The connection between Indonesia and other countries through GVCs may affect the exclusion restriction of our instrumental variable. For instance, global demand for Chinese products can impact Indonesian skills demand through the inputs Indonesia provides to China. We check for this possibility by excluding from the data set the mining and chemical industries, which are strongly involved in GVCs by downstream links –intermediate products Indonesia provides for other countries’ exports (OECD, 2013).

We present the main results for the 2005-2010 sub-period in Tables 26 and 27 for the skills demand model and aggregate labor market indicators models respectively.<sup>7</sup> Results confirm previous findings.

Results for the 2011-2015 sub-period show some differences with respect to previous findings (Tables 28 and 29). We cannot confirm the reduction in the demand for analytical and interpersonal skills and the increase of non-routine manual skills associated with the technology variable. Our previous explanation for this finding was that our proxy for the technological development available for firms in each provincial labor market was capturing a more advanced type of technology and not just the adoption of computers during 2011-2015. The lack of this result when excluding industries more strongly involved in GVCs would indicate that the adoption of more advanced types of technologies took place in these industries. Also contrary to earlier results, we do not find that an increase in employment is associated with an increase in exports and we do find that an increase in technology adoption is associated with a reduction in labor force participation and employment. We argue that industries involved in GVCs explained the previous increase in employment linked to exports and the lack of effect of the technology measure.

# _8.2 Changing the Source of Data for the Skills Measure_

Results on the skills measures presented in the previous section were based on the Philippines STEP Survey. In order to check the sensitivity of our results to the use of a different data source, we follow previous literature in defining skills measures using the U.S. O*Net database. We rely on 2004 O*Net data, the year before our period of analysis, to ensure that the skills measures are not affected by technological advances taking place after that year.

There are differences and similarities between the results using the U.S. O*Net database and our previous analysis, depending on the sub-period. For 2005-2010, the relationships between the technology measure and the demand for different types of skills are similar to the results using the STEP Survey (Table 30). However, we do not confirm the previous impacts of the trade measure and the interaction term on skills composition. By contrast, for the 2011-2015 sub-period the relationship between the trade measure, as well as its interaction with technology, and skills using the U.S. O*Net database are in line with previous estimates. However, we cannot confirm the previous results for the technology variable.

We believe the different results obtained when using the Philippines STEP Survey and U.S. O*Net database can be explained by the different levels and rankings of the skills used in each occupation

> 7 First-stage results for all robustness checks show significant correlations between the instruments and the value of Indonesian exports and its interaction with the technology variables. Tables are available upon request.

18