True $or$ False: $If$ the data for $an$ observation $on$ either the dependent variable $or$ one $of$ the independent variables are missing $at$ random, then the size

$of$ the random sample available from the population must $be$ reduced, which reduces the estimator's precision and introduces a bias.

True

False

Consider the following model examining the determinants $of$ saving:

saving $={}_0+{}_1$ income $+{}_2$ age $+{}_3$ size $+u$

saving $=$ amount $of$ saving each year, $in$ dollars

income $=$ annual income, $in$ dollars

age $=$ a person's age, $in$ years

size $=$ number $of$ people $in$ the family

$u=$ error term

Suppose the survey includes only people older than $40$ years old. This creates $a$

$of$ the parameters $in$ the population model.

Now consider a model examining the determinants $ofan$ hourly wage offer faced $by$ every person $of$ working age. $(Note$: Keep $in$ mind that the

person can either work $at$ that wage $or$ not work. For people who $do$ not work, the wage offer cannot $be$ observed.$)$

$log($ wage ${?}^{(}()o))={}_0+{}_1$ educ $+{}_2$ exper $+{}_3$ age $+u$

$Ifwe$ use a random sample $of$ working people $to$ estimate the equation, will $we$ get biased estimators?

Yes, because selection might $be$ endogenous and can result $in$ a sample selection bias $in$ the OLS estimators.

$No,$ because selection might $be$ endogenous and cannot result $in$ a sample selection bias $in$ the OLS estimators.