Abstract
Using longitudinal data from the Panel Study of Income Dynamics for 1997–2013 and difference-in-differences (DD) and difference-in-difference-in-differences (DDD) techniques, we estimate the effects of minimum wages on absence from work due to own and others’ (such as children’s) illnesses. We use person fixed effects within both linear and two-part models, the latter to explore changes at extensive and intensive margins. A lower educated group (likely affected by minimum wages) is compared with higher educated groups (likely unaffected). Within the lower educated group, we find higher minimum wages are associated with lower rates of absence due to own and others’ illness combined and due to own illness alone, but not associated with absence due to others’ illness. A $1 increase in the real minimum wage results in 19 % (in DD model) and 32 % (DDD) decreases in the absence rate due to own illness evaluated at the mean. These findings are strongest for persons who are not employed year-round and among the lowest wage earners. In additional analysis, we show that these effects are likely not due to changes in labor supply or job-related attributes. Instead, we find a possible mechanism: higher minimum wages improve self-reported health for lower educated workers.
A Appendix
Percentage of workers earning at or below 200 % of the minimum wage, and absence rates by age group, using the baseline sample (≤12 years of education).
Sample size | Percent earning at or below 200 % minimum wage | Absence rate for either reason | Absence rate due to own illness | Absence rate due to others’ illness | |
---|---|---|---|---|---|
16–24 year olds | 1,458 | 77.78 % | 2.22 (5.84) | 1.67 (5.51) | 0.56 (1.87) |
25–40 year olds | 6,481 | 51.33 % | 2.02 (5.31) | 1.40 (4.85) | 0.62(2.18) |
41–64 year olds | 7,725 | 44.78 % | 2.11 (6.12) | 1.67 (5.77) | 0.44 (1.91) |
Note: These statistics are not weighted. Standard deviations are included in parentheses.
Summary statistics of PSID and CPS absence variables 1996–2012.
PSID | CPS | PSID, weighted | CPS, weighted | |
---|---|---|---|---|
≤12 years of education | ||||
Absence rate due to own illness | 1.542 | 1.641 | 1.999 | 1.589 |
(5.483) | (11.462) | (7.077) | (11.286) | |
Absence rate due to others’ illness | 0.502 | 0.426 | 0.388 | 0.420 |
(1.945) | (5.669) | (1.458) | (5.632) | |
13–15 years of education | ||||
Absence rate due to own illness | 1.750 | 1.587 | 1.932 | 1.580 |
(5.847) | (11.053) | (5.921) | (11.049) | |
Absence rate due to others’ illness | 0.709 | 0.572 | 0.515 | 0.561 |
(3.270) | (6.747) | (1.865) | (6.697) | |
13+ years of education | ||||
Absence rate due to own illness | 1.706 | 1.409 | 1.899 | 1.404 |
(5.660) | (10.378) | (5.689) | (10.384) | |
Absence rate due to others’ illness | 0.707 | 0.602 | 0.494 | 0.586 |
(3.266) | (7.019) | (1.779) | (6.932) |
Note: The PSID absence variables are defined as the number of weeks missed divided by total weeks worked and missed in the previous year. In the CPS, absence is measured during the reference week as the gap between usual hours worked and actual hours worked for those who usually work at least 35 hours. Both samples consist of those 25–64 years old hourly paid workers who work at least 35 hours per week. Self-employed are excluded. To make the summary statistics comparable, the PSID sample is restricted to those who usually work more than 35 hours per week. In the CPS, the reason of absence due to others’ illness is not listed. Instead, we include those with absence due to childcare problems and other family/personal obligations in this category. The weighted PSID sample is smaller than the regular sample because only some of the PSID core households are assigned longitudinal sample weights. Standard deviations are included in parentheses.
Effect of minimum wage on weeks worked.
Dependent variable | ≤12 years of education | 13–15 years of education | ≥16 years of education |
---|---|---|---|
Annual weeks worked | −0.083 (0.181) | 0.098 (0.244) | 0.282 (0.448) |
Ln (annual weeks worked) | −0.008 (0.005) | −0.001 (0.008) | 0.007 (0.015) |
Sample size | N = 14,206 | N = 8,282 | N = 2,951 |
Note: Each cell with a coefficient and standard error corresponds to a different regression; six regressions in all. Linear regressions with person fixed effects are applied for all regressions. Standard errors clustered at the state level are included in parentheses. All regressions additionally include the variables in Table 2, year dummies, and month dummies. *, **, *** indicate statistical significance at the 10 %, 5 %, and 1 % level, two-tailed tests, respectively.
Effect of minimum wage on non-health related absences (≤12 years of education).
Linear DD model | Weeks absent due to all non-health reasons combined | Weeks absent due to vacation | Weeks absent due to strike | Weeks absent due to lay-off |
---|---|---|---|---|
Weeks absent | −0.106 | −0.049 | 0.021** | −0.077 |
(0.093) | (0.071) | (0.010) | (0.052) | |
Sample size | 14,206 | 14,206 | 14,206 | 14,206 |
Note: Each cell with a coefficient and standard error corresponds to a different regression; 4 regressions in all. We examine four dependent variables: weeks absent due to all non-health reasons that consist of vacation, strike, and lay-off, and each of the reasons separately. Linear regressions with person fixed effects are applied for all regressions. Standard errors clustered at the state level are included in parentheses. All regressions additionally include the variables in Table 2, year dummies, and month dummies. *, **, *** indicate statistical significance at the 10 %, 5 %, and 1 % level, two-tailed tests, respectively.
Alternative state EPHI and excluding state-level employment controls (≤12 years of education).
Linear DD model | Include state EPHI coverage as additional control | Exclude all state-level employment controls (unemp. rate, mean wages, and EPHI variables) |
---|---|---|
Overall absence | −0.269** | −0.294* |
(0.131) | (0.159) | |
Absence due to own illness | −0.294*** | −0.308** |
(0.104) | (0.129) | |
Absence due to others’ illness | 0.025 | 0.014 |
(0.041) | (0.041) | |
Sample size | 14,206 | 14,206 |
Linear DDD model, 13–15 as comparison group | ||
Overall absence | −0.496** | −0.540** |
(0.223) | (0.207) | |
Absence due to own illness | −0.661** | −0.692*** |
(0.264) | (0.229) | |
Absence due to others’ illness | 0.165 | 0.152 |
(0.124) | (0.127) | |
Sample size | 22,488 | 22,488 |
Linear DDD model, 13+ as comparison group | ||
Overall absence | −0.533*** | −0.581*** |
(0.186) | (0.206) | |
Absence due to own illness | −0.685*** | −0.729*** |
(0.180) | (0.176) | |
Absence due to others’ illness | 0.151 | 0.149 |
(0.113) | (0.110) | |
Sample size | 25,439 | 25,439 |
Note: Each cell with a coefficient and standard error corresponds to a different regression; 18 regressions in all. Linear regressions with person fixed effects are applied for all regressions. Standard errors clustered at the state level are included in parentheses. All regressions additionally include the variables in Table 2, year dummies, and month dummies. *, **, *** indicate statistical significance at the 10 %, 5 %, and 1 % level, two-tailed tests, respectively.
Effect of minimum wage by marital status and gender (≤12 years of education).
Linear DD model | Married | Single | Male | Female |
---|---|---|---|---|
Overall absence | −0.185 | −0.328 | −0.347** | −0.194 |
(0.129) | (0.276) | (0.162) | (0.166) | |
Absence due to own illness | −0.237** | −0.352 | −0.318** | −0.281** |
(0.112) | (0.268) | (0.145) | (0.135) | |
Absence due to others’ illness | 0.052 | 0.024 | −0.029 | 0.087 |
(0.041) | (0.076) | (0.031) | (0.076) | |
Sample size | 9,336 | 4,870 | 7,556 | 6,650 |
Linear DDD model, 13–15 as comparison group | ||||
Overall absence | −0.676** | −0.098 | −0.077 | −0.739** |
(0.335) | (0.428) | (0.289) | (0.309) | |
Absence due to own illness | −0.809** | −0.474 | −0.236 | −0.963*** |
(0.310) | (0.512) | (0.319) | (0.342) | |
Absence due to others’ illness | 0.133 | 0.376 | 0.159* | 0.224 |
(0.096) | (0.368) | (0.093) | (0.220) | |
Sample size | 14,743 | 7,745 | 11,065 | 11,423 |
Linear DDD model, 13+ as comparison group | ||||
Overall absence | −0.695*** | −0.209 | −0.380 | −0.521* |
(0.232) | (0.357) | (0.233) | (0.290) | |
Absence due to own illness | −0.795*** | −0.588 | −0.569** | −0.699*** |
(0.213) | (0.404) | (0.260) | (0.221) | |
Absence due to others’ illness | 0.100 | 0.379 | 0.189** | 0.178 |
(0.090) | (0.305) | (0.082) | (0.220) | |
Sample size | 16,882 | 8,557 | 12,212 | 13,227 |
Note: Each cell with a coefficient and standard error corresponds to a different regression; 36 regressions in all. Linear regressions with person fixed effects are applied for all regressions. Standard errors clustered at the state level are included in parentheses. All regressions additionally include the variables in Table 2, year dummies, and month dummies. *, **, *** indicate statistical significance at the 10 %, 5 %, and 1 % level, two-tailed tests, respectively.
Effect of minimum wage on absence for three alternative industries combined (≤12 years of education).
Linear DD model | Sample includes the following industries: government, agriculture, private households |
---|---|
Overall absence | −0.011 |
(0.307) | |
Absence due to own illness | −0.036 |
(0.245) | |
Absence due to others’ illness | 0.024 |
(0.143) | |
Sample size | 1,131 |
Linear DDD model, 13–15 as comparison group | |
Overall absence | −0.192 |
(0.888) | |
Absence due to own illness | 0.394 |
(0.549) | |
Absence due to others’ illness | −0.586 |
(0.651) | |
Sample size | 2,117 |
Linear DDD model, 13+ as comparison group | |
Overall absence | −0.363 |
(1.160) | |
Absence due to own illness | 0.740 |
(0.583) | |
Absence due to others’ illness | −1.103 |
(1.007) | |
Sample size | 1,782 |
Note: Each cell with a coefficient and standard error corresponds to a different regression; 9 regressions in all. Linear squares regressions with person fixed effects are applied for all regressions. Standard errors clustered at the state level are included in parentheses. All regressions additionally include the variables in Table 2, year dummies, and month dummies. *, **, *** indicate statistical significance at the 10 %, 5 %, and 1 % level, two-tailed tests, respectively.
Using alternative samples: Balanced and movers.
≤12 years of education | ≤12 years of education | |
---|---|---|
Dependent variables | Balanced sample (Standard errors clustered at state level) | Including movers (Standard errors clustered at person level) |
Linear DD model | ||
Overall absence | −0.176 | −0.275* |
(0.117) | (0.147) | |
Absence due to own illness | −0.214* | −0.321** |
(0.109) | (0.140) | |
Absence due to others’ illness | 0.038 | 0.046 |
(0.049) | (0.047) | |
Sample size | 6,619 | 15,715 |
Linear DDD model, 13–15 as comparison group | ||
Overall absence | −0.597* | −0.403 |
(0.335) | (0.278) | |
Absence due to own illness | −0.818*** | −0.586** |
(0.275) | (0.263) | |
Absence due to others’ illness | 0.221 | 0.184 |
(0.166) | (0.137) | |
Sample size | 11,573 | 25,286 |
Linear DDD model, 13+ as comparison group | ||
Overall absence | −0.757* | −0.503** |
(0.383) | (0.245) | |
Absence due to own illness | −0.922** | −0.671*** |
(0.382) | (0.231) | |
Absence due to others’ illness | 0.165 | 0.168 |
(0.210) | (0.108) | |
Sample size | 10,264 | 28,904 |
Note: The balanced sample refers to those respondents who appear in all waves. The “movers” sample is not balanced and includes those who may have migrated across states. Each cell with a coefficient and standard error corresponds to a different regression; 18 regressions in all. Linear regressions with person fixed effects are applied for all regressions. All regressions additionally include the variables in Table 2, year dummies, and month dummies. *, **, *** indicate statistical significance at the 10 %, 5 %, and 1 % level, two-tailed tests, respectively.
Including lead effects.
Dependent variables | Including MWt and MWt+1 | Including MWt and MWt+2 | ||
---|---|---|---|---|
Linear DD model, <=12 years of education | MWt | MWt+1 | MWt | MWt+2 |
Overall absence | −0.110 | −0.271 | −0.271** | 0.012 |
(0.131) | (0.187) | (0.125) | (0.219) | |
Absence due to own illness | −0.163 | −0.218 | −0.290*** | 0.001 |
(0.113) | (0.172) | (0.102) | (0.199) | |
Absence due to others’ illness | 0.052 | −0.052 | 0.019 | 0.010 |
(0.045) | (0.036) | (0.044) | (0.053) | |
Sample size | N = 14,206 | N = 14,206 | ||
Linear DD, 13–15 years of education | ||||
Overall absence | 0.048 | 0.215 | 0.090 | 0.312 |
(0.255) | (0.212) | (0.243) | (0.260) | |
Absence due to own illness | 0.225 | 0.183 | 0.219 | 0.422* |
(0.333) | (0.273) | (0.291) | (0.226) | |
Absence due to others’ illness | −0.177 | 0.032 | −0.129 | −0.109 |
(0.124) | (0.132) | (0.107) | (0.114) | |
Sample size | N = 8,282 | N = 8,282 | ||
Linear DD, 13+ years of education | ||||
Overall absence | 0.131 | 0.188 | 0.208 | 0.114 |
(0.197) | (0.184) | (0.158) | (0.201) | |
Absence due to own illness | 0.263 | 0.196 | 0.301 | 0.277 |
(0.238) | (0.191) | (0.188) | (0.169) | |
Absence due to others’ illness | −0.132 | −0.009 | −0.092 | −0.163* |
(0.103) | (0.104) | (0.094) | (0.096) | |
Sample size | N = 11,233 | N = 11,233 |
Note: Each cell with two coefficients and two standard errors corresponds to a different regression; 18 regressions in all. MWt+1 and MWt+2 are minimum wages for yeas t + 1 and t + 2, respectively. Linear squares regressions with person fixed effects are applied for all regressions. Standard errors clustered at the state level are included in parentheses. All regressions additionally include the variables in Table 2, year dummies, and month dummies. *, **, *** indicate statistical significance at the 10 %, 5 %, and 1 % level, two-tailed tests, respectively.
References
Adams, S., M.L. Blackburn, and C.D. Cotti. 2012. “Minimum Wages and Alcohol-Related Traffic Fatalities among Teens.” The Review of Economics and Statistics 94 (3):828–840.10.1162/REST_a_00199Search in Google Scholar
Allen, S.G. 1981. “An Empirical Model of Work Attendance.” The Review of Economics and Statistics 63 (1):77–87.10.2307/1924220Search in Google Scholar
Averett, S.L., J.K. Smith, and Y. Wang. 2017. “The Effects of Minimum Wages on the Health of Working Teenagers.” Applied Economics Letters 24 (16):1127–1130.10.1080/13504851.2016.1259737Search in Google Scholar
Averett, S.L., and Y. Wang. 2013. “The Effects of Earned Income Tax Credit Payment Expansion on Maternal Smoking.” Health Economics 22 (11):1344–1359.10.1002/hec.2886Search in Google Scholar
Becker, G.S., and C.B. Mulligan. 1997. “The Endogenous Determination of Time Preference.” The Quarterly Journal of Economics 112 (3):729–758.10.1162/003355397555334Search in Google Scholar
Belman, D., and P.J. Wolfson. 2014. What Does the Minimum Wage Do?. Kalamazoo Michigan: W.E. Upjohn Institute for Employment Research.10.17848/9780880994583Search in Google Scholar
Brown, C. 1999. “Minimum Wages, Employment, and the Distribution of Income.” Handbook of Labor Economics 3 (B):2101–2163. North-Holland, Elsevier. Amsterdam, The Netherlands.10.1016/S1573-4463(99)30018-3Search in Google Scholar
Brown, S., and John Sessions. 1996. “The Economics of Absence: Theory and Evidence.” Journal of Economic Surveys 10 (1): 23–53.10.1111/j.1467-6419.1996.tb00002.xSearch in Google Scholar
Bureau of Labor Statistics 2016a. http://www.bls.gov/cps/aa2004/cpsaat46.pdf Accessed October 13, 2016Search in Google Scholar
Bureau of Labor Statistics. 2016b. http://www.bls.gov/schedule/archives/laus_nr.htm. Accessed August 1 2016.Search in Google Scholar
Bureau of Labor Statistics. 2016c. https://www.bls.gov/bls/blsminwagedata.htm. Accessed February 16, 2017.Search in Google Scholar
Callan, M.J., N.W. Shead, and J.M. Olson. 2011. “Personal Relative Deprivation, Delay Discounting, and Gambling.” Journal of Personality and Social Psychology 101: 955–973.10.1037/a0024778Search in Google Scholar
Clark, A.E., and A.J. Oswald. 1996. “Satisfaction and Comparison Income.” Journal of Public Economics 61 (3):359–381.10.1016/0047-2727(95)01564-7Search in Google Scholar
Du, J., and T. Yagihashi. 2017. “Health Capital Investment and Time Spent on Health-Related Activities.” Review of Economics of the Household 15 (4):1215–1248.10.1007/s11150-017-9378-9Search in Google Scholar
Economou, A., and I. Theodossiou. 2011. “Poor and Sick: Estimating the Relationship between Household Income and Health.” Review of Income and Wealth 57 (3):395–411.10.1111/j.1475-4991.2010.00416.xSearch in Google Scholar
Faragher, B.E., M. Cass, and C.L. Cooper. 2005. “The Relationship between Job Satisfaction and Health: A Meta-Analysis.” Occupational and Environmental Medicine 62:105–112.10.1136/oem.2002.006734Search in Google Scholar
Fuchs, V.R 1982. “‘Time Preference and Health: An Exploratory Study.’ Chapter 3.” In Fuchs, V.R. (Ed.), Economic Aspects of Health. 93– 120. Chicago: University of Chicago Press.10.7208/chicago/9780226267944.001.0001Search in Google Scholar
Greenwell, Claire and Centers for Disease Control. 2016 http://www.cdcfoundation.org/businesspulse/healthy-workforce and http://www.cdcfoundation.org/pr/2015/worker-illness-and-injury-costs-us-employers-225-billion-annually Accessed February 16, 2017Search in Google Scholar
Grossman, M. 1972. “On the Concept of Health Capital and the Demand for Health.” Journal of Political Economy 80 (2):223–255.10.1086/259880Search in Google Scholar
Hirsch, B.T., B.E. Kaufman, and T. Zelenska. 2015. “Minimum Wage Channels of Adjustment.” Industrial Relations 54 (2):199–239.10.1111/irel.12091Search in Google Scholar
Horn, B.P., J.C. Maclean, and M.R. Strain. 2017. “Do Minimum Wage Increases Influence Worker Health?” Economic Inquiry 55 (4):1986–2007.10.1111/ecin.12453Search in Google Scholar
Hoynes, H.W., D.L. Miller, and D. Simon. 2015. “Income, the Earned Income Tax Credit, and Infant Health.” American Economic Journal: Economic Policy 7 (1):172–211.10.3386/w18206Search in Google Scholar
Kenkel, D.S., M.D. Schmeiser, and C.J. Urban. 2014. “Is Smoking Inferior? Evidence from Variation in the Earned Income Tax Credit.” Journal of Human Resources 49 (4):1094–1120.10.3386/w20097Search in Google Scholar
Kimmel, J., and R. Connelly. 2007. “Mothers’ Time Choices: Caregiving, Leisure, Home Production, and Paid Work.” Journal of Human Resources 42 (3):643–681.10.3368/jhr.XLII.3.643Search in Google Scholar
Kivimäki, M., J. Head, J.E. Ferrie, M.J. Shipley, J. Vahtera, and M.G. Marmot. 2003. “Sickness Absence as a Global Measure of Health: Evidence from Mortality in the Whitehall II Prospective Cohort Study.” British Medical Journal 327:364–368.10.1136/bmj.327.7411.364Search in Google Scholar
Komro, K.A., M.D. Livingston, S. Markowitz, and A.C. Wagenaar. 2016. “Increased Minimum Wage Linked with Decreased Infant Mortality.” American Journal of Public Health 106 (8):1514–1516.10.2105/AJPH.2016.303268Search in Google Scholar
Kuroki, M. 2017. “Subjective Well-Being and Minimum Wages: Evidence from U.S. States.” Health Economics. August 22, 2017. epublished ahead of print.10.1002/hec.3577Search in Google Scholar
Leigh, J.P. 1983. “Sex Differences in Absenteeism.” Industrial Relations 22 (3):349–361.10.1111/j.1468-232X.1983.tb00940.xSearch in Google Scholar
Leigh, J.P. 1990. “The Decision to Strike as a Decision to Allocate Time.” Applied Economics 22 (9):1249–1266.10.1080/00036849000000044Search in Google Scholar
Lenhart, O. 2017. “The Impact of Minimum Wages on Population Health: Evidence from 24 OECD Countries.” The European Journal of Health Economics 18 (8):1031–1039.10.1007/s10198-016-0847-5Search in Google Scholar
Li, C.-Y., and F.-C. Sung. 1999. “A Review of the Healthy Worker Effect in Occupational Epidemiology.” Occupational Medicine 49 (4):225–229.10.1093/occmed/49.4.225Search in Google Scholar
Lusinyan, L., and L. Bonato. 2007. “Work Absence in Europe.” IMF Staff Papers 54 (3):475–538.10.1057/palgrave.imfsp.9450016Search in Google Scholar
Mani, A., M. Sendhil, S. Eldar, and Z. Jiaying. 2013. “Poverty Impedes Cognitive Function.” Science 341 (6149):976–980.10.1126/science.1238041Search in Google Scholar
Marmot, M. 2004. The Status Syndrome. New York, NY: Owl Books, Henry Holt and Company.10.1111/j.1740-9713.2004.00058.xSearch in Google Scholar
Marmot, M., A. Feene, M. Shipley, F. North, and S. Lenard Syme. 1995. “Sickness Absence as a Measure of Health Status and Functioning: From the UK Whitehall II Study.” Journal of Epidemiology and Community Health 49 (2):124–130.10.1136/jech.49.2.124Search in Google Scholar
Martocchio, J.J. 1989. “Age-Related Differences in Employee Absenteeism: A Meta-Analysis.” Psychology and Aging 4 (4):409–414.10.1037/0882-7974.4.4.409Search in Google Scholar
McCarrier, K.P., F.J. Zimmerman, J.D. Ralston, and D.P. Martin. 2011. “Associations between Minimum Wage Policy and Access to Health Care: Evidence from the Behavioral Risk Factor Surveillance System, 1996–2007.” American Journal of Public Health 101 (2):359–367.10.2105/AJPH.2006.108928Search in Google Scholar
Metzler, D.O., and Z. Chen. 2011. “The Impact of Minimum Wage Rates on Body Weight in the United States.” In Economic Aspects of Obesity, edited by M. Grossman and M. Nh. Chicago, Illinois: University of Chicago Press.10.7208/chicago/9780226310107.003.0002Search in Google Scholar
Mozzafarian, D., E.J. Benjamin, A.S. Go, et al. On behalf of the American Heart Association. 2015. “Heart Disease and Stroke Statistics–2015 Update: A Report from the American Heart Association.” Circulation 131 (4): e29–322.10.1161/CIR.0000000000000152Search in Google Scholar
Neumark, D., M. Schweitzer, and W. Wascher. 2004. “Minimum Wage Effects Throughout the Wage Distribution.” Journal of Human Resources 39 (2):425–450.10.2307/3559021Search in Google Scholar
Neumark, D., and W.L. Wascher. 2007. “Minimum Wages and Employment.” Foundations and Trends® in Microeconomics 3 (1–2):1–182.10.1561/0700000015Search in Google Scholar
Pauly, M.V., S. Nicholson, X. Judy, D. Polsk, P.M. Danzon, J.F. Murray, and M.L. Berger. 2002. “A General Model of the Impact of Absenteeism on Employers and Employees.” Health Economics 11 (3):221–231.10.1002/hec.648Search in Google Scholar
Pouliakas, K., and I. Theodossiou. 2013. “The Economics of Health and Safety at Work: An Interdisciplinary Review of the Theory and Policy.” Journal of Economic Surveys 27 (1): 167–208.10.1111/j.1467-6419.2011.00699.xSearch in Google Scholar
Raissian, K.M., and L.R. Bullinger. 2017. “Money Matters: Does the Minimum Wage Effect Child Maltreatment Rates?” Children and Youth Services Review 72:60–70.10.1016/j.childyouth.2016.09.033Search in Google Scholar
Reeves, A., M. McKee, J. Mackenbach, M. Whitehead, and D. Stuckler. 2017. “Introduction of A National Minimum Wage Reduced Depressive Symptoms in Low-Wage Workers: A Quasi-Natural Experiment in the UK.” Health Economics 26 (5):639–655.10.1002/hec.3336Search in Google Scholar
Royalty, A.B. 2000. “Do Minimum Wage Increases Lower the Probability that Low-Skilled Workers Will Receive Fringe Benefits?” JCPR Working Paper, Northwestern University/University of Chicago Joint Center for Poverty Research.Search in Google Scholar
Sabia, J.J., and R.B. Nielsen. 2015. “Minimum Wages, Poverty, and Material Hardship: New Evidence from the SIPP.” Review of Economics of the Household 13 (1):95–134.10.1007/s11150-012-9171-8Search in Google Scholar
Simon, K.I., and R. Kaestner. 2004. “Do Minimum Wages Affect Non-Wage Job Attributes? Evidence on Fringe Benefits.” Industrial & Labor Relations Review 58 (1):52–70.10.1177/001979390405800103Search in Google Scholar
Smith, T.W., and J.M. Ruiz. 2002. “Psychosocial Influences on the Development and Course of Coronary Heart Disease: Current Status and Implications for Research and Practice.” Journal of Consulting and Clinical Psychology 70 (3):548–568.10.1037/0022-006X.70.3.548Search in Google Scholar
Tsao, T.-Y., K.J. Konty, G. Van Wye et al. 2016. “Estimating Potential Reductions in Premature Mortality in New York City from Raising the Minimum Wage to $15.” American Journal of Public Health 106 (6):1036–1041.10.2105/AJPH.2016.303188Search in Google Scholar
University of Kentucky Center for Poverty Research. 2016. “UKCPR National Welfare Data, 1980-2015.” Gatton College of Business and Economics, University of Kentucky, Lexington, KY. http://www.ukcpr.org/data. Accessed October 2016.Search in Google Scholar
Wehby, G., D. Dhaval, and R. Kaestner. 2016. “Effects of the Minimum Wage on Infant Health.” National Bureau of Economic Research working paper #22373.10.3386/w22373Search in Google Scholar
Wilkinson, R.G. 2005. The Impact of Inequality. How to Make Sick Societies Healthier. New York: The New Press.Search in Google Scholar
Zavodny, M 2000. “The Effect of the Minimum Wage on Employment and Hours.” Labour Economics 7: 729–750.10.1016/S0927-5371(00)00021-XSearch in Google Scholar
© 2018 Walter de Gruyter GmbH, Berlin/Boston