Increased Labor Force Participation

repealed and the set of choices of time- competing options expands, teenagers who tend to be more susceptible to distractions or short- run earning opportunities may invest less in education and ultimately achieve fewer years of schooling.

IV. Potential Channels

A. Increased Labor Force Participation

One possible channel for the decline in years of schooling is through increased labor force participation—as retail activity extends to Sundays, youth could take advantage of newly available employment opportunities on weekends. Marginal youth may ex- pect their future discounted lifetime earnings to be higher from entering the labor force full time rather than completing high school. Working more also could compete with time spent on educational investment and lead to worse educational outcomes. Further, the retail industry is a popular industry for teens because of fl exible hours and limited credential requirements Card 1991. Existing research on the relationship between employment and school leaving deci- sions has produced inconclusive results. On one hand, there is evidence that differ- ences in employment experience at ages younger than 16 have a positive impact on subsequent wages Ruhm 1997 and wage differentials later in life Michael and Tuma 1984. On the other hand, Rothstein 2007 and Eckstein and Wolpin 1999 fi nd that employment during high school leads to worse school performance. Oettinger 1999 also fi nds similar results, and further shows summer employment did not affect grades, suggesting that school year employment affected grades by “crowding out” study time. I fi rst use the March CPS to test the impact of repealing blue laws on employment. The sample is an unbalanced panel of the only eight states among the 16 repeal states and the eight “never” states that are uniquely identifi ed between 1962 and 1976, and all 24 states from 1977 to 2000. The eight states include California, Florida, Indiana, Ohio, Oregon, Pennsylvania, Tennessee, and Texas, among which Ohio, Pennsylva- nia, Texas, and Florida are repeal states. The other 16 states are grouped regionally and cannot be distinguished from the other states in the group until 1977. Thus, the regression sample includes the eight states that are identifi able before 1977 and then incorporate the rest of the states post- 1977. The Repeal dummy is defi ned to be 1 after a state repealed its blue laws. I omit the observations in the year of repeal for that state. The results are reported in Panel A of Table 7. The results suggest that the repeals affected youth employment on the extensive margin by drawing teens into the labor market and especially into the retail industry. However, the repeal of blue laws did not appear to affect youth employment on the intensive margin. Conditional on being in the labor force, neither overall hours worked last week nor weeks worked last year changed signifi cantly, but hours and weeks worked in retail increased, suggesting there may have been substitution of hours towards the retail industry after deregulation. I also experimented with using only the post- 1977 years with all the 24 states, and using only the eight states that were uniquely identifi ed in the post- 1977 years, so that the sample is balanced. The results not shown are qualitatively similar. 15 15. In both samples, the estimate on working in retail and hours worked in retail are positive and signifi cant. In the post- 77 sample, the estimates on overall labor force participation and weeks worked in retail are posi- Lee 301 Table 7 Effect of Repeal on Youth Employment Panel A—March CPS 1962–2000 a Labor Force Participation 1 In Retail Industry 2 Hours Worked Last Week 3 Hours Worked in Retail Last Week 4 Weeks Worked Last Year, Intervalled 5 Weeks Worked in Retail Last Year, Intervalled 6 Repeal 0.0144 0.0145 –0.5318 0.4314 –0.0480 0.0602 0.0071 0.0058 0.3713 0.2103 0.0374 0.0316 Sample size 217,543 217,543 69,303 69,303 69,271 69,271 Panel B—CPS May Extracts 1968–87 b Labor Force Participation In Retail Industry Hours Worked Last Week Hours worked in Retail last week Hourly Earnings, in 2000 Weekly Earnings, in 2000 Repeal 0.0070 0.0102 –0.3817 0.3706 –0.1318 –2.1434 0.0060 0.0049 0.3120 0.2052 0.1439 4.5872 Sample size 109,849 109,849 41,072 41,072 10,815 34,722 continued The Journal of Human Resources 302 Table 7 continued Panel C—CPS Merged Outgoing Rotation Groups 1979–2000 c Labor Force Participation 1 In Retail Industry 2 Hours Worked Last Week 3 Hours Worked in Retail Last Week 4 Weeks Worked Last Year, Intervalled 5 Weeks Worked in Retail Last Year, Intervalled 6 Repeal 0.0046 0.0067 –0.5700 0.2704 –0.0897 –7.3667 0.0140 0.0122 0.4802 0.3290 0.1236 4.7079 Sample size 214,873 214,946 110,385 110,385 110,385 110,385 Notes: signifi cant at 10 percent signifi cant at 5 percent signifi cant at 1 percent. Standard errors in parentheses. Standard errors are produced using the Donald and Lang 2007 two- step procedure. All regressions include fi xed effects for year of birth, state, age, and year fi xed effects. All regressions control for gender, race, marital status, state- specifi c unemployment rate, per capita disposable income, and per capita retail sales, pupil- teacher ratio, average public elementary and secondary school teacher salaries, and average expenditure per student. a, b. The samples in Panels A and B are unbalanced panels of eight states CA, FL, IN, OH, OR, PA, TN, TX that are uniquely identifi ed among the 16 repeal and eight “never” states before 1977. The other 16 states enter the sample after 1977. c. The sample in Panel C is a balanced panel including all 24 states from 1979–2000. Because the March CPS does not contain data on current earnings, I utilize the CPS Mays and MORGs to investigate whether youth earnings were affected. First, I estimate the impact of repeals on the dependent variables as in Panel A, using the same set of controls and specifi cation. The results are presented in Panels B and C of Table 7. As it can be seen, the effect of the repeals on youth employment on the ex- tensive margin fades as the sample period shifts to cover later years. In Panel B, there is still evidence of substitution of hours towards the retail industry, but no impact on overall labor force participation is found. There is no discernible impact on any of the employment variables when the MORGs sample is used. I do not fi nd any effect on hourly or weekly earnings in either sample. One interpretation of these results is that the earlier repeals increased labor force participation among teens, and also led teens who were already in the labor force to substitute work hours into the retail industry. Since it is unclear why working in the retail industry per se would lead teens to do worse in school, a possible mechanism through which the earlier repeals affected educational attainment could be through drawing youth into the labor force. Work could have displaced time spent on educa- tional investment, or led marginal youth to drop out of school altogether, thus contrib- uting to the observed decline in educational attainment. These results are consistent with Goos 2005, who uses the Census of Retail Trade data to show that deregulation increases employment by around 4.4 percent in deregu- lated industries. The results are also in line with those in Skuterud 2005, who fi nds Sunday shopping deregulation increases employment in retail using data from Canada. On the other hand, Gruber and Hungerman do not fi nd any effect of blue laws on employment using the National Longitudinal Survey of Youth GH footnote 17. Simi- larly, Cohen- Zada and Sander 2011 do not fi nd an impact on overall hours worked using data from the General Social Survey. However, these results are not inconsistent with mine. The samples used in GH and Cohen- Zada and Sander 2011 begin in 1979 and 1973 respectively; likewise, I do not fi nd any impact on employment using the MORGs sample, which covers a comparable period from 1979 to 2000.

B. Alcohol and drug use