# **6.1 Data and calibration**

We use the World Input Output Database (WIOD) to calibrate international trade, production functions and labor shares. In the baseline simulation, we use WIOD data for 2005 to calibrate initial trade patterns. We group countries into three broad categories, based on their income per capita, robot density and data availability. The group of countries in the North is composed of Belgium, Germany, Denmark, Finland, France, Italy, Netherlands, Sweden and the United States. The group of countries in the South is composed of Brazil, China, India, Indonesia, Mexico, Turkey and Taiwan, China. Based on these two groupings, we construct the representative countries in the North and South. The group of other developed countries results from the aggregation of other OECD and EU countries for which data are available in WIOD. This group consists of Australia, Austria, Bulgaria, Canada, Czech Republic, Spain, the United Kingdom, Greece, Croatia, Hungary, Ireland, Portugal, the Slovak Republic, Poland, Norway and Switzerland.<sup>14</sup> As detailed in Appendix B3, we have performed robustness checks using a variety of alternative groupings, and found that the results are qualitatively robust.

In the model, workers are perfectly mobile across sectors. Wages are therefore similar across sectors within countries, but differ across countries due to productivity differences. We assume that initial wages are proportional to the average GDP per capita of each group in thousands of U.S. dollars. We set initial wages to 39.5 in the North, 26.7 in Other, and 5.7 in the South. According to our calculations, the average ratio of replaceable tasks in the robotized tradeable sector defined above is approximately 50%, thus we set _K_<sup>_i_</sup> = 0 _._ 5 for this sector and set _K_<sup>_i_</sup> = 0 for the non-robotized sector.<sup>15</sup> We assume that the effective initial robot price (rental rate) is equal to _<u>R</u>_ 59, i.e.<sup>_<u>φi</u>_= 59.ThisparameterizationensuresthatinitialrobotizationintheNorthisequal</sup> _φ_<sup>_i_</sup> _L_<sup>_wR_</sup> to that observed in the data, whereas robotization in Other and South is negligible.<sup>16</sup> We then examine the effects of exogenous reductions in the effective rental rate of robots, from the initial level to 5. This decline in the effective rental rate of robots can be caused by a decline in nominal robot prices and/or an increase in the productivity of robots. We assume that the first and second stage have similar production processes. We calibrate the labor share using information

> 14We excluded small countries (with population up to 3 million) and the Russian Federation (which is neither a member of the OECD nor the EU). We also excluded the Republic of Korea and Japan, which have high robot stocks according the IFR, but were not included in the empirical analysis due to lack of data on labor hours by industry.

> 15We also experimented with _K i_ = 0 _._ 8 for the robotized sector and the qualitative implications were unchanged.

> 16We assume that the price of one robot is equal to $125,000, that robots can work 365 days per year and 24 hours per day, and can be rented for 10% of their price for a year. Then the implied cost of robots relative to the cost of tasks in robotized sector is equal to 1.3% both in the IFR data and in the calibrated model.

21