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H E A L T H E C O N O M I C S L E T T E R
Subjective well‐being and minimum wages: Evidence from U.S. states
Masanori Kuroki
College of Business, Arkansas Tech University, Russellville, AR, USA
Correspondence Masanori Kuroki, College of Business, Arkansas Tech University, 106 West O Street, Russellville, AR 72801, USA. Email: [email protected]
JEL Classification: I31; J30; J83; J88
Summary
This paper investigates whether increases in minimum wages are associated
with higher life satisfaction by using monthly‐level state minimum wages and
individual‐level data from the 2005–2010 Behavioral Risk Factor Surveillance
System. The magnitude I find suggests that a 10% increase in the minimum
wage is associated with a 0.03‐point increase in life satisfaction for workers
without a high school diploma, on a 4‐point scale. Contrary to popular belief
that higher minimum wages hurt business owners, I find little evidence that
higher minimum wages lead to the loss of well‐being among self‐employed
people.
KEYWORDS
happiness, labor market, life satisfaction, minimum wage
1 | INTRODUCTION
This paper investigates whether increases in minimum wages are associated with higher life satisfaction using the Behavioral Risk Factor Surveillance System 2005–2010. Although most economic studies on minimum wages focus of their effects on the labor market,1 how minimum wages affect the well‐being of workers has not been explored exten- sively. There is substantial evidence that subjective well‐being (SWB), such as self‐reported happiness and life satisfac- tion, not only are correlated with better health outcomes but also has a causal effect on health and longevity (Diener & Chan, 2011).2 Therefore, it is important for policymakers to understand the well‐being effects, not just employment effects, of minimum wage legislation.
Higher minimum wages are likely to lead to higher life satisfaction among minimum wage workers for several rea- sons. First, as researchers find that economic deprivation imposes psychological costs (e.g., Diener, Suh, Lucas, & Smith, 1999), increases in the minimum wage have the ability to improve the standard of living for low‐skilled workers. In addi- tion to increased consumption in general, workers may invest in their health, for example, by buying more healthy food or healthcare, which may increase their overall well‐being.3 Second, getting off public assistance and feeling the pride of independence may increase happiness and job satisfaction among minimum‐wage workers. Finally, increases in the
1Previous minimum wage studies on employment either use cross‐state variation in minimum wages over time to estimate effects (Neumark & Wascher, 1992, 2008; Sabia, 2009) or compare adjoining local areas with different minimum wages around the time of a policy change (Card & Krueger, 1994, 2000; Dube, Naidu, & Reich, 2007). Cross‐state studies have tend to find negative effects on employment, while case studies have tended to find small or no employment effects. 2Diener (2008) even argues that a doctor can use a patient's happiness to predict patient's health and longevity. 3However, Horn, Maclean, and Strain (2016) find little evidence that a higher minimum wage increases overall worker health.
Received: 15 January 2017 Revised: 5 July 2017 Accepted: 14 July 2017
DOI: 10.1002/hec.3577
Health Economics. 2018;27:e171–e180. Copyright © 2017 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/hec e171
minimum wage may enhance feelings, among low‐skilled workers, that their fellow citizens and government care about them.
Nevertheless, any labor law is likely to have negative consequences. In addition to the important potential adverse effects on the employment level, it has been pointed out that higher minimum wages hurt small businesses. For example, a recent article in Forbes reports that small businesses are closing due to higher minimum wages and subsequent increases in their business operation costs (Rensi, 2017). Interestingly, there is also a possibility that higher minimum wages lead to lower wages for low‐skilled workers. Recently Lopresti and Mumford (2016) find that small increases in the minimum wage have negative effects on wage growth of low‐wage workers, using the Current Population Survey (CPS) 2005–2008. The authors hypothesize that employers may use a minimum wage increase as a focal point in setting wages—that is, employers would have provided a larger wage increase to the low‐wage workers if there had been no minimum wage increase.
There are at least two studies that look at the link between minimum wages and SWB in the United States, and both studies indicate that an increase in the minimum wage leads to higher levels of self‐reported life satisfaction. Flavin and Shufeldt (2016) find that life satisfaction is higher among low‐income citizens in states that increased their minimum wage, and Pesta, McDaniel, and Bertsch (2010) find that higher minimum wages are correlated with higher well‐being index at the state‐level. However, both studies use states as the units of analysis, and the sample sizes in these studies are only 41 and 50. There does not seem to be any studies that use individual‐level, nonaggregated life satisfaction measures in the literature. Using individual‐level data is crucial in examining the link between minimum wage policy and citizens' well‐being, since the effects of higher minimum wages are unlikely to be uniform, as stated above. This paper's contri- bution is to examine those differential effects.
I find a positive association between life satisfaction of low‐skilled workers and higher minimum wages. The magni- tude I find suggests that a 10% increase in the minimum wage is associated with a 0.03‐point increase in life satisfaction for workers without a high school diploma, on a 4‐point scale. Contrary to popular belief that higher minimum wages hurt business owners, I find little evidence that higher minimum wages lead to the loss of well‐being among self‐ employed people.
2 | DATA AND METHODOLOGY
The dataset I use is the Behavioral Risk Factor Surveillance System Survey (BRFSS), which is a household‐level repeated cross‐sectional survey administered throughout the United States by the U.S. Government's National Center for Chronic Disease Prevention and Health. The measure of life satisfaction is the response, on a 4‐point scale ranging from “Very satisfied” to “Very dissatisfied” to the question, “In general, how satisfied are you with your life?” SWB has been extensively used by economists despite justifiable concerns that people's moods at the time of the survey can bias their SWB. For example, economic studies find that GDP, unemployment, and inflation affect SWB (Di Tella, MacCulloch, & Oswald, 2001, 2003; Wolfers, 2003).
The life satisfaction question has been asked since 2005, except in 2011 and 2012. But due to the changes in weighting methodology and the addition of the cell phone sampling frame, the BRFSS 2011–2015 are not comparable to the BRFSS 2005–2010.4 Therefore, I use 2005, 2006, 2007, 2008, 2009, and 2010. I restrict my analyses to individuals between 18 and 65 years old not residing in unincorporated U.S. territories. I also exclude respondents who refused or were unsure of their response, or whose response is missing, for any of the variables included in my analyses. Lastly, I use the following groups: (a) workers without a high school diploma, (b) workers with a high school diploma (but without a college degree), and (c) self‐employed people. The rationale for excluding workers with more than a high school education is to focus on low‐skilled workers who are most likely to be affected by minimum wage legislation. Self‐employed people are analyzed separately to test the claim that higher minimum wages hurt small business owners. Table 1 shows sum- mary statistics and reveals that a significant proportion (more than 90 percent) of those interviewed in the BRFSS reported either “satisfied” or “very satisfied.”5 High school dropouts are less satisfied with their life than high school graduates, and the self‐employed are more satisfied with their life than workers without a college degree.
4Also, during the period 2013–2015, fewer than 50,000 people were asked about their life satisfaction. 5In the health literature, responses to self‐assessed health questions are shown to have a similarly skewed distribution, as most people choose “good” or “very good” when asked to assess their overall health. Greene, Harris, and Hollingsworth (2015) argue that these inflated responses are inaccurate and thus should not be lightly used to draw policy implications. Thus, it is important to keep in mind that responses to life satisfaction or happiness ques- tions may also be overstated, possibly to make oneself appear more socially acceptable.
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Next, I match people who were surveyed in a particular state, month, and year with the monthly data on minimum wages, which are provided by Vaghul and Zipperer (2016).6 During the period 2004–2010, there was a large number of state‐level minimum wage increases, and the federal minimum wage was increased 3 times (2007, 2008, and 2009). Table A1 in the appendix summarizes these changes. In 2004, seven states raised the minimum wage. In 2005, nine states and the District of Columbia raised the minimum wage, and in 2006, 15 states and the District of Columbia raised the minimum wage. In 2007, 20 states were affected by the federal minimum wage increase, and 25 states raised their min- imum wages. In 2008, 26 states were affected by the federal minimum wage increase, and 17 states and the District of Columbia raised their minimum wages. In 2009, 32 states were affected by the federal minimum wage increase, and 10 states and the District of Columbia raised their minimum wages. In 2010, four states raised their minimum wages, but Colorado decreased its minimum wage $7.28 to $7.24, which was lower than federal minimum wage of $7.25, to adjust for inflation. In the analysis below, the effective minimum wage is defined as the higher of the state and federal minimum wage in each state, and I convert the nominal minimum effective wage to January 2014 dollars using the Consumer Price Index.7
The major methodological issue is to separate confounding factors from the dynamic effects of a minimum wage change. First, as Reich (2009) points out, minimum wages are often enacted when the economy is expanding and unem- ployment is low but the economic conditions could be changing by the time of implementation. Due to this possibility of a delayed effect of minimum wages (Allegretto, Dube, & Reich, 2011; Dube, Lester, & Reich, 2010), I use a 1‐year lag of the minimum wage, and Table 2 shows lagged minimum wages for individuals in the sample.8 Second, states engaging in greater increases in the minimum may be systematically different when it comes to the well‐being of residents. Thus, I include state and time fixed effects and state‐specific linear time trends:
LSist ¼ αXist þ βln MinimumWagest−12ð Þ þ λs þ θt þ τs×tð Þ þ εist;
where LSist refers to the measure of life satisfaction; i, s, and t denote, respectively, individual, state, and time. MinimumWagest − 12 is a 1‐year lag of the minimum wage in state s at time t. Xist is a vector of individual characteristics.
6They constructed panels at daily, monthly, quarterly, and annual frequencies. The state datasets span May 1974 to July 2016. The data is available from: https://github.com/equitablegrowth/VZ_historicalminwage/releases 7Table 24C. Historical Chained Consumer Price Index for All Urban Consumers. Available at https://www.bls.gov/cpi/cpid1702.pdf. 8I also ran specifications with more lags and without any lags. The results do not change substantially.
TABLE 1 Summary statistics
High school dropout High school graduate Self‐employed
Mean life satisfaction 3.27 3.37 3.44 Life satisfaction = 4: Very satisfied 0.34 0.42 0.48 Life satisfaction = 3: Satisfied 0.60 0.53 0.47 Life satisfaction = 2: Dissatisfied 0.05 0.04 0.04 Life satisfaction = 1: Very dissatisfied 0.01 0.01 0.01
Age 37.3 39.7 44.1 Female 0.34 0.47 0.37 White 0.33 0.70 0.73 Black 0.08 0.11 0.07 Asian 0.01 0.02 0.03 Hispanic 0.54 0.13 0.12 Other race 0.04 0.04 0.05 Married 0.52 0.60 0.70 Divorced 0.08 0.10 0.09 Separated 0.05 0.02 0.02 Widowed 0.02 0.02 0.01 Never married 0.22 0.22 0.14 Unmarried couple 0.11 0.04 0.04 Have children 0.63 0.51 0.50 Observations 45,605 503,862 168,981
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T A B L E 2
L ag ge d m in im
u m
w ag es
fo r B eh
av io ra l R is k F ac to r Su
rv ei ll an
ce Sy st em
Su rv ey
re sp o n d en
ts
20 05
20 06
20 07
20 08
20 09
20 10
M in im
u m
w a g e
O b se rv a ti o n s
M in im
u m
w a g e
O b se rv a ti o n s
M in im
u m
w a g e
O b se rv a ti o n s
M in im
u m
w a g e
O b se rv a ti o n s
M in im
u m
w a g e
O b se rv a ti o n s
M in im
u m
w a g e
O b se rv a ti o n s
5. 15
87 ,6 36
5. 15
78 ,3 50
5. 15
77 ,6 38
5. 15
28 ,5 21
5. 85
26 ,6 34
6. 55
30 ,3 27
5. 50
1, 68 9
5. 70
1, 13 0
5. 70
1, 19 1
5. 85
19 ,0 90
6. 15
4, 63 6
6. 85
56 5
6. 15
2, 39 2
6. 00
1, 73 3
5. 85
73 0
6. 15
12 ,0 24
6. 25
2, 14 2
6. 90
1, 37 7
6. 25
3, 34 6
6. 15
4, 99 0
6. 15
4, 12 9
6. 20
1, 54 7
6. 33
48 8
7. 05
75 1
6. 35
33 9
6. 25
2, 35 7
6. 25
39 9
6. 25
3, 78 3
6. 50
3, 24 2
7. 15
5, 71 8
6. 75
8, 37 1
6. 35
1, 14 7
6. 40
12 ,1 66
6. 33
78 1
6. 55
25 ,1 25
7. 21
4, 47 7
7. 05
3, 95 1
6. 50
1, 99 8
6. 50
4, 85 6
6. 50
5, 58 7
6. 65
1, 37 9
7. 25
41 ,0 63
7. 10
1, 56 2
6. 60
85 8
6. 75
12 ,2 22
6. 55
59 4
6. 79
2, 91 1
7. 28
2, 83 1
7. 15
1, 21 0
6. 75
6, 65 4
6. 95
59 8
6. 65
1, 24 4
6. 85
60 5
7. 30
2, 57 5
7. 16
7, 63 7
7. 00
2, 53 5
7. 00
75 0
6. 67
3, 11 7
6. 90
1, 23 7
7. 40
3, 59 9
7. 10
2, 44 7
7. 10
1, 05 5
6. 75
3, 20 6
7. 00
4, 83 2
7. 50
2, 36 8
7. 15
88 5
7. 15
1, 64 9
6. 85
7, 10 3
7. 02
3, 53 0
7. 55
83 5
7. 25
1, 61 0
7. 25
2, 42 2
6. 95
1, 17 1
7. 15
10 ,6 30
7. 75
40 9
7. 35
7, 31 2
7. 40
1, 95 3
7. 00
1, 36 1
7. 25
5, 78 9
8. 00
10 ,1 10
7. 50
1, 47 6
7. 15
9, 64 3
7. 40
2, 62 2
8. 06
2, 11 3
7. 63
7, 80 1
7. 25
2, 19 9
7. 50
48 2
8. 25
30 3
7. 40
1, 30 1
7. 55
24 9
8. 40
1, 12 5
7. 50
9, 69 9
7. 65
1, 48 1
8. 55
5, 00 0
7. 53
2, 38 0
7. 68
2, 11 1
7. 65
1, 64 4
7. 75
1, 09 6
7. 80
1, 39 5
7. 95
1, 01 6
7. 93
6, 78 5
8. 00
7, 89 3
8. 07
5, 42 3
N ot e. T h e fe d er al
m in im
u m
w ag es
ar e sh o w n in
b o ld .
e174 KUROKI
λs and θt are state and time fixed effects, respectively. Finally, state‐specific linear trend variable controls for heterogene- ity in the underlying trends in life satisfaction, where τs denotes the time trend for state s.
3 | REGRESSION RESULTS
Table 3 shows the results. I use a linear model for ease of interpretation, but similar results are obtained from ordered probit models.9 The BRFSS‐provided weights are used to adjust for sampling and nonresponse, and standard errors are clustered at the state level. The first column reveals that high school dropouts get happier when the minimum wage increases. A 10% increase in the minimum wage is associated with approximately a 0.03‐point increase, on a 4‐point scale, in life satisfaction for this group. However, the second column indicates that an increase in the minimum wage is not associated with life satisfaction for high school graduates. Finally, I test the hypothesis that higher minimum wages hurt small business owners by restricting the sample to the self‐employed. Contrary to popular belief, higher min- imum wages are not associated with lower life satisfaction for self‐employed people.
4 | CONCLUSION
This short study examines the direction and magnitude of the impact of minimum wage increases on individuals' life satisfaction in the United States. I find that an increase in minimum wages tends to raise life satisfaction of workers with- out a high school diploma, while that of high school graduates does not seem to be affected. There is little evidence that business owners are hurt by a higher minimum wage. As higher minimum wages may be able to raise the well‐being of low‐skilled workers without hurting business owners, the results provide some evidence that raising minimum wages is largely welfare‐enhancing.
However, the results must be interpreted with caution for two obvious reasons. First, as the sample excluded the unemployed, the study does not take into account low‐skilled workers who might have lost their job because of a higher
9Though using a linear model forces us to assume a constant distance between response categories and treat life satisfaction as interval rather than ordinal, ordinary least squares is commonly used in the literature for ease of interpretation. Moreover, Ferrer‐i‐Carbonell and Frijters (2004) show that whether well‐being is treated as an ordinal or cardinal concept does not substantively affect results.
TABLE 3 Ordinary least squares life satisfaction equation
High school dropout High school graduate Self‐employed
(1) (2) (3)
ln(Lagged minimum wage) 0.273** (0.088) 0.058 (0.036) 0.009 (0.058) Female −0.043*** (0.011) 0.005 (0.004) 0.031*** (0.005) Age/10 −0.006 (0.004) −0.005* (0.002) −0.004 (0.002) Black −0.010 (0.018) −0.053*** (0.007) −0.070*** (0.017) Asian −0.091 (0.046) −0.079** (0.023) −0.108*** (0.019) Hispanic 0.014 (0.020) −0.018** (0.006) −0.014 (0.014) Other race −0.075* (0.029) −0.054*** (0.007) −0.039** (0.012) Divorced −0.178*** (0.025) −0.240*** (0.007) −0.247*** (0.007) Separated −0.186*** (0.017) −0.346*** (0.016) −0.319*** (0.032) Widowed −0.118** (0.035) −0.227*** (0.011) −0.222*** (0.013) Never married −0.128*** (0.021) −0.206*** (0.006) −0.244*** (0.016) Unmarried couple −0.113*** (0.025) −0.182*** (0.008) −0.185*** (0.016) Have children −0.004 (0.009) −0.021*** (0.004) −0.010 (0.007) High school graduate 0.104*** (0.014) College graduate 0.210*** (0.015) Observations 45,605 503,862 168,981 Adj. R‐squared 0.027 0.041 0.061
Robust standard errors clustered at the state level are in parentheses. All regressions include state and year fixed effects and state‐specific linear trends.
***p < .001,
**p < .01,
*p < .05.
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minimum wage. Second, the study does not take into account those who might have had to close their business because of a higher minimum wage. Certainly, these are possible negative well‐being effects of higher minimum wages, which must be weighed against the gain of well‐being among minimum wage workers.
ACKNOWLEDGEMENT
I thank two anonymous referees for their useful suggestions and comments.
ORCID
Masanori Kuroki http://orcid.org/0000-0001-9645-8064
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How to cite this article: Kuroki M. Subjective well‐being and minimum wages: Evidence from U.S. states. Health Economics. 2018;27:e171–e180. https://doi.org/10.1002/hec.3577
APPENDIX
TABLE A1 Minimum wage increases during 2004–2010
Minimum
State Month Wage Change
2004 Connecticut 1 7.10 0.20 Illinois 1 5.50 0.35 Maine 10 6.35 0.10 Oregon 1 7.05 0.15 Rhode Island 1 6.75 0.60 Vermont 1 6.75 0.50 Washington 1 7.16 0.15
2005 District of Columbia 1 6.60 0.45 Florida 6 6.15 1.00 Illinois 1 6.50 1.00 Maine 10 6.50 0.15 Minnesota 8 6.15 1.00 New Jersey 10 6.15 1.00 New York 1 6.00 0.85 Oregon 1 7.25 0.20 Vermont 1 7.00 0.25 Washington 1 7.35 0.19 Wisconsin 6 5.70 0.55
2006 Arkansas 10 6.25 1.10 Connecticut 1 7.40 0.30 District of Columbia 1 7.00 0.40 Florida 1 6.40 0.25 Hawaii 1 6.75 0.50 Maine 10 6.75 0.25 Michigan 10 6.95 1.80 Nevada 12 6.15 1.00 New Jersey 10 7.15 1.00 New York 1 6.75 0.75 Oregon 1 7.50 0.25 Rhode Island 3 7.10 0.35 Vermont 1 7.25 0.25 Washington 1 7.63 0.28 West Virginia 7 5.85 0.70 Wisconsin 6 6.50 0.80
2007 Alabama 8 5.85 0.70 Arizona 1 6.75 1.60 California 1 7.50 0.75 Colorado 1 6.85 1.70 Connecticut 1 7.65 0.25 Delaware 1 6.65 0.50 Florida 1 6.67 0.27
(Continues)
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TABLE A1 (Continued)
Minimum
State Month Wage Change
Georgia 8 5.85 0.70 Hawaii 1 7.25 0.50 Idaho 8 5.85 0.70 Illinois 7 7.50 1.00 Indiana 8 5.85 0.70 Iowa 4 6.20 1.05 Kansas 8 5.85 0.70 Kentucky 7 5.85 0.70 Louisiana 8 5.85 0.70 Maine 10 7.00 0.25 Maryland 1 6.15 1.00 Massachusetts 1 7.50 0.75 Michigan 7 7.15 0.20 Mississippi 8 5.85 0.70 Missouri 1 6.50 1.35 Montana 1 6.15 1.00 Nebraska 8 5.85 0.70 Nevada 7 6.33 0.18 New Hampshire 8 5.85 0.70 New Hampshire 9 6.50 0.65 New Mexico 8 5.85 0.70 New York 1 7.15 0.40 North Carolina 1 6.15 1.00 North Dakota 8 5.85 0.70 Ohio 1 6.85 1.70 Oklahoma 8 5.85 0.70 Oregon 1 7.80 0.30 Pennsylvania 1 6.25 1.10 Pennsylvania 7 7.15 0.90 Rhode Island 1 7.40 0.30 South Carolina 8 5.85 0.70 South Dakota 8 5.85 0.70 Tennessee 8 5.85 0.70 Texas 8 5.85 0.70 Utah 8 5.85 0.70 Vermont 1 7.53 0.28 Virginia 8 5.85 0.70 Washington 1 7.93 0.30 West Virginia 7 6.55 0.70 Wyoming 8 5.85 0.70
2008 Alabama 8 6.55 0.70 Arizona 1 6.90 0.15 Arkansas 8 6.55 0.30 California 1 8.00 0.50 Colorado 1 7.02 0.17 Delaware 1 7.15 0.50 District of Columbia 8 7.55 0.55 Florida 1 6.79 0.12 Georgia 8 6.55 0.70 Idaho 8 6.55 0.70 Illinois 7 7.75 0.25 Indiana 8 6.55 0.70
(Continues)
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TABLE A1 (Continued)
Minimum
State Month Wage Change
Iowa 1 7.25 1.05 Kansas 8 6.55 0.70 Kentucky 7 6.55 0.70 Louisiana 8 6.55 0.70 Maine 10 7.25 0.25 Maryland 8 6.55 0.40 Massachusetts 1 8.00 0.50 Michigan 7 7.40 0.25 Minnesota 8 6.55 0.40 Mississippi 8 6.55 0.70 Missouri 1 6.65 0.15 Montana 1 6.25 0.10 Montana 8 6.55 0.30 Nebraska 8 6.55 0.70 Nevada 7 6.85 0.52 New Hampshire 8 6.55 0.05 New Hampshire 9 7.25 0.70 New Mexico 1 6.50 0.65 New Mexico 8 6.55 0.05 North Carolina 8 6.55 0.40 North Dakota 8 6.55 0.70 Ohio 1 7.00 0.15 Oklahoma 8 6.55 0.70 Oregon 1 7.95 0.15 South Carolina 8 6.55 0.70 South Dakota 8 6.55 0.70 Tennessee 8 6.55 0.70 Texas 8 6.55 0.70 Utah 8 6.55 0.70 Vermont 1 7.68 0.15 Virginia 8 6.55 0.70 Washington 1 8.07 0.14 West Virginia 7 7.25 0.70 Wisconsin 8 6.55 0.05 Wyoming 8 6.55 0.70
2009 Alabama 8 7.25 0.70 Alaska 8 7.25 0.10 Arizona 1 7.25 0.35 Arkansas 8 7.25 0.70 Colorado 1 7.28 0.26 Connecticut 1 8.00 0.35 Delaware 8 7.25 0.10 District of Columbia 8 8.25 0.70 Florida 1 7.21 0.42 Florida 8 7.25 0.04 Georgia 8 7.25 0.70 Idaho 8 7.25 0.70 Illinois 7 8.00 0.25 Indiana 8 7.25 0.70 Kansas 8 7.25 0.70 Kentucky 7 7.25 0.70 Louisiana 8 7.25 0.70
(Continues)
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TABLE A1 (Continued)
Minimum
State Month Wage Change
Maine 10 7.50 0.25 Maryland 8 7.25 0.70 Minnesota 8 7.25 0.70 Mississippi 8 7.25 0.70 Missouri 1 7.05 0.40 Missouri 8 7.25 0.20 Montana 1 6.90 0.35 Montana 8 7.25 0.35 Nebraska 8 7.25 0.70 Nevada 7 7.55 0.70 New Jersey 8 7.25 0.10 New Mexico 1 7.50 0.95 New York 8 7.25 0.10 North Carolina 8 7.25 0.70 North Dakota 8 7.25 0.70 Ohio 1 7.30 0.30 Oklahoma 8 7.25 0.70 Oregon 1 8.40 0.45 Pennsylvania 8 7.25 0.10 South Carolina 8 7.25 0.70 South Dakota 8 7.25 0.70 Tennessee 8 7.25 0.70 Texas 8 7.25 0.70 Utah 8 7.25 0.70 Vermont 1 8.06 0.38 Virginia 8 7.25 0.70 Washington 1 8.55 0.48 Wisconsin 8 7.25 0.70 Wyoming 8 7.25 0.70
2010 Alaska 1 7.75 0.5 Colorado 1 7.25 −0.03 Connecticut 1 8.25 0.25 Illinois 7 8.25 0.25 Nevada 7 8.25 0.70
e180 KUROKI
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