Abstract
Using the first seven waves (1997–2003) of the National Longitudinal Survey of Youth 1997 (NLSY97), this paper investigates the effect of adolescent alcohol use on the amount of transfers they receive from their parents. Exploiting cross state and time variation in the price of alcohol, the main finding is that greater binge drinking among youths is associated with receiving significantly lower parental transfers. From a theoretical standpoint, one way to interpret this finding is to imagine an altruistic parent using pecuniary incentives to influence child behavior. Given that for many teenagers parental allowance is an important component of their income, limiting and monitoring such transfers may help reducing the chances of excessive drinking by youth.
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Notes
An important issue here is the frequency of the missing values and the sensitivity of the results presented in the paper to the case-wise deletion of the missing observations. Due to sample restrictions approximately 36% of the youth-year observations are lost. To test the robustness of the main result to this loss a separate analysis was carried out by setting the missing values equal to zero and then including the indicator variables for missing values in all the regression equations. The main result of the paper remains qualititatively unchanged-parents react to the child’s alcohol consumption by lowering transfers and this reaction continues to remain large and significant in magnitude even after adjusting for missing values. These results are not reported in the paper for brevity and are available upon request from the author.
Given the large proportion of zeros in our dependent variable (amount of transfers) ideally a Tobit type regression model is more appropriate as it will account for the censoring of observations at value zero. The OLS based estimation strategy employed in this paper suffers from the limited dependent variable problem and will generate a bias in the estimates. It is noted in the literature that ignoring censoring typically leads to a downward bias in the estimate Green (1980). This suggests that using Tobit regression model will generate estimates of the effect of drinking on parental transfers which are even bigger than the estimated effects reported in the paper.
According to the National Institute on Alcohol Abuse and Alcoholism [NIAAA], binge drinking is a pattern of drinking alcohol that brings blood alcohol concentration (BAC) to 0.08 g% or above. For the typical adult, this pattern corresponds to consuming five or more drinks for men or four or more drinks for women, in about 2 h. In this regard, the child’s drinking variable used in the paper captures binge drinking by the respondent on more than one occasion in the last 1 month and hence further increases the likelihood of detection by parents, thus making a more legitimate case for parental response.
The mean of actual real family income based on non-missing family income data (not reported in the table) is $65,299. The correlation coefficient between predicted family income and actual family income is 0.94.
Note that the above interpretation is based on the assumption that parents act to influence child behavior driven by altruism. A caveat to this discussion is the extreme case of parent–child exchange models where the parent on aging hopes to be cared for by the child. This kind of framework has been examined by Cox (1987) and other researchers in the literature on the motives behind intergenerational transfers. In such models, the parent may not be trying to influence child substance use behavior but may simply be taking the behavior as given. The substance abuse signals that the child is less likely to provide effectively for the parent at old age and thus may induce the parent to reduce transfers. In such a setting, altruism-based interpretations of our results might not hold in entirety.
The price of beer is used as the relevant substance price because beer is commonly accepted as the preferred alcoholic beverage for youths. This state level price data is taken from “Statewide Availability Data System II: 1933–2003” by Ponicki, W. R. (2004), National Institute on Alcohol Abuse and Alcoholism Research Center Grant P60-AA006282-23, Berkeley, CA: Pacific Institute for Research and Evaluation, Prevention Research Center.
Carpenter and Cook (2008) using repeated cross-section data from the national, state, and local Youth Risk Behavior Surveys over the period 1991–2005 find that a 1-dollar increase in the excise tax per cigarette pack (which serves as a proxy for price) reduces smoking participation by 3–6 percentage points which translates into price elasticities of smoking participation for high school youths in the range −0.23 to −0.56. Grossman (2004) found that changes in price can explain a good deal of the observed changes in cigarette smoking, binge alcohol drinking, and marijuana use by high school seniors. He found that the 70% increase in the real price of cigarettes since 1997 explains most of the reduction in the cigarette smoking participation rate since that year. Further, a 7% increase in the real price of beer between 1990 and 1992 due to the Federal excise tax hike on that beverage in 1991 accounts for almost 90% of the 4 percentage point decline in binge drinking in that period. Loh et al. (2009) use longitudinal survey data from Taiwan and obtain a modest estimate of the short-run price elasticity of demand for cigarettes. Luo et al. (2003) use aggregate time series data from Japan and estimate a larger long-run price elasticity of demand for cigarettes based on the rational addiction model.
Note that this procedure is different from the “pseudo-IV” procedure of simply replacing child substance use in (1) with its predicted value from the Probit regression and estimating the resulting equation by OLS. Here, consistency is not guaranteed unless the first stage is correctly specified and standard errors need to be adjusted.
(i) A caveat to the IV estimation strategy outlined in the paper is that in Eq. 1 due to lack of data, we cannot directly control for parental substance use which may be relevant in determining the child’s substance use and hence the amount of parental transfers. As a result, omitting this variable will lead to a biased estimate of β2. However, it can be shown that the IV estimate of β2 is biased upwards. To see this, consider a regression equation given by
$$ Transfer_{ijt}= \alpha (Child's \ Substance \ Use)_{ijt} + \epsilon_{ijt} $$Then the omitted variable bias of the IV estimator of α caused by ignoring parental substance use is:
$$ plim(\widehat{\alpha_{{IV}}}-\alpha)=\gamma \ {{Cov(P_{jt},Parent's \ Substance \ Use_{ijt})}\over {Cov(P_{jt},Child's \ Substance \ Use_{ijt})}} $$where the instrument P jt is price in state j and year t. Now, both the parent and the child’s substance use are inversely related to the price. Hence, the sign of the above expression depends on the sign of γ which captures the effect of parental alcohol consumption on transfers. It is reasonable to argue that this relationship is negative due to the budget constraint of the parent-the more he spends on his drinking, the less is the money available to transfer to the child. Hence, γ < 0 which implies \(plim(\widehat{\alpha_{IV}}-\alpha)<0\). Because we expect α to be negative, this suggests that \(\widehat{\alpha_{IV}}\) is biased upwards. (ii) Note that there may be other omitted variables in regression (1). However, as long as these are uncorrelated with the instrument (price of beer), they pose no problem for the analysis presented in the paper.
In the literature on the price responsiveness of youths’ substance use, there is a concern that if alcohol manufacturers employ any state-specific pricing then prices may be endogenous to alcohol consumption. To address this issue, I also estimate Eq. 1 using state-level tax data as an instrument. The outcome of this exercise is not reported but is available upon request. The main results of the paper remain qualitatively unchanged and suggest a large negative effect of alcohol consumption on parental transfers.
Note that a non linear first stage regression has been used to generate predicted substance use of the child as an instrument. There is always a question of how much of the identifying variation comes from the instrument (price of alcohol) and how much is simply a manifestation of the non linearity in the first stage. Although there is no easy way of answering this question, based on a private conversation with Jeffrey Wooldridge, one really needs to look at the coefficient of the instrument in the Probit estimation. If it is not significant, then it is very unlikely that the Probit fitted values are an appropriate instrument and consequently it is most likely that this estimation strategy may simply be exploiting nonlinearity in the Probit fitted values. From Table 4, the fact that the coefficient on alcohol price (across all specifications) is statistically significant gives us some faith that the estimation strategy being used is not completely driven by implicit non-linearity in the Probit fitted values.
All the reported regression results are robust to alternative controls for individual and family characteristics. For example, in one specification (not reported) both parents’ education categories, age of the respondent and the number of children under 6 years of age in the household were included and yielded qualitatively similar results.
These proportionate changes are computed from the estimated regression coefficient for substance use in Table 2. Suppose we have the following regression equation:
$$ ln(Y_i) = \beta_0 + \beta_1 D_i +\epsilon_i $$where D i is a dummy variable. Then, the estimated proportionate change in Y i due to the change in the dummy variable value from 0 to 1 is given by:
$$ e^{\widehat{\beta_1}}-1 $$The standard error for this expression can be computed by using the Delta Method.
For brevity the estimation results for alternative sample restrictions are not reported but are available upon request from the author.
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Acknowledgment
I thank Masao Ogaki, David Blau and Pok-sang Lam for comments and suggestions from the initial stages of this research.
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Bhatt, V. Adolescent Alcohol Use and Intergenerational Transfers: Evidence from Micro Data. J Fam Econ Iss 32, 296–307 (2011). https://doi.org/10.1007/s10834-010-9243-y
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DOI: https://doi.org/10.1007/s10834-010-9243-y