Thorsten Beck 1181from a large number of investors, (iii) acquire and process information about enter-
prises and possible investment projects, thus allocating society’s savings to its most
productive use, (iv) monitor investments and exert corporate governance, and (v)
diversify and reduce liquidity and intertemporal risk. However, other models show
that higher returns from better resource allocation may depress saving rates, result-
ing in overall growth rates actually slowing with more effective financial markets
and institutions.^1
While the finding of a positive correlation between indicators of financial
development and economic growth cannot settle this debate, advances in com-
putational capacity and availability of large cross-country datasets with relatively
large time dimensions have enabled researchers to rigorously explore the rela-
tionship between financial development and economic growth. Further, as more
disaggregated datasets have become available, the finance and growth literature
has proceeded from using country-level data, to using industry- and firm-level
data, to more recently using household data. While the cross-country literature
has developed more sophisticated models to address biases introduced by mea-
surement error, reverse causation and omitted variables, the progress to firm- and
household-level data allows not only additional ways to address these biases, but
also tests of the specific channels through which finance might enhance economic
growth.
The econometrics of finance and growth can be summarized in the following
simple regression model:
g(i,t)=y(i,t)−y(i,t− 1 )=α+βif(i,t)+C(i,t)γi+μ(i)+ε(i,t), (25.1)whereyis the log of real GDP per capita or of another measure of welfare,gis
the growth rate ofy,fis an indicator of financial development,Cis a set of con-
ditioning information,μandεare error terms,iis the observational unit – be it
a country, an industry, a firm or a household – andtis the time period. Whileε
is a white-noise error with a mean of zero,μis a country-specific element of the
error term that does not necessarily have a mean of zero. The explanatory vari-
ables are measured either as an average over the sample period or as an initial
value. The sign and significance of the coefficientβiis at the center of the debate.
As discussed in the remainder of this chapter, the estimate ofβican be biased for
a variety of reasons, among them measurement error, reverse causation and omit-
ted variable bias. While the cross-country literature assumesβi=β, with some
research supporting this assumption (Loayza and Ranciere, 2006), the time series
literature does not impose this restriction. Further, several industry- and firm-level
studies test whetherβvaries across industries or firms with different characteristics,
utilizing interaction terms.
This chapter is concerned with an unbiased, consistent and efficient estimator of
βi.^2 In this context, we abstract from a number of other problems in the finance and
growth literature. First, this chapter does not cover problems arising from the lack
of appropriate data, although we are concerned about measurement error in the
financial indicators and the bias this introduces in the estimation. Second, while