Developing countries that hope to transform their economies structurally by anchoring themselves to GVCs are often disappointed by the quality of jobs offshored. The so-called ‘smile curve’ logic asserts that developing countries are mainly being offshored low value added per worker jobs while those with high intellectual and value added content are retained in the industrialized and advanced countries.<sup>5</sup> Thus, there is a rising concern among developing nations that not only are the ‘good jobs’ retained in advanced economies while the ‘bad jobs’ are shuffled to developing countries, but also that this trend is deepening (Baldwin et al., 2014). For example, in the well documented case study for Apple iPod, of a total value of USD 150, only USD 4 can be attributed to local producers in China while the rest of the value accrues to the United States, Japan and the Republic of Korea (Dedrick et al. 2010). Recent work using the OECD Trade in Value Added (TiVA) database suggests that 67% of VA trade accrues to OECD countries while other countries contribute minimally (11% Asian newly industrialized countries such as Singapore; Hong Kong SAR, China; Taiwan, China; Korea; Malaysia; Thailand; the Philippines; 9% for China; 5% for the remaining BRICS countries and only 8% for all other developing economies covered by the database) (e.g. Banga, 2013).

Baldwin (2012) summarizes the location decision of firms in a supply chain context as a trade-off between dispersion (e.g. direct factor costs such as wages) and agglomeration forces (e.g. ‘separation’ costs such as transmission and transport costs). Clustering is a natural outcome in certain sectors and stages such as fashion clothing that involves local or technological spillovers while in others where such benefits are limited, a geographical separation of stages is observed. Dispersion is generally explained by the relatively higher wages of low-skill labor in the ‘headquarter’ economies (e.g. the United States, Germany, Japan) vis-à-vis nearby ‘factory’ economies, which results in a spatial sorting of skill-intensive stages to highwage nations and labor-intensive stages to low-wage nations (Feenstra and Hanson, 1996). Given the skewed distribution of global trade in value added, there are obvious concerns regarding their impact on regional development. The next section sets the stage for examining this more formally.

# **3. Empirical  Strategy**

To assess the impact of participation in GVCs on the city size distribution, we organize our estimation in two stages. In the first stage, we estimate the Zipf’s coefficient, through the following well-known ranksize equation:<sup>6</sup> l l (1) ii,cc,tt cc,tt cc,tt ii,cc,tt ii,cc,tt Where RR = AA is the rank of city −γγ PP + εε _i_ in country _c_ at time _t_ and is the population of the given city _i_ in country _c_ at time _t_ and is a country-year specific intercept term. In sum, an estimate of the Zipf’s coefficient, ii,cc,tt ii,cc,tt , is generated by running a regression of log rank on log city size. The assumption of Pareto distribution RR PP cc,tt is useful in assuring that the Zipf’s coefficient is consistent even when estimated by running a regression AA cc,tt only on the sample of the larger cities, that is, the upper tail of the distribution. γγ In the second stage, we follow Rosen and Resnick (1980), Soo (2005) and Ioannides et al. (2008) in delineating the determinants of city size distribution as measured by the Zipf’s coefficient, : cc,tt γγ (2) where cc,tt cc,tt is a vector of time and/or country specific fixed effect, as appropriate. Time fixed effects capture cc,tt cc,tt γγ = φφ + ββ l XX + ee the linear time trend in city size distribution that affects all countries and may have nothing to do with any cc,tt φφ

> 5 “Smile curve” was introduced by Acer founder and CEO Stan Shih in the early 1990s. There are only a few studies on the smile curve in the economics literature (e.g. Mudambi 2008; Baldwin et al., 2014), although the concept is widely used in the International Business literature (e.g. Yan and Islam, 2011).

> 6 We follow Soo (2005) and Ioannides et al. (2008) in assuming that the city size distribution is Pareto.

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