potential endogeneity of the value of exports and use a measure of global demand for each industrial product, excluding Indonesian products, as an instrument.

We combine four sources of data. First, we use the Indonesian National Labor Force Survey (Sakernas survey) from 2005 to 2015, which collects main labor indicators covering the 33 provinces of the country. With this information, we generate aggregate labor market indicators (labor force participation, employment and unemployment rates) for each province and year, and we construct the occupational structure of employment within each province, industry and year that we use as an input to construct the skills measures. We separate the analysis in two subperiods, 2005-2010 and 2011-2015, to account for the change in the weighting scheme introduced in the Sakernas surveys in 2011. These sub-periods differed greatly: 2005-2010 was characterized by a boom in commodity prices and a large increase in mineral exports, while 2011-2015 exhibited a decline in exports that was larger for mining and mineral products.

Second, we use the 2015-2016 STEP Survey from the Philippines to define five skills measures at the occupation level, following previous work such as Autor et al. (2003) and Acemoglu and Autor (2011). We focus on the Philippines due to the lack of direct information on the skill content of occupations in Indonesia and because the Philippines is a country from the East Asia region with a similar development level. We also construct skills measures using information from the 2004 U.S. O*Net database that we present as a robustness exercise. The skills measures include nonroutine analytical, non-routine interpersonal, non-routine manual, routine manual, and routine cognitive skills.

Data on the Indonesian value of goods exports (trade variable) and the global demand for foreign products excluding Indonesian products (instrumental variable) for 2005 to 2015 come from the UN Comtrade database. Finally, information on computer use at the household level (technology variable) from 2005 to 2015 comes from the Indonesian National Socio-Economic Survey (Susenas).

We formulate the following hypotheses on the relationship between the demand for skills, aggregate labor market indicators, technology adoption at the province level, and exports at the industry level. We expect that changes in a province’s level of technological development will be associated with changes in the structure of employment by skills. Previous literature for developed countries has found that increased use of computers increases the demand for analytical and interpersonal skills and reduces the demand for routine skills (Autor et al., 2003; Autor and Dorn, 2013; Goos et al., 2014). In our analysis we use the share of households with a computer in each province as a proxy for the level of technological development available to firms in that province. This proxy may be signaling the use of other, more advanced technologies, which may in turn lead to different adjustment patterns in the structure of employment by skills (Almeida et al., 2019). In terms of employment indicators, the impact of technology on labor force participation, employment and unemployment rates will depend on the size of the complementarity and substitution effects of technology.

On the other hand, in our econometric analysis we expect an increase in the foreign demand for local products to increase the output level and the demand for all or certain types of skills and to increase employment. This is especially relevant when considering the two sub-periods we use in 3