the connection between _A_<sup> ̄</sup> _i_ and wages, we use data on per capita income as a proxy for labor cost to be matched by the model. In turn, the dispersion parameter _ξ_ is calibrated using the main estimate of Simonovska and Waugh (2014). Data on per capita income is obtained from the Penn World Tables for the year 2000.

Conditional on wages and calibrated parameters, we can compute labor demand for each country. Equality between aggregate labor demand and labor supply is attained by adjusting labor productivity. Of course, productivity is tighly related to wages. As shown in Table 2, there is almost a log-linear (downward-sloping) relationship between wages _wi_ and _A_<sup> ̄</sup> _i_ in our benchmark calibration.

**Coordination costs:** As shown in Proposition 1, the coordination cost parameter _θi_ is a key determinant of the position of a country along the chain, downstream or upstream. A country tends to export final goods when coordination costs are low and export intermediate goods when these costs are higher. Since all countries export a mix of final and intermediate goods, we assume that _θi_ is heterogeneous across varieties _ω_ , as discussed in section 4.3. We assume that it is log-normally distributed. In calibration countries are allowed a different shift parameter _θ_<sup> ̄</sup> _i_ and a different standard deviation _σiθ_ . We use _DX_ as the primary moment to calibrate _θ_<sup> ̄</sup> _i_ and _σiθ_ to fit countries’ intermediate share in total exports. While the correlation of the values in Table 2 is weak (0.11), the prediction of the model is confirmed: countries that are more upstream have higher average values of _θ_<sup> ̄</sup> _i_ . Empirically we note than most of the countries in the model have values of DX near the center of the distribution. Only Indonesia and China stand out as countries that are, respectively, relatively upstream and downstream.

**Transaction costs:** Another key parameter of the model is _γi_ , the cost of transactions between two firms. This cost is assumed to be positive even for transactions that occur within borders. Transaction costs are difficult to estimate in practice, but our model indicates that the grossoutput-to-value-added ratio equals the ratio of coordination and transaction costs parameters _γθii_<sup>andthuscanbeusedtoretrieveanestimateof</sup><sup>_γi_onceweknow</sup><sup>_θ_ ̄</sup><sup>_i_(onaverage).Results</sup> are provided in Table 2. As a credibility check we compare our results to plausible realworld counterparts of the _γi_ parameter using the Doing Business Database (World Bank). Reassuringly, we find expected correlations of our calibrated _γi_ variables with the costs of enforcing a contract claim (0.84), the time to enforce contracts (0.29), and the recovery rate in insolvency proceedings (-0.51).<sup>37</sup>

We note that Singapore has unusually low transaction costs in the calibration, which is consistent with its high gross output to value added ratio. Singapore’s value of _θ_<sup> ̄</sup> _i_ is also relatively

> 37We could regress _γi_ on each type of transaction cost (contract vs. bankruptcy, etc.) but with only 10 countries this regression would be meaningless.

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