Combining labor with capital stock, which is dependent on a country’s technology shifters (A), generates domestic value added. The technology shifter is assumed to be a function of international trade and innovation, which is consistent with the trade literature.

_FVADP_ is a measure of final goods trade to separate a potential positive GVC effect from a simple positive effect of trade openness. For this, we calculate the amount of foreign value added processed or consumed domestically. This covers both imports of final goods and intermediate goods assembled and consumed domestically. As especially the latter part might overlap with GVC trade, we might have a downward bias in our estimates. However, not controlling for openness would prevent us from separating the effects of GVC trade and final goods trade.

Finally, we employ a set of sector-country ( αs,c ), country-year ( αc,t ), and sector-year ( αs,t ) fixed effects to account for potentially correlated omitted variables and take the natural logarithm of all level variables. Note that while our technology shifter does not explicitly take into account innovation (for example, R&D intensity, though, unavailable at the sectoral level for our country sample), we can assume that including three two-dimensional fixed effects picks up most of the differences in innovation.

As we are primarily interested in the contribution of country-specific policy variables to economic upgrading through GVCs, we include an interaction term between a set of national characteristics and our GVC indicators to equation (1): dvas,c,t = α+ β1xs,c,t + β2zk + β3 xs,c,t ∗zc + β<sup>′</sup> Cs,c,t + αs,c + αs,t + αc,t + εs,c,t. (2) As measures for the national characteristics, zc, we employ variables capturing a country’s infrastructure, connectivity, investment and trade policy, business climate and institutions, financial and labor markets, skills and education, innovation and product standards, as well as labor, social, and environmental standards. Due to sometimes incomplete data and to minimize reverse causality, we use the average over the sample period.

We repeat this exercise by splitting the sample into three categories based on the World Bank’s income classification. The categories are low- and lower-middle-, upper-middle-, and high-income countries. As robustness, we also split the countries into three evenly sized groups based on GDP per capita to avoid sample size issues. For a country application of the empirical model, see appendix 1.

It is important to emphasize that our empirical setting does not generate causal estimates on the role of domestic policies for economic upgrading through GVC integration, but rather provides evidence to guide future more directed work. A main advantage of this study is its comprehensive nature covering an extensive part of policy space to detect which policies should be analyzed more closely and which policies might be of second order. However, we do not claim to expose a causal relationship.

# **3.2. Data**

# **_GVC Indicators_**

To calculate our GVC indicators we rely on the OECD ICIO database, which covers 61 countries, 34 industries, and the years 1995, 2000, 2005, and 2008–11. The advantage of the database compared to other ICIOs is the extended country coverage, especially regarding low- and middle-income countries. This is especially important since it allows us to test implicitly if the role of certain policies depends on a country’s stage of development. Since the average per capita income level in the OECD ICIO database is significantly below the level of, for instance, the World Input Output Database (WIOD)—a new ICIO database—differences in significance of certain variables can inform us if a policy is more relevant for either developing or industrialized countries. As robustness, we will use WIOD since it also records 15