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A Study Tried to Find the Determinants of the Increase

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A study tried to find the determinants of the increase in the number of households headed by a female. Using 1940 and 1960 historical census data, a logit model was estimated to predict whether a woman is the head of a household (living on her own)or whether she is living within another's household. The limited dependent variable takes on a value of one if the female lives on her own and is zero if she shares housing. The results for 1960 using 6,051 observations on prime-age whites and 1,294 on nonwhites were as shown in the table:  Regression  (1) White  (2) Nonwhite  Regression model  Logit  Logit  Constant 1.4592.874(0.685)(1.423) Age 0.2750.084(0.037)(0.068) age squared 0.004630.00021(0.00044)(0.00081) education 0.1710.127(0.026)(0.038) farm status 0.6870.498(0.173)(0.346) South 0.3760.520(0.098)(0.180) expected family 0.00180.0011 eamings (0.00019)(0.00024) fanily composition 4.1232.751(0.294)(0.345) Pseudo-R 20.2660.189 Percent Correctly 82.083.4 Predicted \begin{array} { | c | c | c | } \hline \text { Regression } & \text { (1) White } & \text { (2) Nonwhite } \\\hline \text { Regression model } & \text { Logit } & \text { Logit } \\\hline \text { Constant } & 1.459 & - 2.874 \\& ( 0.685 ) & ( 1.423 ) \\\hline \text { Age } & - 0.275 & 0.084 \\& ( 0.037 ) & ( 0.068 ) \\\hline \text { age squared } & 0.00463 & 0.00021 \\& ( 0.00044 ) & ( 0.00081 ) \\\hline \text { education } & - 0.171 & - 0.127 \\& ( 0.026 ) & ( 0.038 ) \\\hline \text { farm status } & - 0.687 & - 0.498 \\& ( 0.173 ) & ( 0.346 ) \\\hline \text { South } & 0.376 & - 0.520 \\& ( 0.098 ) & ( 0.180 ) \\\hline \text { expected family } & 0.0018 & 0.0011 \\\text { eamings } & ( 0.00019 ) & ( 0.00024 ) \\\hline \text { fanily composition } & 4.123 & 2.751 \\& ( 0.294 ) & ( 0.345 ) \\\hline \text { Pseudo-R } 2 & 0.266 & 0.189 \\& & \\\hline \text { Percent Correctly } & 82.0 & 83.4 \\\text { Predicted } & & \\\hline\end{array} where age is measured in years, education is years of schooling of the family head, farm status is a binary variable taking the value of one if the family head lived on a farm, south is a binary variable for living in a certain region of the country, expected family earnings was generated from a separate OLS regression to predict earnings from a set of regressors, and family composition refers to the number of family members under the age of 18 divided by the total number in the family.
The mean values for the variables were as shown in the table.  Variable  (1) White mean  (2) Nonwhite mean  age 46.142.9 age squared 2,263.51,965.6 education 12.610.4 farm status 0.030.02 south 0.30.5 expected family earnings 2,336.41,507.3 family composition 0.20.3\begin{array} { | c | c | c | } \hline \text { Variable } & \text { (1) White mean } & \text { (2) Nonwhite mean } \\\hline \text { age } & 46.1 & 42.9 \\\hline \text { age squared } & 2,263.5 & 1,965.6 \\\hline \text { education } & 12.6 & 10.4 \\\hline \text { farm status } & 0.03 & 0.02 \\\hline \text { south } & 0.3 & 0.5 \\\hline \text { expected family earnings } & 2,336.4 & 1,507.3 \\\hline \text { family composition } & 0.2 & 0.3 \\\hline\end{array} (a)Interpret the results. Do the coefficients have the expected signs? Why do you think age was entered both in levels and in squares?
(b)Calculate the difference in the predicted probability between whites and nonwhites at the sample mean values of the explanatory variables. Why do you think the study did not combine the observations and allowed for a nonwhite binary variable to enter?
(c)What would be the effect on the probability of a nonwhite woman living on her own, if education and family composition were changed from their current mean to the mean of whites, while all other variables were left unchanged at the nonwhite mean values?


Definitions:

Short-run Profits

Profits earned by a firm when it operates under short-term constraints, where at least one input is fixed.

Average Total Cost

The total cost of production divided by the number of units produced, representing the average cost per unit of output.

Average Total Cost

The overall expense of manufacturing divided by the quantity of products made, indicating the expense for each unit of production.

Profit-maximizing

The process of adjusting production and pricing strategies to achieve the highest possible profit from the sale of goods and services.

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