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2. Suppose you are interested in estimating the returns to education. You collect a cross section of data on adults age 25 to 50 and estimate a regression using In wages as the dependent variable and an indicator for having a university degree as the sole explanatory variable. You find a large, statistically significant positive effect. A classmate suggests that the regression may suffer from omitted variable bias and argues that you should collect a second set of observations for the same individuals a few years later and then run a "before and after regression. Relative to the cross section regression, the before and after regression is likely to: a) estimate a smaller coefficient on the degree indicator. b) estimate a larger coefficient on the degree indicator. c) make no sense since the university degree indicator does not change over time for the vast majority of individuals. d) resolve any issues of omitted variable bias. 2. Suppose you are interested in estimating the returns to education. You collect a cross section of data on adults age 25 to 50 and estimate a regression using In wages as the dependent variable and an indicator for having a university degree as the sole explanatory variable. You find a large, statistically significant positive effect. A classmate suggests that the regression may suffer from omitted variable bias and argues that you should collect a second set of observations for the same individuals a few years later and then run a "before and after regression. Relative to the cross section regression, the before and after regression is likely to: a) estimate a smaller coefficient on the degree indicator. b) estimate a larger coefficient on the degree indicator. c) make no sense since the university degree indicator does not change over time for the vast majority of individuals. d) resolve any issues of omitted variable bias