3 GDP per capita. However, unit values are at best a noisy proxy for export quality, because they are also driven by other factors, including production cost differences. The strategies recently developed for quality estimation (including Khandelwal, 2010; Hallak and Schott, 2011; and Feenstra and Romalis, 2014) typically model demand, and in some cases also supply, using explicit microeconomic foundations. However, these methodologies do not allow calculation of a set of quality estimates with large country and time coverage, owing to their significant data requirements.

As a result, much work remains to be done in establishing stylized facts about product quality and, in particular, in linking growth in quality to economic development. Existing work has focused mainly on other questions. For instance, Khandelwal’s (2010) primary aim in calculating quality ladders is to show that U.S. sectors with short quality ladders are exposed to larger employment and output declines resulting from low-wage competition. Hallak (2006) focuses on showing that higher-income economies import more from countries producing high-quality goods. Hallak and Schott (2011) and Feenstra and Romalis (2014) are mainly concerned with decomposing changes in unit values into changes in quality and pure trade-price changes.

This paper yields a series of notable findings, many of them worthy of further research. Quality upgrading is particularly rapid during the early stages of development. There is significant cross-country heterogeneity in the growth rate of quality. Within any given product line, quality converges over time to the world frontier. Institutional quality, liberal trade policies, FDI inflows, and human capital all promote quality upgrading, although their impact varies across sectors. The results suggest that reducing barriers to entry into new sectors can allow economies to benefit from rapid quality convergence over time.

# **2. ESTIMATING PRODUCT QUALITY: METHODOLOGY AND DATA**

Unit values are used by much of the literature to measure product quality because they are readily observable, but suffer from three shortcomings. First, unit values may reflect production costs, or pricing strategies (that is, firms’ choice of mark-up) rather than quality. Second, changes over time in unit values may reflect changes in quality-adjusted prices, in response to supply or demand shocks, rather than changes in quality.<sup>8</sup> Third, if the composition of goods within a product category varies across exporters, the crosscountry differences in unit values among the exporters may reflect these differences in composition, rather than differences in quality.<sup>9</sup> The quality estimates presented here address the first two shortcomings; the last one cannot be addressed if one is to maintain broad country and time coverage.<sup>10</sup> The literature that goes beyond unit values does not provide a set of quality estimates that are well suited for analyzing developments in developing countries. Khandelwal (2010) requires data on market shares of imports relative to corresponding domestic varieties. These are only available for few countries and for limited time periods. Hallak and Schott (2011) require extensive data on tariffs, which are unavailable even for many relatively large countries before 1989.<sup>11</sup> Feenstra and Romalis (2014) require for each product two different unit-value observations, one derived from importer-reported (CIF) and one from exporterreported (FOB) data. However, exporter-reported data are not available for many developing-country 8 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.

9 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.

10 Other papers that focus exclusively on U.S. data (such as Khandelwal, 2012) 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.

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

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