- Alessandro Prati, Luca Ricci, Lone Engbo Christiansen, Stephen Tokarick, and Thierry Tressel
- Published Date:
- March 2011
The database includes data since 1980 for all countries with a population larger than 1 million. A few countries were dropped because of a substantial lack of data or very poor data quality. The database includes 134 countries, of which 31 are high-income countries; 26 are upper-middle-income countries; 36 are lower-middle-income countries; and 41 are low-income countries (based on World Bank Development Indicators, 2008). The low-income country (LIC) sample comprises the last two groups and excludes emerging markets to make the sample as homogeneous as possible (Table A1).1 The high-income country (HIC) sample—mainly used as a comparator group—includes the first two groups and the six countries excluded from the LIC group. The regression sample is often smaller because of the unbalanced nature of the data set. To construct variables relative to trading partners, the analysis uses the weights used by the IMF Information Notice System (IMF-INS) to calculate real effective exchange rates.
The variables used in the regressions are explained below, with a summary of statistics in Table A2.
Real Effective Exchange Rate
The natural logarithm of the consumer price index–based real effective exchange rate was taken from the IMF-INS.
The database also includes a measure of the real exchange rate computed on the basis of the Penn World Table variable P, which captures price levels relative to the United States. The natural logarithm of this variable was taken, and then computed in deviations from trading partners, using the same weights as used in the IMF-INS measure.
|Albania||1, 3||Macedonia, FYR|
|Algeria||3||Madagascar||1, 2, 3|
|Azerbaijan||1, 2, 3||Malawi|
|Bangladesh||1, 2, 3||Mali|
|Belarus||2, 3||Morocco||1, 2, 3|
|Benin||Mozambique||1, 2, 3|
|Bolivia||1, 2, 3||Namibia|
|Burkina Faso||1, 2, 3||Nepal||3|
|Cameroon||1, 2, 3||Niger|
|Chad||Nigeria||1, 2, 3|
|Congo, Dem. Rep. of||Papua New Guinea|
|Congo, Republic of||Paraguay||1, 3|
|Côte d’Ivoire||1, 2, 3||Peru||1, 2, 3|
|Dominican Republic||1, 2, 3||Philippines||1, 2, 3|
|Ecuador||1, 2, 3||Rwanda|
|El Salvador||1, 2, 3||Sierra Leone||2|
|Ethiopia||1, 2, 3||Sri Lanka||1, 2, 3|
|Georgia||3||Syrian Arab Republic|
|Ghana||2, 3||Tanzania||1, 2, 3|
|Guatemala||1, 2, 3||Togo|
|Guinea||Tunisia||1, 2, 3|
|Honduras||2||Uganda||1, 2, 3|
|Jamaica||1, 2, 3||Ukraine||2, 3|
|Jordan||1, 2, 3||Uzbekistan||1|
|Kenya||1, 2, 3||Vietnam||3|
|Kyrgyz Rep.||2, 3||Zambia||2|
|Lao People’s Dem. Rep.|
Black Market Premium
The black market premium was computed as the log difference between the black market nominal exchange rate and the official nominal exchange rate, both in local currency per U.S. dollar. The data are annual averages of the monthly data from Reinhart and Rogoff (2004).
|Current account to GDP||-0.0544||0.0785||-0.4721||0.2478|
|Fiscal balance to GDP1||-0.0123||0.0552||-0.4710||0.3733|
|Net foreign assets to GDP||-0.7162||0.7291||-10.1421||0.4170|
|Oil trade balance to GDP||-0.0063||0.0915||-0.3002||0.6143|
|Income per capita (relative to United States)||0.0682||0.0513||0.0076||0.2671|
|Per capita real GDP growth1||-0.0104||0.0509||-0.3038||0.2800|
|Aid flows to GDP1||0.0874||0.0984||-0.0369||0.8246|
|Net grants to GDP||0.0623||0.0733||0.0008||0.7781|
|Terms of trade, goods and services||4.6358||0.2763||3.7473||5.9550|
|Capital account liberalization (other)||0.4414||0.3366||0.0000||1.0000|
|Capital account liberalization||0.5082||0.2777||0.0000||1.0000|
|Log of REER (IMF-INS)||4.6565||0.3428||2.6794||5.8910|
|Log of REER (PWT)||-0.7972||0.4644||-2.2663||1.5944|
|NFA (w/NPV debt) to trade||-2.5941||3.5943||-24.5817||1.0167|
|Government consumption to GDP1||-0.0378||0.0568||-0.1454||0.3731|
|Terms of trade, goods (log)||4.6677||0.3125||3.2149||6.1624|
|GDP per worker, PWT (log)||8.4248||0.8345||6.6902||10.0493|
|Administered agricultural prices1||0.0223||0.3242||-0.3087||0.9738|
|Maximum agricultural price intervention1||0.1833||0.4988||-0.5313||0.9372|
|Violent political transition||0.0680||0.2518||0.0000||1.0000|
|Public debt to trade||3.2464||3.3911||0.0080||28.7196|
|Relative productivity (log)1||-1.9131||0.6679||-3.5656||0.0631|
|Domestic financial liberalization1||-0.2942||0.1710||-0.6908||0.1458|
|Constraint on executive||3.6352||1.9312||1.0000||7.0000|
|NPV of external debt to trade||2.2657||2.4506||0.0323||21.4463|
Total fertility from the United Nations population data is defined as the average number of children a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality (United Nations, 2006). It is expressed as children per woman.
Infant Mortality Ratio
Infant mortality rate from United Nations (2006) is defined as infant deaths per 1,000 live births.
Old-Age Dependency Ratio
The old-age dependency ratio captures the share of people older than 64, relative to the working-age population, defined as the 15–64-year-old age group. The data were based on UN data, annualized by the World Bank. The variable was computed in deviations from trading partners.
Population growth data were computed from World Bank data, extended with UN projections.
The current-account-to-GDP ratio is based on the IMF’s World Economic Outlook (WEO) data (IMF, 2009).
Net Foreign Assets
Net foreign assets (NFA), lagged one year, was computed relative to average trade for the real exchange rate regressions approach and relative to GDP for the current account regression (net foreign assets regressions encompass various versions of this indicator, as discussed in the text). Data on NFA in nominal terms were provided by Gian Maria Milesi-Ferretti. Trade data are from the WEO or from the IMF’s International Financial Statistics (IFS) spliced with WEO data. For the few countries for which NFA data were unavailable, the cumulative current account was used.
Data on NFA in net present value terms were computed by adding back into NFA the measure of total debt, and then subtracting time series data on the present value of debt from the World Bank. Because the World Bank measure of the present value of debt is based only on long-term public and publicly guaranteed debt as well as use of IMF credit, the analysis also subtracted short- and long-term private nonguaranteed debt (which is not likely to be subject to any substantial element of concessionality). For the few countries without data for the present value of debt, the nominal value was used.
Aid Flows to GDP
This measure of foreign aid was computed by Roodman (2006). It was based on official development assistance data from the Development Assistance Committee of the Organization for Economic Cooperation and Development (OECD) and was computed as total net aid minus debt forgiveness plus offsetting entries for debt relief. The data are in millions of U.S. dollars and are computed relative to WEO nominal GDP in millions of U.S. dollars. The estimations included concessional loans and net grants. The loans were constructed as foreign aid minus the net grants, and net grants were constructed as total grants minus debt forgiveness grants.
Oil Trade Balance
The oil-trade-balance-to-GDP ratio was from the WEO.
The general government balance relative to GDP was obtained using the central government balance for some countries for which the general balance was not available. The data were from the WEO.
Government consumption relative to GDP was from the OECD, spliced with data from the IFS and the WEO.
GDP per Capita Growth
Growth rate of GDP per capita is from the WEO.
The baseline measure of productivity was measured as real purchasing-power-parity GDP per capita from the Penn World Table, extended with data from World Development Indicators (World Bank, 2008).
GDP per Worker
Real GDP per worker is real GDP divided by the number of workers, from the Penn World Table.
Relative Income per Capita
Relative income per capita is purchasing power parity income per capita, relative to the United States. The data were from the World Bank.
Income groups were aggregated on the basis of World Bank income group classifications.
Natural disaster data were from the International Disaster Database of the Centre for Research on the Epidemiology of Disasters, and include binary dummy variables (1 for event, zero for no event) for drought, earthquake, flood, and windstorm. The binary dummy indicator used in the regression analysis is equal to 1 if any of these four indicators is equal to 1, and is equal to zero otherwise.
War dummies from the Uppsala Conflict Data Program/International Peace Research Institute Armed Conflict Dataset, Version 4-2006 (Gleditsch and others, 2002; Harbom, Högbladh, and Wallensteen, 2006), are based on four types of conflicts: (1) extrasystemic armed conflict (between a state and a nonstate group outside its own territory)—these conflicts are by definition territorial because the government side is fighting to retain control of a territory outside the state system); (2) interstate armed conflict (between two or more states); (3) internal armed conflict (between the government of a state and one or more internal opposition groups); and (4) internationalized internal armed conflict (between the government of a state and one or more internal opposition group(s), with intervention from other states on one or both sides). Each of the four types of conflicts was coded as zero: no conflict; 1: minor; 2: intermediate; and 3: war (at least 1,000 deaths per year).
Terms of Trade, Goods
The natural logarithm of terms of trade of goods is from the WEO.
Terms of Trade, Goods and Services
The natural logarithm of terms of trade of goods and services is from the WEO.
Commodity-Based Terms of Trade
The analysis also uses for robustness the ratio of a weighted average price of the main commodity exports to a weighted average price of the main commodity imports. The index for six commodity goods is constructed as in Ricci, Milesi-Ferretti, and Lee (2008). The index for 32 commodities encompasses aluminum, bananas, beef, cocoa, coconut oil, coffee, copper, corn, cotton, crude oil, fishmeal, gold, hard log, hides, iron, lamb, lead, nickel, palm oil, rice, rubber, shrimp, soybean meal, soybean oil, soybeans, sugar, sunflower oil, tea, tin, wheat, wool, and zinc.
Capital Account Liberalization
The capital account liberalization measure from the IMF Research Department’s structural reform database (IMF, 2009) is an index, normalized between zero and 1. It is computed by Dennis Quinn, as an update of Quinn (1997), and measures restrictions on capital account transactions.
An alternative measure of capital account liberalization is taken from Abiad, Detragiache, and Tressel (2008).
Domestic Financial Reforms
The domestic financial reform measure is an index, coded between zero and 1. It is from the IMF Research Department’s structural reform database.
“Administered agricultural prices” is a 0,1 dummy reflecting the strong presence of price controls in the agricultural sector (from the agricultural product market index of the IMF Research Department’s structural reform database).
“Maximum agricultural price intervention” is a 0,1 dummy reflecting the presence of marketing boards setting the prices (from the agricultural product market index of the IMF Research Department’s structural reform database).
“Administered prices” is a measure of price controls based on the share of administered prices in the consumer price index from the European Bank for Reconstruction and Development, available for transition economies only.
Data on trade restrictions are from the IMF Research Department’s structural reform database. The series is based on data for average tariffs and is normalized between zero and 1 on the basis of smallest and largest observation in the sample.
Constraint on the Executive
Constraint on the executive variable (xconst), where negative values have been replaced by a zero, is from the Polity IV project of the Center for Systemic Peace.
Violent Political Transition
This is a dummy variable equal to 1 whenever constraint on the executive is negative, and equal to zero otherwise.
Other controls include a change in the ratio of private credit to GDP, where private credit is defined as credit by domestic depository institutions to the nonbank private sector (see Beck, Demirgüç-Kunt, and Levine, 2000).
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