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Minimum Wage and National Culture Minimum Wage and National Culture
Zhongyu Cao
Part of the Economics Commons
Recommended Citation Recommended Citation Cao, Z.(2019). Minimum Wage and National Culture. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/5263
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By
For the Degree of Doctor of Philosophy in
Economics
University of South Carolina
McKinley Blackburn, Committee Member
Orgul Ozturk, Committee Member
Andrew Hill, Committee Member
Cheryl L. Addy, Vice Provost and Dean of the Graduate School
ii
All Rights Reserved.
iii
ACKNOWLEDGEMENTS
I would like to express the deepest appreciation to my committees, Yi (Crystal)
Zhan, McKinley Blackburn, Orgul Ozturk, and Andrew Hill, for their extremely valuable
support and guidance on both my dissertation and academic path. I very much appreciate
my co-authors, Sadok El Ghoulb, Omrane Guedhamic, Andrew Hill, and Chuck Kwok,
who trained me to become an academic when I work with them. I would like to
acknowledge faculty, staff, and other Ph.D. students in Darla Moore School of Business
for their valuable advice on my research. I also thank the University of South Carolina for
providing the scholarship for me to finish the Ph.D.
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ABSTRACT
Minimum wage adjustment affects economic agent behaviors and leads to an
unintended side effect s. The first chapter of the dissertation studies the impact of the
minimum wage level on the college enrollment. Using institution-level data from IPEDS,
the results show that the real minimum wage level has significant and diverse effects on
different types of enrollment. The results are robust using several alternative specifications
and individual level analysis. The second chapter of the dissertation addresses another side
effect of the minimum wage law: crime. Using U.S. state-level arrest and offense data and
agency-level offense data, this chapter provides empirical evidence that an increase in the
minimum wage leads to less crime. This effect mainly applies to adult but not to youth.
The third chapter of the dissertation studied the impact of national culture on income
inequality, especially Individualism vs. Collectivism aspect. Using a variety of empirical
method to test the effect and mitigate the endogenous problem, the results show that
collectivist societies are associated with higher income inequality, and wealth concentrates
to the richest population.
1.1 INTRODUCTION ................................................................................................. 1
1.3 DATA .................................................................................................................... 11
1.4 RESULTS ............................................................................................................. 13
2.1 INTRODUCTION ............................................................................................... 28
2.4 RESULTS ............................................................................................................. 37
3.1 INTRODUCTION ............................................................................................... 49
3.4 EMPIRICAL RESULTS ...................................................................................... 63
3.5 ROBUSTNESS CHECK ..................................................................................... 78
vii
Table 1.1: Sample Distribution…………………………………………………………12
Table 1.2: Descriptive Statistics……………………………………………………...…12
Table 1.3: Effect of Minimum Wage on Two-year Public Colleges……………….…….15
Table 1.4: Effect of Minimum Wage on Other Types of College………………………..18
Table 1.5: Robustness Check……………………………………………………………20
Table 1.6: Heterogeneous Effect for Two-year Public Colleges…………………………22
Table 1.7: Individual Level Analysis………………………...…………………………..25
Table 1.8: Individual Level Analysis without Some Controls………………………...…26
Table 2.1: Descriptive Statistics for State-level Arrest Rates 1994-2012…….………….33
Table 2.2: Types of Crimes………………………………………………………………34
Table 2.3: Linear Models with Different Fixed Effects……………………………...…38
Table 2.4: Linear Models with State-level Controls and Fixed Effects………………...40
Table 2.5: Log-log Models with State-level Controls and Fixed Effects………………...42
Table 2.6: Linear Models with Dynamic Controls………………………………….……44
Table 2.7: Linear Models for State-level Offense Rates………………………….…….45
Table 2.8: Log-log Models for State-level Offense Rates………………………...…….46
Table 2.9: Models for Agency-level Incident Data…………………………………..…47
viii
Table 3.3: Random Effects Models………………………………………...…………….65
Table 3.4: Random Effects Models with Controls…………………………………...…66
Table 3.5: Income Share of Bottom 20% Poorest Population…………………..………..69
Table 3.6: Income Share of 20%-40% Poorest Population…………………………..…..70
Table 3.7: Income Share of 40%-60% Poorest Population………………………...…….71
Table 3.8: Income Share of 60%-80% Poorest Population…………………………...….72
Table 3.9: Income Share of Richest 20% Population………………………………….…73
Table 3.10: GLOBE Culture Variables………………………………………………...…76
Table 3.11: Would Value Survey Questions…………………………………...…………80
Table A.1: Descriptive Statistics for State-level Arrest Rate 1994-2012…………………93
Table A.2: Descriptive Statistics for State-level Offense Rates 1994-2012……...……….94
Table A.3: Descriptive Statistics for Agency-level Incidence Counts 2005-2012………..94
Table A.4: Linear Models for Adult Arrest Rates…………………………………….…..94
Table A.5: Linear Models for Youth Arrest Rates………………………………………...96
Table B.1: Distribution of Sample………………………………………………………..98
Table B.2: Variable Definition and Source……………………………………….……100
1
1.1 INTRODUCTION
How does minimum wage affect the college enrollment? The minimum wage
draws remarkable attention from policymakers and scholars. According to the Bureau of
Labor Statistics (hereafter BLS), in 2012, the minimum wage law affects around 3.6 million
U.S. workers including tipped workers. The federal minimum wage level is 7.25 dollar per
hour since 2009. Some states have their own minimum wage law and enforce a minimum
wage higher than the federal level. For example, workers in California get paid at minimum
nine dollars per hour in 2015. Due to the large public attention, the influence of minimum
wage had been long studied by scholars. However, most studies focus on the effect on labor
market outcomes such as unemployment or income distribution. So far, no study had linked
the minimum wage to college enrollment.
The investment in human capital is affected by both willingness and ability to pay
for education (Dellas and Sakellaris, 2003). The minimum wage can affect both factors.
On one hand, increasing minimum wage increases the wage of low-skilled workers who
earn the minimum wage. Higher minimum wage level can increase the return to working
1 Cao, Zhongyu and Andrew Hill. To be submitted.
2
for both high-school graduates and college graduates. However, minimum wage mainly
affects low-skilled workers. The expected wage increase should be larger for high-school
graduates than college graduates. This difference enlarges the opportunity cost of attending
a college, leads to a substitution effect. On the other hand, a rise in the minimum wage
level also increases students’ individual and family income, which makes the college
education more affordable (income effect). Therefore, theoretically, the effect of minimum
wage level on college enrollment is a combination of the substitution effect and the income
effect. Furthermore, the reaction of the new college students and the returning students
should be different facing a higher minimum wage level. The returning students are
systematically different from the new students because they have already chosen to attend
a college before and should have a higher expected return to college education. This may
reduce the substitution effect of the returning students. Furthermore, four-year college
students may also have a higher expected wage than community college students. This may
also change the size of substitution effect. Additionally, the tuition of private colleges is
much higher than public colleges. This high cost may reduce the income effect. Therefore,
theoretically, the effect of minimum wage level on college enrollment is unclear. The
further empirical analysis is needed to discover the relationship.
Taking advantage of comprehensive college-level data from Integrated
Postsecondary Education Data System, I test the effect of minimum wage level on
enrollment at two-year public colleges, two-year private colleges, four-year public colleges,
and four-year private colleges covering 3,893 institutions from 2000 to 2015 in U.S. I find
3
that increasing minimum wage level can significantly reduce the part-time freshman
enrollment and increase full-time non-freshman enrollment for two-year public colleges;
increase full-time and part-time freshman enrollment and full-time non-freshman
enrollment for four-year public college; and increase full-time non-freshman enrollment
for four-year private colleges. The results are robust to several tests, such as excluding
unemployment rate, using alternative model, introducing interaction terms between college
characteristics and the minimum wage level, and using Current Population Survey data to
conduct individual level analysis. This paper contributes both to previous enrollment
demand research and minimum wage literature. These findings are important for
policymakers and colleges to forecast student numbers for planning and budgeting facing
the unintentional side effect of minimum wage law adjustment.
The remainder of the paper is organized as follows. The second section develops
theoretical structure and constructs the empirical model. The third section describes data.
The fourth and fifth sections report the results of the main tests and the robustness checks,
and the sixth section concludes the paper.
1.2 THEORY AND MODEL
College enrollment has been long studied. There are extended literature exploring
the factors that can affect the college enrollment, such as students tending to enroll in
colleges during the recessions. Specifically, higher unemployment rate leads to higher
college enrollment (e.g. Betts and McFarland, 1995; Dellas and Sakellaris, 2003; Hillman
4
and Orians, 2013), especially at two-year colleges. Furthermore, family income plays an
important role by affecting the budget constraint. For example, Lovenheim (2011) finds
that housing wealth raises can increase college enrollment. Similarly, a large amount of
literature points out the importance of financial aid on the college choice (e.g. Van der
Klaauw, 20 02). Moreover, Kane (1994) finds that return of college education and the
opportunity cost of attending college may play a role in the college enrollment. However,
Betts and McFarland (1995) find that the earnings of the high-school graduates, two-year
college graduates and four-year college graduates do not significantly affect two-year
college enrollment.
Therefore, following Betts and McFarland (1995), I build a simple income
maximization model of educational choice. Assume that a high school graduate has three
educational choices available: to work directly, to attend a two-year college then work, and
to attend a four-year college then work. The student will choose a path to maximize the
expected future return subject to the student’s budget constraint:
max()
where E denotes expectations of the present value of the expected wage, which is
− =∑ ,−/(1 + )
=1
=3 − 2−
=5 − 4−
where W is the earning in period t with respect to education level, and (1 + ) is the
discount factor. Cost is the cost of education, depending on tuition and financial aid.
5
Given the model, one should expect two-year college enrollment will rise with an
increase of the expected earning of two-year college graduates, financial aid for two-year
college, tuitions for four-year college (when four-year college is not affordable, some
students may attend a two-year college first and then transfer to a four-year college), and
student’s individual or family income level. Two-year college enrollment will fall with an
increase of the expected earnings of high-school graduates, tuition at two-year colleges,
and financial aid for four-year colleges. The effect on the expected earnings of four-year
college graduates may be ambiguous. A rise in the returns to the four-year college may
reduce two-year college enrollment due to the substitution effect. However, if many
students choose to go to a two-year college with the intention of transferring to a four-year
college, the effect will be in the opposite way (Betts and McFarland, 1995).
Similarly, four-year college enrollment should rise with an increase of the
expected earnings of four-year college graduates, financial aid for four-year colleges,
tuition at two-year colleges, and student’s individual or family income levels. Four-year
college enrollment will fall with an increase of the expected earnings of high-school
graduates, the expected earnings of two-year college graduates, tuition at four-year colleges,
and financial aid for two-year colleges.
Furthermore, the current unemployment rate will affect the choice of education.
When the unemployment rate is high, the current expected earning for high-school
graduates is lower because the probability of finding a job is lower. Thus, during recessions,
students tend to go to college when the current return is lower than the future return (Dellas
6
and Sakellaris, 2003), and vice versa during expansions. Therefore, college enrollment
shows a counter-cyclical pattern.
The minimum wage laws can likewise affect the expected return to postsecondary
education, the opportunity cost of attending a college (expected earnings of high-school
graduate), the budget constraints (individual or family income), and unemployment.
Minimum wage law sets a price floor in labor markets. If binding, the adjustment can
directly increase the income of workers who are paid below the new minimum wage level.
Those workers are relatively younger and less-skilled than the average workers (BLS,
2012). Neumark et al. (2004) also point out that low-wage workers are most strongly
affected while higher-wage workers are barely affected. A higher minimum wage increases
the lower boundary of the wage distribution for all education levels. According to BLS, in
2012, 29.5% of workers paid at or below minimum wage had a high-school diploma; 28.4%
of workers were college students and college dropouts; 6.3% of workers had an associate
degree; and 8% of workers had a bachelor's degree and higher education level. A change
in minimum wage should have a larger effect on high-school graduates than college
graduates. Therefore, in the education choice model, an increase of the minimum wage
level should have a relatively larger effect on the expected earnings of high-school
graduates than the expected earnings of college graduates, thus, increasing the opportunity
cost of attending a college. Students at the margin between work and college may be more
likely to substitute college education with work. Therefore, due to this “substitution effect”,
a higher minimum wage level should reduce college enrollment. Furthermore, the return to
7
the community college is lower than four-year college (Kane and Rouse, 1995). Given the
relatively higher earnings of high-school graduates when the minimum wage increases, one
should expect that the substitution effect to be larger for two-year colleges than four-year
colleges.
On the other hand, a rise in minimum wage also leads to a higher individual or
family income, making the college more affordable. Therefore, a higher minimum wage
level can ease the budget constraints, thus, increase the college enrollment, which can be
called “income effect”. Furthermore, two-year colleges have a much lower cost than four-
year colleges (Kane and Rouse, 1999). A change in income may have a larger effect on the
two-year college enrollment. Additionally, students in community college have lower
savings and have little or no access to their parents' assets than four-year college students
(Betts and McFarland, 1995), which also enlarge the income effect. However, the income
effect may work oppositely for students who are more financially restricted and cannot
afford four-year colleges. The students with a tight budget constraint may have to choose
to attend a community college rather than a four-year college, or plan to attend a
community college first and then transfer to a four-year college. If the income effect from
higher minimum wage level is large enough, those students may choose to go the four-year
college directly, which reduces two-year college enrollment and increases four-year college
enrollment.
Combining the substitution effect and the income effect, the overall effect of
increasing minimum wage level on two-year and four-year college enrollment is still
8
Moreover, the college enrollment is affected by unemployment rate. The
minimum wage law may have some effect on the college enrollments though
unemployment. However, there is still not a consensus on unemployment effect due to the
minimum wage law. Some scholars believe that increasing minimum wage level will raise
the cost of the employer, and therefore would adversely affect employment and working
hours for low-wage, low-skill workers who were paid below the new minimum wage level,
especially youth (e.g. Currie and Fallick, 1993; Neumark et al., 2004; Neumark and
Wascher, 2007). Other scholars argue that minimum wage level does not have a strong
effect on unemployment (e.g. Card and Krueger, 1993;). However, Giuliano (2013) finds
that a higher minimum wage level increases teenagers’ employment. Therefore, the effect
minimum wage law adjustment on the college enrollment may be undetermined through
unemployment.
The above theoretical argument is based on the choice of high-school graduates
on whether attending a college for the first time (hereafter freshman choice model).
However, the choice model for the current college students to choose whether stay in the
college or the college dropouts to choose whether return to the college (hereafter non-
freshman choice model) is different. First, the expected earning as a high-school graduate
is substituted by the expected earning with some college if a student chooses to drop out
college or stay as a dropout. The expected return to two-year college and four-year college
stay unchanged. Second, one should expect that the students in two models have
9
systematically different characteristics. The students in the non-freshman choice model are
the ones who already choose to attend the college in the freshman choice model. Therefore,
these students may have higher ability and expected return to college due to self-selection
in the freshman choice model. Other than these two points, the theoretical argument on the
effect of minimum wage on the non-freshman choice model should be same as the
freshman choice model: the substitution effect reduces enrollment and income effect
increases enrollment2. However, the size of these two effects may differ across the two
models.
To sum up, the net effect of minimum wage on the college enrollment is
theoretically ambiguous. Therefore, the following empirical model is estimated:
()
+ 4 () + 5 (−,) + 6 (−,)
+ 7 (2−,) + 8 (4−,)
+ 9(16~19) + 10(20~24)
+ 1116~19 + 1220~24 + +
∗ + +
where is the enrollment in institution i, located in state s, in year t.
2 The income effect for students who plan to transfer from a two-year college to a four-
year college is still ambiguous because higher income makes both type of college more
affordable.
10
is the real effective minimum wage level implemented, which equals to the
federal minimum wage level or the state minimum wage level (whichever is higher). Then,
three institutional level variables are introduced into the model. is the
percentage of first-time degree seeking students who receive any financial aid3. is
the average amount of financial aid received by each first-time degree seeking student from
federal, state, local, and institution program. is the tuition fee reported by the
institution. To capture local labor market conditions, a few state level variables are
controlled. W is the state level average income of workers with respective education level,
which can capture the expected return to different choice. Population of age 16 to 19 and
population of age 20 to 24 are also included. One should expect population of youth is
positively related to the college enrollment. is the state level unemployment
of age group 16 to 19 and 20 to 24. captures the year fixed effect. ∗
captures the unobserved state specific trend. is the college fixed effect
which also capture state fixed effect. Following previous literature studying college
enrollment (e.g. Betts and McFarland, 1995; Hillman and Orians, 2013), a log-log model
is adopted. Therefore, all variables are transformed into their natural log except for the
percentage of student receiving aid and the unemployment rate.
3 Due to the data limitation, only the financial aid variables for the freshman are
availability rather than all undergraduate students.
11
Data System (IPEDS). This dataset covers almost all the higher-education institutions in
the US. because it is mandatory for institutions to report to the system in order to receive
any federal financial aid. The institution-level data include enrollment for the full-time
freshman, part-time freshman, total number of full-time undergraduate students, total
number of part-time undergraduate students, the percentage of freshmen receiving any
financial aid, the average amount of financial aid a freshman received from federal, state,
local, and institution program, and in-district tuition fee4. The institutions are categorized
into four groups: public two-year college, private two-year college, public four-year
college, and private four-year college. The sample distribution is reported in Table 1.1. The
final sample period ranges from 2000 to 2015 5 . The state-level population and
unemployment rate data are obtained from the BLS. The state-level average income of
people with different education levels is calculated from the March Current Population
Survey6. The summary statistics for state level and institution level variables are reported
in Table 1.27.
4 Colleges may charge a different tuition depending on student’s residency. In-district
and out-of-district tuitions (or in-state and out-of-state tuitions) are normally highly
correlated. Thus, only in-district tuition is included in the model. 5 The availability of the financial aid data limits the sample period. 6 The average income is weighted using the CPS final weight. 7 The number of observations for part-time enrollment is smaller because some colleges
do not provide part-time enrollment.
12
Two-year Public College 13,127 1,034
Two-year Private College 3,048 565
Four-year Public College 9,308 669
Four-year Private College 18,673 1625
Total 44,156 3,893
This table reports the number of observations and institutions by different types of college of
the final sample.
State-level Variables
Income with Some College 44,173 27621.3 2960.009 18418.89 39940.27
Income with Associate Degree 44,173 34897.69 4175.458 18911.62 53962.85
Income with Bachelor's or More 44,173 58387.79 7231.48 35284.02 86111.8
Population 16-19 44,173 599.7844 522.0074 20 2222
Population 20-24 44,173 749.9103 671.6121 28 3051
Unemployment Rate 16-19 44,173 18.89 5.66 5.90 49.90
Unemployment Rate 20-24 44,173 10.75 3.33 3.40 22.60
Two-year Public Colleges
Percentage of Student Receiving
Average Amount of Aid 13,127 6,190.60 1,963.15 30 24,984.00
In-district Tuition 13,127 2440.741 1226.867 11 13900
This table reports descriptive statistics for state level and college level variables
Table 1.2: Descriptive Statistics (Continued)
Variable Observations Mean Std.Dev. Min Max
Two-year Private Colleges
13
Percentage of Student Receiving Aid 3,053 88.78 14.57 0 100.00
Four-year Public Colleges
Four-year Private Colleges
This table reports descriptive statistics for state level and college level variables
1.4 RESULTS
The college enrollment may be highly dependent on the college characteristics,
such as capacity limitation, admission requirement, etc. Due to these college-specific
heterogeneities, the OLS estimator may bias. Therefore, a college fixed effect model is
estimated to test the effect of minimum wage on college enrollment to mitigate the bias
from the unobserved time-invariant factors8. Throughout the paper, all regressions include
8 Random effects model is also estimated. But, the rejection of Hausman test indicates
that it underperforms the fixed effects model.
14
the year fixed effects, state-specific trends, college fixed effects, and the standard error is
clustered at the college level.
Table 1.3 Panel A reports the estimation result of the simple fixed effect model
without institution and state level controls for two-year public colleges. The log of real
effective minimum wage does not have a significant effect on the log of full-time freshman
enrollment. However, a 1 percent increase of the minimum wage significantly reduces the
part-time freshman enrollment by 0.256 percent. This effect is not only statistically
significant but also economically meaningful. For example, if the federal minimum wage
raised from 6.55 dollar to 7.25 dollar in 2009, the part-time freshman enrollment for public
community college should increase by 2.73 percent all over the U.S. There are two possible
explanations for the different effect between the full-time and the part-time freshman
enrollment. First, high-school graduates may not consider the change in the labor market
condition caused by the minimum wage adjustment when making choice to attend a public
community college for full-time. Betts and McFarland (1995) point out that part-time
students are generally older and more experienced in the labor market. Thus, they may have
better knowledge on the labor market conditions and respond to the change of minimum
wage. Second, the high-school graduates who want to enroll full-time may respond to the
increase of the minimum wage level. But the substitution effect and income effect have the
similar size for the full-time freshman, and the substitution effect is larger for the part-time
freshman. Empirically, one cannot distinguish the two possible explanations of the different
effect on freshman. Moreover, the effect of non-freshman enrollment shows an opposite
15
pattern. A higher minimum wage level significantly increases the full-time non-freshman
enrollment, but not the part-time non-freshman enrollment. As argued in Section 2, college
students and college dropouts may be systematically different from high-school graduates.
They may have a higher expected return to the college education (because they have
already chosen to attend college). Therefore, the substitution effect for non-freshman may
be smaller than the high-school graduates. Thus, combining the substitution effect and the
income effect, the overall effect may move to the positive side.
Table 1.3: Effect of Minimum Wage on Two-year Public Colleges
Panel A: Fixed Effect Model without Controls
Log Freshman Log Non-freshman
Full-time Part-time Full-time Part-time
(0.0701) (0.116) (0.0434) (0.0511)
Fixed Effects and Trends Yes Yes Yes Yes
Observations 13,127 13,018 13,083 12,971
Number of Institutions 1,034 1,024 1,027 1,018
Panel B: Fixed Effect Model with Controls
(0.0694) (0.116) (0.0433) (0.0513)
Percentage of Student Receiving
(0.0184) (0.0427) (0.0150) (0.0194)
(0.0414) (0.0598) (0.0285) (0.0315)
(0.0485) (0.0840) (0.0320) (0.0409)
16
More
(0.0572) (0.105) (0.0457) (0.0506)
(0.0607) (0.104) (0.0422) (0.0465)
(0.000991) (0.00159) (0.000715) (0.000748)
(0.00173) (0.00310) (0.00142) (0.00142)
Observations 13,127 13,018 13,083 12,971
Number of Institutions 1034 1024 1027 1018
This table reports results from fixed effects regressions of log of two-year public college enrollment on
log of real effective minimum wages. Each column is a separate regression. All estimations include year
fixed effects, college fixed effects and state trends. Standard errors are clustered at institution level. ***
p<0.01, ** p<0.05, * p<0.1
Furthermore, institution and state level control variables are added into the model.
The results are shown in Table 1.3 Panel B. The significance level and the magnitudes do
not noticeably change. The effect of minimum wage remains negative on the part-time
freshman enrollment and positive on the full-time non-freshman enrollment for two-year
public college. Furthermore, consistent with the previous literature, financial aid seems to
increase the non-freshman enrollment. The cost of college is negatively related to the
enrollment. The average income for different education levels generally does not
significantly affect the public community college enrollment, although an unexpected sign
for average income with an associate degree on full-time non-freshman enrollment appears.
17
As expected, youth population is positively related to enrollment. Also, the college
enrollment shows a counter-cyclical pattern for full-time students but not for part-time
students.
Then, the same fixed effect model with all the controls is also applied to the other
three types of colleges. The results are shown in Table 1.4. Panel A shows that there is no
significant effect on all types of enrollment for two-year private college. The possible
explanation is that the cost of two-year private college is high (mean tuition is 2,441 dollars
for public colleges and 12,261 dollars for private colleges), the students need to have a high
expected return to two-year private college and a high income to afford it. This reduces
both the substitution effect and the income effect, then the overall effect. Panel B reports
the results for four-year public college enrollments. A higher minimum wage level
significantly increases the full-time and part-time freshman enrollment and the full-time
non-freshman enrollment. The effect on part-time freshman enrollment is opposite to two-
year public colleges. As argued in Section 2, the return to four-year college is higher than
the two-year college. Thus, for freshman, the substitution effect may be weaker, and the
income effect dominants, making the coefficient positive. Another explanation is also
possible: the student who planning to go to a community college (and may then transfer to
a four-year college) may choose to go to a four-year college directly, given higher income.
Empirically, one cannot distinguish these two explanations. Furthermore, the effect on part-
time freshman is more significant economically: when the federal minimum wage rises
from 6.55 dollar to 7.25 dollar, the part-time freshman enrollment for public four-year
18
college increases by 7.3 percent all over the U.S. On the other hand, the effect on part-time
non-freshman is insignificant. It is possible that more part-time non-freshmen receive a
higher income than the minimum wage level since they are already in the labor force and
have more working experience. Thus, an increase in the minimum wage level does not
strongly affect part-time non-freshmen. Panel C reports the results of four-year private
college enrollment. Only full-time non-freshman enrollment is significantly affected.
Given a higher minimum wage level, more students stay in or return to the four-year private
college.
Table 1.4: Effect of Minimum Wage on Other Types of College
Panel A: Fixed Effect Model for Two-year Private Colleges
(0.236) (0.714) (0.281) (0.446)
Observations 3,048 1,584 2,942 1,496
Panel B: Fixed Effect Model for Four-year Public Colleges
(0.0427) (0.186) (0.0277) (0.0565)
Observations 9,308 8,955 9,307 8,955
Panel C: Fixed Effect Model for Four-year Private Colleges
(0.0680) (0.228) (0.0527) (0.108)
Observations 18,673 11,429 18,640 11,382
This table reports results from fixed effects regressions of log of college enrollment on log of real effective
minimum wages. Each column is a separate regression. All estimations include year fixed effects, college
fixed effects and state trends. Standard errors are clustered at institution level. *** p<0.01, ** p<0.05, *
p<0.1
To sum up, the minimum wage level can significantly reduce the part-time
freshman enrollment and increase full-time non-freshman enrollment for two-year public
colleges; increase full-time and part-time freshman enrollment and full-time non-freshman
enrollment for four-year public college; and increase full-time non-freshman enrollment
for four-year private colleges.
1.5 ROBUSTNESS CHECK
Excluding Unemployment Rate
One may argue that minimum wage adjustment can affect the local unemployment
rate. Controlling the unemployment rate in the model may draw the explanatory power of
the minimum wage and bias the coefficient. Therefore, I replicate the same regressions for
each type of college without controlling unemployment rates. The results for two-year
public college enrollments are shown in Table 1.5 Panel A. The results do not change
noticeably compared with the results from the estimations with unemployment rates. There
is also no outstanding change on the significant level or the magnitude of the coefficients
20
Table 1.5: Robustness Check
Panel A: Fixed Effect Model for Two-year Public Colleges without Unemployment Rate
(0.0706) (0.116) (0.0433) (0.0518)
Observations 13,127 13,018 13,083 12,971
Panel B: Linear Fixed Effect Model for Two-year Public Colleges
Number of Freshman Number of Non-freshman
(7.529) (8.728) (14.91) (27.50)
Observations 13,129 13,020 13,129 13,020
Panel A reports results from fixed effects regressions of log of two-year public college enrollment on log
of real effective minimum wages without controlling unemployment rate. Panel B reports results from
linear fixed effects regressions of number of two-year public college enrollment on the real effective
minimum wages with all linear controls. Each column is a separate regression. All estimations include
year fixed effects, college fixed effects and state trends. Standard errors are clustered at institution level.
*** p<0.01, ** p<0.05, * p<0.1
Alternative Linear Model
Furthermore, to check the robustness of the main results, a linear model is adopted
as an alternative test to estimate the effect of minimum wage on college enrollment. The
9 The results for two-year private college, four-year public college, and four-year private
college are not reported but available upon request.
21
linear models keep all variables in linear form and still control for the fixed effects and
trends. Table 1.5 Panel B shows that, for two-year public college, a higher real minimum
wage level significantly reduces part-time freshman enrollment and full-time non-freshman
enrollment. The results are consistent with the log model.
Introducing Interaction Terms between College Characteristics and the Minimum Wage
Level
Moreover, the estimated size of the reduction of part-time freshman enrollment
for two-year public colleges is close to the size of the sum of the increases of full-time and
part-time freshman enrollment for four-year public colleges10. It is possible that the effect
on comes from the students substituting two-year public colleges with four-year public
colleges. This means that the substitution effect is weak or does not exist. Only income
effect plays a role. Therefore, to further prove the theory developed in Second 2, several
interaction terms between college characteristics and the log of real minimum wage is
introduced into the main model for two-year public college enrollments. Table 1.6 Panel A
shows the results adding an interaction term whether the colleges have higher than median
percentage of student received financial aid11. An increase in minimum wage level can
significantly reduce both full-time and part-time freshman enrollment for colleges with
10 The estimated number of students affected is calculated using the sample mean and the
number of institutions. The estimation may be reliable because the IPEDS covers almost
all the public colleges. 11 The colleges are grouped into two group equally (e.g. 517 vs. 517 colleges): High
Percentage Aid equals to one if the average of percentage aid in the sample period for a
specific college is higher than the median of the average of percentage aid for all
colleges.
22
easier access to financial aid. And, higher minimum wage level still increases full-time
non-freshman enrollment for college with poorer access to financial aid. But the effect
decreases for the colleges with easier access to financial aid. These results indicate that
students are less likely to enter or stay in the colleges with higher probability to get financial
aid. This is plausible because the income effect from a minimum wage increase is smaller
for less financially stressed students. Thus, combining the substitution effect and income
effect, the overall effect shifts negatively. Panel B shows the results of the model adding a
similar interaction of high average amount of financial aid. Although the interaction terms
are statistically insignificant, the sign of all coefficient is negative. These negative signs
tell the similar story that the income effect is weaker for less financially stressed students.
Panel C shows the results adding the interaction terms of high tuition. Increasing minimum
wage level will increase full-time freshman enrollment for colleges which charge the lower
tuition fee. This result is reasonable because increasing minimum wage makes low-cost
colleges more affordable. On the other hand, the income effect is weaker for expensive
colleges. The estimates of minimum wage and the interaction for part-time freshman
enrollment are insignificant. But, the joint F test for the sum of the coefficients of minimum
wage and the interaction shows that the higher minimum wage reduces high tuition college
enrollment.
Panel A: Heterogeneous effect by Percentage of Student Receiving Aid
(0.0973) (0.150) (0.0631) (0.0640)
Log Real Min Wage*High Percentage Aid -0.214* -0.606*** -0.223*** -0.118
(0.119) (0.191) (0.0821) (0.0846)
Observations 13,127 13,018 13,083 12,971
Panel B: Heterogeneous effect by Average Amount of Aid
(0.0981) (0.153) (0.0671) (0.0713)
Log Real Min Wage*High Average Aid -0.141 -0.0224 -0.128 -0.0643
(0.120) (0.179) (0.0877) (0.0901)
Observations 13,127 13,018 13,083 12,971
Panel C: Heterogeneous effect by In-district Tuition
(0.103) (0.168) (0.0647) (0.0734)
Log Real Min Wage*High Tuition -0.272** -0.0313 0.114 -0.113
(0.114) (0.180) (0.0760) (0.0889)
Observations 13,127 13,018 13,083 12,971
This table reports results from fixed effects regressions of log of two-year public college enrollment on
log of real effective minimum wages. Each column is a separate regression. All estimations include year
fixed effects, college fixed effects and state trends. Standard errors are clustered at institution level. ***
p<0.01, ** p<0.05, * p<0.1
24
Individual Level Analysis
Previous analysis is based on institution-level data covering most of the two-year
and four-year colleges. To further support the results, individual-level data analysis is also
employed. March Current Population Survey provides detailed personal information.
However, the college enrollment status only includes whether the respondent is enrolled in
a college as a full-time or part-time student. The further information, such as college type,
first-time enrollment, is not available. Therefore, the individual level analysis is used as a
supplementary robustness test. A logit model is employed. The dependent variable is a
dummy equal to 1 if individual enrolled in a two-year or four-year college. Several
demographic and labor market controls are included, such as age, gender, race, domestic
or foreign-born, marital status, employment status, location type, individual income,
unemployment rate. The sample is restricted to age 16 to 24, from the year 2006 to 2016.
Table 1.7 reports the results. The first column is the estimation of the logit model on the
enrollment of a two-year or four-year college. In the second and the third column, the
dependent variable is enrolled as a full-time or part-time student. The results show a
positive correlation between the real minimum wage and the possibility of enrollment. The
minimum wage may be positively correlated with individual income level. This will only
bias the estimates of real minimum wage downwards. Furthermore, an alternative model
without employment status, income level, and market unemployment rate is also estimated.
Show in Table 1.8, the coefficients of the real minimum wage remain significant and
positive. However, the model may still suffer some endogenous problem, such as missing
25
variables. The individual level analysis suggests that higher minimum wage level increases
college enrollment, supporting the main results from institution level analysis.
Table 1.7: Individual Level Analysis
Variables Enroll in College Full-time Part-time
Real Min Wage 0.0904*** 0.0717*** 0.110***
(0.00945) (0.00991) (0.0198)
(0.0202) (0.0207) (0.0436)
(0.0206) (0.0219) (0.0407)
(0.00380) (0.00394) (0.0102)
(0.00143) (0.00149) (0.00277)
(0.00285) (0.00298) (0.00589)
Observations 170,047 170,047 170,047
This table reports results from the logit model of college enrollment on real effective minimum wages.
Each column is a separate regression. All estimations include year fixed effects. Robust standard errors
in parentheses. *** p<0.01, ** p<0.05, * p<0.1
26
Variables Enroll in College Full-time Part-time
Real Min Wage 0.120*** 0.0965*** 0.144***
(0.00900) (0.00941) (0.0190)
(0.0200) (0.0204) (0.0433)
(0.0205) (0.0218) (0.0405)
Observations 170,047 170,047 170,047
This table reports results from the logit model of college enrollment on real effective minimum wages.
Each column is a separate regression. All estimations include year fixed effects. Robust standard errors
in parentheses. *** p<0.01, ** p<0.05, * p<0.1
1.6 CONCLUSION
This paper fills a gap in the literature by exploring how college enrollment
responds to minimum wage adjustment. The public colleges are mainly affected. Increasing
minimum wage level can significantly reduce the part-time freshman enrollment and
increase full-time non-freshman enrollment for two-year public colleges; increase full-time
and part-time freshman enrollment and full-time non-freshman enrollment for four-year
27
public college; and increase full-time non-freshman enrollment for four-year private
colleges. The effect is not only statistically significant but also has a large economic impact.
A 10 percent increase in the minimum wage level reduces part-time freshman enrollment
by 2.78 percent for two-year public colleges and increases part-time freshman enrollment
by 6.83 percent for four-year public colleges.
The finding of this paper also has policy implications. Given the strong response
to minimum wage change in college enrollments, policymakers should consider linking the
education policy to the minimum wage policy to adjust the resources required by colleges,
students, and potential students. Similarly, colleges should adjust the resources to stabilize
the number and the size of classes and maintain the education quality. If college enrollments
are shocked by the minimum wage adjustment, larger than expected number of students
will enlarge the class size or reduce the average number of courses available (Betts and
McFarland, 1995). Lower than expected enrollment may waste education resources. To
sum up, the change of minimum wage level can affect population’s human capital
accumulation. Policymakers and colleges should be prepared and make plans in advance,
since the minimum wage adjustments are always preannounced.
28
2.1 INTRODUCTION
According to U.S. Bureau of Labor Statistics, in 2014, around 3 million workers
were paid at or under the federal minimum wage level ($7.25 per hour). This considerable
number of workers also have relatively low education level and are younger than the
average worker. One of the targets of minimum wage law is to improve the welfare of these
workers, however, few studies have focused on the impact of minimum wage law on crime.
In 2012, there are 12,197,000 arrests in the United States (i.e. 3,886 arrests per
100,000 population). This large number has drawn much public attention. Therefore,
considerable literature study determinants of crime. A variety of factors (e.g. age, gender,
parenting, schooling, criminal-justice system strength, wage, and employment) can
significantly affect participation in illegal activities. Compared to adults, youth are more
likely to engage in illegal activities (Allan and et. al., 1989; Levitt and Lochner, 2001, etc.).
The probability of committing a crime, as well as of incarceration, decreases with increased
education levels (Lochner and Moretti, 2001; Lochner, 2004). Therefore, workers who will
be affected by minimum wage law might be also associated with relatively higher
probability to commit a crime, since they are younger, and have lower income and lower
education level. Furthermore, an increase in wages would increase the opportunity cost of
29
criminal activity and, therefore, decrease the motivation to commit a crime (Grogger, 1998;
Levitt and Lochner, 2001; Machin and Meghir, 2004; Corman and Mocan, 2005).
Meanwhile, losing one’s job will significantly raise the probability of engaging in illegal
activities because of the loss of stable income. There are many studies, such as Allan and
Steffensmeier (1989), Raphael and Winter-Ebmer (2001), and Lin (2008), discovered that
unemployment would lead to more property crime. Since minimum wage policy can affect
both employment and wage, it is natural to link minimum wage and crime.
The effect of a minimum wage increase on workers’ unemployment and wage is
widely studied. Even though there is not a consonance on the unemployment effect yet,
some scholars believe that increasing minimum wage level will raise the cost of employer,
and therefore would adversely affect employment and working hours for low-wage, low-
skill workers who were paid below the new minimum wage level (Moore,1971; Brown, et
al., 1982; Neumark, et al., 2004; Neumark and Wascher, 2006; Brochu and Green, 2013;
etc.). Workers who lose the job may have a greater incentive to participate in illegal
activities. Meanwhile, increasing minimum wage level can raise the wage for people who
keep employed and earn below the new minimum wage level. An increased income would
decrease the motivation to commit a crime. So theoretically, a minimum wage increase
could either increase or decrease the crime rate: if the unemployment effect is dominant,
the crime rate would raise; if income effect is dominant, the crime rate would decline.
Therefore, the empirical evidence is needed to identify the true effect of a minimum wage
increase on crime.
30
There are a few related papers studied on this effect, Hashimoto (1987),
Chressanthis and Grimes (1990), Hansen and Machin (2002), Beauchamp and Chan (2013),
Agan and Makowsky (2018), and Braun (2019). But their results are inconsistent with each
other. Therefore, this paper can provide a newer and direct evidence of how increasing
minimum wage can affect the crime rate, and how those impacts differ across different
types of crime and age group. Taking advantage of U.S. state-level arrest and offense rates
data from 1994 to 2012, and agency-level incidence counts data, I discovered that
increasing real minimum wage reduces adult total arrest rate, especially robbery, assault,
and burglary. The effects on youth crime are ambiguous, which may suggest that the
income effect diminishes the unemployment effect. Another important result is that only
current real minimum wage level matter, which means that changing nominal minimum
wage level leads to a reduction of arrest rates for the adult group. However, during the
following years, as real minimum wage level keeps decreasing, the arrest rates would climb
back, and the security situation starts worsening. Policy-makers should also recognize and
take this possible cyclical trend into account.
2.2 LITERATURE REVIEW
The determinants of committing a crime have been widely studied, but few studies
focus on the minimum wage law. There are only a few existing related studies, Hashimoto
(1987), Chressanthis and Grimes (1990), Hansen and Machin (2002), Beauchamp and
Chan (2013), and Agan and Makowsky (2018), which use different methods and data to
31
test the effect of minimum wage level on crime empirically.
Hashimoto (1987) and Chressanthis and Grimes (1990) use time-series approach
and find that an increased level of real federal minimum wage would increase youth crime
rates. They point out that this effect may be mainly caused by unemployment effect in
youth labor market. However, due to the data limitation, only federal level variations are
captured, and the state-level heterogeneity is omitted. Beauchamp and Chan (2013) use
NLSY97 survey data and find that workers who were affected by minimum wage policy
are more likely to commit a crime, especially when they were young. The main issue is
that the survey data only include longitudinal data of one cohort, and those individuals are
in similar birth years. Therefore, the sample for youth and adults are actually the same
people. The effect of minimum wage rise on crime may differ across generations. On the
contrary, Hansen and Machin (2002) analyze the effect of implementing the first minimum
wage policy in the UK in 1997 on crime rates, and they find that the increased wage level
leads to a lower crime rate. Furthermore, Agan and Makowsky (2018) use difference-in-
differences strategy and administrative prison release records, they find that higher
minimum wage level reduces the probability for returns to incarcerations for both man and
woman. This study focusses on newly released prisoners rather than the whole population.
Furthermore, Braun (2019) use NLSY97 data as well as U.S. county level crime data and
find that minimum wage level has a U-shaped effect on crime (i.e. ether higher or low
minimum wage level leads to more crime).
Previous studies either focused a particular cohort or non-U. S. sample. Therefore,
32
the contribution of this paper is that it can provide a clearer and newer evidence on the
effect of real minimum wage level on crime, for both youth and adult in U.S.
2.3 DATA AND METHODOLOGY
Since not all crime were known and caught, there is no existing data that record
all illegal activity. Therefore, some proxies measuring crime rate were widely used, such
as arrest records, offense records, incarceration records, self-reported crime participation
from the survey, etc. The primary data used in this paper is state-level arrest rate as a proxy
for crime, rather than arrest count. Because the arrest rates are adjusted by population, thus,
reflect a closer proxy for local security situation. Furthermore, the state-level offense rates
and agency-level incidence counts are also used as supplementary sample.
The state-level arrest rates data is collected from FBI Uniform Crime Reports
(UCR). The data ranges from 1994 to 2012 (because estimation method was different
before and after 1994). Among 51 U.S. state, 14 states have full arrest rate data from 1994
to 2012, and 35 states have 10 years or more data available. The UCR Program counts one
arrest for each separate instance in which a person is arrested, cited, or summoned for an
offense. Because a person may be arrested multiple times during a year, the UCR arrest
figures do not reflect the number of individuals who have been arrested. Rather, the arrest
data show the number of times that persons are arrested, as reported by law enforcement
agencies to the UCR Program. One drawback of this data is that only the most severe
charges would be counted when the suspect faces multiple charges. E.g. when one was
33
arrested for murder during a robbery, this arrest would only count for murder.
The summary statistics for state-level arrest rates are reported in Table 2.1. The
total arrest rate is the total number of arrests for every 100,000 persons in the overall
resident population within the state. The youth arrest rate is the number of arrests of persons
under age 18 for every 100,000 person ages 10 through 17 in the resident population. The
adult arrest rate is calculated using population of 18 years old and older12. The state-level
data includes 30 types of arrest rates, shown in Table 2.213. From Table 2.1, it is easy to
find that youth arrest rates are relatively larger than adult’s, which is consistent with
previous literature. Furthermore, there is a famous downward trend on the crime rate in
recent decades, which is also captured in arrest rates from this dataset. The total arrest rate
fell from 5573 to 3886, violent crime fell from 296 to 166, and property crime fell from
810 to 524. Not only total arrest rate for all age decreased, the arrest rates for different types
of crime also decreased. The same pattern can be found for both youth and adult groups.
However, the arrest rates have their own trend in some states. E.g. there is an upward trend
in Tennessee and a downward trend in Massachusetts. Therefore, the diverse trend across
states should also be considered.
Table 2.1: Descriptive Statistics for State-level Arrest Rates 1994-2012
Variable Observations Mean Std.Dev. Min Max
Youth arrest rate (per 100,000 population, 10-17 years old)
Total 617 7374.72 3269.19 1312.00 23025.00
12 The adult arrest rates and youth arrest rates are both higher than all age arrest rate
because youth arrest rates do not include children under 10 years old. 13 Appendix A, Table A1 reports the summary statistics for each types of crime.
34
Adult arrest rate (per 100,000 population, 18 and older)
Total 617 5564.56 1475.71 2294.00 10714.00
Violent Crime 617 196.75 105.95 27.00 967.00
Property Crime 617 563.32 173.20 220.00 1166.00
All age arrest rate (per 100,000 population, all age)
Total 617 5015.15 1292.92 2244.00 9089.00
Violent Crime 617 176.78 93.16 26.00 887.00
Property Crime 617 614.73 191.24 245.00 1345.00
Nominal Minimum Wage 1026 5.72 1.13 4.25 9.04
Police Expenditure 950 0.04 0.02 0.00 0.21
Education Expenditure 950 1.52 0.51 0.47 3.74
Welfare Expenditure 950 1.07 0.44 0.26 2.84
Unemployment 969 5.57 1.95 2.30 13.70
This table shows summary statistics of variables. The arrest rate is the number of arrests for every 100,000
persons from the respective group of the resident population within the state. The adult arrest rates and
youth arrest rates are both higher than all age arrest rate because youth arrest rates do not include
children under 10 years old. The police expenditure, education expenditure, and welfare expenditure are
in thousand dollars per capita. The unemployment is in percentage.
Table 2.2: Types of Crimes
Type of crime Violent crime Property crime Other
Street crime
Counterfeiting
35
Other
offenses
Because of the limitations of the arrest rate data, I use two supplementary datasets:
the state-level offense rates data and agency-level incidence counts data. The state-level
offense rates data is also collected from UCR, it counts incidences known to police per
100,000 persons. It may better represent security situation since it represents how many
crimes knows in the local community. Furthermore, incidences reported to police should
be less affected by other factors, such as arrest standards and police effort. The data ranges
from 1994 to 2012 and covers all 51 states, which provides a balanced sample14. However,
the data are estimated, and its quality may not be guaranteed. Furthermore, the offense rates
data do not report all types of crime and the total offense rate cannot be observed. Since
we cannot identify the offender’s identity, the offense rates only cover all age group and
cannot distinguish between youth and adults.
The agency-level incidence counts provide the number of incidences reported to
14 Appendix A, Table A2 reports summary statistics for state-level offense rates.
36
local police department. The data is collected from FBI’s National Incident-Based
Reporting System (NIBRS), ranges from 2005 to 2012, and includes 5840 agencies15. The
agency-level data provides an opportunity to detect the effect of minimum wage level on
crime at the local level rather than aggregated state-level. However, the data also suffers
two major issues. First, the agency heterogeneity is hard to capture. The agencies have
different police jurisdiction size and population coverage. Some agencies may cover more
than one county, and some may share a county together. To mitigate this heterogeneity, I
computed a pseudo offense rate: the number of incidences divided by county population.
However, those rates are only suggestive since actual population coverages are unknown.
Second, the agencies voluntarily report to the NIBRS program, which leads to potential
self-selection bias. Therefore, the state-level offense rates data and agency-level incidence
count data are supplementary.
During 1994 to 2012, all states changed their effective minimum wage level
(because federal minimum wage level changed). The effective minimum wage is the
maximum between federal and state minimum wage level. Each state changed their state-
specific minimum wage level more than 5 times on average, which provides enough
variation for the estimation.
To analyze the effect of minimum wage on arrest rates, the main specification used
is a linear model controlled with state fix effects, year fix effects and state trends, i.e.
15 Appendix A, Table A3 reports summary statistics for agency-level incidence count.
37
+ (1)
Where is arrest rate of crime type j in State i year t. is the
real effective minimum wage level in State i and year t (in 2006 dollar). are state-level
controls including unemployment rate (in percentage), police protection expenditure,
education expenditure, and welfare expenditure (in thousand dollars per capita). ∗
is the interaction of state dummy and linear time. is the year fixed
effects. is the state fixed effects. The state fixed effects are included to capture the
unobserved time-invariant heterogeneity across states. The year fixed was included to
capture the shock in each year at the nation level such as financial crisis. The interaction of
state dummy and linear time captures the unobservable factors which can affect arrest rates
and correlate with time, and also captures the potential different trend of arrest rates in each
state. captures the shocks from unobservable factors that cannot explained by
minimum wage and controls, time variant, not linearly correlated with time, and
heterogeneous across states within single year.
In other specifications, is replaced by state-level offense rates or
agency-level incidence counts. The results of those model are presented in next section.
2.4 RESULTS
Table 2.3 shows the results of models with only the main independent variable,
38
the real minimum wage, and different fixed effects. The results from basic models show
that there are significant negative correlations between real minimum wage level and total
arrest rates for youth, adult and all age group. However, after controlled for the fixed effects,
both significant level and magnitude of estimates changes. Solely controlled for the state
fixed effects, the magnitude and significant level do not largely differ from the basic model.
But after controlled for the year fixed effects, the real minimum wage level does not
significantly affect youth arrest rate, and the effects on adult and all age group remain
significant but the magnitudes reduced. Column 5 shows the results of linear model
controlling for year fixed effects, state fixed effects, and state-specific trends. The results
show that the unobserved time-variant factors and state-specific trends can significantly
affect the estimation and should be included in the main model. Therefore, the rest of
estimations all include the year fixed effects, state fixed effects, and state trends.
Table 2.3: Linear Models with Different Fixed Effects
Panel A: Youth total arrest rate (per 100,000 population)
(1) (2) (3) (4) (5)
Variable Basic Year FE State FE Year and State FE State Trend
Real Minimum Wage -1,242*** -145.3 -1,226*** -125.9 -25.90
(152.6) (126.7) (154.7) (127.5) (88.01)
Year Fixed Effects √ √ √
State Fixed Effects √ √ √
(1) (2) (3) (4) (5)
Real Minimum Wage -419.3*** -213.6*** -407.2*** -184.1*** -141.1**
(57.08) (67.08) (57.43) (66.63) (52.78)
39
Panel C: All age total arrest rate (per 100,000 population)
(1) (2) (3) (4) (5)
Real Minimum Wage -441.3*** -152.7*** -428.0*** -129.7** -102.3**
(54.82) (57.25) (55.30) (57.30) (48.10)
Year Fixed Effects √ √ √
State Fixed Effects √ √ √
Number of States 44
This table reports the estimation results of linear models of total arrest rate with respects to real minimum
wage and different fixed effects. Each column reports a separated regression. All regressions do not include
state-level controls. *** p<0.01, ** p<0.05, * p<0.1
Table 2.4 shows the main results of Equation (1): arrest rates of youth, adult
and all age group on real minimum wage, state-level controls and all fixed effects. In Panel
A, the results show that real effective minimum wage level does not have a significant
impact on youth total, violent crime, property crime, and other crime arrest rate. For all age
group, in Panel C, one dollar increase on real minimum wage level would reduce total
arrest rate by 107.9, which is a 2.78% reduction relative to 2012 federal level, violent crime
by 7.22 (4.34%), and other crime by 93.41 (2.92%). Contrary to previous studies, property
crime rate is not significantly affected. Those effects of all age group were mainly driven
by adult group: in Panel B, one dollar increase in minimum wage level would reduce total
arrest rate by 156.4, and violent crime arrest rate by 9.829. Similarly, there is no significant
effect found from property crime. It is clear that, for adults, income effect dominant
40
unemployment effect, thus fewer adults would be arrested for violent crime due to
minimum wage rise. The ambiguous results for youth could be explained by two reasons:
first, minimum wage level cannot affect the labor market conditions for youth, thus cannot
affect the decision to involve in criminal activity. Second, the unemployment effect offsets
the income effects. Previous studies found that a minimum wage law change can
significantly affect youth labor market condition (Williams and Mills, 2001; Giuliano,
2013). Therefore, the second explanation might be more appropriate. The labor market
preference may shift: employers may tend to hire older and more skilled workers when
minimum wage rise (Moore,1971; Brown, et al., 1982; Neumark and Wascher, 2006).
Therefore, the unemployment effect for youth might be larger than adults.
Table 2.4Linear Models with State-level Controls and Fixed Effects
Panel A: Youth arrest rates (per 100,000 population)
(1) (2) (3) (4)
Real Minimum Wage -17.22 -2.915 -21.40 7.098
(161.7) (6.855) (31.64) (141.2)
(1) (2) (3) (4)
Real Minimum Wage -156.4*** -9.829** -9.365 -137.2***
(52.13) (4.072) (11.63) (49.46)
(1) (2) (3) (4)
Real Minimum Wage -107.9** -7.216* -7.245 -93.41**
(45.27) (3.627) (10.08) (44.02)
Number of States 44
This table reports the estimation results of linear models of arrest rates with respects to effective real
minimum wage. Each column reports a separated regression. All regressions include state-level controls
and fixed effects. Standard errors are clustered at state level. *** p<0.01, ** p<0.05, * p<0.1
The effect of minimum wage level on major categories of crime is important. It is
also critical to investigate how people respond to minimum wage level by looking at
different types of crime. Previous literature (Grogger, 1988; Machin and Meghir, 2004;
Corman and Mocan, 2005) found that property related crime is more likely to be affected
by income and unemployment status, especially for street crime (i.e. homicide, assault,
rape, robbery, and burglary, larceny, and auto theft, arson)16 (Lochner, 2004). Therefore,
those street crimes are expected to be affected by minimum wage level. On the other hand,
committing a white-collar crime (i.e. forgery, fraud, embezzlement, counterfeiting)
requires a higher level of skill than a street crime (Lochner, 2004). Thus, minimum wage
level should have less impact on white-collar crime, since the policy mainly targets low-
skilled workers. My results support the expectation. Among 30 types of arrest rates17 ,
higher real minimum wage level leads to lower robbery, assault, burglary, possess stolen
property and sex offenses arrest rates for adult group. However, for youth18 , only sex
offenses arrest rate is significantly affected.
16 The effect of real minimum wage level on the sum of street crimes is also tested.
However, the coefficient is not significant. 17 Results of all 30 types of arrest rates for adult group are shown in Appendix A, Table
A4. 18 Results of all 30 types of arrest rates for youth group are shown in Appendix A, Table
A5.
42
2.5 ROBUSTNESS CHECK
In this section, few different specifications and supplementary data are used to
check the robustness of the results.
First, the effect of real minimum wage level on arrest rates may not be linear.
Therefore, same models were estimated using natural log of the real minimum wage, arrest
rates, and all controls. The results are shown in Table 2.5. The results from log-log models
are consistent with ones from the linear model. From Table 2.5, it is clear that a 13.8%
increase on real minimum wage level (one dollar increase from $7.25) will reduce total
adult arrest rate by 2.13%, and violent crime arrest rate by 3.78%. The effects are weaker
for all age group, only violent crime is significantly affected. Consistent with the linear
model, the effects on youth arrest rates are ambiguous.
Table 2.5: Log-log Models with State-level Controls and Fixed Effects
Panel A: Youth arrest rates (per 100,000 population)
(1) (2) (3) (4)
Variable Log Total Log Violent Crime Log Property Crime Log Other Crime
Log Real Minimum Wage -0.0611 -0.129 0.00634 -0.0579
(0.110) (0.158) (0.102) (0.128)
(1) (2) (3) (4)
Log Real Minimum Wage -0.154** -0.274** -0.0454 -0.148**
(0.0582) (0.113) (0.123) (0.0614)
(1) (2) (3) (4)
Log Real Minimum Wage -0.124** -0.222** -0.0311 -0.118*
43
Number of States 44
This table reports the estimation results of linear models of the log of arrest rates with respects to log of
effective real minimum wage. Each column reports a separated regression. All regressions include log
form state-level controls and fixed effects. Standard errors are clustered at state level. *** p<0.01, **
p<0.05, * p<0.1
Second, to detect whether it is just a coincident that real minimum wage and arrest
rates move simultaneously, I also add different dynamic controls into the main specification.
Table 2.6 reports the results of the models for current and lag/lead real minimum wage
level on adult total arrest rate and violent crime arrest rate. Column (1) and (2) controls for
real minimum wage level in future one year or two years. Possibly due to multicollinearity,
estimators for both current and future real minimum wage level are not significant.
However, in Column (4) and (5), the estimator for current real minimum wage level
remains significant and the magnitude does not differ much from the main specification
(Column (3)). In Table 2.6 Column (6), all dynamic controls are included and none of the
estimators are significant due to multicollinearity for adult total arrest rate. However, for
adult violent crime arrest rate, the current real minimum wage level remains significant.
Those results support the causal effect of real minimum wage level on crime. Furthermore,
the results also implicate that minimum wage workers respond only to current real
minimum wage level. This means that crime rate will decrease during the year of
implementing new minimum wage policy. In the later years, the crime rate will not bounce
back immediately, but keep increasing gradually since real minimum wage keeps
44
decreasing due to inflation. Thus, this special pattern of real minimum wage leads to a
cyclical effect on crime.
Panel A: Adult Total Arrest Rate (per 100,000 population)
(1) (2) (3) (4) (5) (6)
Variable Adult Total Arrest Rate (per 100,000 population)
t+2 Real
(56.77) (46.01) (52.13) (61.10) (56.14) (49.88)
t-1 Real Minimum Wage -3.820 26.10
(51.36) (52.27)
(50.92) (70.15)
Panel B: Adult Violent Crime Arrest Rate (per 100,000 population)
(1) (2) (3) (4) (5) (6)
Variable Adult Violent Crime Arrest Rate (per 100,000 population)
t+2 Real
(5.406) (4.477) (4.072) (4.409) (4.198) (4.551)
t-1 Real Minimum Wage 1.804 -0.374
(3.212) (3.723)
(2.787) (3.368)
Number of States 44 44 44 44 43 42
This table reports the estimation results of linear models of arrest rates with respects to current, and lag/lead
of effective real minimum wage level. Each column reports a separated regression. All regressions include
45
state-level controls and fixed effects. Standard errors are clustered at state level. *** p<0.01, ** p<0.05, *
p<0.1
Third, supplementary data is used to check whether the effect is consistent. It is
possible that arrest standard or police effort can affect arrest rate. Therefore, I replicate the
same analysis on offense rate data, since the number of incidences reported to police may
better indicate local security situation. Table 2.7 reports the results of Equation (1) using
different state-level offense rates. Observations for D.C. are dropped due to lack of
expenditure data. Therefore 50 states are included. For violent crime and murder, the
coefficients are significantly negative. Table 2.8 reports the results of the log-log model for
state-level offense rates, it shows that an increase on real minimum wage level leads to less
violent crime, murder, robbery, and assaults. Those results provide further evidence that
higher real minimum wage level is associated with less crime in state-level.
Table 2.7Linear Models for State-level Offense Rates
Panel A: All age Violent Crime offense rates (per 100,000 population)
(1) (2) (3) (4) (5)
Variable
Violent
(5.003)
(0.0908
Number of States 50
Panel B: All age Property Crime offense rates (per 100,000 population)
(6) (7) (8) (9)
(39.47) (8.855) (24.15) (15.84)
Number of States 50
This table reports the estimation results of linear models of offense rates with respects to effective real
Table 2.8Log-log Models for State-level Offense Rates
Panel A: All age Violent Crime offense rates (per 100,000 population)
(1) (2) (3) (4) (5)
Variable
(0.0674) (0.152) (0.0713) (0.0830) (0.106)
Observations 950
Number of States 50
Panel B: All age Property Crime offense rates (per 100,000 population)
(6) (7) (8) (9)
(0.0760) (0.0946) (0.0670) (0.146)
Number of States 50
This table reports the estimation results of linear models of offense rates with respects to effective real
I also apply same technique on agency-level incidence count data to check
whether this effect remains at agency-level. The results are shown in Table 2.9. The
coefficients from violent crime and property crime are negatively significant in the linear
47
model in panel A. In panel B, the log-log model shows a similar negative effect on violent
crime. In panel C, the pseudo incidence rate for violent crime and property crime is also
negatively affected by real minimum wage level. Therefore, the results from agency-level
incidence data consolidate the robustness of the effect. As mentioned in the previous
section, the data quality of state-level offense rates and agency-level incidence count is less
reliable than arrest rates data, those results are supplementary evidence for the effect of
minimum wage on crime.
Panel A: Incident Counts
(2.309) (9.268)
(1) (2)
Log Real Minimum Wage -0.600*** -0.231
(0.210) (0.164)
(1) (2)
Real Minimum Wage -5.967*** -12.14**
(1.867) (5.142)
Observations 37,394
Number of Agencies 5,840
This table reports the estimation results of linear models of incidence count, the log of incidence count and
pseudo incidence rates with respects to log of effective nominal minimum wage. Each column reports a
separated regression. All regressions include state-level controls and fixed effects. Standard errors are
clustered at state level. *** p<0.01, ** p<0.05, * p<0.1
48
2.6 CONCLUSION
Taking advantage of state-level arrests data from 1994 to 2012, I found that an
increase on real minimum wage level can significantly reduce adult arrest rates but not for
youth. This paper provides a newer and clearer evidence of one aspect of the effects of
minimum wage policy. This effect is robust using different specification and data.
Increasing the real minimum wage level by dollar leads to a 2.78% reduction on total arrest
rate relative to 2012 level. Furthermore, due to the cyclical pattern of real minimum wage,
the effect on crime rates also follows a cyclical trend. When nominal minimum wage
increase, as well as real minimum wage, the crime rate would decrease. In following years,
as inflation reducing the real minimum wage level, the crime rate should climb back. This
effect is not negligible, thus, policy-makers should recognize this unintended side effect of
minimum wage policy and take this effect into account.
49
3.1 INTRODUCTION
Are collectivistic societies associated with lower income inequality? People may
have a pre-judgment that collectivistic societies emphasize group harmony and collective
distribution of resources and, therefore, should have lower income inequality. However,
the reality may not reflect this expectation. China is a typical collectivistic society; the
United States is a typical individualistic society. Yet, they have almost the same level of
income inequality, as measured by the Gini coefficient. Therefore, this paper provides
constructive theoretical framework and robust empirical evidence to investigate the
relationship between collectivism and income inequality.
Hofstede (2001) defines culture as “the collective programming of the mind that
distinguishes people of one country, region or group from people from other countries,
regions or groups.” Culture shapes people’s perception and defines what is important. After
Hofstede constructed his famous cultural dimensions, scholars have studied the
relationship between culture and different types of social phenomena in management,
marketing, international business, economics, and other fields. In recent years, economists
19 Cao, Zhongyu, Sadok El Ghoul, Omrane Guedhami, and Chuck Kwok. To be
submitted.
50
have noticed the importance of culture in explaining the cross-nation variation of many
economic variables: long-term economic growth rate and wealth of nations
(Gorodnichenko and Roland, 2010, 2011); institutions such as financial systems, legal
institutions, and democracy (Kwok and Tadesse, 2006; Alesina and Giuliano, 2015); and
microeconomic issues such as entrepreneurship (Hayton et al., 2002; Gorodnichenko and
Roland, 2010). However, there are few studies that focus on whether national culture can
explain prolonged variation of income inequality across countries. Malinoski (2012), find
that individualism and long-term orientation shows a negative relationship with the Gini
coefficient. Nikolaev, Boudreaux, and Salahodjaev (2017) find that individualistic societies
have higher level of income inequality than collectivistic societies. Yet, this paper takes a
deeper look at individualism/collectivistic aspect and find that individualism/collectivistic
may have a diverse effect on income inequality.
Scholars that study the determinants of income inequality find that GDP per capita
(Kuznet, 1955; Thornton, 2001), unemployment (Mocan, 1999), corruption (Li et al., 2000;
Gupta et al. 2002), education attainment (Gregorio and Lee, 2002), democracy (Reuveny
and Li, 2003), inflation and government spending (Blejer and Guererro,1990; Albanesi,
2007), openness (Reuveny and Li, 2003; Meschi and Vivarelli, 2009; Jaumotte et al. 2013),
natural resources endowment and population in the agriculture sector (Bourguignon and
Morrisson, 1998; Jaumotte et al. 2013), and financial development (Clarke et al. 2006;
Beck et al., 2007; Shahbaz and Islam, 2011) can significantly affect income inequality. This
previous research provides political implications for governments to promote the
51
egalitarian distribution of income. Nevertheless, we think that culture as a major
determinant of differences in income distribution across countries has been largely ignored.
Culture, as an informal institution, should have an important impact on formal
institutions and human activities, since culture is “the collective programming of the mind”
and therefore affects income inequality (Hofstede, 2001). This paper focuses on both
Hofstede’s individualism-collectivism culture dimension and institutional collectivism and
in-group collectivism from GLOBE (House et al., 2004) project. According to Chui and
Kwok (2008), “Individualistic values thus emphasize independence and encourage the
pursuit of individual achievements, whereas collectivistic values stress group
embeddedness and group harmony.” Theoretically, the relationship between individualism-
collectivism and income inequality is ambiguous. On one hand (denoted as H1),
collectivistic societies may be associated with lower income inequality. Collectivistic
societies promote group harmony, thus social norms and formal institutions would reflect
this preference

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Minimum Wage and National Culture - Scholar Commons