1154 The Methods of Growth Econometrics
as in Easterly (1996), and the consequences of the debt crisis for investment, as in
Warner (1992).
The obvious analogue for growth econometrics is to study the time paths of
variables such as output growth, investment and total factor productivity (TFP)
growth, before and after discrete events such as democratizations. When using
fixed effects combined with a binary indicator to capture discrete events, the logic
of the approach is similar to the differences-in-differences estimator in the literature
on program evaluation. Examples include the work of Giavazzi and Tabellini (2005)
on economic and political liberalizations, Henry (2000, 2003) on stock market
liberalization, Papaioannou and Siourounis (2007) and Rodrik and Wacziarg (2005)
on democratizations, and Wacziarg and Welch (2003) on trade reforms. Depending
on the context, one can also study the response of other variables in a way that
is informative about the channels of influence. For example, in the case of trade
reform, it is natural to study the response of the trade share, as in the work of
Wacziarg and Welch.
The rigor of this method should not be exaggerated. As with any other approach
to empirical growth, one has to be cautious about inferring a causal effect. This is
clear from drawing explicitly on the literature on treatment effects and program
evaluation.^22 In the study of growth, “treatments” such as democratization are
clearly not exogenously assigned, but are events that have arisen endogenously.
This means, for example, that treatment and control groups may differ system-
atically, either in terms of time-invariant unobservables, or in factors that vary
over time. Methods based on fixed effects can address the first of these considera-
tions, but allowing for the second is more complicated. To illustrate the problem,
Papaioannou and Siourounis (2007) draw a useful analogy between democratiza-
tions and “Ashenfelter’s dip” in the program evaluation literature. It is possible
that countries experiencing a downturn or weak economic performance are espe-
cially likely to democratize, in which case the estimated effect of democratization
risks conflating the true effect with the effects of a separate recovery from the
pre-treatment “dip.”
Moreover, in growth applications, the treatment effects are highly likely to be
heterogeneous across countries and over time. They may depend, for example, on
whether a policy change is seen as temporary or permanent, as Pritchett (2000a)
observes. In these circumstances, the ability to quantify even an average treatment
effect is strongly circumscribed. It may still be possible to identify the direction
of effects, and here the limited number of observations does have one advantage.
With a small number of cases to examine, it is easy for the researcher to present
a graphical analysis that allows readers to gauge the extent of heterogeneity in
responses, and the overall pattern. Another useful and informative approach,
adopted by Papaioannou and Siourounis (2007), is to estimate a model that allows
the treatment effect to vary over time, using the methods developed in Laporte
and Windmeijer (2005).
There is one remaining problem to note. When growth researchers look at the
effects of discrete events, they typically study the effects on serially correlated out-
comes such as output or investment. A particular concern in cross-country samples