Why Doesn't Capital Flow from Rich to Poor Countries? An Empirical Investigation

TL;DR: This paper examined the empirical role of difierent explanations for the lack of flow of capital from rich to poor countries, including differences in fundamentals across countries and capital market imperfections, and showed that during 1970-2000 low institutional quality is the leading explanation.

Abstract: We examine the empirical role of difierent explanations for the lack of ∞ows of capital from rich to poor countries|the \Lucas Paradox." The theoretical explanations include difierences in fundamentals across countries and capital market imperfections. We show that during 1970i2000 low institutional quality is the leading explanation. For example, improving Peru’s institutional quality to Australia’s level, implies a quadrupling of foreign investment. Recent studies emphasize the role of institutions for achieving higher levels of income, but remain silent on the speciflc mechanisms. Our results indicate that foreign investment might be a channel through which institutions afiect long-run development.

Summary

1 Introduction

• The standard neoclassical theory predicts that capital should flow from rich to poor countries.
• The ordinary least squares (OLS) estimates show that improving the quality of institutions to the U.K.’s level from that of Turkey’s implies a 60% increase in foreign investment.

2 Conceptual Issues

• Agents can borrow and lend capital internationally.
• Atf ′(kjt), (2) where f(.) is the net of depreciation production function in per capita terms and k denotes capital per capita.
• As explained in the introduction, the theoretical explanations for this paradoxical pattern can be grouped as differences in fundamentals across flows to countries where the physical marginal product of capital is the highest, a corollary on which the authors provide systematic evidence.
• The authors investigate each group in detail below.

Missing Factors of Production

• One of the explanations for the lack of capital flows from rich to poor countries is the existence of other factors—such as human capital and land—that positively affect the returns to capital but are generally ignored by the conventional neoclassical approach.
• If human capital positively affects capital’s return, less capital tends to flow to countries with lower endowments of human capital.
• Thus, if the production function is in fact given by Yt = AtF (Kt, Zt, Lt) = AtKαt Z β t L 1−α−β t , (3) where Zt denotes another factor that affects the production process, then (2) misrepresents the implied capital flows.

Government Policies

• Government policies can be another impediment to the flows and the convergence of the returns.
• Differences across countries in government tax policies can lead to substantial differences in capital-labor ratios.
• Inflation may work as a tax and decrease the return to capital.
• In addition, the government can explicitly limit capital flows by imposing capital controls.
• The authors can model the effect of these distortive government policies by assuming that governments tax capital’s return at a rate τ , which differs across countries.

Institutional Structure and Total Factor Productivity

• They consist of both informal constraints (traditions, customs) and formal rules (rules, laws, constitutions).
• Policies are choices made within a political and social structure, i.e., within a set of institutions.
• Thus institutional weaknesses create a wedge between expected returns and ex-post returns.
• The authors model these as differences in the parameter At, which captures differences in overall efficiency in the production across countries.
• Indeed, as Prescott (1998) argues, the efficient use of the existing technology or the resistance to the adoption of new ones depends on the “arrangements” a society employs.

2.2 International Capital Market Imperfections

• Asymmetric Information Asymmetric information problems, intrinsic to capital markets, can be ex-ante (adverse selection), interim (moral hazard) or ex-post (costly state verification).
• In general, under asymmetric information, the main implications of the neoclassical model regarding the capital flows tend not to hold.
• In a model with moral hazard, for example, where lenders cannot monitor borrowers’ 15See Parente and Prescott (2000) and Rajan and Zingales (2003).
• 16Kalemli-Ozcan, Reshef, Sorensen, and Yosha (2003) show that capital flows to high productivity states within the U.S., where there is a common institutional structure.
• This result is consistent with the prediction of a neoclassical model with TFP differences.

Sovereign Risk

• Sovereign risk is defined as any situation where a sovereign defaults on loan contracts with foreigners, seizes foreign assets located within its borders, or prevents domestic residents from fully meeting obligations to foreign contracts.
• He maintains that investors in India faced the same rules and regulations as the investors in the U.K.
• Following Obstfeld and Rogoff (1995), the authors considered international capital market imperfections only those related to sovereign enforcement problems or those based on information asymmetries.
• The authors put all domestic distortions under fundamentals since they affect capital’s productivity.

Capital Flows

• The International Financial Statistics (IFS) issued by the International Monetary Fund (IMF) is the standard data source for annual capital inflows.
• 21Until the mid 1970s—following the shutting down of the international markets in the 1930s—debt flows to most developing countries were generally restricted to international organizations/government-to-government loans.
• The authors calculate annual inflows of direct and portfolio equity investment out of the stocks in the KLSV and LM data sets as the yearly change in the stock of foreign claims on domestic capital.
• The authors use the logarithm of GDP per capita (PPP) in 1970 on the right hand side in each regression to capture the “Lucas Paradox”, in other words, the positive significance of this variable demonstrates the presence of the “Paradox.”.

International Capital Market Imperfections

• It is difficult to obtain the appropriate information (from an investment point of view) about a country without visiting the country and therefore how far away that country is located could be a concern.
• ”29 Recently distance has been used a proxy for the international capital market failures, mainly asymmetric information.
• The authors interpret the results as fund managers exploiting informational advantages in their selection of nearby stocks.
• The authors construct a similar variable called “distantness,” which is the weighted average of the distances from the capital city of the particular country to the capital cities of the other countries, using the GDP shares of the other countries as weights.
• The authors construct this variable following KalemliOzcan, Sorensen, and Yosha (2003).

3.2 Correlations

• In general, most of the correlations are all below 0.50, with the clear exception of GDP, institutions and schooling.
• Log GDP per capita and institutional quality are highly correlated in all three samples and so are log GDP per capita and log schooling.
• Since the main point of their analysis is to find out which of the explanatory variables remove the “Lucas Paradox,” it is very important to look at the role of each variable one at a time and also in a multiple regression framework given the high correlations.
• Table 4 shows the correlations between the main explanatory variables and the additional control variables that are used in the robustness analysis.

Are the Results Driven by Multicollinearity?

• One might worry that the results are spurious due to the high correlation between GDP per capita and institutions.
• The authors undertake a number of tests to show that indeed they are capturing the independent effect of institutions and multicollinearity is not driving their results.
• A of the figure 4 plots the residuals from the regression of average inflows of direct and portfolio equity investment per capita on average institutional quality against the residuals from the regression of log GDP per capita in 1970 on average institutional quality.
• It is clear from the figure that their results are not driven by capital account liberalization episodes but rather by countries, which, ceteris paribus, have very high levels of institutional quality, such as Denmark, Sweden, Netherlands, Norway, and U.K.
• Figure 5 repeats the same exercise for their “preferred” KLSV data “base” sample.

4 Institutions and the “Lucas Paradox:” IV Estimates

• It is possible that the capital inflows affect the institutional quality of a country.
• Most countries’ legal rules, either through colonialism, conquest, or outright borrowing, can be traced to one of four distinct European legal systems: English common law, French civil law, German civil law, and Scandinavian civil law.
• Instrumenting average institutional quality with other instruments—British legal origin and English language—column (1) shows that log European settler mortality is excludable from the main regression.
• The authors cannot reject the hypothesis that their instruments are appropriate since all of p-values far exceed the conventional 5% significance level.

5 Conclusion

• The authors objective in this paper has been to analyze empirically the role of different theoretical explanations behind the lack of flows of capital from rich countries to poor ones.
• As the world economy collapsed into depression in the 1930s, so did the international capital markets.
• The authors argue that it is differences in institutional quality among the poor and rich countries.

Figures

Table C-1: Monte Carlo Exercise

Table 2: Descriptive Statistics for the Additional Control Variables

Table C-2: Perturbation Exercise

Table D-4: Robustness Checks for OLS Regressions of Capital Inflows per capita - LM Flows Data

Table D-3: Robustness Checks for OLS Regressions of Capital Inflows per capita III- KLSV Data

Figure 3: Evolution of Institutional Quality Index, 1984–2000: Sub-components for Developing Countries

Table 9: OLS Regressions of Capital Inflows per capita - KLSV Flows Data

Table 1: Descriptive Statistics

Figure 2: Evolution of Institutional Quality Index, 1984–2000: Sub-components

Table D-2: Robustness Checks for OLS Regressions of Capital Inflows per capita II- KLSV Data

Table 10: IV Regressions of Capital Inflows per capita - KLSV Flows Data

Table 5: OLS Regressions of Capital Inflows per capita I - IMF Flows Data

Figure 4: Conditional Correlation Plots from Regression of Inflows of Capital per capita on Institutional Quality and GDP per capita: Base Sample of 81 Countries

Table 4: Correlations between the Main and the Additional Control Variables

Table 6: OLS Regressions of Capital Inflows per capita II- IMF Flows Data

Figure 5: Conditional Correlation Plots from Regression of Inflows of Capital per capita on Institutional Quality and Log GDP per capita: Base Sample of 58 Countries

Table 11: Tests for Validity and Excludability of Instruments - KLSV Flows Data

Table D-1: Robustness Checks for OLS Regressions of Capital Inflows per capita I- KLSV Data

Table 7: OLS Regressions of Capital Inflows per capita - KLSV Flows Data

Table 3: Correlation Matrix for the Main Explanatory Variables

Figure C-1: Monte Carlo Distribution of Perturbed Regression Coefficients

Full text

NBER WORKING PAPER SERIES
WHY DOESN’T CAPITAL FLOW FROM RICH TO POOR COUNTRIES?
AN EMPIRICAL INVESTIGATION
Laura Alfaro
Sebnem Kalemli-Ozcan
Vadym Volosovych
Working Paper 11901
http://www.nber.org/papers/w11901
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
December 2005
Corresponding author: Sebnem Kalemli-Ozcan, Department of Economics, University of Houston, Houston,
TX, 77204 (e-mail: Sebnem.Kalemli-Ozcan@mail.uh.edu). We thank Rawi Abdelal, Daron Acemoglu, Dan
Berkowitz, Rafael Di Tella, Alex Dyck, Simon Johnson, Michael Klein, Aart Kraay, Robert Lucas, Dani
Rodrik, Ken Rogoff, Julio Rotemberg, Bent Sorensen, Federico Sturzenegger, two anonymous referees, and
participants at various seminars and conferences for their valuable comments and suggestions. We are
grateful to Doug Bond, Philip Lane, Norman Loayza, Gian Maria Milesi-Ferretti, and Shang-Jin Wei for
kindly providing us with their data. The views expressed herein are those of the author(s) and do not
necessarily reflect the views of the National Bureau of Economic Research.
©2005 by Laura Alfaro, Sebnem Kalemli-Ozcan, and Vadym Volosovych. All rights reserved. Short
sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full
credit, including © notice, is given to the source.

Why Doesn’t Capital Flow from Rich to Poor Countries? An Empirical Investigation
Laura Alfaro, Sebnem Kalemli-Ozcan, and Vadym Volosovych
NBER Working Paper No. 11901
December 2005
JEL No. F21, F41, O1
ABSTRACT
We examine the empirical role of different explanations for the lack of flows of capital from rich to
poor countries the "Lucas Paradox." The theoretical explanations include differences in fundamentals
across countries and capital market imperfections. We show that during 1970-2000 low institutional
quality is the leading explanation. For example, improving Peru's institutional quality to Australia's
level, implies a quadrupling of foreign investment. Recent studies emphasize the role of institutions
for achieving higher levels of income, but remain silent on the specific mechanisms. Our results
indicate that foreign investment might be a channel through which institutions affect long-run
development.
Laura Alfaro
Harvard Business School
lalfaro@hbs.edu
Sebnem Kalemli-Ozcan
University of Houston
Department of Economics
Houston, TX 77204
and NBER
skalemli@mail.uh.edu
Vadym Volosovych
University of Houston
vvolosovych@gmail.com

1 Introduction
The standard neoclassical theory predicts that capital should flow from rich to poor countries.
Under the usual assumptions of countries producing the same goods with the same constant returns
to scale production technology using capital and labor as factors of production, differences in income
per capita reflect differences in capital per capita. Thus, if capital were allowed to flow freely, new
investments would occur only in the poorer economy, and this would continue to be true until the
return to investments were equalized in all the countries. However, in his now classic example,
Lucas (1990) compares the U.S. and India in 1988 and demonstrates that, if the neoclassical model
were true, the marginal product of capital in India should be about 58 times that of the U.S. In
face of such return differentials, all capital should flow from the U.S. to India. In practice, we
do not observe such flows. Lucas questions the validity of the assumptions that give rise to these
differences in the marginal pro duct of capital and asks what assumptions should replace these.
According to Lucas, this is the central question of economic development.
Lucas’ work has generated an extensive theoretical literature. Researchers, including Lucas
himself, show that with slight modifications of the standard neoclassical theory, the “Paradox”
disappears. These theoretical explanations for the “Lucas Paradox” can be grouped into two
categories. The first group includes differences in fundamentals that affect the production structure
of the economy, such as technological differences, missing factors of production, government policies,
and the institutional structure.
1
The second group of explanations focuses on international capital
market imperfections, mainly sovereign risk and asymmetric information. Although capital has a
high return in developing countries, it does not go there b ecause of the market failures.
2
According
to Lucas, international capital market failures, or “political risk” as he puts it, cannot explain the
lack of flows before 1945 since during that time most of the “third world” was subject to European
legal arrangements imposed through colonialism. Hence, investors in the developed countries, such
as the U.K., could expect contracts to be enforced in the same way in both the U.K. and India.
3
1
See King and Rebelo (1993), Razin and Yuen (1994), Gomme (1993), and Tornell and Velasco (1992). Lucas
finds that accounting for the differences in human capital quality across countries significantly reduces the return
differentials and considering the role of human capital externalities eliminates the return differentials. However,
his calculations assume that the externalities from the country’s stock of human capital accrue entirely to the pro-
ducers within the country, i.e., all knowledge spillovers are local. This assumption is at odds with the evidence of
quantitatively significant international knowledge spillovers; see Helpman (2004).
2
See Gertler and Rogoff (1990) and Gordon and Bovenberg (1996).
3
Before 1945 European imperial p owers granted trading rights to monopoly companies, an action that created
one-way flows. In theory a large capital exporting economy can limit capital flows in order to push interest rates in a
favorable direction. Gordon and Bovenberg (1996) note that there is little evidence of large countries restrict capital
flows for this purpose.
1

However, the British institutions in India do not necessarily have the same quality as the British
institutions in the U.S. and Australia. As shown by Acemoglu, Johnson, and Robinson (2001, 2002),
if European settlement was discouraged by diseases or if surplus extraction was more beneficial,
then the European colonizers set up an institutional structure where the protection of property
rights was weak.
Our objective in this paper is to investigate the role of the different theoretical explanations
for the lack of flows of capital from rich countries to poor countries in a systematic empirical
framework.
4
We show that during the period 1970−2000 low institutional quality is the leading
explanation for the “Lucas Paradox.” The ordinary least squares (OLS) estimates show that im-
proving the quality of institutions to the U.K.’s level from that of Turkey’s implies a 60% increase in
foreign investment. The instrumental variable (IV) estimates imply an even larger effect: improving
Peru’s institutional quality to Australia’s level, implies a quadrupling of foreign investment.
5
An excellent example for the role of institutional quality in attracting foreign capital is Intel’s
decision to locate in Costa Rica in 1996.
6
In the final stage of the decision process, the short list
included Mexico and Costa Rica. The two countries have similar GDP per capita in U.S. dollars
(close to $3000 at that time), albeit Mexico is a much larger country. Both countries have similar
levels of adult literacy rates. However, given the overall size of Intel’s investment relative to the
size of the economy, one important concern in the decision process was the absolute availability of
engineers and technically trained graduates, which favored Mexico. Hence, one cannot argue that
human capital was a defining issue in Intel’s final choice. Instead, Costa Rica’s stability and lower
corruption levels tilted the balance in favor of the country. As noted by Spar (1998), Mexico’s offer
to make “exceptions” to the existing rules for Intel only in contrast to Costa Rica’s approach of
making any concession made to Intel available to all other investors was an important reason in the
final decision. Another example is the recent boom in foreign direct investment (FDI) in Turkey.
This boom is similar to what Portugal and Greece observed after joining in the EU. Turkey became
an official accession country on October 3rd, 2005 and started entry negotiations. In a recent
article, Champion and von Reppert-Bismarck (2005), argue that these official entry negotiations
would force Turkey to become more like the “EU countries” in its banking sector, its antitrust law,
its regulation, and its policies, which in turn will attract foreign investment. Turkey has undertaken
4
Obstfeld (1995) argues that the most direct approach would be to compare capital’s rate of return in different
countries. Unfortunately, it is difficult to find internationally comparable measures of after tax returns to capital.
5
Both Turkey and Peru are in the bottom 25th percentile in the distribution of the index of institutions, whereas
Australia and the U.K. are in the top 75th percentile.
6
See Spar (1998) and Larrain, Lopez-Calva and Rodriguez-Clare (2000).
2

major institutional reform and constitutional change in the past 2 years, including the 2003 FDI law
that cuts the official procedures from 15 to 3 for foreign investors. Multinational companies such as
Metro AG, PSA Peugeot Citroen, Vodafone PLC, and France Telekom are increasing their FDI to
Turkey, arguing that the investor protection and overall investment climate improved considerably
as a result of these reforms. As a result, FDI flows has boomed from an average of well under $1
billion in the 1990s to $2.6 billion in last year and more than a $5 billion projected for 2005.
The “Lucas Paradox” is related to the major “puzzles” in international macroeconomics and
finance.
7
These include the high correlation between savings and investment in OECD countries
(the Feldstein-Horioka puzzle); the lack of overseas investment by the home country residents
(the home bias puzzle); and the low correlations of consumption growth across countries (the
risk sharing puzzle). All of these puzzles stem from the lack of international capital flows, more
specifically, the lack of international equity holdings. However, the empirical literature on these
issues is extremely thin and not in agreement. In particular, we still do not know what is more
important in explaining the “Lucas Paradox”: fundamentals or market failures? Some researchers
provide indirect historical evidence that schooling, natural resources, and demographic factors
are the reasons for the European investment into the “new world.”
8
The empirical literature on
the determinants of capital flows has focused on the role of external (push) and internal (pull)
factors. Researchers find that external factors, mostly low interest rates in the developed nations,
in particular in the U.S., played an important role in accounting for the renewal of foreign lending
to developing countries in the 1990s.
9
The literature pays particular attention to the determinants
of FDI and shows that government size, political stability, and openness play an important role.
10
In terms of the determinants of bilateral equity flows and external debt some studies find support
for theories emphasizing imperfections in international credit markets.
11
These papers, however,
have not paid particular attention to the role of institutions in shaping international capital flows
over the long-run.
12
7
See Obstfeld and Rogoff (2000) for an overview of the major puzzles in international economies.
8
In the context of British overseas investment before World War I, O’Rourke and Williamson (1999) find that
British capital chased European emigrants, where both were seeking cheap land and natural resources. Clemens and
Williamson (2004), using data on British investment in 34 countries during 19th century, show that two thirds of the
British capital exports went to the labor-scarce new world and only about one quarter of it went to labor abundant
Asia and Africa because of similar reasons.
9
See Calvo, Leiderman and Reinhart (1996).
10
See Edwards (1991) and Wei and Wu (2002).
11
See Lane (2004) and Portes and Rey (2005).
12
Using firm-level data Stulz (2005) and Doidge, Karolyi and Stulz (2004) show that the institutions of the country
in which a firm is located affect how investors receive a return from investing in the firm. Specifically, they show that
almost all of the variation in governance ratings across firms in less developed countries is attributable to country
characteristics. The implication of their work is that weak institutions at the country-level can explain the lack of
3

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Nber working paper series why doesn’t capital flow from rich to poor countries? an empirical investigation" ?

The authors examine the empirical role of different explanations for the lack of flows of capital from rich to poor countries the `` Lucas Paradox. `` The authors show that during 1970-2000 low institutional quality is the leading explanation. 

Q2. What have the authors stated for future works in "Nber working paper series why doesn’t capital flow from rich to poor countries? an empirical investigation" ?

The authors undertake a systematic empirical study to evaluate the role of the alternative explanations behind the “ Lucas Paradox, ” which include differences in fundamentals and capital market imperfections. Capital flows began to increase starting in the 1960s, and further expanded in the 1970s after the demise of the Bretton Woods system. Their results suggest that policies aimed at strengthening the protection of property rights, reducing corruption, increasing government stability, bureaucratic quality and law and order should be at the top of the list of policy makers seeking to increase capital inflows to poor countries. 

Q3. Why do the authors prefer to use total foreign equity flows in the analysis?

In addition, because of missing portfolio data (some countries tend not to receive portfolio flows, in part due to lack of functioning stock markets), the authors prefer to use total foreign equity flows in the analysis, which is the sum of inflows of direct and portfolio equity investment. 

Q4. How do the authors calculate the great arc distance between each capital city?

The authors use Arcview software to obtain latitude and longitude of each capital city and calculate the great arc distance between each pair. 

Q5. What does Lucas find to be the important factor in reducing the return differentials?

Lucas finds that accounting for the differences in human capital quality across countries significantly reduces the return differentials and considering the role of human capital externalities eliminates the return differentials. 

Q6. What is the main reason why the authors do not see more capital flows to developing countries?

They argue the following: “[T]he fact that so many poor countries are in default on their debts, that so little funds are channeled through equity, and that overall private lending rises more than proportionately with wealth, all strongly support the view that political risk is the main reason why the authors do not see more capital flows to developing countries. 

Q7. How many years of schooling did the educated country have?

GDP per capita in 1970 varies between 500 PPP U.S. dollars to 23,000 PPP U.S. dollars; and the most educated country has 11 years of schooling as opposed to 0 in the least educated country. 

Q8. What is the standard data source for annual capital flows?

7The International Financial Statistics (IFS) issued by the International Monetary Fund (IMF) is the standard data source for annual capital inflows. 

Q9. How can the authors model the effect of distortive government policies?

The authors can model the effect of these distortive government policies by assuming that governments tax capital’s return at a rate τ , which differs across countries. 

Q10. What is the main implications of the neoclassical model regarding the capital flows?

In general, under asymmetric information, the main implications of the neoclassical model regarding the capital flows tend not to hold. 

Q11. What are the main reasons why multinational companies are increasing their FDI to Turkey?

Multinational companies such as Metro AG, PSA Peugeot Citroen, Vodafone PLC, and France Telekom are increasing their FDI to Turkey, arguing that the investor protection and overall investment climate improved considerably as a result of these reforms. 

Q12. What is the effect of the overidentification test on the log settler mortality?

The authors use Hansen’s overidentification test (J-test) to check the null hypothesis41This is similar to the first stage regression in Acemoglu, Johnson and Robinson (2001), where they regress the average risk of expropriation (which is one the components of their index of institutions) on log settler mortality.