Abstract
On the basis of prior research findings that employed youth, and especially intensively employed youth, have higher rates of delinquent behavior and lower academic achievement, scholars have called for limits on the maximum number of hours per week that teenagers are allowed to work. We use the National Longitudinal Survey of Youth 1997 to assess the claim that employment and work hours are causally related to adolescent problem behavior. We utilize a change model with age-graded child labor laws governing the number of hours per week allowed during the school year as instrumental variables. We find that these work laws lead to additional number of hours worked by youth, which then lead to increased high school dropout but decreased delinquency. Although counterintuitive, this result is consistent with existing evidence about the effect of employment on crime for adults and the impact of dropout on youth crime.
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Notes
By 1913 all but nine states had child labor laws which fixed age 14 as the minimum age for factory work, while a majority of states had 14 years as the minimum age for employment in stores and other workplaces.
Powerless to affect labor practices within states, Congress passed the Keating-Owen Act in 1916 which prohibited the interstate transportation of goods produced by factories or shops that employed children under 14 years of age, or children under the age of 16 who worked at night or for more than 8 h per day. The Supreme Court struck down this law 2 years later in 1918 in Hammer vs. Dagenhart (247 U.S. 251). In 1919, Congress passed the Child Labor Tax Law which placed a 10% excise tax on the net profits of factories and mines employing children. The Supreme Court struck down this law as unconstitutional in 1922 in Bailey vs. Drexel Furniture Company (259 U.S. 20). In 1924, Congress then passed a constitutional amendment giving the federal government the power to regulate child labor, but too few states ratified the proposed amendment and it consequently did not take effect.
It is noteworthy that virtually all of these commissions made their recommendation for increased involvement of school-going youth in the workplace with little or no empirical evidence on the beneficial effect of adolescent employment (see Ruhm 1995).
The bill died in committee but was resubmitted in the 109th Congress (2005−2006) as H.R. 2870, where it was also tabled without resolution. As of this writing (May 2008), the bill has not yet been resubmitted in the 110th Congress.
These findings led Tyler (2003, p. 405) to recommend that “if a primary policy objective is to maximize twelve-grade academic achievement, then states should possibly consider more restrictive child labor laws for 16−17-year-olds.”
We present our empirical results from unweighted analyses. However, we hasten to add that we re-estimated all of our models using normed sampling weights to adjust for the minority oversample, and our findings were virtually identical.
By using a variety scores for delinquent behavior and substance, we settled on a compromise between single binary indicators and frequency scales. We employed variety scores because frequency scales are generally dominated by less serious offenses. Furthermore, prior research has demonstrated that variety scores are as reliable as frequency scales (Hindelang et al. 1981).
Youth who have dropped out of school, not surprisingly, report an unusually high number of suspensions. The pattern of significance is unchanged when we exclude them. In fact, by excluding dropouts our point estimates for work intensity in the school suspension models are somewhat more conservative.
This variable unfortunately suffers from a substantial degree of non-response. During waves two (1998) and seven (2003), high-school transcripts were requested from the last school of record for every individual who was over 18, who was no longer enrolled in high school, and who had provided written consent. Of the 8,984 youth in the NLSY97, survey staff were successful in collecting high-school transcripts for 6,232 (69.4%). Transcript data are missing because student consent was refused (n = 1,734), the student was still in high school and the transcript was not requested (n = 132), the school was unable to locate the student record (n = 544), and the school or district refused to turn over the transcript (n = 342).
These variables are dummy coded. For example, if a respondent is eligible to drop out of high school under state law by virtue of her age at interview, we code her “1” on school dropout eligibility and “0” otherwise. Similarly, statutorily age-eligible youths are coded “1” on consensual sexual intercourse and unrestricted driving privileges. See Appendix for further detail. These control variables are intended to absorb variation in other state statutes with age-eligibility conditions that might be correlated with the prevailing child labor law. We are careful to ensure as much as possible that youth employment is being driven by change in work-eligibility under the child labor law rather than by dropout-eligibility under school attendance laws, for instance (for a study of the impact of school attendance laws on behavior, see Lochner and Moretti 2004).
The same figures for cumulative employment are 42.8% in the month before turning 16, 63.4% 6 months into the 16th year, and 72.8% in the month before turning 17.
Figure 3 demonstrates that, while change in work intensity is correlated with change in child labor laws, the correlation is modest. An important limitation of the instrumental variables estimator is that, while consistent, in finite samples it is known to be biased in the direction of the least squares estimator. This problem is exacerbated in the “weak instrument” case. In such a situation, first-stage diagnostics become paramount in evaluating the validity of the chosen instruments. The first-stage F and partial R-square for the instruments are common metrics (Shea 1997; Staiger and Stock, 1997), with larger values obviously preferred. Our estimate of the relative bias is derived from Bound et al.’s (1995, pp. 449–450) approximation, given by the formula: \( 1 - (\tau ^{2} /k){}_{1}F_{1} (1, (k + 2)/2; - \tau ^{2} /2) \) where k is the number of instruments, τ 2/k is the F-statistic for the joint significance of the instruments, and 1 F 1(•,•;•) is the confluent hypergeometric function evaluated at the argument. By this metric, smaller values are preferable and indicate less finite-sample bias in the FEIV model relative to the FE model. From our first-stage models, we estimate the finite-sample bias to be at most 4.5% (from Model 4), which is quite respectable.
Given space constraints, we only briefly describe some of the more important sensitivity analyses that we conducted. Our results were substantially similar across the robustness tests. First, we experimented with different measures of employment in addition to the continuous measure of work intensity used here—a dichotomous indicator for employment, a continuous measure of work intensity limited to youth who were employed at age 16, and dichotomous indicators for moderate (1–20 h per week) and intensive (over 20 h per week) employment. Second, we evaluated the sensitivity of our estimates to the exclusion of subsets of the instrumental variables set. Third, we substituted a series of dummy variables for the state child labor laws rather than continuous measures by grouping states with similar restrictions (e.g., up to 20 h maximum allowed per week, 21–30 h maximum, 31–40 h maximum, and so on). Fourth, we substituted the mean number of hours worked per week during the nine months of the school year, rather than during the entire calendar year. Fifth, to be sure that our instruments were identifying only variation in work intensity at the first stage, we controlled for several other characteristics of youth jobs, including the number of different jobs, the total number of weeks worked, and hourly wages. Sixth, we systematically removed states one at a time to ensure that outlying states were not exerting inordinate influence on our point estimates. Seventh, to be sure that our results were unaffected by period variation, we removed one interview wave at a time from our models.
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Acknowledgments
This research was supported by grants to Bushway and Apel from the National Institute of Child Health and Human Development (No. R03-HD049703-01) and the National Science Foundation (No. SES-0452982). The authors wish to extend their sincere thanks to several individuals who read and commented on previous versions of this manuscript: Laura Dugan, Jeff Fagan, Jeff Kling, John Laub, Jens Ludwig, Seth Sanders, and John Tyler.
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Appendix
Appendix
Variable definitions
Variable | Definition |
|---|---|
Adolescent work involvement | |
Formal employment | =1 if respondent was employed for at least 1 week in a formal job since the last interview |
Work intensity | Number of hours worked per week, on average, computed as the total number of hours worked since the last interview, divided by the total number of weeks worked since the last interview |
Deviant behavior, academic achievement, & precocious transitions | |
Delinquent behavior | Variety score representing the sum of the number of different kinds of illegal behaviors committed since the last interview: (1) vandalism; (2) minor theft (under 50 dollars); (3) serious theft (over 50 dollars); (4) “other” property crime (e.g., fencing, receiving, possessing, or selling stolen property); (5) aggravated assault; (6) sold or helped sell drugs; (7) handgun possession |
Arrest | =1 if respondent was arrested by police or taken into custody for an illegal or delinquent offense since the last interview, excluding minor traffic violations |
Substance use | Variety score representing the sum of the number of different kinds of substances consumed since the last interview: (1) cigarettes; (2) alcohol; (3) marijuana |
School suspension | Number of days suspended since the last interview (censored at 30). Excludes respondents who are not enrolled in school at both interviews |
Transcript grades | Grade point average (on a 4.0 scale), created from the school transcript file, for all terms that overlap with the reference period, weighted according to the number of term days that occur since the last interview |
School dropout | =1 if respondent is not currently enrolled in school and does not have a high school diploma. Respondents with a G.E.D. are classified as dropouts. Note that school dropout is not an absorbing state. That is, a respondent may be classified as a dropout in one time period but return to school the next |
Individual-level control variables | |
Residential location | |
Central city | =1 if respondent lives in a metropolitan statistical area (MSA) in the central city |
Suburbs | =1 if respondent lives in MSA not in the central city |
Rural | =1 if respondent does not live in MSA |
Dwelling type | |
House, condo, or farm | =1 if respondent lives in a house, condo, townhouse, row house, farm, or ranch |
Apartment or flat | =1 if respondent lives in an apartment or flat |
Other dwelling | =1 if respondent lives in some other type of dwelling (e.g., hotel/motel, rooming house, trailer) |
Residential mobility | Mean number of different residences per year since age 12 |
Household size | Number of people that currently live in household |
Highest grade attended | Highest grade attended as of the interview |
Reached puberty | =1 if pubertal changes seem completed (for boys) or youth has had a menstrual period (for girls) |
Have driver’s license | =1 if respondent has a driver’s license |
Worked in an informal job | =1 if respondent worked in an informal (“freelance”) job or was self-employed since the last interview |
Earned an allowance | =1 if respondent received an allowance from family in the previous year |
School dropout eligible | =1 if respondent is eligible to drop out of school under state statute |
Sexual consent eligible | =1 if respondent is eligible to consent to sexual intercourse under state statute |
Unrestricted driving eligible | =1 if respondent is eligible to drive with no restrictions (e.g., passenger restrictions, driving curfew) under state statute |
State-level control variables | |
Gross domestic product | State gross domestic product. This variable is logged and in 2000 dollars |
Income per capita | Per capita personal income. This variable is logged and in 2000 dollars |
Transfer payments | Total benefits disbursed for public assistance medical care (Medicaid), supplemental security income (SSI), family assistance (AFDC/TANF), food stamps (WIC), and unemployment insurance (UI). This variable is logged and in 2000 dollars |
Total employment | Total number of full- and part-time jobs, in tens of millions |
Percent retail industry | Percentage of total employment in the retail industry |
Percent service industry | Percentage of total employment in the service industry |
Youth labor supply | Resident population of 18–24 year olds, in tens of thousands |
Unemployment rate | Percentage of individuals not employed but participating in the labor force |
Female L.F.P. rate | Percentage of females participating in the labor force |
Union membership | Percentage of workers in labor unions |
School enrollment rate | Percentage of 15–17 year olds enrolled in school |
Per pupil expenditure | Average per pupil expenditure in average daily attendance. This variable is logged and in 2000 dollars |
Index crime rate | Number of index crimes, per 100,000 population |
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Apel, R., Bushway, S.D., Paternoster, R. et al. Using State Child Labor Laws to Identify the Causal Effect of Youth Employment on Deviant Behavior and Academic Achievement. J Quant Criminol 24, 337–362 (2008). https://doi.org/10.1007/s10940-008-9055-5
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DOI: https://doi.org/10.1007/s10940-008-9055-5