The rise of deep determinants
Robbert Maseland, University of Groningen
1. Introduction
In recent decades, explanations of economic growth have
increasingly focused on deep determinants of differences in
development (Spolaore & Wacziarg 2013). Contemporary
differences in economic performance are related to institutional
quality which can be traced back to historical events (e.g. Ashraf
and Galor 2011; Dell 2010; Tabellini 2010) and natural
circumstances (e.g. Ashraf and Galor 2013; Easterly and Levine
2003; Haber and Menaldo 2011; Sokoloff and Engerman 2000). This
literature suggests that initial differences in formal and informal
institutions tend to persist for long periods of time, only
incrementally changing in response to further random events or
changes in political interests (ARJ 2001; Francois & Zabonjik
2005; North 1990). Such institutional persistence subsequently
provides a substantial part of the explanation for enduring income
inequality between societies.
Why do differences in institutional and economic development
persist? Spolaore & Wacziarg (2013) identify biological
transmission channels (Ahsraf & Galor 2013; Galor & Moaz
2002; Clark 2007) and mechanisms of cultural transmission (Bisin
& Verdier 2000; Gorodnichenko & Roland 2010; Greif 2006;
Guiso, Sapienza & Zingales 2008; Tabellini 2010). In biological
transmission, members of a society inherit certain biological
traits that impact development directly from their ancestors. Under
cultural transmission, older generations transmit ideas, norms,
values and beliefs to the newer ones through socialization.
Stability may then come from interdependencies among economic
institutions (Hall and Soskice 2001), between economic
institutions, mental models and processes of learning (Bisin and
Verdier 2000; North 1990, 2005), or between economic and political
institutions (Acemoglu, Johnson, Robinson 2005). Institutions
provide incentives, thus creating vested interests in a given
institutional structure. Learning and the development of ideas and
beliefs is therefore biased in accordance with the structure,
cementing initial institutions. Politically, the beneficiaries of
an institutional incentive structure are often able to transform
their economic gains into political power, which enables them to
resist institutional change (Acemoglu and Robinson 2012). In both
arguments observed differences in institutional quality have deep
roots and will disappear only very gradually.
Curiously enough, the thesis that the impact of historical and
natural circumstances is stable or at most slowly declining has
rarely been tested directly. Studies claiming to study long-term
persistence usually use a cross-sectional set-up, tracing current
institutional and economic differences to historical or natural
differences (Spoloare & Wacziarg 2013). Yet this only tells us
that deep determinants matter now. What we do not know is whether
the impact of deep determinants has always been this big, and
whether it is stable or slowly receding, as the theoretical
argument wants it.
In this paper, we trace the evolution of explanatory power of
deep determinants for institutional quality over time. We do so by
estimating reduced form regressions of institutional indicators
(taken from the Polity IV dataset) on a broad range of popular deep
determinants for each year since 1850. We subsequently study the
evolution of the proportion of explained variance and the
standardized coefficients as indications of the impact of deep
determinants and the room societies have to escape from the
deterministic trend. Doing so, we find that for a wide range of
historical and natural determinants proposed in the literature, the
impact of natural and historical roots on comparative institutional
and economic development is a fairly recent phenomenon, and that
the explanatory power of deep determinants has been consistently
increasing. The room for societies to escape the historical and
environmental straitjacket seems to narrow.
We conclude that the impact of deep determinants on development
is historically contingent. Their ability to explain differences in
institutional quality seems not to be universal, but to be a
product of the latter half of the 20th century. For institutional
theories of comparative development, our results provide something
of a puzzle. Rather than having a long-term persistent effect, deep
determinants have begun to substantially matter only recently. This
seems to disqualify much of the literature about the
intergenerational transmission of development (Spolaore &
Wacziarg 2013), because it has focused on the wrong problem. The
question is not what accounts for long-term persistence, but which
aspects of our current economic system render deep determinants so
important nowadays. More in general, the results indicate that we
need to be much more aware of the historical contingency of our
explanations. When nature or history matters, it is because they
have been made to matter. From the perspective of policy, this is
good news: where Francois and Zabonjik (2005, 51) argue that due to
the impact of inherited conditions ‘late developing countries may
not easily be able to transplant the modes of production that have
proved useful in the West’, our results suggest that this impact of
deep determinants is conditional and therefore subject to changes
in the global economic system. There is a way out. The current
challenge for institutional theory is to find it.
In the remainder of this paper, we first review the literature
about deep determinants and the intergenerational transfer of
development. From this discussion, we derive the proposition that
the impact of deep determinants is consistent but slowly receding
over time. In section 2, we explain the data and methods used to
test this proposition. Section 3 provides the results, showing that
for most determinants, explanatory power is steadily increasing. We
round off with a conclusion and discussion.
2. Deep determinants and the long-term persistence of
development
Ever since Adam Smith’s ‘The Wealth of Nations’ (1776), scholars
have been debating the question why some societies have grown more
prosperous than others. Traditionally, answers to this question
focused on factors such as investment, population growth and
technological progress. More recently, differences in governance,
and economic and political institutions have been put forward as
important factors (Acemoglu, Johnson and Robinson 2001; Easterly
and Levine 2003; Kaufmann, Kraay and Zoido-Lobatón 1999; Rodrik,
Subramanian and Trebbi, 2004). By now, it has become widely
accepted that differences in institutions provide a big part of the
explanation for differences in development. What is less clear,
however, is where these differences in institutions come from. A
burgeoning new literature attempts answer this question by relating
developmental differences to so-called deep determinants:
differences in historical, geographical and natural conditions
(e.g. Ashraf & Galor 2013; Comin, Easterly & Gong 2010;
Easterly and Levine 2003; LaPorta et al 1998, 2008; Spolaore &
Wacziarg 2013; Tabellini 2010).
Within this literature, two main strands can be distinguished.
First, a large body of research has focused on natural
circumstances as explanation for differences in development. This
literature assesses the impact of geographic and biological
conditions such as access to waterways (Rappaport and Sachs 2003),
suitability to agriculture and domestication (Diamond 1997 ; Olsson
and Hibbs 2005), or disease environment (Maseland 2013; Sachs 2001;
Sachs and Malaney 2002). These differences in conditions may have
either a direct effect on development, burdening hot countries with
lower agricultural productivity and a higher transmission of
diseases for example (Sachs 2001); or an indirect effect through
historical channels. An example of the latter is the famous thesis
by Diamond (1997), claiming that a number of environmental
advantages enjoyed by Eurasians enabled the adoption of agriculture
and pastoralism sooner than elsewhere, providing Eurasia with a
head start in the development of larger population centres and
subsequent technological innovation (Ashraf & Galor 2011;
Diamond 1997; Olsson & Hibbs 2005). Other examples include the
arguments by Engerman & Sokoloff (1997, 2002), Acemoglu,
Johnson & Robinson (2001), Easterly and Levine (2003) about the
effects of crops and diseases on settlement of European colonizers,
suggesting that differences in settlement patterns resulted in
differences in institutional quality. In this perspective, initial
differences in bio-geographical circumstances matter because they
have set in motion divergent evolutionary paths.
Second, various authors have proposed a wide range of
socio-historical factors as determinants of development (e.g.
Becker & Woesman 2009; Barro & McCleary 2003; Comin,
Easterly & Gong 2010; Guiso, Sapienza & Zingales 2008;
LaPorta, Lopez de Silanes & Shleifer 2008; Putterman & Weil
2010; Tabellini 2010). Again, determinants may either have a direct
on present institutions and economic performance, or an indirect
effect through affecting institutional development in the past, the
effects of which still can be felt today. Guiso, Sapienza &
Zingales (2003) and Noland (2005), for example, argue that present
day religious denominations directly shape economic attitudes and
impact economic growth. By contrast, Becker & Woesman (2009)
show that any effect of Protestantism on growth runs through a
historical effect on literacy rates, setting of a higher rate of
human capital development that persists until today. In line with
the latter argument, Guiso et al (2008) claim that differences in
social capital between Italian cities reflect the impact of
different experiences in medieval times. Greif and Tabellini (2010)
argue that the emergence of clans in China and cities in Europe in
medieval times as dominant forms of institutional enforcement set
off both regions on diverging institutional trajectories. LaPorta
et al (2008) claim that differences in the origin of legal systems,
which to some extent reflect the borders of the former Roman
empire, have a persistent effect on institutional and economic
development. Not going back as far as that, Tabellini (2010) shows
that differences in 19th century literacy rates and political
institutions in regions of Europe still can be felt in today’s
economic attitudes and outcomes.
Institutional persistence
Both direct and indirect arguments imply the presence of
long-term institutional persistence (Acemoglu and Robinson 2008;
Guiso et al 2008) in institutional development. When deep
determinants affect development directly, this impact remains as
long as the determinants themselves do not change. The reasons for
institutional persistence in the indirect channel are more complex.
Several arguments may be found in the literature.
First, North (1990, 1991, 2005) argues that institutional
persistence is a consequence of symbiosis between institutions and
organizations, and, more importantly, interdependencies between
institutions and learning. In this argument, an institutional
environment provides an incentive structure to economic agents.
Organizations are created to respond to these incentives. Since the
investment in the creation and maintenance of such organizations
would become irrelevant upon changes in institutional arrangements,
agents have a strong incentive to try to maintain the status quo.
In addition, both agents’ investment in knowledge development and
their ability to develop new ideas and insights is shaped by
pre-existing social structures. The knowledge agents seek to
acquire is the type of knowledge that is most profitable given the
set of existing institutional incentives (North 1991). This sets
societies off on a path of technology development that is strongly
intertwined with its institutional system. The most important
mechanism for institutional persistence in North’s thinking,
however, is the fact that any new information that we learn is
filtered and shaped by our pre-existing mental models (North 1991,
2005). This implies that any information that potentially
undermines our inherited cultural beliefs is likely to be rejected
or re-interpreted in a way that fits within our mental models. As a
result, learning will further support and cement inherited ideas,
making radical change unlikely. In a similar argument, Bisin and
Verdier (2000) argue that the transmission of knowledge and values
from one generation to the next is determined by the decisions of
parents, who interpret this decision through their own cultural
perspectives. Consequentially, ideas, beliefs and values are
usually subject to incremental change only.
A second line of literature stresses symbiosis between
institutions and political power as a cause for institutional
persistence. This argument is most clearly visible in the work of
Acemoglu and Robinson (Acemoglu and Robinson 2000, 2001, 2006,
2008). In this line of reasoning, a distinction is made between
economic and political institutions. Economic institutions
determine the distribution of profits, and their shape is a
function of the distribution of interests and political power in
society so that powerful groups set economic institutions to
further their interests. Political power is a combination of de
jure and de facto political power. De jure power stems from formal
political institutions, while de facto power results from
investment in lobbying and other forms of influence. In the
argument of Acemoglu and Robinson (2008), the economic institutions
prevalent in a society may favour some groups over others, giving
them the means to invest in more de facto political power. In turn,
they are likely to use this power to exert pressure to keep
economic institutions as they are. A system emerges that is highly
resistant to change. Even when formal political institutions change
towards a more inclusive de jure system, elites respond to this by
increasing their investment in de facto power and making sure that
economic institutions are left intact (Acemoglu and Robinson 2008).
This symbiosis between economic institutions and political power is
thought to explain the long term persistence of institutional
arrangements introduced in colonial times (Acemoglu Johnson and
Robinson 2001). Once arrangements are in place in a society that
disenfranchise large parts of the population, it is hard to change
them into a more inclusive institutional system.
Third, institutional persistence has been argued to be a product
of interdependencies between economic institutions. This argument
is most closely associated with the work of Hall and Soskice
(2001), who claim that any economic institution is part of a
coherent system that makes up a distinct variety of capitalism.
Illustrating their argument with examples from Germany and the
United States, they show that the functioning of institutional
arrangements is dependent on the presence of other, complementary
institutions. The pervasiveness of on the job training in the
German “coordinated market economy”, for example, is dependent on
the presence of collective wage bargaining institutions, as these
prevent hold-up by individual workers. In the American “liberal
market economy”, lacking such collective bargaining, workers invest
in training themselves and focus on acquiring more general skills
that make them attractive to a large range of employers, rather
than on the highly specialized, firm-specific skills that are
typical for the German system. Careers are advanced by switching
employers in the US, which is related to higher labour market
flexibility (Hall and Soskice 2001). Institutional
complementarities such as these imply that profound institutional
change is unlikely. Reforming one institution in isolation is not
effective since its functioning depends on its position in the
entire institutional setting. What is more, different institutional
arrangements give rise to different comparative institutional
advantages, making sure that different industries and interests
arise under different varieties of capitalism. Not only the ability
to change institutions, but also the incentive to change is reduced
as a result. Much like in the argument about economic and political
institutions of Acemoglu and Robinson (2006, 2008),
interdependencies among institutions result in a lock-in effect
leading to long-term institutional persistence.
Their differences notwithstanding, all these arguments maintain
that deep determinants should have a long-term, persistent effect
on economic and institutional development. In general, empirical
support for this argument has come from cross-sectional studies,
showing that deep determinants affect economic performance and
institutional quality today (e.g. Acemoglu, Johnson & Robinson
2001; Ashraf & Galor 2011, 2013; Becker & Woesman 2009;
Comin, Easterly & Gong 2010; , Easterly and Levine 2003;
Engerman & Sokoloff 1997, 2002; Guiso, Sapienza & Zingales
2008; LaPorta, Lopez de Silanes & Shleifer 2008; Olsson &
Hibbs 2005; Putterman & Weil 2010; Tabellini 2010). Results
such as these are seen as evidence of a long-term,
intergenerational transmission of development (Spolaore and
Wacziarg 2013). History lingers in the institutional legacy of
societies. However, the fact that deep determinants make their
presence felt today does not necessarily mean that they have done
so in the past. Cross-sectional results in a single period cannot
prove institutional persistence by themselves. A required
additional assumption is that any effect of a natural or historical
determinant that is found today is a remnant of an effect that was
equally strong or even stronger initially and has been present ever
since. More specifically, if we take the distinction between direct
and indirect channels through which deep determinants matter into
account, we expect that direct effects of deep determinants are
stable over time, while indirect effects—i.e. effects through
historical legacies—slowly wear off over time. In other words,
Hypothesis 1: The effect of deep determinants directly affecting
contemporary economic performance is stable over time.
Hypothesis 2: The effect of deep determinants directly affecting
contemporary institutional quality is stable over time.
Hypothesis 3: The effect of deep determinants affecting economic
performance through their historical legacy is slowly decreasing
over time.
Hypothesis 4: The effect of deep determinants affecting
institutional quality through their historical legacy is slowly
decreasing over time.
Data & Methodology
In order to test these hypotheses, we follow a simple
methodology that consists of two steps. First, we use a broad set
of ´deep determinants´ that have been proposed in some of the most
prominent studies in economics and relate these to measures of
institutional quality and economic performance one by one. We run
separate regressions for each year in our study, varying the
dependent variable but leaving the presumably time-invariant deep
determinants constant. Doing so, we save the r-squares of the
regression and the standardized coefficients of the deep
determinants. We interpret the coefficients as a measure of
explanatory power of the deep determinant and the r-squares as
indicators of the potential for countries to dodge the
deep-determined trend. In a second step, we assess whether these
r-squares and standardized coefficients are stable over time,
decreasing or increasing.
In order to ensure comparability across years, we restrict our
sample of countries to those that have observations for all years
in the period under study. In our main analysis, we investigate the
period 1965-2012, which allows us to include most former colonies
in the investigation, maximizing the scope of the investigation. In
a second analysis, we focus on the period 1850-2012, which allows
us to look at longer time trends but only for the small sample of
countries that already existed by 1850. Table A1 in the appendix
provides the lists of countries included in both samples.
Dependent variables
In order to measure institutional quality we use the indicator
of constraints on the executive taken from Polity IV. This measure
has been used as a proxy for property rights protection and shown
to be capturing the most important element of institutional quality
(Acemoglu, Johnson & Robinson 2001; Acemoglu & Johnson
2005). The index runs from 1 to 7, with a score of 1 reflecting
unlimited executive authority and a score of 7 executive parity of
subordination. Yearly interpolation has been used to predict values
in years of political upheaval. A potential problem of this
variable is that it is truncated at the low and high ends. For our
specific purposes, a concern may be that in case of a general trend
towards more executive subordination, the explanatory power of any
determinant will decrease over time because the countries with the
‘highest’ institutional quality are not able to rise further due to
scale limits, while the others are catching up. We normalize the
index for each year to reduce the problem, but it remains a
potential concern. Our second dependent variable is economic
performance, measured by the log of income per capita levels taken
from the World Development Indicators.
Bio-geographical determinants
We first explore a set of natural and geographical conditions
proposed in the literature as deep determinants of development. A
first indicator is the access to waterways, measured as the average
distance of a country to the nearest sea-navigable river or
ice-free coastline in thousands of km (Gallup, Sachs and Mellinger
1999). Better access to waterways is thought facilitate the
diffusion of technology between centres of population. Similarly,
Diamond (1997) argues that the orientation of the continental axis
determines the transmission of climate-dependent technology between
societies. To capture this effect, we include the ratio of the
largest longitudinal distance to the largest latitudinal distance
of a continent (Olsson and Hibbs 2005). Ashraf and Galor (2013)
argue that genetic diversity affects the levels of trust and
innovativeness of society, affecting long run technological
development and growth. We include their measure of genetic
diversity (adjusted to population) to study this effect.
We also explore a number of variables related to the suitability
for agriculture and pastoralism. A measure of rainfall is taken
from Ashraf and Galor (2013). This indicator reflects the average
monthly precipitation between 1961 and 1990, and is based on
Nordhaus (2006) and New et al (2002). A measure of the number of
pre-historically available domesticable plants and animals is taken
from Olsson and Hibbs (2005). We include the percentage of arable
land in a country (World Development Indicators) and measures of
soil fertility and the suitability of land for agriculture
(Michalopoulos 2011, based on Ramankutty et al 2002).
To capture the effect of climatic conditions on productivity and
proneness to diseases, we use a measure of the share of the
population living in temperate zones (Gallup, Sachs and Mellinger
1999), the average monthly temperature (Ashraf and Galor 2013;
Nordhaus 2006), climatic suitability for agriculture (Olsson and
Hibbs 2005), absolute latitude, and the percentage of the
population at risk of contracting malaria (Ashraf and Galor 2013;
Gallup and Sachs 2001).
Socio-Historical determinants
In addition to the set of bio-geographical determinants, we
explore the temporal consistency of a number of historical
variables. Several authors have argued that differences in the
level of technology and development arising early on in history
continue to affect development today (Ashraf and Galor 2011; Comin,
Easterley and Gong 2010; Putterman and Weil 2010). Variables
attesting to such an effect are a measure of the years passed since
the Neolithic revolution took place for a population, and the
distance to the technological frontier in year 1AD (Ashraf and
Galor 2013). Following up on the claim of Acemoglu, Johnson and
Robinson (2001) that differences in settler mortality affected
settlement patterns of colonies and subsequent institutional and
economic development, we include their measure of settler mortality
and the share of the population of European descent in 1900
(Acemoglu, Johnson & Robinson 2001). Legal origin dummies are
taken from La Porta et al. (1999). From this dataset, we also
obtain a measure of the shares of the population affiliated with
major world religions. Finally, we include the ethnic
fractionalisation index constructed by Alesina et al (2003).
Results
In our first set of analyses, we look at the impact of deep
determinants on differences in economic performance. To start, we
look at bio-geographical conditions which are more closely related
to direct effects. Figure 1a present the temporal trends in
absolute standardized coefficients (left) and r-squares (right) and
for various bio-geographical determinants.
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A first observation is that the effect of determinants do not
appear to be robust over time. Effect sizes and explained variance
vary substantially between periods. For some determinants, such as
genetic diversity, the r-square is zero for a large part of the
period under investigation, indicating that when research into the
effects of ruggedness (e.g. Nunn & Puga 2007)had been done in
the 1970s, it would not have generated any significant results.
Second, results generally indicate an increasing trend in both the
proportion of variance explained by deep determinants and their
explanatory power. Increases are often quite substantial. For
genetic diversity and terrain ruggedness, for example, the
standardized coefficient more than quadruples in size over the
given period.
Figure 1b presents the same information for determinants more
associated with indirect, historical legacy effects. Results give
rise to the same conclusions, although there is somewhat more
variation. Effect sizes and explanatory power are generally
inconsistent over time and seem to be subject to an increasing
trend. Early technology and settlement patterns also portray
increasing effect sizes, but inconsistent r-squares. The effects of
Protestantism and Catholicism are the true exceptions. While also
not being robust over time, the effect of these religions seems to
rapidly decline, even when the negative impact of Islam
increases.
Figure 2 presents the same exercise, but with institutional
quality as dependent variable. Section 2a presents the results for
bio-geographical determinants, and 2b the results for
socio-historical factors. Again, we find increases in both effect
size and the proportion of variance explained across the board.
Effects are again somewhat less pronounced and consistent for
historical legacy determinants. The impacts of some determinants,
such as settler mortality, the availability of domesticable animals
and legal origin seem to become smaller after peaking earlier.
While this only adds to the picture of intertemporal inconsistency,
they dodge the general trend of increases in effect size and
explanatory power. Overall, increases are the rule and they are
often even more substantial than in the case of income. The effect
of soil fertility, for example, increases by a factor 6 between
1960 and 2012, and the explained variance of this variable alone
grows from close to zero to almost 20%. For the proportion of
arable land, the effect size even rises ten-fold.
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To see whether the increases over time are statistically
significant, Tables 1 and 2 present regression results for both
r-squares and absolute standardized coefficients on year, for
institutional quality and income respectively. The first column
shows the regression coefficient of the first-step standardized
coefficients regressed on time, and the associated t-statistic. The
second column does the same for the regression of r-squares on
time. The third column provides the percentage of years in which
the effect of the determinant on economic performance is
statistically significant (to be added).
These more formal tests confirm the general picture emerging
from Figures 1 and 2. For most determinants, both effect sizes and
the proportion of variance explained significantly increases over
time. As a result, a large portion of the determinants do not seem
to be robustly significant. This does not mean that deep
determinants do not matter, but it does indicate that their effect
is contingent on the conditions as they have existed in the global
economy for the past decades. Apparently, most determinants have
become important only recently and are becoming increasingly
dominant.
Conclusion & discussion
In this paper, we have taken to the test the implicit argument
in the literature about deep determinants of growth that any effect
of deep determinants today is a remnant of such effects in the
past. By re-regressing measures of economic performance and
institutional quality on a large number of deep determinants for
various years since 1850, we have shown that the effect of deep
determinants is far from robust to changes in the year of
measurement of the dependent variables. More strikingly, we show
that for most determinants proposed in the literature, a consistent
upward trend in both effect size and proportion of variance
explained exists. In contrast to what one theoretically would
expect, the effect of natural circumstances or historical events is
not wearing off, but is increasing. In fact, for many determinants,
no significant effects on institutions or economic outcomes would
have been found had research been done a few decades earlier.
What to make of this? To be sure, this analysis does not
invalidate the literature into the deep determinants of growth and
institutional development. Rather, it confirms that the effect of
deep determinants is real, but that it is something recent instead
of long-standing. This compels us to look again more at the present
rather than the past in order to understand current differences in
economic and institutional performance. Rather than further
expanding the list of deep determinants of development, our
research should focus on explaining what it is in the current
economic system that has made deep determinants suddenly so
prominent. If we are able to answer that question, we can also
begin to understand what countries with less fortunate natural and
historical legacies may do to escape from their pre-‘determined’
path of development. Instead of focusing on the historical and
natural factors that are holding some societies back, institutional
economics may focus on what may be done to reduce their effect.
From the perspective of economic growth policies, it is not the
pre-determined trend that matters. It is the capacity to deviate
from this trend.
References (to be updated)
· Acemoglu, Daron and James A. Robinson 2012. Why Nations Fail.
New York: Crown Publishers.
· Acemoglu, Daron, Simon Johnson, and James A. Robinson . 2005.
“Institutions as a Fundamental Cause of Long-Run Growth.” In
Handbook of Economic Growth, Volume 1A, edited by Philippe Aghion
and Steven N. urlauf, 385–472. Amsterdam and Boston: Elsevier,
North-Holland.
· Acemoglu, Daron, Simon Johnson, and James A. Robinson. 2001.
The Colonial Origins of Comparative Development: An Empirical
Investigation. American Economic Review 91:5, 1369–1401.
· Dell, Melissa. 2010. The persistent effects of Peru’s mining
Mita. Econometrica, 78(6) 1863–1903.
· Easterly, William & Levine, Ross, 2003. Tropics, germs,
and crops: how endowments influence economic development. Journal
of Monetary Economics, 50(1), 3-39.
· Haber, Stephen and Menaldo, Victor A. 2011. Rainfall, Human
Capital, and Democracy. Available at SSRN:
http://dx.doi.org/10.2139/ssrn.1667332.
· LaPorta, R., Lopez-de-Silanes, F. & Shleifer, A. 2008.
“The economic consequences of legal origin,” Journal of Economic
Literature, 46.2: 285–332.
· North, D. 1991. Institutions. Journal of Economic
Perspectives, 5:1, 97-112
· North, D. 2005. Understanding the process of economic change,
Princeton: Princeton University Press, pp 11-80.
· North, Douglass. 1990. Institutions, Institutional Change, and
Economic Performance. Cambridge, UK: Cambridge University
Press.
· Nunn N. 2008. The Long Term Effects of Africa's Slave Trades.
Quarterly Journal of Economics. 123(1):139-176.
· Sokoloff, Kenneth L., and Stanley L. Engerman 2000. History
Lessons: Institutions, Factor Endowments, and Paths of Development
in the New World. Journal of Economic Perspectives 14:3,
217–232.
· Spolaore, Enrico, and Romain Wacziarg. 2013. How Deep are the
Roots of Economic Development? Journal of Economic Literature
51(2), 325–36.
· Tabellini, Guido. 2008. Culture and institutions: economic
development in the regions of Europe. Journal of the European
Economic Association 8 (4): 677-716.
Figure 1a The temporal stability of the effect of natural
determinants on income
Figure 1b The temporal stability of the effect of historical
legacies on income
Figure 2a The temporal stability of the effect of natural
determinants on institutional quality
Figure 2b The temporal stability of the effect of historical
legacies on institutional quality
Table 1 Temporal stability of effects of deep determinants on
institutional quality
Beta (absolute)
R-square
Latitude
.004* (7.28)
.003* (12.1)
Neolithic
7.25e-3* (5.50)
7.29e-4* (5.27)
Malaria
.011*(8.09)
.005* (11.39)
Access to waterways
.004*(6.77)
.002*(9.26)
Ruggedness
.030*(9.25)
6.82e-3*(8.57)
Soil
.020*(20.2)
.004*(21.9)
Suitability for agriculture
.006*(17.0)
.003*(18.0)
Islam
1.56e-4* (9.97)
.004* (12.45)
Catholicism
2.03e-4*(13.0)
.003*(13.6)
Protestantism
-1.67e-4* (9.6)
-.002*(8.34)
Climate
.002*(6.94)
.003*(13.5)
Technology 1AD
.002*(7.64)
4.44e-3*(15.2)
Settler Mortality
.003* (4.55)
-.003* (3.79)
European settlers 1900
9.22e-5*(7.31)
.005*(12.0)
Temperature
1.90e-4* (3.38)
.002*(5.84)
Population in temperate zone
.006*(5.81)
.003*(11.0)
Ethnic fractionalization
.009*(4.96)
.002*(7.51)
Civil law
.018*(10.3)
-.003*(10.3)
Table 2 Temporal stability of the effects of deep determinants
on income
Beta (absolute)
R-square
Latitude
2.17* (19.5)
9.04* (7.64)
Neolithic
.018* (53.4)
.003* (61.0)
Malaria
.011*(8.09)
.005* (11.39)
Access to waterways
.013*(21.8)
.002*(21.5)
Ruggedness
.027*(36.6)
2.71e-3*(16.5)
Soil
.006*(12.8)
2.85*(8.14)
Suitability for agriculture
1.48e-3*(9.44)
3.96e-5*(8.24)
Islam
9.46e-4* (22.6)
8.52e-4* (9.96)
Catholicism
-2.03e-4*(6.59)
-.001*(37.0)
Protestantism
-2.68e-5* (3.97)
-.001*(18.6)
Climate
.005*(17.8)
.001*(12.8)
Technology 1AD
8.11e-4*(4.70)
-7.82e-5*(1.04)
Settler Mortality
.005* (33.7)
.004* (26.6)
European settlers 1900
6.05e-5*(12.8)
-.001*(7.12)
Temperature
4.78e-4* (26.3)
.001*(14.4)
Population in temperate zone
.010*(15.4)
.002*(14.7)
Ethnic fractionalization
.025*(31.8)
.004*(73.5)
Civil law
.006*(12.5)
1.79e-4*(7.26)
-.5
0
.5
1
beta
196019701980199020002010
year
Ruggedness
0
.005
.01
.015
R-Square
196019701980199020002010
year
Ruggedness
.2
.3
.4
.5
.6
beta
196019701980199020002010
year
Soil suitability
.005
.01
.015
.02
.025
R-Square
196019701980199020002010
year
Soil Suitability
-.04
-.02
0
.02
.04
.06
beta
196019701980199020002010
year
Suitability for agriculture
0
.0005
.001
.0015
.002
.0025
R-Square
196019701980199020002010
year
Suitability for Agriculture
-.09
-.08
-.07
-.06
beta
196019701980199020002010
year
Temperature
.28
.3
.32
.34
.36
.38
R-Square
196019701980199020002010
year
Temperature
.5
.6
.7
.8
beta
196019701980199020002010
year
Climate
.4
.42
.44
.46
.48
R-Square
196019701980199020002010
year
Climate
1
1.2
1.4
1.6
1.8
beta
196019701980199020002010
year
Distance to waterways
.14
.16
.18
.2
.22
.24
R-Square
196019701980199020002010
year
Distance to waterways
-2.2
-2
-1.8
-1.6
beta
196019701980199020002010
year
Malaria
.4
.45
.5
.55
R-Square
196019701980199020002010
year
Malaria
1.4
1.6
1.8
2
beta
196019701980199020002010
year
Population in temperate zones
.38
.4
.42
.44
.46
.48
R-Square
196019701980199020002010
year
Population in temperate zones
.8
1
1.2
1.4
1.6
1.8
beta
196019701980199020002010
year
Time since neolithic revolution
.1
.15
.2
.25
R-Square
196019701980199020002010
year
Time since neolithic revolution
-.3
-.2
-.1
0
.1
beta
196019701980199020002010
year
Civil Law
0
.005
.01
.015
R-Square
196019701980199020002010
year
Civil law
-.013
-.012
-.011
-.01
-.009
-.008
beta
196019701980199020002010
year
Islam
.06
.07
.08
.09
.1
.11
R-Square
196019701980199020002010
year
Islam
.016
.017
.018
.019
.02
beta
196019701980199020002010
year
Protestantism
.08
.1
.12
.14
.16
.18
R-Square
196019701980199020002010
year
Protestantism
.008
.0085
.009
.0095
.01
beta
196019701980199020002010
year
Catholicism
.06
.08
.1
.12
.14
R-Square
196019701980199020002010
year
Catholicism
.1
.12
.14
.16
.18
.2
beta
196019701980199020002010
year
Domesticable animals
.2
.25
.3
.35
.4
.45
R-Square
196019701980199020002010
year
Domesticable animals
-3
-2.5
-2
-1.5
beta
196019701980199020002010
year
Ethnic fractionalization
.2
.25
.3
.35
.4
R-Square
196019701980199020002010
year
Ethnic fractionalization
.017
.018
.019
.02
.021
beta
196019701980199020002010
year
European settlers 1900
.5
.52
.54
.56
.58
.6
R-Square
196019701980199020002010
year
European settlers 1900
-.65
-.6
-.55
-.5
-.45
-.4
beta
196019701980199020002010
year
Settler mortality
.35
.4
.45
.5
.55
R-Square
196019701980199020002010
year
Settler mortality
-.26
-.24
-.22
-.2
-.18
beta
196019701980199020002010
year
Distance to technological frontier 1AD
.06
.07
.08
.09
R-Square
196019701980199020002010
year
Distance to technological frontier 1AD
.2
.3
.4
.5
beta
196019701980199020002010
year
Absolute latitude
0
.05
.1
.15
.2
R-Square
196019701980199020002010
year
Absolute latitude
-15
-10
-5
0
5
10
beta
196019701980199020002010
year
Genetic diversity
0
.02
.04
.06
R-Square
196019701980199020002010
year
Genetic diversity
-1.6
-1.4
-1.2
-1
-.8
beta
196019701980199020002010
year
Malaria
.1
.2
.3
.4
.5
R-Square
196019701980199020002010
year
Malaria
.8
1
1.2
1.4
beta
196019701980199020002010
year
Population in temperate zones
.1
.15
.2
.25
.3
R-Square
196019701980199020002010
year
Population in temperate zones
1
1.1
1.2
1.3
1.4
beta
196019701980199020002010
year
Distance to waterways
.1
.15
.2
.25
R-Square
196019701980199020002010
year
Distance to waterways
.25
.3
.35
.4
.45
.5
beta
196019701980199020002010
year
Climate
.05
.1
.15
.2
.25
.3
R-Square
196019701980199020002010
year
Climate
-.06
-.05
-.04
-.03
beta
196019701980199020002010
year
Temperature
.1
.15
.2
.25
.3
R-Square
196019701980199020002010
year
Temperature
.035
.04
.045
beta
196019701980199020002010
year
Absolute latitude
0
.1
.2
.3
.4
beta
196019701980199020002010
year
Suitability for argiculture
0
.05
.1
.15
R-Square
196019701980199020002010
year
Suitability for agriculture
.2
.4
.6
.8
1
1.2
beta
196019701980199020002010
year
Soil fertility
0
.05
.1
.15
.2
R-Square
196019701980199020002010
year
Soil fertility
-.5
0
.5
1
1.5
2
beta
196019701980199020002010
year
Ruggedness
0
.02
.04
.06
.08
R-Square
196019701980199020002010
year
Ruggedness
0
.2
.4
.6
.8
1
beta
196019701980199020002010
year
Time since neolithic revolution
0
.05
.1
R-Square
196019701980199020002010
year
Time since neolithic revolution
-.6
-.4
-.2
0
.2
beta
196019701980199020002010
year
Civil law
0
.02
.04
.06
.08
R-Square
196019701980199020002010
year
Civil law
.34
.36
.38
.4
.42
rsqrlat_abst
196019701980199020002010
year
Absolute Latitude
.012
.014
.016
.018
.02
.022
beta
196019701980199020002010
year
Protestantism
.05
.1
.15
.2
R-Square
196019701980199020002010
year
Protestantism
0
.002
.004
.006
.008
.01
beta
196019701980199020002010
year
Catholicism
0
.05
.1
.15
R-Square
196019701980199020002010
year
Catholicism
-.016
-.014
-.012
-.01
-.008
-.006
beta
196019701980199020002010
year
Islam
.05
.1
.15
.2
.25
.3
R-Square
196019701980199020002010
year
Islam
-2
-1.8
-1.6
-1.4
-1.2
-1
beta
196019701980199020002010
year
Ethnic fractionalization
.05
.1
.15
.2
.25
R-Square
196019701980199020002010
year
Ethnic fractionalization
.008
.01
.012
.014
.016
beta
196019701980199020002010
year
European descent 1900
.1
.2
.3
.4
.5
R-Square
196019701980199020002010
year
European descent 1900
200
400
600
800
1000
beta
196019701980199020002010
year
Genetic diversity
-.5
-.4
-.3
-.2
beta
196019701980199020002010
year
Settler mortality
.1
.2
.3
.4
.5
R-Square
196019701980199020002010
year
Settler mortality
.06
.08
.1
.12
.14
beta
196019701980199020002010
year
Domestiable animals
.1
.15
.2
.25
.3
R-Square
196019701980199020002010
year
Domesticable animals
-.15
-.1
-.05
0
.05
beta
196019701980199020002010
year
Technology in 1AD
0
.005
.01
.015
.02
.025
R-Square
196019701980199020002010
year
Technology in 1AD
.02
.04
.06
.08
.1
R-Square
196019701980199020002010
year
Genetic Diversity
