Focusing on the Fundamental Causes of Growth
The research of economists shows that successful economies are those with
high rates of accumulation of human and physical capital together with
sustained technological progress. But this conclusion then raises the crucial
question: why do some nations successfully achieve this outcome while
others fail? Olson (1996) has highlighted the fact that high rates of growth
seem to occur in a subset of poor countries rather than in all low-income
countries as the transitional dynamics of the Solow neoclassical growth model
imply. Given that capital and technology can migrate across political boundaries,
the persistence of significant differences in the level of output per
worker suggests the presence of persistent barriers to growth and development
(Parente and Prescott, 2000). An obvious deterrent to the free flow of
capital from rich to poor countries arises from the greater risk involved in
investing in countries characterized by macroeconomic instability, trade barriers,
inadequate infrastructure, poor education, ethnic diversity, widespread
corruption, political instability, disadvantageous geography and frequent policy
reversals. To understand why some countries have performed so much better
than others with respect to growth it is therefore necessary to go beyond the
proximate causes of growth and delve into the wider fundamental determinants.
This implies that we cannot hope to find the key determinants of
economic growth by using narrow economic analysis alone. To explain growth
‘miracles’ and ‘disasters’ requires an understanding of the history of the
countries being investigated as well as how policy choices are made within an
institutional structure involving political distortions.
Dani Rodrik (2003) has provided a useful framework for highlighting the
distinction between the proximate and fundamental determinants of eco
nomic growth. Figure 11.8, adapted from Rodrik, captures the main factors
that determine the size and growth of any economy. Referring back to equation
(11.4), in the upper part of Figure 11.8 we can see the influence of the
proximate determinants of growth, with output being directly influenced by
an economy’s endowments of labour (Lt), physical capital (Kt), natural resources
(Nt) and the productivity of these resources (At). The impact of both
technical and allocative efficiency is captured within the productivity variable.
In the lower portion of Figure 11.8 we observe the major fundamental
determinants of economic growth, including social capability (St). Rodrik
provides a threefold taxonomy of the fundamental determinants of growth,
namely geography, integration and institutions. These categories highlight
three major research areas, within a voluminous and rapidly expanding literature,
that have dominated growth analysis in recent years. Many social scientists
would argue forcefully that the influence of culture should be added to the list
of important deeper determinants of economic performance. It is certainly the
case that economic historians have given much greater consideration to culture
as a determinant of economic performance than economists. For example,
David Landes argues that ‘Culture Makes Almost All the Difference’ (Harrison
and Huntington, 2000). For other interesting discussions of the influence of
culture on economic growth and development the reader should consult
Huntington, 1996; Temin, 1997; Landes, 1998; Lal, 1999; Dasgupta and
Serageldin, 2000; Barro and McCleary, 2003; Grief, 2003.
As Rodrik points out, the central question in growth analysis is: which of
the causal relationships in Figure 11.8 matters most? However, Rodrik also
notes that geography is the only exogenous factor in his threefold taxonomy,
with integration and institutions ‘co-evolving with economic performance’.
The causal interrelationships between the variables in Figure 11.8, indicated
by the two-way direction of some of the arrows, suggest that there are
complex feedback effects at work. Therefore empirical work, in the form of
endless cross-country regressions, that attempts to establish clear lines of
causality must be treated with ‘extreme care’.
The research of economists shows that successful economies are those with
high rates of accumulation of human and physical capital together with
sustained technological progress. But this conclusion then raises the crucial
question: why do some nations successfully achieve this outcome while
others fail? Olson (1996) has highlighted the fact that high rates of growth
seem to occur in a subset of poor countries rather than in all low-income
countries as the transitional dynamics of the Solow neoclassical growth model
imply. Given that capital and technology can migrate across political boundaries,
the persistence of significant differences in the level of output per
worker suggests the presence of persistent barriers to growth and development
(Parente and Prescott, 2000). An obvious deterrent to the free flow of
capital from rich to poor countries arises from the greater risk involved in
investing in countries characterized by macroeconomic instability, trade barriers,
inadequate infrastructure, poor education, ethnic diversity, widespread
corruption, political instability, disadvantageous geography and frequent policy
reversals. To understand why some countries have performed so much better
than others with respect to growth it is therefore necessary to go beyond the
proximate causes of growth and delve into the wider fundamental determinants.
This implies that we cannot hope to find the key determinants of
economic growth by using narrow economic analysis alone. To explain growth
‘miracles’ and ‘disasters’ requires an understanding of the history of the
countries being investigated as well as how policy choices are made within an
institutional structure involving political distortions.
Dani Rodrik (2003) has provided a useful framework for highlighting the
distinction between the proximate and fundamental determinants of eco
nomic growth. Figure 11.8, adapted from Rodrik, captures the main factors
that determine the size and growth of any economy. Referring back to equation
(11.4), in the upper part of Figure 11.8 we can see the influence of the
proximate determinants of growth, with output being directly influenced by
an economy’s endowments of labour (Lt), physical capital (Kt), natural resources
(Nt) and the productivity of these resources (At). The impact of both
technical and allocative efficiency is captured within the productivity variable.
In the lower portion of Figure 11.8 we observe the major fundamental
determinants of economic growth, including social capability (St). Rodrik
provides a threefold taxonomy of the fundamental determinants of growth,
namely geography, integration and institutions. These categories highlight
three major research areas, within a voluminous and rapidly expanding literature,
that have dominated growth analysis in recent years. Many social scientists
would argue forcefully that the influence of culture should be added to the list
of important deeper determinants of economic performance. It is certainly the
case that economic historians have given much greater consideration to culture
as a determinant of economic performance than economists. For example,
David Landes argues that ‘Culture Makes Almost All the Difference’ (Harrison
and Huntington, 2000). For other interesting discussions of the influence of
culture on economic growth and development the reader should consult
Huntington, 1996; Temin, 1997; Landes, 1998; Lal, 1999; Dasgupta and
Serageldin, 2000; Barro and McCleary, 2003; Grief, 2003.
As Rodrik points out, the central question in growth analysis is: which of
the causal relationships in Figure 11.8 matters most? However, Rodrik also
notes that geography is the only exogenous factor in his threefold taxonomy,
with integration and institutions ‘co-evolving with economic performance’.
The causal interrelationships between the variables in Figure 11.8, indicated
by the two-way direction of some of the arrows, suggest that there are
complex feedback effects at work. Therefore empirical work, in the form of
endless cross-country regressions, that attempts to establish clear lines of
causality must be treated with ‘extreme care’.
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