The remainder of this paper is organized as follows. The second section discusses the methodology and data used to measure and labor and jobs content of exports to construct the LACEX database. The third section discusses how the labor content of exports has evolved globally, and the fourth section turns attention to the jobs content. These developments are explored across income groups and regions, sectors, as well as at the country level. The concluding remarks are in the fifth section of the paper.

# **2. Data and methodology**

We compute the labor value added and jobs content of exports on the basis of a panel of global IO and other aggregate data from the Global Trade Analysis Project (GTAP). GTAP represents a massive combined effort of international institutions and universities. Over time, the data set has grown to include more countries and more sectors. The latest version, GTAP 9, has data on 129 countries/regions and 57 sectors (Narayanan et al., 2012). Table A1 in Appendix 1 provides the description of the 57 GTAP sectors.

## _<u>Why GTAP</u>_

Although recent years have seen the emergence of different databases of global IO tables, we believe the GTAP data offer a balance between quality and country coverage, making it more suitable than other alternatives for our purposes. There are at least three other databases that could be used to compute the labor contained in exports: the World Input-Output Database (WIOD), the OECD-WTO Trade in Value Added (TiVA) Database and the UNCTAD-Eora Database. These data differ in their coverage of countries, sectors, and years as well as in overall accuracy.<sup>5</sup> WIOD and TiVA include a limited number of developing countries, although they account for most of world trade in value added, which is generated by high income countries. Because they comprise only high income or large middle income countries, these databases tend to have great levels of accuracy. On the other hand, the Eora database has the widest country coverage, with 187 countries in total, but only 25 sectors. However, the data at this stage do not ensure consistency in terms of aggregation and reconciliation of trade flows. This can generate problems of imbalances in the data, which can be particularly relevant for smaller developing countries.<sup>6</sup> GTAP data strike a balance between the accuracy of the data (including a rigorous submission process for national IO tables), the global consistency of the national account and trade data across countries, and the relatively wide coverage of developing countries. GTAP uses various data sources and estimates missing data. The GTAP IO data also include social accounting matrices that cover additional countries where no official IO tables are available. While the global coverage is smaller than Eora (120 versus 187 countries), the countries covered in the GTAP data account for over 90% of developing countries’ population.<sup>7</sup> In addition, GTAP disaggregates the factors of production into skilled and unskilled labor.

There are also certain drawbacks in using GTAP to compute such data, which should be acknowledged upfront. The quality of the data critically hinges on the underlying IO tables and social accounting matrices that each country produces autonomously. While GTAP researchers check and validate these data before using it, their quality cannot be fully observed. That can be a problem especially for certain sectors in specific countries. A second problem relates to the frequency of the data. As the production of GTAP data is complex (it needs global consistency and requires multiple data inputs and iterations), its frequency is not regular and the data suffer from non-negligible time lags. For example, the latest data currently available are from 2011. For these reasons, in the future it will be important to reconcile GTAP with the

> 5 See Tukker and Dietzenbacher (2013) for a review comparing the various databases.

> 6 The Eora developers explain that “for some sectors imbalance remains. Generally this imbalance is small, e.g. <5% for bigger economies, but can be up to 20% (IIRC) in the worst case sectors, e.g. manufactured goods from Mongolia, or similar sectors where we have poor or conflicting input data” (http://worldmrio.com/EoraFAQ.pdf).

> 7 In the longer term, as the database matures, and assuming it shows a pattern of regular updating, one may consider moving partially or fully to Eora for updating of the database developed herein.

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