Many cities in California have passed Inclusionary Zoning policies (also known as below-market housing mandates) as an attempt to make housing more affordable. These policies require developers to sell some units below the market price on a percentage of the new homes built. For example, in a development of 10 new homes each with market value $850,000, the developer may have to sell 5 of the units at $180,000. Kang examine the effects of such policies on house prices and number of housing units available using 1990 (before policy impact) and 2000 (after policy impact) census data on California cities. Use means.dat for the following questions.

1. Using only the data for 2000, compare the sample means of LNPRICE and LNUNITS for cities with an Inclusionary Zoning policy, IZLAW=1, to those without policy, IZLAW=0. Based on these estimates, what is the percentage difference in prices and number of units for cities with and without the law? [Hint: use the simple rule that 100[ln(y1)-ln(y0)] is the approximate percentage difference between y0 and y1.] Does the law achieve its purpose?
2. Use LNPRICE and LNUNITS in difference-in-difference regressions, with explanatory variables D, the indicator variable for year 2000; IZLAW, and the interaction of D and IZLAW. Is the estimate of the treatment effect statistically significant, and of the anticipated sign?
3. To the regression in (b) add the control variable LMEDHHINC. Interpret the estimate of the new variable, including its sign and significance. How does the addition affect the estimates of the treatment effect?
4. To the regression in (c) add the variables EDUCTTAIN, PROPOVERTY, and LPOP. Interpret the estimates of these new variables, including their signs and significance. How do these additions affect the estimates of the treatment effect?