This paper applies machine learning to recreate to a high degree of accuracy the OECD's Services Trade Restrictiveness Index (STRI) to provide quantitative evidence on the restrictiveness of services policies in 2016 for a sample of developing countries, using regulatory data collected by the World Bank and WTO. Resulting estimates are used to extend the OECD STRI approach to 23 additional countries, producing what we term a Services Policy Index (SPI). Converting the SPI to ad valorem equivalent terms shows that services policies are typically much more restrictive than tariffs on imports of goods, in particular in professional services and telecommunications. The SPI has strong explanatory power for bilateral trade in services at the sectoral level, as well as for aggregate goods and services trade.
