Growth and Risk: A View From International Trade...
Transcript of Growth and Risk: A View From International Trade...
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Very Preliminary, do not cite
Growth and Risk: A View From International Trade
Pravin Krishna
and
William F. Maloney
March 9, 2009
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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.
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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
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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.
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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.
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( ) ( )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
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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.
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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.
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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
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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