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Effects of Minimum Wages on Absence from Work Due to Illness

  • Juan Du EMAIL logo and J. Paul Leigh

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

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 sizePercent earning at or below 200 % minimum wageAbsence rate for either reasonAbsence rate due to own illnessAbsence rate due to others’ illness
16–24 year olds1,45877.78 %2.22 (5.84)1.67 (5.51)0.56 (1.87)
25–40 year olds6,48151.33 %2.02 (5.31)1.40 (4.85)0.62(2.18)
41–64 year olds7,72544.78 %2.11 (6.12)1.67 (5.77)0.44 (1.91)
  1. Note: These statistics are not weighted. Standard deviations are included in parentheses.

Table 12:

Summary statistics of PSID and CPS absence variables 1996–2012.

PSIDCPSPSID, weightedCPS, weighted
≤12 years of education
Absence rate due to own illness1.5421.6411.9991.589
(5.483)(11.462)(7.077)(11.286)
Absence rate due to others’ illness0.5020.4260.3880.420
(1.945)(5.669)(1.458)(5.632)
13–15 years of education
Absence rate due to own illness1.7501.5871.9321.580
(5.847)(11.053)(5.921)(11.049)
Absence rate due to others’ illness0.7090.5720.5150.561
(3.270)(6.747)(1.865)(6.697)
13+ years of education
Absence rate due to own illness1.7061.4091.8991.404
(5.660)(10.378)(5.689)(10.384)
Absence rate due to others’ illness0.7070.6020.4940.586
(3.266)(7.019)(1.779)(6.932)
  1. 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 education13–15 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 sizeN = 14,206N = 8,282N = 2,951
  1. 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 modelWeeks absent due to all non-health reasons combinedWeeks absent due to vacationWeeks absent due to strikeWeeks absent due to lay-off
Weeks absent−0.106−0.0490.021**−0.077
(0.093)(0.071)(0.010)(0.052)
Sample size14,20614,20614,20614,206
  1. 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 modelInclude state EPHI coverage as additional controlExclude 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’ illness0.0250.014
(0.041)(0.041)
Sample size14,20614,206
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’ illness0.1650.152
(0.124)(0.127)
Sample size22,48822,488
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’ illness0.1510.149
(0.113)(0.110)
Sample size25,43925,439
  1. 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 modelMarriedSingleMaleFemale
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’ illness0.0520.024−0.0290.087
(0.041)(0.076)(0.031)(0.076)
Sample size9,3364,8707,5566,650
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’ illness0.1330.3760.159*0.224
(0.096)(0.368)(0.093)(0.220)
Sample size14,7437,74511,06511,423
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’ illness0.1000.3790.189**0.178
(0.090)(0.305)(0.082)(0.220)
Sample size16,8828,55712,21213,227
  1. 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 modelSample 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’ illness0.024
(0.143)
Sample size1,131
Linear DDD model, 13–15 as comparison group
Overall absence−0.192
(0.888)
Absence due to own illness0.394
(0.549)
Absence due to others’ illness−0.586
(0.651)
Sample size2,117
Linear DDD model, 13+ as comparison group
Overall absence−0.363
(1.160)
Absence due to own illness0.740
(0.583)
Absence due to others’ illness−1.103
(1.007)
Sample size1,782
  1. 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 variablesBalanced 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’ illness0.0380.046
(0.049)(0.047)
Sample size6,61915,715
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’ illness0.2210.184
(0.166)(0.137)
Sample size11,57325,286
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’ illness0.1650.168
(0.210)(0.108)
Sample size10,26428,904
  1. 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 variablesIncluding MWt and MWt+1Including MWt and MWt+2
Linear DD model, <=12 years of educationMWtMWt+1MWtMWt+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’ illness0.052−0.0520.0190.010
(0.045)(0.036)(0.044)(0.053)
Sample sizeN = 14,206N = 14,206
Linear DD, 13–15 years of education
Overall absence0.0480.2150.0900.312
(0.255)(0.212)(0.243)(0.260)
Absence due to own illness0.2250.1830.2190.422*
(0.333)(0.273)(0.291)(0.226)
Absence due to others’ illness−0.1770.032−0.129−0.109
(0.124)(0.132)(0.107)(0.114)
Sample sizeN = 8,282N = 8,282
Linear DD, 13+ years of education
Overall absence0.1310.1880.2080.114
(0.197)(0.184)(0.158)(0.201)
Absence due to own illness0.2630.1960.3010.277
(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 sizeN = 11,233N = 11,233
  1. 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.

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Published Online: 2018-1-23

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