metadata: authors: 0: author: Chuhan, Punam
1: author: Claessens,Constantijn A.
2: author: Mamingi,
count: Asia,Caribbean,Latin America
docna: 0: docna: Equity and bond flows to Asia and Latin
            America : the role of global and country factors
docty: Policy Research Working Paper
subtopic: Investment and Investment Climate,Public Sector Economics,Macroeconomic Management,Public Finance Decentralization and
            Poverty Reduction,Financial Structures,Crime and Society
teratopic: Environment,Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: The authors investigate what has
            motivated the large portfolio flows to several developing
            countries in recent years. Using monthly data on U.S.
            capital flows to nine Latin American and nine Asian
            countries (instead of monthly reserves data), they analyze
            the behavior of bond and equity flows to those countries.
            Using panel data, they find that global factors - such as a
            drop in U.S. interest rates and the slowdown in U.S.
            industrial production - are important in explaining capital
            inflows. But country developments are at least as important
            in determining those flows, especially for Asia. They also
            find that equity flows are more sensitive than bond flows to
            global factors, but that bond flows are generally more
            sensitive to a country's credit rating and to the
            secondary market price of debt.
repnme: repnme: Equity and bond flows to Asia and Latin
            America : the role of global and country factors
display_title: Equity and bond flows to Asia and
            Latin America : the role of global and country factors
listing_relative_url: /research/1993/07/698971/equity-bond-flows-asia-latin-america-role-global-country-factors
topicv3: Capital Account m1326277 1134,Treasury m1327009 1045,Central Banks m1327453 1235,Black Market m1439229 BLA-3388,Investment Entry m1504647 2544
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Environment,Macroeconomics and Economic Growth,Finance and Financial Sector Development
origu: International Economics Department
no_of_pages: 44
model_extractions: <!-- Start of picture text --> Polley Researh<br>Debt and Intornallonal Finance<br>WPS 1160<br><!-- End of picture text -->

- This paper a product of the Debt and International Finance Division, International Economics - Departnent is part of a larger effort in the department to study the determinants of capital flows to developing countries. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rose Vo, room S8-042, extension 31047. (July 1993, 36 pages).

Chuhan, Claessens, and Mamingi investigate what has motivated the large portfolio flows to several developing countries in recent years.

Using monthly data on U.S. capital flows to nine Latin American and nine Asian countries (instead of monthly reserves data), they analyze the behavior of bond and equity flows to those countries.

Using panel data, **Ley** find that global factors - such as a drop in U.S. interest rates and the slowdown in U.S. industrial production - are important in explaining capital inflows. But country developments are at least as important in detennining those flows, especially for Asia.

They also find that equity flows are more sensitive than bond flows to global factors, but that bond flows are generally more sensitive to a courntry's credit rating and to the secondary market price of debt.

ThePolicy Research Working PaperSeries disseminates thefindings of workunder way in theBank. Anobj-ctiveof the series is to get these flndings out **quickly,** even if presentations are less than fully polished. The findings, **int** rpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Disseminadon Center
Equity and Bond Flows to Latin America and Asia: The Role of Global and Country Factors by Punam Chuhan, Stijn Claessens and Nlandu Maniingi World Bank The authors are in the Debt and International Finance Division of the World Bank. The opinions expressed do not necessarily represent those of the World Bank or its Board of Directors. We would like to thank Carmnen Reinhardt, Eduardo Fernandez-Arias, Ron Johannes and Vikram Nehru for useful comments, Gary Lee of the US Treasury Department for providing the data, Anthony Rodrigues for useful discussions on econometric techniques, Lois Stekler for helpful conversations on the data, Cliff Papik for support with the data, and Rose Vo for preparing the paper.
# i

# Summary

This paper investigates the factors (global and country-specific) motivating the large capital flows to a number of developing countries in recent years, extending previous work by Calvo et al. (1993). The paper estimates the influence of global factors such as US interest rates and US industrial activity on capital flows to developing countries. It alse explores the importance of country-specific developments in explaining these flows. The paper includes the secondary market price of a country's debt (when available), the country's credit rating, the stock price-earnings ratio (when available), the relative return on the domestic stock market (when available), and the black market prcmium as additional explanatory variables.' Because t&. global factors in the study directly represent developments within the US, we only consider monthly US capital flows. The paper distinguishes between the different types of US capital flows to allow for variation in the determinants of these flows. The focus is on equity and bond flows, the two most important components of portfolio flows. The study thus examines US portfolio equity and bond flows to 9 Latin American and 9 Asian countries.

On the econometric side, the paper employs a panel data approach, which is an appropriate technique for investigating the effects of a common set of global factors across a group of countries. At the same time, thMs approach allows for country-specific effects. The panel data methodology also reduces the problem of multicollinearity among variables.

We find that although global factors, such as the drop in US interest rates and the slowdown in US industrial production, are important in explaining capital inflows, domestic factors in the developing countries are at least as important in determining these flows. About half of the explained increase in flows to the Latin American countries in our sample can be attributed to the drop in US interest rates and the slowdown in the US economy. For the Asian countries, by contrast, country-specific factors are estimated to be three to four tiues more important than global factors in motivating the flows. A reversal equal to one standard deviation of the favorable movements in these global factors would motivate an annual outflow of about $3 billion for the two regions combined.

Among country-specific factors, we find that an across-the-board, one standard deviation increase in the institutional investor credit rating of the 9 Asian countries (the average rating for the countries over the period was 59 out of a maximum of 100) would lead to an increase in annual bond flows of about $3 billion. Capital inflows to the Latin American countries are much less sensitive to a combined index of their credit rating and secondary market price; here, a one standard deviation increase would only lead to an increase in annual bond flows of about $1 billion (note that their credit rating (28) is about half as high as for Asia).

'Since several of these variables are not available for all countries in our study, we use a smaller sample in some estimations.
ii We also find evidence of variation in the sensitivity of the two flows to the explanatory variables. Equity flows are more sensitive than bond flows to global factors, although the relative sensitivity of each type of flow to US interest rates and U.S. industrial activity is similar -by region. As expected, equity flows are more sensitive to a country's price-earnings ratio (with a negative sign) and rates of return on domestic stock markets (relative to the U.S.) than are bond flows. Bond flows, however, are generally more sensitive to a country's credit rating and secondary market price of debt than equity flows. While our evidence is not conclusive, these findings may be interpreted as being consistent with the possibility of credit rationing in the fixed-income market, something one would not expect in the equity markets.
# **TABLE OF CONTENTS**

|I.|Introduction||1|
|---|---|---|---|
|II.|Motivations|of Capital Flows|4|
|II1.|Data||6|
|IV.|Methodolog|y and Result|16|
|V.|Conclusion||26|
|Refer|ence||27|
|Anne|x I||29|
|Figur|es<br>|||
||Figure 1.<br>|Bond Flows to Latin America<br>|8<br>|
||Figure 2.|Bond Flows to Asia<br>|8<br>|
||Figure 3.<br>|Equitv Flows to Latin America<br>|9<br>|
||Figure 4.<br>|Equity Flows To Asia<br>|9|
||Figure 5.|Regional Monthly Averages for Credit Ratings:<br>||
|||Latin America and Asia<br>|11|
||Figure 6.|Monthly Average for Secondary Market Prices:<br>||
|||Latin America: 1988.01-1992.09<br>|11<br>|
||Figure 7.<br>|Black Market Premiums: Latin America<br>|12<br>|
||Figure 8.|Black Market Premiums: Asia|12<br>|
||Figure 9.|US Short-Run Interest Rate<br>|13<br>|
||Figure 10.|US Industrial Production Index in Deviation Fro,n Time<sup>Trend</sup>|13|
|Table|s<br>|||
||Table 1.|Portfolio and other Long-Term Flows to all Developing<sup>Countries</sup><br>|2<br>|
||Table 2.<br>|Some Statistics for Net Equity Flows and Gross Bonds<sup>Flows</sup><br>|10<br>|
||Table 3.|Panel Data Correlations: 1988.01-1992.09<br>|14<br>|
||Table 4.|Panel Data Correlations Among Explanatory Variables|15|
||Table 5.|Panel Data Estimates: Equity Flows, 1988.01-1992.09<br>||
|||Latin America (9 countries) - Asia (9 countries)<br>|18|
||Table 6.|Panel Data Estimates: Bond Flows, 1988.01-1992.09<br>||
|||Latin America (9 countries) - Asia (9 countries)|19|
||Table 7.|Panel Date Estimates: Equity Flows, 1988.01-1992.09||
|||Latin America (6 countries) - Asia (7 countries)<br>|20|
||Table 8.|Panel Data Estimates: Bond Flows, 1988.01-1992.09||
|||La-in America (6 countries) - Asia (7 countries)<br>|21<br>|
||Table 9.|Standardized Slope Coefficients|23|
||Table 10.|<br>Elasticities|5|
1

# **I.** Introduction

Several " atin American countries have received large capital inflows in recent years, reversing a trend of outflows for most of the 1980s. Mexico, for example, saw its net external financing increase in the 1990-1992 period to 5.9% of its GDP, compared to an average net outflow of 0.7% in the 1983-1989 period (IMF, 1992b). Some Asian countries have also experienced large capital inflows in recent years: for developing countries in East Asia and Pacific current-account deficits as a share of GNP averaged 1.8 percent in 1991-1992, compared to 0.6 percent in 1989-1990 (World Bank, 1992).

Much of tkis new capital inflow has been in the form of portfolio investment, i.e., bonds and equities.' Portfolio equity and bond flows to all developing countries were $17.8 billion in 1991, and an estimated $31.3 billion in 1992 (Table 1). These flows are concentrated among a small group of '"emerging" developing countries. For example, five major Latin American countries (Argentina, Brazil, Chile, Mexico, and Venezuela) received over 55 percent of portfolio flows to all developing countries in 1992, and seven South and East Asian countries (China, India, Indonesia, Korea, Malaysia, Philippines, and Thailand) received another 26 percent. This concentration implies that portfolio flows are an important source of finance for some developing countries. For Latin America, for example, portfolio flows accounted for about 50 percent of the region's overall net external long-term financing in 1991 (World Bank, 1992). While other developing countries have not participated to the same degree in this increase in portfolio flows, they have still seen an increase in overall capital inflows compared to much of the 1980s. The current account deficit for all developing countries as a percentage of GNP more than doubled to 1.4 percent in 1991 compared to the average for 1987-1990, and official foreign exchange reserves in many developing countries are now at historically high levels.

The recent surge in capital flows to many developing countries has raised questions regarding the characteristics of these flows, the most important being whether these flows are temporary or permanent. As a large number of developing countries with a wide spectrum of domestic economic conditions--macroeconomic policy and performance--have experienced these flows, the notion has arisen that portfolio flows may be driven by a common set of favorable global factors, factors which could reverse themselves in the future.

'Bond flows have gone to both private and public sector entities, with close to 50 percent going to the private sector in 1992. Equity flows take a number of forms: direct equity purchases by investors in the host stock markets; investments through country funds; issue of rights on equities held by depository institutions (American Depository Receipts, ADRs, and Global Depository Receipts, GDRs); and direct foreign equity offerings. In the last three years equity flows have largely taken pla t!--ough ADRs. Next in importance have been (closed-end) - country funds, followed by direct purc& and foreign equity offerings.
2

|Table 1: Portfolio and<br>|other Long<br>(net inflows|-Term Flow<br>in billions o|s to all Deve<br>f US$)|loping Cou|ntries<br>l|
|---|---|---|---|---|---|
|Type of Flow|1989|1990<br>1|<br>1901|1992|1989-92|
|Bond flows|3.5|4.7|10.2|21.7|40.1|
|Equity Flows|3.5|3.8|7.6|9.6|24.4|
|Country funds|2.2|2.9|1.2|1.3|7.5|
|International Issues|---|0.1|4.9|6.5|11.6|
|Direct Equity|1.3|0.8|1.5|1.8|_5.3_|
|Net FDI|23.3|24.0|23.9|38.3|119.5|
|Official Flows (excluding<br>grants)|19.9|28.2|31.3|31.1|110.5|
|Commercial Banks|6.3|-4.1|3.9|||
|Other Private LT|2.7|12.4|2.8|4.2|28.2|
|Total|59.2|69.0|89.7|104.9|347.1|
|Memo: All Portfolio Flows|7.0|_8.5_|17.8|31.3|_64.5_|

Source: World Bank, 1992.

In a recent paper, Calvo et al. (1993) have argued the importance of global factors-particularly the role of US interest rates (which declined precipitously over the period), the recent US recession (third quarter 1990 to first quarter 1991) and the slowdown in US industrial production over the 1989-1992 period--in expla: iing these inflows. Using monthly international reserves figures as proxies for capital flows (because they lack monthly data on capital flows), as well as monthly real exchange rate movements as indicators of these flows, Calvo et al. evaluate the experience for 10 Latin American countries for the period January 1988 to July 1992. They also derive the first two principal components for a series of global variables (various US interest rates, deviations from the trend in real disposable income, and indexes on returns in US stock and real estate markets). Then using the first two principal components on the series of global variables and the series on reserves and exchange rates themselves in a structural VAR-model, they estimate that about 50 percent of the variance of monthly forecast errors in the real exchange rates and reserves is accounted for by the global variables. Based on these results, they suggest that a reversal of favorable global conditions could induce a capital outflow from these countries.

Like the Calvo et al. study, this paper also investiga:es the factors motivaLing the refent capital inflows. This study differs from the formner, however, in that it uses a different data set,
3 a number of alternative explanatory variables, and a different -onometric methodology.<sup>2</sup> Most in portantly, the paper directly investigates the behavior of capital inflows by employing monthly da&a on capital flows instead of data on reserves and real exchange rates. Official reserve, accumulation and capital inflows are not necessarily identical, particularly when the capital inflows are largely to the pri"ate sector. The relationship between these two variables depends on, among other things, the degree and type of government (exchange rate) intervention. While there has been a substantial degree of central bank intervention in these countries, and capital inflows and reserves do tend to co-move, the co-movement is `mperfect and differs across countries (Calvo et al. point out, for example, th **t** in Brazil and Uruguay there is no comovement). Indeed, our results indicate that reserves are weakly correlated with portfolio flows, implying that reserves are a poor proxy for these flows (Annex 1 provides the details).

This study also distinguishes between th- different types of portfolio capital inflows to allow for variation in the deterninants of these flows. The focus is on equity and bond flows, the two most important components of portfolio flows. Because the global factors in the study directly represent developments within the US, we only consider US flows. The study thus focuses on US portfolio equity and bond flows to 9 Latin American and 9 Asian countries.

While the paper assesses the importarce of global factors 'n explaining pcr:folio flows, it also attempts to systematically explore the influence of country-specific factors in explaining Lnese flows. The paper includes the secondary market price of a country's debt (when available), the country's credit rating, the price-earnings ratio (when available), the return on the domestic stock market (when available), and the black market premium as explanatory variables. Since several of these variables are not available for all countries in our study, we use a smaller sample in some estimations. On the econometric side, the paper employs a panel data approach, which is an appropriate tezhnique for investigating the effects of a common set of exogenous factors across a group of countries. At the same time, this approach also allows for country-specific effects.

The econometric results point tc, the importance of global and country-specific factors in influencing these capital inflows. To the extent that we are able to explain the flows, about half of the explained increase in the flows to the Latin American countries in our sample can be attributed to the drop in US interest rates and the slowdown in the US economy. For the Asian countries, country-specific factors are three to four times more important than global factors in motivating these flows. A reversal equal to one standard deviation of the favorable movements in these global factors would rotivate an annual outflow of about $3 billion for the two regions combined.

Among country-specific factors, we find that an across the board, one standard deviation increase in the institutional investor credit rating of the 9 Asian countries (the average rating for 2In addition, while the Calvo et al. paper has a forecasting focus, our aim is primarily to analyze the impact of different scenarios for the external environment on capital flows.
4 the countri,..s over the period was 59 out of a maximum of 100), would lead to an increase in annual bond flows of about $3 billion. Capital inflows to the Latin American countries are much less sensitive to a combined index of their credit rating and secondary market price; here, a one standard deviation increase would only lead to an increase in annual bond flows of about $1 billion (note that their credit rating (28) is about half as high as for Asia).

We also find evidence of variation in the sensitivity of the two flows to the explanatory variables. Equity flows are more sensitive than bond flows to US interest rates and U.S. industrial activity, although the relative sensitivity of each type of flow to these global factors is similar by region. As expected, equity flows are also more sensitive to a country's priceearnings ratio (with a negative sign) and rates of return on domestic stock marklets (relative to the U.S.) than are bond flows. Bond flows, however, are generally more sensitive to a country's credit rating and secondary market price of debt than equity flows. While our eviden,:e is not conclusive, these findings may be interpreted as being consistent with the possibility of credit rationing in tlhe fixed-income market, something one would not expect in the equity markets.

The rest of the paper is organized as follows. Section II outlines some theoretical considerations regarding the motivations of capital flows. Section m describes the data. Section IV presents the methodology and the empirical results. The concluding section outlines some directions for further research in this area.

# H. Motivations of Capital Flows

The motivation of capital flows has long been a subject of research in economics and many tests of the degree of capital mobility among countries have been performed. Frankel (1992) provides a general survey of this literature. Montiel (1993) provides an overview of this literature as it deals with developing countries and also presents some new estimates n-,garding a number of empirical tests for developing countries. In general, this research has not ben able to explain some observed characteristics of international capital flows. For example, the Feldstein-Horiok}a finding (of low capital mobility), first identified in 1980, still largely .remains a puzzle as there are many other indications that, from an asset pricing and gross capital flow point of view, industrial country international capital markets are well integrated. In practice, this means that it has proven difficult to model capital flows in a world in which capital is imperfectly mobile.<sup>3</sup> The finance perspective on the issue of capital flows is also useful. In portfolio models, because asset allocation is based on relative tradeoffs between expected risk and return, capital flows can be experted to be motivated by changes in perceived relative risk and returns. Much 3When capital is imperfectly mobile, proper modelling of capital flows requires that the source of the imperfection be modelled. Also see Frankel (1992).
_5_ of the early international portfolio models followed the lobin-Brainard model (e.g., Branson and Henderson, 1985) where assets are assumed to be imperfect substitutes and asset demand functions are estimated. But few recent papers follow this approach to modelling capital flows (e. g., McKibbin and Sachs (1991) assume assets are perfect substitutes). And recent empirical international finance papers deal with tests on asset-prices and do not concern themselves with the implications for equilibrium adjustments in stock positions, i.e., capital flows. Moreover, research in this area with a specific focus on capital flows to developing countries is limited.

Nonetheless, the literature allows us to identify some factors which are likely to play an important role in motivating capital flows, once the special situation of (indebted) developing countries (which are likely credit rationed) is taken into account. First, there are countryspecific factors reflecting the opportunities and risks of investing in the country. Rates of return on stock markets, credit ratings, and secondary market prices of sovereign debt are thus likely to play a role in influencing the flows. Most of these indicators had an upward trend over the study period for the developing countries in our samp;_. Rates of returns on stock (equity) markets in many developing countries increased sharply in the last few years: over 1988-92, the IFC dollar-based composite index rose 294.2 percent for Latin America and 49.5 percent for Asia, compared to 108.4 percent for the S&P 500. Care should of course be taken to distinguish the ex-ante rates of return (which can motivate capital flows) from the measured ex-post rates. The credit ratings of many major developing countries also improvwd. For our sample of 9 Latin American countries the credit rating rose from an average of 27 points out of 100 in 1988 to 31 in 1992. Secondary market prices were higher by approximately two-thirds over end- 1989 (using the weighted price for the group of severely indebted developing countries). Furthermore, there was a general trend towards a larger role of the private sector in these developing countries, along with liberalization of their economies, especially opening up of the capital account.<sup>4</sup> Second, global factors may also have played an important influence. US short-term interest rates fell dramatically, by about half since 1988. For example, the three-month treasury bill rate was 3.3 percent at the end of 1992, compared to 5.9 percent at the beginning of 1988. Likewise, LIBOR on six-month US dollar deposits was 7.4 percent at the beginning of 1988, and s!9od at 3.7 percent at the end of 1992. Over this per;nd, three-year US interest rates fell by 2.7 percentage points from 7.9 percent in the beginning of 1988. Other rates of return in the US, e.g., on real estate, have also been low in this period. Declining returns in the US markets may not only have made it more attractive for US investors to seek higher returns abroad, but may also have induced holders of flight capital to repatriate their funds. The slowdown in the US economy over the 1989-1992 period may have further cor 'ibuted to the outflow of capital from the US. Regulatory and other changes in industrial coui..ies (such as the introduction of 4For general surveys of the experiences and (macro-economic) issues involved with liberalization of the capital account, see Hanson (1992), Mathieson and Rojas-Suarez (1992), and Reisen and Fischer (1993).
6 Rule 144A5 in the US) may have also facilitated access to international markets, particularly for portfolio flows (see fttrther IMF (1992), Calvo et al. (1993) and World Bank (1992)). These global factors take on an increased importance given that the recent capital flows have gone to developing countries with a wide spectrum of domestic policies and economic developments.

The inportant (policy) question now is to what extent the capital inflows are a function of country-specific factors and to what extent of factors in industrial countries: i.e, has the recent decline in (US) interest rates been an important "push" factor or have the improved economic prospects in many develcping -ountries been "pulling" flows tc these countries?

# **III.** Data

Data Sources The study uses monthly US capital flow data or gross and net purchases of non-US long-term securities from 9 Latin American countries and 9 Asian counltries for the period January 1988 through September 1992.6 The data was obtained from the US Treasury Department and is part of the Treasury's International Capital Rep,rts (TIC).<sup>7</sup> TIC data is 5Rule 144A, which was introduced in April 1990, has facilitated private pla^ements in the US market by circumventing onerous filing requirements and easing restrictions on resale of privately placed securities.

6The Latin America countries are Argentina, Brazil, Chile, Colombia, Ecuador, Jamaica, Mexico, Uruguay, and Venezuela. The Asian countries in the sample are China, India, Indonesia, Korea, Malaysia, Pakistan, Philippi e-s, Taiwan (China) and Thailand. Our data also covered three more Latin American countries (Panama, Peru and Trinidad and Tobago), but these countries were dropped because the flows either reflected the special banking status or the country (Panama) or were insignificant (Peru, and Trinidad and Tobago). In Asia, we dropped Hcig-Kong from our sample because of the country's status as a international financial center. The results for a smaller group of countries (six Latin American and seven Asian) are also reported.

7We would like to thank Mr. Gary Lee of the US Treasury for his cooperation in providing the data. Quarterly data are published in Treasury Bulletin, Capital Movements section, Table CM-V-4, Foreign Purchases and Sales of Long-Term Securities, by Type and Country. The data cover transactions executed in the US for the accounts of foreigners, and transactions abroad for the accounts of ieporting institutions by their domestic customers. The data includes new issues as well as transactions in outstanding issues. The data are collected by the Treasury from financial intermediaries (banks, brokers and other entities) in the US through so-called International Capital Form S reports. The data do not cover any direct dealings of US (institutional or individual) investors with foreign intermediaries (or direct borrowers or lenders) as these bypass the reporting system. This is probably more of a problem for industrial countries as for these countries institutional investors in the US may be willing to deal directly with the foreign entity, something which seems less likely in case of the less known (corporations in) developing countries. The data on bonds cover the purchase and sales of foreign securities in the US from and to the developing country. The data may thus include
7 disaggregated by type of capital flow, e.g., bonds (corporate),<sup>equities, and US government</sup> bonds. The total number of subcategories for long-term securities is<sup>7.</sup> In this study we focus on equity and bond flows only and exclude,<sup>for example, sales and</sup> purchases of money market CDs and commercial paper.<sup>8</sup> We correct the bond flows for the issuance of bonds under public debt conversions and the Brady<sup>plan (bonds issued in the debt-</sup> for-debt exchanges as well as any new money bonds) because these<sup>flows do not represent any</sup> voluntary new financing. This adjustment is done by including<sup>dummies for the months of</sup> issuance.<sup>9</sup> Although in principle we aim to model net<sup>capital flows, in this study we focus on</sup> gross bond inflows instead of net bond inflows. We prefer to use<sup>gross bond flows because net</sup> bond flows are influenced by countries' gross purchases<sup>of foreign assets (central banks'</sup> sterilization and other reserve operations, including the repurchases<sup>of the countries' own</sup> .,al obligations). A case in point is Mexico, where<sup>the central bank bought back $7.2</sup> billion of public external debt over the two-year period preceding July<sup>1992, mostly bonds issued</sup> under the 1989 Brady agreement (IMF, 1992b). For<sup>equity flows, on the other hand, we can</sup> correctly study the net flow figures as these are not influenced<sup>by central banks' operations (few</sup> central banks hold non-fixed income securities).

Capital Flow Data The bulk of US equity and bond<sup>flows to developing countries are</sup> concentrated in the 17 countries (Taiwan (China)<sup>is a high-income country) in our sample. In</sup> 1992, these countries' share was 88 percent for gross equities<sup>(97 percent for net equities) and</sup> 74 percent for gross bonds. The US share in total equity<sup>and bond flows to the sample countries</sup> (excluding Taiwan (China)) is likewise large, 67<sup>percent of net equities and 50 percent of bonds</sup> in 1992. The large size of these shares implies that our data<sup>covers a substantial amount of</sup> portfolio flows to developing countries. The US share<sup>in total portfolio flows is much larger</sup> for the Latin American countries than for the Asian<sup>ones, however. As a result, the capital</sup> flows studied here cover a larger percent of overall<sup>capital inflows to Latin America than to</sup> Asia. For selected Latin American and Asian countries,<sup>US gross bond purchases and US net</sup> equity purchases are presented in figures I to 4. Table 2 presents<sup>the annual averages of the two</sup> transactions in bonds not issued by the developing<sup>country in question nor by US entities (e.g.,</sup> US residents would purchase non-Mexican, non-US<sup>bonds from Mexico), but these transactions</sup> are likely to be small, and in any case, likely to be<sup>motivated by the same factors underlying the</sup> trading or issuing of bonds from the developing ceuntry<sup>itself. Data limitations do not allow for</sup> a systematic cross-check, but for countries for which<sup>other data were available the gross iriflow</sup> figures for bonds correspond closely to estimates<sup>from other sources on the amount of new</sup> bonds issued by these countries in the US (on an<sup>annual basis).</sup> OTIC-dat **a** re also collected on other cross-border<sup>claims. Stekler and Truman (1992) draw</sup> attention to some of the Froblems with the TIC-data<sup>as it concerns U.S. nonbank claims on</sup> foreign banks, short-term negotiable instruments<sup>(e.g., commercial paper), and trade-credits</sup> received and extended.

9In our sample, this happened in February 1988 (the so called Mexico-Morgan deal) and March 1990 for Mexico, December 1990 for Venezuela,<sup>and February 1991 for Uruguay.</sup>
8

# types of flows, as well as the stpudard deviations and some other statistics, by region.

<!-- Start of picture text --> Bond F I ows to Lat iri Amer- I ca<br>Soo 800 ~~~~~Cuelectud coufltr-I6)<br>400<br>200<br>200-<br>B on(d F I o)ws to( As i a<br>600 -_.<br>0-<br>50 *;<br>19O6-01 1988-07 1989-01 1989-07 1990-01 1990-07 1991-01 1991-07 1992-01 1992-07<br>a Korge I- MneISSIa X Vhf I lOandI<br>F%we 2<br><!-- End of picture text -->
9 <!-- Start of picture text --> Equ I ty F I ows to Lat i n Amer- i ca<br>1._<br>01.2 g~~~~~~~~~~e *r_* OO:, Ijw,t X<br>0.<br>-0.2<br>-0.4<br>-0.2<br>-1<br>1999-01 199E-07 1909.-01 1989-07 1990-01 1990-07 1991-01 1991-0? 1902-01 1992-07<br>0- -<br>+ Mexoico e 9raz11<br><!-- End of picture text --> Fi"ue **3** <!-- Start of picture text --> EqpuTty Floiws to Asia<br>-100<br>90-<br>70-<br>80-<br>40-<br>20-<br>-10<br>-20<br>-30<br>-40<br>19S8-01 18998-07 1089i-01 1889i-07 1890-01 1990-07 19391-01 1001-07 1992-01 1992-07<br><!-- End of picture text --> <!-- Start of picture text --> Figur 4<br><!-- End of picture text -->
10 Table 2. Some Statistics for Net<sup>Equity Flows and Gross Bond Flows</sup> (millions of dollars, on annual basis,<sup>1988 -</sup><sup>**1992)**</sup>

||||Latin America||Asia||
|---|---|---|---|---|---|---|
|||Net Equity|Gross Bonds|Gross Bonds<br>(without Brady|Net<sup>Equity</sup><br>)|Gross Bonds|
|1988|Sum|177.000|658.000<br>|388.000<br>|132.000<br>|2239.000<br>|
||Average|19.667|73.111|43.111|14.667|2d8.778|
|1989|Sum|376.000|567.000<br>|567.%00<br>|61.000<br>|2920.000<br>|
||Average|41.778|63.000|63.000|6.778|324.444|
|1990|Sum|1206.000<br>|9673.000<br>|829.000<br>|192.000<br>|2890.000<br>|
||Average|134.000|1074.778|92.111|21.333|321.111|
|1991|Sum|2343.000|2870.000<br>|2306.000<br>|220.000<br>|3985.000<br>|
||Average|260.333|318.889|256.222|24.444|442.778|
|1992|Sum|3170.000|4011.000<br>|4011.000<br>|740.000<br>|3837.000<br>|
||Average|352.222|445.667|445.667|82.222|426.000|
|Adjusted|Sum|4226.667<br>|5348.000<br>|5348.000<br>|986.667<br>|5116.000<br>|
|1992|Average|469.630|594.222|594.222|109.630|568.444|
||sUM|7272.000|17779.00|8101.000|1345.000|15871.00|
||MEAN|1530.947|3742.947|1705.474|283.158|3341.263|
||St. D|1328.517|2956.418|1589.646<br>|279.622<br>|775.669<br>|
||CV|0.868|0.790|0.932|0.987|0.232|

Note: Fourth Column: Gross bond<sup>flows without Brady; Sum: annual sum for all the countries for a particular year;</sup> Average: Sum divided by the number<sup>of countries; Adjusted 1992: the nine months of 1992 grossed to a yearly basis;</sup> SUM: the sum for the whole period;<sup>MEAN = (SUM*12)/57; CV: Coefficient of Variation = standard deviation/</sup> MEAN.

Country-Specific Variables. The<sup>study employs several domestic equity, credit, and</sup> exchange market variables as explanatory<sup>factors. The two equity market variables-- price</sup> earnings ratios and rates of return<sup>on domestic stock markets--are from the IFC's Emerging</sup> Markets Data Base. The country credit<sup>variable was constructed by using Institutional Investor's</sup> semi-annual country credit rating. Secondary<sup>market debt prices are from Salomon Brothers</sup>
11 (Figures 5 and 6). **'1** Black market exchange rate premiums are dervied from data on black market exchange rates in the World Currency Yearbook and the official exchange rates presented in the IFS (Figures 7 and 8).1" <!-- Start of picture text --> Regional Monthly Avera1es Monthly Average for Secondary Market Prices:<br>Latin America (LAC and Asia (ASIA) LtnAerica: 1988.01-1992.09<br>1988.01 - 1992.09<br>55 2<br>1988 198 1990 19S1 1992 1988 1989 1990 l9S1 19920<br>45<br>40<br>2 ~~~~~~~~~~~~~~~~~~~~~~50<br>eo --. ,-A ASS3.,.,,,,,........... -. .. .. ..... .. ... ...........<br>35~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'<br>30 45<br>8,25 -40<br>109.. 1989.1990 1991 1992 1988 1989.1990.1 9 91 1992<br><!-- End of picture text --> Figure **S** Figure 6 '"The credit ratings are available on a semi-annual basis. For intermittent months, the semiannual ratings are kept at their latest value. The secondary market prices are end-of-month prices. Prices are not corrected for the fact that some of the claims are collateralized with riskfree assets, assets whose prices themselves depend on interest rates. Secondary market prices are not available for Asia (debt trades in general at par, except for the Philippines). Individual country credit ratings and secondary market prices are used in estimations.

"The premiums are occasionally negative as there is some non-synchroneity between the black market rates and the official exchange rates, as well as excess supply in the black market itself.
<!-- Start of picture text --> pLIpU es seu!dd!IL4d uD4sP4Od --- l<br>Z6ADIN L 6JdV 06.Jo4 69q.j ur<br>-~~~~~~~~~~~~~~~~S<br>OL.<br>(se!.J4unoo peosejes)<br>DIS SLUnl!Wujd 49>bJDov4 >p1ie<br><!-- End of picture text --> <!-- Start of picture text --> olnzOeuAes IZD.8 o &o u!4usBJV mI<br>.............. Z6AoI . ...... ....... I. 6-Jd' 06JOV4 ........ 69q*j .. .... .. g guor .. , O S-<br>-0<br>-00<br>osz<br>(sepjunoo pe84oe9s)<br>ooLJiLuV uilo- :sLun!Lue-j te1>1DVM >14>Die<br><!-- End of picture text -->
Global Variables. The key global variables are US interest rates and US industrial production. We employ three short-term nominal interest rates (including LlBOR on 6-month US dollar deposits (Figure 9), line 60ldd in IFS), one medium-termn and one long-term nominal interest rate, as well as the first principal component of the these five interest rates. Real interest rate series are constructed by subtracting the past twelve month US CPI rate (line 64 in IFS) from the nominal interest rates. The US industrial activity variable (US INP) is constructed as the deviation of the US industrial production index (line 66..c in IFS) from a time trend (Figure 10).

<!-- Start of picture text --> US Industrial Production Index In Deviation trom<br>US8 Short-Run Itr0t - 0mon LIBOR): Time Trend: 1988.01 - 1992.09<br>.<br>11988L 190 1 1998 181990<br><!-- End of picture text --> Figure9 FLgure 10 Correlations between flows and explanatory variables. Table 3 reports the panel data correlation coefficients between gross bond flows and net equity flows and the independent variables. The correlations are presented by region and for both nominal and real interest rates. Most correlation coefficients correspond to our priors: negative between US interest rates and flows, US industrial production variable and flows, and black market premiums and flows; and positive between credit ratings and flows, and secondary market prices and flows.
14 Table 3. Panel Data Correlations: 1988.01 - 1992.09

|Lat|in Americ|a||Asia|||||
|---|---|---|---|---|---|---|---|---|
||Net<br>Equity|Net<br>Equity<br>(with<br>real<br>interest<br>rates)|Gross<br>Bond|Gross<br>Bond<br>(with<br>real<br>interest<br>rates)|Net<br>Equity|Net<br>Equity<br>(with<br>real<br>interest<br>rates)|Gross<br>Bond|Gross<br>Bond<br>(with<br>real<br>interest<br>rates)|
|Credit|0.1_4||0.253||0.052||0.334||
|Second.|0.029||0.142||N.A.||N.A.||
|US INP|-0.129||-0.282||-0.222||-0.12"'||
|Black|_-0.054_||-0.134||-0.083||0.141||
|USAT|-0.094|-0.094|-0.324|-0.273|-0.253|-0.221|-0.117|-0.143|
|USAC|-0.099|-0.103|-0.329|-0.295|-0.256|-0.235|-0.122|-0.147|
|USAG|-0.092|-0.024|-0.315|-0.006|-0.211|0.016|-0.119|-0.071|
|USAL|-0.100|-0.102|-0.332|-0.295|-0.253|-0.229|-0.128|-0.153|
|USA3i|-0.092|-0.075|-0.336|-0.219|-0.234|-0.141|-0.123|-0.135|
|PCi|-0.098|-0.093|-0.336|-0.260|-0.248|-0.195|-0.125|-0.148|

Sources: International Financial Statistics, IMF. US Treasury, Salomon Brothers Institutional Investors, and World Currency Year book. Note: USAT, USAC, USAG, USAL and USA3i interest rates are US Treasury bill rate (IFS line 66c), certificate of deposit rate (IFS line 601c), long term rate (ten-year: IFS line 61), LIBOR (three-month: IFS line 60ldd) and medium term rate (threeyear: IFS line 61a), respectively. PCi stands for the first principal component of the five interest rates (it explains 95 % of the variance of these (nominal) interest rates).<sup>12</sup> Brady bonds have been taken out of gross bond flows for Latin America (the results are very different with Brady bonds incorporated). N.A. denotes not available.

Table 4 reports the correlation coefficients among the explanatory variables by region. As expected, the various interest i-ates are highly correlated. The correlation between these interest rates and the industrial production variable is likewise high. There is also a significant (negative) correlation between secondary market prices and interest rates (and the industrial production variable). As interest rates dxclined over much of this period, secondary market '<sup>2</sup> When using real interest rates, the first principal component explains equally well the individual real interest rates, except for the long-term (ten-year) real interest rate, where only 51 % is explained by the first principal component.
15 prices increased, especially prices of fixed-interest-rate bonds."<sup>3</sup> In the case of T,atin America, a significant (negative) corTelation is also registered between interest rates and country credit rating. Black market premiums are significantly related with the secondary market prices (available for Latin America only) and the industrial production variable.

Table 4: Panel Data Correlations among Explanatory Variables 1988.01 - 1992.09

||Credit<br>Rating|Second.<br>Market<br>Price|US<br>Industrial<br>Product.|Black<br>Market<br>Premium|USAT|USAG|PCi|
|---|---|---|---|---|---|---|---|
|||Latin|America|||||
|Credit|1.000|||||||
|Second.|0.703|1.000||||||
|US INP|-0.210|-0.357|1.000|||||
|Black|-0.060|-0.179|0.300|1.000||||
|USAT|-0.224|-0.407|0.873|0.237|1.000|||
|USAG|-0.180|-0.313|0.799|0.313|0.816|1.000||
|PCi|-0.219|-0.391|0.887|0.286|0.976|0.912|1.000|
|Credit|1.000||Asia|||||
|Second.|NA|1.000||||||
|US INP|0.003|N.A.|1.000|||||
|Black|0.048|N.A.|0.070|1.000||||
|USAT|0.012|N.A.|0.873|0.085|1.000|||
|USAG|-0.001|N.A.|0.799|0.049|0.816|1.000||
|PCi|0.007|N.A.|0.887|0.076|0.976|0.912|1.000|

Sources: See Table 3.

Note: See Table 3 for the acronyms used. The lowest correlation between pairs of interest rates is 0.816 (between USAG and USAT). N.A. denotes not available.

13When interest rates fall prces of collateralized bonds will increase because the present value of collateral increases. Prices of uncollateralized, variable interest rate bonds can also be expected to rise when interest rates fall when payments on the bonds arc to some degree independent of the payment obligation, and in general, as the solvency of the country (discounted present value of future trade and/or fiscal balances) rises as interest rates fall.
16

# IV. **Methodology and Results**

Methodology. We use a panel data approach to analyze capital flows to Latin America and Asia. This approach is employed for several reasons. First, the panel data estimation method is among the most efficient techniques to analyze the impact of a common set of global factors -ross a diverse group of countries. Second, this structure acknowledges that each country can have its own characteristics (country-specific effects), which can be correlated or uncorrelated with some or all the explanatory variables. Third, it is an appropriate method to alleviate the effects of omitted (missing and/or unobserved) time-invariant variables that are correlated with the explanatory variables. Fourth, the panel data technique solves or reduces some econometric problems by increasing the data points (increasing the degrees of freedom) and decreasing the collinearity among explanatory variables.

We investigate both gross and net flows. For reasons indicated above, we only discuss gross bond flows and net equity flows here. The model that we estimate is of the following type: Yit = a; + **X3t 0** + **u,t** (1) where i=1,2,...,N is a country index, t=1,2,...,Tis a time index, Yit is the explained variable (either net equity or gross bond flows), a; is the country specific constant which can be either fixed or random, Xi, is a set of explanatory variables, ,B is a vector of slope parameters, and **uit** is the usual random disturbance. Model (1) states that the slope of each explanatory variable is the same across all the countries and the differences among the countries are captured by the latent variable a;. The model can be rewritten as follows:

**Yit** = a; + **x'A** + z, y + **Uit**

where x,, is a set of country-specific factors (e.g., credit rating, secondary market price, price earnings ratio, and black market premium) and z, is a set of time-invariant variables which capture global factors (e.g., US interest rates and US industrial activity).

The appropriate estimation technique for model (1) or (2) crucially depends, among other factors, on the nature of the latent variable a,. If a, is fixed, i.e. correlated with some of the explanatory variables, and the model is over identified, then the two-stage least squares method (2SLS) a la Hausman-Taylor (1981) provides efficient estimates of a ad y. If _a1_ is fixed and the model is just identified, then 2SLS and within-estimator are equally efficient. If _a 1_ is random, that is uncorrelated with all the explanatory variables, then the generalized least square estimator, a weighted average of between-group and within-group estimators, is the appropriate technique.

Thus, prior to using any particular estimation technique, we test for the hypothesis that Ho: E **(ac I xt,** z;) = 0 against **HI:** E (a1 **I** x,,, z;) **;# 0.** The relevant x<sup>2</sup> test statistic (see Hausman and Taylor (1981, pp. i382-1383) is:
17 <!-- Start of picture text --> x2 V-4(3)<br><!-- End of picture text -->

# where **_4=0wP_GLs'_** _and ow_ OG,S stand for within and GLS estimators, respectively,

> _V=cov(4)=cov(OO,)-cov(G,,,)_ and k, the number of parameters of interest, is the degree of

freedom. The acceptance of Ho means that a, is random. Before reporting the results, we point out that in many cases the models passed the F-test of whether "to pool or not to pool. " Although the panel data approach is designed to reduce the problem of multicollinearity, we also use principal components of explanatory variables to further attenuate this problem (multicollinearity is manifested in the form of large standard errors or wrong coefficient signs as seen in several tables in Annex 2).14 We regroup variables which are both highly correlatedi and which are believed to exert a similar qualitative influence on flows. A case in point is the first principal component of a country's credit rating and its secondary market price. We interpret this first principal component as an index of country-specific credit standing (PCS).<sup>15</sup> A first principal component of five US interest rates and the US industrial production variable is also constructed and is viewed as an index of global factors (PH).

Results. Our findings support the importance of both country-specific and global factors in explaining capital flows to emerging countries in Latin America and Asia. There is some variation in the results by region and by type of flow, however. In addition, there is also some variation in results across models arising from the use of nominal versus real interest rates, particularly for Latin America. The adjusted R1 in the different models are high for Latin America and low for Asia. The high level of the statistic in Latin America is mainly explained by the dummies which capture the privatization (Telmex) phenomenon for equity flows and the Brady Plan for bonl flows."<sup>6</sup> In fact, a lower **_R2_** is generally expected for these types of models. Table _5_ and 6 present the panel data estimates for bond and equity flows to all Latin American (9) and Asian (9) countries in our sample. Tables 7 and 8 present these same results for a slightly smaller groups (6 and 7 countrie, respectively) for which data on price-earnings ratios and rates of return on domestic stock markets were available .

**14** To detect multicollinearity, one should primarily look at the variance of residuals of the model, the R2, and the different **_R2,_ (12** from the regression of one explanatoLy variable on the other explanatory variables (Maddala 1988, p. 223-249)).

lS Although the two variables are not the only country-specific factors, in these models they seem to be the most important ones. Note that their index is not derived for Asia for lack of availability of secondary market prices data.

> 6 In fact, if we adjust for these dummies, the slope coefficients of the explanatory variables are not affected, but a much lower R<sup>2</sup> results (i.e., for bonds, it drops from .98 to 0.16).
18 Table 5: Panel Data Fstimates: Equity Flows, 1988.01-1992.09 Latin America (9 countries) - Asia (9 countries).

||Lat|in America||Asia|
|---|---|---|---|---|
||Nominal interest<br>rates|Real interest<br>rates|Nominal interest<br>rates|Real interest<br>rates|
|Method|GLS|GLS|Within|Within|
|Credit rating|||.396**<br>(0.131)|0.359**<br>(0.017)|
|PCS|-1.367<br>(5.377)|2.458<br>(4.626)|||
|PH|-10.539*<br>(5.339)|-6.505<br>(4.587)|-2.347**<br>(0.380)|-1.924<br>(0.385)|
|Adjusted R<sup>2</sup>|0.292|0.289|0.065|0.042|
|X2|4-0.025|-0.032|6.976<br>(0.008)|_5.595_<br>(0.018)|
|NOBS|513|513|513|513|

Note: Mothod: estimation technique; PH: first prinoipal component of tde five types of interest rates and US industrial production (it explains 93 % of the variance of these variables if nominal interest rates are used and 79% if real interest ratw ar used); PCS: first principal component of country's credit rating and secondary market prices obtained by stacking the corresponding individual first principal components (the least explained PCS is 85%); figures in parentheses are standard errors of coeffioients or p-value for kl, which tests for independence of either PCS or credit rating with the latent variable; columns 2 and 4 use nominal interest rates; column 3 and 5 use real interest rates; NOBS: number of observations; '"o, **_"*',_** and **_"**":_** significant at tde 10%, _5%_ and 1% level, respectively; and for Ladn Ameica, 3 dummnies are used for equity flows to capture the privatization phenomenon (Telmex). Toh choice of estimation technique depends on the value of _x_<sup>_2_</sup> in the following way: if p < level of significance (1 % or 5%), we refect H. and use either 2SLS or within-estamtor. If 2 is negative or p 2 level of significance, we use GLS.
19 Table 6: Panel Data Estimates: Bond Flows, 1988.01-1992.09 Latin America (9 countries) - Asia (9 countries).

||La|tin America||Asia|
|---|---|---|---|---|
||Nominal interest<br>rates|Real interest rates|Nominal interest<br>rates|<br>real interest rates|
|Method|GLS|GLS|GLS|GLS|
|Credit rating|||1.318<br>(0.281)|1.304-<br>(0.281)|
|PCS|3.8930<br>(2.431)|8.329-<br>(2.114)|||
|Pll|-13.094|-8.737-|-8.370-|-9.563**|
||(2.413)|(2.104)|(2.628)|(2.620)|
|Adjusted R'|0.982|0.982|0.041|0.047|
|_X'1_|-0.0381|-0.0002|0.0003<br>(0.9871)|0.0181<br>(0.8930)|
|NOBS|513|513|513|513|

Note: See Table 5. Three dummies are used for Latin America to capture<sup>the Brady Plan effect. One dummy is</sup> eliminated because with 4 dummies the algorithm for GLS breaks down (square root of negative element).<sup>"#n, 'o',</sup> "*", and "**': significant at the _15%,_ 10%, _5%_ and **_I_** % levels, respectively.
20 Table 7: Panel Data Estimates: Equity Flows, 1988.01-1992.09 - Latin Ameriza (6 countries) Asia (7 countries).

||Lati<br>|n America<br>||Asia<br>|
|---|---|---|---|---|
||Nominal interesi<br>rates|Real interest<br>rates|Nominal interest<br>rates|<br>Real interest<br>rates|
|Method|GLS|GLS|GLS|GLS|
|Credit rating<br>PCS|1.690<br>(8.174)|7.278<br>(7.480)|0.108*<br>(0.051)|0.106*<br>(0.052)|
|Return|-0.039<br>(0.316)|-0.C61<br>(0.316)|0.0020<br>0.001|0.002?<br>0.001|
|Price-Earning Ratio|-0.9850<br>(0.928)|-0.654<br>(0.894)|-0. 182**<br>(0.065)|-0. 185**<br>(0.066)|
|P11|-18.141*<br>(8.212)|-11.066°<br>(6.913)|-2.007**<br>(0.479)|-1.564**<br>(0.485)|
|Adjusted E?|0.292|0.290|0.0S3|0.036|
|_xi[_|-0.430|-3.633|0.976<br>(0.323)|0.000<br>(0.994)|
|NOBS|342|342|399|399|

Note: See Table 5.

Latin America: Argentina, Brazil, Chile, Colombia, Mexico and Venezuela. Asia: India, Korea, Malaysia, Philippines, Pakistan, Taiwan and Thailand. Return: differential of rates of return (rates of return of domestic stock markets - US rates of return). As in Table S, three dummies are used for equity flows in Latin America. "#", "o", "a", and "**f:significant at the _15%,_ 10%, _5%_ and I % levels, respectively.
21

**Table 8: Panel Data Estimates: Bond Flows, 1988.01-1992.09** - **Latin America (6 countries)  Asia (7 countries).**

||**Lati**|**n America**||**Asia**|
|---|---|---|---|---|
||Nominal interest<br>rates|Real interest<br>rates|Nominal interest<br>rates|<br>Real interest<br>rates|
|Method|GLS|GLS|GLS|GLS|
|Credit rating<br>PCS|7.374*|12.708**|1.643**<br>(0.318)|1.599**<br>(0.315)|
||(3.534)|(3.328)|||
|Return|-0.046|-0.067|0.003|0.003|
||(0.137)|(0.139)|0.009|0.009|
|Price-Earning Ratio|-0.860*|**_-0.5010_**|-1.**171****|**-1. 101****|
||(0.412)|(0.406)|(0.426)|(0.428)|
|**PH**|-20.316**<br>(3.567)|-13.406<br>(3.052)|-10.001**<br>(3.205)|-0.494**<br>(3.218)|
|Adjusted R`|0.984|0.983|0.075|0.078|
|AI|-0.016|-0.037|0.035<br>(0.851)|0.040<br>(0.842)|
|NOBS|342|342|399|399|

Note: See Table 7. Two dummies are used for Latin America to carture the Brady Plan.

The impact of country-specific factors varies by type of flow and by region. For bond flows, the importance of the credit rating variable (in Asian countries) or the first principal component of credit rating and secondary market price (in Latin American countries) appears to be clearly established (see Table 6 and 8 as well as Table AS and A7 in Annex 2), but not for equity flows. Indeed, the "t" statistics of the first principal component of credit rating and secondary market prices are not significantly different from zero (at the 15 % level of significance) for equity flows to Latin America. In fact, the big standard errors for this countryspecific factor emphasize that multicollinearity is still a problem here.'" Among other countryspecific factors, the price-earning ratios are consistently significant across models, but the domestic stock market return variable behaves differently for Asia and Latin America. The 17 This is confirmed, for example by using Klein's rule (R<sup>2</sup> **<** R2i: 0.309 < 0.440) in Table 8 (first regression). Note that multicollinearity does not affect the index of global factors because the latter has big coefficients.
22 latter has both the right sign and is some times significant for Asia, but it is wrongly signed and insignificant for Latin America. The lack of significance of this variable is mainly explained by the fact that these rates of return are ex-post measured rates and tend to be volatile. Obviously, ex-ante measures would be more useful, but this would require a full fledged assetpricing model.'<sup>8</sup> The black market premium is not a significant explanatory variable in our model, even though this variable has been found to be a useful indicator for the degree of (exchange rate) distortions in developing countries (World Bakik, 1991). The lack of explanatory power of this variable probably stems form the fact that few developing countries in the sample maintained significant black market premiums during this period as many countries unified their exchange rates.

Our results confirm the importance of global factors. The first principal component of US interest rates and US industrial production is always significant and exerts a negative influence on flows. When the first principal component is replaced by the underlying interest rates and the industrial production variable, the impact of the individual variables is not clear cut in many instances. This ambiguity arises from multicollinearity among these variables (Annex tables Al to A4). Nevertheless, we can tentatively assert that the effect of US interest rates is more important than that of US industrial production. We should, however, point out that the five interest rates do not necessarily display the same behavior.

Standardized Coefficients and Elasticities. In order to interpret the relative importance of various explanatory factors within each regression, we compute standardized slope coefficients (the coefficients multiplied by the standard deviation of the independent variable) (Table 9). The absolute values of the relevant standardized coefficients can be summed to obtain the importance of all global and all country-specific factors.

"<sup>8</sup> Another reason for (he weak performance of this variable could be that the IFC's total return indexes do not reflect returns on domestic stocks that are actually accessible to foreign investors. In this context, the IFC's new investable index (just released) might be more appropriate for measuring domestic stock market returns.
23 Table 9: Standardized Slope Coefricients (based on Tables 7 and 8)

||Equity<br>Nominal<br>interest<br>rates|Real<br>interest<br>rates|Bon<br>Nominal<br>interest<br>rates|d<br>Re ii<br>interest<br>rates|
|---|---|---|---|---|
|||Lati|n America||
|PCS|1.678's|6.816n"|7.320|12.614|
|Return|_NS_|NS|NS|NS|
|Price-<br>earning<br>ratios|-8.598|-5.764|-7.506|-4.375|
|P11|-18.141|-11.066|-20.316|-13.406|
|||Asia|||
|Credit<br>rating|1.974|1.937|30.030|29.230|
|Return|1.180|1.180|1.570|1.638|
|Price-<br>earning<br>ratios|-1.916|-1.948|-12.334|-11.596|
|P11|-2.007|-1.564|-10.001|-10.494|

Notes: Variables other than the principal components have been standardized; PCS: first principal component of credit rating and secondary market prices (see notes tor Table 5); Pll: first principal component of interest rates and US industrial production index; NS: not significant.

The results vary by region and type of flows (and whether real or nominal interest rates are used). For Latin America, the sums of the absolute value of the standardized coefficients for PCS, return, and price-earnings ratio are about the same as the coefficients for the global factor (PII). The eplication for this region is that global variables are as important as domestic variables in explaining both bond and equity flows. For Asia, the country-specific variables are three to four times more important than the global variables in explaining both equity and bond flows. The credit rating variable is particularly important in explaining bond flows (a standardized coefficient of about 30) to this region.
24 The standardized coefficients do not allow us to compare the relative sensitivities across regions and by type of flows because the absolute levels of the flows are very different (see Table 2). For this purpose, we compute elasticities (Table 10).'9 As expected, equity flows are more sensitive to a country's price-earnings ratio (with a negative sign) and rates of return on domestic stock markets (relative to the U.S.) than are bond flows. The elasticities also reveal some other interesting differences. The most important is that the elasticities for equity flows with respect to global factors are consistently higher than for bonds flows for both regions. This could be interpreted as a sign of some credit rationing it, the fixed-income market. The effect of credit rationing (which characterized Latin America) would be to make bond flows less interest sensitive--the supply curve would bend backward. This is not likely to happen for equity flows as these flows are not associated with a fixed payment obligation and, consequently, are not susceptible to (this type of) credit rationing. We would, therefore, expect equity flows to be more interest rate sensitive. Further evidence of this effect is found in the fact that credit rating is (marginally) more important for bond flows to Asia than for equity flows to that region.<sup>20</sup> '<sup>9</sup> MTe slope coefficients divided by the mean of the dependent variable and multiplied by the mean of the independent variable. For the principal components, we use the maximum values to calculate elasticities instead of the mean which is zero. Note that the elasticities vary depending on whether the equation is estimated using real or nominal interest mtes. 20These results should, however, be interpreted carefully as equity flows from the US to Asia are very small (only 1/20th of the bond flows to that region).
25 Table 10: Elasticities (Based on Tables 7 and 8)

||Equity<br>Nominal<br>Real<br>interest<br>interest<br>rates<br>rates|B<br>Nominal<br>interest<br>rates|ond<br>Real<br>interest<br>rates|
|---|---|---|---|
|||Latin America||
|PCS|0.171m<br>0.739"|0.311|0.571|
|Return|NS<br>NS|NS|NS|
|Price-<br>earning<br>ratios|_-0.581_<br>-0.586|-0.225|-0.131|
|P11|-1.331<br>-0.848|-0.660|-0.455|
||Asia|||
|Credit<br>rating|2.085<br>2.046|2.739|2.718|
|Return|0.507<br>0.507|NS|NS|
|Price-<br>earning<br>ratios|-1.516<br>-1.587|-0.884|-0.832|
|Pl|-1.168<br>-0.954|-0.514|-0.563|

Notes: Variables are defined as in Tables 7 and 8; elasticity= coefficient times (x/y) where x and y stand for the mean of the independent variable and that of the dependent variable, respectively; for the first principal components, we use the maximum value of the variable instead of the mean because the latter is zero. NS: not significant.
26

# V. Conclusions

Rather than repeating our conclusions, which are contained in the Introduction, we would like to indicate several directions for further research in this area. On the data side, it would be useful to increase the sample of developing countries, e.g., include Africa or the Middle-East. Some countries in these regions (e.g., Egypt) have also received large flows in recent years and it would be useful to see if these flows are motivated by the same factors. Second, industrial country (e.g., G-7) factors could be added to model the tradeoff for US investors between investing in various markets. This exercise would require including flows from the US to these industrial countries (and vice-versa) in the panel estimations. If it were found that flows from the US to all countries (developing as well as industrial) increased, then our result on the importance of country-specific factors in attracting these flows would be weakened.

Turning to methodology, a simultaneous equation error comi-porient .model _c^u=l_ be **us9A** in order to account for possible simultaneity among variables (e.g., interest rates could influence secondary market prices and the creditworthiness of a country; the latter two variables could be related; and capital inflows themselves could affect rates of return on stock markets). Further, if forecasting is one of the research objectives, then a "VAR-panel" approach could be employed.21 21See Husain and Jun (1992) and Fry (1993) for a simultaneous equation approach of modelling the interactions between capital flows, on the one hand, and aggregate savings and growth on the other hand.