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Export Quality in Advanced and Developing Economies: Evidence from a New Data Set

IMF Economic Review volume 68, pages 421–451 (2020)Cite this article

Abstract

This paper develops new estimates of export quality, based on bilateral data, which are far more extensive than previous efforts. The data cover 166 countries and more than 800 products over the period 1962–2014. The analysis finds that, within any given product line, export quality on average converges rapidly across countries. However, there is also significant cross-country heterogeneity in the growth rate of quality. Institutional quality, liberal trade policies, foreign direct investment inflows, and human capital all promote quality upgrading, although their impacts vary across sectors.

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Notes

  1. For instance, unit values for cotton shirts imported from Japan are 30 times higher than those from the Philippines.

  2. Defined as those products predominantly produced by high-income economies. While higher-income countries also tend to produce higher-quality varieties, the concepts of quality and sophistication are quite different. Quality refers to the relative price of a country’s varieties within their respective product lines. Product sophistication, as in Hausmann et al. (2007), assesses the composition of the aggregate export basket.

  3. Starting production of higher-quality varieties need not imply abandoning production of lower-quality varieties, particularly if the latter are better suited to some destination. Mukerji and Panagariya (2009) note that the USA produces goods at a large variety of quality levels. Nonetheless, the average quality within 4-digit product categories, which is the focus of our study, tends to be higher in higher-income economies.

  4. Regarding proximity, Bahar et al. (2014) documents that the probability of a country exporting a new type of good is significantly (over 50%) larger if a neighboring country is a successful exporter of the same good.

  5. Hallak and Schott’s (2011) results suggest for instance that Malaysia continually upgrades quality, but this does not show in unit values because of falling world prices for electronics, the country’s main export.

  6. Similarly, quality measures will be affected by introduction of new products, if the initial quality level produced in these new products varies substantially from the average quality of existing products in the category.

  7. Other papers that focus exclusively on US data (such as Khandelwal 2010) can address this last issue by using HS 10-digit data. However, data at such a high level of disaggregation are not widely available for developing countries.

  8. Also, data on tariffs in the Long Time Series TRAINS database, which goes back to the 1970s, do not cover low-income countries well.

  9. The key difference is that we directly use unit values at the SITC 4-digit level, whereas Hallak gathers unit values at the 10-digit level and then normalizes them into a price index for each 2-digit “sector”.

  10. This approach builds on Schott (2004), who showed that unit values for any given product vary systematically with exporter relative factor endowments, as proxied by GDP per capita.

  11. Hallak (2006) uses distance to the USA instead of distance to the importer, because it only focuses on prices of exports to the USA. Harrigan et al. (2011) find that the correlation between export prices and distance is due to a composition, or “Washington apples”, effect. They also find that US firms charge higher prices to larger and richer markets.

  12. It includes indicator variables for a common border, a common language, the existence of a preferential trade agreement, a colonial relationship, and a common colonizer.

  13. Where a unit value for the preceding year is not available (for instance, because the good was not traded), we use the unit value in the closest available preceding year, going back up to 5 years. If unit values are not available in any of the preceding 5 years, the observation is excluded from the estimation.

  14. In (4), the term \(- \frac{{\delta \xi_{\text{mxt}} }}{{\zeta_{1} }}\) is set to its expectation of zero: it cannot be separately identified, as it constitutes part of \(\xi^{{\prime }}_{\text{mxt}}\). As pointed out in Hallak (2006), estimating export quality may reflect omitted factors affecting export prices in (1), such as sector-specific technological advantages not well proxied by GDP per capita, and could persist over time. This should be borne in mind when interpreting the results.

  15. This number is smaller than the 45.3 million potential combinations in the data set because of: (1) missing observations for other regressors, primarily per capita income; and (2) elimination of outliers (see footnote 20).

  16. The preference for quality parameter δ will vary across sectors. Therefore, when quality estimates are later aggregated across sectors, the procedures necessarily also aggregate across these heterogeneous preferences for quality. The level term \(- \frac{{\delta \zeta_{0} }}{{\zeta_{1} }}\) is of no significance, given our subsequent normalization of the quality estimates.

  17. Changes in the higher-level (including country-level) quality estimates will in general reflect both quality changes within disaggregated sectors, and reallocation across sectors with different quality levels. If the composition of exports is shifting toward product lines characterized by low quality levels, it is possible for the quality of any given product to be rising sharply, but country-level quality to rise slowly (or indeed decline).

  18. The only exceptions to this methodology are export flows as reported by the USA, which take precedence over importer-reported flows.

  19. SITC 4-digit-plus products were dropped if they met either of two criteria for smallness. First, the product comprises less than 1% of the total observations or trade value of the corresponding SITC 4-digit product. Second, the product has less than 1000 observations and comprises less than 25% of the total observations or trade value of the corresponding SITC 4-digit product. In addition, outliers were eliminated by excluding any observation where: (1) the quantity equals unity; or (2) the total trade value is less than $7500 at 1989 prices; or (3) the unit value lies above the 95th percentile, or below the 5th percentile, of all unit values for that product, with all values expressed in 1989 prices.

  20. It is also possible that in various sectors (say, fast fashion, toys, and some appliances) the demand for quality may not have increased in line with income in the recent past, because consumers have instead preferred to replace products more frequently as the novelty factor wears off.

  21. In addition, our trade price vector is defined differently from Hallak (2006). The latter, using US data only, computes Fisher price indexes for each SITC 2-digit sector starting from 10-digit sectors. This paper uses directly the unit values of SITC 4-digit-plus products.

  22. Specifically, we use a longer lag on the unit value: instead of using the unit value in the closest available preceding year, going back up to 5 years, we use the unit value in the furthest available preceding year, going back up to 10 years. Annex B documents that the results do not change significantly, focusing on two of the sectors discussed further below—the automotive and clothing sectors. This test is admittedly not conclusive. That said, other tests, such as controlling for exporter–year fixed effects, would not be feasible owing to collinearity with other covariates defined at the exporter–year level.

  23. Market share is measured as a country’s exports as a percentage of total world exports of that product.

  24. The correlation between income and unit values for non-agricultural commodities is relatively weak.

  25. Countries are classified as small states if their population is smaller than 1.5 million in either 2010 or 2011, using Penn World Tables (2010) and World Development Indicators (2011) data. This classification does not include fuel exporters that are high income (as per World Bank definition), including in particular Bahrain, Brunei, and Equatorial Guinea. Countries are classified as commodity exporters, following the IMF World Economic Outlook classification, if commodities on average exceed 50% of total exports.

  26. For instance, red wine, Arabica coffee, and shrimp and prawns constitute examples of agricultural products with particularly long quality ladders (cf. Lederman and Maloney 2012, Box. 5.1).

  27. Similar results are obtained using the Kaufmann–Kraay–Mastruzzi indicators.

  28. We do not include country–time fixed effects, because the determinants we are primarily interested in only vary along the country–time dimension.

  29. The precise results of these robustness checks are available upon request.

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Authors and Affiliations

  1. IMF, Washington, USA

    Christian Henn, Chris Papageorgiou & Nikola Spatafora

  2. The World Bank, Washington, USA

    Jose Manuel Romero

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  1. Christian Henn
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  2. Chris Papageorgiou
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Correspondence to Nikola Spatafora.

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All data and estimates referenced in the paper may be downloaded at http://data.imf.org/exportquality. We thank Ricardo Hausmann for particularly enlightening discussions. We are also grateful to Irena Asmundson, Andrew Berg, Hugh Bredenkamp, Amit Khandelwal, Aaditya Mattoo, Camelia Minoiu, Cathy Pattillo, Fidel Perez-Sebastian, Michele Ruta, Romain Wacziarg, and participants in seminars at Clemson University, EBRD, Florida International University, Harvard University, IMF, National University of Singapore, Oxford University, University of Washington, World Bank, and WTO for useful comments. Zidong An, Lisa Kolovich, Freddy Rojas, and Ke Wang provided outstanding research assistance. This work benefited from the financial support of the U.K.’s Department for International Development (DFID). The paper should not be reported as representing the views of the IMF, WTO, World Bank, or DFID.

Electronic supplementary material

Appendix: Robustness of Product Quality Estimates

Appendix: Robustness of Product Quality Estimates

1.1 Consistency of Quality Estimates over Time

Export quality was estimated separately over three sub-periods, each covering 14 or 16 years: 1968–1982, 1983–1997, and 1998–2014. The estimates obtained using these sub-periods imply trends that follow closely those generated by the estimates obtained using the full sample, 1962–2014. This suggests that the coefficients are stable over time, and that the data set can indeed be used to obtain a measure of export quality that is comparable over time. For instance, compare the estimates obtained using the full sample and using the three separate sub-periods for the U.S. Passenger Motor Cars sector (SITC 7321) and for the Argentine Pumps and Centrifuges sector (SITC 7192) (Fig. 8).

Fig. 8
figure 8

Quality estimates over different sub-periods

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More systematically, for each country, sector, and year, we also compute the absolute value of the difference between the quality values estimated using the full sample and using the 1968–1982 sub-period. We then average this across all countries and sectors for each year between 1968 and 1982. The results do not show substantial differences; in each year, the average difference is less than 0.1 (Table 13).

Table 13 Quality estimates: differences between full sample and subsample 1968–82
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1.2 Instrument Validity

Export quality was estimated using as the main instrument (a) the nearest lag of unit values; or (b) the furthest lag of unit values (see Sect. 2 in main text for details). The estimates were comparable across the two procedures, as illustrated below for the automotive and the clothing sectors (Figs. 9, 10). The only exception is the car sector in Germany; here, the significant differences likely reflect differences in the data before and after the unification of East and West Germany.

Fig. 9
figure 9

Automotive sector

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Fig. 10
figure 10

Clothing sector

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Henn, C., Papageorgiou, C., Romero, J.M. et al. Export Quality in Advanced and Developing Economies: Evidence from a New Data Set. IMF Econ Rev 68, 421–451 (2020). https://doi.org/10.1057/s41308-020-00110-8

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