use of both routine and non-routine manual tasks, shifting the composition of employment towards lower-skilled workers.

Third, regarding the interplay between technology and trade, Poole et al. (2017) for Vietnam show that the increase in the use of non-routine manual and routine tasks as a result of an increase in exports is smaller in areas where technology access is larger, providing evidence that technological progress is associated with a reduction in the demand for routine tasks. Artuç et al. (2018) find that Mexican exposure to U.S. automation reduces exports from Mexico to the United States, reduces manufacturing employment in areas where the initial share of jobs susceptible to automation in the United States was larger, and increases unemployment and informal employment. All these impacts are larger for unskilled than for skilled workers and for male than for female workers.

# **3. Data Sources and Definitions of Variables**

In this paper we combine several data sets covering the period 2005-2015. First, we use information from the August wave of the Indonesian National Labor Force Survey (Sakernas). During this period, the survey was representative at the province level and covered all 33 provinces of the country. Starting in 2011 the Sakernas survey changed the weighting scheme. This change leads us to separate the analysis in two sub-period: 2005-2010 and 2011-2015. Using the Sakernas survey we construct aggregate labor market indicators that we use as outcome variables in our econometric analysis: the labor force participation, employment and unemployment rates for the working age population (15 years old and more) and for different population sub-groups (men and women, and low- and high-educated workers). We also calculate the share of each occupation in total province-industry-year employment that we use as inputs to construct our skills measures (see below). Finally, we use the Sakernas survey to generate variables used as controls in the econometric analysis. These variables include the age composition of the population and the share of urban population in each province and year, and the composition of industrial employment by educational level and by gender in each year of data.

Second, we constructed a variable indicating the skill content of occupational groups. Data on types of skills by occupation are not available for Indonesia. Many studies in a similar situation rely on the U.S. Department of Labor’s O*Net database, which collects occupation-specific attributes for detailed occupations in the United States. However, the United States and Indonesia differ greatly in their levels of development and production technologies, which could affect both the levels and rankings of the skills used in each occupation. Another possibility is to use information from the World Bank’s STEP (Skills Towards Employment and Productivity) survey of working-age people (15-64), which covers Vietnam and the Philippines in East Asia. Unfortunately, the survey mostly covers urban areas, which makes it less relevant for Indonesia where only half of the population is urban. On balance, we decided to use the 2015-2016 STEP survey of the Philippines for our main results and the U.S. O*Net database as a robustness exercise.<sup>3</sup>

> 3 We decided to use the STEP survey of the Philippines instead of Vietnam because for the latter country the survey only covers the main cities.

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