Paul Johnson, Steven Durlauf and Jonathan Temple 1169controlling for some variables that are hard to measure at the country level, such
as cultural factors. By comparing experiences across regions, there may also be
scope for identifying events that correspond more closely to natural experiments
than those found in cross-country data. Work such as that by Besley and Burgess
(2000, 2002, 2004), using panel data on Indian states, shows the potential of such
an approach. In working with such data more closely, one of the main challenges
will be to develop empirical frameworks that incorporate movements of capital
and labor between regions: clearly, regions within countries should only rarely be
treated as closed economies. Shioji (2001) is an example of how analysis using
regional data can take this into account.
Even with better data, at finer levels of disaggregation, the problem of omit-
ted variables can only be alleviated, not resolved. It is possible to argue that the
problem applies equally to historical research and case studies, but at least in these
instances, the researcher may have some grasp of important forces that are difficult
to quantify. Since growth researchers naturally gravitate towards determinants of
growth that can be analyzed statistically, there is an ever-present danger that the
empirical literature, even taken as a whole, yields a rather partial and unbalanced
picture of the forces that truly matter. Even a growth model with high explanatory
power, in a statistical sense, has to be seen as a rather provisional set of ideas about
the forces that drive growth and development.
This brings us to our final points. We once again emphasize that empirical
progress on the major growth questions requires attention to qualitative sources
such as historical narratives and studies by country experts. One example we have
given concerns the validity of instrumental variables: understanding the historical
experiences of various countries seems critical for determining whether exclusion
restrictions are plausible. In this regard work such as that of Acemogluet al.(2001,
2002) is exemplary. More generally, nothing in the empirical growth literature
suggests that issues of long-term development can be disassociated from the his-
torical and cultural factors that fascinated commentators such as Max Weber, and
the examination of these factors must rely at least partly on case studies, or risk
missing some of the most interesting and important issues.
These questions have been asked for many decades, and the quest to understand
the wealth of nations is as old as the discipline of economics itself. In contrast,
growth econometrics is an area of research that is still in its infancy. Researchers
in this field have shown flexibility in responding to the specific challenges and
questions that arise in this context. They have introduced a number of statistical
methods into applied economics, including classification and regression tree algo-
rithms, robust estimation, threshold models and Bayesian model averaging, all of
which appear to have wider utility. As with any new literature, especially one tack-
ling questions as complex as these, it is easy to identify significant limitations of
the existing evidence, and of the tools that are currently applied. Yet it seems clear
to us that significant progress has been and continues to be made, even from the
vantage point of our (2005) review. We therefore see good reasons for continued
optimism.