diff --git a/OLSvsIV_simulation_study.tex b/OLSvsIV_simulation_study.tex
index 8f45dcb..84a649e 100644
--- a/OLSvsIV_simulation_study.tex
+++ b/OLSvsIV_simulation_study.tex
@@ -34,7 +34,7 @@ \subsection{OLS versus TSLS Example}
 For any values of $N$ and $\pi$ there is a value of $\rho$ below which OLS outperforms TSLS: as $N$ and $\pi$ increase this value approaches zero; as they decrease it approaches one.
 In practice, of course, $\rho$ in unknown so we cannot tell which of OLS and TSLS is to be preferred \emph{a priori}.
 If we make it our policy to always use TSLS we will protect ourselves against bias at the potential cost of very high variance.
-On the other hand, we make it our policy to always use OLS we protect ourselves against high variance at the potential cost of severe bias. 
+If, on the other hand, we make it our policy to always use OLS then we protect ourselves against high variance at the potential cost of severe bias. 
 FMSC represents a compromise between these two extremes that does not require advance knowledge of $\rho$. 
 When the RMSE of TSLS is high, the FMSC behaves more like OLS; when the RMSE of OLS is high it behaves more like TSLS.
 Because the FMSC is itself a random variable, however, it sometimes makes moment selection mistakes.\footnote{For more discussion of this point, see Section \ref{sec:avg}.} 
diff --git a/empirical_example.tex b/empirical_example.tex
index f0d8f3e..3bae6d8 100644
--- a/empirical_example.tex
+++ b/empirical_example.tex
@@ -13,7 +13,7 @@ \section{Empirical Example: Geography or Institutions?}
 
 In this section, I revisit and expand upon the instrument selection exercise given in Table 2 of \cite{Carstensen2006} using the FMSC and corrected confidence intervals described above. 
 All results in this section are calculated by TSLS using the formulas from Section \ref{sec:chooseIVexample} and the variables described in Table \ref{tab:desc}, with ln\emph{gdpc} as the outcome  variable and \emph{rule} and \emph{malfal} as measures of institutions and malaria transmission.
-I this exercise I imagine two hypothetical econometricians.
+In this exercise I imagine two hypothetical econometricians.
 The first, like \cite{Sachs} and \cite{Carstensen2006}, seeks the best possible estimate of the causal effect of malaria transmission, $\beta_3$, after controlling for institutions by selecting over a number of possible instruments.
 The second, in contrast, seeks the best possible estimate of the causal effect of \emph{institutions}, $\beta_2$, after controlling for malaria transmission by selecting over the same collection of instruments.
 After estimating their desired target parameters, both econometricians also wish to report valid confidence intervals that account for the additional uncertainty introduced by instrument selection.