The debt-growth nexus in poor countries: a reassessment

Presbitero, Andrea F. Working Paper The debt-growth nexus in poor countries: a reassessment Economics Discussion Papers, No. 2007-17 Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Presbitero, Andrea F. (2007) : The debt-growth nexus in poor countries: a reassessment, Economics Discussion Papers, No. 2007-17, Kiel Institute for the World Economy (IfW), Kiel This Version is available at: https://hdl.handle.net/10419/17940 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc/2.0/de/deed.en discussion Papers Discussion Paper 2007-17 April 24, 2007 THE DEBT-GROWTH NEXUS IN POOR COUNTRIES: A REASSESSMENT Andrea F. Presbitero Università Politecnica delle Marche Abstract: This paper investigates the relationship between external indebtedness and economic growth, with a particular attention to LICs, for which the theoretical arguments of debt overhang and liquidity constraint have to be reconsidered. The estimation of a growth model, with a panel of 121 developing countries, supports a negative and linear relationship between past values of the NPV of external public debt and current economic growth. This could be due to the “extended debt overhang”, according to which a large indebtedness leads to misallocation of capital and discourage long-term investment and structural reforms. JEL: C33, F34, H63, O11 Keywords: External Debt, HIPC, Debt Relief, Economic Growth Correspondence: Andrea F. Presbitero, Università Politecnica delle Marche – Dipartimento di Economia, Piazza Martelli 8, 60121 Ancona - Italy. E-mail: [email protected]. http://www.economics-ejournal.org/economics/discussionpapers © Author(s) 2007. This work is licensed under a Creative Commons License - Attribution-NonCommercial 2.0 Germany 1. Introduction Debt relief is nowadays one of the critical issues on the policy agenda of governments and international institutions. At the G8 summit at Gleneagles and at the following meetings donors and the international community agreed to further debt cancellation to the Highly Indebted Poor Countries (HIPC): as a result, in March 2006, the Multilateral Debt Relief Initiative (MDRI) was introduced as a new policy tool to provide additional support and financing to the world’s poorest and most indebted countries. Namely, all countries reaching completion point under the HIPC Initiative will receive up-front and irrevocable cancellation of their external debt owed to the World Bank, the African Development Bank and the IMF. The presence of a large indebtedness has different effects on poor countries, not only related to their macroeconomic performance, but also to political and institutional aspects. Besides, the HIPC Initiative and the MDRI deals with the critical issue of debt sustainability, given that one of their targets is avoiding the build up of a new stock of external debt. Nevertheless, this paper focuses exclusively on the economic consequences of high debts in poor countries, providing a re-examination of the channels through which external debt impinges on investment and on economic growth, building on a stream of literature that aims to assess the relationship between external debt and GDP growth. According to the debt overhang effect (Krugman, 1988 and Sachs, 1989) a large debt burden squeezes investments, because returns are “taxed away” by foreign creditors. This theoretical argument was developed in response to the Latin American crisis of the 1980s, which affected Middle Income countries and debts contracted mainly with private creditors. However, the current debt crisis involves Low Income countries, mainly located in Sub-Saharan African, without market access and highly dependant on concessional external lending. Notwithstanding bilateral and multilateral debt relief, they keep on receiving large inflows of external credit at high concessional terms by multilateral institutions. Hence, the lack of sudden stops in external assistance and the continuous process of debt rescheduling and restructuring is expected to reduce the disincentive effect of external public debt. The current situation seems to adapt better to an extensive interpretation of debt overhang, which implies a disincentive on investments in human capital and new technologies, and the government’s willingness to adopt structural reforms and fiscal adjustments, leading to a poverty trap (Sachs, 2002). Besides, the uncertainty associated with the level of external public debt (i.e. risk of default, rescheduling and arrears) increases the volatility of future inflows, leading to a situation in which investors are likely to exercise the “waiting” option (Serven, 1996). Thus, an unstable macroeconomic environment (i.e. high and volatile inflation and interest rates) is likely to generate a misallocation of resources, maybe due to short-termism, which reduces the efficiency and productivity of capital, leading to a slowdown of economic growth. 2 Some earlier papers1 suggest that the debt-growth relationship follows a bell-shaped curve, since, beyond a certain debt ratio2, the impact of the stock of external debt on growth becomes negative. Two recent papers, Cordella et al. (2005) and Imbs and Ranciere (2005), move from the previous literature and extend the analysis: the former argue that the relation is a “modified Debt-Laffer curve” because, over a certain threshold, the debt effect on growth is nil, creating a sort of debt irrelevance zone; the latter use a non parametric technique to support the bell shaped curve, arguing that better institutions reduce the magnitude of the debt overhang. Also debt flows could affect economic performance, if a reduction in current debt service increases the current level of investments, for any given level of future indebtedness (liquidity constraint effect). However, empirical findings on the effectiveness the crowding out of investment are debatable3. In sum, the empirical evidence on the debt-growth nexus is unclear, since econometric results lack of robustness (Moss and Chiang, 2003). This field of research has to deal with the issues of omitted variables and causality, since it is not clear and necessary that high debt causes low growth. It could or the other way round, or debt and growth could be both determined by policies and institutions. We try to address the problem of causation taking past, instead of current, values of the debt ratio as explanatory variables and we include an institutional indicator to verify if debt has a direct effect on growth4. The first contribution of this work is that we do not find any evidence of an inverse U-shaped curve representing the debt-growth relation. External public debt in the previous period is negatively associated with current economic growth, even controlling for policies and institutions. A further step aims to disentangle the negative debt effect in Low and Middle Income countries, on the ground that debt overhang could be reduced or avoided in LICs thanks to the continuous external borrowing. Our results are not conclusive, but they suggest the possibility that the negative effect of debt on growth is lower in the poorest countries. The second contribution of the paper concerns the discussion on the channels through which external debt affects economic growth: the estimation of a total investment and a public investment equations does not find any relationship between external debt and investment rate. A lower GDP growth is not due to lower capital accumulation, but to any factors responsible for total factor productivity growth (Pattillo et al. 2004). A possible interpretation could rely on the “extended debt overhang”, according to which the uncertainty and the instability created by a large external debt cause less efficient and short term investment and the lack of structural reforms. We also find that debt service 1 See: Elbadawi et al. (1997), Pattillo et al. (2002 and 2004), Clements et al. (2003). 2 The main measures of external indebtedness are the ratios of external debt over GDP and over exports. 3 Pattillo et al. (2002, 2004) do not support the liquidity constraint, while Chowdhury (2004), Clements et al. (2003), Elbadawi et al., (1997) and Hansen (2004) find that both debt burden and debt service obligations squeeze investment and economic performance. Cohen (1993), instead, rejects the debt overhang hypothesis and supports the crowding out effect. 4 If the inclusion of an institutional variable does not affect the significance of the debt ratio, we can be more confident on the authenticity of the relationship between debt and growth. 3 obligations crowd out total (and not public) investment, only in Low Income countries. Thus, we could guess that debt service soaks up resources and reduces the credit from the banking system to private sector. Eventually, the paper underlines the great relevance of macroeconomic management and market oriented policies to trigger economic growth. Therefore, in order to reap of the benefit from a reduction in external debt, it is necessary that governments have the incentives to keep on pursuing structural adjustments and reforms. On the contrary, without conditionality, moral hazard issues could prevent these improvements and hinder economic growth. The remainder of the paper is as follows: next section presents the dataset, its sources and its descriptive statistics in Low and Middle Income countries. Section 3 deals with the growth model and discusses some methodological issues (sub-section 3.1) and the results. Section 4 is about the investment models, while the last section wraps up, draws the main policy recommendations and presents some open questions. Summary Tables and the list of variables are presented in the Annexes. 2. Institutional Indicators and Descriptive Statistics The dataset covers 121 developing countries over the period 1980-2004. The main sources are the World Development Indicators (WDI) and the Global Development Finance 2005 of the World Bank. Other data comes from the World Economic Outlook (IMF) and from a number of IMF Country Report Staff Papers. The historical series on the Net Present Value of Public and PublicGuaranteed (PPG) external debt is an internal dataset of The World Bank constructed by Yuri Dikhanov (2004). The educational indicators – the gross primary and secondary enrolment rates – are constructed updating the Barro-Lee dataset5 with data from the WDI 2005. To take into account the institutional aspect we use the Country Policy and Institutional Assessments (CPIA) score, which is a confidential indicator of the quality of policies and institutions developed by the World Bank6. The CPIA assesses the quality of a country’s present policy and institutional framework. Their ratings, ranging from 1 (low) to 6 (high), are based on all key factors that foster pro-poor growth and poverty alleviation (Economic Management, Structural Policies, Policies for Social Inclusion/Equity, Public Sector Management and Institutions). The broad coverage – the CPIA index is available for 136 countries – and the long time horizon (1977-2004) makes this indicator very useful for this panel analysis, since it overcome the usually lack of historical data for institutional indicators. 5 http://www.cid.harvard.edu/ciddata/ciddata.html (last accessed: February, 2007). 6 The datasets on the NPV of PPG external debt and on the CPIA ratings were given to the author when he was an intern at the PRMED (Economic Policy and Debt Department) at The World Bank. The author thanks L. Bandiera and V. Nehru for the provision of the data. 4 To wash out any business cycle variation, we take 5 year average of the data, ending with 5 observations in time7. Eventually, the plot of the data helped to highlight some outliers, generally related to the first observations in the former communist countries. The sample includes both Low and Middle-Income countries, so that we end up with an heterogeneous sample of countries, which could to be affected in different ways by debt dynamics. The summary statistics of the main variables, (Table 1A, in Annex A), highlight large differences between sub-samples. Middle Income countries are characterized by larger investment (INV) and revenues (REV), higher education (PEDUC and SEDUC), and stronger economic growth (GROWTH). The quality of policies and institutions (CPIA) is better in the richest countries of the sample. The level of public investment (PUBINV) is, instead, larger in the poorest countries, even if the difference is small. The macroeconomic structure in HIPCs present the worst scenario, with an average annual growth rate of 2.9%, lower levels of investment, education, and worse institutional quality. The comparison of the debt indicators shows that the external debt to GDP ratio (DGDP) is 55.2 in MICs and 96.6 in LICs at nominal values and 35 and 59 respectively in Present Value terms (NPVDGDP). The NPV of debt to export ratio (NPVDXTS), which is the basic indicator implemented in the HIPC Initiative, is below the threshold of 150 for MICs (123.8), while it is above in LICs (315) and HIPCs (391). Debt service (TDSGDP), instead, is larger in Middle Income than in Low-Income countries (6.2% of GDP versus 4.5%). In the HIPCs, debt service is larger than in the overall sample of LICs, because of the larger stock of external debt, but still below the level reached in MICs, thanks to concessional lending. Nevertheless, since the crowding out effect concerns the budget constraint, what really matters is the share of revenues designed to repay debt obligations: given their poor revenues, in Low Income countries even a smaller debt service might crowd out investment. The correlation analysis (Tables 2A-3A) underlines that past values of external debt, the variability of inflation and the exchange are significantly associated with lower economic growth, while public and total investment, debt service and education are positively related to GDP growth, even if, for education and debt service, the correlations are smaller and not significant in LICs. We observe a positive and significant correlation between the logarithm of investment (LINV) and debt service, revenues, CPIA, primary education, economic growth and GDP, both in the entire sample and in LICs, while the correlation with external debt ratios is generally not significant. With respect to public investments (PUBINV), they are positively correlated with GDP growth, revenues and the institutional indicator, negatively with the level of GDP, while the correlation with the external debt ratios is not significant. This brief description of the data underlines differences in the macroeconomic environment between Low and Middle Income countries: in order to provide more reliable indications of debt effects in the poorest countries, we 7 For the education variable, instead of taking the five year average, we consider the enrolment rate in the first year of the 5-year period. 5 will control the robustness of our findings estimating the model in different subsamples and allowing for heterogeneity in the debt effects on growth. 3. The Growth Model The growth equation that has to be estimated (1) is: (1) () ∑∑ == −++++−+=Δ k jiti hithhitjjitit ndebtxyy 1 2 1 1 1 εγδβα and it is equivalent to the dynamic panel model (2): (2) ∑∑ == −+++++= k jiti hithhitjjitit ndebtxyy 1 2 1 1 εγδβα where yit is the logarithm of per capita GDP at Purchasing Power Parity of country i at time t (and ∆y is the GDP growth rate calculated as log difference), yit-1 is the log of lagged income, xitj is a set of control variables, debtith are different indicators of the external public debt stocks and flows, ni captures the effects of the country i that are time invariant, and the classical error term εit is referred to the variability across time and countries. We move from the standard growth model and we add debt variables – the logarithm of debt service and the log of the external public debt-to-GDP ratio in the previous period - and the institutional variable, the CPIA index. The other control variables are the log of investments, the log of the primary enrolment rate, the rate of growth of terms of trade, and some financial indicators – the log of the change in the exchange rate and the variability of inflation8. Methodology The dynamic structure of the model9 makes the OLS estimator upwards biased and inconsistent, since the lagged level of income is correlated with the error term. The within transformation does not solve the problem, because of a downward bias (Nickel, 1981) and inconsistency. A possible solution is represented by the Generalized Method of Moments (GMM) technique. Blundell and Bond (1997) show that when β approaches to one, so that the dependent variable follows a path close to a random walk, the differenced-GMM (Arellano and Bond, 1991) has poor finite sample properties, and it is downwards biased, especially when T is small. Bond et al. (2001) argue that this is likely to be a 8 The exchange rate is defined as national currency per US dollar, while the variability of inflation - defined as the standard deviation of inflation in the five-year period - could be thought as a measure of macroeconomic instability. 9 We present the methodological issues referring to the growth model, since they can be easily extended at the investment equation, discussed in section 4. 6 serious issue for autoregressive model, like the growth equation (2), when the per capita GDP is observed in 3 or 5 years averages and T is necessarily small. Therefore, Blundell and Bond (1997) propose another estimator – the SystemGMM (thereafter, BB) – derived from the estimation of a system of two simultaneous equations, one in levels (with lagged first differences as instruments) and the other in first differences (with lagged levels as instruments). In multivariate dynamic panel models, the BB estimator is shown to perform better than the differenced-GMM when series are persistent (β close to unity) and there is a dramatic reduction in the finite sample bias due to the exploitation of additional moment conditions (Blundell et al. 2000). In presence of heteroscedasticity and serial correlation, the two-step System-GMM uses a consistent estimate of the weighting matrix, taking the residuals from the one-step estimate (Davidson and MacKinnon, 2004). Though asymptotically more efficient, the two-step GMM presents estimates of the standard errors that tend to be severely downward biased. However, it is possible to solve this problem using the finite-sample correction to the two-step covariance matrix derived by Windmeijer, which can make two-step robust GMM estimates more efficient than one-step robust ones, especially for system GMM (Roodman, 2003). Bond et al. (2001) provide a useful insight in the GMM estimation of dynamic growth models10, arguing that the pooled OLS and the LSDV estimators should be considered respectively as the upper and lower bound. As a result, whether the differenced GMM coefficient is close to or lower than the within group one, this is likely a sign that the estimates are biased downward (maybe because of a weak instrument problem). Thus, if this is the case, the use of System-GMM is highly recommended, and its estimates should lie between OLS and LSDV. This conclusion is supported by the empirical testing of the augmented Solow model (Hoeffler, 2002 and Nkurunziza and Bates, 2002). Presbitero (2006) estimates a model similar to (2) showing that the System-GMM is a good estimator, at least better than the differenced-GMM, which is severely downward biased. In particular, there is evidence that using results obtained with the System GMM confirm that: • the system-GMM lies between the upper and lower bound represented by OLS and LSDV, • there is a gain in efficiency, and • the instrument set is valid11. Empirical results The results (Table 1) show the presence of conditional convergence and a positive effect of education and investment on economic growth. Terms of trade 10 One of the main problems of using the GMM estimators with macroeconomic and cross country data is that they are generally developed for micro data, in which the spatial dimension is very large, and their properties are valid asymptotically. 11 Whether these three conditions are met, the two-step system-GMM results can be taken as a benchmark for growth regressions (Bond et al., 2001, 2004, Nkurunzita and Bates, 2003, Hoeffler, 2002). 7 have generally a positive impact too, while openness is not significant. The higher the volatility of the inflation rate, the more unstable is the macroeconomic positive impact on growth, since a one point increase in the CPIA score is associated with an increase in GDP growth of around 1.3 percentage points. The estimates support the existence of a negative relation between the past debt values and current growth, while debt service is not significant. We check and validate this relevant results with different debt indicators – face and discounted values and their ratios over GDP and exports12. All the specifications pass the Hansen-J statistic test for Over-Identifying Restrictions (OIR), confirming that the instrument set can be considered valid, the F-test for the overall significance of the regression and the Arellano-Bond tests for serial correlation13, supporting the model specification. The main findings on the debt-growth nexus do not change if we exclude some variables or if we control for secondary education or for the exchange rate14. The estimation of the growth equation without the investment variable shows that the exclusion of investment does not increase substantially the debt effect, since the coefficients on debt are not statistically different comparing columns 1 and 2 in Tables 1 and 2. Therefore, external indebtedness is not a constraint to the level of investment. Lower growth, thus, could not be explained by lower capital accumulation, but by other factors affecting total factor productivity (Pattillo et al., 2004). An extensive interpretation of debt overhang suggests that large debts would imply a misallocation of resources, with agents preferring less efficient investment project, because of uncertainty and shorttermism. Eventually, there is no evidence of a bell shaped relation between debt and growth: the inclusion of the quadratic term in the preferred specification (column 6), in fact, does not change the impact of other variables on economic growth, but makes the debt ratios no more significant. In particular, we are able to show how the presence of the Debt-Laffer curve depends on the exclusion of the institutional control and on the use of current debt ratios (column 3). Nonetheless, the inclusion of the CPIA score (column 4) or the use of past instead of current debt ratios (column 5) makes the Debt-Laffer curve not significant. The estimation of the debt growth nexus in the entire sample might not be truly informative because of the heterogeneity of the countries analyzed. A first strategy to address this problem is the estimation of the model for the two subsamples, allowing all the explanatory variables to have different effects on 12 The last two columns of Table 1 report the estimates obtained using current instead of past values of the debt ratio: the linear negative relationship is still significant and its magnitude is larger. Since column 1 is the preferred specification, thereafter we take the NPV of external public debt-to-GDP ratio as main debt indicator. 13 If the model is well specified we expect to reject the null of not autocorrelation of the first order (AB1), and to not reject the hypothesis of no autocorrelation of the second order (AB2). 14 Secondary enrolment rate is a positive and significant determinant of the growth rate, while the change in the log of the exchange rate has a negative impact on GDP growth. In other words, a devaluation of the exchange rate reduces economic growth, according to a recent contribution by Frankel (2005), who stresses the contractionary effects of devaluation in developing countries, mainly due to balance sheet effects on financial sector. Results are not shown for reason of space and available from the Author on request. 8 Dikhanov, Yuri, 2004, “Historical Present Value of Debt in Developing Economies: 1980-2002”, manuscript, The World Bank. Elbadawi, Ibrahim A., Benno J. Ndulu, and Njuguna Ndung’u, 1999, “Debt Overhang and Economic Growth in Sub-Saharan Africa”, chapter 5 in External Finance for Low-Income Countries, ed. by Iqbal, Zubair, and Ravi Kanbur (Washington D.C: IMF Institute). Frankel, Jeffrey A., 2005, “Mundell-Fleming Lecture: Contractionary Currency Crashes in Developing Countries”, IMF Staff Papers, Vol. 52, No. 2, September 2005. Further information Hansen, Henrik, 2004, “The Impact of External Aid and External Debt on Growth and Investment”. chapter 7 in Debt Relief for Poor Countries, ed. by Addison, Hansen and Tarp. Hoeffler, Anke E., 2002, “The augmented Solow model and the African growth debate”, Oxford Bulletin of Economics and Statistics, Vol. 64, No. 2, pp. 135-158. Further information in IDEAS/RePEc Imbs, Jean, and Romain Ranciere, 2005, “The Overhang Hangover”, World Bank Policy Research Working Paper, No. 3673, August 2005. Further information in IDEAS/RePEc Krugman, Paul, 1988, “Financing vs. Forgiving a Debt Overhang”, Journal of Development Economics, No. 29, pp. 253-268. Further information in IDEAS/RePEc Moss, Todd J., and Hanley S. Chiang, 2003, The Other Costs of High Debt in Poor Countries: Growth, Policy Dynamics, and Institutions”, Issue Paper on Debt Sustainability, Center for Global Development, Washington DC, August 2003. Nickell, Stephen, 1981, “Biases in Dynamic Models with Fixed Effects”. Econometrica, Vol. 49, No. 6, November 1981, pp. 1417 – 1426. Further information in IDEAS/RePEc Nkurunziza, Janvier D., and Robert H. Bates, 2003, “Political Institutions and Economic Growth in Africa”, CSAE Working Paper, No. 2003-03. Further information in IDEAS/RePEc Pattillo, Catherine, Helene Poirson, and Luca Ricci, 2002, “External Debt and Growth”, IMF Working Paper, No. 02/69. Further information in IDEAS/RePEc ----------2004, “What Are the Channels Through Which External Debt Affects Growth?”, IMF Working Paper, No. 04/15. Further information in IDEAS/RePEc Presbitero, Andrea F., 2006, “The Debt-Growth Nexus: a Dynamic Panel Data Estimation”, Rivista Italiana degli Economisti, Vol. 11, No. 3, in press. Further information in IDEAS/RePEc Roodman, David, 2003, “XTABOND2: Stata module to extend xtabond dynamic panel data estimator," Statistical Software Components S435901, Boston College Department of Economics, revised 22 Apr 2005. Further information in IDEAS/RePEc 15 Sachs, Jeffrey D., 1989, “The Debt Overhang of Developing Countries”. In Debt, Stabilization and Development, by Calvo, Guillermo A., Ronald Findlay, Pentti Kouri, and Jorge Braga de Macedo, (Oxford: Basil Blackwell). ----------2002, “Resolving the Debt Crisis of Low-Income Countries”, Brookings Papers on Economic Activity, Vol. 1, pp. 257-286. Further information Serven, Luis, 1996, “Irreversibility, Uncertainty and Private Investment: Analytical Issues and Some Lessons for Africa”, The World Bank, Mimeo, December 1999. Further information in IDEAS/RePEc 16 Table 1: The Growth Model: different debt indicators Dependent variable: GDP growth (1) (2) (3) (4) (5) (6) GDP (-1) -1.68** -1.38** -1.77** -1.50** -2.69** -2.45** (0.41) (0.46) (0.39) (0.53) (0.54) (0.48) NPVDGDP (-1) -0.83** (0.31) NPVDXTS (-1) -0.45* (0.27) DGDP (-1) -0.91** (0.37) DXTS (-1) -0.34 (0.34) DGDP -0.99** (0.51) DXTS -1.10** (0.45) LTDSGDP 0.48 -0.15 0.27 -0.27 2.12** 2.43** (0.53) (0.55) (0.54) (0.57) (0.67) (0.75) LINV 2.67** 2.40** 2.68** 2.46** 4.18** 4.36** (0.85) (0.95) (0.87) (0.88) (1.27) (1.00) PEDUC 3.83** 3.52** 3.68** 3.54** 3.10** 2.40* (1.35) (1.48) (1.37) (1.29) (1.30) (1.40) TOT 0.06** 0.06** 0.05** 0.05* 0.04 0.04 (0.03) (0.03) (0.03) (0.03) (0.04) (0.03) OPEN -0.60 -0.67 -0.73 -0.42 -0.89 -2.44** (0.75) (0.77) (0.68) (0.88) (0.92) (1.04) INFL -0.0004** -0.0005** -0.0005** -0.0005** -0.0003 -0.0002 (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) CPIA 1.23** 1.29** 1.31** 1.38** 0.81** 0.94** (0.30) (0.30) (0.32) (0.30) (0.39) (0.34) CONSTANT -8.15 -7.80 -5.31 -8.52 -1.67 6.38 (6.72) (7.60) (6.91) (7.99) (6.19) (8.40) OIR test (p-value) 0.374 0.321 0.366 0.367 0.499 0.578 AB(1) 0.003 0.003 0.003 0.020 0.002 0.002 AB(2) 0.917 0.998 0.918 0.967 0.859 0.900 No. Obs. 410 405 409 412 427 421 No. Obs. Per group 3.42 3.38 3.41 3.43 3.56 3.51 F-test 14.64 18.77 15.4 17.46 28.29 21.07 Notes: Robust standard errors are in brackets. Two and one star (*) mean, respectively, a 5% and 10% significance level. All variables are five-year average. All regressions include time dummies not shown for the sake of brevity. AR(1) and AR(2) are the Arellano and Bond autocorrelation tests of first and second order (the null is no autocorrelation), the F-test refers to the significance of the regression, and the OIR test is the Hansen test for over-identifying restrictions (the null is the validity of the instrument set). 17 Table 2: The Growth Model: without investment and non-linearities. Dependent variable: GDP growth (1) (2) (3) (4) (5) (6) -1.34** -1.13** -3.32** -2.50** -2.43** -1.71** GDP (-1) (0.58) (0.66) (0.70) (0.54) (0.45) (0.42) -0.83** 0.09 -0.35 NPVDGDP (-1) (0.31) (0.98) (0.91) -0.20 -0.07 [NPVDGDP (-1)]^2 (0.14) (0.13) -0.44** NPVDXTS (-1) (0.27) NPVDGDP 3.08* 2.39 (1.77) (2.22) -0.66** -0.48* [NPVDGDP]^2 (0.24) (0.28) LTDSGDP 0.51 0.22 3.27** 1.89** 1.38** 0.45 (0.69) (0.72) (0.80) (0.61) (0.51) (0.46) LINV 4.31** 3.54** 3.69** 2.48** (0.98) (1.09) (0.87) (0.75) PEDUC 5.30** 4.97 5.24** 4.03** 5.25** 4.10** (1.63) (1.59) (1.74) (1.41) (1.36) (1.37) TOT 0.06** 0.05 0.00 0.03 0.05* 0.05** (0.03) (0.03) (0.04) (0.04) (0.03) (0.02) OPEN -0.06 -0.22 -1.39 -0.95 -0.30 -0.50 (0.90) (0.97) (0.87) (0.85) (0.72) (0.73) INFL -0.0005** -0.0006** -0.0002 -0.0002 -0.0005** -0.0004** (0.0002) (0.0002) (0.0003) (0.0002) (0.0002) (0.0002) CPIA 1.24** 1.32** 0.76* 1.25** (0.32) (0.31) (0.42) (0.31) CONSTANT -11.92 -11.74 -8.51 -10.18 -11.92* -9.68 (8.80) (9.50) (8.00) (6.71) (6.44) (6.77) OIR test (p-value) 0.428 0.372 0.329 0.448 0.430 0.402 AB(1) 0.003 0.003 0.009 0.002 0.004 0.003 AB(2) 0.638 0.749 0.650 0.698 0.981 0.938 No. Obs. 410 405 438 427 417 410 No. Obs. Per group 3.42 3.38 3.65 3.56 3.48 3.42 F-test 10.42 12.2 12.08 14.13 22.11 19.3 Notes: Robust standard errors are in brackets. Two and one star (*) mean, respectively, a 5% and 10% significance level. All variables are five-year average. All regressions include time dummies not shown for the sake of brevity. AR(1) and AR(2) are the Arellano and Bond autocorrelation tests of first and second order (the null is no autocorrelation), the F-test refers to the significance of the regression, and the OIR test is the Hansen test for over-identifying restrictions (the null is the validity of the instrument set). 18 Table 3: Growth Equation, Interaction term and LIC dummy. Dependent variable: GDP growth (1) (2) (3) (4) GDP (-1) -2.82** -2.91** -2.95** -2.77** (1.02) (0.80) (0.83) (0.74) NPVDGDP (-1) -1.00** -0.54 -0.93** -0.86* (0.41) (0.45) (0.39) (0.43) [NPVDGDP (-1)]*LIC -0.05 -0.19 -0.05 -0.02 (0.55) (0.66) (0.49) (0.51) LIC -2.23 -1.52 -2.43 -2.11 (2.65) (2.65) (2.23) (2.12) LTDSGDP 0.34 0.25 0.58 0.71 (0.51) (0.54) (0.51) (0.48) LINV 2.12** 2.88** 2.32** 2.09** (0.80) (0.75) (0.80) (0.84) OPEN -0.96 -1.12 (0.68) (0.75) PEDUC 2.97* 3.48** 4.50** (1.77) (1.55) (1.11) SEDUC 1.51** (0.70) TOT 0.06** 0.05** 0.05** 0.05* (0.02) (0.02) (0.02) (0.03) INFL -0.0003 -0.0003 -0.0003 (0.0003) (0.0002) (0.0002) RER -0.33 (0.22) CPIA 1.18** 1.06** 1.16** 0.95** (0.27) (0.23) (0.28) (0.31) CONSTANT 6.08 11.26 7.73 3.95 (14.70) (7.89) (10.53) (8.73) OIR test (p-value) 0.413 0.223 0.422 0.31 AB(1) 0.003 0.004 0.003 0.003 AB(2) 0.995 0.686 0.964 0.394 No. Obs. 410 406 410 406 No. Obs. Per group 3.42 3.41 3.42 3.41 Test LIC (p-value) 0.233 0.175 0.054 0.188 F-test 12.64 9.82 11.06 11.75 Notes: Robust standard errors are in brackets. Two and one star (*) mean, respectively, a 5% and 10% significance level. All variables are five-year average. All regressions include time dummies not shown for the sake of brevity. AR(1) and AR(2) are the Arellano and Bond autocorrelation tests of first and second order (the null is no autocorrelation), the F-test refers to the significance of the regression, and the OIR test is the Hansen test for over-identifying restrictions (the null is the validity of the instrument set). Test LIC is a t-test for joint hypothesis of the annulment of the coefficients on the LIC dummy and on the interaction term. 19 Table 4: Total Investment Equation Dependent variable: Total Investment All sample LIC MIC All sample LIC MIC INV (-1) 0.49** 0.50** 0.33** 0.50** 0.50** 0.37** (0.08) (0.10) (0.10) (0.07) (0.09) (0.00) NPVDGDP (-1) 0.88** 0.20 0.59 (0.42) (0.54) (0.53) NPVDXTS (-1) 0.39 0.19 0.44 (0.35) (0.52) (0.36) LTDSGDP -0.21 -0.38** 0.09 -0.17 -0.36* 0.10 (0.13) (0.16) (0.14) (0.12) (0.19) (0.11) REV 0.31** 0.32** 0.15* 0.37** 0.29** 0.19** (0.09) (0.12) (0.08) (0.08) (0.14) (0.09) GROWTH 0.36** 0.36** 0.20 0.33** 0.39 0.15 (0.17) (0.15) (0.14) (0.14) (0.26) (0.15) CPIA 0.86* 1.95** 0.46 0.83* 2.22** 0.59 (0.51) (0.74) (0.59) (0.44) (0.95) (0.58) LIC -0.16 0.11 (0.81) (0.69) CONSTANT -1.66 -4.05 6.90* -2.19 -3.59 4.77 (2.59 (2.54) (3.91) (2.89) (4.32) (3.84) OIR test (p-value) 0.120 0.999 0.674 0.491 0.996 0.814 AB(1) 0.005 0.105 0.027 0.007 0.086 0.018 AB(2) 0.276 0.297 0.689 0.245 0.280 0.750 No. Obs. 391 176 215 386 174 212 No. Obs. Per group 3.49 3.74 3.31 3.45 3.7 3.26 F-test 12.03 25.42 3.83 16.31 27.60 7.07 Notes: Robust standard errors are in brackets. Two and one star (*) mean, respectively, a 5% and 10% significance level. All variables are five-year average. All regressions include time dummies not shown for the sake of brevity. AR(1) and AR(2) are the Arellano and Bond autocorrelation tests of first and second order (the null is no autocorrelation), the F-test refers to the significance of the regression, and the OIR test is the Hansen test for over-identifying restrictions (the null is the validity of the instrument set). 20 Table 5: Public Investment Equation Dependent variable: Public Investment All sample LIC MIC PUBINV (-1) 0.42** 0.36** 0.46** (0.09) (0.11) (0.11) NPVDGDP (-1) 0.48 -0.29 0.85** (0.34) (0.66) (0.28) TDSGDP -0.07 -0.20 0.02 (0.07) (0.16) (0.07) PRINV -0.07 -0.18** -0.19** (0.05) (0.09) (0.07) REV 0.26** 0.27** 0.13** (0.07) (0.06) (0.06) GROWTH 0.13** 0.15** 0.13 (0.05) (0.07) (0.10) CPIA 0.79** 1.29* 0.70 (0.32) (0.73) (0.42) LIC 1.88** (0.63) CONSTANT -4.97** -1.62 -2.84 (1.49) (2.26) (2.93) OIR test (p-value) 0.560 1.000 0.988 AB(1) 0.007 0.044 0.032 AB(2) 0.691 0.841 0.640 No. Obs. 360 168 192 No. Obs. Per group 3.36 3.65 3.15 F-test 15.04 10.69 23.45 Notes: Robust standard errors are in brackets. Two and one star (*) mean, respectively, a 5% and 10% significance level. All variables are five-year average. All regressions include time dummies not shown for the sake of brevity. AR(1) and AR(2) are the Arellano and Bond autocorrelation tests of first and second order (the null is no autocorrelation), the F-test refers to the significance of the regression, and the OIR test is the Hansen test for over-identifying restrictions (the null is the validity of the instrument set). 21 22 Annex A: List of Variables Variable Definition Source GDP Logarithm of per capita GDP, measured at Purchasing Power Parity. The World Bank GROWTH GDP growth rate, calculated as log difference. The World Bank NPVDGDP Logarithm of the Net Present Value of PPG external debt-to-GDP ratio. The World Bank, Dikhanov (2004) NPVDXTS Logarithm of the Net Present Value of PPG external debt-to-exports ratio. The World Bank, Dikhanov (2004) DGDP Logarithm of PPG external debt-to-GDP ratio. The World Bank DXTS Logarithm of PPG external debt-to-exports ratio. The World Bank LTDSGDP Logarithm of Total Debt Service-to-GDP ratio. The World Bank TDSGDP Total Debt Service-to-GDP ratio. The World Bank LINV Logarithm of Gross Fixed Capital Formation, as percentage of GDP. The World Bank and IMF INV Gross Fixed Capital Formation, as percentage of GDP. The World Bank and IMF PUBINV Total Gross Public Capital Formation, as percentage of GDP. The World Bank and IMF PRINV Total Gross Private Capital Formation, as percentage of GDP. The World Bank and IMF REV Central Government Total Revenues, including grants, as percentage of GDP. The World Bank and IMF PEDUC Logarithm of Gross Primary Enrolment Rate, in the first year of the 5-year period. The World Bank and Barro-Lee dataset SEDUC Logarithm of Gross Secondary Enrolment Rate, in the first year of the 5-year period. The World Bank and Barro-Lee dataset TOT The growth rate of the Terms of Trade. The World Bank and IMF OPEN Logarithm of openness, defined as Exports plus Imports over GDP. The World Bank INFL Standard Deviation of Inflation (consumer price) over the five-year period. The World Bank and IMF RER Logarithm of the change in the real exchange rate, defined as national currency per US dollar. The World Bank CPIA Country Policy and Institutional Assessments score. The World Bank LIC Dummy for Low Income Countries, according to the GDF classification. The World Bank Notes: All variables, except PEDUC, SEDUC and LIC, are five-year averages Annex B: Tables Table 1A: Summary Statistics GROWTH DXTS DGDP NPVDGDP NPVDXTS TDSGDP INV PUBINV REV PEDUC SEDUC CPIA MIC Mean 4.210465 194.0707 55.19176 35.42635 123.8681 6.182921 22.97128 7.251964 24.96846 103.616 64.09186 3.606392 Median 4.343414 151.3348 44.85499 25.3587 87.85905 5.531419 22.15975 5.925061 23.28295 103.3027 66.72 3.6375 Sd. Dev. 4.095351 248.4029 48.09289 33.75073 178.5734 4.443062 7.166159 4.891902 9.899317 15.43577 24.61565 0.789402 obs. 327 303 308 313 308 308 330 296 317 338 329 295 HIPC Mean 2.936606 650.2239 122.4667 75.40727 390.935 5.216383 17.32015 8.062695 19.32867 76.54697 22.04934 3.097056 Median 3.184185 453.3207 100.5347 58.16979 259.4368 3.995039 16.20101 7.013128 18.47676 74.836 17.5025 3.1865 Sd. Dev. 3.175678 592.7778 88.40223 68.68234 439.436 4.645291 7.180072 5.236216 7.272531 27.35326 17.5202 0.685727 obs. 170 166 168 168 166 168 170 166 165 169 161 169 LIC Mean 3.21908 527.801 96.63205 59.32402 315.3279 4.460259 18.62104 8.124525 19.43138 80.84227 28.55437 3.110183 Median 3.771996 362.944 78.19771 44.11967 195.845 3.591579 17.01445 6.99377 18.09523 78.94 21.44 3.205 Sd. Dev. 3.992074 545.45 75.69175 59.26672 394.1437 3.642276 8.446576 6.243141 8.620467 26.19487 23.7435 0.647655 obs. 248 235 237 237 235 237 248 233 232 247 239 237 Total Mean 3.782876 339.8451 73.21258 45.72407 206.7282 5.4338 21.10474 7.636287 22.62856 94.00044 49.13859 3.385337 Median 4.061909 227.011 57.10055 32.28211 135.2018 4.339733 20.20491 6.642166 21.24626 99.548 46.82333 3.429125 Sd. Dev. 4.077347 437.9443 64.91199 47.93343 306.8339 4.198369 8.029097 5.539364 9.763646 23.52167 29.92494 0.769668 obs. 575 538 545 550 543 545 578 529 549 585 568 532 Notes: The debt ratios (NPVDGDP and NPVDXTS) refer to the original ratios, not to the logarithms. 23 Table 2A: Pairwise correlations, entire sample All sample GROWTH GDP(-1) LTDSGDP NPVDXTS (-1) NPVDGDP (-1) LINV PUBINV PEDUC TOT REV RER INFL CPIA 1 GROWTH 575 -0.0192 1 GDP(-1) 455 459 0.1807* 0.2502* 1 LTDSGDP 542 450 545 -0.1860* -0.4176* 0.0451 1 NPVDXTS(-1) 422 424 423 424 -0.2058* -0.2131* 0.2863* 0.8260* 1 NPVDGDP(-1) 427 429 428 423 429 0.2704* 0.2587* 0.1923* -0.1716* -0.0241 1 LINV 573 458 544 423 428 578 0.2058* -0.1839* -0.048 0.0347 0.0543 0.4321* 1 PUBINV 527 430 510 405 409 528 529 0.1310* 0.4793* 0.1444* -0.2156* -0.0932 0.3256* -0.0301 1 PEDUC 559 453 535 420 425 563 517 584 -0.0522 0.0512 -0.1243* -0.0538 -0.0277 0.0226 0.0031 0.0225 1 TOT 570 455 540 420 425 573 524 579 598 0.0834 0.2447* 0.1727* -0.2547* 0.010 0.4428* 0.3673* 0.0956* -0.0093 1 REV 532 427 509 396 401 533 489 535 547 552 -0.4656* 0.0061 -0.2110* 0.1524* 0.0604 -0.1380* -0.1400* 0.0435 0.0916* -0.1467* 1 RER 570 453 539 420 425 570 524 576 590 548 595 -0.2736* 0.0438 -0.1409* 0.1159* 0.1101* -0.0984* -0.0652 0.0139 0.0358 -0.0800 0.5579* 1 INFL 573 458 545 424 429 576 527 582 596 547 593 601 0.2311* 0.3460* 0.2757* -0.2526* -0.1846* 0.2935* 0.0979* 0.2237* -0.0443 0.1250* -0.2129* -0.1731* 1 CPIA 530 439 523 416 421 531 497 521 527 498 527 532 532 Note: A star means a 5% level of significance, the second row shows the number of observations. 24