# Datafile Name: Wages and Hours # Reference: D.H. Greenberg and M. Kosters, Income Guarantees and the Working Poor, The Rand Corporation (R-579-OEO), %% December, 1970. # Authorization: free use # Description: The data are from a national sample of 6000 households with a male head earning less than $15,000 annually in 1966. The data were clasified into 39 demographic groups for analysis. The study was undertaken in the context of proposals for a guaranteed annual wage (negative income tax). At issue was the response of labor supply (average hours) to increasing hourly wages. The study was undertaken to estimate this response from available data # Number of cases: 39 # Variable Names: # HRS: Average hours worked during the year # WPH: Average hourly wage ($) # ERSP: Average yearly earnings of spouse ($) # NEIN: Average yearly non-earned income # SCH: Average highest grade of school completed # The Data: HRS WPH ERSP SCH NEIN 2157 2.905 1121 10.5 380 2174 2.97 1128 10.5 398 2062 2.35 1214 8.9 185 2111 2.511 1203 11.5 117 2134 2.791 1013 8.8 730 2185 3.04 1135 10.7 382 2210 3.222 1100 11.2 474 2105 2.493 1180 9.3 255 2267 2.838 1298 11.1 431 2205 2.356 885 9.5 373 2121 2.922 1251 10.3 312 2109 2.499 1207 8.9 271 2108 2.796 1036 9.2 259 2047 2.453 1213 9.1 139 2174 3.582 1141 11.7 498 2067 2.909 1805 10.5 239 2159 2.511 1075 9.5 308 2257 2.516 1093 10.1 392 1985 1.423 553 6.6 146 2184 3.636 1091 11.6 560 2084 2.983 1327 10.2 296 2051 2.573 1194 9.1 172 2127 3.262 1226 10.8 408 2102 3.234 1188 10.7 352 2098 2.28 973 8.4 272 2042 2.304 1085 8.2 140 2181 2.912 1072 10.2 383 2186 3.015 1122 10.9 352 2108 2.786 1757 10.2 506 2188 3.01 990 10.6 374 2203 3.273 1109 11 430 2077 1.901 350 8.2 95 2196 3.009 947 10.6 342 2093 1.899 342 8.1 120 2173 2.959 1116 10.5 387 2179 2.971 1128 10.5 397 2200 2.98 1126 10.6 393 2197 3.413 1078 11.3 512

3. Multivariate Regression. The file called PS2Wage.txt contains worker wages data for 39 demographic groups. Your goal here is to study the effect of change in hourly wage (WPH) on the supply of labor measured in hours worked (HRS). The task is complicated by the fact that factors other than hourly wage affect the supply of labor. We will in particular consider two such additional factors: spouse's annual income (ERSP) and the number of years of education (SCH). (a) Assume the labor supply depends on both the wage paid and how much the spouse makes as in the following multivariate regression model, HRS = Bo + B.WPH + B2ERSP + u (i) Discuss what sign do you expect for B1 and B2 (ii) Estimate the regression using OLS. Are your coefficient estimates significant and have the sign you hypothesized in (i)? (iii) Provide interpretation for the intercept term Bo. Does your estimate for Bo make sense? (b) Delete the 19th observation and rerun the multivariate regression. What do you observe when comparing the results to those in (a)? (c) It is natural to expect the spouse's income to be a substitute for own income. Test whether it is a perfect substitute as follows. Let WPH and ERSP be the averages of WPH and ERSP respectively. Specify the null hypothesis as Ho: B. WPH = -B2ERSP. What is your conclusion? (d) Finally, since both HRS and WPH are endogenous, we may be interested in estimating the following multivariate regression model instead, WPH HRS = Yo + y SCL + y2NEIN + Y3ERSP + e where NEIN is non-wage income. Interpret your coefficient estimates and test their significance. Do all coefficient signs make sense? 3. Multivariate Regression. The file called PS2Wage.txt contains worker wages data for 39 demographic groups. Your goal here is to study the effect of change in hourly wage (WPH) on the supply of labor measured in hours worked (HRS). The task is complicated by the fact that factors other than hourly wage affect the supply of labor. We will in particular consider two such additional factors: spouse's annual income (ERSP) and the number of years of education (SCH). (a) Assume the labor supply depends on both the wage paid and how much the spouse makes as in the following multivariate regression model, HRS = Bo + B.WPH + B2ERSP + u (i) Discuss what sign do you expect for B1 and B2 (ii) Estimate the regression using OLS. Are your coefficient estimates significant and have the sign you hypothesized in (i)? (iii) Provide interpretation for the intercept term Bo. Does your estimate for Bo make sense? (b) Delete the 19th observation and rerun the multivariate regression. What do you observe when comparing the results to those in (a)? (c) It is natural to expect the spouse's income to be a substitute for own income. Test whether it is a perfect substitute as follows. Let WPH and ERSP be the averages of WPH and ERSP respectively. Specify the null hypothesis as Ho: B. WPH = -B2ERSP. What is your conclusion? (d) Finally, since both HRS and WPH are endogenous, we may be interested in estimating the following multivariate regression model instead, WPH HRS = Yo + y SCL + y2NEIN + Y3ERSP + e where NEIN is non-wage income. Interpret your coefficient estimates and test their significance. Do all coefficient signs make sense