Abstract

AbstractThis 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.

Highlights

  • Services play an important role in economic development

  • This situation began to change in the late 2000s with a World Bank project to collect information on services trade and investment policies and to create services trade restrictiveness indicators (STRIs) that constitute a numerical summary of applied services policies believed to affect trade flows (Borchert et al, 2014)

  • As it is important for empirical analysis to have data availability across all relevant data points, we limit consideration to the countries and sectors we have identified as satisfying that criterion

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Summary

Introduction

Services play an important role in economic development. Because services account for a significant share of total output in even very poor countries, the operation of services sectors matters for overall economic performance. Data on services activities in developing economies has been improving, in part as the result of periodic firm-level surveys that have resulted in large panel datasets (e.g., the World Bank enterprise surveys), comparable information on external service-sector policies of developing countries is very limited, information on policies is often patchy at best, and time series data on relevant policy variables generally are not available on a cross-country, comparable basis This situation began to change in the late 2000s with a World Bank project to collect information on services trade and investment policies and to create services trade restrictiveness indicators (STRIs) that constitute a numerical summary of applied services policies believed to affect trade flows (Borchert et al, 2014).

New Data on Services Policies
Constructing an Index of Services Policies from I-TIP Data
Developing Services Policy Indices with Simple Machine Learning
Validating the SPI with Trade Data
Findings
Conclusion
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