnings: Hours as Quartile

| (1) | (2) | (3) | (4) |
---|---|---|---|---|---
| Beland (2015, Table A2) |  $Q_{1}$ Hours |  $Q_{2}$ Hours |  $Q_{3}$ Hours |
Democrat |  -0.024∗∗ |  -0.033∗ |  -0.023∗∗∗ | -0.010 |
| (-2.16) | (-1.88) | (-2.86) | (-0.79) |
Black $\times$ Democrat |  0.050∗ | 0.063 | 0.033 | -0.011 |
| (1.99) | (1.29) | (1.60) | (-0.36) |
Black |  -0.164∗∗∗ |  -0.160∗∗∗ |  -0.100∗∗∗ |  -0.120∗∗∗ |
| (-10.34) | (-4.28) | (-6.51) | (-6.07) |
$N$ | 1921186 | 396069 | 1103159 | 421958 |

  * •

Notes: Quartiles are governed by total hours worked. Control variables include
highest level of education, marital status, age, age two, age three, age four,
a female dummy, state fixed effects, and year fixed effects. Outcome variables
are expressed in log form. Results are clustered at the state level. t
statistics in parentheses.

  * •

*** Significant at the 1 percent level.

  * •

** Significant at the 5 percent level.

  * •

* Significant at the 10 percent level.

Table 9: Dependent variable, Wages (Earnings)

| Quartile (Total Earnings) |
---|---|---
Quartile (Hours) |  $Q_{1}$ Earning |  $Q_{2}$ Earnings |  $Q_{3}$ Earnings |  $Q_{4}$ Earnings |
$Q_{1}$ Hours | 0.003 | 0.004 | 0.020 | -0.130 |
| (0.06) | (0.19) | (0.93) | (-1.30) |
$Q_{2}$ Hours | -0.348 | 0.004 | 0.005 | 0.013 |
| (-1.35) | (0.38) | (0.81) | (0.97) |
$Q_{3}$ Hours | 0.591 | -0.048 | -0.001 | 0.011 |
| (0.34) | (-0.92) | (-0.07) | (0.51) |

  * •

Notes: Control variables include highest level of education, marital status,
age, age two, age three, age four, a female dummy, state fixed effects, and
year fixed effects. Outcome variables are expressed in log form. Results are
clustered at the state level. t statistics in parentheses.

  * •

*** Significant at the 1 percent level.

  * •

** Significant at the 5 percent level.

  * •

* Significant at the 10 percent level.

Table 10: Dependent variable, Total Hours

| Quartile (Total Earnings) |
---|---|---
Quartile (Hours) |  $Q_{1}$ Earning |  $Q_{2}$ Earnings |  $Q_{3}$ Earnings |  $Q_{4}$ Earnings |
$Q_{1}$ Hours | 0.069 | -0.003 |  0.129∗∗∗ | -0.010 |
| (0.83) | (-0.16) | (2.81) | (-0.06) |
$Q_{2}$ Hours | -0.035 | -0.008 | 0.001 |  0.004∗∗ |
| (-0.97) | (-1.49) | (0.21) | (2.08) |
$Q_{3}$ Hours |  -0.293∗∗ | -0.002 | 0.008 | 0.014 |
| (-2.45) | (-0.11) | (0.77) | (1.15) |

  * •

Notes: Control variables include highest level of education, marital status,
age, age two, age three, age four, a female dummy, state fixed effects, and
year fixed effects. Outcome variables are expressed in log form. Results are
clustered at the state level. t statistics in parentheses.

  * •

*** Significant at the 1 percent level.

  * •

** Significant at the 5 percent level.

  * •

* Significant at the 10 percent level.