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Appendix I: List of Countries in the Sample

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Note: LICs: IMF PRGT-eligible countries. Advanced and Emerging: IMF World Economic Outlook classification Countries with a star are included in the OLS and Quantile augmented regressions (Table 3 and 4)

Appendix II: Data Description

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1

We would like to thank Kalpana Kochhar, Hugh Bredenkamp, Prakash Loungani, Adrian Peralta-Alva, Bruce Quillin and other colleagues in the IMF’s Middle East and Central Asia Department for their helpful comments and suggestions. This working paper is part of a research project on macroeconomic policy in low-income countries and was partially supported by the United Kingdom’s Department for International Development (DFID).

2

By focusing on manufacturing, we only examine a subset of the share of value added in the industrial sector. Mining, construction and utilities are not considered. Services include wholesale and retail trade; hotels and restaurants; transport; telecommunications; financial and insurance services; other business services; and community, social, and personal services.

3

Compared with Bah (2011), this paper uses a larger sample of countries and a more recent time period. Bah uses a sample of 9 developed and 38 developing countries for the period 1965-2000 for the developing country sample, and unequally-spaced intervals (20 year from 1870 to 1950 and 5 years between 1955 and 2000) for the period 1870-2000 for the developed country sample. Another difference is that this paper uses real value added as the dependent variable, whereas Bah uses nominal GDP shares.

4

More precisely, the figures plot the corrected shares (actual – (country fixed effects – average fixed effect)) against the fitted values from the regression.

5

The World Bank classifies countries into low-income, lower middle-income, higher middle-income, and high-income, based on the countries’ gross national income (GNI) per capita in current prices since 1989. The World Bank updates the original thresholds by adjusting them for international inflation, the average inflation of the Euro Zone, Japan, the UK, and the United States. By adjusting for inflation, the thresholds remain constant in real terms over time. In this paper, the World Bank income thresholds were extended back to 1970 using GNI per capita data from the World Indicators.

6

See Koenker and Hallock (2001) for an introduction to quantile regressions.

7

In particular, OLS estimation assumes that the conditional distribution is homogenous, implying that the estimates of the relationship between the dependent variable and the independent variables are the same at all points of the conditional distribution. No assumptions about the homogeneity of the conditional distribution are needed in quantile regression.

8

The UN Statistics Division provides continuous coverage on real and nominal value added by economic activity for a large number of countries between 1970 and 2010 and makes an explicit effort to harmonize the national accounts data so as to ensure comparability across different countries.

9

Given the paucity of data on capital stocks for a large number of countries, this variable is not included in the baseline regressions but is considered in the robustness section.

10

The agricultural reform index measures the extent of public intervention in the market of each country’s main agricultural export commodity. It includes the presence of export marketing boards and the incidence of administered prices. The networks index captures the degree of liberalization in the telecommunication and electricity markets, including the extent of competition in the provision of these services and the existence of an independent regulator.

11

Using a generalized linear model, and imposing that the share be between 0 and 100, yields similar results.

12

Note that the sample size is smaller than in the baseline regressions given the paucity of data on reform variables for some countries.

13

The sample size now drops from 93 to 71 countries as not all indicators are available for every country.

Benchmarking Structural Transformation Across the World
Author: Ms. Era Dabla-Norris, Mr. Alun H. Thomas, Mr. Rodrigo Garcia-Verdu, and Ms. Yingyuan Chen