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Why Doesn’t Capital Flow from Rich to Poor Countries?
An Empirical Investigation
Laura Alfaro
Harvard Business School
Sebnem Kalemli Ozcan
Vadym Volosovych
University of Houston, NBER
University of Houston
In his seminal AAE PP
paper, Lucas shows that
…
Motivation
Lucas (1990): Contrary to what the neoclassical theory predicts, not
enough capital flows from rich to poor countries!
• Standard Assumptions (2 countries, same goods, CRS
production function, factors K, L)
Yt = At F(Kt, Lt ) = At Kt Lt1-
(1)
•  in y are due to  in k  with perfect capital mobility, returns
to k converge;
As he mentions, he has ruled everything
At f ( kit ) = rt =At f ( kjt )
• Lucas (1990) shows:
MPKIndia1988 = 58 MPKUSA 1988
else. So, if trade in capital is free and
competitive, capital should go to poor –
capital scarce countries where returns should
be high. However, although capital does go
to poor countries, not enough capital seems
to flow to poor countries – at least not the
levels predicted by the neoclassical theory. In
this now classical example, Lucas compares
returns in India and the US.
(2)
Clearly some of the assumptions must be wrong, but which ones. And he finishes by saying that this a central question for
economic development.
Theoretical Explanations
1. Differences in Fundamentals ( F( . ) or A )
 Differences in the productivity of capital
•
Omitted Factors
At f ( kit, zit ) = rt =At f ( kit, zjt )
(Lucas, 1990).
•
(3)
Lucas: HK
Government Policies
At f (kit )( 1-it ) = rt =At f( kit )( 1-jt )
(4)
(Razin and Yuen, 1994).
•
Institutions – Incentive Structure
Ait f ( kit ) = rt =Ajt f ( kit )
(Tornell and Velasco, 1992).
(5)
Eichengree – cultural and tech capacity
matter. Prescott – tech adopted depends
organization of society.
Although capital is productive, it does not go there due to market failures. We roughly divide them in sovereign risk
and int. capital market failures.
Theoretical Explanations
2. International Capital Market Failures
Poor country acquires K from
the rich – expected to repay
tomorrow.
• Sovereign Risk: absence of a supranational legal authority that
can enforce international borrowing agreements
• Asymmetric information (capital markets): adverse selection,
moral hazard, costly state verification
(Gertler and Rogoff, 1990; Gordon and Bovenberg, 1996).
Assumption:problems –
greater across borders
Empirical Literature
• Lucas Paradox is related to major puzzles in International
Economics: Feldstein-Horioka, Home bias, Risk Sharing
- Lack of flows / lack of foreign equity holding.
• Focus on determinants of FDI, debt, equity.
(Calvo et. al, 1993, 1996; Edwards, 1991; Wei and Wu, 2001;
Lane 2000; Portes and Rey, 2000).
• Indirect historical evidence on Lucas Paradox
(Clemens and Williamson, 2003).
… However, empirical literature: indirect, no consensus…
… We still don’t know
1. Which of the theoretical explanations for the Lucas Paradox
are empirically relevant?
• Lucas (1990): Human Capital Externalities if all knowledge
spillovers are local
 All benefits of the country’s stock of human capital accrue
entirely to producers within the country (?)
• Which benefits do really end in the border? (Rules, laws…)
Mexico Story; CR Story
… We still don’t know
1. Which of the theoretical explanations for the Lucas Paradox
are empirically relevant?
• Lucas (1990): Human Capital Externalities if all knowledge
spillovers are local
 All benefits of the country’s stock of human capital accrue
entirely to producers within the country (?)
• Which benefits do really end in the border? (Rules, laws…)
2. What role do institutions play for capital flows?
Aim of the Paper
 Investigate the role of different theoretical explanations for the
Lucas Paradox in a systematic empirical study.
• What role do institutional quality play for capital flows?
Results:
For the period 1970-2000, the most important variable in
explaining the Lucas Paradox is:
Institutional Quality
Institutions and Economic Growth
• Countries with better institutions -- secure property rights -invest more in physical capital, use factors more efficiently and
achieve greater level of income.
(North, 1981; Jones and Hall, 1998; Acemoglu et al. 2001,
2002).
• We find that “good institutions” also shape international capital
flows.
Outline
• Introduction and Motivation
• Data
• Empirical Results
– Main Results
– Robustness
– Endogeneity
• Conclusions
Many robustness – I
won’t have time to go
over everything –
Empirical Strategy: Long Term Analysis
1) Cross-sectional regression – whole sample (1970 - 2000).
2) Cross-sectional regressions – sub-periods.
LHS Variables
•
Average Capital Inflows per Capita
Explain issues with debt:
measurement after Debt
crisis – government –
consumption smoothing + we
want private decision.
Capital Inflows
For our question distinction
FDI – portfolio not relevant
Inflows of Equity
Portfolio
FDI
Inflows of Debt
LHS: Net Inflows of Capital
1. Inflows of Capital: Inflows of direct and portfolio equity
investment, 81 countries (WS: 98), 1970-2000, (IMF, IFS).
2. Inflows of Capital (FDI + portfolio): Change in the stock of
foreign claims on domestic capital; 58 countries (WS: 61),
1970-1997, (Kraay, Loayza, Serven and Ventura, 2000, 2005)
(KLSV).
3. Inflows of Capital (FDI+ portfolio): Change in the stock of
portfolio equity and direct investment liabilities; 56 countries,
(WS: 60), 1970-1998, (Lane and Milessi-Feretti, 2001) (LM).
4. Inflows of Capital + Inflows of Debt: 3 + change in the stock
of portfolio debt liabilities and other investment liabilities; 56
countries, 1971-1998, (LM).
RHS Variables: Fundamentals
• Measure of Lucas Paradox
– GDP per capita (PPP and non PPP)
– Capital stock per capita
• Missing Factors
– Initial values of human capital (years of total schooling,
higher schooling)
• Government Policies
– Restrictions: Capital Controls (IMF, AREAER).
• Institutional Quality
As North (1995) argues, institutions provide the incentive structure of an economy.The work by North (1981) and
2002) argue that institutions - social, legal and political organizations of a society - shape its economic
performance. Institutions, most likely, affect economic performance through their effect on investment decisions
by protecting the property rights of entrepreneurs against the government and other segments of society and
preventing elites from blocking the adoption of new technologies. In general, weak property rights due to poor
institutions can lead to lack of productive capacities or uncertainty in returns in an economy.
Fundamentals: Institutions
• North (1995) defines institutions as the humanly devised constraints
that structure political, economic and social interaction;
- Informal constraints (traditions, customs)
- Formal rules (constitutions, laws, property rights)
* Rules of the game  Incentive structure of the Economy
• How can we measure Institutional Quality?
– Composite Political Safety Index (ICRG)
Institutions: Composite Political Safety Index
–
–
–
–
–
–
–
–
–
–
–
Government Stability (0-12)
Internal Conflict (0-12)
External Conflict (0-12)
No-Corruption (0-6)
Investment Profile (0-12)
Non-Militarized Politics (0-6)
Protection form Religious Tensions (0-6)
Law and Order (0-6)
Protection form Ethnic Tensions (0-12)
Democratic Accountability (0-6)
Bureaucratic Quality (0-6)
Source: ICRG
RHS Variables: Robustness for Fundamentals
 Inflation Volatility
 Land
 Government Infrastructure (paved roads)
Inst – asymmetric info
 Each component of the capital controls index; removal
of as one or
clearly define
the other
capital controls
Some are hard to
 Corporate Taxes
 Restrictions to and Incentives for foreign investment
 Trade
 TFP (residual)
 Financial Market Development (credit and capital markets)
RHS Variable: Capital Market Imperfections
• Asymmetric Information (frictions in information flows)
– Distantness: weighted average of the distance form the capital city
of a country to the capital cities of the other countries, using GDP
shares as weights; (Wei and Wu, 2001; Coval and Moskowitz,
2001; Portes and Rey, 2002; Kalemli-Ozcan et al., 2003).
– Reuters: Number of times a country is quoted in Reuters, by Doug
Bond.
– Accounting standards (transparency)
– Foreign Banks: share of banks in total with 10% (50%) foreign
capital.
• Sovereign Risk: Sovereign Ratings (S&P), Moody’s.
Inflows of Direct and Portfolio Equity per Capita 1996 US$
Equity Inflows per Capita to Rich and Poor Countries,
1970-2000
29,000
24,000
19,000
14,000
9,000
4,000
-1,000
1970-1974
1975-1979
Rich Countries
1980-1984
1985-1989
1990-1994
Poor Countries
1995-2000
OLS Regression of Capital Inflows per capita – IMF Flows Data
The partial R^2 is 0.0 for the log GDP per capita, whereas it is 0.13 for the index of institutions as seen by comparing columns (3) and (4).
To get a sense of the magnitude of the effect of institutional quality on inflows of direct and portfolio equity investment per capita, let's
consider two countries such as Guyana and Italy: if we move up from the 25 percentile (Guyana) to the 75 percentile (Italy) in the
distribution of the index of institutions, based on the results shown in column (4), we have 187.54 dollars more inflows per capita over the
sample period on average. This represents a 60% increase in inflows per capita over the sample mean, which is 117.34 dollars, therefore it
has quite an effect.
OLS Regression of Capital Inflows per capita – IMF Flows Data II
Partial Correlations
OLS Regression of Capital Inflows per capita – KLSV Flows Data
Robustness I: OLS Regression of Capital Inflows per capita – KLSV Flows Data
Robustness II: OLS Regression of Capital Inflows per capita – KLSV Flows Data
Robustness III: OLS Regression of Capital Inflows per capita – KLSV Flows Data
Robustness V: OLS-- Capital Inflows per capita – KLSV Data: Institutions ICRG
Robustness IV: OLS Regression of Capital Inflows per capita – KLSV Flows Data
Robustness V: OLS Regression of Capital Inflows per capita – LM Flows Data
Robustness IV: OLS Regression of Capital Inflows per capita – Debt Flows, LM Data
Multicollinearity: Diagnostic Tests
• Residual regressions
– Variable-specific component of the institutions index
(residual regression inst. on GDPpc ) has explanatory power;
the variable-specific component of GDPpc does not.
• Monte Carlo simulations (fake data with characteristics of our
data).
• Perturbation exercise based on Beaton, Rubin, and Barone
(1976).
• Condition index as in Belsley (1991).
• None of the robustness regressions show any big sign and
magnitude change (typical indicators of multicollinearity).
Endogeneity
• Capital flows can generate incentives to reform and create
investor friendly environments, (Rajan and Zingales, 2003)
• Ex-post bias –perceptions
 Regress Capital inflows (1985-1997) on pre-sample
institutions (1984).
 Empirical Strategy: IV Estimation (mortality – 34 countries).
OLS Regression of Capital Inflows per capita – KLSV Data: Initial Values
IV Regression of Capital Inflows per capita – KLSV Flows Data
Conclusions
• We investigate the role of the different theoretical explanations for the
Lucas Paradox in an empirical framework.
– Institutional Quality is the most important factor that explains the
Lucas Paradox between 1971-1998.
• Our work is silent on: how to get “good” institutions:
– Not easy!
• Welfare implications and growth effects of capital flows:
– Institutions also matter for the effectiveness of capital flows on
growth (Alfaro et al., 2003; Eichengreen, 2003; Klein, 2003)
* *Better institutions: attract foreign capital + allow host countries to
maximize benefits of such investments.
Robustness VI: OLS -- Capital Inflows per capita – KLSV Flows Data: Institutions, Polity
Test of Instruments – KLSV Flows Data
OLS Regression of Capital Inflows per capita – IMF Flows Data II
Partial Correlations