OECD imports: diversification of suppliers and quality search


We posit that OECD buyers are in a continuous search for best quality suppliers from developing countries. We build a simple model of adverse selection and quality screening which captures this feature. The model predicts that diversification happens by “bouts”, or temporary episodes, during which OECD buyers search for high-quality suppliers. Each diversification episode is followed by a phase of re-concentration on the best performers, until those fail (which happens stochastically), triggering new search phases. The model also shows that concentration across origin is highly volatile, especially for goods with high-quality heterogeneity. Finally, as the set of suppliers expands and buyers continue sampling, the overall trend is an increased diversification across time. We empirical explore these conjectures using OECD imports over time (1963–2006) and measuring their concentration across 250 origin countries at the product level (1,300 products). We provide strong empirical evidence corroborating the model predictions.

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  1. 1.

    Data in this introduction are from the World Bank WDI database.

  2. 2.

    As mentioned above Jaimovich (2012) studies geographic import diversification. We add to this paper by focusing on the OECD as importers and most importantly by providing and testing a model where quality search leads to specific form of geographic import diversification over time.

  3. 3.

    The number of ISO 9000 certificates comes from the ISO Survey of ISO 9000 and ISO 14000 certificates—Tenth cycle provided by the International Organization of Standardization. We obtain data for industrial employment combining data on share of industrial employment in total employment, share of employment in population and population from the World Development Indicators (World Bank 2011).

  4. 4.

    Whether unit values are good proxies of product quality is disputable. Differences in unit value may reflect both changes in product quality and/or firms' markups. In our context however, higher markups could easily be associated with performing suppliers as buyers keep high unit-price suppliers only if they are reliable. Note that unit values are still widely used in the literature discussing issues related to product quality (see for example Kugler and Verhoogen 2012; Manova and Zhang 2012).

  5. 5.

    This trend in diversification reversed itself in recent years; we show however in Sect. 3 that this is entirely explained by the rising share of Chinese products in OECD imports. Concentration indices keep on decreasing monotonically when China is excluded.

  6. 6.

    Baldwin and Harrigan (2011) develop a general equilibrium model based on Melitz (2003) with a taste for quality so that the lowest prices goods are not necessarily the most competitive.

  7. 7.

    As in the incomplete contract literature (see Antràs 2011 for a survey in the context of international trade), there is some uncertainty about the quality of the relationship between buyers and suppliers. Our focus is however different. In our work, the organizational form of the transaction (outsourcing or integration) is not the central question and termination does not incur any cost. Our model is better understood as outsourcing contracts where buyers shop around for the best suppliers rather than buyers and sellers adapting contracts in order to improve the transaction.

  8. 8.

    The problem of selecting the stochastic process that delivers the highest expected reward among a set of independent processes is known in the statistical-decision literature as a “multi-armed bandit” problem. One strategy, called “epsilon-first”, consists of a sampling (exploratory) phase during which several “levers” are tried, after which the experimenter sticks to the lever for which he has the most optimistic belief based on information gathered during the sampling phase.

  9. 9.

    In our 3-period model, the event that \( p_{2}^{y} < p_{2}^{x} \) implies that y had a defect in period 1 while x did not. Then, if fortunes are reversed in period 2 (x has a defect while y has not), it is easily verified that posteriors at the beginning of period 3 will be just equal for x and y. So, at best, the buyer will be indifferent between x and y in period 3. In (8), we have thus \( \phi^{y} = 0 \) and, given the multiplicative form of Ω, the value of the information is nil: There is no reason to keep on sampling after period 1. In a 4-period framework, at the cost of tedious algebra it is (relatively) straightforward to show that a reversal of beliefs is possible with two successive lucky draws on y and two unlucky ones on x, and so, continued sampling (using both suppliers) can be optimal in period 2.

  10. 10.

    Note that in this setup there can be no “informational cascade”. An informational cascade (Bikhchandani et al. 1992) can take place when a sequence of actors make binary decisions on a singe issue (say, buying or selling a stock) based on a noisy signal about the correct decision and on the observed behaviour of past players. Each player forms his own belief based on a weighted average of his signal and past players’ actions, with weight on the latter that increases with the number of past players. Bikhchandani et al. show that there exists a critical number n such that, if n players observe the wrong signal and act accordingly, the (n + 1)st will discard his own signal and follow the crowd. From then on, the herd behaviour cannot be reversed. Our setup is different because the buyer is repeatedly getting information about his supplier, whereas in an informational cascade the individual experimenter gets only one signal that he compares with the actions of other (past) players.

  11. 11.

    Note that these studies consider new products at the HS6 level. The number of new producers is obviously much larger.

  12. 12.

    Failure may also be triggered endogenously by moral hazard if incumbents slacken the monitoring effort as time passes. For a reputational model with both selection and moral hazard, see e.g. Laeven and Perotti (2001).

  13. 13.

    However, supply shocks knocking out suppliers periodically could also create exogenous volatility at the extensive margin. This is to be kept in mind in the empirical exploration that follows, as baseline volatility is unlikely to be exactly zero.

  14. 14.

    Parteka (2010) found very high correlations between the different measures of concentration (Theil and Herfindahl indices are correlated at 0.95). Results in her paper do not depend on the choice of the concentration index. We re-run all specifications of the present paper using the Herfindahl index and found similar results as the ones presented here. These results are available upon request. We decided not to use the Gini coefficient because of the issues associated with this concentration index. The Gini coefficient is a numerical representation of the degree of concentration and represents the distance between the Lorentz curve and the 45° line (egalitarian distribution). There are two issues with Gini coefficients. First, they place more weight on changes in the middle part of the distribution. If a transfer occurs from a larger number of exporters to a smaller number of exporters, it has a greater effect on the Gini if these numbers of exporters are near the middle rather than at the extremes of the distribution. Second, if the Lorentz curves cross, it is impossible to summarize the distribution in a single statistic without introducing value judgements. While studying concentration of import across time these issue should be relevant. Herfindahl and Theil indices are robust to these sensitivity issues [on this, see Sen (1997)].

  15. 15.

    In order to calculate the Theil index and capture the evolution in geographic import concentration, we need to define a potential number of source countries than is constant over time. In effect, if we let n k vary over time, (say by making it equivalent to the number of countries that export a specific product each year), we cannot disentangle concentration/diversification on actual suppliers from changes in the universe of potential suppliers. For example, an increase in the Theil index may be caused by either concentration of imports on fewer sources or an increase in the number of potential sources. Our model however suggests that concentration occurs because buyers select the best suppliers after testing them. If n k varies, we may observe concentration while the number of actual suppliers does not change, which would be misleading. Similarly, the geographic diversification of imports across time could results from the elimination of some countries from the set of potential suppliers. The OECD is not more diversified in the sense that it tests extra supply sources. As we are interested in the importers selections of source countries and its link with unit values, we do not want our concentration index to be modified by changes on the supplier side.

  16. 16.

    Cadot et al. (2011) show that the Theil index can be decomposed into between- and within-groups components with a partition of lines into active and inactive ones which result in a perfect mapping with the extensive and intensive margins of trade.

  17. 17.

    The definition of OECD countries used in this paper includes the 29 countries that belonged to the OECD in 2006, i.e. Australia, Austria, Belgium-Luxembourg, Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Italy, Ireland, Japan, Spain, Korea, Mexico, the Netherlands, New-Zealand, Norway, Poland, Portugal, the Slovak Republic, Sweden, Switzerland, Turkey, the United Kingdom and the United States.

  18. 18.

    We do not believe that the level of disaggregation (SITC 5-digit or HS6) matters for our analysis: although Theil variations may results from composition effect at the sub-product level, such variations randomly correlate with product unit value. In order to confront our main findings to the choice of disaggregation of the database, we re-run our main regression (Table 1) on the sub-sample of SITC codes that correspond to only one (alternatively two or less) HS6 lines. Results are similar to the ones obtain with the full SITC database and are available upon request.

  19. 19.

    Note that importer-product as well importers fixed effects control for late appearance of some OECD countries within the database (e.g., we have data for Poland, the Slovak Republic and the Czech Republic starting in the early 90’s). Year fixed effects control for exogenous shocks that may affect several OECD countries on their sourcing behaviour alike.

  20. 20.

    For every product, Theil and unit value volatilities correspond to simple averages over countries belonging to the OECD.

  21. 21.

    95 % confidence interval is also reported.

  22. 22.

    Non-parametric “smoother” regression consists on re-estimating the regression for overlapping samples centered on each observation.

  23. 23.

    Figures including all suppliers and using simple averages of number of exporters to OECD at the product (SITC4) level are very similar to the one presented here and are available upon request.


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Research on this paper was supported by a Grant from the World Bank. Support from France’s Agence Nationale de la Recherche under “Investissement d’Avenir” Grant ANR-10-LABX-14-01 is gratefully acknowledged. Without implicating them, the authors would like to thank Daniel Lederman, William Maloney, Marcelo Olarreaga, and an anonymous internal reviewer for useful comments. We are grateful to Madina Kukenova for her research assistantship on a previous version of the paper.

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Correspondence to Vanessa Strauss-Kahn.



The expression for the expected period-3 gain, as of the beginning of period 2, given that the buyer kept only one supplier, x, is

$$ E\left( {\left. {V_{3} } \right|I_{2} } \right) = q_{2}^{x} E\left( {\left. {V_{3} } \right|\zeta_{2}^{x} = 1} \right) + \left( {1 - q_{2}^{x} } \right)E\left( {\left. {V_{3} } \right|\zeta_{2}^{x} = 0} \right) $$

where the probability of no defect in period 2 given information at the beginning of period 2, \( q_{2}^{x} \), is

$$ q_{2}^{x} \equiv \Pr \left( {\zeta_{2}^{x} = 1\left| {I_{2} } \right.} \right) = p_{2}^{x} \lambda^{G} + \left( {1 - p_{2}^{x} } \right)\lambda^{B} , $$

and the expected gain in period 3 is

$$ E\left( {\left. {V_{3} } \right|\zeta_{2}^{x} = 1} \right) = p_{3}^{x} \left( {\zeta_{2}^{x} = 1} \right)\left( {2\bar{\pi }^{G} } \right) + \left[ {1 - p_{3}^{x} \left( {\zeta_{2}^{x} = 1} \right)} \right]\left( {2\bar{\pi }^{B} } \right) $$

given no defect in period 2 and

$$ E\left( {\left. {V_{3} } \right|\zeta_{2}^{x} = 0} \right) = p_{3}^{x} \left( {\zeta_{2}^{x} = 0} \right)\left( {2\bar{\pi }^{G} } \right) + \left[ {1 - p_{3}^{x} \left( {\zeta_{2}^{x} = 0} \right)} \right]\left( {2\bar{\pi }^{B} } \right) $$

given a defect in period 2. Finally, the probability of supplier x being of the good type is, by Bayes’ rule,

$$ p_{3}^{x} \left( {\zeta_{2}^{x} = 1} \right) = \Pr \left( {\left. G \right|\zeta_{2}^{x} = 1} \right) = \frac{{\lambda^{G} p_{2}^{x} }}{{\lambda^{G} p_{2}^{x} + \lambda^{B} \left( {1 - p_{2}^{x} } \right)}} $$

given no defect in period 2 and

$$ p_{3}^{x} \left( {\zeta_{2}^{x} = 0} \right) = \Pr \left( {\left. G \right|\zeta_{2}^{x} = 0} \right) = \frac{{\left( {1 - \lambda^{G} } \right)p_{2}^{x} }}{{\left( {1 - \lambda^{G} } \right)p_{2}^{x} + \left( {1 - \lambda^{B} } \right)\left( {1 - p_{2}^{x} } \right)}} $$

given a defect. Substituting these expressions into (14) and simplifying gives expression (7) in the text.

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Cadot, O., Carrère, C. & Strauss-Kahn, V. OECD imports: diversification of suppliers and quality search. Rev World Econ 150, 1–24 (2014). https://doi.org/10.1007/s10290-013-0172-3

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  • Import diversification
  • International trade
  • OECD
  • Developing countries
  • Suppliers search

JEL Classification

  • F1
  • O11