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Binge drinking and labor market success: a longitudinal study on young people

An Erratum to this article was published on 08 January 2010


This paper presents a two-equation model of joint outcomes on an individual's decision to binge-drink and on his/her annual labor market earnings. The primary data source is the 1979 cohort of the National Longitudinal Survey of Youth, 1979–1994. We show that binge-drinking behavior is quite alcohol-price responsive and is a rational addiction. A new result is that an individual's decision to binge-drink has a statistically significant negative effect on his/her earnings. Furthermore, we conducted simulations of the short-run and long-run impacts of increasing the alcohol price. They showed that that the tendency for an individual to binge-drink heavily is reduced significantly, and the reduction is greater in the long- than short-run simulation. Also, an individual's annual earnings were increased. However, in the structural model, an individual's earnings have no significant effect on his/her tendency to engage in binge drinking. Our results contradict earlier findings from cross-sectional evidence that showed increased alcohol consumption raised an individual's earnings or wages.

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  1. US per capita consumption of alcohol peaked in 1980–1981, and in 1984, the federal government imposed transportation legislation requiring that states wanting federal highway aid must raise their legal minimum drinking age to 21 years.

  2. The NLSY79 asked binge-drinking questions only in 1979, 1980, 1983–1985, 1988, 1989, and 1992, one half of the years in the 1979–1994 period. Hence, a differences-in-difference model would require discarding a large amount of the available binge-drinking data and would preclude testing the rational addiction hypothesis. With a structural model, we can bring all of this data to bear on the parameters in the binge-drinking equation, including the hypothesis of rational addiction.

  3. The labor economics literature is mixed in its treatment of the number of children as predetermined and endogenous. Since family size is not the primary focus of this study and the panel data are annual, an individual's number of children by age group is treated as predetermined.

  4. A good review of the estimation methods for simultaneous equations with limited dependent variables can be found in Amemiya (1974, 1978, and 1979), Lee (1982), and Maddala (1983). We also instrument the lead and lag values of binge drinking because they are endogenous.

  5. The instrumental variable estimation method is similar to the two-stage least square estimation method, and the estimation in the first stage is statistically significant. The chi-square statistics for the demand for health, current binge drinking, lag binge drinking, and lead binge drinking in the first stage are 4,838, 9,561, 9,481, and 10,071 respectively.

  6. The R 2 for the local real alcohol price equation is high (0.75), and thus, we do not have a weak correlation between the instrument and the endogenous/stochastic explanatory variable (see Bound et al. 1995). The regression results are available upon request.

  7. The number of groups and boundaries on the categories are, of course, somewhat arbitrary. Compared to binge-drinking measures that are continuous, our measure has two advantages. First, we do not force a linear incremental relationship on the severity of binge drinking. Having no occurrences of binge drinking (being in group 1) is much different than binge drinking with one, two, or three occasions per month, but more frequent binge drinking (i.e., three or more times per month) may be very different than lesser values. Second, our measure of binge drinking minimizes the problems of memory lapses—and measurement errors—in those individuals who have a high frequency of binge drinking, e.g., our binge-drinking measure treats individuals who report four or more occurrences of binge drinking in the past 30 days equally rather than needing to accurately distinguish between, for example, four or six or ten occurrences.

  8. Before 1982, the Cost of Living Index only surveyed prices of liquor. Regression analysis was applied to obtain the predicted prices of beer and wine for 1979–1982. We regressed state prices for beer and wine between 1982 and 1996 on state excise tax rates, indicators for geographical regions, and a time trend. The data on state excise tax rates come from the Brewers Almanac.

  9. If the respondents did not live in the same state or the same city in consecutive years, the lead and lag alcohol prices are simply calculated as the average of the pure alcohol prices in the two different cities or states.

  10. The derivation of the price of ethyl alcohol per gallon is as follows: [(beer price per gallon/alcohol content)×consumption share of beer per capita]+[(wine price per gallon/alcohol content)×consumption share of wine per capita]+[(liquor price per gallon/alcohol content)×consumption share of liquor per capita].

  11. This procedure was suggested to us by Wayne Fuller.

  12. Human growth seems to have functional consequences for physical and mental development, health, personality, and personal appearance that have been shown to affect labor productivity and/or hours of work (Steckel 1995; Fogel 1994; Strauss and Thomas 1998; Biddle and Hamermesh 1998). Also, height (and weight) at early ages have predictive power for the onset of chronic diseases and premature mortality in middle and late ages (Fogel 1999; Fogel and Costa 1997). Steckel (1995) shows that the average height of US adult, native-born, white males rose steadily in the 75 years before the birth cohort of 1955 but then peaked and declined to 1965, being about 1% below trend in 1965, before starting to increase again.

  13. First-stage estimates are available from the authors for all variables that are instrumented.

  14. Some other differences are as follows. In model 1, the estimated coefficients for HEIGHT and FATHER's ED in the ln EARNINGS equation are negative and significant, but they are positive and significant in model 2. The latter results seem most plausible. Also, in model 1, the estimated coefficient of SCHOOLING in the ln EARNINGS equation is 0.06, but it is 0.01 in model 2.

  15. If an individual is living in an urban area, this increases his/her demand for binge drinking, holding the price of alcohol constant perhaps due to a “wetter” local alcohol culture in urban than in rural areas. Also, when an individual is married, his/her demand for binge drinking is reduced, which is consistent with greater personal responsibilities than when he/she is not married.

  16. The details of the derivation are available upon request.

  17. The computations also depend on how quickly the effects of the past and anticipated future values of variables diminish in their impact on the current value of the endogenous variables. The unstable root 1.93 implies that the effects of anticipated future values of variables are approximately zero after 18 years. The stable root 0.4 suggests that the effects from past values of variables will approach zero in 10 years. Therefore, the calculation in each year uses the actual values of lead variables for 18 years in the future and the values of lagged variables for 10 years in the past. We impose a stability condition to derive the long-run price elasticities.


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The authors acknowledge helpful comments and suggestions from Wayne Fuller, Hal Stern, Peter Orazam, Justin Tobias an anonymous referee, and Daniel Hamermesh, coeditor. They also acknowledge cooperation from the US Department of Labor for access to the NLSY79 panel with geocodes, agreement no. 96–26, and the Iowa Agriculture and Home Economics Experiment Station for financial assistance.

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Correspondence to Wallace E. Huffman.

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Responsible editor: Daniel Hamermesh

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Keng, SH., Huffman, W.E. Binge drinking and labor market success: a longitudinal study on young people. J Popul Econ 20, 35–54 (2007).

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  • Binge drinking
  • Earnings
  • Rational addiction
  • Health
  • Labor productivity
  • Panel data

JEL Classification

  • J10
  • J22
  • J24