Effects of Minimum Wages on Absence from Work Due to Illness

Juan Du; J. Paul Leigh

doi:10.1515/bejeap-2017-0097

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Effects of Minimum Wages on Absence from Work Due to Illness

Juan Du und J. Paul Leigh

Veröffentlicht/Copyright: 23. Januar 2018

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Aus der Zeitschrift The B.E. Journal of Economic Analysis & Policy Band 18 Heft 1

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.

Keywords: absence; health; income inequality; low wages

A Appendix

Table 11:

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.

Table 12:

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
Absence rate due to own illness	(5.483)	(11.462)	(7.077)	(11.286)
Absence rate due to others’ illness	0.502	0.426	0.388	0.420
Absence rate due to others’ illness	(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
Absence rate due to own illness	(5.847)	(11.053)	(5.921)	(11.049)
Absence rate due to others’ illness	0.709	0.572	0.515	0.561
Absence rate due to others’ illness	(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
Absence rate due to own illness	(5.660)	(10.378)	(5.689)	(10.384)
Absence rate due to others’ illness	0.707	0.602	0.494	0.586
Absence rate due to others’ illness	(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.

Table 13:

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.

Table 14:

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
Weeks absent	(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.

Table 15:

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*
Overall absence	(0.131)	(0.159)
Absence due to own illness	−0.294***	−0.308**
Absence due to own illness	(0.104)	(0.129)
Absence due to others’ illness	0.025	0.014
Absence due to others’ illness	(0.041)	(0.041)
Sample size	14,206	14,206
Linear DDD model, 13–15 as comparison group
Overall absence	−0.496**	−0.540**
Overall absence	(0.223)	(0.207)
Absence due to own illness	−0.661**	−0.692***
Absence due to own illness	(0.264)	(0.229)
Absence due to others’ illness	0.165	0.152
Absence due to others’ illness	(0.124)	(0.127)
Sample size	22,488	22,488
Linear DDD model, 13+ as comparison group
Overall absence	−0.533***	−0.581***
Overall absence	(0.186)	(0.206)
Absence due to own illness	−0.685***	−0.729***
Absence due to own illness	(0.180)	(0.176)
Absence due to others’ illness	0.151	0.149
Absence due to others’ illness	(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.

Table 16:

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
Overall absence	(0.129)	(0.276)	(0.162)	(0.166)
Absence due to own illness	−0.237**	−0.352	−0.318**	−0.281**
Absence due to own illness	(0.112)	(0.268)	(0.145)	(0.135)
Absence due to others’ illness	0.052	0.024	−0.029	0.087
Absence due to others’ illness	(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**
Overall absence	(0.335)	(0.428)	(0.289)	(0.309)
Absence due to own illness	−0.809**	−0.474	−0.236	−0.963***
Absence due to own illness	(0.310)	(0.512)	(0.319)	(0.342)
Absence due to others’ illness	0.133	0.376	0.159*	0.224
Absence due to others’ illness	(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*
Overall absence	(0.232)	(0.357)	(0.233)	(0.290)
Absence due to own illness	−0.795***	−0.588	−0.569**	−0.699***
Absence due to own illness	(0.213)	(0.404)	(0.260)	(0.221)
Absence due to others’ illness	0.100	0.379	0.189**	0.178
Absence due to others’ illness	(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.

Table 17:

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
Overall absence	(0.307)
Absence due to own illness	−0.036
Absence due to own illness	(0.245)
Absence due to others’ illness	0.024
Absence due to others’ illness	(0.143)
Sample size	1,131
Linear DDD model, 13–15 as comparison group
Overall absence	−0.192
Overall absence	(0.888)
Absence due to own illness	0.394
Absence due to own illness	(0.549)
Absence due to others’ illness	−0.586
Absence due to others’ illness	(0.651)
Sample size	2,117
Linear DDD model, 13+ as comparison group
Overall absence	−0.363
Overall absence	(1.160)
Absence due to own illness	0.740
Absence due to own illness	(0.583)
Absence due to others’ illness	−1.103
Absence due to others’ illness	(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.

Table 18:

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*
Overall absence	(0.117)	(0.147)
Absence due to own illness	−0.214*	−0.321**
Absence due to own illness	(0.109)	(0.140)
Absence due to others’ illness	0.038	0.046
Absence due to others’ illness	(0.049)	(0.047)
Sample size	6,619	15,715
Linear DDD model, 13–15 as comparison group
Overall absence	−0.597*	−0.403
Overall absence	(0.335)	(0.278)
Absence due to own illness	−0.818***	−0.586**
Absence due to own illness	(0.275)	(0.263)
Absence due to others’ illness	0.221	0.184
Absence due to others’ illness	(0.166)	(0.137)
Sample size	11,573	25,286
Linear DDD model, 13+ as comparison group
Overall absence	−0.757*	−0.503**
Overall absence	(0.383)	(0.245)
Absence due to own illness	−0.922**	−0.671***
Absence due to own illness	(0.382)	(0.231)
Absence due to others’ illness	0.165	0.168
Absence due to others’ illness	(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.

Table 19:

Including lead effects.

Dependent variables	Including MW_t and MW_t+1		Including MW_t and MW_t+2
Linear DD model, <=12 years of education	MW_t	MW_t+1	MW_t	MW_t+2
Overall absence	−0.110	−0.271	−0.271**	0.012
Overall absence	(0.131)	(0.187)	(0.125)	(0.219)
Absence due to own illness	−0.163	−0.218	−0.290***	0.001
Absence due to own illness	(0.113)	(0.172)	(0.102)	(0.199)
Absence due to others’ illness	0.052	−0.052	0.019	0.010
Absence due to others’ illness	(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
Overall absence	(0.255)	(0.212)	(0.243)	(0.260)
Absence due to own illness	0.225	0.183	0.219	0.422*
Absence due to own illness	(0.333)	(0.273)	(0.291)	(0.226)
Absence due to others’ illness	−0.177	0.032	−0.129	−0.109
Absence due to others’ illness	(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
Overall absence	(0.197)	(0.184)	(0.158)	(0.201)
Absence due to own illness	0.263	0.196	0.301	0.277
Absence due to own illness	(0.238)	(0.191)	(0.188)	(0.169)
Absence due to others’ illness	−0.132	−0.009	−0.092	−0.163*
Absence due to others’ illness	(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. MW_t+1 and MW_t+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.

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Published Online: 2018-1-23

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Artikel in diesem Heft

https://doi.org/10.1515/bejeap-2017-0097

Schlagwörter für diesen Artikel

absence; health; income inequality; low wages