Question

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 classified 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

Approach: Our plan is to divide up the work where one person will tackle each question proposed above. We hope to find the best fitting simple linear regression between hours and wages. We also plan to gather an analysis of the labor supply to increasing hourly wage.

Number of cases:   39

Variable Names:

HRS: Average hours worked during the year

WAGE: Average hourly wage ($)

ERSP: Average yearly earnings of spouse ($)

ERNO: Average yearly earnings of other family members ($)

NEIN: Average yearly non-earned income

ASSET: Average family asset holdings (Bank account, etc.) ($)

AGE: Average age of respondent

DEP: Average number of dependents

RACE: Percent of white respondents

SCHOOL: Average highest grade of school completed

The Data (also attached in another excel file):  

HRS    RATE ERSP ERNO NEIN ASSET AGE     DEP RACE SCHOOL

2157   2.905   1121   291   380   7250   38.5   2.340   32.1   10.5

2174   2.970   1128   301   398   7744   39.3   2.335   31.2   10.5

2062   2.350   1214   326   185   3068   40.1   2.851   *   8.9

2111   2.511   1203   49   117   1632   22.4   1.159   27.5   11.5

2134   2.791   1013   594   730   12710   57.7   1.229   32.5   8.8

2185   3.040   1135   287   382   7706   38.6   2.602   31.4   10.7

2210   3.222   1100   295   474   9338   39.0   2.187   10.1   11.2

2105   2.493   1180   310   255   4730   39.9   2.616   71.1   9.3

2267   2.838   1298   252   431   8317   38.9   2.024   9.7   11.1

2205   2.356   885   264   373   6789   38.8   2.662   25.2   9.5

2121   2.922   1251   328   312   5907   39.8   2.287   51.1   10.3

2109   2.499   1207   347   271   5069   39.7   3.193   *   8.9

2108   2.796   1036   300   259   4614   38.2   2.040   *   9.2

2047   2.453   1213   297   139   1987   40.3   2.545   *   9.1

2174   3.582   1141   414   498   10239   40.0   2.064   *   11.7

2067   2.909   1805   290   239   4439   39.1   2.301   *   10.5

2159   2.511   1075   289   308   5621   39.3   2.486   43.6   9.5

2257   2.516   1093   176   392   7293   37.9   2.042   *   10.1

1985   1.423   553   381   146   1866   40.6   3.833   *   6.6

2184   3.636   1091   291   560   11240   39.1   2.328   13.6   11.6

2084   2.983   1327   331   296   5653   39.8   2.208   58.4   10.2

2051   2.573   1194   279   172   2806   40.0   2.362   77.9   9.1

2127   3.262   1226   314   408   8042   39.5   2.259   39.2   10.8

2102   3.234   1188   414   352   7557   39.8   2.019   29.8   10.7

2098   2.280   973   364   272   4400   40.6   2.661   53.6   8.4

2042   2.304   1085   328   140   1739   41.8   2.444   83.1   8.2

2181   2.912   1072   304   383   7340   39.0   2.337   30.2   10.2

2186   3.015   1122   30   352   7292   37.2   2.046   29.5   10.9

2108   2.786   1757   *   506   9658   43.4   *   32.6   10.2

2188   3.010   990   366   374   7325   38.4   2.847   30.9   10.6

2203   3.273   *   *   430   8221   38.2   2.324   22.1   11.0

2077   1.901   350   209   95   1370   37.4   4.158   61.3   8.2

2196   3.009   947   294   342   6888   37.5   3.047   31.8   10.6

2093   1.899   342   311   120   1425   37.5   4.512   62.8   8.1

2173   2.959   1116   296   387   7625   39.2   2.342   31.0   10.5

2179   2.971   1128   312   397   7779   39.4   2.341   31.2   10.5

2200   2.980   1126   204   393   7885   39.2   2.341   31.0   10.6

2052   2.630   *   *   154   3331   40.5   *   45.8   10.3

2197   3.413   1078   300   512   10450   39.1   2.297   15.5   11.3

Pleas answer the following using SAS and leave the code as well

The comparison between the correlation of wages and ages, and wages and schooling.

Answer 1

The data can be imported using the following code:

PROC IMPORT DATAFILE ="path name where file is saved"

              OUT = MONEY

              DBMS = TAB

              REPLACE;

RUN;

Here we first need to form a new variable with HRS and RATE to get WAGE using the formula WAGE=RATE*HRS

PROC SQL;

SELECT HRS,RATE,AGE,SCHOOL,HRS*RATE AS WAGE FORMAT 10.2

FROM WORK.MONEY;

QUIT;

To compare the correlation between the variables wages and ages, and wages and schooling we can find correlation coefficient separately or use the correlation matrix with the variables wages, ages and schooling

PROC COR DATA=MONEY;

VAR WAGE AGE SCHOOL;

RUN;

In order to see correlation between wage and age refer to the cell corresponding to wage and age variables and you can find the correlation coefficient and for wage and school refer to cell corresponding to wage and school variables. Based on the value we can make a comparison.For example, whether both have a positive correlation with wage or a negative. If correlation is higher for wage vs school than wage vs age then we can infer schooling years have more positive impact on wage than age and vice versa.