Discussion Paper No. 967

THE DEEP HISTORICAL ROOTS
OF MACROECONOMIC VOLATILITY

Sam Hak Kan TangCharles Ka Yui Leung

April 2016

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

The Deep Historical Roots of Macroeconomic Volatility
Sam Hak Kan Tanga and Charles Ka Yui Leungb

This version: April 2016

Abstract
We present cross-country evidence that a country’s macroeconomic volatility, measured
either by the standard deviation of output growth or the occurrence of trend-growth breaks, issignificantly affected by the country’s historical variables. In particular, countries with longer
histories of state-level political institutions experience less macroeconomic volatility in postwar periods. Robustness checks reveal that the effect of this historical variable on volatility
remains significant and substantial after controlling for a host of structural variables
investigated in previous studies. We also find that the state history variable is more importantin countries with a higher level of macroeconomic volatility.

JEL classification: O10, O33, O43

Keywords: Macroeconomic volatility, history of State-level institution, History of agriculture,
Technology adoption, Political stability.
a.

Business School, the University of Western Australia, 35 Stirling Highway, Crawley WA 6009, Australia.
Tel: +618-6488-2931. Fax: +618-6488-1016. E-mail: sam.tang@uwa.edu.au.
b.
Department of Economics and Finance, the City University of Hong Kong, Kowloon Tong, Hong Kong. Email: kycleung@cityu.edu.hk.
Acknowledgments. We would like to thank Harry Bloch, who provided invaluable comments on an
earlier draft of this paper. Constructive comments from Sumru Altug, Josef Brada, Cem Cakmakli,
Stanley Engerman, Inci Gumus, Keiya Minamimura, Tomomi Miyazaki, Tim Riddiough, Masahiko
Shibamoto, and seminar participants at the Kobe University, the University of Western Australia, Koc
Macroeconomic Workshop and World Congress of Comparative Economics in Rome, and especiallyanonymous referees are also acknowledged. We thank the University of Western Australia and theCity University of Hong Kong for their financial support. The work described in this paper waspartially supported by a grant from the Research Grants Council of the Hong Kong Special
Administrative Region, China [Project No. CityU 146112]. All remaining errors are our own.

1.

Introduction
Macroeconomic volatility varies significantly across nations. And much effort has been

devoted to study the reasons behind and whether there are rooms for policies to improve thesituation. In this paper, we ask the following questions: do historical variables affectmacroeconomic volatility today? If so, which variables are most important?
Clearly, these research questions can be interpreted as natural extensions of somegrowing literatures. For instance, in the recent growth literature, the search for thefundamental factors of economic prosperity has traced its roots to variables of pre-historic
times, dating back to more than 10,000 B.C.E. This line of research has produced empiricalevidence that current cross-country income differences are related to several key historicalvariables: the biogeographic conditions that determined the timing of the Neolithic
Revolution (Diamond, 1997; Hibbs and Olsson, 2004; Olsson and Hibbs, 2005), the historyof state-level political institutions (Bockstette, Chanda and Putterman, 2002; Putterman andWeil, 2010), the history of agriculture (Putterman and Weil, 2010), the history of technology
adoption (Comin, Easterly and Gong, 2010), genetic distance from the frontier (Spolaore andWacziarg, 2009), and genetic diversity of the population (Ashraf and Galor, 2013).1 Theseseminal studies have not only provided stimulating insights, but have also made available richdatasets of historical variables that have stimulated further research in this direction. Ourstudies take advantage of these datasets and study the question related to macroeconomic
volatility rather than long run growth.
This paper is also inspired by the literature which focuses on the fundamental factorsof macroeconomic volatility, especially in developing countries. Those studies show that
distortionary or discretionary policies that cause high macroeconomic volatility and economic
1

See Engerman and Sokoloff (2008), Spolaore and Wacziarg (2013), among others, for a summary of thisgrowing body of literature. Nunn (2009) provides a survey of the literature on the importance of history to
economic development.

1

crises are themselves the “symptoms” of weak institutional quality, unfavorable geography orless democratic political systems. Among others, Rodrik (1999) argues that divided societies
and weak conflict management at the institutional level amplify external shocks, causing
volatile growth and crises. Acemoglu, Johnson, Roberson and Thaicharoen (2003)(henceforth AJRT, 2003) show that countries that inherited more “extractive” institutions
from their former European colonizers are more likely to experience high volatility andeconomic crises during post-war periods. Fatá s and Mihov (2003) find that institutional
environments that impose few constraints on governance via checks and balances experiencegreater discretion in fiscal policy, which causes macroeconomic instability. Malik andTemple (2009) provide evidence that remote countries with poor market access are more
likely to have undiversified exports and to experience greater volatility in output growth.
There is also substantial evidence that less democratic countries experience more
macroeconomic volatility. For instance, Mobarak (2005) finds that countries with Muslimmajority populations are less democratic, which increases volatility. Our work is closely
related to research on the fundamental factors shaping macroeconomic volatility. However,
we look much more deeply into history to determine whether and how human evolution in
the last 10,000 years continues to influence macroeconomic volatility today.
This paper makes the following contribution to the literature. We show that countries
with longer histories of state-level political institutions experience less macroeconomic
volatility in post-war periods.2 The effect of this historical variable is manifested not only inhigh-frequency volatility, measured by the standard deviation of annual output growth, but in
medium-term volatility, measured by the occurrence of trend-growth breaks. In addition,
2

A country’s history of state-level political institutions is measured by the extent to which the country wascontrolled by a government above tribal level from 1 to 1950 C.E. This historical variable is adjusted for the
proportion of the territory of the modern country ruled by the government during this period, and also forwhether its government was foreign or locally based. See Section 2 for a detailed description of this historicalvariable. See also Acemoglu et al. (2013) for a related analysis of “weak states.”

2

robustness checks reveal that the effect of this historical variable on volatility remains
significant and substantial after controlling for a host of structural variables investigated inprevious studies. Moreover, adding current income to the list of regressors does not eliminate
the significant effect of this historical variable on volatility, suggesting that state history
affects current macroeconomic volatility through independent channels, rather than throughthe growth channel alone.

Why does the history of state-level political institutions have such a strong influenceon macroeconomic volatility and its proximate factors today? Diamond (1997) argues thatpeople living in countries with long histories of nationhood are more likely to have a sense ofcommon identity and a common language. A common language also reinforces national
identity. Clots-Figueras and Masella (2013) show that teaching Catalan in Catalonian schools
instils greater loyalty to the Catalan identity, and affects political preferences and attitudes
toward the organization of the state. Sharing an identity and a language fosters trust andsocial interaction, which are important components of social capital (Temple, 1998). Knackand Keefer (1997) find that social capital, as measured by trust and civic norms, is higher incountries that are less polarized in terms of ethnicity and class. Putterman and Weil (2010)
show that trust, control, respect, obedience and thriftiness are all positively affected by statehistory. In addition, a unified state with a common national identity and language is lesslikely to experience the devastating effects of civil war and other forms of political instability(Easterly and Levine, 1997). Bockstette, Chanda and Putterman (2002) argue that throughlearning by doing, public administration is more effective in long-standing states. Also, longstanding states may develop better bureaucratic discipline and hierarchical control. Hence,
countries with longer histories of nationhood are likely to be more stable because they may

3have higher social capital, fewer civil wars and other forms of political instability, more
effective administration, and greater bureaucratic discipline and hierarchical control.
Recent studies also point out that state antiquity plays an important role in thedevelopment of financial institutions of modern states that helps lower macroeconomic
volatility of these states. For instance, a longer history of state-level political institutions
contributes to state capacity in collecting tax revenue and enforcing contracts, and hence
affects the level of financial development. A study by Ang (2013a) finds that state antiquity
provides the basis for explaining the fundamental sources of variations in financial
development between countries today. Besley and Persson (2009, 2010) argue that legal andfiscal state capacity are built upon past investments in common interest public goods such as
fighting external wars, political stability, and inclusive political institutions, all of which may
be positively linked to state antiquity. Dincecco and Prado (2012) confirm that war casualties
sustained in pre-modern wars affect the long-term development of a country’s fiscal
institutions which in turn affects the country’s economic performance and labour productivity
today. La Porta et al. (1997) emphasize on legal traditions in affecting the development offinancial institutions. They argue that British common law provides better protection ofprivate property rights compared to that of French civil law, and hence enjoys higher levels offinancial development. An important message being shared by all these studies is that alonger history of state-level political institutions may enhance the state’s fiscal and legal
capacity, leading to higher levels of financial development and better economic performances
including greater economic stability.
The remainder of this paper is divided into four sections. Section 2 presents thedefinitions and constructions of the two measures of macroeconomic volatility and thehistorical variables used in the regressions. Section 3 presents empirical evidence for linking
historical variables to macroeconomic volatility. In particular, we show in Section 3 that the

4history of state-level political institutions has an important role in explaining macroeconomic
volatility today, especially in more volatile countries. Section 4 conducts robustness checks to
see how sensitive are our baseline results to using alternative periods of state history,
controlling for the volatility of state history, using instrumental variable estimation to control
for potential endogeneity bias, controlling for more confounding variables, and using statehistory that has not been adjusted for world population migration since 1500 C.E.. In the lastsection, we summarize our findings and conclude the paper.

2.

Data Description

This section describes the main variables used in this study, with particular attention given to
two measures of macroeconomic volatility and four historical variables. A detailed
description of the other variables used in the regressions is provided in Appendix A.

2.1

Macroeconomic Volatility—Output Volatility and Occurrence of Trend-Growth
BreaksThe vast majority of researchers examining macroeconomic volatility use the standard
deviation of output growth as a measure of macroeconomic volatility (Klomp and Haan,
2009). Output volatility is measured by calculating the standard deviation of the annual
growth rates of real gross domestic product (GDP) per capita between 1960 and 2011. The
annual growth rates of real GDP are log differences of annual GDP per capita in constantlocal currency. The standard deviation of annual output growth captures the total variation in
output growth due to business-cycle fluctuations, crises, and changes in trends; not just thevariation arising from uncertainty or unpredictability. It is worth noting that output variation
incurs some costs even when the variation is fully anticipated, especially when individuals
have limited opportunities for consumption smoothing.

5

Although the standard deviation of output growth captures (relatively) high-frequency
shocks, some researchers argue that this measure is not ideal when a country’s economic
growth is characterized by frequent episodes of accelerated growth and growth breaks, whichis the case for many developing countries. For instance, Pritchett (2000) describes thedifferent patterns of growth of developing countries as hills among plateaus, mountains, andplains. Jones and Olken (2008) find that most countries experience growth miracles andfailures over substantial periods, and that productivity plays a significant role in accounting
for patterns of start-stop growth. Hence, we also consider the relationship between historicalvariables and medium-run trend-growth volatility measured by the occurrence of trendgrowth breaks, which is a dummy variable that takes the value of 1 if a country experiences atleast one structural break (trend-growth break) during the sample period, and 0 otherwise.
The data on structural breaks are taken from Jones and Olken (2008), who use a method
developed by Bai and Perron (1998, 2003) to identify 73 structural breaks in 48 of 125
countries with at least 20 years of Penn World Table data.

2.2

History of State-Level Political Institutions

The history of state-level political institutions is the “State Antiquity Index Version 3.1”
obtained from Putterman (2004), which measures the existence of any form of central statefrom 1 to 1950 C.E. in the history of a modern country. The index adjusts for the scope of astate’s geographical control between 1 and 1950 C.E., and whether the state was indigenous
or controlled by an outside power during that period. The index is constructed using thefollowing formula:


where

∑
∑

1.05
,
50 ∗ 1.05

is the state-history variable for country i in the 50-year period t. For each of the 39

50-year periods, a country receives 1 point if it has a government above the tribal level (and 0

6points otherwise), 1 point if its government is locally based (0.5 points if the country iscontrolled by an outside power, and 0.75 points if local and foreign powers share its
governance), and 1 point if the government controls more than 50 percent of the territory of
the modern country (0.75 points if 25-50 percent of the country’s territory is under
government control, 0.5 points if 10-25 percent is under government control, and 0.3 points if
less than 10 percent is under government control). The three scores are then multiplied byeach other, and the result is multiplied by 50 to give

. Take, for example, a country with aforeign government above the tribal level that ruled 30 percent of the territory of the modern
country. This country has anvalue of 18.75 (1 x 0.5 x 0.75 x 50) for the 50-year period t.

As older data are less reliable,

is thus discounted by additional 5 percent for each

50-year period before 1901-1950 C.E. For example, anvalue of 18.75 for the 50-year

period from 1801 to 1850 is discounted by (1.05)-2 to give 17.01. Finally, dividing thediscounted sum of the state-history variables,, by the maximum achievable value gives thestate-history index, which varies between 0 and 1.
The state-history index measures a country’s history of state-level political institutions
between 1 and 1950 C.E. However, some countries have experienced massive population
migration since 1500 C.E. as a result of European colonization. Consequently, to measure thestate history of the ancestors of the current population of a country, as opposed to the statehistory of the country, we use the World Migration Matrix (WMM) to adjust the state-history
index for population migration since 1500 C.E. More specifically, we multiply the statehistory index by the WMM, which is constructed by Putterman and Weil (2010). The WMM
gives the share of the population in every country in 2000 C.E. descended from people indifferent source countries in 1500 C.E. The adjustment is made because the history of acountry’s current population has a greater effect than the history of the country on the
country’s economic outcomes. Most countries have experienced little population migration,

7and thus show little difference between the two variables. In some cases, however, the historyof a country’s current population and the history of that country are substantially different,
due to extensive population migration. It is also worth noting that the main empirical resultsof this paper are unchanged even without adjusting the historical variables for population
migration, as shown in the robustness check in section 4.

2.3

History of Agriculture

Drawing on a variety of sources, Putterman and Trainor (2006) assemble data on thetransition from hunting and gathering to agriculture. The variable gives the number of years(in thousands) prior to 2000 since a significant number of people in an area within the
country’s present borders met most of their food needs by cultivating crops. Similar to thehistory of state-level political institutions, this variable is adjusted for population migration
using the WMM.

2.4

History of Technology Adoption

We use technology-adoption indices constructed by Comin, Easterly and Gong (2010) tomeasure the level of technology adoption in the agricultural, transportation, communications,industry, and military sectors of a maximum of 135 countries in 1000 B.C.E, 1 C.E. and 1500
C.E.9 The agricultural technology adoption index measures the extent to which agriculture
had been adopted by a given country in the given year. The transportation technology
adoption index indicates whether human, animals or vehicles were used for transportation.
The communications technology adoption index reflects the presence of non-written records
and true writing. The existence of pottery is used to measure the country’s adoption of
9

There are studies which employ post-war technological progress to explain the post-war macroeconomic
volatility, such as Leung et al. (2006), Tang et al. (2008). However, there may be a reverse causation (RC) issue.
Thus, using variables on the history of technology adoption to explain the post-war macroeconomic volatility
may mitigate the RC problem.

8

industrial technology, and the existence of metalwork and bronze instruments is regarded as
an indicator of the adoption of military technology. Each of the overall technology adoption
indices denotes the country’s average adoption level across these sectors, and is expressed ininterval form, as [0,1]. Comin, Easterly and Gong (2010) use Peregrine’s (2003) study as themain source of data for 1000 B.C.E. and 1 C.E. As Comin, Easterly and Gong (2010) use
more than 170 source materials to construct the data for 1500 C.E., the technology-adoption
index for 1500 C.E. has a more detailed classification for each type of technology. For
example, transportation technology is classified by the presence of ships of different
capabilities, the wheel, the magnetic compass and horse-powered vehicles. Similar to theother historical variables, the technology-adoption indices are adjusted for population
migration using the WMM.

2.5

Genetic Distance

Genetic distance or

distance is also known as the “co-ancestor coefficient”, which we

obtained from Spolaore and Wacziarg (2009). It measures genetic differences based on
indices of heterozygosity, which measure the probability that two alleles at a given locus
selected at random from two populations will be different. If the allele distributions are
identical across the two populations,takes a value of 0; if the allele distributions differ,is positive. A higher value ofrepresents a larger genetic difference. It is assumed thatthe diffusion of technology may be more efficient in genetically similar countries. We also
assume that England was the most technologically advanced country in 1500 C.E., and thus
our genetic distance variables refers to genetic difference between England and each of the9other countries in the dataset.10 Similar to the other historical variables, this variable isadjusted for population migration using the WMM.

3.
3.1

Linking Historical Variables to Macroeconomic Volatility
Descriptive Evidence

Figure 1 provides a scatterplot of the history of state-level political institutions and output
volatility. The diagram depicts a negative relationship between the two variables, which does
not appear to be caused by the few outliers in the plot. The line of best fit indicates thatcountries with longer histories of state-level political institutions tend to experience less
output volatility. The heteroskadastically robust t-statistic for state history is -3.50 in abivariate regression of output volatility, which is statistically significant at the 0.1-percent
level. Six former command economies (Armenia, Azerbaijan, Bosnia and Herzegovina,
Georgia, Serbia, and Tajikistan) show relatively high output volatility (above 0.1) and havelong histories of state-level political institutions (above 0.5). Most developed, high-income
countries lie in the lower right-hand corner of the diagram, reflecting their low output
volatility (below 0.05) and their relatively long histories of state-level political institutions(above 0.6).
[Insert Figure 1 about here]
Figure 2 shows the relationship between the history of state-level political institutions
and the frequency of growth breaks for three groups of countries. The first group of countries
is defined by a state-history index greater than 0.75. The countries in the second group havestate-history indices in the intermediate range, between 0.25 and 0.75. The countries in the10

Scholars are increasingly linking genetic differences with cultural conditions and political behavior, as well aswith the diffusion of technology. In the very long run, cultural formation may also be shaped by genetic
differences. Therefore, the potential effects of genetic differences on political behavior should not be neglected.
See Fowler and Schreiber (2008), among others, for a review of that literature.

10third group have state-history indices lower than 0.25. The average state-history indices forthe first, second and third groups of countries are 0.85, 0.53, and 0.15, respectively. As
shown in Figure 2, the average numbers of trend-growth breaks for the first, second and thirdgroups are 0.5, 0.63, and 0.91, respectively. The countries with the longest state histories
have roughly half the average number of trend-growth breaks exhibited by the countries withthe shortest state histories (0.5 versus 0.91). Figure 2 thus illustrates the relationship thatcountries with longer histories of state-level political institutions experience less medium-run
trend-growth volatility.
[Insert Figure 2 about here]

3.2

Econometric Evidence

Figures 1 and 2 illustrate a negative relationship between the history of state-level politicalinstitutions and macroeconomic volatility. However, the observed relationship may result
from other, deeper determinants of macroeconomic volatility, such as geography.
Macroeconomic volatility may also be affected by other historical variables related to thehistory of state-level political institutions. In this section, we address these possibilities bydetermining whether the relationship between state history and macroeconomic volatility issensitive to the introduction of other control and historical variables to the regressions.
Formally, we consider the following econometric model:
(1)

∙

∙

where the dependent variable

denotes one of the two measures of macroeconomic

volatility for country i;

is a historical variable; and Wi is a vector of the control

variables. The residual,, is assumed to be iid. The parameter of special interest is, whichprovides an estimate of the relationship between a given historical variable andmacroeconomic volatility. Clearly, as macroeconomic volatility in a post-war period cannot

11affect historical variables prior to that period, reverse causality is unlikely to affect the resultsof Equation (1). However, if certain structural factors are not included in the regression, they
may be argued to be responsible for the observed results. Therefore, the potential
misspecification issue must be fully addressed in Equation (1). Following the recent literaturein the area, several structural factors are added to Equation (1) to control for their effects on
macroeconomic volatility.
[Insert Table 1 about here]
Table 1 reports the results of the ordinary least squares (OLS) regression of Equation(1) when macroeconomic volatility is measured by the standard deviation of output growth.
Columns 1-6 of Panel A report the results of setting

as each of the six historicalvariables on the right-hand side of Equation (1). These variables are the history of state-level
political institutions (column 1), the history of agriculture (column 2), the technologyadoption indices for 1000 B.C.E. (column 3), 1 C.E. (column 4) and 1500 C.E. (column 5),
and genetic distance (column 6). Also included in the regressions are absolute latitude,
arable-land area (in log form), and dummy variables for tropical regions, landlocked areas,islands, and continents of Africa, Asia and Europe (omitting America and Oceania). We use
these geographic variables because climate, humidity, soil conditions, disease environment
and market access may directly and/or indirectly affect human motivation, enterprise,
agricultural productivity, institutions and trade, which have been shown to be important
determinants of growth and volatility.12

12

The literature on the effects of geography on economic performance is prolific. It can be divided into two

main groups. Some researchers argue that factors such as tropical or landlocked geography directly inhibit
growth and development through poor soil quality, a high prevalence of crop pests and parasites, a lack of coaldeposits, an infectious-germ ecology, high transport costs, and the inhibition of motivation to work (see, forexample, Bloom et al., 1998). Other authors find strong evidence that these geographical factors (tropics, germs
and crops) only indirectly affect country income, through institutional factors (see, for example, Engerman and12Panel A of Table 1 shows that the history of state-level political institutions is the
only historical variable with a statistically significant effect on the standard deviation of
output growth in post-war periods. The estimate of -0.049 for the state-history variable shownin column 1 has a t-statistic (in absolute values) of 2.93, which is statistically significant at
the 1-percent level. This finding indicates that output volatility is reduced by roughly 0.5
percent when the state-history index is increased by 0.1 points. The estimate is also
economically large, because the presence of state history explains almost all of the crosscountry average output volatility (the average standard deviation for output growth is 5.38
percent across all of the countries in the sample). The value of R2 in column 1 is 17 percent,which is substantially larger than the values of other R2 in Panel A, suggesting that the historyof state-level political institutions has much larger explanatory power of output volatility thanother historical variables. Also, Panel A, Table 1, shows that being an island country is
estimated to reduce output volatility significantly in Column 1, 4, 5 and 6. Moreover, Africanand Asian countries consistently show higher output volatility compared to the rest of theworld.
Panel B of Table 1 displays the results of adding another historical variable to theregression when the regression already includes the history of state-level political institutions
and the baseline control variables. Column 1 of Panel B, Table 1 shows that the estimate forstate history increases from -0.049 to -0.057 and remains statistically significant at the 1percent level when both state history and agricultural history are included in the regression.
The effect of agricultural history is estimated at 3.16e-06, with a t-statistic of 1.86, indicatingthat output volatility increases by around 0.32 percent for every 1,000 years of agricultural
history. However, it is worth noting that the correlation between state history and agricultural
history is moderately high (with a correlation coefficient of 0.68), which makes it fairly
Sokoloff, 1997; Acemoglu, Johnson and Roberson, 2002; Easterly and Levine, 2003). Island countries have also
been found to have better institutions than countries with land borders (Fors, 2014).

13difficult to separate the effects of these two historical variables. Similarly, the results incolumns 2, 3, 4 and 5 of Panel B, Table 1 show that the estimate for the history of state-level
political institutions remains roughly the same and is statistically significant regardless ofwhether other historical variables are added to the regression. However, the other historicalvariables are of little statistical significance, suggesting that state history is the mostimportant historical variable explaining output volatility in post-war periods.
If all of the historical variables are considered to be closely related, and collectively toreflect a country’s historical development, combining the historical variables to produce asingle variable is justified (Ang, 2013b).13 Column 6 of Panel B, Table 1 shows the results ofusing the first principal component (FPC) of all of the historical variables to explain output
volatility in post-war periods.14 The result shows that the estimate for the first principal
component of all the historical variables is negative and statistically significant at the 5percent level. This finding supports the claim that countries with long histories ofdevelopment experience lower output volatility today.
[Insert Table 2 about here]
The OLS results provided in Table 1 show that on average a strong negative
relationship is observed between state history and output volatility. These results, however,
do not tell us whether the effect of state history on output volatility is more important for
certain countries than the others. Thus, we ask not only the question, ‘does state history
matters for output volatility?’, but also ask the other question, ‘for which group of countries
state history matters most?’ To address this question, we run a quantile regression (QR)
(Koenker, 2005). The results are reported in Table 2 and indicate that the strength of the13

The correlation coefficients of the six historical variables are presented in Table B.1, Appendix B. The results

show that the six historical variables are all significantly correlated at the 1-percent level.
14

Table B.2 in Appendix B reports the explanatory power of each principal component of the historicalvariables. The first principal component accounts for 67 percent of the variation in the historical variables.

14

relationship between state history and output volatility increases from the bottom to the top of
the conditional distribution. At the 0.05 quantile, the relationship is estimated as -0.025,
which is statistically significant but less than the OLS estimate of -0.049. From the 0.25
quantile to the 0.50 quantile and thence to the 0.75 quantile, the estimated relationshipincreases from -0.027 to -0.029 to -0.057, all of which are statistically significant at theconventional levels. The estimated relationship at the 0.75 quantile is almost twice that
estimated at the 0.25 quantile. These results reveal that state history matters most for
countries with relatively high volatility. The results also imply that it may be difficult for
low-income developing countries to reduce their output volatility if much of their volatility is
the result of being an inexperienced state.
[Insert Table 3 about here]
The results reported in Table 1 and 2 are generated from OLS and QR in which thestandard deviation of output growth is used as the dependent variable. We now seek to
confirm that the principal finding is not caused either by our use of the standard deviation of
output growth to measure macroeconomic volatility, or by the OLS methodology. Table 3
reports the results of our probit regression of Equation (1) when macroeconomic volatility ismeasured in terms of the occurrence of trend-growth breaks. Column 1 of Panel A, Table 3
shows that the estimated marginal effect of state history on the occurrence of trend-growth
breaks is -0.559, with a t-statistic of 1.82, which is statistically significant at the 10-percent
level. This finding suggests that countries with short or no histories of state-level politicalinstitutions are roughly 56 percent more likely to experience at least one major trend-growth
break than countries with long histories of state-level political institutions. Columns 2 and 4
of Panel A, Table 3 show, respectively, that agricultural history and the technology-adoption
index in 1 C.E. have a statistically significant negative effect on the likelihood ofexperiencing a trend-growth break. In particular, the technology-adoption index in 1 C.E. has

15

the most statistically significant marginal effect: it is estimated at -0.497, with a t-statistic of
4.04. This estimate indicates that a country with full technological adoption in 1 C.E.
(technology-adoption index of 1) is roughly 50 percent less likely to experience a trendgrowth break today than countries in which little or no technology had been adopted in 1 C.E.
Panel B of Table 3 reports the results of adding another historical variable to a probit
regression that already includes the technology-adoption index for 1 C.E. and the baseline
control variables. The estimated marginal effect of the technology-adoption index in 1 C.E.
remains highly statistically significant across columns 1 to 5 of Panel B, Table 3, whereas theestimated marginal effects of the other historical variables are not statistically significant.
These results suggest that the technology-adoption index in 1 C.E. is the most important
historical variable affecting the occurrence of trend-growth breaks in a post-war period.
Thus far, we have described the introduction of separate historical variables to theregression of the occurrence of trend-growth breaks. In practice, however, the historicalvariables are correlated. In the appendix, we show that the first principal component of thesix historical variables explains about two thirds of the variation in all of the historicalvariables. In column 6 of Panel B, Table 3, we thus present the results of the regression when
all of the six historical variables are replaced by the FPC. The estimated marginal effect of
the FPC is -0.145, with a t-statistic of 2.89, which is statistically significant at the 1-percent
level. This estimate suggests that countries with longer histories of development are lesslikely to experience trend-growth breaks.
In sum, the results presented in this section indicate that historical variables in thecollective have a highly significant influence on macroeconomic volatility, measured aseither the standard deviation of output growth or the occurrence of trend-growth breaks.
Thus, countries with long histories of development are shown to experience more stable
growth. When the six historical variables are considered separately, however, only the history

16of state-level political institutions is found to significantly affect both the standard deviationof output growth and the occurrence of trend-growth breaks. The technology-adoption index
in 1 C.E. is found to have a highly significant effect on the occurrence of growth breaks(which captures medium-term shocks), but not on the standard deviation of output growth(which captures short-term shocks).

4.

Further Robustness Checks

In this section, we further investigate the robustness of our baseline results by using
alternative periods of state history, controlling for the volatility of state history, using
instrumental variable estimation to control for potential endogeneity bias, excluding Africancountries from the regression, adding 15 climate and soil variables as controls, andcontrolling for European population, ethnic fractionalization, democratic institutions andcurrent income. The purpose of these exercises is to check whether our principal finding isaltered by varying the sample or the control variables. Reassuringly, our principal finding thatstate history is the most historical variable in explaining current macroeconomic volatility islargely unaffected by the sensitivity checks.
[Insert Table 4 about here]
Table 4 reports the regression results when the periods of state history are between 1
and 1950 C.E. (column 1), 1501 and 1950 C.E. (column 2), 1001 and 1500 C.E. (column 3),
501 and 1000 C.E. (column 4), and 1 and 500 C.E. (column 5). In Table 4, the volatility ofstate history is also added in the baseline regression. The volatility of state history is thestandard deviation of state-history variable,, for every 50-year periods from 1 to 1950 C.E.

This variable attempts to distinguish countries which are consistently ruled by state-levelinstitutions from other countries which might swing between state-level institutions andfragmented governments. Column 1 shows that the estimated coefficient of -0.046 for state17

history is slightly smaller in magnitude than the baseline result of -0.049, but is still
statistically significant at the 1-percent level. Hence, controlling for the volatility of statehistory does not appear to alter our principal finding.
In Column 2, the result shows that if we use only the last 450 years (1501-1950 C.E.)
of state history to explain macroeconomic volatility, the estimated coefficient increases to0.056 , which is also statistically significant at the 1-percent level. In Column 3 to 5, we
examine the effect of the state history in different periods on the macroeconomic volatility,
controlling for other factors. More specifically, the estimated coefficients of state history forthe periods 1001-1500 C.E., 501-1000 C.E., and 1-500 C.E. are -0.015, -0.019, and -0.023
respectively. Although these estimates are smaller than that of the most recent historicalperiod of 1501-1950 C.E., they are still statistically significant at the conventional levels.
Thus, Column 1 to 5 of Table 4 demonstrate that state history of the most recent historicalperiod of 1501-1950 C.E. exerts the largest effect on macroeconomic volatility today.
A question that needs to be addressed is potential endogeneity bias. Reverse causality
is not the main issue for the history of state-level political institutions, but omitted-variablebias may be an issue because we are not able to control for country-specific factors using
fixed effects. The direction of the bias, if any, depends on the correlations between theomitted variable and macroeconomic volatility and also between the omitted variable andstate history. If, for example, the omitted variable is negatively correlated with both
macroeconomic volatility and state history, then the OLS estimate would likely underestimate
the reductive effect of state history on macroeconomic volatility. We attempt to overcome
this potential bias by using instrumental variable estimation in Column 6, Table 4.
In Column 6, the endogenous variables are state history and the volatility of statehistory since 1 C.E. The instruments are population size at 1 C.E., urbanization index at 1000
B.C.E., agriculture history and adoption of military technology at 1000 B.C.E. To be an18

appropriate instrument, it has to meet the conditions of relevance and exogeneity. Surplus
theories hypothesize that the availability of agricultural surplus supported increasing
population and urbanization, which formed the basis for the emergence of social stratification
and a hierarchical political structure for distributing the surplus.15 Hence, population at 1 C.E.
and urbanization at 1000 B.C.E. are likely to be positively correlated with state history. Also,
following Ang (2015), we add agriculture history and adoption of military technology in the
instrument set.
Results of the first-stage regressions are reported in Column 5 and 6, Panel B, Table
6. In Column 5, state history is significantly explained by population size at 1 C.E. at the 1percent significance level, while other instruments are not significant at the conventional
levels. In Column 6, the volatility of state history is only marginally explained by the historyof agriculture. The (adjusted) R-squares indicate that the instrument set is capable ofexplaining 60% of the variation in state history and 27% of the variation in the volatility ofstate history. The Shea partial R-squares of 25% and 14% respectively for state history andthe volatility of state history are relatively large to suggest that the instrument set is relevantin explaining the endogenous variables. The F-test for the joint significance of the
instruments rejects the null hypothesis of no effect from the instrument set for the first-stage
regressions (9.10 for state history and 5.3 for the volatility of state history). These diagnostic
statistics of the first-stage regressions thus appear to suggest that the instruments are relevant.
Results of the second-stage regression are reported in Column 6, Panel A, Table 4.
The reductive effect of state history on macroeconomic volatility is estimated at -0.084,
which is statistically significant at the 5-percent significance level. Compared to the baseline
result of -0.049, the IV result is substantially higher, indicating that the OLS estimate
underestimates the reductive effect of state history due to omitted-variable bias. The Sargan-

15

See, for example, Aspromourgos (1996) and Meek (1976) for surveys of surplus theories.

19

Hansen test is a test of over-identifying restrictions, i.e., whether the instruments explain
macroeconomic volatility beyond their effects on state history and the volatility of statehistory. A rejection of the null suggests the instruments are invalid. The P-values of theSargan-Hansen test reported in Table 4 is 0.775, which does not reject the null hypothesis andhence suggests the validity of our instrument set. It is reassuring to find that the OLS estimate
underestimates the effect of state history, suggesting that even without correcting forpotential endogeneity bias the reductive effect of state history on macroeconomic volatility is
still highly significant and substantial in magnitude.
[Insert Table 5 about here]
Table 5 reports the regression results of further robustness checks. We begin with the“African factor.” As can be seen from Figure 1, many African countries experience both highoutput volatility and relatively short history of state-level political institutions. We are thenconcerned that the estimated effect of state history is mainly caused by the difference
between African countries and the rest of the world. Having included the dummy variable for
Africa in the baseline regressions mitigates but not eliminates the concern. We thus exclude
the African countries from the sample and report the regression results in Column 1, Table 5:
the estimated effect of state history on output volatility increases from -0.049 to -0.067 andremains statistically significant at the one percent level in Panel A. In Panel B, the estimatedmarginal effect of state history on the occurrence of trend-growth breaks also increases from 0.559 to -0.665 and remains statistically significant at the 10 percent level. Moreover, thegoodness-of-fit improves in both regressions (R2 = 0.25). We can now be confident that theestimated effect of state history on macroeconomic volatility is not due to the inclusion of
African countries in the sample.
The next concern is the extent of controls for geography. In the baseline regressions,
we include absolute latitude and a tropical dummy to capture the potential effects of climates20

and soils on macroeconomic volatility and state history. Now, we ask whether a moredetailed classification of climates and soils can better capture their effects. Column 2, Table
5, reports the regression results when we include four temperature, four humidity and sevensoil zones from Parker (1997) in the baseline regressions: the results in Panel A show that themagnitude of the state history estimate is reduced to -0.037 from the baseline result of -0.049,
but remains statistically significant at the 5 percent level. The F-test for the joint effect of the
15 additional climate and soil variables gives a P-value of 0.81, indicating that they are notjointly significant in explaining output volatility. In Panel B, the t-statistic of the estimatedmarginal effect of state history increases from 1.82 in the baseline result to 2.12 after the
additional climate and soil variables are included in the regression. Again, the joint estimatedeffect of the additional climate and soil variables is not statistically significant (P-value =0.24). In short, the estimated effects of state history on both output volatility and theoccurrence of trend-growth breaks remain robust after including the 15 additional controls forclimates and soils.
We then address the concern related to the “European factor.” Easterly and Levine(2012) show that the share of the European population in colonial times has a large andsignificant impact on income per capita today. They argue that European colonizers broughtwith them social values, ideologies and norms in addition to general education, scientific andtechnological knowledge, knowledge of access to international markets, and the humancapital needed in building quality institutions. Clearly, this is also related to the literature on
the role of human capital in long-run development, including Galor and Weil (2000), Glaeser
et al. (2004), and Galor, Moav and Vollrath (2009), Hanushek and Woessmann (2012a, b,2016), among others. As such, the share of the European population can be an importantfactor not only for long-run growth but also for macroeconomic volatility. Using Putterman
and Weil’s (2010) World Migration Matrix, we construct a variable ‘European’ which is the

21share of the year 2000 population in every country that is descended from people in Belgium,
France, Germany, Italy, the Netherlands, Portugal, Spain and the United Kingdom in the year
1500 C.E. Following Spolaore and Wacziarg (2013), we restrict our attention to a sample ofcountries with fewer than 30 percent of Europeans.
Column 3 of Panel A, Table 5, shows that the estimated effect of ‘European’ on
output volatility is negative and statistically significant at the 10 percent level. Because thehistory of state-level political institutions has already been adjusted for population migration
since 1500 C.E., the European variable represents the effects of European ancestry on output
volatility over and above what has already been explained by the state history of European
ancestry. The estimate indicates that countries with a larger share of current population
descended from Europe experience less output volatility. More importantly, the estimate forstate history remains unchanged from the baseline result and statistically significant at the 5
percent level even given the presence of ‘European’ in the regression.
Column 3 of Panel B, Table 5, shows that the estimated effect of ‘European’ on theoccurrence of trend-growth breaks is negative, but statistically insignificant at conventional
levels. In contrast, the estimated marginal effect of state history is -0.587 and is statisticallysignificant at the 5 percent level. Hence, even though European is important for output
volatility, it does not undermine the strength of the linkage between state history andmacroeconomic volatility.
Rodrik (1999) shows that divided societies and weak institutions of conflict
management amplify external shocks, causing volatile growth and crises. He adopts income
inequality and ethnolinguistic fragmentation as indicators of divided societies. Inspired by his
research, we use the variable ‘ethnic fractionalization,’ which is defined as the probability
that two individuals selected at random from a country will be from different ethnic groups(Fearon, 2003). It is expected that greater ethnic fractionalization is associated with more

22

divided countries, which hinders the ability of the authorities to manage external shocks(Alesina and La Ferrara, 2004; Yuki, 2015).
Column 4 of Table 5 reports the results when ethnic fractionalization is added to thebaseline regressions. In Panel A, the estimated effect of ethnic fractionalization on output
volatility is positive and statistically significant at the 10 percent level (0.019, t-statistic =1.76). Greater ethnic fractionalization is thus associated with greater output volatility, whichis consistent with our expectation. On the contrary, the estimated marginal effect of ethnic
fractionalization on the occurrence of trend-growth breaks is negative and statisticallysignificant at the 5 percent level (-0.573, t-statistic = 2.29) in Panel B, indicating that greater
ethnic fractionalization is associated with a smaller chance of trend-growth breaks. Hence,
the results for ethnic fractionalization are mixed as it increases the high-frequency output
volatility, but reduces the occurrence of medium-run trend-growth breaks.16 On the other
hand, the estimates of state history, which remain to be negative and statistically significant,are robust to the addition of ethnic fractionalization in both panels.

We also consider the “democratic factor.” Column 5, Table 5, checks whether thestate history estimate would be affected when democracy is added to the baseline regressions.
Previous studies find that democratic countries are less volatile (Rodrik, 2000; Mobarak,
2005, Cuberes and Jerzmanowski, 2009). Using Polity2 from the POLITY IV PROJECT(Marshall, Gurr and Jaggers, 2013) to measure democracy, we confirm the finding ofprevious studies that democracy has a significant stabilizing effect on both high-frequency
output volatility (-0.002, t-statistic = 5.89) and medium-run trend-growth breaks (-0.023, t16

We regard this result of ethnic fractionalization in reducing medium-run trend-growth volatility interesting,and it may highlight the potential dual roles of ethnic diversity on macroeconomic volatility. On the one hand,
ethnic fractionalization increases social divides and conflicts, but, on the other, it may increase ideas andinnovations. Ashraf and Galor (2013) demonstrate that human genetic diversity has a hump-shaped effect on thelogged population density in 1500 C.E.

23

statistic = 1.64). More importantly, the magnitudes and statistical significances of the statehistory estimates in Column 5 of Panel A and B are roughly the same as their baseline results
after controlling for the effect of democracy. Hence, the evidence suggests that state history
and democracy both affect macroeconomic volatility significantly.
We also consider another robustness checks related to less developed countries, which
is well known to be more volatile than developed ones (Lucas, 1988). Koren and Tenreyro(2007) show that as countries develop, they move their productive structure from morevolatile to less volatile sectors. Therefore, if state history reduces macroeconomic volatility ofa country primarily through enhancing its growth and development, then controlling for thestages of development in the regression should render the estimate for state history
insignificant, but the estimate for development significant. If, however, state history operates
primarily through routes other than development to affect macroeconomic volatility, thebaseline estimate for state history should be unaffected by controlling for the stages ofdevelopment. Clearly, state history can affect macroeconomic volatility through both routes,
and in which case the magnitude of the state history estimate should be reduced but remain
statistically significant.
Column 6 of Table 5 reports the results when log income per capita in 2002 is added
to the baseline regressions to control for the stages of development. Results in Panel A showthat the estimate of state history is substantially smaller in both magnitude and statisticalsignificance (-0.027, t-statistic = 1.88) than the baseline result after controlling fordevelopment. However, it is still statistically significant at the 10 percent level in explaining
output volatility. Moreover, the estimate for log income per capita in 2002 is highly
significant (-0.011, t-statistic = 2.85). Taken together, these results appear to suggest thatstate history reduces output volatility of a country by both enhancing the country’s
development as well as by operating independently through other routes.

24

Results in Column 6 of Panel B, Table 5, indicate that controlling for development
leaves the state history estimate unchanged from the baseline result in the regression of theoccurrence of trend-growth breaks. The estimate for log income per capita in 2002, however,is non-significant. Hence, the evidence suggests that the stages of development appear to beimportant for high-frequency output volatility, but not so for the occurrence of medium-run
trend-growth breaks.
In sum, regressions in Table 5 reports results from a set of regressions which include
additional variables which are well-known to be related to macroeconomic volatility. It isreassuring to find that the baseline estimate of state history is largely unaffected by the
inclusion of these addition controls. Furthermore, we confirm findings of previous studies
that the share of European population, ethnic fractionalization, democratic institutions, anddevelopment all play a role in affecting macroeconomic volatility today.
[Insert Table 6 about here]
The historical variables used in the baseline regressions above have all been adjusted
using the World Migration Matrix. We now come to investigate whether the principal finding
would be altered by the use of historical variables which have not been adjusted for world
population migration since 1500 C.E. Table 6 reports the regression results when each of thesix historical variables that has not been adjusted for population migration is used in the
regression to explain macroeconomic volatility. Panel A of Table 6 shows that the unadjusted
state history is still the only historical variable out of all the other unadjusted historicalvariables that has a significant reductive effect on macroeconomic volatility. The estimate of-0.036 for the unadjusted state history is smaller than the baseline estimate of -0.049 for the
adjusted state history, but is statistically significant at the 1-percent level. In the Panel B of
Table 6, the significant reductive effect of unadjusted state history is shown to be unaffectedby adding another unadjusted historical variable in the regression. In sum, results in Table 6

25

reaffirm the principal finding that state history is the most important historical variableaccounting for current macroeconomic volatility even when it is not adjusted for world
population migration since 1500 C.E.

5.

Conclusions
This study is motivated by the recent finding that historical variables play a criticalrole in explaining current economic performance. While most existing studies focus on theeffect of historical variables on the differences in income level or income inequality, we takea step further to examine whether and how historical variables affect macroeconomic
volatility, measured either by high-frequency output volatility or by medium-run trendgrowth breaks. The results of the study contribute to the ongoing debate regarding thefundamental determinants of macroeconomic volatility.
We also conduct a large number of robustness checks and find that among the six
historical variables previously shown to be important determinants of income, only thehistory of state-level political institutions significantly affects both the high-frequency output
volatility and the occurrence of medium-run trend-growth breaks. The other historicalvariables found to affect medium-run trend-growth breaks, but not high-frequency output
volatility, are agricultural history and the adoption of technology in 1 C.E. Thus, thehistorical variables do not all provide equally important explanations of macroeconomic
volatility, despite their important roles in determining differences in income, as shown inprevious studies. We also find that the state history variable is more important in countries
with a higher level of macroeconomic volatility. More recent history also tends to mattermore. We leave it to the future research to investigate the mechanism behind all thesefindings.

26

Figure 1: Scatterplot of State History and Output Volatility

standard deviation of output growth

0.25

0.20

LBR
BIH
AZE ARMTJKGEO SRB

0.15

TKM RWALTUGABAGO UKRALBMDATCD KGZLVABLRRUSKAZSYRDZA
CHN
IRN
ETH
ZWE
EST
NICNGACUBMOZZARNERJOR
TGO
ARG
MMR
HUN
LSO
BDICOG MWI MRT
ROM
SLE
CMR
AFG
SVK
SDN
MNG
VEN
UZB
PER
DOM
GUY
CIV
BGR
HTIJAM
0.05CHL LBY URY
MLI
BWA
PNG
SVN
KENZMBPANCZECYPMARSWZMDG FJIGHAPRY GRC
HKG
SLV
BGD
HRV
TURSGPCAF SENMKD
BRAIDN
THA PRT
JPNKORYEM
BOL
GMB BFAMEX
ECU HNDIRLMYS
ISR MUSCRIINDTUNFIN
POLLAO
PHLUGA
BEN
KHM
ESP
EGY
NPL
ZAF
ITA
CPVGTMSWE USA
DNK
PAK
CAN
GBR
NLD
COL
BEL
D
EU
VNM
LKA
AUT
NOR NZLFRA
GIN
AUS
CHE

0.10

0.05

0.00
0

0.2

0.4

0.6
state history

0.8

Notes: See Section 2 in text for discussions of state history and output volatility.

1

1.2

27

Figure 2: State History and Frequency of Trend-Growth Breaks
1.00
0.90

0.91
0.85

0.80
0.70

0.63

0.60
0.50

0.53

0.50
0.40
0.30
0.20

0.15

0.10
0.00
1

2
state history

3
breaks

Notes: See Section 2 in text for discussions of state history and trend-growth breaks (breaks).

28

Table 1: Historical Variables and Volatility of Output Growth (OLS Regressions)
Dependent variable is the standard deviation of annual output growth (1960-2011)
Panel A1
2
3
4
5
6
State history-0.049***(2.93)
Agri. history
5.13e-7(0.29)
Technology
0.008
1000BC(0.88)
Technology-0.0071CE(0.92)
Technology-0.011
1500CE(0.80)
Genetic
4.74e-6
distance(0.77)
Absolute
0.000
0.000
0.000
0.001*
0.000
0.000
latitude(0.64)(0.96)(0.65)(1.77)(0.19)(0.85)
Tropics
0.001
0.007
0.007
0.004
-0.0030.004(0.07)(0.93)(0.79)(0.41)(0.27)(0.43)
Log of-0.000-0.002-0.001-0.003**-0.002-0.002arable land
(0.23)(1.02)(0.57)(2.06)(1.37)(0.87)
Landlocked
0.001
0.002
0.005
-0.007-0.0080.000(0.00)(0.26)(0.79)(1.05)(1.12)(0.05)
Island-0.010*-0.009-0.004-0.015***-0.015**-0.009*(1.89)(1.65)(0.53)(2.81)(2.35)(1.98)
Africa0.004
0.014**
0.013**
0.016***
0.014**
0.009(0.80)(2.11)(2.29)(2.81)(2.16)(1.29)
Asia
0.012**
0.007
0.007
0.019***
0.013
0.006
(2.13)(1.28)(1.19)(2.77)(1.52)(1.15)
Europe0.000-0.001-0.001-0.0090.001-0.000(0.03)(0.08)(0.15)(1.10)(0.11)(0.00)
Observations
117
117
102
122
114
117
R2
0.17
0.07
0.07
0.14
0.10
0.08
Panel B
Agri.
Technology Technology Technology
Genetic
FPC ofhistory
1000BC
1CE
1500CE
distance
historyState history-0.057***-0.052***-0.045**-0.050**-0.054***(3.24)(3.15)(2.59)(2.61)(2.67)
Column3.16e-06*
0.014*-0.0020.009-4.73e-06-0.004**variable(1.86)(1.67)(0.25)(0.80)(0.60)(2.06)
Baseline
Yes
Yes
Yes
Yes
Yes
Yes
controls
Observations
117
98
113
105
117
113
R2
0.19
0.21
0.18
0.19
0.18
0.12
Notes:
1. *** indicates significance at the 1-percent level, ** at the 5-percent level, and * at the 10-percent level.
2. Absolute values of heteroskasdastically robust t-statistics are in the parentheses. All regressions include an
unreported constant.
3. Baseline controls include absolute latitude, logged arable land area, and dummies for tropics, landlocked,
island, Africa, Asia and Europe. FPC is the first principal component of historical variables.
4. See Section 2 in text and Appendix A for discussions of the variables.

29

Table 2: State History and Volatility of Output Growth (Quantile Regressions)
Dependent variable is the standard deviation of annual output growth (1960-2011)
OLS
Quantile
0.05
0.25
0.50
0.75
State history-0.049***-0.025*-0.027***-0.029**-0.057***(2.93)(1.88)(2.66)(1.82)(2.64)
Absolute
0.000
0.000
0.000
0.000
0.000
latitude(0.64)(0.60)(0.16)(0.91)(0.31)
Tropics
0.001
0.001
0.004
0.004
0.005
(0.07)(0.11)(0.41)(0.58)(0.33)
Log of-0.000-0.001-0.0000.000
0.003
arable land
(0.23)(0.66)(0.24)(0.02)(1.03)
Landlocked
0.001
0.008
0.001
0.001
-0.007(0.00)(1.33)(0.20)(0.10)(0.65)
Island-0.010*-0.0010.001-0.001-0.013(1.89)(0.27)(0.16)(0.21)(0.98)
Africa0.004-0.004
0.006
0.005
0.001
(0.80)(0.55)(1.05)(0.98)(0.09)
Asia
0.012**
0.002
0.008
0.004
0.013
(2.13)(0.49)(1.59)(0.72)(1.63)
Europe0.000-0.013-0.001-0.0140.002(0.03)(1.00)(0.10)(1.38)(0.12)
Observations
117
117
117
117
117
R2
0.17
0.10
0.13
0.10
0.10
Notes:
1. *** indicates significance at the 1-percent level, ** at the 5-percent level, and * at the 10-percent level.
2. Absolute values of heteroskasdastically robust t-statistics are in the parentheses for OLS, and bootstrappedfor quantiles.
3. All regressions include an unreported constant.
4. See Section 2 in text and Appendix A for discussions of the variables.

30

Table 3: Historical Variables and Trend-Growth Breaks (Probit Regressions)
Dependent variable is a dummy variable indicating the presence of trend-growth breaks
Panel A1
2
3
4
5
6
State history-0.559*(1.82)
Agri. history-7.94e-5*(1.77)
Technology-0.075
1000BC(0.36)
Technology-0.497***1CE(4.04)
Technology-0.0881500CE(0.26)
Genetic
1.82e-4
distance(1.38)
Absolute
0.006
0.000
0.004
0.005
0.002
0.005
latitude(0.70)(0.05)(0.44)(0.65)(0.24)(0.56)
Tropics
0.238
0.083
0.238
0.159
0.163
0.174
(1.04)(0.35)(0.95)(0.76)(0.63)(0.74)
Log of0.062**
0.049*
0.029
0.057**
0.034
0.052*
arable land
(2.12)(1.68)(0.83)(2.24)(1.16)(1.89)
Landlocked-0.054-0.0930.020-0.027-0.035-0.109(0.47)(0.78)(0.15)(0.25)(0.27)(0.89)
Island-0.057-0.1180.080
0.009-0.011-0.045(0.37)(0.71)(0.31)(0.06)(0.07)(0.30)
Africa-0.046-0.1280.011-0.0770.007-0.076(0.31)(0.72)(0.08)(0.59)(0.04)(0.44)
Asia
0.238*
0.288*
0.260
0.214*
0.210
0.130(1.66)(1.92)(1.54)(1.66)(1.34)(0.89)
Europe0.424**
0.494***
0.411**
0.477***
0.439**
0.446**(2.30)(2.73)(2.10)(2.89)(2.39)(2.41)
Observations
94
94
83
95
93
94
Pseudo R2
0.13
0.13
0.09
0.20
0.09
0.12
Panel B
State
Agri.
Technology Technology
Genetic
FPC ofhistoryhistory
1000BC
1500CE
distance
history
Technology-0.433***-0.458***-0.584***-0.634***-0.521***
1CE(3.27)(3.31)(3.17)(4.34)(3.39)
Column-0.306-2.92e-5
0.303
0.430
-2.38e-5-0.145***variable(0.93)(0.59)(1.19)(1.09)(0.15)(2.89)
Baseline
Yes
Yes
Yes
Yes
Yes
Yes
controls
Observations
91
91
81
89
91
91
Pseudo R2
0.19
0.19
0.16
0.21
0.19
0.18
Notes:
1. *** indicates significance at the 1-percent level, ** at the 5-percent level, and * at the 10-percent level.
2. Absolute values of heteroskasdastically robust t-statistics are in the parentheses.
3. All regressions include an unreported constant.
4. Baseline controls include absolute latitude, logged arable land area, and dummies for tropics, landlocked,
island, Africa, Asia and Europe. FPC is the first principal component of historical variables.
5. See Section 2 in text and Appendix A for discussions of the variables.

31

Table 4: Alternative Periods of State History and Volatility of Output Growth
Dependent variable is the standard deviation of annual output growth (1960-2011)
Panel AOLS (1OLS (1501 OLS (1001- OLS (501- OLS (1-500 IV (1-1950
1950 CE)-1950 CE)
1500 CE)
1000 CE)
CE)
CE)
1
2
3
4
5
6
State history-0.046***-0.056***-0.015*-0.019**-0.023**-0.084**(2.78)(2.93)(1.87)(2.59)(2.39)(2.25)
Volatility of-0.001*-0.000-0.001*-0.001**-0.001***-0.003**state history(1.68)(0.32)(1.84)(2.45)(2.66)(2.10)
Absolute
0.000
0.001
0.001
0.000
0.001
0.001***
latitude(1.25)(1.45)(1.56)(1.40)(1.54)(3.04)
Tropics
0.008
0.011
0.013*
0.011
0.012
0.035***(0.92)(1.35)(1.76)(1.57)(1.65)(2.80)
Log of-0.001-0.001-0.002-0.001-0.001
0.002
arable land
(0.43)(0.43)(0.92)(0.80)(0.79)(1.03)
Landlocked
0.002
0.002
0.003
0.003
0.002
0.006
(0.25)(0.37)(0.43)(0.49)(0.38)(0.93)
Island-0.011**-0.012**-0.012**-0.011**-0.013**-0.004(2.06)(2.19)(2.30)(2.19)(2.51)(0.45)
Africa0.004
0.002
0.015**
0.012**
0.011**-0.003(0.72)(0.37)(2.26)(2.06)(2.01)(0.34)
Asia
0.013**
0.010*
0.017**
0.016***
0.015**
0.014*(2.23)(1.84)(2.33)(2.62)(2.48)(1.95)
Europe-0.001-0.003
0.006
0.005
0.002
-0.011(0.07)(0.37)(0.64)(0.50)(0.27)(1.13)
Observations
113
113
113
113
113
85
R2
0.20
0.21
0.13
0.15
0.15
0.07
Hansen-J
0.775
test (p value)
Panel B: First-stage regressions
State
Volatility ofhistory
state history
Population
0.061***
0.787
at 1 CE
(3.93)(1.48)
Urbanization-0.010-0.879at 1000 BCE(0.35)(1.05)
Agriculture
7.95e-6-9.96e-4*
history(0.37)(1.69)
Military tech
0.025
-2.970at 1000 BCE(0.23)(1.28)
Controls inYes
Yes
the 2nd stage
R2 (Adj.)
0.589
0.270
Shea Partial
0.247
0.135
R2
F-test excl.
9.10
5.28
instruments
Notes:
1. *** indicates significance at the 1-percent level, ** at the 5-percent level, and * at the 10-percent level.
2. Absolute values of heteroskasdastically robust t-statistics are in the parentheses. All regressions include an unreported
constant.
3. See Section 2 in text and Appendix A for discussions of the variables.

32

Table 5: Further Robustness Checks
ExcludingAfricaControlling Controlling Controlling Controlling Controllingfor climate
for
for
for
for
and soil
Europeanethnicity
democracy
income
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Dependent variable is the standard deviation of annual output growth (OLS regressions)
State history-0.067***-0.037**-0.043**-0.044***-0.055***-0.027*(3.54)(2.07)(2.38)(2.74)(3.39)(1.88)
P-value for[0.81]
climate & soil
European share-0.073*of population(1.93)
Ethnic0.019*
fractionalization(1.79)
Democratic-0.002***
institutions(5.89)
Log income in-0.011***2002(2.85)
Baseline
Yes
Yes
Yes
Yes
Yes
Yes
controls
Observations
80
117
89
116
116
111
R2
0.25
0.36
0.15
0.19
0.29
0.26
Panel B: Dependent variable is a dummy variable indicating the presence of trend-growth breaks(Probit regressions)
State history-0.665*-0.582**-0.587**-0.710**-0.632**-0.578*(1.72)(2.12)(2.02)(2.34)(2.05)(1.81)
P-value for[0.24]
climate & soil
European share-0.128of population(0.17)
Ethnic-0.573**
fractionalization(2.29)
Democratic-0.023*
institutions(1.64)
Log income in
0.076
2002
(1.02)
Baseline
Yes
Yes
Yes
Yes
Yes
Yes
controls
Observations
61
89
67
93
94
93
Pseudo R2
0.25
0.23
0.19
0.17
0.15
0.13
Notes:
1. *** indicates significance at the 1-percent level, ** at the 5-percent level, and * at the 10-percent level.
2. Absolute values of heteroskasdastically robust t-statistics are in the parentheses.
3. All regressions include an unreported constant.
4. Baseline controls include absolute latitude, logged arable land area, and dummies for tropics, landlocked,
island, Africa, Asia and Europe.
5. See Section 2 in text and Appendix A for discussions of the variables.

33

Table 6: Baseline Results (Without Adjusting for Population Migration)
Dependent variable is the standard deviation of annual output growth (1960-2011)
Panel A1
2
3
4
5
6
State history-0.036***(2.63)
Agri. history
8.79e-07(0.58)
Technology
0.011
1000BC(1.40)
Technology-0.0071CE(0.93)
Technology-0.0101500CE(0.53)
Genetic-1.06e-07
distance(0.02)
Absolute
0.000
0.000
0.000
0.001*-4.98e-06
0.000
latitude(0.99)(1.09)(0.60)(1.69)(0.01)(0.81)
Tropics
0.008
0.009
0.008
0.004
-0.0030.002(0.93)(1.23)(0.83)(0.43)(0.32)(0.20)
Log of-0.001-0.002-0.001-0.003**-0.002-0.004**arable land
(0.67)(1.35)(0.69)(2.13)(1.23)(2.51)
Landlocked
0.002
0.003
0.005
-0.007-0.010-0.005(0.24)(0.42)(0.77)(1.00)(1.41)(0.74)
Island-0.013**-0.008-0.004-0.016***-0.013**-0.017***(2.40)(1.42)(0.53)(2.86)(2.14)(3.21)
Africa0.015**
0.013**
0.010*
0.019**
0.020**
0.016***(2.48)(2.23)(1.68)(2.60)(2.59)(2.73)
Asia
0.023***
0.007
0.004
0.023***
0.018**
0.020***(2.87)(0.94)(0.59)(2.83)(2.05)(2.75)
Europe0.010-0.004-0.005-0.0050.003-0.002(1.01)(0.37)(0.53)(0.47)(0.20)(0.16)
Observations
113
113
103
123
111
128
R2
0.15
0.09
0.08
0.14
0.11
0.13
Panel B
Agri.
Technology Technology Technology
Genetic
FPC ofhistory
1000BC
1CE
1500CE
distance
historyState history-0.042***-0.043***-0.030**-0.029*-0.041**(2.93)(3.12)(2.18)(1.90)(2.36)
Column2.79e-06*
0.018**-0.0050.007-5.68e-06-0.001variable(1.79)(2.48)(0.68)(0.53)(0.72)(0.43)
Baseline
Yes
Yes
Yes
Yes
Yes
Yes
controls
Observations
113
95
109
99
111
84
R2
0.17
0.22
0.17
0.17
0.17
0.14
Notes:
1. *** indicates significance at the 1-percent level, ** at the 5-percent level, and * at the 10-percent level.
2. Absolute values of heteroskasdastically robust t-statistics are in the parentheses. All regressions include an
unreported constant.
3. Baseline controls include absolute latitude, logged arable land area, and dummies for tropics, landlocked,
island, Africa, Asia and Europe. FPC is the first principal component of historical variables.
4. See Section 2 in text and Appendix A for discussions of the variables.

34

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39

Appendix
The appendix is organized as follows:Appendix A provides more details on data definitions and sources.



Appendix B provides the supplementary results behind the principal component
analysis relating to historical variables and covariates of output volatility.

Appendix C provides a list of countries included in the study.

40

Appendix A: Data
 Absolute latitude: absolute value of the latitude of the country (THE WORLDFACTBOOK, CIA).
 Arable land area: log of total arable land area in sq. km (THE WORLD FACTBOOK,CIA).
 Climate and soil: climate includes five measures of temperatures (average, minimum
monthly high, maximum monthly high, minimum monthly low, and maximum monthly
low, all in centigrade), and four measures of humidity (morning minimum, morning
maximum, afternoon minimum and afternoon maximum, all in percent). Soil includes
seven types of soil qualities (steppe [low latitude], desert [low latitude], steppe [middle
latitude], desert [middle latitude], dry steppe wasteland, desert dry winter, and highland).
(Parker, 1997).
 Colonial origins: are four dummy variables representing former colonies of the UK,
France, Spain or others. It takes on the value of one if the country is a former colony of
one of the above-mentioned countries, zero otherwise. (THE WORLD FACTBOOK, CIA).
 Democracy institutions: is the variable Polity2 that is calculated by subtracting the
autocratic score from the democratic score for each country. If a country has a“standardized authority score” (66, 77 or 88) for a particular year, it will be converted to“conventional polity scores”. This variable has a range from -10 to 10, with 10 being theperfect score for democracy (POLITY IV PROJECT, Marshall, Gurr, and Jaggers, 2013).
 Ethnic fractionalization: is the probability that two individuals selected at random from acountry will be from different ethnic groups (Fearon, 2003).
 European share of population: is the share of the year 2000 population in every country
that is descended from people in Belgium, France, Germany, Italy, the Netherlands,

41

Portugal, Spain and the United Kingdom in the year 1500CE (Constructed using
Putterman and Weil’s (2010) World Migration Matrix).
 Income level: is the log of Purchasing Power Parity adjusted per capita GDP in 2002
(rgdpch, Penn World Tables, version 6.3).
 Island: is a dummy variable taking on the value of one if a country is an island, zerootherwise (THE WORLD FACTBOOK, CIA).
 Malaria prevalence: is the index of malaria prevalence in 1966 (Gallop et al., 200).
 Neo-Europes: is a dummy variable taking on the value of one if a country is Australia,
Canada, New Zealand or the US, zero otherwise.
 Political stability: is the Political Risk Index from International Country Risk Guide for theyear 2000, which is an index between zero and one, with one being perfectly politicallystable.
 Protestant share: is the fraction of population protestant in 1960 (Barro, 1999)
 Tropics: a dummy variable taking on the value of one if the country is tropical and zerootherwise (Comin, Easterly and Gong, 2010).

42

Appendix B
Table B.1: Correlation Coefficients of Historical Variables

Statehistory
Agricultural
history
Technology
at 1,000BC
Technology
at 1CE
Technology
at 1,500CE
Genetic
distanceStatehistory
1.00

Agricultural
history

Technology
at 1,000BC

Technology
at 1CE

Technology
at 1,500CE

0.68***

1.00

0.41***

0.66***

1.00

0.60***

0.55***

0.64***

1.00

0.61***

0.69***

0.49***

0.59***

1.00-0.55***-0.67***-0.53***-0.59***-0.74***

Genetic
distance

1.00

Note: *** indicates significance at the 1-percent level.

Table B.2: Explanatory Power of Each Principal Component of Historical Variables
PC
1
2
3
4
5
6

Percentage explained
0.67
0.11
0.08
0.07
0.04
0.03

Cumulative
0.67
0.78
0.86
0.93
0.97
1.00

Table B.3: Correlation Coefficients of Proximate Factors
Political volatility
Political volatility
Discretionary
fiscal policy
Financial
development
FDI inflow

Discretionary
fiscal policy

Financial
development

1.00-0.37***

1.00

0.46***-0.51***

1.00

0.18

-0.12

0.14

FDI inflow

1.00

Note: *** indicates significance at the 1-percent level.

Table B.4: Explanatory Power of Each Principal Component of Proximate Factors
PC
1
2
3
4

Percentage explained
0.49
0.24
0.16
0.12

Cumulative
0.49
0.73
0.88
1.00

43

Appendix C: A List of Countries
Country Name
Afghanistan
Albania
Algeria
AngolaArgentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
BelarusBelgium
Benin
Bolivia
Bosnia & Herzegovina
Botswana
BrazilBulgaria
Burkina Faso
BurundiCambodia
Cameroon
CanadaCape VerdeCentral African Rep.
Chad
Chile
China
Colombia
Congo, Dem. Rep.
Congo, Rep.
Costa Rica
Cote d'Ivoire
Croatia
Cuba
CyprusCzech Republic
Denmark
Dominican Republic
EcuadorEgypt, Arab Rep.
El Salvador
Estonia
Ethiopia
Fiji
FinlandFranceGabon
Gambia, The
Georgia
Germany
Ghana
GreeceGuatemalaGuineaGuyanaHaiti
HondurasHong Kong
Hungary
India
Indonesia
Iran, Islamic Rep.
IrelandIsraelItaly
JamaicaJapan
JordanKazakhstan
Kenya
Korea, Rep.

Kyrgyz Republic
Lao PDR
LatviaLesothoLiberia
Libya
Lithuania
Macedonia, FYR
Madagascar
MalawiMalaysia
Mali
Mauritania
Mauritius
MexicoMoldova
Mongolia
MoroccoMozambique
Myanmar
Nepal
Netherlands
New Zealand
Nicaragua
Niger
Nigeria
NorwayPakistan
PanamaPapua New Guinea
Paraguay
Peru
Philippines
PolandPortugal
Romania

Russian Federation
RwandaSenegalSerbiaSierra Leone
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
Sudan
Swaziland
SwedenSwitzerland
Syrian Arab Republic
Tajikistan
ThailandTogo
Trinidad and Tobago
Tunisia
TurkeyTurkmenistan
UgandaUkraineUnited Kingdom
United States
Uruguay
Uzbekistan
Venezuela, RB
Vietnam
Yemen, Rep.
ZambiaZimbabwe