The questions below are based on a dataset containing the characteristics of 5,407 households. In particular, we will use the following variables: - size: home size, measured in square feet. - hhinc:...

The questions below are based on a dataset containing the characteristics of 5,407
households. In particular, we will use the following variables:
- size: home size, measured in square feet.
- hhinc: annual household net income, measured in euros.
- owner: dummy variable equal to 1 if the living space is owned, and 0 if the living
space is rented.
- hhsize1: dummy variable equal to 1 if the number of household’s members is 1 or
2, and 0 otherwise.
- hhsize2: dummy variable equal to 1 if the number of household’s members is 3 or
4, and 0 otherwise.
- hhsize3: dummy variable equal to 1 if the number of household’s members is 5 or
above 5, and 0 otherwise.
- edu: education level of household’s members.

We conduct a simple regression of size on hhinc, now using robust standard errors.
The regression output is reported in Table 2. Why are the estimated coefficients in
Table 2 equal to the estimated coefficients in Table 1? Do the conclusions on the
statistical significance at 5% of the coefficient of hhinc change between Tables 1 and
2?

Extracted text: Table 1 • regress size hhinc Source df MS Number of obs 5,407 F(1, 5405) 1613.34 1 263141566 Model 263141566 Prob > F 0.0000 Residual R-squared Adj R-squared Total 1.1447e+09 5,406 211749.457 Root MSE size Coefficient Std. err. t P>|t| [95% conf. interval] hhinc .0082545 .0002055 40.17 0.000 .0078516 .0086574 -cons 800.9835 9.404917 85.17 0.000 782.5461 819.4209


Extracted text: Table 2 · regress size hhinc, robust Linear regression Number of obs 5,407 F(1, 5405) 134.25 Prob > F 0.0000 R-squared 0.2299 Root MSE 403.86 Robust size Coefficient std. err. t P>|t| [95% conf. intervall hhinc .0082545 .0007124 11.59 „cons 800.9835 25.72644 31.13