Suppose you have two years' worth $of$ panel data $on$ wages and work experience $of$ adults; however, the data set has $no$ further information $on$ the

characteristics $of$ the individuals $in$ the data set. Specifically, you have cross$-$sectional wage and work experience data $on$ individuals $in2006$ and

cross$-$sectional data $on$ those same individuals $in2012.$ You plan $to$ use the following fixed$-$effects model $to$ analyze the effects $of$ work experience $on$

wages:

$log($ wage ${?}_{(}()it))={}_0+{}_{0}yr{10}_t+{}_1$ exper ${?}_{(}()it)+a_i+u_{it}$

where

wage ${?}_{(}()it)=$ yearly wage $of$ individual $iat$ time $tin$ dollars

$yr{10}_t=1in$ the year $2010(t=2),$ and $=0$ otherwise $(t=1)$

exper ${?}^{(}()2)=$ years $of$ work experience $of$ individual $i,at$ time $t$

$a_i=$ unobserved $(time-$invariant$)$ effect

$u_{it}=$ idiosyncratic error

Two other factors that can influence wage, which you have not controlled for $in$ your model, are race and professional certification.

Use the following table $to$ indicate which term $in$ the fixed$-$effects model captures the effect $of$ race, which term captures the effect $of$ professional

certification, and which term captures the effect $of$ work experience.