metadata: authors: 0: author: Krishna,Pravin
1: author: Levchenko,Andrei A.
2: author: Maloney,William F.
count: World
docna: 0: docna: Growth and Risk : A View from International Trade
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Trade and Services,Legal Institutions of the Market Economy,Transport Services,Private Sector Development Law,Marketing,Private Sector Economics
teratopic: International Economics and Trade,Public Sector Development,Transport,Law and Development,Private Sector Development
abstracts: cdata!: This paper studies the cross-country
            patterns of risky innovation and growth through the lens of
            international trade. It uses a simple theoretical framework
            of risky quality upgrading by firms under varying levels of
            financial development to derive two predictions. First, the
            mean rate of quality growth and the corresponding
            cross-sectional variance of quality growth in a country are
            positively correlated. Second, both the mean and variance of
            quality changes are positively correlated with the
            country's level of financial development. The paper
            tests these two hypotheses using data on disaggregated
            (Harmonized System 10- digit) bilateral exports to the
            United States. The patterns in the data are consistent with
            the theory. The mean and the variance of quality growth are
            strongly positively correlated with each other. Countries
            with greater financial depth are systematically
            characterized by higher mean and higher variance in the
            growth of product quality. The findings suggest a
            mean-variance trade-off in product quality improvements
            along the development path. Increases in financial depth do
            not imply lower variability of changes in the product space.
repnme: repnme: Growth and Risk : A View from International Trade
display_title: Growth and Risk : A View from
            International Trade
listing_relative_url: /research/2020/06/32164686/growth-risk-view-international-trade
topicv3: Private Sector Development m1326060 1921,Transport m1326661 135,Trade m1326924 698,Trade in Services m1326928 780,Financial Institutions m1505015 3797
publishtoextweb_dt: 2020-06-23T00:00:00Z
historic_topic: International Economics and Trade,Public Sector Development,Transport,Law and Development,Private Sector Development
origu: VP, EFI Practice Group (GGEVP)
no_of_pages: 25
author_keys: 0: author_key: 645248
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2: author_key: 93000
model_extractions: Policy Research Working Paper 9296

# Growth and Risk

A View from International Trade _Pravin Krishna Andrei A. Levchenko William F. Maloney_ Equitable Growth, Finance and Institutions Practice Group Office of the Chief Economist June 2020
Policy Research Working Paper 9296

# **Abstract**

This paper studies the cross-country patterns of risky innovation and growth through the lens of international trade. It uses a simple theoretical framework of risky quality upgrading by firms under varying levels of financial development to derive two predictions. First, the mean rate of quality growth and the corresponding cross-sectional variance of quality growth in a country are positively correlated. Second, both the mean and variance of quality changes are positively correlated with the country’s level of financial development. The paper tests these two hypotheses using data on disaggregated (Harmonized System 10− digit) bilateral exports to the United States. The patterns in the data are consistent with the theory. The mean and the variance of quality growth are strongly positively correlated with each other. Countries with greater financial depth are systematically characterized by higher mean and higher variance in the growth of product quality. The findings suggest a mean-variance trade-off in product quality improvements along the development path. Increases in financial depth do not imply lower variability of changes in the product space.

This paper is a product of the Office of the Chief Economist, Equitable Growth, Finance and Institutions Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at wmaloney@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Growth and Risk: A View from International Trade<sup>_∗_</sup> Pravin Krishna<sup>_†_</sup> Andrei A. Levchenko<sup>_‡_</sup> William F. Maloney<sup>_§_</sup> JEL Codes: F14, O3, O4 Keywords: Product quality, financial development, risk

> _∗_ We are grateful to Amit Khandelwal for his help and advice in generating his quality measures and Peter Schott for advising on data. We thank Andr ́es Rodr ́ıguez-Clare for invaluable comments on a previous version.

> _†_ Johns Hopkins University and NBER, `Pravin` ~~`K`~~ `rishna@jhu.edu` .

> _‡_ University of Michigan, NBER, and CEPR, `alev@umich.edu` .

> _§_ World Bank, `Wmaloney@worldbank.org` .
# **1 Introduction**

Economic development is unavoidably a series of wagers; the returns to investments in physical, human and knowledge capital are invariably accompanied by risk.<sup>1</sup> A substantial theoretical literature has argued that the evolution of a country’s product basket along its development path (i.e., the evolution of the goods it produces and their quality) will depend on the relevant domestic and international institutions that allow agents to manage these risks. The lack of financial depth and the consequent inability of domestic producers to diversify risk implies that poorer countries will be less able to take on innovative but risky projects compared to rich countries.<sup>2</sup> Do countries, in practice, differ systematically in the evolution of their product space? If so, what do these differences depend upon? This paper explores these questions by studying cross-country patterns of product quality growth through the lens of international trade – in particular by looking at the evolution of countries’ export baskets over time.

We begin in Section 2 with a theoretical framework that describes risky quality upgrading by firms in economic environments characterized by different levels of financial development. Each firm has a choice of either producing a basic variety of its product with certainty, or undertaking a risky investment required for a quality upgrade. As in Kugler and Verhoogen (2012), upgrading quality requires the firm to use higher-cost inputs (which could be either more skilled labor or more expensive materials). Firms are risk averse and the ability of agents to diversify individual firm production risks through the financial system will determine their production choices. With low financial development, risks cannot be diversified and entrepreneurs consume the ex-post profits of their firms. Under a high level of financial development, firms will diversify production risks and thus have a greater willingness to take on risky quality-upgrading projects. The framework yields two results. First the mean rate of quality growth (across products) and the corresponding cross-sectional variance of quality growth in a country are positively correlated. Second, both the mean and the cross-sectional variance of quality changes in a country are positively correlated with the country’s level

> 1See, for instance, Acemoglu and Zilibotti (1997), Kerr, Nanda, and Rhodes-Kropf (2014) and Manso (2016).

> 2See, for instance, Greenwood and Jovanovic (1990) who 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. Acemoglu, Antr`as, and Helpman (2007) argue that weak domestic 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) difficult would also cause poorer countries to specialize in less risky products. Kletzer and Bardhan (1987), Beck (2002), Do and Levchenko (2007), and Hausmann, Hwang, and Rodrik (2007), among others, explore the links between financial development and patterns of production specialization.

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of financial development. This suggests a mean-variance “tradeoff” in quality growth along the development path. Rapid improvements in product quality will be more likely feasible in countries with greater financial depth.

Section 3 evaluates these theoretical predictions using data on disaggregated (HS10) bilateral exports to the United States for 1990-2000. Product quality is proxied by unit values.<sup>3</sup><sup>_,_4</sup> The advantage of using international trade data is that information on exports is available at a much finer level of disaggregation than domestic production, especially for a large sample of countries. The implicit assumption is that the export basket of any country to the United States represents its technological frontier, and any domestic innovations in product quality are reflected in changes in unit values of products in the export basket to the United States. Further, given both our theoretical focus and data limitations, we focus on quality changes in existing products (i.e. the “intensive margin”), rather than the introduction of altogether new products (the “extensive margin”).

Both of the main theoretical predictions discussed above find support in the data. The mean of quality growth and the variance of quality growth are strongly correlated with each other. Countries with greater financial depth exhibit systematically higher mean and higher variance in the growth of product quality.

Our results speak to a number of interrelated issues in the literature on economic growth, technology and convergence across countries. It is well established that countries have not converged in their income levels – despite a range of theoretical mechanisms identified by the literature, such as international technology transfer and trade that could drive convergence. Our findings are consistent with the lack of income convergence across countries because they suggest that the combination of risk associated with technology upgrading and institutional weaknesses that prevent insurance against risk may be a barrier to technological adoption. The implied lack of convergence in product quality is also consistent with the well known finding by Schott (2004) that higher income countries exhibit systematically higher quality of exports.

> 3We follow a number of papers in the recent international trade literature that have used unit values as a measure of quality, such as Schott (2004). In an influential contribution, Khandelwal (2010) proposes a methodology to estimate product quality that also incorporates information on market shares. However, Feenstra and Romalis (2014) find that “much of the variation in unit values is explained by quality, so quality-adjusted prices vary much less than the raw unit values or than the quality-adjusted estimates” of Khandelwal (2010) and others. Regardless, while our analysis focuses on product unit values, in our empirical analysis we also discuss the robustness of our findings to using Khandelwal (2010)’s alternative methodology.

> 4The literature has variously explored the correlates and determinants of product quality. Schott (2004) and Hummels and Klenow (2005) show that unit values increase with level of development. Hallak and Sivadasan (2013) argue that quality improvements represent the accumulation of “caliber,” a factor of production distinct from productivity, while Sutton (2001) sees both quality and productivity as resulting from research and development.

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Our results also inform the literature on technological change, growth and financial depth which has argued that technological change may be risky and that the inability to diversify this risk impedes technological progress. For instance, Doraszelski and Jaumandreu (2013) find that engaging in research and development roughly doubles the degree of uncertainty in the evolution of a producer’s productivity level. Foster and Rosenzweig (2010) argue, in the context of small scale agriculture, that risk, “the incompleteness of insurance, and credit availability play an important role in delaying the adoption of profitable new technologies..” Relatedly, Gorodnichenko and Schnitzer (2013) provide evidence that financial constraints affect the incentives of firms to innovate, which ultimately limits the ability of poor countries to catch up technologically with rich ones. Our finding that quality growth is higher in countries with greater financial depth complements these findings. Overall, our paper supports the view that the systematic differences in the evolution of countries’ product baskets are in part due to disparities in financial development. The upgrading of product quality requires the undertaking of risk. Inability to diversify and mitigate this risk is a barrier to development.

# **2 Analytical Framework**

This section presents a model of risky quality upgrading by firms. Each firm has a choice of either producing a basic variety of its product with certainty, or engaging in a risky quality upgrade. Specifically, the firm can undertake the investment required for a quality upgrade, but success is not certain. As in Kugler and Verhoogen (2012), upgrading quality requires the firm to use higher-cost inputs (which could be either more skilled labor or more expensive materials).<sup>5</sup> As in Greenwood and Jovanovic (1990), firms (or more precisely the shareholders of firms) are risk averse. Thus, the ability of agents to diversify individual firm production risks through the financial system will determine the production choices of firms. In a low financial development setting, risks cannot be diversified and each entrepreneur consumes the ex post profits of his/her firm. Each firm then chooses the safe production technology, prices are low, and the cross-sectional dispersion of price changes is zero.

Under the high level of financial development, firms will diversify production risks. In that case, all firms will choose to invest in the risky quality upgrading project. As a result, average price changes will be higher than in the low financial development equilibrium, but there will also be strictly positive cross-sectional dispersion in product price changes.

> 5This assumption implies that, consistent with empirical evidence, firms selling higher quality products charge higher prices.

3
# **2.1 Preferences and Technology**

There are two periods, indexed by _t_ = [0 _,_ 1]. There is a continuum of monopolistically competitive firms of mass 1 indexed by _k ∈_ [0 _,_ 1], each with the ability to produce a unique variety. Each firm faces isoelastic demand for its product: where _xt_ ( _k_ ) is quantity demanded, _pt_ ( _k_ ) is price, _qt_ ( _k_ ) is a variety-specific demand shifter which we think as quality, and _A_ is market-wide demand shifter.<sup>6</sup> Firms produce with an exogenously given non-time varying unit input requirement _a_ ( _k_ ) _∈_ [ _amin,_ 1] with finite support, and firm _k_ can convert _a_ ( _k_ ) units of the input bundle into one physical unit of its product. There are no fixed costs for setting up production (or, alternatively, those fixed costs have already been paid by all firms). Firm _k_ ’s bundle at time _t_ costs _ct_ ( _k_ ). While we do not need to take an explicit stand on the composition of the input bundle, it can consist of labor (possibly differentiated by skill), capital, and material inputs (both possibly heterogeneous). All inputs are generically firm-specific, and thus the overall cost of the input bundle _ct_ ( _k_ ) is firm-specific as well.

Standard profit-maximization steps given demand (1) lead to the well-known result that prices are a constant markup over marginal cost, and variable profits are a constant fraction of firm sales,

# **2.2 Risky Quality Upgrading**

We assume that this set of firms is small relative to the overall size of this market, so that their behavior, even as a group, does not affect the overall market demand shifter _A_ .<sup>7</sup> We also assume that _A_ does not vary across periods. This market demand could in principle be a combination of domestic and foreign demands.

> 6The utility function underlying this demand for an individual variety is

> 7 Precisely, we assume that the continuum of firms analyzed here is a small subset of the total set of varieties Ωthat enters the utility function specified in footnote 1.

4
At _t_ = 0, all firms are constrained to produce a basic product with the basic input bundle. Without loss of generality, we normalize the cost of the basic input bundle _c_ 0( _k_ ) to 1, and the quantity shifter _q_ 0( _k_ ) of the basic product to 1 as well. The firm profits when selling basic product are: where the subscript _b_ stands for “basic.” For _t_ = 1, firms have a choice over product quality that is subject to uncertainty. In particular, each firm has the option to keep producing under basic quality. This choice carries no additional costs and no uncertainty.

Alternatively, firms can invest in risky quality upgrading. They pay a fixed cost _f_ before realization of uncertainty. This fixed cost can be anything that has an impact on the consumer valuation of the product: product development, market research to determine the optimal product design, advertising expenditure, etc. With probability _φ_ the investment is successful, and consumers like the product. In that case, the firm’s quality becomes _q_ 1( _k_ ) = _q >_ 1. At the same time, in order to produce a higher quality product, the firms need better inputs. These could be higher-end material inputs, or higher-skilled workers.<sup>8</sup> To produce higher quality final product, the cost of the required input bundle is _c_ 1( _k_ ) = _c >_ 1. When the quality-upgrading investment is successful, the firm thus earns profits of: However, with probability 1 _− φ_ the quality-upgrading investment is not successful, and consumers are not convinced that the firm’s product is of higher quality. In that case, the firm reverts back to the basic product, and its profits are: which is of course simply equal _πb_ ( _k_ ) _− f_ .

To make the analysis interesting and obtain the main result in the simplest way, we make the following assumption.

This assumption states that expected profits from quality upgrading are greater than basic profits, for all firms. Of course, if this were not the case, then the safe project has a higher

> 8Kugler and Verhoogen (2012) document the positive relationship between firms’ product quality and unit costs.

5
mean return than the risky project, and the risky project never gets implemented. Expressed in terms of exogenous parameters of the model, A1 amounts to: Obviously, a necessary condition for A1 to be satisfied is that _q > c_ : the increase in demand the firm gets from a successful quality-upgrading effort more than compensates for the increase in unit cost that it must incur.

# **2.3 Financial Markets**

Entrepreneurs that run firms are risk averse. They make the quality-upgrading choice for its firm and financial market portfolio decisions (if any) to maximize expected utility where _I_ is the ex post income, and _u_ ( _._ ) is a standard concave utility function. The ex-post income need not coincide with firm profits, as will become clear below.

To make the analysis interesting and state the results in the clearest way, we make the following assumption: This assumption states that the expected utility when consuming profits from quality upgrading is lower than the expected utility of consuming basic profits, for all firms. If this were not the case, agents would always choose the quality upgrade, irrespective of the financial market structure. Of course, A2 can be satisfied with a sufficiently concave _u_ ( _._ ) even as A1 is satisfied simultaneously.

There are two levels of financial development: low and high. Under low financial development any financial transactions between owners of firms are ruled out. Per standard arguments, this can be due to poor ability of the financial system to monitor actions or outcomes, and/or to the inability of the legal system in the country to enforce contracts.

Under high financial development, firms can trade claims to each other’s profits. For simplicity, we assume that there are no frictions to doing this, that is, the good financial system reaches the first-best risk-diversification allocation. In that case, firms will perfectly pool risks among themselves. In practice, each entrepreneur will sell all the shares in its firm to the other firms, and buy the market portfolio containing the shares of all other firms.

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# **2.4 Equilibrium Production Allocations**

The low and high financial development economies will reach different real allocations. Under low financial development, each firm consumes its own ex post profits. Thus, when making the quality upgrading decision, it compares the expected utility from consuming the profits under quality upgrading (the left-hand side of eq. (2)) to the expected utility from consuming the profits from selling the basic product (the right-hand side of eq. (2)). Under A2, all firms in the low-financial development economy will choose not to engage in quality upgrading. The price charged by each firm will be the same in the two periods: prices will be equal to _p_ 0( _k_ ) = _p_ 1( _k_ ) = _ε−ε_ 1<sup>_a_(</sup><sup>_k_).Thusboththeaveragepricechangeandthecross-</sup> sectional variance of price changes will be zero: _E_ (∆ _p_ ( _k_ )) = 0 and _V ar_ (∆ _p_ ( _k_ )) = 0, where ∆ _p_ ( _k_ ) _≡ p_ 1( _k_ ) _/p_ 0( _k_ ) _−_ 1 is the growth rate in the unit price: the theoretical counterpart of the object of the empirical analysis below.

Under high financial development, all firms will be consuming the expected profits at _t_ = 1 (since there is a continuum of firms, the law of large numbers operates exactly and there is no uncertainty in aggregate). By A1, each firm will choose to upgrade quality.

The average proportional change in goods prices and the cross-sectional variability in price changes will be different from the low financial development case. Some of the prices (those for firms with unsuccessful quality upgrading outcomes) will be _p_ 1( _k_ ) = _p_ 0( _k_ ) = _ε−ε_ 1<sup>_a_(</sup><sup>_k_),</sup> and thus their growth rate will be zero: ∆ _p_ ( _k_ ) = 0. On the other hand, for the successful firms the price will be _p_ 1( _k_ ) = _ε−ε_ 1<sup>_a_(</sup><sup>_k_)</sup><sup>_c_,andthustheproportionalchangewillbe</sup><sup>_c −_1.</sup> Then mean price change, _E_ (∆ _p_ ( _k_ )) = _φ_ ( _c −_ 1) + (1 _− φ_ )0 = _φ_ ( _c −_ 1), is higher than in the low financial development equilibrium.

There will also be cross-sectional variation in prices. The variance of prices changes across firms _V ar_ (∆ _p_ ( _k_ )) = _φ_ (( _c −_ 1) _− φ_ ( _c −_ 1))<sup>2</sup> + (1 _− φ_ )(0 _− φ_ ( _c −_ 1))<sup>2</sup> = _φ_ (1 _− φ_ )( _c −_ 1)<sup>2</sup> , which is strictly positive. The discussion above proves the following proposition.

**_Proposition 1_** _Suppose that there are N >_ 1 _countries that differ among themselves in their level of financial development. In the cross-section of countries:_

_1. The mean growth rate in firm price and the cross-sectional variance of price changes are positively correlated: the countries with a lower mean growth rate in price also exhibit a lower variance of price growth rates._ _2. Both the mean price change and the cross-sectional variance of price changes are positively correlated with financial development. High financial development countries have both higher average growth rates of prices and a higher variance of growth rates of prices._ 7
# **3 Evidence**

Proposition 1 summarizes the main theoretical results concerning the correlation between mean quality growth and variance of quality growth as well as the positive relationship between the mean and the variance of quality growth and financial development in a crosssection of countries. We now turn to the empirical evidence for these predictions.

## **3.1 Data**

We use data from disaggregated (HS10) bilateral exports to the United States for the years 1989 to 2001, sourced from the NBER Trade Data Set, a detailed description of which is provided by Feenstra, Romalis, and Schott (2002). The data sum across U.S. ports and modes of transportation. In total, there are 16,926 HS10 products and 178 countries. The number of products per country ranges from 1 to 9907, with a mean number across countries of 5385. We focus on manufacturing industries (SITC 5-8). In keeping with the spirit of the underlying theoretical analysis, we exclude “homogeneous goods” as defined by Rauch (1999a).

As is well known, the trade data in the NBER data set are quite noisy. In addition to data reporting and entry errors, there are product classification errors due underlying product heterogeneity. Further, as Besedes and Prusa (2006) note, there are changes in product categorization across time. For instance, a given HS10 product category may split into several HS10 codes over time and, alternately, multiple HS10 codes may be merged into one HS10 category, due to the evolution of new products as well as the attrition of old ones. To deal with the noise in the data, we trim the data along two dimensions. First, we exclude all observations for which the dataset reports a quantity of one unit or value less than $7500 1989 dollars. Second, as in Khandelwal (2010), we remove varieties with extreme unit values that fall below the 5<sup>th</sup> percentile or above the 95<sup>th</sup> percentile within the industry. To mitigate the effects of outlier observations on our dispersion measure, we drop those goods for which the growth rate of quality on annual basis is greater than 2 (that is, those products whose unit value more than doubled).<sup>9</sup> These filters, taken together result in a loss of 7.99% percent of trade volume. Additional filters of the data, as part of robustness analysis, are considered in the following sections.

Product unit values (trade value divided by trade quantity) serve as a proxy for product quality. To make statements about a country’s innovations in product quality, we implicitly

> 9In a separate calculation, we also consider only those goods which additionally saw no downward movement in unit values as it is not clear what would cause such a downgrading of quality. Results were similar and are available upon request.

8
assume that the export basket of any country to the United States represents the exporting country’s technological frontier. Thus any domestic innovations in product quality in an exporting country are reflected in changes in unit values of products in its export basket to the United States.

Denote the unit value of product _i_ in country _c_ at time _t_ by _uict_ . To capture the central tendency of quality growth in any exporting country _j_ , we calculate first the time-average of the growth rate of unit values of its exports of product _i_ across _T_ consecutive years: and then take the median of this growth rate across _Ic_ products in this exporting country: The dispersion in quality growth across products in _c_ is measured as the variance across growth rates of the _Ic_ products exported by country _c_ : To assess quantitatively the relationship between the growth rate of product quality and the dispersion in product quality growth as predicted by the theory, we examine the correlation between _μc_ and the standard deviation in quality growth, _σc_ using the following bivariate specification: where _εc_ is the error term.

# **3.2 Main Results**

Figure 1 plots _μc_ against _σc_ . Panel (a) restricts the sample to only those countries that export more than 50 products to the United States. There is a strong positive relationship between the mean rate of quality growth _μc_ and the standard deviation of quality growth, _σc_ . Panels (b) and (c) plot _μc_ against _σc_ restricting the sample to countries with more than 100 products exported and more than 500 products exported to the United States, respectively. The positive link between _μc_ and _σc_ holds regardless of whether we consider the full group of countries exporting to the United States or the smaller group of countries that export a large range of products.

Columns 1, 2, and 3 of Appendix Table A1 report the underlying regression estimates. The correlation between _μc_ and _σc_ is positive and statistically significant at the 1 percent level in every case. The relationship is economically significant: a 10 percent increase in the value of _σc_ is associated with about a 10 percent increase in the value of _μc_ .

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Figure 1: Quality Growth vs. Dispersion <!-- Start of picture text --> .08 .08<br>United Kingdom United Kingdom<br>JapanSwitzld JapanSwitzld<br>.06 Portugal ArgentItalySwedenAustraliaFranceIrelandAustriaGermanyNethldsDenma .06 Portugal ItalySwedenAustraliaFranceAustriaGermanyNethldsDenma<br>Hongkong Bel_lux Norway Hongkong<br>.04 Bngldsh Sri_lka El SalvadorPeruEgyptVenezGuatmalaIndonesPolandPhilippinesThailandArab_emColombiaBrazilChinaIndiaTaiwanKoreaSpainIsraelCanadaMexicoSingaporeFinland .04 Sri_lka GuatmalaPhilippinesThailandColombiaBrazilChinaIndiaTaiwanKoreaSpainIsraelCanadaMexicoBel_luxSingaporeFinland<br>Bngldsh Indones<br>Dom_repPakistan South AfricaTurkeyMalaysia Pakistan TurkeyMalaysia<br>Macau Dom_rep<br>.02 Cos_rica Macau<br>Hondura .02<br>.04 .06 .08 .1 .04 .06 .08 .1<br>Standard deviation Standard deviation<br>(a) Countries with > 50 Products (b) Countries with > 100 Products<br>.08<br>United Kingdom<br>.07 Japan Switzld<br>.06 France<br>Italy Germany<br>Hongkong<br>.05<br>China Taiwan Canada Mexico<br>.04 Korea<br>.07 .075 .08 .085 .09 .095<br>Standard deviation<br>Quality growth Quality growth<br>Quality growth<br><!-- End of picture text -->

# **(c) Countries with** _>_ **500 Products**

**Notes:** This figure plots the median quality growth against the standard deviation of quality growth for countries that export at least 50 (panel a), 100 (panel b), or 500 (panel c) HS10 products to the US.

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# **3.2.1 Controlling for Sectoral Composition**

The pattern documented above may be due to inherent differences across broad sectors in their mean and variance. To explore the role of the sectoral dimension, we compute _μpc_ and _σpc_ for countries and broad sectors _p_ , and project these on country and broad sector fixed Panel (a) of Figure 2 plots the product fixed effects for the mean and standard deviation _δp_<sup>_μ_</sup> and _δp_<sup>_σ_against each other.It is indeed the case that across broad product groups, the mean-</sup> variance relationship is positive. For instance, instruments and electronic products have both higher mean and higher variance than food products and textiles. This relationship is statistically significant, with the coefficient and standard error reported in Column 5 of Appendix Table A1.

Since richer countries also generally produce more sophisticated products, we may therefore worry that product composition rather than country characteristics drive our results. However, country differences are equally pronounced after controlling for product group fixed effects. Panel (b) of Figure 2 plots the country effects _δc_<sup>_μ_and</sup><sup>_δ_</sup> _c_<sup>_σ_resultingfromestimating</sup> (3)-(4). Again, the relationship is positive, with high-income countries displaying higher mean and variance profiles. Column 4 of Appendix Table A1 confirms the statistical significance of the relationship. The findings suggest that countries with high mean and variance combinations not only produce a basket containing on average higher risk-return sectors, but also within broad sectors choose higher-risk investments.

# **3.2.2 Robustness to an Alternate Measure of Product Quality**

Finally, we consider an alternate measure of product quality and product quality growth. In an important recent paper, Khandelwal (2010) has argued that unobserved supply shocks can cause price differentials that do not correspond to differences in quality. The paper proposes a method to filter the raw unit value measures through a demand system in order to get to an improved measure of product quality. A product is judged to have higher quality if the demand for the good is higher, controlling for prices. In a nutshell, a better measure of quality than the price (or unit value) would be market share conditional on price, since a higher unit value with little market share may well represent higher costs rather than higher quality. We follow the Khandelwal (2010) procedure and back out these measures of quality. The Appendix contains a brief summary of the procedure; the reader is referred to 11
Figure 2: Quality Growth vs. Dispersion: Sectoral Dimension <!-- Start of picture text --> .08 .1<br>PrintingFurniture<br>Industrial_machinery<br>United_Kingdom<br>LeatherTransportation Fabricated_metalElectronic .08 Japan Switzld Nethlds<br>.06 Lumber Instruments Portugal FranceAustralia<br>Rubber_plastics Peru SwedenArgentAustriaItalyIrelandBel_luxNorwayGermanyDenmark<br>.04 Petroleum Apparel TextilePaper Stone_ceramic .06 Bngldsh VenezSri_lkaEgyptIndonesPolandEl_SalvadorThailandSouth_AfricaPhilippinesBrazilGuatmalaChinaTaiwanKoreaTurkeyColombiaIndiaSpainCanadaHongkongIsraelMexicoSingaporeFinland<br>.04 Macau Dom_repPakistanMalaysia<br>Chemicals<br>Cos_rica<br>Hondura<br>Food Primary_metal<br>.02 .02<br>.03 .04 .05 .06 .07 .04 .05 .06 .07 .08<br>Standard deviation (Product dummies) Standard deviation (Country dummies)<br>(a) Sector Fixed Effects (b) Country Fixed Effects<br>Drif (Product Dummies)<br>Quality growth (Country dummies)<br><!-- End of picture text -->

**Notes:** Panel (a) plots the product group fixed effects for the mean _δp_<sup>_μ_and standard deviation</sup><sup>_δ_</sup> _p_<sup>_σ_.Panel (b)</sup> plots the country fixed effects for the mean _δc_<sup>_μ_andstandarddeviation</sup><sup>_δ_</sup> _c_<sup>_σ_.Bothsetsoffixedeffectsresult</sup> from estimating (3)-(4). The sample is countries that export at least 50 HS10 products to the US.

the original paper for the full detail. The correlation between the two measures is around 0.4.<sup>10</sup> Appendix Table A2 shows that the correlation between risk and return is robust to using the filtered measure of quality.

# **3.2.3 Correlation with Financial Depth**

An important feature of Figure 1 is that high values of _μc_ and high values of _σc_ are generally seen together for countries with high income levels. For instance, the high mean-variance countries include the United Kingdom, Japan, Denmark and Switzerland, and the low meanvariance countries include the Dominican Republic, Pakistan and Honduras. This is broadly – consistent with our theoretical hypothesis that countries with greater financial depth gen– erally high income countries will have large mean-variance combinations for unit value growth.

Figure 3 presents suggestive support for this hypothesis by showing a positive unconditional relationship between quality growth and financial depth proxied by Private Credit by Deposit Money Banks and Other Financial Institutions.

To formalize this further, we fit the following relationship between quality growth and

> 10Both measures are also positively correlated with the exporting country’s income, as reported in previous studies.

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financial depth in our sample of countries: where _FINc_ denotes the level of financial development in country _c_ . We experiment with four indicators of financial depth: (i) Private Credit by Deposit Money Banks; (ii) Private Credit by Deposit Money Banks and Other Financial Institutions; (iii) Deposit Money Bank Assets; (iv) Stock Market Capitalization, each expressed as a share of GDP. The source for financial depth data is Beck, Demirg ̈u ̧c-Kunt, and Levine (2010).

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|(12)<br>arket<br> _GDP_|0.004*<br>(0.002)<br>0.005***<br>(0.001)<br>-0.013**<br>(0.005)<br>0.007<br>(0.009)<br>44<br>0.526<br>Private Credit by<br>pitalization/GDP.<br> This table reports<br>products exported<br> Rica, Dominican|
|---|---|
|(11)<br> D: Stock M<br>_talization / _|0.001<br>(0.002)<br>0.005***<br>(0.001)<br>0.003<br>(0.009)<br>44<br>0.443<br>Panel B:<br>arket Ca<br>0% level. <br>least 50 <br>re, Costa|
|(10)<br>Panel <br>_Capi_|0.005**<br>(0.002)<br>0.045***<br>(0.002)<br>44<br>0.136<br>ks/GDP;<br>: Stock M<br>at the 1<br> with at <br>, Singapo|
|(9)<br>t Money<br> _GDP_|0.014***<br>(0.004)<br>0.004***<br>(0.001)<br>-0.016***<br>(0.003)<br>0.004<br>(0.009)<br>46<br>0.67<br>Money Ban<br>P; Panel D<br> signifcant<br> countries <br> are Macao|
|(8)<br>C: Deposi<br> _Assets /_|0.009*<br>(0.005)<br>0.004***<br>(0.002)<br>0.001<br>(0.011)<br>46<br>0.467<br>eposit<br>ets/GD<br>level; *: <br>ample is<br>y==1|
|(7)<br>Panel <br>_Bank_|0.019***<br>(0.004)<br><br>0.033***<br>(0.003)<br>46<br>0.365<br>redit by D<br>y Bank Ass<br>at the 5%<br>nt. The s<br>hore dumm|
|(6)<br>redit by<br> _GDP_|0.009**<br>(0.004)<br>0.005***<br>(0.001)<br>-0.016***<br>(0.004)<br>-0.003<br>(0.010)<br>46<br>0.609<br>Private C<br>sit Mone<br>gnifcant<br>evelopme<br>d by of-s|
|(5)<br>: Private C<br> _and OFI/_|0.003<br>(0.005)<br>0.006***<br>(0.002)<br>-0.007<br>(0.012)<br>46<br>0.425<br>Panel A:<br>l C: Depo<br>evel; *: si<br>nancial d<br> capture<br> Africa.|
|(4)<br>Panel B<br>_DMB_|0.015***<br>(0.004)<br>0.037***<br>(0.004)<br>46<br>0.224<br>l depth. <br>DP; Pane<br>the 1% l<br>ures of f<br>. Locales<br>d, South|
|(3)<br>dit by<br> _/ GDP_|0.015***<br>(0.004)<br>0.004***<br>(0.001)<br>0.016***<br>(0.003)<br>0.004<br>(0.010)<br>46<br>0.657<br>fnancia<br>tions/G<br>fcant at<br>nt meas<br>ropped<br>itzerlan|
|re<br>_ks_|-<br>r<br>u<br>i<br>e<br> d<br>w|
|(2)<br>: Private C<br>_Money Ban_|0.009*<br>(0.005)<br>0.005***<br>(0.002)<br>0<br>(0.012)<br>46<br>0.457<br>proxies fo<br>cial Instit<br> ***: sign<br> for difer<br>200% are<br>mbourg, S|
|(1)<br>Panel A<br>_Deposit _|0.020***<br>(0.004)<br>0.034***<br>(0.003)<br>46<br>0.345<br>owth on<br>er Finan<br>entheses. <br>tion (5) <br>ter than <br>ng, Luxe|
|ariable:<br>_owth_|able<br> capita)<br>ality gr<br>and Oth<br>s in par<br>ng equa<br>es grea<br>ong Ko|
|t v<br> _gr_|vari<br> per <br>ons<br><br>qu<br>ks<br>ror<br>ati<br> rat<br> H|
|en<br>_ity_|l <br>P <br>e<br>t<br>ti<br>ed<br>of<br>n<br>r<br>m<br>h <br>a,|
|Depend<br>_Qual_|Financia<br>Ln (GD<br>Of-shor<br>Constan<br>Observa<br>R-squar<br>_Note:_ Regression<br>Deposit Money Ba<br>Robust standard e<br>the results of esti<br>to the US. Growt<br>Republic, Malaysi|

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Figure 3: Quality Growth vs. Financial Depth <!-- Start of picture text --> Risk vs. Financial Depth<br>Countries with more than 50 products. Slope = .015 (t−stat=3.56)<br>.08<br>United Kingdom<br>Japan Swi<br>Australia<br>Denmark Nethlds<br>.06 Argent SwedenFrance Ireland Portugal<br>Italy Austria Germany<br>Hongkon<br>Bel_luxNorway<br>MexicoPeruBrazilPhilippinesFinland Israel Canada Singapore<br>.04 VenezGuatmalaPolandIndiaSri_lkaColombiaEl SalvadorEgypt Spain ThailandKorea<br>IndonesBngldsh<br>Turkey South AfricaMalaysia<br>Pakistan<br>Dom_rep<br>Macau<br>.02<br>Cos_rica<br>Hondura<br>0 .5 1 1.5<br>Private Credit by Deposit Money Banks and Other Financial Institutions / GDP<br>Quality growth<br><!-- End of picture text -->

**Notes:** This figure plots the median quality growth against financial development, for countries that export at least 50 HS10 products to the US. Slope = 0.15 ( _t_ -stat=3.56).

Table 1 reports the results. Columns 1, 4, 7, and 10 show that in a bivariate regression, there is a significant and positive relationship between all four measures of financial development and average quality growth. Since financial development increases with income levels, it may be that our proxies are capturing other correlates of overall development. Hence, columns 2, 5, 8, and 11 control for GDP per capita. The magnitude and significance of the coefficient on the financial development proxy falls, losing significance in panels B and D. This result turns out to be partly due to a few countries showing conditionally very high rates of financial development without corresponding quality growth. The overwhelming majority of these countries turn out to be financial off-shoring destinations. Columns 3, 6, 9, and 12 control for this by including an indicator variable for offshoring locales.<sup>11</sup> Our financial depth proxies again emerge as strongly significant.

# **4 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

> 11Offshoring locales are Macao, Singapore, Costa Rica, Dominican Republic, Malaysia, Hong Kong, Luxembourg, Switzerland, and South Africa. See International Monetary Fund (2006) for a complete discussion.

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one decade, 1990-2001, the paper documents how the first two moments of quality growth vary across countries and products. Three stylized facts emerge from our analysis. First, there is a strong positive relationship between the mean and the variance of quality growth, consistent with a risk-return tradeoff. Second, developing countries occupy the less risky parts of the frontier. Finally, we find that the different positions that countries occupy along the risk-return frontier are explained by financial market depth, even conditional on per capita income.

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