capital account liberalization: theory, evidence, …hutch/241b/ec 241b syllabus...journal of...

Journal of Economic Literature Vol. XLV (December 2007), pp. 887–935

Capital Account Liberalization: Theory,Evidence, and Speculation

PETER BLAIR HENRY∗

Research on the macroeconomic impact of capital account liberalization finds few, ifany, robust effects of liberalization on real variables. In contrast to the prevailing wis-dom, I argue that the textbook theory of liberalization holds up quite well to a criticalreading of this literature. Most papers that find no effect of liberalization on real vari-ables tell us nothing about the empirical validity of the theory because they do notreally test it. This paper explains why it is that most studies do not really address thetheory they set out to test. It also discusses what is necessary to test the theory andexamines papers that have done so. Studies that actually test the theory show that lib-eralization has significant effects on the cost of capital, investment, and economicgrowth.

887

1. Introduction

Acapital account liberalization is a deci-sion by a country’s government to move

from a closed capital account regime, wherecapital may not move freely in and out of thecountry, to an open capital account system inwhich capital can enter and leave at will.Broadly speaking, there are two starkly dif-ferent views about the wisdom of capitalaccount liberalization as a policy choice fordeveloping countries.

The first view, Allocative Efficiency, drawsheavily on the predictions of the standardneoclassical growth model pioneered byRobert M. Solow (1956). In the neoclassicalmodel, liberalizing the capital account facili-tates a more efficient international allocationof resources and produces all kinds of salu-brious effects. Resources flow from capital-abundant developed countries, where thereturn to capital is low, to capital-scarcedeveloping countries where the return tocapital is high. The flow of resources into the

∗ Henry: Stanford University, Brookings Institution,and National Bureau of Economic Research. I gratefullyacknowledge financial support from a NSF CAREERAward, the John and Cynthia Fry Gunn FacultyFellowship, and the Freeman Spogli Institute forInternational Studies, and the Stanford Center forInternational Development. I thank Anusha Chari, RogerGordon, Diana Kirk, John McMillan, Diego Sasson, andtwo anonymous referees for extensive comments on earli-er drafts. Barry Eichengreen, Kristin Forbes, MichaelHutchison, Don Lessard, Ross Levine, Karen Lewis, Rick

Mishkin, Paul Romer, Andrew Rose, Jay Shambaugh,René Stultz, Romain Wacziarg, and seminar participantsat the Brookings Institution, the Federal Reserve Bank ofSan Francisco, the International Monetary Fund,Stanford, Washington University in St. Louis, U.C.-SantaCruz, and Yale also provided helpful comments. SandraBerg provided stellar editorial assistance. Finally, I thankSir K. Dwight Venner and the Eastern Caribbean CentralBank for hosting me in the summer of 1994, when I firststarted thinking about the issues addressed in the paper.

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developing countries reduces their cost ofcapital, triggering a temporary increase ininvestment and growth that permanentlyraises their standard of living (StanleyFischer 1998, 2003; Maurice Obstfeld 1998;Kenneth S. Rogoff 1999; Lawrence H.Summers 2000). Motivated in part by theprospective gains from incorporatingAllocative Effciency arguments into theireconomic policies, dozens of developing-country governments from Santiago to Seoulimplemented some form of capital accountliberalization during the past quarter century.

The alternative view regards AllocativeEfficiency as a fanciful attempt to extend theresults on the gains to international trade ingoods to international trade in assets. Thepredictions of Allocative Efficiency hold onlywhen the economy suffers from no distor-tions other than barriers to free capital flows.Because there are many other distortions indeveloping countries, skeptics argue that thetheoretical predictions of the neoclassicalmodel bear little resemblance to the realityof capital account policy. Dani Rodrik’s(1998) provocatively titled article “WhoNeeds Capital Account Convertibility?” bestcharacterizes this view.1 Rodrik’s empiricalanalysis finds no correlation between theopenness of countries’ capital accounts andthe amount they invest or the rate at whichthey grow. He concludes that the benefits ofan open capital account, if indeed they exist,are not readily apparent, but that the costsare manifestly evident in the form of recur-rent emerging-markets crises.

In the wake of Rodrik’s polemic, evidenceseemingly continues to mount in support ofthe view that capital account liberalizationhas no impact on investment, growth, or anyother real variable with significant welfareimplications. For example, in his survey ofthe research on capital account liberaliza-tion, Barry Eichengreen (2001) concludesthat the literature finds, at best, ambiguous

evidence that liberalization has any impacton growth. In another review of the litera-ture, Hali J. Edison et al. (2004) survey tenstudies of liberalization and document thatonly three uncover an unambiguously posi-tive effect of liberalization on growth.Finally, in their comprehensive survey of theresearch on financial globalization, Eswar S.Prasad et al. (2003) extend the Edison et al.(2004) survey to fourteen studies, but stillfind only three that document a significantpositive relationship between internationalfinancial integration and economic growth.Prasad et al. (2003) conclude that “. . . anobjective reading of the vast research effortto date suggests that there is no strong,robust, and uniform support for the theoret-ical argument that financial globalization perse delivers a higher rate of economicgrowth.”

In contrast to existing surveys, this articledemonstrates that a critical reading of theliterature reveals that the textbook theory ofliberalization stands up to the data quitewell. It is true that most papers find no effectof liberalization on growth. But these paperstell us nothing about the empirical validity ofthe theory. They perform purely cross-sec-tional regressions that look for a positive cor-relation between capital account opennessand economic growth, implicitly testingwhether capital account policy has perma-nent effects on differences in long-rungrowth rates across countries. The funda-mental problem with this approach is thatthe neoclassical model provides no theoreti-cal basis for conducting such tests. Themodel makes no predictions about the cor-relation between capital account opennessand long-run growth rates across countries,and certainly does not suggest the causal linkneeded to justify cross-sectional regressions.

What the neoclassical model does predictis that liberalizing the capital account of acapital-poor country will temporarilyincrease the growth rate of its GDP percapita. The temporary increase in growthmatters, because it permanently raises the

888 Journal of Economic Literature, Vol. XLV (December 2007)

1 See also Jagdish Bhagwati (1998) and Joseph E.Stiglitz (1999, 2000, 2002).


country’s standard of living. However, as it isthe increase in the level of GDP per capitathat is permanent—not its rate of growth—theory dictates that one tests for either a per-manent level effect or a temporary growtheffect. Testing for a permanent growth effectmakes no sense because capital accumula-tion, which is subject to diminishing returns,is the only channel through which liberaliza-tion affects growth in the neoclassical model.

A small but growing branch of the litera-ture takes the time series nature of the neo-classical model’s prediction seriously. It doesso by investigating whether countries investmore and grow faster in the immediateaftermath of a discrete change in their capi-tal account policy. In contrast to work thatlooks for a permanent impact of capitalaccount openness on growth across coun-tries, papers in the policy-experiment genrefind that opening the capital account withina given country consistently generates eco-nomically large and statistically significanteffects, not only on economic growth butalso on the cost of capital and investment.

While the policy-experiment literatureremoves any serious doubts about whetherliberalization has real effects, its findingsraise more questions than they resolve. Arethe magnitudes of the documented effectsplausible? Do the transmission mechanismsemphasized by the theory really drive theresults or are other forces at work? With theirfocus on aggregate data, papers in the policy-experiment genre do not have enough empir-ical power to be of any use. Fortunately, anew wave of papers demonstrates how tomake progress on these and other importantquestions by using firm-level data. Analyzingcapital account liberalization at the level ofthe firm instead of the country providesgreater clarity about the channels throughwhich liberalization affects the real economy.

Disaggregating the data also brings clarityto the contentious debate on liberalization andcrises. Because there are many different waysto liberalize the capital account, when tryingto determine whether liberalizations cause

crises, it is critical to specify exactly what kindof liberalization you mean. At a minimum, thedistinction between debt and equity is critical.Recent research demonstrates that liberaliza-tion of debt flows—particularly short-term,dollar-denominated debt flows—may causeproblems. On the other hand, all the evidencewe have indicates that countries derive sub-stantial benefits from opening their equitymarkets to foreign investors.

The rest of this paper proceeds as follows.Section 2 presents an organizing theoreticalframework. Section 3 reviews the literatureon the cross-sectional approach to the macro-economic effects of capital account liberaliza-tion. Section 4 uses the framework fromsection 2 to explain why the cross-sectional lit-erature finds no real effects of liberalization.Section 5 discusses the policy-experimentapproach to the macroeconomic effects of lib-eralization. Section 6 discusses problems withthe policy-experiment approach. Section 7reviews recent advances using firm-level data.Section 8 examines whether liberalizationscause crises. Section 9 concludes.

2. Capital Account Liberalization and theNeoclassical Growth Model

This section illustrates the fundamentalpredictions of the neoclassical growth modelabout the impact of capital account liberal-ization on a developing country. The frame-work is not novel, but it brings clarity to thediscussion of the empirical literature thatfollows in section 3.

Assume that output is produced using cap-ital, labor, and a Cobb–Douglas productionfunction with labor-augmenting technologicalprogress:

(1) .

Let k = A⎯KL be the amount of capital per

unit of effective labor and y = AY⎯L the amount

of output per unit of effective labor. Usingthis notation and the homogeneity of theproduction function we have:

Y F K AL K AL= ( ) = ( ) −, α α1

889Henry: Capital Account Liberalization


(2) .

Let s denote the fraction of nationalincome that is saved each period andassume that capital depreciates at the rate δ,the labor force grows at the rate n, and totalfactor productivity grows at the rate g.Saving each period builds up the nationalcapital stock and helps to make capital moreabundant. Depreciation, a growing popula-tion, and rising total factor productivity allwork in the other direction making capitalless abundant. The following equation sum-marizes the net effect of all these forces onthe evolution of capital per unit of effectivelabor:

(3) k.(t) .

When k.(t) = 0, the economy is in the steady

state depicted by point A in figure 1. Atpoint A, the ratio of capital to effective labor(k) is constant. In contrast, the steady-statelevel of capital (K) is not constant, but grow-ing at the rate n + g. Output per worker (Y−L)grows at the rate g. Finally, the steady statemarginal product of capital equals the interestrate plus the depreciation rate:

sf k t n g k t( )= ( ) − + +( ) ( )δ

y f k k= ( ) = α (4) .

Equation (4) gives a general expression ofthe equilibrium condition for investment.This equation has important implications forthe dynamics of a country’s investment andgrowth in the aftermath of capital accountliberalization because the impact of liberal-ization works through the cost of capital. Letr∗ denote the exogenously given world inter-est rate. The standard assumption in the lit-erature is that r∗ is less than r because therest of the world has more capital per unit ofeffective labor than the developing country.It is also standard to assume that the devel-oping country is small, which means thatnothing it does affects world prices.

Under these assumptions, when thedeveloping country liberalizes, capital surgesin to exploit the difference between theworld interest rate and the country’s rate ofreturn to capital. The absence of any fric-tions in the model means that the country’sratio of capital to effective labor jumpsimmediately to its postliberalization, steady-state level. Figure 1 depicts this jump as arightward shift of the vertical dashed line

′ = +f k rs state( ). δ


Figure 1. Capital Account Liberalization in the Neoclassical Growth Model

ks.state kk∗s.state

A

sf(k)

(n + g + δ)kOutput Per Unit ofEffective Labor


from ks.state to k∗s.state. In the postliberaliza-

tion steady state, the marginal product ofcapital equals the world interest rate plus therate of depreciation:

(5) .

On the one hand, the immediate jump tothe new steady state illustrates the problemwith cross-sectional regressions writ large:instantaneous convergence generates a one-time spike in the growth rate of output perworker in the year of liberalization followedby a return to normalcy in all subsequentyears. Cross-sectional regressions designed tomeasure long-run differences in growth ratesacross countries are ill-suited to detect theshort-lived changes implied by instantaneousconvergence.

On the other hand, it is reasonable toregard instantaneous convergence as anunattractive feature of the model ratherthan a serious practical challenge to thecross-sectional regression approach. Section4.1 demonstrates in detail that the inabilityof cross-sectional studies to detect theimpact of liberalization on growth remains aproblem under slower, more realistic speedsof transition. For now, the vital point to noteis that, during the country’s transition to thepostliberalization steady state, the capitalstock grows faster than it did before the tran-sition. To see why, recall that in the preliber-alization steady state the ratio of capital toeffective labor (ks.state) is constant and thestock of capital (K) grows at the rate n + g. Inthe postliberalization steady state, the ratioof capital to effective labor (k∗

s.state) is alsoconstant and the capital stock once againgrows at the rate n + g. However, becausek∗

s.state > ks.state, it follows that the growthrate of K exceeds n + g during the transition.

The temporary increase in the growth rateof capital has implications for economicgrowth through the formula for the growthrate of output per worker:

k.

γY = �− + g.L− k

′ ( ) = +f k rs state.∗ ∗ δ

Since the growth rate of K exceeds n + gduring the transition, k

.−k must be greater than

0 during the corresponding interval of time.Therefore, the growth rate of output perworker also increases temporarily.

Figure 2 illustrates the hypotheticalresponse of the interest rate, the growthrates of capital and output per worker, andthe natural log of output per worker underthe more palatable assumption that theinterest rate converges immediately uponliberalization but the ratio of capital toeffective labor does not. Figures 3 through5 (see pages 906–08), which display thereal-life responses of the cost of capital,investment, and growth during eighteencapital account liberalization episodes from1986 to 1991, bear a striking resemblanceto the panels in figure 2. Section 5 discuss-es the construction of figures 3 through 5in detail, but even this preliminary glanceat the data raises an obvious issue. If thesimple time series evidence lines up withthe theory, then why does a large literaturefind no real effects of liberalization? I havealready hinted at part of the answer, butthe full story requires a more thoroughaccount of the previous literature.

3. The Cross-Sectional Approach toMeasuring the Impact of LiberalizationDoes capital account liberalization pro-

mote a more efficient allocation of resourcesacross countries? This section describes themethodology, data, and results associatedwith the traditional, cross-sectional approachto this question.

Attempts to measure the impact of capitalaccount liberalization on economic perform-ance require information on countries’ capi-tal account policies. The InternationalMonetary Fund’s (IMF) Annual Report onExchange Arrangements and ExchangeRestrictions (AREAER) provides the mostreadily available, standardized source ofsuch information. Available since 1967, theAREAER provides a list of the rules and reg-ulations governing resident and nonresident




Figure 2. The Impact of Liberalization on the Cost of Capital, Investment, and Growth

Interest Rate

Growth Rate of K

r

r∗

to

Panel A: The Cost of Capital

t

tot

to

to

t

t

Panel B: Investment

n + g

Panel D: Level of GDP Per Worker

Panel C: Growth Rate of GDP per WorkerGrowth Rate of Y−L

ln (Y−L)

g

Permanent effect of liberalization


capital-account transactions in each country,a table that summarizes the presence ofrestrictions, and a qualitative judgment onwhether the country has an open or closedcapital account. For the editions of AREAERpublished between 1967 and 1996, the sum-mary table contains a single line (line E2)entitled, “Restrictions on payments for capi-tal transactions.” The presence of a bulletpoint in line E2 indicates that the countryhas some form of restrictions on capitalaccount transactions. In effect, line E2 deliv-ers a binary judgment that takes on the valueone if any capital controls are in place andzero otherwise.

The typical study maps the qualitativeinformation from line E2 into a quantitativemeasure of capital account openness by tal-lying the number of years that each countrywas judged to have an open capital account.That tally is then divided by the total num-ber of years in the period to produce a num-ber called SHARE—the fraction of yearsover a given period that the IMF judged thecountry to be free from “restrictions on pay-ments for capital account transactions.” Forexample, if a country had an open capitalaccount for three of the ten years from 1986to 1995, then SHARE is equal to 0.3.

3.1 Empirical Methodology

Papers that use the variable SHARE assessthe real effects of capital account liberaliza-tion by running some variant of the followingcross-country growth regression:

(6).........GROWTHi = a0 + a1 • SHAREi

+ CONTROLSi + εi,

where GROWTHi denotes the averagegrowth rate of real GDP per capita in coun-try i over the time period in question—typi-cally about twenty years—and the variableCONTROLSi serves as a compact stand-infor the usual set of control variables used incross-country growth regressions in the tradi-tion of Robert J. Barro (1991). With the vari-able SHARE in hand, estimating equation (6)is a relatively straightforward exercise

because data on the left-hand-side variableare readily available from standard sourcessuch as the IMF’s International FinancialStatistics.

Because equation (6) does not containtime subscripts, it makes no use of the varia-tion in growth rates or openness over timewithin a given country. In other words, itidentifies the impact of capital account poli-cy exclusively through the cross-countryvariation in average growth rates and frac-tion of years open. In effect, the estimationprocedure tests for a permanent impact ofliberalization on growth, measuring whethercountries with open capital accounts havehigher long-run growth rates than countrieswhose capital accounts are closed.

The problem with this test is that we havealready seen (in section 2) that the neoclassi-cal model does not predict that countrieswith open capital accounts will have higherlong-run growth rates than countries withclosed capital accounts. In the neoclassicalmodel, differences in long-run growth ratesacross countries are driven exclusively bydifferences in their growth rates of total fac-tor productivity (TFP). Because it containsno channel through which capital accountliberalization affects TFP growth, strictlyspeaking, the model provides no theoreticalbasis for estimating equation (6).2 In otherwords, papers that estimate the effects ofcapital account openness on growth do notprovide a test of any causal theory. What thetheory does predict is that capital-poor coun-tries will experience a permanent fall in theircost of capital and a temporary increase inthe growth rate of their capital stock andGDP per capita when they liberalize.

Testing whether countries with more opencapital accounts invest more or grow faster


2 Foreign direct investment (FDI) is one form of capi-tal inflow that could bring technology in addition to capi-tal. If FDI brings new technology, it could generateknowledge spillovers that result in TFP growth. Section5.4 considers the empirical relevance of this considerationas well as other extensions of the neoclassical model inwhich liberalization might have an impact on TFP growth.


than countries with closed capital accounts isnot logically equivalent to testing whethercountries that liberalize experience a tempo-rary increase in investment and growth.Moreover, the following example demon-strates that there are nontrivial examples inwhich the purely cross-sectional regressionframework leads to specious conclusionsabout the data.

3.1.1 Numerical Example of the Pitfalls ofthe Cross-Sectional RegressionApproach

Consider two countries (A and B) that arein steady state and have the same level andgrowth rate of total factor productivity.Country A has a closed capital account whilecountry B has no capital account restrictionswhatsoever. Assume that both countries aresmall and that the world interest rate r∗ islower than r, the domestic interest rate incountry A. Since country B is open and facesthe world interest rate, r∗, country B willhave a higher ratio of capital to effectivelabor than country A. Because both coun-tries are in steady state and have identicalgrowth rates of total factor productivity, theywill also have identical growth rates of GDPper capita (although B has a higher level ofGDP per capita).

Now consider the path of these twoeconomies over the next twenty years underthe following scenario: country A keeps all ofits restrictions on capital in place for the firstten years but removes all restrictions on cap-ital inflows and outflows for the second tenyears while country B, on the other hand,makes no change in its capital account poli-cy and remains completely open for theentire twenty-year period.

The transition dynamics in this examplefollow immediately from the discussion insection 2. Country B continues to grow at itssteady-state rate because nothing there haschanged. To understand what happens tocountry A when it liberalizes, recognize thatas soon as it does so it will face the same costof capital as country B. Therefore, when

country A reaches its new steady state, it willhave the same ratio of capital to effectivelabor as country B. For country A to reach itsnew, higher, steady-state ratio of capital toeffective labor, the growth rate of its capitalstock must exceed its growth rate of effectivelabor throughout the transition to its newequilibrium. Since A began in a steady statewhere capital and effective labor were grow-ing at the same rate, the growth rate of A’scapital stock must increase temporarily.

The temporary increase in the growth rateof A’s capital stock will also generate a tempo-rary increase in the growth rate of its GDP percapita. Since GDP per capita in A and B weregrowing at the same rate before the shock, itfollows that A grows faster than B throughoutA’s transition to its new steady state. BecauseA grows faster than B throughout the transi-tion, it also follows that A will have a higheraverage growth rate for the twenty-year peri-od under consideration than country B.

What would we conclude about theimpact of capital account openness ongrowth if, using data from multiple countrieslike A and B, we ran a regression ofGROWTH on SHARE? The answer is thatwe would conclude that capital accountopenness has a negative impact on growth.To see why, remember that A’s capitalaccount was open for ten years and closedfor ten. This means that SHARE for countryA is 0.5, a smaller number than the value ofSHARE for country B, which is equal to 1.Since A has faster growth and a lower valueof SHARE over the period than country B, itfollows that regressing GROWTH onSHARE would produce a negative coeffi-cient on SHARE, with the attendant (andspecious) conclusion that liberalizationexerts a negative impact on growth. Yet inthis example it is clear that country A’s deci-sion to liberalize capital inflows does lead toa temporary increase in growth. The strictlycross-sectional regression framework is sim-ply powerless to detect the effect.

Although the cross-sectional approach doesnot test the predictions of the neoclassical


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growth model, a large number of papers useequation (6), or a close cousin, as theirbenchmark of analysis. Of the fourteen stud-ies of liberalization and growth summarizedin table 3 of the survey by Prasad et al.(2003), twelve perform cross-sectionalregressions. The corresponding numbersfrom other surveys are nine of ten in table 6of Edison et al. (2004); eleven of twelve intable 1 of César Calderón, Norman Loayza,and Klaus Schmidt-Hebbel (2004); andtwenty-two of twenty-five in M. Ayhan Koseet al. (2006). Given the prevalence of thecross-sectional regression approach in theliterature on the macroeconomic effects ofcapital account liberalization, it is importantto spell out what these papers do and theresults they find.

3.2 Evidence

Attempts to quantify the real effects ofcapital account liberalization using SHAREhave not been kind to the AllocativeEfficiency view. Most of the publishedpapers in the cross-sectional genre find noeffect of liberalization on investment orgrowth. In the most prominent example ofsuch papers, Rodrik (1998) examines therelation between liberalization and growthin a sample of 100 developed and developingcountries from 1975 to 1989. Specifically,Rodrik estimates regressions with averagegrowth rates of GDP per capita on the left-hand side and SHARE on the right-handside. His regressions show no statistically sig-nificant correlation between growth andSHARE. Since the theoretical channel fromliberalization to growth operates throughcapital accumulation, Rodrik also regressesthe average ratio of investment-to-GDP onSHARE and again finds no statistically sig-nificant correlation. He concludes that thereis no evidence to suggest that countries withfewer restrictions on capital movementsgrow faster, or invest more, than countrieswith greater restrictions.

One problem with the variable SHARE isthat it is constructed from a binary measure of

capital controls that treats all countries aseither completely closed or completely open.As such, it does not allow for varying degreesof intensity across countries that are classifiedas open. In an attempt to address this prob-lem, Dennis Quinn (1997) uses the informa-tion in the AREAER to create a measure ofthe intensity of capital account openness.Quinn’s measure splits capital account trans-actions into two categories: capital accountreceipts and capital account payments. Foreach of the two categories, he assigns a coun-try a score between 0 and 2 based on a read-ing of the qualitative description of theAREAER reports. A score of 0 indicates themost restrictive of regimes, where paymentsfor capital transactions are completely forbid-den, while a score of 2 indicates that capitaltransactions are not subject to any taxes orrestrictions. Intermediate scores—brokeninto increments of 0.5—indicate intermediatelevels of restrictiveness. Quinn then adds thescores for the two categories to create a vari-able called CAPITAL, which ranges from 0 to4. Quinn’s indicators are available annuallyfrom 1950 to 1997 for twenty-oneOrganisation for Economic Co-operation andDevelopment (OECD) countries. For theforty-three non-OECD countries in his sam-ple, they are available for four different years:1958, 1973, 1982, and 1988. Quinn runscross-sectional regressions of average annualgrowth rates on changes in CAPITAL andfinds a positive and significant correlation.

Quinn’s data provide a novel attempt atquantifying the intensity of capital accountopenness, but there are two reasons whythey may be of limited use for understand-ing the impact of capital account liberaliza-tion on developing countries. First, Quinn’sdata for non-OECD countries end in 1988and, therefore, do not cover the most rapidperiod of capital account liberalization inthe developing world (the late 1980s andearly 1990s). Given the time span of hisdata, Quinn’s results may reflect the impactof the move from closed to open capitalmarkets among the developed nations. For



example, in 1973 eleven OECD countriesand twenty-six developing countries scored2 or lower on Quinn’s index. In 1988, thenumber of OECD countries with a score of2 or lower fell to one. In contrast, the num-ber of developing countries with a score of 2or lower rose to thirty-two. Second, Quinn,like Rodrik, also uses a strictly cross-section-al regression framework, so his results implythat easing restrictions on capital accounttransactions have permanent effects ongrowth. Since theory predicts only a tempo-rary effect, it is not clear how to interpretthis result (more on this in section 4.1).

Whatever the correct interpretation ofQuinn’s results may be, in finding a positiveeffect of liberalization on growth, his studyis an outlier among the most heavily citedpapers that assess the macroeconomicimpact of capital account liberalizationusing cross-sectional regressions. Forinstance, using data on a sample of devel-oped and developing countries from 1976to 1995, Ross Levine and Sara Zervos(1998b) examine whether capital accountliberalization has an effect on investment.Like Rodrik and Quinn, they also test forpermanent effects, but use a differentmeasure of liberalization. Specifically,Levine and Zervos ask whether countriesexperience a permanent increase in thegrowth rate of their capital stocks whentheir stock markets become more integrat-ed with the rest of the world. They arguethat emerging stock markets became moreintegrated in the 1980s and use 1985 as acommon break point across all countries.Next, they examine whether the growthrate of the capital stock of the countries intheir sample increased after 1985. Theyfind no evidence that this is the case andconclude that capital account liberalizationhas no effect on investment.

Among the entire class of papers that testwhether countries with more open capitalaccounts have permanently higher growthrates than those that do not, the bulk of evi-dence supports the findings of Rodrik (1998)

and Levine and Zervos (1998b) over Quinn(1997).3 For example, of the ten studies ofliberalization surveyed in Edison et al.(2002), only three find an unambiguouslypositive effect of liberalization on growth(see their table 6). Prasad et al. (2003)extend the Edison et al. survey to fourteenstudies, but still find only three papers thatuncover a significant positive relationshipbetween international financial integrationand economic growth.

A number of papers debate whether theabsence of significant evidence is a conse-quence of trying to find an unconditionalcorrelation between openness and long-rungrowth when the relationship between thetwo variables is more complicated. Becausecountries with strong institutions stand togain more from capital account liberaliza-tion, the relationship between growth andcapital account policy may be a conditionalone that depends on the strength of a coun-try’s institutions.4 Michael W. Klein andGiovanni P. Olivei (2007) explore thishypothesis using data on sixty-seven coun-tries from 1976 to 1995. They find a signifi-cant correlation between SHARE andgrowth, but the developed countries in thesample drive the results. There is no signifi-cant correlation between growth andSHARE for the non-OECD countries, lead-ing the authors to conclude that capitalaccount liberalization only promotes finan-cial development when done in an environ-ment that is supportive in terms of its overallinstitutional development.

Sebastian Edwards (2001) uses Quinn’sCAPITAL measure and the variableSHARE to conduct formal tests for a condi-tional relationship between capital controlsand long-run growth rates in a sample oftwenty industrial and emerging economiesduring the 1980s. To test whether the


3 See also Alberto Alesina, Vittorio Grilli, and GianMaria Milesi-Ferretti (1994), Grilli and Milesi-Ferretti(1995), Aart Kraay (1998), and Edison et al. (2002).

4 See Kose et al. (2006) for an extensive review of theliterature on the conditional effects of liberalization.


impact of capital account policy variesaccording to a country’s level of develop-ment, Edwards interacts each country’scapital account openness measure with itslevel of GDP per capita in 1980. He findsevidence that an open capital account posi-tively affects growth after a countryachieves a certain degree of economicdevelopment.

Carlos Arteta, Eichengreen, and CharlesWyplosz (2001) take exception withEdwards’s results on the grounds that he (1)uses Quinn’s measure in 1973 and 1988 only;(2) weights observations by GDP per capitain 1985, thereby giving richer countries ahigher weight; and (3) uses instruments thatmay not be appropriate. Using data on sixty-one countries from 1973 to 1992, Arteta,Eichengreen, and Wyplosz perform cross-sectional regressions using Ordinary LeastSquares and Instrumental Variables. Theyfind a fragile association between growth andcapital account liberalization and concludethat there is little evidence to suggest thatcapital account liberalization has a largerimpact in high- and middle-income countriesthan in poor developing ones.

Like Arteta, Eichengreen, and Wyplosz(2001), Kraay (1998) also finds no evidence ofa conditional relationship between capitalaccount liberalization and growth.5 In fact,while future work may prove otherwise, itseems fair to say that the absence of signifi-cant results in the cross-sectional literature isnot a consequence of failing to condition theirtests on the level of institutional develop-ment.6 Rather, the next section demonstratesthat the ostensible lack of an effect of liberal-ization on real variables stems from a numberof more basic issues common to papers in thecross-sectional literature—those that test for

the conditional and unconditional impact ofliberalization on growth alike.

4. The Reasons Why Cross-SectionalStudies Find No Impact of Liberalization

on Growth

There are at least three reasons why stud-ies that use purely cross-sectional regres-sions find no impact of liberalization ongrowth (although not all three reasons applyto all studies). First, as mentioned earlier,cross-sectional studies test for a permanenteffect of liberalization on growth when the-ory says that the impact should be tempo-rary. Second, many of the papers in thisliterature include both developed anddeveloping countries in the sample whentheory argues for a separate examination ofthe two types of countries.7 Third, the vari-able SHARE contains important sources ofmeasurement error that hinder empiricalattempts to capture the impact of liberaliza-tion on the real economy. I now discuss theempirical consequences of each of thesethree points.

4.1 What Does “Temporary” Mean in theContext of the Neoclassical Model?

The extent to which it really matterswhether you test for a permanent versustemporary growth effect of liberalizationdepends on exactly what “temporary” meansin the context of the neoclassical model.Testing for a permanent effect of liberaliza-tion probably does little harm to the data ifthe growth rate of output remains substan-tially above its steady-state value for rough-ly the same length of time as the intervalover which cross-sectional studies computethe growth rates used in their regressions(thirty years or so). If the period of elevatedeconomic growth is significantly shorter,


5 Kraay’s paper also conducts within-country analysessimilar in spirit to the policy-experiment approach dis-cussed in section 5. He finds no impact of liberalization,but the dates in his analysis come from a different meansof identifying liberalization episodes.

6 This is not to deny the importance of institutionalconsiderations.

7 Some of the papers in this literature do acknowledgedifferences between countries (see the discussion in sec-tion 3.3). But papers that do acknowledge the importanceof country heterogeneity also use the SHARE variable totest for permanent effects of liberalization.


however, then the distinction between per-manent and temporary matters a great deal.

The critical question, then, is the follow-ing: For how many years following a liber-alization is it reasonable to expectcapital-deepening-induced growth that islarge enough to detect with a cross-section-al growth regression? There are two casesto consider. The first case—instantaneousconvergence—was dismissed in section 2 asan artifact of the neoclassical model. Wenow consider the second case—when thespeed of transition is slower, in accordancewith reality.

There are a variety of formal ways to slowdown the speed of transition. In the discus-sion which follows, I do so by treating thetransition from ks.state to k∗

s.state as if it cameabout from a permanent increase in a closedcountry’s savings rate. The savings-rateassumption offers several advantages indetermining how long the growth effect ofliberalization remains detectable.

First, an increase in the savings rate hasthe same qualitative implications as a liberal-ization: for a given r and r∗, there exists apermanent increase in the savings rate suchthat the country’s new steady state ratio ofcapital to effective labor following theincrease would be exactly the same as undera liberalization. Second, the savings-rateassumption facilitates a more concise exposi-tion than introducing adjustment costs (orsome other theoretical friction) to slowdown the installation of capital. Third, andmost importantly, the speed of convergenceunder the savings-rate assumption is actual-ly slower than it would be in the canonicalmodel of a small open economy with adjust-ment costs (see Barro and Xavier Sala-i-Martin 1995, chapter 2). A slower speed ofconvergence implies a longer period of timeduring which the economy’s output growthremains elevated above its steady-statevalue. This means that relative to other ana-lytical methods of slowing down the transi-tion dynamics, the savings-rate assumptionemployed in the next several paragraphs

actually understates the importance of testingfor a temporary effect and therefore over-states the statistical power of cross-sectionalregressions.

By how much does the country’s growthrate in each year following the liberalizationexceed the growth rate that would haveoccurred had the liberalization never takenplace? To answer this question, recall thatthe growth rate of output per worker at anypoint in time is:

k.(t)

.A(t)γY(t) = � ⎯− + ⎯⎯ .

L− k(t) A(t)

Before liberalization, the economy is insteady state with k.(t)⎯k(t)− = 0 and

.A(t)⎯A(t)− = g. Be-cause liberalization has no impact on totalfactor productivity in the model, it followsthat the deviation of the growth rate of out-put per worker from its steady-state value(g) in the aftermath of liberalizationequals k.(t)⎯k(t)−. To calculate the size of the out-put growth deviation at any point in time, wetherefore need to know the entire time pathof k(t) during the economy’s transition to itsnew steady state. Since we are treating con-vergence from ks.state to k∗

s.state as though itcomes from an increase in the savings rate,standard arguments show that:

(7) ,

where λ = (1 − �)(n + g + δ).Using equation (7) to compute the time

path of k(t) requires values of three keyparameters: ks.state—the steady-state ratio ofcapital to effective labor before liberaliza-tion, k∗

s.state—the postliberalization steady-state ratio of capital to effective labor, andλ —the speed of convergence.

Pinning down the parameters ks.state andk∗

s.state requires assumptions about the dif-ference between the autarky interest rate inthe developing country and the interestrate in the rest of the world. I begin byassuming that the autarky interest rate inthe developing country is twice as high asthe world interest rate, with r = 0.16 and

k t k k k es state s state s statet( ) ( )= + − −

. . .∗ ∗ λ



r∗ = 0.08. At these rates, ks.state = 2.15 andk∗

s.state = 4.63.8The simplest way to pin down λ is to plug

reasonable values for the capital share (�),population growth (n), total factor produc-tivity growth (g), and the depreciation rate(δ) into the formula λ = (1 − �)(n + g + δ).The combination of a capital share of one-third, population growth and TFP growthbetween 1 and 2 percent, and depreciationof 4 percent, places λ at about 4 percent peryear. Alternatively, one can obtain the speedof convergence from cross-country growthregressions. Early estimates implied speedsof convergence of about 2 percent per year(N. Gregory Mankiw, David Romer, andDavid N. Weil 1992; Barro and Sala-i-Martin1992). Subsequent work argues that the 2percent estimates are inaccurate for a varietyof methodological reasons (FrancescoCaselli, Gerardo Esquivel, and FernandoLefort 1996). Caselli, Esquivel, and Lefortfind that the speed of convergence is about10 percent per year when estimated appro-priately. In what follows, I perform calcula-tions with a speed of convergence in themid-range of those used in the literature(λ = 0.04). The basic conclusion—that thereis no statistically detectable impact of liber-alization on growth over long horizons—remains intact for speeds of convergence atthe high (λ = 0.1) and low (λ = 0.02) ends ofthe spectrum.

With λ = 0.04, ks.state = 2.15, and k∗s.state

= 4.63, equation (7) implies that the averagegrowth rate of k(t) in the first five years fol-lowing liberalization is large: 3.8 percentper year. Multiplying 3.8 by the elasticity ofoutput with respect to capital (1/3), givesthe average deviation of the growth rate of

output per worker from its long-run steady-state value during the first five years postlib-eralization: 1.27 percentage points per year.

After year 5, the impact of liberalizationon growth drops off considerably. Fromyears six through thirty, the average annualgrowth rate of k(t) is 0.0154 or 1.5 percentper year. This translates into an averageannual growth rate of output per workerthat is 0.0051, or 0.51 percentage pointsabove its steady-state value. I focus on thegrowth impact in years six and beyondbecause this period constitutes the differ-ence between the policy experimentapproach to measuring the impact of liber-alization (see figures 3 through 5) and thecross-sectional regression approach. Giventhat difference, the key question is the fol-lowing: Can a cross-sectional regression dis-tinguish a liberalization-induced growtheffect of 0.51 percentage points per yearfrom the usual noise in the data? Comparingthe magnitude of the hypothetical growtheffect (0.0051) to that of the standard errorswe typically see in regressions of growth onSHARE provides a rough and ready answerto the question. For instance, Rodrik (1998)reports standard errors of 0.006 on the vari-able SHARE. Dividing 0.0051 by 0.006gives a t-statistic of 0.85—less than half themagnitude required for statistical signifi-cance. In contrast, the t-statistic on theaverage effect over the first five years is 2.11(.0127 divided by 0.006).

Changing the speed of convergence hasno material impact on the calculations.When λ = 0.02, the average annual growtheffect of liberalization between years six andthirty is 0.0041, with a corresponding t-sta-tistic of 0.69. For λ = 0.1, the average annu-al growth effect over the same horizon is0.0045, with a t-statistic of 0.75. Similarly,increasing the autarky rate interest rate inthe developing country to three times thelevel in the rest of the world also does notalter the results. Now, it is far from clear thatthe rate of return to capital in developingcountries is even twice as high as in the


8 From the first order condition for investment, equa-tion (4), it follows that

.

Plugging in r = 0.16, δ = 0.04, and � = 1/3 givesks.state = 2.15. The analogous set of calculations gives thevalue for k∗

s.state.

kr

s state. = +⎛⎝⎜

⎞⎠⎟

−δα

α1

1


developed world (see section 6.1.2), so play-ing with a threefold differential begins tostrain credulity. Nevertheless, I do so toillustrate the likely futility of the cross-sectional approach. Holding λ constant at0.04 and increasing r to three times the levelof r∗, the average annual growth effect risesto 0.008. With standard errors on the orderof 0.006, a growth effect of 0.008 is still toosmall to attain statistical significance in across-sectional regression.

One potential concern about the calcula-tions in this subsection is that they focus ongrowth in GDP per worker while cross-sectional studies of liberalization focus ongrowth in GDP per capita. If the growth rateof the labor force exceeds the growth rate ofthe population, then calculations of statisti-cal significance using growth in GDP perworker will be smaller than calculationsbased on growth in GDP per capita, therebyunderstating the true statistical power ofcross-sectional regressions.

Data on the growth of the labor force andpopulation in developing countries speak tothe issue. Between 1960 and 1997, the pop-ulation in low- and middle-income develop-ing countries grew at an average annual rateof 2.2 percent per year while the labor forcegrew at 2.1 percent per year (World Bank1999). Since the growth rate of the popula-tion actually exceeded the growth rate of thelabor force, if anything, calculations usingoutput per worker actually overstate theimpact of liberalization on the growth rate ofGDP per capita.

In other words, the calculations in thissubsection underscore the empirical rele-vance of the critique that cross-sectionalregressions are unlikely to capture the trueimpact of capital account liberalizationbecause they test for permanent instead oftemporary effects. Although the impact ofliberalization on growth remains positive fora long time, the statistically significant por-tion of that impact occurs in the immediate-to-near aftermath of liberalization. Trying todetect this near-term change by running

cross-sectional regressions on averagegrowth rates over long time horizons is notan effective empirical strategy.

4.2 Country Heterogeneity

The second reason why cross-sectionalstudies may fail to find significant effects ofliberalization is that they lump both devel-oped and developing countries together intheir samples. Including both sets of coun-tries increases sample size, but doing sowithout employing an empirical methodolo-gy that explicitly recognizes the fundamen-tal theoretical difference betweendeveloped and developing countries under-mines a study’s ability to interpret the data.The neoclassical model predicts that capitalaccount liberalization will have a differentimpact on a developing country than on adeveloped one. Developing countries arecapital poor and should experience net cap-ital inflows, a permanent fall in their cost ofcapital, and a temporary increase in growthwhen they remove all restrictions on capitalflows. In contrast, capital-rich, developedcountries should experience exactly theopposite effects.9 For instance, with bothdeveloping and developed countries includ-ed in their samples, the Rodrik (1998) andLevine and Zervos (1998b) results may sug-gest that capital account liberalization hasno effect on investment and growth, but theresults may also reflect the opposing effectsof liberalization in the developing- anddeveloped-country subsamples.

Developing and developed countries alsoimplemented the process of capital accountliberalization at different times. Developedcountries liberalized in the late 1970s andearly 1980s (Helmut Reisen and BernhardFischer 1993). Most developing countriesdid not liberalize until the late 1980s andearly 1990s (Donald J. Mathieson and


9 Although the rich country’s GDP falls because itexports capital, its GNP will rise as it receives the incomethat accrues to that capital. I discuss the Lucas (1990)paradox in section 5.


Liliana Rojas-Suárez 1993). The differencesin liberalization dates highlight an addition-al limitation of the Rodrik paper. Sincedeveloping-country liberalizations com-menced in the late 1980s and early 1990s,empirical attempts to determine whetherliberalization boosted investment andgrowth should use data that extend into the1990s. Because Rodrik’s data end in 1989,they exclude developing-country economicperformance in precisely the period whenwe would expect to see the effects of liber-alization. Therefore, it is not clear thatRodrik’s data tell us anything about theimpact of capital account liberalization ongrowth in developing countries.10

4.3 Measurement Error Problems withLiberalization Indicators

Cross-sectional studies conclude that capi-tal account liberalization has no real effectsbecause they do not find any correlationbetween economic growth and broad meas-ures of capital account openness such asSHARE. As discussed in section 3.1, studiesconstruct the variable SHARE using the judg-ment on capital account openness containedin line E2 of the IMF’s AREAER. In turn, thejudgment in the AREAER is based on therestrictions applied to an exhaustive list of allpossible capital account transactions.11 Whenthe IMF changes its assessment of a country’scapital account openness, the AREAER pro-vides no information on the specific aspect ofthe capital account the country liberalized.Because the underlying data provide no indi-cation of what has been liberalized, neithercan any index that is based on such data.Without any indication of what drives thevariation in SHARE, it is also unclear how tomap that variation to a well-articulated modelfor the purpose of empirical estimation.

For example, AREAER does not tellwhether the IMF made a change in thejudgment about the openness of a country’scapital account because of an easing ofrestrictions on capital inflows or capital out-flows. This distinction matters. Theory pre-dicts that when a capital-poor countryliberalizes capital inflows it will experience apermanent fall in its cost of capital and atemporary increase in the growth rate of itscapital stock and GDP per capita. In princi-ple, if that same developing country were toliberalize capital outflows, nothing wouldhappen to its cost of capital, investment, orGDP.

If the goal is to understand whether capi-tal account liberalization has real effects,then theory tells us that the right question toask is the following: Do we see a fall in thecost of capital and a temporary increase ininvestment and growth after developingcountries liberalize restrictions on capitalinflows? The most direct and transparentway of answering this question is to examinewhether the behavior of the cost of capital,investment, and growth in the immediateaftermath of liberalization differs from itsbehavior in the immediately preceding peri-od. We now discuss a class of papers whichattempt to do just that.

5. The Policy-Experiment Approach toCapital Account Liberalization

Examining the behavior of real variablesbefore and after the removal of restrictionson capital inflows requires informationabout the date on which the restrictionswere lifted. The policy-experiment approachgrapples with the complexity of identifyingliberalization dates by narrowing the scopeof the problem. Instead of trying to deter-mine the date on which the entire capi-tal account was judged to be open, as in the cross-sectional approach, the policy-experiment literature tries to identify thefirst point in time that a country liberalizes aspecific aspect of its capital account policy.


10 Figure 5A of Kose et al. (2006) looks at the data from1985 to 2004. Like Rodrik, they find no significant rela-tionship between growth and capital account openness.The discussion in section 4.1 of this paper explains why.

11 For an extensive enumeration of these transactions,see the appendix of Kose et al. (2006).


One example of liberalizing a specificaspect of the capital account is a decision bya country’s government to permit foreignersto purchase shares of companies listed on thedomestic stock market. Liberalizing restric-tions on the ownership of domestic sharesenables foreign capital to flow into a part ofthe country’s economy from which it was pre-viously prohibited. Just such a policy changeoccurred repeatedly in the late 1980s andearly 1990s as a number of developing coun-tries opened their stock markets to foreigninvestors for the first time (Stijn Claessensand Moon-Whoan Rhee 1994). These stockmarket liberalizations constitute discreteremovals of barriers to capital inflows thatbear close resemblance to the model in sec-tion 2. As the closest empirical analogue tothe textbook example, stock market liberal-izations provide real-world policy experi-ments that allow researchers to evaluate theempirical validity of the two competing viewsof capital account liberalization outlined inthe introduction.

Since the stock market is forward looking,the critical question is when does the marketfirst learn of a credible, impending liberal-ization? In principle, obtaining this informa-tion simply involves identifying the date onwhich the government declares that foreign-ers may purchase domestic shares. In prac-tice, the liberalization process is not sotransparent. The literature uses official poli-cy decree dates when they are available, butin many cases there is no obvious govern-ment declaration or policy decree to whichone can refer (see, for example, Levine andZervos 1994). When policy decree dates arenot available, papers in this area typicallypursue two approaches.

First, many countries initially permittedforeign ownership through closed-end coun-try funds. Since one presumably needs gov-ernment permission to establish a fund, thefirst country fund establishment date servesas a proxy for the official implementationdate. The second way of indirectly capturingofficial implementation dates is to monitor

the International Finance Corporation’sInvestibility Index (Claessens and Rhee1994). The investibility index is the ratio ofthe market capitalization of stocks that for-eigners can legally hold to total market capi-talization. As such, a large jump in theinvestibility index provides indirect evidenceof an official liberalization. In the end, thepolicy-experiment literature defines the dateof a country’s first stock market liberalizationas the first month with a verifiable occur-rence of any of the following: liberalizationby policy decree, establishment of the firstcountry fund, or an increase in the investibil-ity index beyond a certain threshold.12

An important limitation of the policy-experiment approach is that relatively fewdeveloping countries have a stock market,publish reliable stock market data, and alsoimplemented a liberalization. Hence, thenumber of countries that can feasibly beincluded under the stock-market-liberaliza-tion-based implementation of the policy-experiment approach is much smaller thanin purely cross-sectional studies (an issue Idiscuss further in section 6.1.3).

Table 1 presents a list of eighteen devel-oping countries that liberalized their stockmarkets between 1986 and 1993. The thirdcolumn of the table indicates that the modalindicator of liberalization for this sample isthe establishment of a closed-end countryfund. Relative to the broader indicatorsemployed elsewhere in the literature, theestablishment of a country fund, in particu-lar, and stock market liberalizations in gen-eral may seem like a narrow way to definecapital account liberalization.

But again, it is precisely the narrowness ofstock market liberalizations that make themmore useful than broad indicators of capitalaccount liberalization for testing the implica-tions of the theory. For all of the reasonsexplained in section 4.3, broad indicators,


12 See Geert Bekaert and Campbell R. Harvey (2000)and Peter Blair Henry (2000b) for further details ondetermining the dates of stock market liberalizations.


such as the IMF’s, introduce significantmeasurement error. Since measurementerror reduces the statistical power of anyregression, it is important to focus on policyexperiments where the true variation in thedata is large relative to the noise. Stock mar-ket liberalizations provide just such experi-ments, because they constitute a radical shiftin the degree of capital account openness(Jeffrey A. Frankel 1994).

In addition to identifying episodes of largechanges in capital account openness, focus-ing narrowly on stock market liberalization

offers another advantage. In contrast withthe unspecified easing of restrictions indi-cated by movements in the variableSHARE, there is no theoretical ambiguityabout the expected impact of lifting restric-tions on the flow of capital into the stockmarket of a developing country. Since stocksare risky, however, using stock market liber-alizations to examine the impact of capitalaccount liberalization on economic per-formance raises issues of uncertainty notaddressed by the deterministic frameworkoutlined in section 2.


TABLE 1DATES OF STOCK MARKET LIBERALIZATIONS AND MAJOR ECONOMIC REFORMS

Country Year of Means of Stabilization Trade Privatization Brady PlanLiberalization Liberalization Program Liberalization Debt Relief

Argentina November 1989 Policy Decree November 1989 April 1991 February 1988 April 1992Brazil March 1988 Country Fund January 1989 April 1990 July 1990 August 1992Chile May 1987 Country Fund August 1985 1976 1988 NAColombia December 1991 Policy Decree NA 1986 1991 NAIndia June 1986 Country Fund November 1981 1994 1991 NAIndonesia September 1989 Policy Decree May 1973 1970 1991 NAJordan December 1995 Policy Decree May 1994 1965 January 1995 June 1993Malaysia May 1987 Country Fund NA 1963 1988 NAMexico May 1989 Policy Decree May 1989 July 1986 November 1988 September 1989Nigeria August 1995 Policy Decree January 1991 NA July 1988 March 1991Pakistan February 1991 Policy Decree September 1993 2001 1990 NAPhilippines May 1986 Country Fund October 1986 November 1988 June 1988 August 1989South June 1987 Country Fund July 1985 1968 NA NAKoreaTaiwan May 1986 Country Fund NA 1963 NA NAThailand September 1987 Country Fund June 1985 Always Open 1988 NATurkey August 1989 Policy Decree July 1994 1989 1988 NAVenezuela January 1990 Policy Decree June 1989 May 1989∗ April 1991 June 1990Zimbabwe June1993 Policy Decree September 1992 NA 1994 NA

Notes: The capital account liberalization dates identified in this table are the dates on which the eighteen countriesin Column 1 eased restrictions prohibiting foreign ownership of domestic stocks. The liberalization dates in Column2 are an amalgamation of those in Henry (2000a), Bekaert and Harvey (2000), Kim and Singal (2000), and Levineand Zervos (1994). Column 3 indicates the form of the liberalization, policy decree or country fund opening;Section 5 of the paper contains a detailed description of both forms. Columns 4 through 7 list the dates of majoreconomic reforms that occurred around the same time as the capital account liberalizations. The stabilization pro-gram dates in column 4 come from Henry (2002) and various issues of the IMF Annual Reports. Column 5 liststrade liberalization dates from Sachs and Warner (1995). The privatization dates in column 6 come from thePrivatization Data Base maintained by the World Bank. Finally, column 7 lists the month and year that each countryreceived debt relief under the Brady Plan. The debt relief dates come from Cline (1995), Lexis Nexis, and variousissues of the Economist Intelligence Unit. ∗Venezuela reversed its trade liberalization reforms in 1993.


5.1 Extending the Theoretical Frameworkto Incorporate Uncertainty

This subsection shows that the funda-mental predictions of the deterministicneoclassical model of capital account liber-alization extend to the risky-asset setting ofthe policy-experiment approach.13 In theabsence of uncertainty, it is optimal toinvest until the marginal product of capitalequals the interest rate. When the payofffrom investing in capital is risky, the inter-est rate is no longer the relevant hurdle ratefor investment. Optimality in a risky worldrequires that investment take place untilthe expected marginal product of capitalequals the interest rate plus a risk premiumto compensate for the uncertain return tocapital.

For a country that has not yet opened itsstock market to the rest of the world, thismeans that the first-order condition forinvestment is f ′(k)e = r + θ + δ, where f ′(k)e

denotes the expected marginal product ofcapital, r is the domestic interest rate, θ isthe aggregate equity premium, and δ is therate of depreciation of the capital stock. Inthe context of the capital asset pricing model(CAPM), θ is equal to the price of risk, γ,times the variance of the market return, sothat the first-order condition for investmentunder uncertainty is:

(8) (r∼M) + δ,

where r∼M is the risky return to investing inthe market.

Starting from the equilibrium in equation(8), the issue is whether liberalizationreduces the cost of capital as it does whenthere is no uncertainty.14 The first compo-nent of the cost of capital is the domestic

′ = +f k r Vare( ) γ

interest rate (r), which responds the sameway to liberalization as in the case of perfectcertainty—it falls to the world interest rate(r∗). The second component of the cost ofcapital is the aggregate equity premium.Whereas the equity premium before liberal-ization is equal to the price of risk times thevariance of the local market return, the equi-ty premium after liberalization is equal tothe price of risk times the covariance of thelocal market with the rest of the world (seeStulz 1999b).

The change in both components of thecost of capital means that following liberal-ization the first-order condition for invest-ment becomes:

(9) (r∼M,r∼W) + δ.

Because the world interest rate (r∗) is lessthan the domestic interest rate (r), compar-ing the right-hand-side of equation (9) withthat of (8) indicates that liberalization willreduce the cost of capital if the variance ofthe domestic market return is greater thanits covariance with the world market.Historical stock returns show that this condi-tion holds for emerging stock markets (Stulz1999b). Stock market liberalization there-fore reduces the cost of capital, and theattendant predictions about investment andgrowth follow accordingly.

5.2 Evidence: The Cost of Capital,Investment, and Growth

We do not observe the cost of capitaldirectly, but we do observe stock prices.Because stock prices and the cost of capitalmove inversely, we should observe a one-time revaluation of the stock market if lib-eralization reduces the cost of capital. In asample of twelve emerging economies thatliberalized between 1986 and 1991, theaverage country experienced a revaluationof 26 percent in real dollar terms (Henry2000b). E. Han Kim and Vijay Singal (2000)and Rodolfo Martell and Stulz (2003) alsodocument a sharp rise in stock prices in

′ = +f k r Cove( ) ∗ γ


13 The discussion in this section borrows heavily fromJeremy I. Bulow and Summers (1984) and René M. Stulz(1999b).

14 For expositional convenience, I assume that liberal-ization has no effect on the marginal product of capital.Section 7.1 discusses what happens when this assumptiondoes not hold.


conjunction with liberalizations.15 Consis-tent with these revaluations, liberalizationsalso coincide with an average fall in dividendyields of 5 to 75 basis points (Bekaert andHarvey 2000).16 While the magnitude of theeffect differs slightly depending on the sam-ple of countries and the exact liberalizationdates applied, there is broad agreementacross all policy-experiment studies that lib-eralization reduces the cost of capital.

Since liberalizations reduce the cost ofcapital, some projects that were negative netpresent value before liberalization becomeworthwhile to undertake once the govern-ment permits foreigners to hold domesticshares. Consequently, the rate of capitalaccumulation should rise until the marginalproduct of capital is driven down to thecountry’s new, lower cost of capital. If invest-ment increases temporarily, so too shouldthe growth rate of GDP per capita.

The policy-experiment approach tests theinvestment and growth predictions by esti-mating panel regressions of the followingform:

(10)

.

The variable GROWTH genericallydenotes the growth rate of the variable ofinterest: the capital stock in the case of theinvestment regressions and GDP per capita.The variable LIBERALIZE is a country-spe-cific dummy variable that takes on the valueone in the year that country i liberalizes(year [0]) and each of the subsequent fiveyears (year [+1] through year [+5]).

Equation (10) highlights two importantdimensions along which the policy-experi-ment approach differs from the traditional

a LIBERALIZEit it+ +1 ∗ εGROWTH ait = 0

cross-sectional regression approach typifiedby equation (6). First, equation (10) usesevery annual observation of each country’sgrowth rate to exploit the variation in growthrates within countries over time. In contrast,equation (6) uses a single number on theleft-hand-side—the average growth rateover the time period in question.

Second, the definition of the variable LIB-ERALIZE ensures that equation (10) testsfor a temporary effect of liberalization ongrowth instead of a permanent one as inequation (6). If liberalization causes a tem-porary increase in growth, then estimatingequation (10) should produce a positive andsignificant estimate of the coefficient on thevariable LIBERALIZE. In other words, thecoefficient on LIBERALIZE measures theaverage annual abnormal growth during thefive-year window subsequent to the imple-mentation of a liberalization. It is natural toask why policy-experiment studies choose afive-year liberalization window and how sen-sitive the results are to that choice. It followsfrom the discussion in section 4.1 that theresults should be sensitive to the choice ofwindow length and that a short window offive years or less is theoretically appropriate.

The behavior of both investment andGDP in the aftermath of liberalizations sup-ports the predictions of the neoclassicalmodel. For the eighteen countries in table 1,the raw growth rate of the capital stock risesfrom an average of 5.4 percent per year inthe five years preceding liberalization to anaverage of 6.5 percent in the five-yearpostliberalization period (Henry 2003).Similarly, in a sample of eleven emergingeconomies, stock market liberalization leadsto a 22-percentage-point increase in thegrowth rate of real private investment(Henry 2000a). Of the eleven countries inthe sample, only two do not experienceabnormally high rates of investment in thefirst year after liberalization, and only one inthe second year after liberalization. Bekaert,Harvey, and Christian Lundblad (2005) findthat the growth rate of GDP per capita


15 A related literature shows that firms in emergingmarkets experience stock price increases when they listtheir shares on stock exchanges in developed countries(see G. Andrew Karolyi 1998 and Stephen R. Foersterand Karolyi 1999).

16 See also Vihang R. Errunza and Darius P. Miller(2000).


increases by a percentage point per annum.An important question that I defer until section 6.3 is whether the growth and investment numbers are mutually consistent.

Figures 3 through 5 summarize the prin-cipal findings of the policy-experiment liter-ature by plotting the cost of capital,investment, and growth in event time forthe eighteen countries listed in table 1.17

When developing countries liberalize capi-tal flows into their stock markets, the cost ofcapital falls and investment increases alongwith the growth rate of GDP per capita. In

contrast to the cross-sectional studiesreviewed in section 3, the evidence from thepolicy-experiment approach demonstratesthat the real-world impact of capital accountliberalization is quite consistent with theAllocative Efficiency view.

5.3 Economic Reforms

The policy-experiment evidence is alsoconsistent with interpretations in which lib-eralization plays no causal role. Figure 3,which uses the dividend yield as a proxy forthe cost of capital, helps to demonstrate thepoint. The theoretical justification for inter-preting a fall in the dividend yield as a reduc-tion in the cost of capital comes from MyronJ. Gordon (1962). In the Gordon model, the


17 The data used to construct figure 4, the averagegrowth rate of the real capital stock for the eighteen coun-tries in listed in table 1, come from Barry P. Bosworth andSusan M. Collins (2003).

Figure 3. The Cost of Capital Falls When Countries Liberalize the Capital Account

Notes: Figure 3 plots the average value of the aggregate dividend yield in event time for the eighteencountries listed in table 1. The x axis denotes time in years relative to the liberalization year: “0” denotesthe year in which the liberalization took place, “−1” denotes the year prior to liberalization, “+1” denotesthe year after liberalization, and so on.

6

5.5

5

4.4

4

3.5

3

2.5

2−5 −4 −3 −2 –1 0 1 2 3 4 5

Year Relative to Liberalization

Div

iden

d Yi

eld

(Per

cent

)


dividend yield equals the cost of capital, ρ,minus the (constant) expected future growthrate of dividends: D /P = ρ − ge.18 If thegrowth rate of dividends does not changewith liberalization, then a fall in the dividendyield implies a fall in the cost of capital.Because liberalizations are part of a generalprocess that involves substantial macroeco-nomic reforms, however, there is a strongpossibility that large changes in expectedfuture growth rates do occur at the sametime that countries liberalize.

Columns 4 through 7 of table 1 demon-strate that the timing of economic reforms—inflation stabilization, trade liberalization,and privatization—poses a significant chal-lenge to the central policy-experiment find-ings reviewed in section 5.2. Stabilizinginflation drives up asset prices, investment,and output (Michael Bruno and William

Easterly 1998; Guillermo A. Calvo andCarlos A. Végh 1999; Easterly 1996; Fischer,Ratna Sahay, and Végh 2002; Henry 2000a,2002). When a country liberalizes trade,domestic production shifts toward the coun-try’s comparative advantage, thereby increas-ing its output for a given stock of capital andlabor. Accordingly, trade liberalization alsogenerates a temporary increase in the growthrate of GDP and a permanent increase in thelevel (Anne O. Krueger 1997, 1998; JeffreyD. Sachs and Andrew M. Warner 1995;Frankel and Romer 1999; Romain Wacziargand Karen Horn Welch 2003). Privatizationraises the efficiency and value of formerlystate-owned enterprises (William L.Megginson and Jeffry M. Netter 2001).Finally, in return for adopting reforms, manycountries received debt relief under theBrady Plan. Debt relief also has a positiveimpact on stock prices, investment, and out-put in certain emerging economies (SerkanArslanalp and Henry 2005, 2006a, 2006b).


Figure 4. Investment Booms When Countries Liberalize the Capital AccountNotes: Figure 4 plots the average growth rate of the aggregate capital stock in event time for the eighteencountries listed in table 1. The x axis denotes time in years relative to the liberalization year: “0” denotesthe year in which the liberalization took place, “−1” denotes the year prior to liberalization, “+1” denotesthe year after liberalization, and so on.

7.5

7

6.5

6

5.5

5

4.5

4

Gro

wth

Rat

e of

the

Cap

ital S

tock

(Pe

rcen

t)

Year Relative to Liberalization−5 −4 −3 −2 −1 0 1 2 3 4 5

18 Recall that ρ = r + γVar(r~M) before liberalization andρ = r∗ + γ COV(r~M,r~W) after liberalization.


Papers in the policy-experiment literatureapproach adopt different methods of trying todisentangle the impact of liberalization fromthat of other economic reforms. Some paperstreat reforms and liberalization symmetrical-ly, constructing dummy variables that take onthe value one in the year a reform program ofa particular type begins and each of the fivesubsequent years.19 Using the variableREFORMS as a compact stand-in for the fullcomplement of the reform dummies, the keyregression then simply becomes an extensionof equation (10):

(11)

+ .

Other papers, instead of conducting abefore-and-after analysis of reforms, use

REFORMSit i+ ε tt

GROWTH a a LIBERALIZEit it= +0 1 ∗

continuous proxies such as the level of infla-tion and trade openness.20 Kose et al. (2006)discuss the relative merits of both approach-es on page 19 of their survey.

5.4 Policy Endogeneity

The impact of stock market liberalizationon stock prices, dividend yields, investment,and growth remains statistically and eco-nomically significant after controlling forreforms (Bekaert and Harvey 2000; Bekaert,Harvey, and Lundblad 2005; Henry 2000a,2000b). But an even more difficult interpre-tation problem remains. Do liberalizationsdrive up stock prices, investment, and eco-nomic growth or does causation run theother way round? Because liberalizing thestock market during bad times may draw


Figure 5. The Growth Rate of GDP Per Capita Rises When Countries Liberalize the Capital Account

Notes: Figure 5 plots the average growth rate of GDP per capita in event time for the eighteen countries listed in table1. The x axis denotes time in years relative to the liberalization year: “0” denotes the year in which the liberalization tookplace, “−1” denotes the year prior to liberalization, “+1” denotes the year after liberalization, and so on.

5

4.5

4

3.5

3

2.5

2

1.5

1

.5

0

Gro

wth

Rat

e of

GD

P Pe

r C

apita

(Pe

rcen

t)

Year Relative to Liberalization

−5 −4 −3 −2 −1 0 1 2 3 4 5

19 See, for example, Henry (2000b).20 See, for instance, Bekaert and Harvey (2000) and

Bekaert, Harvey, and Lundblad (2005).


criticism for selling off the country at firesale prices, politicians may be may be moreinclined to open up when times are good(Summers 1994). Liberalization may also bethe country-level equivalent of an initialpublic offering (Martell and Stulz 2003). Youdon’t have an IPO when things are goingpoorly; you have one when valuations arehigh because of exceptional growth opportu-nities that require financing or because theshares are simply overvalued. To the extentthat the economy’s current and expectedfuture performance influence policymakers’decision to liberalize, the endogeneity prob-lem is quite real.

In trying to think of a legitimate instru-mental variable for this problem, one isstruck by the distinct lack of variables thatare correlated with the decision to liberal-ize but uncorrelated with the stock marketor macroeconomic fundamentals. Changesof political regime are a natural candidatebut, in addition to being correlated with lib-eralizations, political changes also bringnew economic programs that influencemarket expectations. IMF programs do notmake good instruments because, contraryto popular wisdom, the IMF did not insiston capital account liberalization as an ele-ment of conditionality during its programsat the time (see Fischer 1998). Becausenone of the papers in the policy-experimentliterature are fully able to dispel with con-cerns about endogeneity, the economicallylarge and statistically significant correla-tions between liberalization and real vari-ables uncovered by the policy-experimentliterature require a measured interpreta-tion.

6. Problems with the Policy-ExperimentApproach

The strength of the policy-experimentapproach is that it provides a clear picture ofwhat happens to developing countries whenthey liberalize capital inflows. The weaknessof the policy-experiment approach is that it

raises more questions than it answers (evenputting aside the issue of endogeneity raisedat the end of section 5). This section of thepaper addresses three of those questions.

6.1 Why Is the Financial Impact ofLiberalization So Small?

Liberalization drives up the value of thestock market by roughly 30 percent in realdollar terms. This is a large number relativeto most event studies of the stock marketconducted using U.S. data (see A. CraigMacKinlay 1997). But it is small in compari-son with theoretical predictions about theimpact of capital account liberalization.Casual observation suggests that developingcountries have much lower ratios of capital tolabor than rich countries. If the neoclassicalmodel provides a reasonable description ofthe world, then we should observe a muchlarger financial impact when developingcountries permit capital flows into their stockmarkets (Stulz 1999a, 1999c, 2005).

There are at least three possible explana-tions for the relatively modest increase instock prices (decrease in the cost of capital)associated with liberalizations. The first is thatthe policy-experiment approach treats liberal-ization as a one-shot event when it is really anincremental process. The second explanationis that the rate of return to capital in develop-ing countries may not really be that muchhigher than in the developed world (RobertE. Lucas 1990; Mankiw 1995). The third isthat developing countries do have substantial-ly higher rates of return but the presence ofcapital market imperfections causes much ofthe return differential to persist, even in theface of free capital flows. The next three sub-sections elaborate on each of these points inturn.

6.1.1 Treating Liberalization as a BinaryProcess May Understate Its TrueImpact

The policy-experiment literature esti-mates the change in the cost of capital thatoccurs the first time a country opens its stock



market to foreign investors. This approachpresents three problems that could poten-tially explain the relatively small valuationeffects. First, stock price effects at the dateof liberalization may be small if the liberal-izations were anticipated. Stock prices doincrease in anticipation of liberalization butthe preliberalization run-up in valuations isnot large enough to account for the puzzle(Henry 2000b).

Second, investors may perceive that theliberalization will be reversed. If foreigninvestors believe that the government willreverse the policy at some point in thefuture, then they will value domestic sharesless highly than if the policy change was fullycredible. Although Malaysia in 1997 is theonly case of an actual reversal (see section6.2), liberalizations may suffer from a sort ofpeso problem that reduces the magnitude ofthe revaluation.21

The third problem is that countries sel-dom move from having a completely closedstock market to one that is fully open. Mostcountries undergo several stock market lib-eralizations subsequent to the first. SouthKorea provides a good example. SouthKorea began allowing foreigners very limit-ed access to its stock market through closed-end country funds as early as 1982, but didnot start lifting its statutory ceiling on for-eign investment until 1992.22 Even then,South Korea did not finish lifting its regula-tory ceiling until 1998 (Inseok Shin andChangyun Park forthcoming).

If a country takes several episodes toopen its stock market completely, then look-ing exclusively at the stock market responseto its initial opening may understate thetotal financial impact of the liberalization

process (Stulz 1999b). One way of address-ing the issue is to estimate the marketresponse to every opening, sum up all of theresponses, and use the resulting number asa proxy for the total liberalization effect. Itturns out that stock market responses to lib-eralizations subsequent to the first are fairlysmall, so the incremental-opening hypothe-sis cannot account for the small financialimpact of liberalization.

Another alternative is to estimate the totalfinancial impact using continuous measuresof liberalization such as the IFC’s investibleindex (Edison and Francis E. Warnock2003). Again, the investible index indicatesthe fraction of the domestic stock marketcapitalization that foreign investors maylegally own. In contrast to the policy-experi-ment approach, which estimates the one-time response of the stock market at thetime of a large, discrete change in theinvestible index, the continuous approachregresses returns or dividend yields on thelevel of the investibility index over the entiresample. The continuous approach alsoreveals a small change in the cost of capital.The estimated cumulative fall in the divi-dend yield is about 140 basis points (FrankDe Jong and Frans A. De Roon 2005). All inall, the evidence suggests that the dichotomybetween the binary measure of liberalizationused by policy-experiment papers and themore gradual nature of the process cannotexplain the relatively small financial impactof liberalizations.

6.1.2 Poor Institutions in DevelopingCountries May Reduce the Return toCapital

Large differences in capital-to-laborratios across countries imply large differ-ences in rates of return to capital only if allcountries have the same level of total factorproductivity. In standard production theory,the level of total factor productivity is cap-tured by the parameter A, which is usuallytaken to be an index of technology or ideas.But it is important to remember that, for a


21 See Rawi Abdelal and Laura Alfaro (2002, 2003) fora detailed discussion of the imposition of capital controlsin Malaysia. The reversal of open capital markets by Thaiauthorities in December 2006 lasted less than 24 hours,but the sharp impact it had on valuations reinforces thepoint.

22 See Keith Park and Antoine W. Van Agtmael (1993),Margaret M. Price (1994), and Ian M. Wilson (1992).


given stock of technology, total factor pro-ductivity measures the general efficiencywith which an economy transforms capitaland labor into output (Solow 2001). Holdingtechnology constant, many factors mayinfluence the general level of efficiency.

For instance, weak institutions and inap-propriate government regulation can distorteconomic decision making, reduce total fac-tor productivity, and lead to lower rates ofreturn than would otherwise prevail (ArnoldC. Harberger 1998). The rate of return tocapital in emerging economies may not bemuch higher than in rich countries if emerg-ing-market governments fail to create “anenvironment that supports productive activ-ities and encourages capital accumulation,skill acquisition, invention and technologytransfer” (Robert E. Hall and Charles I.Jones 1999).

To illustrate the point, table 2 demonstratesthe gap between the G-7 countries and theeighteen emerging economies that liberalizedtheir stock markets using some frequentlyemployed measures of the quality of econom-ic institutions. Column 2 shows that, accord-ing to the Hall and Jones (1999) measure ofsocial infrastructure, the median G-7 countryranks fourteenth of 130 countries, while themedian emerging economy ranks sixty-fourth. Columns 3 through 5, which presentanalogous measures using the HeritageHouse’s Index of Economic Freedom, theWorld Bank’s Ease of Doing Business Index,and the World Economic Forum’s GlobalCompetitiveness Index, display a similarpattern.

In other words, the relatively small finan-cial effects of liberalization may simply indi-cate that the return to capital in developingcountries is not that much higher than in thedeveloped world. Recent work producesindirect estimates of rates of return that sup-port this view (see, for example, Caselli 2005and Caselli and James Feyrer 2007). Thereare still relatively few studies that attempt tomeasure the rate of return to capital indeveloping countries directly. In my view,

this continues to be an important gap in theliterature.

6.1.3 Capital Market Imperfections

A third explanation for the relatively smallfinancial impact of liberalization is thatdeveloping countries really do have higherrates of return to capital but the presence ofcapital market imperfections, such as agencyproblems, asymmetric information, and poorinvestor protection, leads to a persistentreturn differential between rich and poorcountries (Stulz 2005).

Investor protection matters. Whereaccounting standards and enforcement bod-ies do not exist to restrain insiders, resourcesinvested in a company may be wasted onsuperfluous managerial perks or even stolenoutright (Michael C. Jensen 1986). Theinsiders may be controlling shareholders,such as a founding family, a firm’s top man-agers, or both. Because outside investorsknow less than insiders about the firm’sprospects and the behavior of its managers,they will demand higher returns or simplynot invest.

Strong investor protection can help to mit-igate problems of agency and asymmetricinformation by providing minority sharehold-ers with mechanisms to restrain insiders. Alarge body of research demonstrates that thedegree to which a country’s laws protect thelegal rights of minority shareholders exerts asignificant influence on the size and robust-ness of capital markets (Rafael La Porta et al.1997, 1998, 2002). Firms located in countrieswith strong investor protection have greateraccess to external finance, invest more, andhave higher valuations than their counter-parts in countries with weak investor protec-tion (La Porta et al. 1997, 1998, 2002; AndreiShleifer and Robert W. Vishny 1997;Raghuram G. Rajan 2000).23


23 In a related paper, Simon Johnson et al. (2000) showthat measures of investor protection do a better job ofexplaining stock market declines during the Asian Crisisthan do standard macro variables.


Conversely, investors will stay away fromcountries in which investor protection isweak (Rudiger Dornbusch 2000, p. 92).Table 3 shows that the eighteen emergingeconomies that liberalized their stock mar-kets rank lower than developed countrieson five commonly used measures ofinvestor protection: the rule of law, efficien-cy of the judicial system, contract repudia-tion, expropriation risk, and the accountingsystem. Weak investor protection decreaseseffective returns, thereby reducing theincentive for capital to flow from rich to

poor countries (Shleifer and DanielWolfenzon 2002).24 Because investors inemerging economies receive less protectionthan their counterparts in rich countries,lifting restrictions on capital inflows maygenerate smaller changes in asset prices andcapital flows than would occur if emergingeconomies gave investors the same averagelevel of protection they receive in developedeconomies.


24 See also the discussion in Charles P. Himmelberg,Robert Glenn Hubbard, and Inessa Love (2004).

TABLE 2THE ECONOMIC INFRASTRUCTURE OF EMERGING MARKETS IS WEAKER THAN THAT OF DEVELOPED COUNTRIES

Hall and Jones Heritage House World Bank Doing World EconomicIndex Index Business Index Forum Index

Argentina 77 23 101 69Brazil 68 87 121 66Chile 43 15 28 27Colombia 79 73 79 65India 87 126 134 43Indonesia 45 76 135 50Jordan 34 58 78 52Malaysia 22 42 25 26Mexico 64 91 43 58Nigeria 116 97 108 101Pakistan 113 107 74 91Philippines 90 69 126 71South Korea 33 33 23 24Taiwan 28 16 47 13Thailand 20 38 18 35Turkey 71 49 91 59Venezuela 53 99 104 88Zimbabwe 94 122 153 119Emerging Market Average 63 68 83 59Developed Country Average 14 14 23 15

Notes: Table 2 compares the average quality of the economic infrastructure of the eighteen emerging economiesthat liberalized (see table 1) with that of the G-7 countries, using four commonly employed measures of the qual-ity of economic institutions. All four indices rank countries in descending order of quality (e.g., a ranking of 1indicates superior quality, while a ranking of 130 indicates poor quality). The rankings in column 2 are based onthe Hall and Jones (1999) measure of social infrastructure, which ranks 130 countries. Column 3 uses HeritageHouse’s Index of Economic Freedom (Heritage House 2006), which ranks 161 countries. Column 4 uses theWorld Bank’s Ease of Doing Business Index (World Bank 2006), which ranks 175 countries. The rankings in col-umn 5 are based on the World Economic Forum’s Global Competitiveness Index (World Economic Forum 2006),which ranks 125 countries. “Average Emerging Market” is the average ranking, on each index, of the eighteenemerging economies that liberalized. “Average Developed Country” is the average G-7 country ranking on eachindex.


Although table 3 corroborates the basicstory, an important question for futureresearch is the extent to which the capital-market-imperfections view truly explains therelatively small financial impact of liberaliza-tion. For instance, an empirical prediction ofthe market-imperfections view is that theimpact of liberalization should be larger incountries that have stronger investor protec-tion. Unfortunately, the policy-experimentapproach is not well-suited to test this predic-tion. With typically no more than twenty-fivecountries that meet the criteria for sampleinclusion, there are simply not enough datapoints to conduct precise tests of the cross-sectional prediction that liberalization will

have a larger impact in countries withstronger investor protection.25

In a related exercise that is not a study ofliberalization per se, Alfaro, SebnemKalemli-Ozcan, and Vadym Volosovych(forthcoming) try to determine whether thecapital-market-imperfections view or theweak-institutions view has greater empiri-cal relevance for the observed pattern of


25 Menzie D. Chinn and Hiro Ito (2006) do find thatcountries whose financial systems have a higher level oflegal and institutional development benefit more fromcapital account openness but, like other cross-sectionalstudies, the paper examines the correlation betweenopenness and financial development, not the impact ofopening on financial development.

TABLE 3INVESTOR PROTECTION IN EMERGING MARKETS IS WEAKER THAN IN DEVELOPED COUNTRIES

Rule of Judicial Contract Expropriation AccountingLaw Efficiency Repudiation Risk Standards

Argentina 5.4 6.0 4.9 5.9 4.5Brazil 6.3 5.8 6.3 7.6 5.4Chile 7.0 7.3 6.8 7.5 5.2Colombia 2.1 7.3 7.0 7.0 5India 4.2 8.0 6.1 7.8 5.7Indonesia 4.0 2.5 6.1 7.2 NAJordan 4.4 8.7 4.9 6.1 NAMalaysia 6.8 9.0 7.4 8.0 7.6Mexico 5.4 6.0 6.6 7.3 6Nigeria 2.7 7.3 4.4 5.3 5.9Pakistan 3.0 5.0 4.9 5.6 NAPhilippines 2.7 4.8 4.8 5.2 6.5South Korea 5.4 6.0 8.6 8.3 6.2Taiwan NA NA NA NA NAThailand 6.3 3.3 7.6 7.4 6.4Turkey 5.2 4.0 6.0 7.0 5.1Venezuela 6.4 6.5 6.3 6.9 4Zimbabwe 3.7 7.5 5.0 5.6 NAEmerging Market Mean 4.8 6.2 6.1 6.8 5.7Developed Country Mean 9.1 9.2 9.2 9.5 6.4

Notes: Table 3 compares the average quality of investor protection in the eighteen emerging economies that lib-eralized (see table 1) with that of the G-7 countries, using five measures employed by LaPorta, López-de-Silanes,Shleifer, and Vishny (1998). All scores are based on a scale of 1 to 10, with 10 indicating the highest quality and 1the lowest. The source for all of the numbers in this table is LaPorta, López-de-Silanes, Shleifer, and Vishny(1998).


asset prices and capital flows to emergingeconomies. They construct a measure ofthe strength of institutions for a large cross-section of countries using variables fromPolitical Risk Service’s InternationalCountry Risk Guide. As a proxy for inter-national capital market imperfections, theyconstruct a measure of economic “remote-ness” that they interpret as capturing infor-mational frictions as in Joshua D. Coval andTobias J. Moskowitz (1999, 2001) andRichard Portes and Helene Rey (2005).

In running horse races between the twoviews, Alfaro, Kalemli-Ozcan, andVolosovych (forthcoming) find that theirmeasure of institutions explains over half ofthe variation in FDI and portfolio flows as afraction of GDP. They interpret their find-ing as indicative of the primacy of weakinstitutions over capital market imperfec-tions for explaining the paucity of capitalflows to developing countries. One problemwith their interpretation is that it is not clearthat economic remoteness is the right proxyfor capital market imperfections such asagency problems and investor protection.Furthermore, it is also difficult to say whereinstitutional quality ends and capital marketimperfections begin. Poor institutions (e.g.,the absence of property rights) lead to mar-ket imperfections such as weak investor pro-tection. While it is not clear that one cansuccessfully distinguish between the institu-tions view and the capital market imperfec-tions view using aggregate cross-countrydata, the Alfaro, Kalemli-Ozcan, andVolosovych (forthcoming) paper does pro-vide empirical support for the notion thatthe myriad of potential distortions notexplicitly modeled in the neoclassical frame-work can account for some of the Lucas(1990) paradox.26

6.2 Can We Believe the InvestmentResults?

Although the financial impact of stockmarket liberalization is small relative to pre-dictions, the evidence in section 5.2 demon-strates that it is apparently still large enoughto have significant effects on real invest-ment. However, it is not clear how muchconfidence we should place in results thatattribute economywide investment booms toa policy change that affects directly onlythose firms that are traded on the stock mar-ket. In most emerging economies, the eco-nomic activity of stock-market-listed firmsaccounts for a relatively small fraction ofGDP, so it is natural to ask whether theaggregate investment booms that occur inthe aftermath of liberalizations may be plau-sibly linked with events that partially open asingle component of the capital account.

For instance, the establishment of a coun-try fund is the modal means through whichthe policy-experiment approach identifiescapital account liberalization dates. The sizeof a country fund is typically small relative tothe size of the liberalizing country’s capitalstock. The Chilean liberalization listed intable 1 illustrates the point. Chile liberalizedits stock market in May 1987 and the vehiclethrough which it did so was the TorontoTrust Mutual Fund, a Canadian closed-endfund with a net asset value of 37.7 millionU.S. dollars.27 A capital inflow of this size isnot large enough to account for the size ofChile’s subsequent investment boom.Because the capital stock of publicly tradedChilean firms is a subset of the entire econ-omy’s, to illustrate the point it is sufficient toshow that 37.7 million dollars is small rela-tive to the increase in the capital stock of thepublicly traded firms. In 1987, Chile’s stockmarket capitalization was 5.34 billion U.S.dollars and the ratio of the market value ofassets to book value was 0.7. This means that


26 For more on the distinction between credit marketand governance institutions, and an empirical attempt todisentangle them, see the discussion of Jiandong Ju andShang-Jin Wei (2006) in Kose et al. (2006).

27 See Park and Van Agtmael (1993), Price (1994), andWilson (1992).


the book value of assets was roughly 7.63 bil-lion U.S. dollars at the time Chile liberal-ized. In the five years after liberalization,Chile experienced abnormal capital stockgrowth of 2.2 percentage points per year.Applying this growth number to the capitalstock in a continuously compounded fashionfor the next five years adds an additional 890million dollars of productive assets to theeconomy. The 37.7 million dollar capitalinflow can account for less than 5 percent ofthis increase.

The apparent incongruity of these num-bers forces harder thinking about the validi-ty of the empirical link betweenliberalization and real investment. In doingso, an important point to keep in mind is thatthe policy-experiment approach uses coun-try-fund openings as observable de factoindicators of harder-to-pinpoint de jure pol-icy changes.28 If country-fund dates are validproxies for the occurrence of broader,undocumented liberalizations, then signifi-cant quantities of capital may flow inthrough stock markets that are not part ofany particular country fund. If that is thecase, then the size of the initial country fundwill understate the quantity of resources thatstock market liberalizations make availableto finance an increase in the domestic capi-tal stock. Three facts suggest that closed-endcountry fund dates do indeed provide non-specious indicators of a larger move towardopen capital markets.

First, a steady stream of country funds andissuances of American Depository Receiptstypically follow stock market liberalizations(Karolyi 2004; Juan Carlos Gozzi, Levine,and Sergio L. Schmukler forthcoming). Inthe case of Chile, six additional countryfunds with a cumulative net asset value of991.8 million dollars were establishedbetween 1987 and 1992.29 More generally,

table 4 shows that aggregate net equityinflows to emerging equity markets risesharply following the median date of coun-try-fund openings. In principle, when capitalflows into the stock market, the economy asa whole need not experience net capitalinflows because foreign investors could beswapping debt for equity or the countrycould experience debt outflows that are larg-er than the inflow of portfolio equity. Inpractice, however, the epoch of stock marketliberalization coincided with a period ofstrong net capital inflows to developingcountries (Calvo, Leonardo Leiderman, andCarmen M. Reinhart 1996).

Second, with the sole exception ofMalaysia during the Asian Crisis of 1997–98,none of the stock market liberalization datesfrom table 1 were followed by a reversal offreedom of foreign access. Together with thefirst fact, this second fact confirms that stockmarket liberalizations indicate a discretechange in capital account policy that signi-fies the beginning of a steady march towardgreater freedom of capital inflows.

Third, stock market liberalizations coincidewith a significant increase in the importationof capital goods. In a sample of twenty-fivecountries that liberalized their stock marketsbetween 1980 and 1997, liberalization leadsto a 6 percent increase in capital goods as afraction of total imports and the share of totalmachine imports to GDP rises by 12 percent(Alfaro and Eliza Hammel 2007). Becausedeveloping countries do not produce a signif-icant portion of the capital goods they use,the observation that imports of capital goodsrise in concert with the advent of portfolioequity inflows increases confidence in earli-er work on liberalization and aggregateinvestment.

6.3 Are the Growth Effects Plausible?

The increase in economic growth due toliberalization is about one percent per yearafter controlling for a host of variables(Bekaert, Harvey, and Lundblad 2005). Thatestimate is inconsistent with the neoclassical


28 See Kose et al. (2006) for a detailed discussion of defacto versus de jure indicators.

29 See Park and Van Agtmael (1993), Price (1994), andWilson (1992).


theory on which it is based.30 To see why,recall that the growth rate of capital increas-es by about one percent per year followingstock market liberalizations and that theelasticity of output with respect to capital isroughly one-third. Therefore, a one-per-centage-point increase in the growth rate ofthe capital stock cannot raise the growth rateof GDP per capita by much more than one-third of a percentage point.

Within the standard neoclassical frame-work, any increase in the growth rate of out-put not due to an increase in the growth rateof capital and labor must be the result of anincrease in the growth rate of total factor pro-ductivity (TFP). For the eighteen countries

in table 1, the growth rate of TFP does risefrom an average of 0.19 percent per year inthe five years preceding stock market liberal-ization to an average of 1.82 percent per yearin the subsequent five. But one cannot gliblyattribute the increase in TFP growth to stockmarket liberalization. The operative channelfrom liberalization to growth in the neoclassi-cal model runs strictly through capital accu-mulation. Total factor productivity does notenter into the story.31

Of course, if one is willing to step outsidethe neoclassical model, there are many sto-ries in which liberalization can raise total


30 See the discussion on page 19 and page 22 of Kose etal. (2006).

31 Pierre-Olivier Gourinchas and Olivier Jeanne (2006)argue that the welfare effects of capital account liberaliza-tion in the neoclassical model are de minimus preciselybecause liberalization has no impact on total factor pro-ductivity.

TABLE 4COMPOSITION OF CAPITAL INFLOWS TO DEVELOPING COUNTRIES 1970–95

Average 1970–74 Average 1975–79 Average 1980–84 Average 1985–89 Average 1990–95Millions Percent Millions Percent Millions Percent Millions Percent Millions Percentof dollars of total of dollars of total of dollars of total of dollars of total of dollars of total

Net Resource 12529.1 32836.8 51604.7 32726.2 90184.1FlowsNet Flows 10121.3 80.8 27151.3 82.7 42374.6 82.1 20563.4 62.8 45316.2 50.2on Debt(PPG+PNG)Public and 5628.0 44.9 18014.0 54.9 28383.4 55.0 14844.5 45.4 12820.5 14.2PubliclyGuaranteedDebt (PPG)Private 4493.4 35.9 9137.3 27.8 13991.2 27.1 5718.9 17.5 32495.8 36.0NonguaranteedDebt (PNG)Foreign Direct 1798.6 14.4 4247.2 12.9 6871.7 13.3 9006.5 27.5 24993.8 27.7InvestmentPortfolio Equity 0.0 0.0 0.0 0.0 27.0 0.1 762.2 2.3 16855.0 18.7Grants 609.2 4.9 1438.3 4.4 2331.3 4.5 2394.2 7.3 3019.0 3.3

Notes: This table presents data on the composition of aggregate net resource flows to the eighteen developingcountries listed in table 1 (with the exceptions of South Korea and Taiwan, for which no data were available)from 1970 to 1995. All data come from the World Bank’s Global Development Finance Data Base. The first col-umn of the table lists all the components of net resource flows. Net resource flows are the sum of net resourceflows on public and publicly guaranteed (PPG) debt, private nonguaranteed (PNG) debt, foreign direct invest-ment, portfolio equity, and official grants. Public and publicly guaranteed debt is an external obligation of a pri-vate debtor that is guaranteed for repayment by a public entity. Private nonguaranteed external debt is an exter-nal obligation of a private debtor that is not guaranteed for repayment by a public entity. Columns 2 through 6display the data as averages over five-year intervals.


factor productivity. For instance, liberaliza-tion may enable firms to import more pro-ductive machinery (e.g., tractors instead ofhoes) that effectively shift production in thecountry closer to the world technology fron-tier. Also, FDI in the form of green fieldinvestments or the purchase of a majoritystake of a domestic firm may bring newtechnology and management techniquesthat increase the efficiency of the acquiredfirm and generate economywide knowledgespillovers. For instance, Frederic S.Mishkin (2006) argues that developingcountries can import greater financial effi-ciency by allowing foreign investors to takecontrolling stakes in domestic financialfirms and bring in state-of-the-art financialintermediation practices. More generally,he articulates how capital account liberal-ization could raise total factor productivityby imposing capital market discipline ongovernments, breaking up local monopo-lies, and broadly promoting a whole rangeof institutional improvements that Kose etal. (2006) refer to as “collateral benefits” offinancial globalization.

These stories are theoretically plausiblebut empirically unsubstantiated. There was asignificant increase in the TFP growth ofMexican plants that changed from domesticto majority foreign ownership in 1989(Francisco Pérez González 2005). Similarly,FDI in Venezuela also increased TFPgrowth at the plant level (Brian J. Aitken andAnn E. Harrison 1999).32 But there is no evi-dence that plant-level productivity gainsgenerate economywide knowledge spilloversthat stimulate higher aggregate TFP growth(Mona Haddad and Harrison 1993; Aitkenand Harrison 1999; Holger Gorg and DavidGreenaway 2004).

Similarly, there is no rigorous evidencethat capital account liberalization improvesthe allocative efficiency of the domestic

financial system.33 It is true that capitalaccount liberalization within a country tendsto increase financial development (Levine2001; Levine and Zervos 1998a, 1998b). It isalso true that countries at high levels offinancial development allocate capital moreefficiently than countries at low levels offinancial development (Love 2003; JeffreyWurgler 2000; Rajan and Luigi Zingales1998; Eichengreen 2004). The temptation,of course, is to invoke transitivity and con-clude that capital account liberalizationincreases domestic allocative efficiency. Theproblem with such logic is that documentinga positive correlation between the efficiencyof capital allocation and financial develop-ment in a cross section of countries does notpermit us to infer that more financial devel-opment within a given country will improveits allocative efficiency. Without a convincingbody of time series evidence that the qualityof a country’s capital allocation improves asits level of financial development rises, nobasis exists for concluding that liberalizationindirectly improves the efficiency of domes-tic capital allocation through its impact onfinancial development.34

I am not arguing that one cannot tell sto-ries in which capital account liberalizationinduces higher TFP growth. Again, once youstep outside the neoclassical model, thereare many possible channels. Liberalizationmay ease liquidity constraints (NandiniGupta and Kathy Yuan 2005; Todd Mitton2006), thereby enabling firms to adopt tech-nologies that they could not finance prior tothe liberalization. Liberalization also facili-tates increased risk sharing, which mightencourage investment in riskier, highergrowth technologies in the spirit of Obstfeld(1994), Levine (1997), or Robert G. King


32 For an analysis of the anticipated productivity gainsfrom FDI using stock market data, see Anusha Chari,Paige P. Ouimet, and Linda L. Tesar (2004).

33 In fact, many economists argue for enhancingdomestic financial efficiency before liberalizing the capi-tal account (see Dornbusch 1983; Edwards 1984; RonaldI. McKinnon 1991; Mishkin 2007).

34 Arturo Galindo, Fabio Schiantarelli, and AndrewWeiss (2007) provide some of the first evidence on thissubject of which I am aware.


and Levine (1993). The point is that, whilethe developing countries in this sample mayhave increased their rate of adoption of newproduction technologies during the late1980s and early 1990s, there is no sound wayto draw such a conclusion from aggregatedata.

In contrast, the aggregate TFP growthnumbers are consistent with the readilyobservable evidence on economic reforms.As discussed in section 6.1.2, economicreforms that raise the efficiency of a givenstock of capital and labor will increase TFPand again table 1 shows that stock marketliberalizations typically coincide with a raftof other such reforms. Of course, the onlyway to distinguish between competing theo-ries that can explain higher TFP in the after-math of liberalization is to confront thesetheories with data that have the power tomake such fine distinctions. Recent studiesof liberalization that move from aggregate tofirm-level data show the way forward.

7. Firm-Level Perspectives on CapitalAccount Liberalization

Perhaps the most important shortcomingof aggregate data is its limited ability to helpus understand whether countries that liber-alize efficiently allocate the capital that flowsin. While the rise in aggregate stock pricesand investment documented in the literaturesuggests that liberalization promotes someefficient movement of capital between coun-tries, it says nothing about the efficiency ofcapital allocation within countries. Within-country efficiency would require that thestream of capital from developed countriesget allocated to the highest return sectorswithin the emerging economies to which itflows.

The efficiency of capital allocation isclosely related to the cost of capital. Forinstance, the larger the fall in a country’scost of capital, the more its investmentshould increase. In other words, cross-coun-try changes in investment in the aftermath

of liberalizations should be negatively corre-lated with cross-country changes in the costof capital on impact. The problem is that thesmall-sample-size problem inherent in thepolicy-experiment approach once again doesnot provide sufficient power to say anythingmeaningful about the empirical relevance ofthis hypothesis.

Similarly, aggregate data are of little use inhelping us understand the role of interna-tional risk sharing, a subject that also hasimportant efficiency implications and is ofbroad interest in the literature.35 Becausethe cost of capital matters for investment andrisk sharing matters for the cost of capital, itis natural to wonder about the importance ofrisk sharing relative to the fall in the interestrate for the overall change in liberalizingcountries’ cost of capital. Unfortunately,studies of liberalization that use aggregatedata provide no help on this point. For eachcountry, aggregate data supply one observ-able proxy for the change in the cost of capi-tal—the stock market revaluation discussedin section 5. But there are two forces at work:the change in risk sharing and the change inthe interest rate. Since one data point is notsufficient to identify two effects, it is impossi-ble to disentangle them using aggregatedata.36

Firm-level data, on the other hand, pro-vide more than sufficient degrees of free-dom with which to disentangle the impact ofrisk sharing from that of the interest rate.This section reviews how recent work usessuch data to gain a better understanding ofrisk sharing and the allocation of capitalwithin countries. More generally, this sec-tion explains how recent firm-level studies


35 For surveys of the literature on international risksharing, see Obstfeld and Rogoff (1996), chapter 5.

36 If we could observe internal, market-determinedrisk-free rates before and after liberalizations, disentan-gling the two effects would be straightforward. The prob-lem is that almost all of the countries listed in table 1 hadsome form of financial repression in place at the time theyliberalized (see tables 1, 4, and 5 in John Williamson andMolly Mahar 1998). Hence, we do not observe market-determined, risk-free rates.


enhance our understanding of the realeffects of liberalization.

7.1 Risk Sharing and Asset Prices

The country-level organizing frameworkin section 5.1 extends naturally to a firm-level setting that lays bare the risk-sharingcontent of stock-price revaluations. The keyidea is that the changes that occur in firms’stock prices at the time of liberalizationembody signals about the impact of liberal-ization on the firms’ fundamentals. If capitalallocation is efficient, then firms’ investmentdecisions should respond to these signals.

An efficient allocation of capital is one thatsatisfies the first-order condition for invest-ment. Recall that, before liberalizationoccurs, the first-order condition for aggre-gate investment requires the expected mar-ginal product of the aggregate capital stockto be equal to the aggregate cost of equitycapital plus the rate of depreciation. The dif-ference between the first-order conditionfor the economy as a whole and that for anindividual firm lies in the equity premium.Whereas the aggregate equity premium isproportional to the variance of the return onthe domestic market, Var(r∼M), the equitypremium for an individual firm is propor-tional to the covariance of the firm’s returnwith the local market.

Written in symbols to express it more con-cisely, the first-order condition for an individ-ual firm’s investment before liberalization is:

(12) (r∼i,r∼M) + δ,

where the i subscripts indicate that we arenow talking about a single firm instead ofthe entire economy. Liberalizing a country’sstock market changes the relevant source ofsystematic risk for pricing a firm’s sharesfrom the local stock market index to a worldstock market index. Due to this change, thefirst-order condition for investment afterliberalization is:

(13) (r∼i,r∼W) + δ,′ = +f k r COVie( )∗ ∗ γ

′ = +f k r COVie( ) γ

where COV(r∼i,r∼W) is the covariance of firmi’s return with the world market. Subtractingequation (13) from equation (12) gives a use-ful expression for the change in the first-order condition from before-to-afterliberalization:

(14) ,

where DIFCOVi = COV(r∼i,r∼M) − COV(r∼i,r∼W).The right-hand-side of equation (14) high-

lights the two channels through which liber-alization changes a firm’s cost of capital.Moving from left to right, the first term isexactly the same as in the aggregate case: afall in the risk-free rate as the country switch-es from financial autarky to financial integra-tion with the world market. This is a commonshock to all firms in the economy. The secondeffect is unique to each firm: the greater thecovariance of the firm’s stock return with thelocal market relative to the covariance of itsreturn with the world market, the larger thechange in the firm-specific component of itscost of capital.

In the pristine world of theory, liberaliza-tion does not alter the firm’s expected futurecash flow and the response of the firm’s stockprice to the news of the liberalization mirrorsexactly the change in its cost of capital. Thefirm’s stock price will increase if liberalizationreduces the cost of capital and vice versa. Inother words, equation (14) predicts that eachfirm’s revaluation will have an intercept termand a slope term. The intercept term shouldbe the same across all firms within a givencountry. As for the slope effect, if risk sharingmatters, then the revaluations that firmsexperience should be an increasing functionof the variable DIFCOV.

In the murky world of empirical work, lib-eralizations coincide with other economicreforms that undoubtedly affect expectedfuture cash flows. Whereas aggregate studiesattempt to disentangle the impact of liberal-ization from contemporaneous economicreforms by using rough macroeconomicindicators as proxies for cash flows (see

� ′ = − +f k r r DIFCOVie

i( ) ( )∗ γ



equation (11) in section 5.3), firm-levelstudies control directly for changes in cashflows by using the real value of sales andearnings taken from firms’ income state-ments. More generally, although the econo-my as a whole benefits from liberalization,firms that gain more will expand, whilethose that gain less (or lose) may contract.37

Because the process of liberalization gener-ates losers as well as winners, it raisesimportant questions of political economythat aggregate data cannot answer. In con-trast, firm-level data allow researchers tocontrol for the differential impact of liberal-ization across various sectors of the econo-my and therefore may help to answer someof these questions (see section 8.2).

Recent work supports the two asset-pric-ing predictions of equation (14). First, firmsexperience significant stock price revalua-tions during liberalizations, controlling forthe impact of contemporaneous reforms onexpected future cash flows (Chari and Henry2004; Dilip K. Patro and John K. Wald2005). Second, the stock price change asso-ciated with liberalization is positively andsignificantly related to DIFCOV. In a sampleof 430 firms from eight countries, the aver-age firm-level revaluation is about 15 per-cent in real dollar terms, changes infirm-specific covariances explain roughlyone-third of the revaluation, and the com-mon shock is the same for all firms (Chariand Henry 2004).

The finding that stock prices of firms inemerging markets move in accordance withchanges in systematic risk provides some ofthe first empirical support for the CAPM ina domestic or international setting.38 It also

runs counter to recent work which finds thatstock returns in emerging economies gener-ally contain little firm-specific information(Randall K. Morck, Bernard Yeung, andWayne Yu 2000). These two facts make youwonder whether the firm-level asset pricingresults are spurious. Two observations sug-gest otherwise.

First, the empirical design of the firm-level policy-experiment approach gives itpower to uncover cross-sectional relation-ships between expected returns and system-atic risk that are difficult to detect in othersettings. Covariances are measured witherror (Fama and Kenneth R. French 2004),and measurement error reduces the statisti-cal power of any regression. Instead of test-ing for a relationship between levels ofreturns and levels of systematic risk, thefirm-level policy-experiment approachfocuses on episodes where there are largechanges in both risk and returns. The mag-nitude of the liberalization-induced changesin expected returns and systematic risk asso-ciated with opening up the economy to for-eign capital flows may simply dominate theattenuating effects of measurement errorthat usually plague efforts to find cross-sec-tional pricing relationships. Similarly,changes in emerging-market stock pricesmay convey little firm-specific informationin general, but they do convey such informa-tion during episodes like liberalizationswhen the magnitude of the information issufficiently large.39

Second, using a similar approach to that ofChari and Henry (2004), Tomas Dvorak andRichard Podpiera (2006) also find that largechanges in systematic risk have explanatorypower for changes in the cross-section ofexpected returns. The policy experimentthat provides the key source of variation forDvorak and Podpiera’s exercise is the addi-tion of eight central and Eastern European


37 See, for example, the impact of liberalization on theexpansion of big versus small firms documented in Gozzi,Levine, and Schmukler (forthcoming).

38 Christopher Polk, Samuel Thompson, and TuomoVuolteenaho (2006) also argue that the CAPM matters forthe cross section of stock prices. For surveys of theCAPM’s empirical record, see Eugene F. Fama (1991),John H. Cochrane (1999), and John Y. Campbell (2000).Karolyi and Stulz (2001) discuss the international CAPM.

39 The firm-specific information in stock prices alsorises as countries adopt greater capital market openness(Kan Li et al. 2004).


countries into the European Union. Dvorakand Podpiera (2006) argue that accessioninto the European Union integrated thestock markets of the acceding countries withthe rest of the world and thereby changedthe source of systematic risk facing investorsin the countries that gained membership. Intheir sample of seventy-four firms, the dif-ference between the beta of a firm’s stockreturn with the local market and its beta withthe world market explains about 22 percentof the typical stock price revaluation thatoccurs with accession.

7.2 Risk Sharing and Resource Allocation

While the correlation between changes inreturns and changes in systematic risk pro-vide some evidence of within-country effi-ciency at the level of asset pricing, the morepressing economic question is whether realresource allocation—investment—respondsaccordingly.40 As the benchmark for deter-mining the firm’s hurdle rate for investmentswitches from the local stock market index toa world stock market index, efficiencyrequires that allocation of the firm’s physicalcapital also shift in accordance with thechange in the source of the country’s system-atic risk.41 In order to restore equilibrium,the increase in the firm’s capital stock mustbe large enough to drive the expected mar-ginal product down to the lower, postliberal-ization cost of capital. Equation (14)implicitly defines the size of the requiredincrease and therefore delivers two testablepredictions about investment.

The first prediction is that the commonshock to the cost of capital should cause theaverage investment rate of all firms to rise.

The second prediction is that, given thecommon shock, the firm-specific shock (thechange in covariance) implies that firmswhose equity premia fall should invest evenmore than those whose premia rise. In otherwords, if physical capital is reallocated in linewith the optimal smoothing of productionrisk, then high DIFCOV firms should expe-rience faster capital stock growth than lowDIFCOV firms following liberalization.

The first prediction about liberalizationand firm-level investment finds empiricalsupport in the literature. The growth rate ofthe average firm’s capital stock exceeds itspreliberalization mean by an average of 3.8percentage points per year (Chari andHenry forthcoming). This effect is muchlarger than the corresponding increase in theaggregate capital stock over the same timeperiod (1.1 percentage points per year).Because stock market liberalizations mostdirectly affect the investment incentives ofpublicly traded firms, the firm-level invest-ment results are more credible than theaggregate results and make a stronger casethat liberalization does, indeed, have realeffects.42

The second prediction about liberalizationand firm-level investment enjoys rather lessempirical success. There is no evidence thatphysical investment responds to changes insystematic risk, and firm-specific changes inequity premia (the DIFCOV variable) havean economically trivial and statisticallyinsignificant effect on changes in invest-ment.

The finding that firms’ investment deci-sions are insensitive to firm-specific changesin their cost of capital delivers a powerfulblow to the Allocative Efficiency view of lib-eralization. On the other hand, with littleevidence from developed markets to suggest


40 The idea of trying to relate changes in investment tothe liberalization-induced changes in stock prices followsin the spirit of earlier work that tries to relate changes ininvestment to changes in stock prices more generally(Olivier Blanchard, Changyong Rhee, and Summers 1993;Fischer and Robert C. Merton 1984; James Tobin andWilliam C. Brainard 1977).

41 For a helpful exposition on the resource-allocationimplications of changes in systematic risk, see Tesar andIngrid M. Werner (1998), pp. 290–92.

42 The firm-level investment data used by Chari andHenry (forthcoming) comes from the InternationalFinance Corporation’s Corporate Finance Database. Fora detailed description of this dataset, see Asli Demirgüç-Kunt and Vojislav Maksimovic (1998) and LaurenceBooth et al. (2001).


that exposure to aggregate covariance riskdrives expected returns, testing the hypothe-sis that firms in developing countries allocatephysical investment in accordance with theCAPM may seem to fly in the face of allcommon sense.

But if the risk-sharing-resource-allocationhypothesis is a straw man, then it is a verypopular one. Virtually all studies of interna-tional risk sharing lean heavily on the intuitionthat the gains to trade in risky assets stemfrom the difference between the variance ofdomestic returns (consumption) and thecovariance of domestic returns (consump-tion) with the rest of the world (see, for exam-ple, Stefano G. Athanasoulis and Eric VanWincoop 2000; Obstfeld and Rogoff 1996,chapter 5; Karen K. Lewis 1999, 2000; andVan Wincoop 1994, 1999).43 Furthermore,the evidence shows that firm-level stockprices do move in conjunction with liberaliza-tion-induced changes in systematic risk. Sothe real issue is why the reductions in riskpremia do not cause firms to adopt projects àla Obstfeld (1994)—that were too risky toundertake in the absence of international risksharing.

7.3 Other Firm-Level Approaches toLiberalization and Efficiency

Testing whether liberalization-inducedchanges in risk drive changes in asset pricesand investment provides one way of evaluat-ing efficiency, but it is also useful to adopt abroader perspective—one that is groundedin theory but not tied so restrictively to theCAPM. There is much to be learned aboutthe impact of capital account policy on realvariables by looking at firm-level data from anumber of different perspectives.

For instance, another way to tackle thequestion of liberalization and allocative

efficiency is to turn the policy-experimentapproach on its head. In addition to lookingat how firms respond to liberalization, itmay be just as instructive to study howfirms respond when countries imposerestrictions on capital inflows (Kristin J.Forbes 2007b). If capital account liberaliza-tion enhances efficiency, then imposingcapital controls should diminish efficiencyin at least two important ways. First, capitalcontrols may reduce the supply of capital,thereby raising the cost of borrowing andtightening the financing constraints facedby domestic firms. Second, by reducing thesupply of capital, capital controls candecrease competition and market disci-pline, permitting firms that might not sur-vive if their competitors had access tocredit to flourish behind closed borders(Rajan and Zingales 2003; Johnson andMitton 2003; Morck, David A. Stangeland,and Yeung 2000).

A number of papers examine the extent towhich the presence of capital controls exac-erbates the financing constraints faced byvarious kinds of firms. In the spirit of StevenM. Fazzari, Hubbard, and Bruce C. Petersen(1988), the basic test involves the examina-tion of the sensitivity of investment to cashflow.44 Forbes (2007a) uses the 1991–98episode of El Encaje, the tax on capitalinflows to Chile, as a policy experiment withwhich to assess the efficiency implications ofimposing capital controls.45 Her analysisbegins with the following observation. Indeveloped countries, small, publicly tradedfirms exhibit higher investment growth thanlarge, publicly traded firms. Forbes (2007a)finds that this is also the case in Chile before1991 and after 1998. During El Encaje, how-ever, she finds that the investment growth ofsmall firms drops below that of large firms.She goes on to show that El Encaje increasedthe sensitivity of investment to cash flow for


43 An important difference between the aggregatestudies to which I refer in this citation and the firm-levelpolicy-experiment papers is that the aggregate studiesfocus on the implications of risk-sharing for consumptionand welfare gains as opposed to investment and produc-tion. Section 7.4 discusses this distinction in further detail.

44 For a detailed discussion of the literature on financingconstraints and investment, see Hubbard (1998).

45 Francisco A. Gallego and F. Leonardo Hernandez(2003) also conduct an analysis of El Encaje.


small, publicly traded firms whereas the sen-sitivity of large firms was unaffected. Ineconomies in which small firms drive pro-ductivity increases, Forbes’s results mightsuggest one channel through which capitalaccount liberalization could affect TFP. Toknow whether this is the case, we need moreresearch on the extent to which large versussmall firms drive TFP growth in developingcountries.

In contrast to Forbes’s case study,Harrison, Love, and Margaret S. McMillan(2004) use a cross-country, firm-level paneldata set to study the impact of capitalaccount restrictions. They take their meas-ures of capital account restrictions from theIMF publication AREAER discussed in sec-tion 3. Like Forbes (2007a), they find thatthe presence of capital account restrictionsincreases the sensitivity of firms’ investmentto cash flow. They also document that thesensitivity of domestically owned firms’investment to cash flow is greater than thatof firms with foreign ownership or assets.

In a paper that reverts back to the easing ofcapital controls, Luc Laeven (2003) con-structs a cross-country data set to address thequestion of whether financial liberalizationeases financing constraints and increasescompetition. Financial liberalization typicallyrefers to the removal of interest rate ceilings,directed credit, and other such distortions inthe domestic financial markets.46 Laeven’spaper has implications for capital-accountliberalization because his measure of finan-cial liberalization captures the impact of FDI(i.e., foreign entry) into the domestic bankingsector. As such, it has some power to speak tothe issue of whether foreign bank entryincreases the supply of capital and makes thedomestic lending market more competitive.He finds that the sensitivity of small firms’investment to cash flow falls by 80 percent asa result of financial liberalization. He alsofinds that the investment of large firms

becomes more sensitive to cash flow afterfinancial liberalization and interprets thisfinding as evidence that large firms may havehad access to preferential credit beforefinancial liberalization.

One important weakness of firm-levelpapers that examine the efficiency implica-tions of capital controls is their interpretationof the sensitivity of investment to cash flow asa measure of firm-financing constraints. Iffirms face financing frictions, then theirinvestment will be sensitive to cash flow. Butthe converse of the preceding statementneed not be true: sensitivity of investment tocash flow does not imply that firms facefinancial constraints (Steven N. Kaplan andZingales 1997, 2000; Jeremy C. Stein 2003).

While there are multiple interpretations ofthe investment–cash-flow sensitivity ofemerging-market firms and it remains to beseen how all of the results reviewed in thissection will stand up to further scrutiny, thereis little question that papers which use firm-level data provide two steps forward: (1) theyincrease our understanding of the transmis-sion mechanisms through which capital-account policy affects the real economy and(2) they provide a blueprint of how to con-struct firm-level identification strategies fordisentangling those mechanisms.

7.4 Important Areas for Future Research:Capital Outflows and ConsumptionData

In spite of the progress made in this area,an important limitation of existing firm-levelstudies on the benefits of risk sharing is theiralmost-exclusive focus on (1) the investmentand production side of the economy asopposed to consumption and welfare and (2)the removal of restrictions on capital inflowsas opposed to capital outflows.

The largest future welfare gains to capitalaccount liberalization in developing coun-tries such as China and India are likely tocome from the reduction in consumption andincome volatility that will occur when theirgovernments liberalize capital outflows,


46 See McKinnon (1973), Edward S. Shaw (1973), andCarlos Diaz-Alejandro (1985).


thereby permitting their citizens to holdassets abroad and unload some of the sys-tematic risk of the domestic economy.Beyond the headline-grabbing examples ofChina and India, there may also be large wel-fare gains to permitting capital outflows fromcountries such as Chile, Malaysia, andTaiwan that have government-managed or government-mandated funded retirementprograms that are heavily tilted towarddomestic assets.

Because of developing countries’ long his-tory of legal restrictions on capital outflows,past research in this area focused on waysthat these countries could enjoy the welfarebenefits of external portfolio diversificationwithout permitting net capital outflows(Donald R. Lessard 1973, 1974, 1994; ZviBodie and Merton 2002). With the rise ofsovereign wealth funds and the apparentimpetus toward greater easing of restrictionson the investment of capital abroad, therewould seem to be ample opportunities toidentify relevant policy experiments in thearea of liberalization of capital outflows.47

In light of these recent developments, theglaring absence of empirical research on theactual (as opposed to hypothetical) welfareimpact of the liberalization of capital outflowsfrom developing countries practically cries outfor further research. Given the difficultieswith aggregate data mentioned earlier, sub-stantial contributions to research in the area ofrisk sharing, welfare, and the liberalization ofcapital outflows are most likely to come fromanalyses of household-level consumption andportfolio data. Using the cross-sectional varia-tion in such data in conjunction with ma-jor policy changes may lead to useful and convincing empirical identification strategies.

8. Do Liberalizations Cause Crises?

Stock market liberalizations deliver unde-niable benefits over the short-to-medium

term but, viewed over a more extensive hori-zon, the data raise questions about thelonger-run cost–benefit trade-off. Forinstance, extending figures 4 and 5 to tenyears beyond the liberalization dates in table1 would reveal the collapse in investmentand output associated with the Asian Crisis.In addition to its effect on real variables,there is the additional question of whetherliberalizations also amplify the cycle in assetprices (Graciela Laura Kaminsky andSchmukler 2002). For instance, in contrastto the efficient-markets prediction that thereshould be no revaluation of assets once mar-kets are open to foreign investors, equityprices continue to rise for two to three yearsafter stock market liberalizations but thenlose significant value over the longer term(Martell and Stulz 2003). At first glance, theboom–bust cycle in output, investment, andasset prices seems consistent with the viewthat liberalizations cause crises, but thereare at least three important points to keep inmind.

First, crises occur not only in countriesthat liberalize the capital account, but also inthose where capital controls are in place(Forbes 2007b). There is, in fact, some sys-tematic evidence that the occurrence ofcrises and the imposition of capital controlsare positively correlated (Reuven Glick andMichael Hutchison 2000; Eichengreen2003). So it is equally plausible that bad pol-icy precipitates crises and countries imposecapital controls as a way to postpone thefinancial-market consequences (such asdepreciation of the currency) of weak ordeteriorating fundamentals. In other words,at least some of the existing evidence sup-ports the notion that poor macroeconomicpolicies, not capital account liberalizations,cause crises.

Second, when trying to determine whetherliberalizations or fundamentals cause crises,it is important to think critically about timing.The median stock market liberalization datein table 1 is 1989—five years prior to theMexican Crisis of December 1994 and


47 For a discussion of the welfare implications of sover-eign wealth funds and capital outflows from emergingmarkets, see Summers (2006).


almost a decade before the Asian crisis of1997. Given the length of time between theonset of stock market liberalizations andthe rash of subsequent emerging-marketfinancial crises, any causal link between thetwo is far from obvious. Of course, theseeds of a misguided policy may take a longtime to yield their bitter fruit, so lags in tim-ing alone cannot dismiss the possibility thatstock market liberalizations played a role.Nevertheless, the more proximate andplausible cause of the crises would appearto be a combination of fixed exchange ratesand the build-up of short-term dollar-denominated debt: tesobonos in the case ofMexico and interbank loans in the case ofAsia (Fischer 2001; Krueger 2000; Mishkin1997, 2000; Obstfeld and Rogoff 1995).

The question of whether stock market lib-eralizations or short-term debt caused theMexican and Asian crises raises the third andmost important point: cost–benefit analyses ofcapital account liberalization do not makesense without specifying exactly what is meantby the term “capital account liberalization.”In its broadest form, a capital account liberal-ization can be any decision by a country’s gov-ernment that allows capital to flow morefreely in and (or) out of that country. Allowingdomestic businesses to take out loans fromforeign banks, permitting foreigners to pur-chase domestic government debt instru-ments, and allowing foreigners to invest in thedomestic stock market are but three exam-ples. At a minimum, we need to distinguishbetween two categories of liberalization:those that involve debt and those that involveequity. The distinction matters because theanswer to the question—do liberalizationscause crises—depends critically on whetheryou are talking about the liberalization of debtor equity flows.

8.1 The Importance of Distinguishing Debtfrom Equity

A debt contract requires regular paymentsregardless of the borrower’s circumstances.This means that, when bad news arises,

creditors rush to get their money while theycan. An equity contract, on the other hand,involves risk-sharing—large payouts forshareholders when times are good and littleto nothing when times are bad. It may seemthat foreign purchase of equities on thedomestic stock market could be reversed ifand when foreign investors become con-cerned about a country’s prospects, but share-holders cannot simply demand their moneyback. They have to sell their shares and priceswill drop as soon as other market participants(domestic or foreign) anticipate the increasein supply. Furthermore, as prices fall, expect-ed returns rise so that the incentive to sellequity is no longer as strong (Hyuk Choe,Bong-Chan Kho, and Stulz 1999). In otherwords, servicing an equity contract involvesprocyclical payments that tend to stabilize thebalance of payments, whereas debt servicepayments are countercyclical and have theopposite effect.

Because it does not embody risk-sharing,excessive reliance on debt can cause finan-cial distress. In the 1970s, developing-coun-try governments obtained large quantities offloating-rate commercial bank loans. TheDebt Crisis of the 1980s then demonstratedthat debt contracts can induce large ineffi-ciencies when economic conditions turn outto be worse than anticipated at the time thecontract was signed (Fischer 1987). Nor is itsimply the flow of asset-service paymentsthat is more likely to vary in a stabilizing wayfor equity than for debt. “The debt crisiswas caused most immediately and powerful-ly by the cutoff in new lending without anysimilar curb on the requirement to payamortization” (Williamson 1997, p. 288).

Williamson’s point about the cessation ofnew lending rings particularly true in thecontext of the further distinction betweenbonds and bank loans. The nature of banklending makes that form of capital flows farmore volatile than portfolio bond or equityflows. For instance, domestic banks borrowshort-term in the foreign interbank marketwith the expectation that they will be able to



roll over the loans. The potential problemshere are twofold. First, the decision bylenders to call in loans and cease rollingthem over tends to be driven not by idiosyn-cratic shocks to individual borrowers butcommon shocks to the domestic economy(Wendy Dobson and Gary Clyde Hufbauer2001). Second, common shocks dominateidiosyncratic shocks in emerging markets(Morck, Yeung, and Yu 2000). Taken togeth-er, these two facts create the potential forlarge-scale reversals of short-term interbankloans.

Yet the vast majority of bank lending toemerging markets takes the form of short-term bank loans (loans with a maturity of lessthan a year).48 At the end of 1997, 55 per-cent of foreign bank loans worldwide wereshort-term and one third of these loans wereof the interbank variety. For instance, for-eign capital flows to Thailand peaked at 25.5billion dollars in 1995; 75 percent of this sumtook the form of bank loans; two thirds ofthese loans had maturities of less than a year,and the majority went to Thai banks andnonbank financial intermediaries (Martin N.Baily, Diana Farrell, and Susan Lund 2000).

The critical point about interbank loans isthat they may be used by the recipient banksto make loans to finance domestic projectsthat may not be short-term, thereby creatinga liability mismatch. These liability mis-matches wreak havoc on the domestic econ-omy in the face of external shocks. Excessiveshort-term borrowing in dollars from foreignbanks by Asian banks, companies, and gov-ernments played a central role in the onsetof the Asian Financial Crisis (Jason Furmanand Stiglitz 1998; Steven Radelet and Sachs1998). In essence, the mismatch betweenthe term structure of Asian borrowers’ assets(long term) and their dollar-denominated

external liabilities (short term) meant thatany bad news that made their lenders reluc-tant to extend new loans was bound to createa liquidity problem.

The tendency toward sudden reversal ofshort-term bank flows underscores the cen-tral point of the discussion. Whereas portfo-lio flows adjust to shocks through changes inprices, short-term bank loans adjust throughquantities (Dobson and Hufbauer 2001).Consequently, bank lending to emergingmarkets is far more volatile than portfolioinvestment in bonds or equities (Kose et al.2006).49 Again, the Asian Crisis highlightsthe difference in volatility. In 1996, the fiveAsian Crisis countries (Indonesia, Malaysia,Philippines, South Korea, Thailand)received 47.8 billion dollars of capitalinflows in the form of bank loans; in 1997,they experienced a collective outflow of 29.9billion dollars—a reversal of almost 80 bil-lion dollars in a one-year period (Baily,Farrell, and Lund 2000).

In contrast to the abrupt reversal in bankflows, portfolio flows remained positive inAsia throughout 1997. Baily, Farrell, andLund (2000) document that aggregate port-folio flows to the crisis countries fell byabout half but remained positive.Furthermore, the aggregate pattern holds atthe individual country level for South Korea,Malyasia, and Thailand. In the case ofThailand, even as foreign banks were refus-ing to roll over loans, portfolio flowsincreased by 70 percent between the secondand third quarters of 1997 and remainedpositive through the first half of 1998. Theauthors document that Indonesia did experi-ence net portfolio outflows in the fourthquarter of 1997 but they turned positiveagain by the middle of 1998.

Beyond the Asian Crisis, heavy short-termborrowing in dollars played a central role inprecipitating almost every emerging-marketfinancial crisis during the 1990s (Dornbusch


48 As I discuss shortly, it is important to note thatbonds, which now comprise a much larger share of totaldebt flows to developing countries than they did in the1970s and 1980s, are potentially less problematic thanbank loans.

49 It is important to note that investment in bonds canalso be volatile if the bonds are sufficiently short-term.


2000; Martin Feldstein 2002). A generalfinding is that the ratio of short-term debt toreserves predicts crises, and greater short-term exposure predicts more severe criseswhen positive capital flows turn negative(Rodrik and Andres Velasco 2000).

8.2 If Debt Is So Risky, Why Do We See SoMuch of It?

Table 4 illustrates the dominance of debtover equity by breaking the composition ofnet capital flows to the eighteen developingcountries listed in table 1 into five major cat-egories: public and publicly guaranteed debtflows, private nonguaranteed debt flows,FDI, portfolio equity, and grants. The sumof the first two categories reflects all debtflows. From 1970 to 1984, debt typicallyaccounted for about 80 percent of all capitalflows to developing countries. If debt consti-tutes such a dangerous form of lending, whydo we do we see so much of it relative toequity? One obvious reason follows directlyfrom the discussion in section 5: prior to thelatter half of the 1980s, developing countrieslargely banned foreigners from holdingdomestic shares.

If debt is so risky relative to equity, thenwhy do governments liberalize debt inflowsby removing restrictions on offshore borrow-ing by domestic banks while retaining hardlimits on foreign participation in the equitymarket? There are number of competingexplanations for the relatively slow liberal-ization of equity markets, all of whichdeserve more serious consideration in futureresearch than the speculative treatment Igive them in the next few sentences.Domestic capitalists in the nonfinancial sec-tor of the economy may favor liberalizationbecause it reduces their cost of capital. Butthese capitalists may also be reluctant to cre-ate the necessary preconditions for success-ful equity market liberalization (Shleifer andWolfenzon 2002). Alternatively, there maybe competing interests. Liberalization gen-erates aggregate welfare gains, but theremay also be potential losers who oppose the

process (Rajan and Zingales 2003). Forexample, domestic banks may lose monopolyrents because liberalizations provide domes-tic firms with alternative sources of financ-ing. In turn, large, nonfinancial firms may beworse off because new sources of financingfor their smaller, more financially con-strained industry peers may increase prod-uct market competition (Chari and Guptaforthcoming). Finally, governments maydelay equity market liberalizations for anentirely different set of considerations.Future research should address these issuesusing firm-level data.

Whatever explains the delay, with theadvent of stock market liberalization, portfo-lio equity as a fraction of total capital inflowsrose from less than 0.1 percent in 1980–84 to18.7 percent in 1990–95—an increase ofalmost two-hundredfold. Debt as a fractionof total capital flows fell from 82 percent in1980–84 to 50 percent in 1990–95. FDIflows as a fraction of total capital flowsincreased from 13 percent in 1980–84 to 28percent in 1990–95. However, the flow ofportfolio equity to emerging markets hasslowed since the boom of the early 1990s(Dilek Aykut, Himmat Kalsi, and DilipRatha 2003). And while developing coun-tries’ ratios of external debt to equity fellbetween 1997 and 2001, it is still not clearthat they have fallen to prudent levels (PhilipSuttle 2003, p. 9). Hence, the recentincrease in portfolio equity and FDI inflowsnotwithstanding, developing countries stilllean heavily toward debt.50

The observed pattern of debt and equityflows is an equilibrium outcome, resultingfrom the optimal response of borrowers andlenders to a given set of institutionalarrangements. Therefore, the critical issue iswhat distortions in the international financialsystem produce incentives that lead to so


50 Home bias, the fact that foreign investors hold fewerforeign securities than they should, does not explain whythe composition of securities they do hold favors debt sostrongly. Lewis (1999) surveys the home-bias literature.


much debt and so little equity. One promi-nent example of an incentive-distorting insti-tutional arrangement was the Basel CapitalAccord of 1988 (Basel I).

Drafted in response to the 1980s debt cri-sis, Basel I tried to reduce systemic risk bylinking banks’ capital adequacy ratios to theriskiness of their loans. Under Basel I, bankshad to set aside capital equal to at least 8percent of their risk-adjusted assets to pro-vide a buffer in case of a loss on those assets.The problem was that Basel I’s system forrisk-adjusting assets created an unintention-al bias toward short-term lending to emerg-ing markets. Lending to banks in OECDcountries received a 20 percent risk weight-ing regardless of the maturity structure ofthe loan. For loans to non-OECD countries,short-term loans received a 20 percentweight, long-term loans a 100 percentweight (Rudi Bonte 1999).

As a consequence of Basel I’s weightingsystem, short-term, foreign currency lendingto banks in emerging markets required onlyone fifth of the capital required of long-termloans to banks in emerging markets and nomore capital than a long-term loan to a bankin an OECD country. Skewing the incentivestructure for banks in G-10 countries to lendshort-term to emerging markets had pre-dictable consequences. As discussed in sec-tion 8.1, short-term, dollar-denominatedloans, most of which originated withEuropean and Japanese banks, constitutedthe lion’s share of debt contracts in the EastAsian Crisis. The eventual implementation ofBasel II may help alleviate the bias towardshort-term debt in Basel I, but Rogoff (1999)identifies three other sources of debt bias inthe international financial system not directlyrelated to capital adequacy ratios.

First, by making it less risky to holddeposits, deposit insurance in creditor anddebtor countries increases the deposit baseand expands the size of the banking system.Implicit subsidies are also a problem. As thesize of banking system expands, it becomesincreasingly difficult for the government to

credibly commit to not bail out the bankingsystem in the event of a financial meltdown(George A. Akerlof and Pual M. Romer1993). Somewhat perversely, deposit insur-ance may also raise the probability of bankfailures by reducing the incentive for depos-itors to monitor the lending practices of thefinancial institutions where they keep theirmoney.51

Second, the international financial systemprotects the rights of debt holders more vig-ilantly than those of equity holders. G-7lenders to emerging-market countries canresort to G-7 courts in the event of a debtdispute, but there is no such avenue ofrecourse for G-7 holders of emerging-mar-ket equity (Bulow 2002; Rogoff 1999;Obstfeld 1998).

Third, the underdevelopment of equitymarkets in emerging economies exacerbatesthe problem of preferential treatment ofdebtholders in G-7 courts. The point here isclosely related to the discussion in section6.1.3. A lack of transparency in emergingequity markets makes foreigners reluctant toinvest, and weak protection of the rights ofequity investors reinforces the tendency ofcapital suppliers to purchase debt instead ofequity (Henry and Peter LombardLorentzen 2003).

9. Conclusion

Writing about capital account liberaliza-tion in 1998, Bhagwati threw down thegauntlet, declaring “It is time to shift theburden of proof from those who oppose tothose who favor liberated capital” (Bhagwati1998). The explosion of papers written onthe subject since that time indicates the seri-ousness with which the profession has takenhis challenge.

There is little evidence that economicgrowth and capital account openness are


51 Rogoff (1999) also argues that funds from interna-tional financial institutions aimed at helping distressed-country debtors also provide an implicit subsidy to G-7debt holders that encourages debt financing over equity.


positively correlated across countries. Butthere is lots of evidence that opening thecapital account leads countries to temporar-ily invest more and grow faster than they didwhen their capital accounts were closed.Why does so much of the literature focus onthe relationship between openness and long-run growth when the predictions of the neo-classical model all point toward theshort-run impact of a country opening up?Part of the answer is tradition. Cross-sec-tional regressions of national growth rates onpolicy variables have been around for awhile, so the gravitational pull of thatmethodological approach is quite strong.But I also think that the answer has some-thing to do with a professional obsession.There has always been a great deal of inter-est in uncovering policies that increase thesteady-state rate of growth. As a conse-quence, economists tend to ignore theimportance of short-run increases that per-manently raise the path of national incometo a higher but parallel trajectory (Harberger2005; Solow 2000, pp. 182–83).

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