1

Very Preliminary, do not cite

Growth and Risk: A View From International Trade

Pravin Krishna

and

William F. Maloney

March 9, 2009

2

I. Introduction

Economic development is unavoidably a series of wagers. Investments in

physical, human and knowledge capital (R&D) are made with an expectation of return,

but with cognizance of the accompanying risk. A recent literature (Acemoglu and

Zilibotti 1997) has moved the inability of poor countries to diversify this risk combined

with the indivisibility of many projects, as the central explanation for the perverse

phenomenon of both low growth and high volatility.1 However, the issue remains

germane for advanced countries: ongoing growth is thought to depend on investments in

supplying specialized, hence inherently risky production inputs (see, for example Romer

1990 and Grossman and Helpman 1991).

Several factors have been forwarded as impeding countries from taking on riskier

projects. The financial sector is seen as central. Greenwood and Jovanovic (1990) argue

that financial intermediates encourage high-yield investments and growth by performing

dual roles: pooling idiosyncratic investment risks and eliminating ex-ante downside

uncertainty about rates of return. Obstfeld (1994) sees international asset trade as

encouraging all countries to shift from low-return, safe investments toward high return,

risky investments. Grossman and Razin (1985) argue that that multinational corporations

may take on more risky production techniques within a country because they are more

diversified internationally than locals firms. In the area of trade, Baldwin (1985) argues

that the differential ability of investors to diversify leads the country with better capital

markets to exports the ‘risky’, and hence higher return, good.2 However, finance need

not be the only barrier to countries taking on riskier projects. To the degree that Pasteur

is right that “chance favors the prepared mind,” an inability to resolve the well-known

1 Do and Levchenko also postulate a model where financial services are endogenous and hence countries producing low finance intensive goods will have financial markets that cannot support taking on more risky goods. 22 The association of increasingly complex or involved products suggests that the diversification channel need not be the only financial barrier, and that barriers need not, in fact, originate in the financial sector. Kletzer and Bardan (1987) argue that more sophisticated manufactured finished products require more credit to cover selling and distribution costs than primary or intermediate products, hence imperfections in credit markets, even where technology and endowments are identical, can lead to specialization of countries with higher levels of sovereign risk or imperfect domestic credit markets in less sophisticated products. Beck (2002) builds a model where manufacturing, due to exhibiting increasing returns to scale, is more finance intensive due to increasing returns to scale.

3

market failures and again, indivisibilities surrounding innovation and R&D would leave

poorer countries restricted to less complex, and less risky products (For a recent

application that emphasizes appropriation externalities over finance, see Hausmann,

Hwang and Rodrik 2007). Further, as Acemoglu. Johnson and Robinson (2002) and

Levchenko (2007) argue, weak supporting institutions that either exclude entrepreneurs,

create additional uncertainty in the rules of the game, or make managing the implications

of loss (for instance, bankruptcy law) would also cause countries to specialize in lower

risk.

To date, the evidence of these effects, while compelling, has been largely

historical and anecdotal. This paper studies the dynamics of product quality changes in

US imports to explore the tradeoff between risk and return in quality improvements in the

exporting countries.3,4 Across one decade, 1990-2001 the paper documents how the first

two moments of quality growth vary across countries and products. Both dimensions are

central to the development debate. On the one hand, the literature above suggests that

distinct country contexts will lead to different investment choices. On the other, a long

and continuing literature stresses the importance of the type of goods countries produce

and export. In particular, natural resource based goods have been seen as being

intrinsically more volatile, yet with fewer possibilities for growth overall (see, for

instance, Matsuyama on the latter). Hausmann, Hwang and Rodrik (2007) have led a

resurgence of interest on the qualities of different products and their development impact

more generally. The dichotomy is clearly overdrawn since country characteristics also

inform the composition of the export basket, however parsing out the contribution of each

is important to the debate.

3 We follow the recent literature in international trade that has treated unit values, as a measure of quality. Kandelwahl has argued that additional information on the relative demand for products needs to be incorporated to make true quality comparisons. For out purposes, we assume that, on average, the raw unit values capture differences in quality, albeit with measurement error. 4 Schott and Hummels and Klenow have shown that unit values increase with level of development, though the implications of this observation are only in the incipient phases of being teased out. At one extreme, Hallak and Sivadasan (2007) have argued that such improvements represent the accumulation of “caliber”, a factor of production distinct from what drives pure productivity growth-a high productivity country can produce low quality. Sutton (1998) on the other hand, views both quality and productivity as broadly emerging from the undertaking of research and development.4

4

Two stylized facts emerge from our analysis. First, we identify a strong positive

relationship between the mean and the variance of quality growth, consistent with a risk-

return trade off. Second, developing countries occupy the less risky parts of the frontier.

This appears to stem partly from the goods individual countries find themselves

producing. Poor countries tend to take smaller risks and experience lower growth than

the rich countries that are already closer to the quality frontier. This suggests that, much

as the theoretically expected convergence in incomes has been elusive to document,

convergence in unit values is likely to be as well. But the relationship also reflects the

choice of bets (technology, quality choices) that countries make within very

disaggregated product categories. We also find that the different positions that countries

occupy on the risk return frontier are explained by factors such as the degree of R&D

spending and financial market depth.

II. International Trade Data We follow Feenstra (1996), Schott (2003), and Hummels and Klenow (2005)

among others in exploring the Harmonized System (HS) Imports, Commodity by

Country, 1989-2001. This dataset, records all US imports at the 10-digit HS level,

currently the highest degree of disaggregation available. The dataset was compiled by

Feenstra (1996) using official customs records from the US Census Bureau. This dataset

contains imports values and quantities as well as entries for tariffs and transportation

costs. The so-called “general imports” categories (as opposed to the “imports for

consumption” and “customs import values”) were used. In long format, the dataset

contains more than two million observations, representing US imports from 179 countries

Although HS 10 data exists from earlier decades, a significant change in the

system of classification could potentially confound the results and we choose to work

within the 1989-2001 cohort. We drop observations from 1989 to lessen the problems

associated with the reclassification of the previous soviet states. All imports under

$25,000 dollars were also dropped and so were observations with zero quantities.

5

Similarly, products differentiated by trade agreement were aggregated by country, year

and HS code. Lastly, the two different country codes for Yugoslavia were consolidated.

A Schott notes, the unit values in this data set are not perfect. Underlying

product heterogeneity and classification error have been identified as two major sources

of error. Further, Besedes and Prusa (2006) note, there are changes in categorization

across time. There are categories that split into several HS-10 codes and HS-10 codes

that correspond to more than one category. The reason behind the mismatch is not clear

and may involve the evolution of new products as well as the attrition of old ones. US

documentation offers no unambiguous explanations. Eliminating these products excludes

about 15% of the sample.

Finally, unit values are calculated simply as the quotient of general imports values

and quantities. Perhaps as a result of the residual misclassification issues identified

immediately above, the data showed evidence of serious outliers. We approach this in

two ways. The first, as in previous work with this data, is to trim at the 10-90th

percentiles.5 Second we employ conditional median regression as an alternative approach

to summarizing the data that is less sensitive to the outliers which are common in this

data. This is a subset of Quantile analysis (Koenker and Bassett (1978)) where curves are

estimated such that approximately τ% of the residuals lie below the regression line and

(100-τ)% above. Though a clear advantage of quantile regression is that it permits

sketching the entire distribution and not just the central tendency, it (and the median more

generally) also offers greater robustness at the trade off of some efficiency.6 Implicitly, it

puts no weight on the distance from the fitted curve, as opposed to OLS where

minimizing the quadratic weighting of deviations from the fitted curve gives

disproportionate weight to outliers. The τ-th quantile of Y conditional on Z is given by:

5 For robustness, the estimates were conducted using the full sample as well as trimmed and winsorized samples with different cutoffs. A winsorized sample rolls back and retains extreme values (at the bottom or top X%), whereas a trimmed sample simply drops extreme observations. (Angrist and Krueger, 1999). 6 In the case where the errors are symmetrically distributed, the conditional median and means are equivalent. Where they are not, then in addition to losing efficiency we are no longer estimating the central tendency as captured by the conditional mean, but the conditional median. The parameter values must be interpreted as such.

6

( ) ( )τβτ íii ZZYQ ′=

where β(τ) is the slope of the quantile line and thus gives the effect of changes in Z on

the τ-th conditional quantile of Y. Median regression (τ = 0.5) leaves half of the

residuals above and below the regression line, and gives the same results as ordinary least

squares when the distribution is symmetric.

Though clearly the magnitudes and levels of significance change across

estimation techniques, the overall picture remains robust.

III. The Risk-Return Tradeoff in Quality Growth

We focus first on two variables the drift and standard deviation of the unit values:

Drift: The unit value drift is estimated as the country median of the row average of

growth of consecutive unit values by product. Misclassification or misreporting may

give rise to very large jumps that contaminate the row average. This is largely attenuated

by taking the median at the country level.

Standard Deviation: Analogously, the country variance is estimated as the conditional

median of the row variance across goods and taking the median value by country.

Both variables are estimated from a median regression of the dependent variable on

country fixed effects.

Two stylized facts emerge robustly from plotting the two series against each other

(figure 1). First, there is a striking upward sloping relationship between drift and standard

deviation. This pattern is highly suggestive of a standard risk-return relationship

although caution must be taken with interpretation. The individual country points do not

represent the aggregate risk-return combination, capturing a portfolio of goods and

7

potential covariances among them. They are rather the central tendency among goods in

the export basket.

The relationship described appears statistically robust. To begin, we estimate

iiii εσβσβμ ++= 221 (1)

by both robust OLS and median regression as a first approximation. We find median

country drift (μi) and its standard deviation σi and its square to be statistically

significantly related (table 1). This is particularly the case in the median regression where

the level term enters at the 1% and the quadratic at the 10%. Employing OLS, the

coefficients are broadly similar although only the standard deviation is significant at the

10% level.

Second, Figure 1 also suggests that, strikingly, poor countries occupy the lower

risk-return positions on average, while richer countries occupy the higher. Despite some

outliers along one or the other dimension with relatively few observations (e.g. Angola,

Bermuda, Kiribati, Zambia, Togo, Zaire), it is clear that it is the industrialized countries

that occupy both higher levels of risk and return. Broadly speaking, the UK, Switzerland,

Germany, the Netherlands, Italy Denmark, France and Sweden and Japan form a “high

performing” group. The next group arguably Is formed by Austria, Israel, Norway,

Australia, Hong Kong, Ireland, Canada, Singapore, the Benelux countries and Spain.) A

very dense cloud follows whose leading edge contains middle income countries such as

Russia, South Korea, Brazil, Mexico, Taiwan, Brazil, New Zealand and Portugal well as

India and China. Moving deeper into the cloud reveals poorer developing countries as

well as the Eastern European countries who, during this period, were in their transition to

more capitalist economies. Those with negative drift (and low standard deviation)

include central Asian countries such as Turkmenistan, Tajikistan, Uzbekistan, Vietnam,

and numerous African countries.

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Columns 1 and 2 of Table 2 confirm that both standard deviation and drift are, in

fact, individually positively related to GDP per capita.7 This is a central and provocative

finding. It suggests that Schott‘s (2003) positive static relationship between unit values

and level of development is occurring in a dynamic context of divergence. Further, it is

consistent with the previously discussed literature that sees the inability of poorer

countries to take on (diversify) risk as a central barrier to economic growth.

IV. Environment or Product?

Before exploring what lies behind the observed correlation with GDP, we ask

whether the risk return profile is a characteristic of countries, or is intrinsic to the kinds of

goods (j) they produce. We decompose the drift and standard deviation of a particular

country-product combination into product and country fixed effects:

jiijji εμμμ ++= (2)

jiijji ησσσ ++=

Ideally, this could be achieved by incorporating a full set of country and product

dummies in the median regression. However, estimating 14,000 product specific effects

are problematic in a QREG context and we proceed in two steps. First, we estimate the

median product drift and SD terms and then “product difference” the data by taking the

deviations of each μji-μj, σji-σj. Though the number of observations per fixed effect is not

sufficiently large to provide precise estimates, this is not required for the second step

estimator to be consistent. We then estimate the median of the country fixed effects and

plot these in figure 2. Figure 2A magnifies the high concentration found around the

origin.

Again, there is evidence of an upward sloping relationship between risk and

return although in this case the marginal increase in standard deviation buys 7 The equations are estimated individually since, when the covariate set is identical across equations, there are no gains from a SUR approach.

9

progressively less additional drift. Columns 3 and 4 of Table 1 again confirm a very

strong concave relationship in the median regression and a positive and significant linear

relationship with OLS. This implies that even within a given product categories, the risk

return trade off holds: moving up along the quality ladder requires investing in risky

projects and countries that place riskier bets on average have a higher return.

And again, we find that both drift and standard deviation are overall increasing in

GDP: developing countries within the same good choose less risky techniques within a

product category (table 2, columns 3 and 4). Though consistent with the greater

dispersion in the graph, the overall explanatory power and level of significance is less

than in the unconditional cases, the relationship is significant in the median regression

and in the OLS at 10%, Again, clearly, richer countries are at the top of the curve with

the UK, Germany, France Switzerland, and Bermuda among the highest drift countries.

A level down we find Italy, Netherlands, Denmark, Sweden, Australia and Austria and

Portugal although also Tanzania, Cameroon, Sri Lanka and Uzbekistan. The high

concentration around zero is due to a combination of the correction for fixed effects and

the median regression. Countries which are unique exporters of a good will necessarily

have a zero value once fixed effects have been stripped out. Given the very high level of

disaggregation, it is not unreasonable that many countries should have a non trivial

number of zeros and should these lie in the center of their distribution, the median

regression will return zero as the “central tendency.” Lying below the fitted median curve

we find several countries with large numbers of exported products: Greece, Brazil, India

Morocco, South Korea, Ireland Romania, Pakistan South Africa, Turkey, Ireland. The

interpretation here would be that, within a product category, these countries tend invest in

underperforming projects given their level of risk.

Products

Figure 3 presents the complementary relationship, plotting the drift of a country’s

basket of goods if every element had the median growth rate. In practice this implies

running μj on country fixed effects. Both visual inspection and columns 5 and 6 of table

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1 suggest a very strong convex upward sloping relationship between standard deviation

and growth. Unsurprisingly given that virtually all outliers have been stripped out by

construction, the OLS and Median regressions are extremely close. This suggests that, in

fact, different goods do present opportunities for high return investments.

And, again, rich countries are at the high end of the curve. Columns 5 and 6 of

table 2 again suggest a strongly significant upward sloping relationship with GDP.

Denmark, Norway, Finland, Ireland and Malta anchor the top of the fitted line and are

followed down the fitted median line by Sweden, Switzerland, Guadalupe, Austria and

the upper third includes most of the OECD. Central African Republic, Congo, Equatorial

Guinea, Georgia and the Netherland Antilles appears striking over performers although

this is largely driven by being highly concentrated in one or two commodities that have

performed very well across this period.8 The bulk of poor countries are found along the

lower half of the curve.

Since these results are purely artifacts of the composition of each country’s export

basket, we should expect to see strong differences among product categories with poor

countries more specialized in low risk low return goods. Since we would like to have

more precise estimates of the product fixed effect that are possible at the HS10 level, we

aggregate up to the HS-1 with 16 categories and HS-3 with 177 to identify pure product

effects. Figure 4a, confirms both conjectures. Electrical and machinery (84-85),

miscellaneous (90-97), a category that includes measurement, time pieces, optics, arms

and ammunition, and toys, and transportation (41-43) are the areas with the highest

variance and rate of return. Overall, more natural resource and, broadly speaking, less

sophisticated goods where developing countries often specialize are found in the lower

part of the curve. That said, chemicals and allied products, plastic and rubber, both of

which are seen as more capital intensive products in the traditional Leamer categorization

are substantially below leather and wood products.

8 In particular, Congo and Central Africa export only a few products each most related to diamonds. Equatorial Guinea exports plywood, but also seems driven by perhaps re-exports of steel pressure containers. The Netherland Antilles and perhaps Georgia also appear to be driven by transshipments of heavy equipment and manufactures that may have been loosely classified.

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Jumping to a higher level of disaggregation at HS-3 again preserves the profile

(figure 4b). Here, the star performers are concentrated in the 80 and 90s categories. In

particular, agricultural processing machinery (843), Optical machinery (901), printing

related machinery (944), oscilloscopes (903). confirm that the relationship between risk

and return is, again, significant, albeit with some ambiguity about the sign of the

coefficient on the variance.

Two findings merit special note. First, the risk return relationship, even at this

very aggregated level, is very tight with both very significant coefficients and a high

degree of explanatory power (Columns 7 and 8 in table 1). The few major outliers are sui

generis. For instance, 970 is works of art, collector’s pieces and antique. Otherwise,

there are fairly few products offering unusually high returns for a given level of risk.

. Second, natural resource based goods, for instance, minerals, vegetables, forest

products seem to show lower variance in unit values. That is, commodities do not appear

to show higher volatility than manufactures. Thus, the concern in the resource curse

literature of high risk, low return is not obviously supported. It may be that natural

resource exporting countries have more volatile export revenues, but this perhaps arises

because of the lack of diversification of their overall export portfolio, not anything

intrinsic to the individual goods comprising it.

The annex presents two additional summaries of the data. The first is the raw data

decomposition in to product and country fixed effects. The second plots the median

predicted values for the unconditional, product fixed effect, and country fixed effects

specifications. Although the relationship between risk and return exists in all cases, it

appears that the products countries are in, as opposed to the investment projects they

undertake within the product class, are the principle drivers of the unconditional

relationship.

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V. Location on Risk-Return Profile – Explanatory Factors

The literature summarized above suggests a number of variables which may

determine a country’s position on the risk-return frontier. These could be influential

either through the selection of goods that countries export, or the projects they undertake

within these sectors. Though we are restricted by limited data (approximately 160

observations) in this section we investigate if there may be some correlation with readily

available measures of financial intermediation, resolution of market failures relating to

innovation, and institutions. As proxies we use

Financial Intermediation: We employ financial depth as measured by private by deposit

money banks as a share of GDP.9 The data is taken from the 2006 revision of Beck el al.

(2000). The private credit variable measures credit issued to the private sector, as

opposed to credit issued to governments and public enterprises by intermediaries other

than the central bank.

Resolution of Innovation Related Market Failures/Innovative Effort: Consistent with

much of the microeconomic literature we employ total real R&D expenditures as a

measure of innovation effort. The data are derived ultimately from national surveys that

use as a common definition of expenditures that include “fundamental and applied

research as well as experimental development.” 10 The data thus include not only the

basic science expected in the more advanced countries, but also investments in the

adoption and adaptation of existing technologies often thought more germane to

developing countries. The series are constructed based on underlying data published by

UNESCO, the OECD, the Ibero-American Science and Technology Indicators Network

(RICYT) and the Taiwan Statistical Data Book.

9 Private credit by deposit money banks to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. 10 UNESCO Statistical Yearbook (1980) pg. 742. Definitions are common to the OECD, Ibero American Science and Technology Indicators Network (RICYT), World Bank ,and Taiwan Statistical Yearbook and all are based on the Frascatti manual definition.

13

Institutions: To account for the role of institutions, we employ the commonly used

executive constraints proxy (see, for example, Acemoglu and Robinson 2002, 2005 and

Glaeser et al. 2004),.11 The data come from the Polity IV database, and has a value that

ranges between 1 and 7, with higher values representing less executive-branch discretion.

This property rights variable was chosen because it focuses on the relationship between

property rights institutions and political institutions. This measure is also procedural and

hence not a consequence of dictatorial choices.

GDP/Capita: Other data used for this study includes information on international

economic activity and income level. GDP per capita data was obtained from the World

Development Indicators (WDI) and the Global Development Finance (GDF) databases of

the World Bank.12 This data is adjusted for Purchasing Power Parity (PPP) and is

recorded in constant 2005 US dollars. Regional membership and income status was

determined according to the World Bank’s FY2005 official classification. OECD

membership was obtained from the official website of this organization.13

We begin by running both drift and standard deviation against each potentially

explanatory variable individually, both with robust OLS and median regression to

minimize the influence of outliers. As coverage varies by proxy, the number of

observations varies from roughly 75 to 165. We exploit the full sample available in each

case. We then combine the proxies and GDP to attempt to rule out correlations with

omitted variables.

Table 3 suggests that all variables enter individually significantly in both the drift

and standard deviation regressions, with only institutions falling below the 5% level in

the OLS specification. In the combined drift regression, financial depth and R&D enter

significantly. In the OLS regression, GDP per capita also remains significant, perhaps

suggesting additional development related variables that are not accounted for. In the

11 Lederman and Maloney (2008). 12 http://publications.worldbank.org/GDF/ http://publications.worldbank.org/WDI/ 13 http://www.oecd.org/countrieslist/0,3351,en_33873108_33844430_1_1_1_1_1,00.html

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standard deviation specifications, Financial depth, R&D and GDP are significant in the

OLS regressions although financial depth drops out in the median regression.

As an attempt to reduce the possible endogeneity of the regressors, table 3 breaks

up the sample into quinquennia and lags the proxies. The exercise cannot be pushed too

hard since all variables are highly persistent and by lagging we may be introducing noise

and little else. That said, again, all free standing variables enter very significantly in the

drift median regressions although GDP and institutions lose significant when estimated

by OLS. When combined, only GDP survives. In the estimations of standard deviation,

all variables are significant freestanding in both OLS and QREG estimations. However,

only R&D is robust to both estimation techniques. Financial depth is significant at the

10% level in OLS.

Further attempts were made to instrument both institutions and R&D with the

now standard settler mortality and population variables however, the sample size shrank

to 29 and little was significant (R&D more so). We do not report these results.

In sum, there is evidence that the variables we have considered here are all

plausibly related to a county’s position on the risk return frontier. However, in combined

regressions that attempt to control for the strong correlations of these variables with

development, the sample is severely restricted, and the correlations become less clear.

R&D and, to a somewhat lesser degree, financial depth emerge as the most robust

proxies.

VI. Conclusion

This paper studies the dynamics of product quality changes in US imports to explore the

tradeoff between risk and return in quality improvements in the exporting countries.

Across one decade, 1990-2001, the paper documents how the first two moments of

quality growth vary across countries and products. Two stylized facts emerge from our

analysis. First, we identify a strong positive relationship between the mean and the

15

variance of quality growth, consistent with a risk-return trade off. Second, developing

countries occupy the less risky parts of the frontier. We find that the different positions

that countries occupy along the risk return frontier are explained to some degree by such

factors as the degree of R&D spending and financial market depth.

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Hummels, D and P. J. Klenow (2005) “The Variety and Quality of a Nation’s Exports” American Economic Review 95(3):704-723. Khandelwal, A. (2007) “The Long and Short (of) Quality Ladders.” Mimeograph. Columbia Business School. Levchenko, A. A. (2007) Institutional quality and international Trade, Review of Economic Studies 74:791-819. Obstfeld, M (1994) Risk Taking, Global diversification and Growth, The American Economic Review, Vol 84:5 (Dec 1994) pp 1310-1329. UNESCO (1980) Statistical Yearbook. United Nations, Paris.

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Figure 1: Unit Values: Drift and Standard Deviation, Unconditional Quantile Regression 1990-2001

ALBANIA

ALGERIA

ANGOLA

ARAB_EM

ARGENT

ARMENIA

ASIA_NES

AUSTRAL

AUSTRIA

AZERBAIJ

BAHAMAS

BAHRAIN

BARBADO

BEL_LUX

BELARUS

BELIZEBENIN

BERMUDA

BNGLDSHBOLIVIA BOSNIA-H

BRAZILBULGARIA

BURMA

BURUNDI

CAMBOD CAMEROON

CANADA

CHILE CHINACOLOMBIA

CONGO

COS_RICACROATIACYPRUS

CZECHO

CZECHREP

DENMARK

DOM_REPECUADOR

EGYPT

ESTONIA

ETHIOPIA

FIJI FINLAND

FRANCE

GABONGEORGIA

GERMAN

GHANA

GILBRALT

GREECE

GREENLD

GUADLPE

GUATMALA

GUINEA

GUYANA

HAITIHONDURA

HONGKONG

HUNGARY

ICELAND

INDIA

INDONES

IRELANDISRAEL

ITALY

IVY_CST

JAMAICA

JAPAN

JORDON

KAZAKHST

KENYA

KIRIBATI

KOREA_S

KUWAITLAO

LATVIA

LEBANON

LIBERIALITHUANI

MACAUMACEDONI

MADAGAS

MALAWI

MALAYSIA

MALI

MALTA

MAURITN

MEXICO

MOLDOVA

MONGOLA

MOROCCO

MOZAMBQ

MRITIUS

N_ANTIL

NEPAL

NETHLDS

NEW_CALE

NEW_GUIN

NEW_ZEAL

NICARAGA

NIGER

NIGERIA NORWAY

OMAN

PAKISTAN

PANAMA

PARAGUA

PERU

PHIL

POLANDPORTUGAL

QATAR

ROMANIA RUSSIA

RWANDA

S_AFRICA

SALVADRSAMOA

SD_ARAB

SENEGAL

SIER_LN

SINGAPR

SLOVAKIA

SLOVENIA

SPAINSRI_LKA

ST_K_NEV

SUDAN

SURINAMSWEDEN

SWITZLD

SYRIA

TAIWAN

TAJIKIST

TANZANIATHAILAND

TOGO

TRINIDAD

TUNISIA

TURKEY

TURKMENI

UGANDA

UKINGDOM

UKRAINE

URUGUAYUS_NES

USSR

UZBEKIST

VENEZ

VIETNAM

YEMEN_N

YUGOSLAV

ZAIRE

ZAMBIA

ZIMBABWE

-0.075

-0.025

0.025

0.075

0.125

0.05 0.15 0.25 0.35 0.45 0.55

Standard Deviation (country dummies)

Drif

t (co

untr

y du

mm

ies)

Figure 2: Unit Values: Drift and Standard Deviation, Quantile Regression with Product Fixed Effects 1990-2001

ALBANIA

ALGERIA

ANGOLA

ARAB_EM

ARGENT

ARMENIA

ASIA_NESAUSTRALAUSTRIA

AZERBAIJBAHAMAS

BAHRAIN

BARBADO

BEL_LUX

BELARUS

BELIZE

BENIN

BERMUDA

BNGLDSH BOLIVIA

BOSNIA-H

BRAZILBULGARIA

BURMA

BURUNDI

CAMBOD

CAMEROONCANADA

CHAD

CHILECHINACOLOMBIA

CONGO

COS_RICACROATIA

CYPRUSCZECHO

CZECHREP

DENMARKDOM_REPECUADOR

EGYPTESTONIA

ETHIOPIA

FIJI FINLANDFR_IND_O

FRANCE

GABON

GEORGIA

GERMAN

GHANAGILBRALT

GREECEGREENLD

GUADLPE

GUATMALA

GUINEA

GUYANA

HAITI HONDURAHONGKONGHUNGARY

ICELANDINDIAINDONESIRELAND

ISRAEL ITALY

IVY_CST

JAMAICAJAPAN

JORDON

KAZAKHSTKENYA

KIRIBATI

KOREA_SKUWAITLAO

LATVIA

LEBANON

LIBERIA

LITHUANI

MACAUMACEDONI

MADAGAS

MALAWI

MALAYSIA

MALI

MALTA

MAURITN

MEXICO

MOLDOVA

MONGOLA

MOROCCO

MOZAMBQ

MRITIUS

N_ANTIL

NEPAL

NETHLDS

NEW_CALE

NEW_GUIN

NEW_ZEALNICARAGA

NIGER

NIGERIA

NORWAY OMAN

PAKISTAN

PANAMAPARAGUA

PERUPHIL POLANDPORTUGAL

QATAR

ROMANIARUSSIA

RWANDA

S_AFRICA

SALVADR

SAMOASD_ARAB

SENEGAL

SIER_LN

SINGAPRSLOVAKIA

SLOVENIA

SOMALIA

SP_MQEL

SPAINSRI_LKAST_K_NEV SUDAN SURINAMSWEDENSWITZLD

SYRIA

TAIWAN

TAJIKIST

TANZANIATHAILANDTRINIDAD

TUNISIATURKEY

TURKMENI

UGANDA

UKINGDOM

UKRAINE

URUGUAYUS_NES

USSR

UZBEKISTVENEZ

VIETNAM

YEMEN_N

YUGOSLAV

ZAIRE

ZIMBABWE

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1

Standard Deviation (country dummies)

Drif

t (co

untr

y du

mm

ies)

19

Figure 2A: Unit Values: Drift and Standard Deviation, Quantile Regression with Product Fixed Effects 1990-2001 (Zoomed)

ZIMBABWE

VENEZ

UZBEKIST

US_NES

URUGUAY

UGANDA

TURKEY

TUNISIA

TRINIDAD

THAILAND

TANZANIA

TAIWAN

SYRIA

SWITZLD

SWEDEN

SURINAMSUDAN

ST_K_NEV

SRI_LKA

SPAIN

SLOVAKIA

SINGAPR

SD_ARAB

SAMOA

SALVADR

S_AFRICA

RUSSIA

ROMANIA

PORTUGAL

POLAND

PHILPERU PANAMA

PAKISTAN

OMANNORWAY

NIGERIA

NICARAGA

NEW_ZEAL

NETHLDS

NEPAL

N_ANTIL

MRITIUS

MOZAMBQ

MOROCCO

MONGOLA

MOLDOVA

MEXICO

MALAYSIA

MALAWI

MACEDONI

MACAU

LITHUANI

LEBANON

LAO

KUWAIT

KOREA_S

KENYAKAZAKHST

JAPAN

JAMAICA

IVY_CST

ITALY

ISRAEL

IRELAND

INDONESINDIA

ICELAND

HUNGARY

HONGKONGHONDURA

HAITI

GUYANA

GUATMALA

GREENLD

GREECE

GERMAN

FRANCE

FR_IND_O

FINLAND

FIJI

EGYPT

ECUADOR

DOM_REP

DENMARK

CZECHREP

CZECHO

CROATIACOS_RICA

COLOMBIA

CHINACHILE

CANADA

CAMEROON

CAMBOD

BULGARIA

BRAZIL

BOLIVIABNGLDSH

BERMUDA

BELIZE

BEL_LUX

BAHRAIN

BAHAMAS

AUSTRIAAUSTRAL

ASIA_NES

ARGENT

ARAB_EM

ALGERIA

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

-0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05

Standard Deviation (country dummies)

Drif

t (co

untr

y du

mm

ies)

Figure 3: Unit Values: Drift and Standard Deviation, Quantile Regression with Country Fixed Effects

ZIMBABWE

ZAMBIA

ZAIRE

YUGOSLAV

YEMEN_N

VIETNAM

VENEZ

UZBEKIST

USSR

US_NES

URUGUAY

UKRAINE

UKINGDOM

UGANDA

TURKMENI

TURKEY

TUNISIA

TRINIDAD

TOGO

THAILAND

TANZANIA

TAJIKIST

TAIWAN

SYRIA

SWITZLDSWEDEN

SURINAM

SUDAN

ST_K_NEV

SRI_LKA

SPAIN

SP_MQEL

SOMALIA

SLOVENIASLOVAKIA

SINGAPR

SIER_LN

SEYCHEL

SENEGALSD_ARAB

SAMOA

SALVADR

S_AFRICA

RWANDA

RUSSIA

ROMANIA

QATAR

PORTUGALPOLANDPHIL

PERU

PARAGUA

PANAMA

PAKISTANOMAN

NORWAY

NIGERIA

NIGER

NICARAGA

NEW_ZEAL

NEW_GUINNEW_CALE

NETHLDS

NEPAL

N_ANTIL

MRITIUSMOZAMBQ MOROCCO

MONGOLA

MOLDOVA

MEXICOMAURITN

MALTA

MALI

MALAYSIA

MALAWI

MADAGAS

MACEDONI

MACAU

LITHUANI

LIBERIA

LEBANON

LATVIA

LAO

KYRGYZST

KUWAIT

KOREA_S

KIRIBATI KENYA

KAZAKHST

JORDON

JAPAN

JAMAICA

IVY_CST

ITALY

ISRAEL

IRELAND

IRAQ

IRAN

INDONES

INDIA

ICELANDHUNGARY

HONGKONG

HONDURA

HAITIGUYANA

GUINEA

GUATMALA

GUADLPE

GREENLD

GREECE

GILBRALT

GHANA

GERMAN_E

GERMAN

GEORGIA

GAMBIA

GABON

G_BISAU

FRANCE

FR_IND_O

FR_GUIAN

FINLAND

FIJIFALK_IS

ETHIOPIA

ESTONIA

EQ_GNEA

EGYPT

ECUADOR

DOM_REPDJIBOUTI

DENMARK

CZECHREP

CZECHO

CYPRUS

CROATIACOS_RICA

CONGO

COLOMBIA

CHINA

CHILE

CHAD

CANADA

CAMEROON

CAMBOD

C_AFRICA

BURUNDI

BURMA

BURKINA

BULGARIA

BRAZIL

BOSNIA-H

BOLIVIA

BNGLDSH

BERMUDA

BENIN

BELIZE

BELARUS

BEL_LUX

BARBADO

BAHRAIN

BAHAMAS

AZERBAIJ

AUSTRIA

AUSTRAL

ASIA_NES

ARMENIA

ARGENT

ARAB_EM

ALGERIA

ALBANIA

-0.025

-0.015

-0.005

0.005

0.015

0.025

0.035

0.045

-0.125 -0.075 -0.025 0.025 0.075 0.125

Standard Deviation (country dummies)

Drif

t (c

ount

ry d

umm

ies)

20

Figure 4: Unit Values: Drift and Standard Deviation, Country Fixed Effects, HS-1 1990-2001

woodprods (44-49)

vegetables (06-15)

transp (86-89)

textiles (50-63)

stoneglass (68-71)

plasticrubber (39-40)

misc (90-97)

minerals (25-27)

metals (72-83)

machinelec (84-85)

leatherprods (41-43)

foothead (64-67)

foods (16-24)

chemallied (28-38)

animals (01-05)

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

-0.2 -0.1 0 0.1 0.2 0.3

Standard Dev iation (Product Dummies)

Drif

t (Pr

oduc

t Dum

mie

s)

Figure 5: Unit Values: Drift and Standard Deviation, Country Fixed Effects, HS-3 1990-2001

981

980

970

961960950

940

930920

911

91091

903

902

901

900

90

890880

871

870

860

854853

852

851850

848

847

846845

844

843

842

841

840831

830

821820

811

81081

800

80790780

761

760 750741

740

732

731

730

722721720

711710

71701

700

70

691

690681

680670

660

650

640

631

630621620611610

600

60

591

590581

580

570

560551

550540

531

530

521520511 510

51

500

50 491

490

482

481480

470

460

450442

441

440430

420

411 410

41

401400

40

392391

390

382 381

380

370

360350340

330

321

320

310

300

30

294

293292291

290

285

284

283282

281 280

271

270

262261

260

253

252

251 250240

230

22021021 200190180

170160152

151150

140130121

120

11010020

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

-0.35 -0.15 0.05 0.25 0.45 0.65 0.85

Standard Dev iation (Product Dummies)

Drif

t (Pr

oduc

t Dum

mie

s)

21

Table 1: Risk Return Regressions Dependent Variable: Drift

(1) (2) (3) (4) (5) (6) (7) (8)OLS QREG OLS QREG OLS QREG OLS QREG

Standard Deviation 0.10 0.18 0.18 0.13 0.20 0.19 0.30 0.30(1.94) (8.48)** (2.08)* (7.89)** (21.32)** (26.28)** (21.25)** (42.64)**

Variance 0.41 0.28 -0.54 -0.87 0.41 0.42 0.09 0.07(0.90) (1.80) (0.86) (7.18)** (3.44)** (4.05)** (2.72)** (3.70)**

Constant -0.05 -0.04 -0.01 -0.01 0.00 0.00 -0.01 -0.01(5.46)** (11.10)** (5.44)** (15.52)** (7.40)** (8.37)** (2.48)* (4.56)**

Observations 163 163 159 159 173 173 173 173R-squared / Pseudo R squared 0.04 0.13 0.16 0.14 0.77 0.59 0.87 0.66Robust t statistics in parentheses, and absolute value of t statistics in parentheses, * significant at 5%; ** significant at 1%Drift, standard deviation, and variance: country dummies of quantile regression first stage estimationProduct fixed effects: de-median drift, standard deviation and variance valuesCountry fixed effects: median drift, standard deviation and variance values

Unconditional Product Fixed Effects Country Fixed Effects HS-3 (Country FE)

22

Table 2: Contemporaneous Drift and Standard Deviation Regressions on GDP, 1990-2001 Cross Section Dependent Variable: Drift

(1) (2) (3) (4) (5) (6)OLS QREG OLS QREG OLS QREG

GDP 1.1E-06 1.1E-06 4.0E-07 2.0E-07 6.0E-07 7.0E-07(3.21)** (5.00)** (2.02)* (1.89) (4.85)** (8.82)**

Constant -0.06 -0.06 -0.02 -0.01 0.00 0.00(10.89)** (21.84)** (5.08)** (8.49)** (0.33) (3.21)**

Observations 157 157 153 153 164 164R-squared / Pseudo R-squared 0.05 0.06 0.02 0.02 0.17 0.13Absolute value of t statistics in parentheses, robust standard errors, * significant at 5%; ** significant at 1%Drift: estimated quantile regression country coefficient, GDP: decade average per capita PPP U$2005 GDP

Dependent Variable: Standard Deviation(1) (2) (3) (4) (5) (6)

OLS QREG OLS QREG OLS QREGGDP 4.8E-06 5.1E-06 1.4E-06 5.0E-07 2.6E-06 4.1E-06

(5.30)** (10.19)** (4.60)** (2.97)** (5.09)** (9.57)**Constant -0.13 -0.15 -0.03 0.00 -0.03 -0.05

(12.43)** (22.45)** (4.55)** (2.05)* (5.71)** (8.44)**Observations 157 157 153 153 164 164R-squared / Pseudo R-squared 0.20 0.11 0.08 0.03 0.21 0.14Absolute value of t statistics in parentheses, robust standard errors, * significant at 5%; ** significant at 1%Standard Deviation: estimated quantile regression country coefficient, GDP: decade average per capita PPP U$2005 GDP

UNCOND PFE CFE

UNCOND PFE CFE

23

Table 3: Determinants of location on Risk Return Frontier (Contemporaneous), 1990-2001 Cross Section Dependent Variable: Drift (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

OLS QREG OLS QREG OLS QREG OLS QREG OLS QREGGDP 1.1E-06 1.1E-06 6.0E-07 4.0E-07

(3.21)** (5.00)** (1.97) (1.03)Financial Depth 0.04 0.04 0.02 0.02

(4.39)** (4.16)** (2.37)* (2.21)*Institutions 4.0E-03 4.2E-03 -3.1E-04 3.1E-04

(1.87) (4.68)** (0.16) (0.19)R&D 0.02 0.01 0.01 0.01

(5.24)** (6.57)** (2.64)* (2.40)*Constant -0.06 -0.06 -0.07 -0.07 -0.07 -0.08 -0.06 -0.06 -0.07 -0.07

(10.89)** (21.84)** (12.35)** (13.70)** (5.79)** (16.60)** (20.19)** (21.46)** (5.71)** (7.60)**Observations 157 157 132 132 133 133 78 78 68 68R-squared / Pseudo R-squared 0.05 0.06 0.12 0.07 0.04 0.07 0.30 0.16 0.52 0.27Absolute value of t statistics in parentheses, robust standard errors, * significant at 5%; ** significant at 1%Drift: estimated quantile regression country coefficient, Financial depth: decade average private credit by deposit money banks/GDP, institutions: decade average executive contraints, R&D: decade average research and development expenditure/GDP , GDP: decade average per capita PPP U$2005 GDP

Dependent Variable: Standard Deviation (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)OLS QREG OLS QREG OLS QREG OLS QREG OLS QREG

GDP 4.8E-06 5.1E-06 2.8E-06 2.4E-06(5.30)** (10.19)** (2.24)* (2.07)*

Financial Depth 0.12 0.12 0.05 0.04(4.51)** (7.33)** (2.06)* (1.36)

Institutions 0.01 0.01 -0.01 1.7E-03(2.89)** (2.88)** (0.70) (0.28)

R&D 0.08 0.08 0.05 0.05(7.11)** (9.05)** (3.16)** (3.61)**

Constant -0.13 -0.15 -0.13 -0.15 -0.15 -0.16 -0.14 -0.14 -0.13 -0.17(12.43)** (22.45)** (9.83)** (16.61)** (6.80)** (10.50)** (11.92)** (12.98)** (2.23)* (5.35)**

Observations 157 157 132 132 133 133 78 78 68 68R-squared / Pseudo R-squared 0.20 0.11 0.17 0.09 0.07 0.03 0.40 0.28 0.55 0.40Absolute value of t statistics in parentheses, robust standard errors, * significant at 5%; ** significant at 1%Standard Deviation: estimated quantile regression country coefficient, Financial depth: decade average private credit by deposit money banks/GDP, institutions: decade average executive contraints, R&D: decade average research and development expenditure/GDP , GDP: decade average per capita PPP U$2005 GDP

24

Table 4: Determinants of location on Risk Return Frontier (Lags), 1996-2001 Cross Section Dependent Variable: Drift (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

OLS QREG OLS QREG OLS QREG OLS QREG OLS QREGGDP 6.0E-07 1.0E-06 9.0E-07 1.2E-06

(1.62) (4.90)** (2.24)* (2.93)**Financial Depth 0.02 0.02 0.01 -0.01

(2.61)* (2.74)** (0.75) (0.66)Institutions 1.5E-03 3.4E-03 6.3E-04 9.0E-04

(0.67) (2.86)** (0.41) (0.55)R&D 0.01 0.01 3.8E-03 4.8E-03

(4.88)** (3.21)** (1.41) (1.14)Constant -0.06 -0.07 -0.07 -0.07 -0.07 -0.08 -0.07 -0.07 -0.08 -0.08

(9.30)** (25.48)** (11.00)** (18.99)** (5.15)** (13.46)** (24.18)** (14.06)** (8.66)** (8.90)**Observations 153 153 126 126 130 130 75 75 65 65R-squared / Pseudo R-squared 0.01 0.04 0.04 0.04 0.00 0.03 0.24 0.11 0.40 0.25Absolute value of t statistics in parentheses, robust standard errors, * significant at 5%; ** significant at 1%Drift: estimated quinquennium 2 quantile regression country coefficient, Financial depth: private credit by deposit money banks/GDP quinquennium 1, institutions: executive contraints quinquennium 1, R&D: research and development expenditure/GDP quinquennium 1, GDP: per capita PPP U$2005 GDP quinquennium 1

Dependent Variable: Standard Deviation (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)OLS QREG OLS QREG OLS QREG OLS QREG OLS QREG

GDP 3.5E-06 4.5E-06 2.6E-06 3.1E-06(2.97)** (7.70)** (1.48) (1.62)

Financial Depth 0.12 0.12 0.06 0.04(3.76)** (7.08)** (1.79) (0.90)

Institutions 0.01 0.01 -5.0E-03 -5.6E-04(1.99)* (2.68)** (0.60) (0.08)

R&D 0.07 0.07 0.05 0.05(6.27)** (8.93)** (2.29)* (2.50)*

Constant -0.08 -0.12 -0.09 -0.12 -0.10 -0.12 -0.11 -0.11 -0.11 -0.13(4.41)** (15.53)** (5.34)** (14.96)** (3.63)** (8.00)** (8.40)** (10.54)** (2.29)* (3.44)**

Observations 153 153 126 126 130 130 75 75 65 65R-squared / Pseudo R-squared 0.05 0.07 0.10 0.11 0.03 0.03 0.35 0.25 0.52 0.36Absolute value of t statistics in parentheses, robust standard errors, * significant at 5%; ** significant at 1%Standard deviation: estimated quinquennium 2 quantile regression country coefficient, Financial depth: private credit by deposit money banks/GDP quinquennium 1, institutions: executive contraints quinquennium 1, R&D: research and development expenditure/GDP quinquennium 1, GDP: per capita PPP U$2005 GDP quinquennium 1

25

Annex: Risk-Return Decomposition

Raw Data Decomposition (Centered)

-0.275

-0.175

-0.075

0.025

0.125

0.225

-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

Standard Deviation (country dummies)

Drif

t (c

ount

ry d

umm

ies)

UNCOND PFE CFE

Quantile Regressions Decomposition

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

Standard Deviation UV

Gro

wth

UV

UNCOND PFE CFE

26

Quantile Regressions Decomposition

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

Standard Deviation UV

Gro

wth

UV

UNCOND PFE CFE