Democratic development and the resource curse in Latin American & Caribbean economies: A panel data analysis

Transcription

1 Democratic development and the resource curse in Latin American & Caribbean economies: A panel data analysis Lars Sorge 1,2 1 DIW Berlin, Department of Energy, Transportation, Environment 2 Berlin University of Technology, Workgroup for Infrastructure Policy (WIP) Berlin, 10 July 2018 Lars Sorge Berlin, 10 July / 31

2 Motivation Motivation Relevance (Sinnott el al., 2010): Latin America is one of the resource richest regions of the world 4 out of the 20 largest oil producing countries in the world are located there (Venezuela, Mexico, Brazil, and Colombia) some of the largest producers of other mineral resources are part of the region It is fair to say that as a general conclusion, there is currently no consensus regarding the existence of a natural resource curse. (Badeeb et al., 2017) Lars Sorge Berlin, 10 July / 31

3 Contribution Contribution Research question: Does resource dependence has a negative impact on economic growth and if so, can a certain level of democratic quality diminish the detrimental effect of resource richness on economic growth? focus on Latin American & Caribbean (LAC) economies application of heterogeneous dynamic panel estimation techniques transparent documentation of data and method Lars Sorge Berlin, 10 July / 31

4 Agenda Agenda 1 Resource curse 2 Literature 3 Data and empirical strategy Data Empirical specification Cross-section dependence test Second-generation panel unit root test Panel ARDL approach 4 Empirical results 5 Conclusions Lars Sorge Berlin, 10 July / 31

5 Resource curse Natural resources from an economic perspective Natural resource wealth (Humphreys et al., 2007): does not need to be produced but only extracted can occur relatively independent of other economic activities many natural resources are nonrenewable rather like an asset than like a source of income Advantages for long-term economic development (Sachs, 2007): boost real living standards by financing higher levels of public and private consumption finance higher levels of investment out of natural resource revenues compensate the lack of fiscal resources needed to finance core public goods Lars Sorge Berlin, 10 July / 31

6 Resource curse The evolution of the resource curse Figure: Own illustration based on Badeeb et al. (2017) One of the surprising features of modern economic growth is that economies with abundant natural resources have tended to grow less rapidly than natural-resource-scarce economies. (Sachs and Warner, 1995) Lars Sorge Berlin, 10 July / 31

7 Literature Related literature The role of institutional quality: Arezki and van der Ploeg (2007), Boschini et al. (2007), Horvath and Zeynalov (2014), Isham et al. (2005), Mehlum et al. (2006), Kolstad and Wiig (2009), Sarmidi et al. (2014) resource curse can be avoided if institutional quality is sufficiently high Natural resources and democracy in Latin American: Dunning (2008), Ross (2009), and Haber and Menaldo (2011) positive relationship between natural resources and democracy Latin American (Blanco and Grier, 2012): What is the effect of resource dependence on factor accumulation? 17 Latin American countries from 1975 to 2004 resource dependence has no significant direct effect on physical and human capital Lars Sorge Berlin, 10 July / 31

8 Data and empirical strategy Variables Commonly used indicators within the resource curse framework: resource dependence: rents from natural resources over GDP or share of mineral/fuel exports in total exports resource abundance: subsoil wealth or deposits of natural resources Variables: GDP: GDP per capita (constant 2010 USD) C: Gross capital formation (billion constant 2010 USD) L: Labor force, total (million) NR: Total natural resources rents (% of GDP) INS: Polity2 index (Haber and Menaldo, 2011) T : Trade (% of GDP) FDI : Foreign direct investment, net inflows (billion current USD) Lars Sorge Berlin, 10 July / 31

9 Data and empirical strategy Data Lars Sorge Berlin, 10 July / 31

10 Data and empirical strategy Empirical specification Model 1: GDP it = β 0i + β 1i C it + β 2i L it + β 3i NR it + β 4i INS it +β 5i (NR it INS it ) + ɛ it Model 2: GDP it = β 0i + β 1i C it + β 2i L it + β 3i NR it + β 4i INS it +β 5i (NR it INS it ) + β 6i T it + ɛ it Model 3: GDP it = β 0i + β 1i C it + β 2i L it + β 3i NR it + β 4i INS it +β 5i (NR it INS it ) + β 6i T it + β 7i FDI it + ɛ it The resource curse will be eliminated ( δgdp it δnr it = β 3i + β 5i INS it 0) when institutional quality is greater than the required threshold INS it β 3i β 5i. Lars Sorge Berlin, 10 July / 31

11 Data and empirical strategy Empirical strategy 1 detect contemporaneous correlation among countries after controlling for individual characteristics (i.e. global shocks, local interactions) 2 test for unit roots in the presence of cross-section dependence from a single common factor 3 dynamic heterogeneous panel autoregressive distributed-lag (ARDL) approach to analyse if a resource curse in the LAC region is present Lars Sorge Berlin, 10 July / 31

12 Data and empirical strategy Cross-section dependence test contemporaneous correlation among countries that is left over after controlling for individual characteristics (Moscone and Tosetti, 2009) first-generation panel methods assume cross-sectional independence Pesaran (2004) CD test is robust to the presence of nonstationary processes, parameter heterogeneity or structural breaks,... and perfoms well in small samples. Lars Sorge Berlin, 10 July / 31

13 Data and empirical strategy Second-generation panel unit root test using nonstationary variables can lead to apparently significant regression results although the data is unrelated Pesaran (2007) CIPS panel unit root test Cross-sectionally augmented Im-Pesaran-Shin (2003) (IPS) test J J y it = δ i d t + ρ i y i,t 1 + c i y t 1 + d ij y t j + β ij y i,t j + ɛ it j=0 j=1 H 0 : ρ i = 0 is tested against H 1 : ρ i < 0 and H 1 : ρ i = 0 Lars Sorge Berlin, 10 July / 31

14 Data and empirical strategy Panel ARDL approach I estimation of the effect of natural resources on econmic growth identification of a threshold for democratic quality at which the resource curse vanishes ARDL(p,q) model variables which have a different order of integration can be used irrespective whether the variables of interest are I(0) or I(1) inclusion of lags for the dependent and independent variables reduces problems resulting from endogeneity Lars Sorge Berlin, 10 July / 31

15 Data and empirical strategy Panel ARDL approach II VECM representation of the ARDL(p,q) model p 1 q 1 GDP it = β 0i + φ i (GDP i,t 1 θ i X it ) + λ ij GDP i,t 1 + δij X i,t j + ɛ it j=1 j=0 X it = C it, L it, NR it, INS it, (NR it INS it ), T it, FDI it is the set of regressors denotes the first difference operator j is the number of lags (set to one based on the SBIC criterion) φ i is the error correction or speed of adjustment term a negative coefficient on the error-correction term not lower than -2 provides evidence for a long-run relationship and stability of the model (Loayza et al., 2006) Lars Sorge Berlin, 10 July / 31

16 Data and empirical strategy MG and PMG estimation techniques Mean Group estimation (Pesaran and Smith, 1995) allows the country specific intercepts, the short- and long-run dynamics, and the error variances to differ across countries does not impose any homogeneity restrictions on the parameters for the cross-section members Pooled Mean Group estimation (Pesaran et al., 1999) intrecepts, short-run coefficients, and error variance are determined cross-section specific the long-run parameters are constrained to be equal across the groups Which estimator to choose? the test of difference in these models is performed using the Hausman (1987) specification test Lars Sorge Berlin, 10 July / 31

17 Empirical Results Empirical results I Peasaran (2004) CD test: all series are highly dependent across the LAC panel Pesaran (2007) panel unit root test: strong evidence that a panel unit root in the variables exists NR,T, and interaction term (NR INS) are stationary in levels (I(0)), all variables are stationary in their first difference I(1) Implications first generation panel data methods are inappropriate mixed order of intergration justifies panel ARDL apporach Lars Sorge Berlin, 10 July / 31

18 Empirical Results Empirical results II: PMG estimates Lars Sorge Berlin, 10 July / 31

19 Conclusions and future research Preliminary conclusion I coefficient for NR is negative, thus confirming the resource curse level of democratic development is important to neutralize the effect of the resource curse in LAC economies estimated tresholds: only Venezuela, RB has insufficient institutional quality to insulate the economy from the resource curse How to improve the quality of democratic institutions? Lars Sorge Berlin, 10 July / 31

20 Conclusions and future research Preliminary conclusion II Democracy itself is based on the principle of transparent governance: transparency is a key principle to resource management (Haufler, 2010) transparency in revenue allocations enables people to hold others accountable for their actions transparency is a way to lightly regulate extractive private sector activities disclosure by both firms and governments regarding: details of the call for proposals and bidding process for natural resources exploration and development contracts payments made by companies to governments the location of resources and size of reserves government budgets for distributing resource rents empowerment of civil society Lars Sorge Berlin, 10 July / 31

21 Conclusions and future research Preliminary conclusion III Limitations: little variation in INS missing data for other resource dependent countries such as Chile, Guyana, and Suriname Future research: link between corruption and the resource curse in LAC region high degree of corruption might explain negative sign of POL reducing corruption can improve inequal distribution from natural resource income Uruguay Trinidad and Tobago Argentina Peru Panama Colombia Brazil El Salvador Bolivia Ecuador Mexico Honduras Dominican Republic Guatemala Nicaragua Venezuela 0 (high) (low) Corruption Perceptions Index 2017 Figure: Transparency International (2018) Lars Sorge Berlin, 10 July / 31

22 Thank you. lars.sorge[at]diw.de Lars Sorge Berlin, 10 July / 31

23 References I Arezki, R., and van der Ploeg, F., (2007). Can the natural resource curse be turned into a blessing? The role of trade policies and institutions. In CEPR discussion papers C.E.P.R. Badeeb, R.A., Lean, H.H. and Clark, J., (2017). The evolution of the natural resource curse thesis: A critical literature survey. Resources Policy 51: Blanco, L. and Grier, R., (2012). Natural resource dependence and the accumulation of physical and human capital in Latin America. Resources Policy 37(3): Boschini, A. D., Pettersson, J., and Roine, J., (2007). Resource curse or not: A question of appropriability. Scandinavian Journal of Economics 109(3): Corden, W.M. and Neary, J.P., (1982). Booming sector and de-industrialisation in a small open economy. The economic journal 92(368): Dunning, T., (2008). Crude Democracy: Natural Resource Wealth and Political Regimes. Cambridge University Press: New York, United States. Lars Sorge Berlin, 10 July / 31

24 References II Gylfason, T., (2001). Natural resources, education, and economic development. Eur. Econ. Rev. 45(4): Haber, S. and Menaldo, V., (2011). Do natural resources fuel authoritarianism? A reappraisal of the resource curse. American political science Review 105(1): Haufler, V., (2010). Disclosure as governance: the extractive industries transparency initiative and resource management in the developing world. Global Environmental Politics 10(3): Hausman, J.A., (1978). Specification tests in econometrics. Econometrica: Journal of the econometric society 46(6): Humphreys, M., Sachs, J., Stiglitz, J.E., (2007). Escaping the Resource Curse. Columbia University Press, New York, United States. Horvath, R., and Zeynalov, A., (2014). The natural resource curse in post-soviet countries: The role of institutions and trade policies. In IOS working papers 341. Institute for East and Southeast European Studies. Lars Sorge Berlin, 10 July / 31

25 References III Hotelling, H., The economics of exhaustible resources. Journal of political Economy 39(2): Im, K.S., Pesaran, M.H. and Shin, Y., (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics 115(1): Isham, J., Woolcock, M., Pritchett, L., and Busby, G., (2005). The varieties of resource experience: Natural resource export structures and the political economy of economic growth. World Bank Economic Review 19(2): Loayza, N.V. and Ranciere, R., (2006). Financial development, financial fragility, and growth. Journal of Money, Credit and Banking 38(4): Kolstad, I., and Wiig, A., (2009). Is transparency the key to reducing corruption in resource-rich countries? World Development 37(3): Mehlum, H., Moene, K., and Torvik, R., (2006). Cursed by Resources or Institutions? The World Economy 29(8): Lars Sorge Berlin, 10 July / 31

26 References IV Pesaran, M.H., (2004). General diagnostic tests for cross section dependence in panels. CESifo Working Paper Series No Pesaran, M.H., (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22 (2), pp Pesaran, M.H. and Smith, R., (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of econometrics 68(1): Pesaran, M.H., Shin, Y. and Smith, R.P., (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association 94(446): Ross, M., (2009). Oil and Democracy Revisited. Manuscript, Department of Political Science, University of California Los Angeles, access date: 3 July 2018 Sachs, J., Warner, A.M., (1995). Natural Resources Abundance and economic growth. National bureau for Economic Research. NBER Working Paper Lars Sorge Berlin, 10 July / 31

27 References V Sachs, J.D., (2007). How to handle the macroeconomics of oil wealth. In: Humphreys, M., Sachs, J., Stiglitz, J. (Eds.), Chapter 7 in Escaping the Resource Curse. Columbia University Press, New York, United States. Sarmidi, T., Hook Law, S. and Jafari, Y., (2014). Resource curse: new evidence on the role of institutions. International Economic Journal 28(1): Sinnott, E., Nashat, B. and De la Torre, A., (2010). Natural resources in Latin America and the Caribbean: beyond booms and busts? The World Bank. Washington, D.C., United States. Stiglitz, J., (1974a). Growth with exhaustible natural resources: efficient and optimal growth paths. The review of economic studies 41: Stiglitz, J.E., (1974). Growth with exhaustible natural resources: the competitive economy. The Review of Economic Studies 41: Solow, R.M., (1974a). Intergenerational equity and exhaustible resources. The review of economic studies 41: Lars Sorge Berlin, 10 July / 31

28 References VI Solow, R.M., (1974b). The Economics of Resources or the Resources of Economics. American Economic Review 64(2): 1-14 Transparency International, (2018). Transparency International Corruption Perceptions Index 2017, corruption_perceptions_index_2017#resources, access date: 3 July 2018 Lars Sorge Berlin, 10 July / 31

29 Back up: I Lars Sorge Berlin, 10 July / 31

30 Back up II Model 1 GDP it = φ i (GDP i,t 1 θ 0i θ 1i C it θ 2i L it θ 3i NR it θ 4i INS it θ 5i (NR it INS it )) + δ 11i C it + δ 21i L it + δ 31i NR it +δ 41i INS it + δ 51i ( NR it INS it ) + ɛ it number of lags j are determined using information criteria (SBIC) Important missing countries: Chile (10.49%), Guyana (19.31%), Suriname (29.02%) Lars Sorge Berlin, 10 July / 31

31 Back up: III Uruguay Trinidad and Tobago Argentina Peru Panama Colombia Brazil El Salvador Bolivia Ecuador Mexico Honduras Dominican Republic Guatemala Nicaragua Venezuela CPI 2017 Polity2 Lars Sorge Berlin, 10 July / 31