Public Finance and Cost Benefit 1. What\'s a commitment contract 2. if you regre

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Public Finance and Cost Benefit

1. What's a commitment contract

2.  if you regress a variable on just a constant (really a column of ones), the constant will be the sample mean of that variable. Convince yourself of this by regressing age on just a constant and comparing it to the mean as calculated by the summarize command. Next, sort the dataset by male, then summarize age by male to obtain the sample mean of age by gender. What's the difference in the sample means? Finally, regress age on a constant and male. How does the coefficient on male compare to the difference between the sample means for males and females? This exercise is meant to show that when you regress a variable on dummy variables without any variables of other types, the coefficients on the dummy variables show the differences in sample means between the various groups indicated by the dummy variables. What do the t-statistics in these regressions tell you?

by male summarize age male 0 Mean sed. Dev. Min Max Variable 468 41.01 496 11.295 42 21 13 male Mean Std. Dev. Variable Max age 515 38.24078 10.39194 male Vazz able Mean sed. Dev. Min Max regress age male 983 Source SS df MS Number of obs F (1 981) 16.08 Model 1886.98746 1 1886.98746 Prob F 0.0001 Residual 115091.039 981 117.320121 squared 0.0161 Ad j R squared 0.0151 982 119.122227 Root MSE Total 116978.026 10.831 Coef. Std Err P>lt I 1953 Conf. Interval male -2.774181 .69172 98 -4.01 0.000 -4. 131621 -1.41674 41.01496 .5006836 81.92 0.000 40.03242 41.99749

Explanation / Answer

Question 1

Commitment contract is the contract which is legally binding commitment and in which you will have the choice between charity, an anti-charity or an individual.

Question 2

Here, we want to study the effect of the dummy variables or constants on the regression variables. Here, we want to regress the variable age on the gender of the participants. First we introduce the dummy variable as male coded as 0 and female as 1. The mean of the variable age for male is given as 41.01 with the standard deviation of 11.30, however the mean of the variable age for female is given as 38.24 with the standard deviation of 10.39. There is one observation missing for which the information for gender is missing. For the given regression model with dependent variable age based on the independent variable gender, the test statistic value is given as F = 16.08 and the p-value is given as 0.0001. This p-value is very small and it is less than the level of significance or alpha value 0.01, so we reject the null hypothesis that there is no any significant relationship exists between the two variables such as age and gender. If we compare the effect of the dummy variable on the means, it is observed that there is significant difference in the average values. The t test used in this regression model is helpful for checking the significance of the coefficients of the regression equation. From the given regression model, it is observed that the p-value for slope for variable male is given as 0.00 which is less than alpha 0.01, so we reject the null hypothesis that the slope is not statistically significant. This means the slope of the regression equation is statistically significant. Also, the constant or y-intercept of the least squares regression line is statistically significant as the p-value is given as 0.00.

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