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The study explores how differences in rural community contexts relate to early adolescent alcohol use. Data were gathered from 1,424 adolescents in the sixth through eighth grades in 22 rural Northern Plains communities, as well as 790 adults, parents, teachers, and community leaders. Multilevel modeling analyses revealed that community supportiveness, as perceived by adolescents, but not adults, was associated with less lifetime and past month alcohol use, and for past month use, this relationship was stronger than perceived peer drinking or parental closeness. Perceived peer drinking and parental closeness were not associated with past month use. Adolescents experiencing family economic strain did not report greater lifetime or past month use, but living in a disadvantaged community was associated with greater past month use. Relatively affluent adolescents reported greater past month use when living in a poor community than did poorer adolescents, highlighting relationship complexity between economic disadvantage and alcohol use.

Many improvements have been proposed for the basic gravity model specification, most of which are confirmed by standard statistical tests due to the large number of observations often used to estimate such models. We use Monte Carlo experiments to examine situations in which features of models may be found statistically significant (or insignificant) when it is known ex ante that they are absent (or present) in the underlying data process. Erroneous assumptions about the presence or absence of lagged dependent variables, fixed effects, free-trade associations and custom unions are shown to introduce an economically important bias in estimates of the coefficients of interest, and in some cases to be confirmed spuriously. Policy effects for such initiatives as free trade associations and currency unions can also be confirmed spuriously when they do not exist in the data-generating process. 17 Here is another, more positive interpretation: since gravity models routinely have outstanding fits, fixed-effects models among others, perhaps the under-4-percent R-squared finding in column 4 can be taken as evidence that researchers are seldom in the position of estimating an LDV world with an FE model.

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We offer insights on how distance-related trade costs may best be inferred from price-dispersion measures. Using a simple spatial model of price dispersion, we argue that measures of price dispersion that are not spatially informed can mislead researchers into concluding that distance-related costs are small even when such costs are the major determinant of price dispersion. With intra-United States data on eleven goods, we find that distance-related costs are large and are indeed underestimated when inferred from standard, non-spatial, price dispersion measures. Our empirical findings have implications for studies of market integration policies (such as trade liberalization and currency unions) and the significance of economic geography.