Consistency of estimator Regression: Wage=b0+b1collegegrad, where collegegrad is a dummy variable. Suppose you want to estimate the wage ratio between college graduates and non-college graduates. Is the estimator theta=b0/b1 consistent?
My thinking is that if we could increase our sample size to infinity, we would cover the entire population and thus get the true ratio, so the estimator is consistent. Am I correct, or am I missing something?
 A: To close this one:
We have the regression 
$$w = b_0+b_1d + u$$
where $d$ is a binary $1/0$ random variable, and $u$ is an independent error term, with $E(u\mid d) = 0$ and so also $E(u)=0$. The size of the i.i.d. sample is $n$ and we denote by $n_1$ the number of observations for which $d=1$ and by $n_0$ the number of the rest.  
One can find that the OLS estimator gives
$$\hat b_0 = \frac 1{n_0}\sum_{d_i=0}w_i = \frac 1{n_0}\left(n_0b_0+\sum_{d_i=0}u_i\right) = b_0 + \frac 1{(n_0/n)}\left(\frac 1n\sum_{d_i=0}u_i\right) \tag{1}$$
and
$$\hat b_1 = \frac{n}{n_1n_0}\sum_{d_i=0}w_i - \frac 1{n_0}\sum_{i=1}^nw_i$$
$$=\frac{n}{n_1n_0}\left(n_1b_0+n_1b_1+\sum_{d_i=1}u_i\right) - \frac 1{n_0}\left(nb_0+n_1b_1+\sum_{i=1}^nu_i\right)$$
$$=b_1 +\frac{n}{n_1n_0}\sum_{d_i=1}u_i - \frac 1{n_0}\sum_{i=1}^nu_i$$
$$\Rightarrow \hat b_1 = b_1 +\frac 1{(n_1/n)}\frac 1n\sum_{d_i=1}u_i - \frac 1{(n_0/n)}\frac 1n\sum_{d_i=0}u_i \tag{2}$$
So
$$\text{plim}\hat b_0 = b_0 + \text{plim}\left[\frac 1{(n_0/n)}\right]\cdot \text{plim}\left(\frac 1n\sum_{d_i=0}u_i\right)$$
and by the Law of Large Numbers
$$= b_0 + [1/P(d=0)]\cdot \left(\frac 1n\sum_{d_i=0}E(u_i)\right) = b_0$$
since $E(u_i)=0$, and analogously for $\hat b_1$.
So both coefficient estimators are consistent therefore the expression
$$\frac {\hat b_0}{ \hat b_0 + \hat b_1} \xrightarrow{p} \frac {b_0}{b_0 + b_1}$$
since the probability limit distributes, when it is a constant, as a consequence of the Continuous Mapping (Mann-Wald) Theorem.
