Abstract
One of the most significant differences between developing countries and today’s advanced states is the fact that many developing countries rely heavily on one or several natural resources. That such dependence shapes the state’s ability to tax—its fiscal capacity—is commonly argued in the political science and applied development literatures. This paper approaches the issue from an economic angle. Our analytical foundation builds upon a novel theoretical framework, and allows us to model fiscal capacity as an ex ante investment under uncertainty. For our panel of 30 hydrocarbon-rich economies, instrumental-variables results provide strong empirical support for our theoretical proposition: resource intensification weakens state-building by impeding the state’s fiscal capacity. This result provides an inaugural validation of the economic analytics of state-capacity determinants: understanding these determinants serves to build stronger states and support sustainable paths of development. Our result also suggests that one of the main tools of fiscal policy-analysis in resource-rich economies, namely optimal taxation, could gain in practical relevance by incorporating capacity-constraints into the analytical fiscal-framework.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The non-resource tax effort is computed as the ratio of (central government) tax-revenue derived from the non-resource economy to non-resource GDP; please see Appendix for further definitions and summary statistics.
The majority of hydrocarbons naturally occur in crude oil and in natural gases.
Resource intensity: ratio of (central government) resource-related revenue to total revenue; please see Appendix.
Please see Table 1, Note (6) for definition of regime-type classification, and Appendix for further description.
Systematically, regressions were run with and without Norway: all results discussed in this paper are robust to the country’s exclusion; this robustness to ‘outliers’ justifies Norway’s presence in the sample as alleviating concerns over sample selection bias. Presented results include Norway; those excluding Norway are available upon request.
Please see Table 1, Note (4) for characterisation of income-classification.
Graphs are readily available and gladly provided upon request.
In a presentation related to their work on state-capacities (http://siteresources.worldbank.org/DEC/), Torsten Persson asks whether “the multidimensional problem of weak states is not the development problem?”
They include the importance of common interest public goods, the level of wealth, political stability, and the distribution of economic and political power.
In particular, we thank Mauricio Villafuerte and Paolo Medas for supplying the data.
An estimation technique which combines within-group variation and between-group variation (such as Generalised Least Squares) would be inconsistent in our setting, as we cannot assume that the unobserved country-specific effect is not correlated with the set of country-specific (observable) structural determinants.
This can be seen if one expresses both the non-resource tax effort and the resource intensity variables in terms of only resource-related and total components. We thank Prof. Guy Michaels for leading on to this issue.
Please see Appendix for full description of how these variables were derived.
In this case, oil price in US$ will be co-linear in time effects, and one would have to exclude the year-dummies from a 2SLS regression. Interacting oil prices in US$ with national exchange rates which exhibit no particular time-trend alleviates the time-trend in prices, and one can use oil prices as instruments while keeping the time effects.
A proof can be found in Angrist and Pischke (2009).
Because 2SLS is biased towards OLS estimates, the Hausman specification tests may incorrectly fail to reject the null hypothesis. Because of its low power, we refrain from reporting values of the Hausman test.
Jackknife 2SLS is an alternative to LIML: regression results (readily available) are similar to a 0.01 difference.
All regressions include the set of structural covariates. Full tables are provided upon request.
Our measure of institutional quality is the sum of bureaucratic quality, governmental stability, and corruption in the governing circles. Please see Appendix for further discussion of this measurement.
Please see Appendix for a further discussion of Polity 2’s definitions and methods of aggregation.
References
Anderson, T., & Rubin, H. (1949). Estimation of the parameters of a single equation in a complete set of stochastic equations. Annals of Mathematical Statistics, 20.
Angrist, J., & Pischke, J-S. (2009). Mostly harmless econometrics. Princeton University Press.
Benabou, R. (1997). “Inequality and growth”, NBER Macroeconomics Annual 1996. MIT Press.
Besley, T., & Persson, T. (2009). The origins of state capacity: Property rights, taxation and politics. American Economic Review, 99.
Besley, T., & Persson, T. (2010). State capacity, conflict and development. Econometrica, 78.
Brautigam, D. (2008). Taxation and state-building in developing countries. In D. Brautigam, O-H. Fjeldstad, & M. Moore (Eds.), Taxation and state-building in developing countries. Cambridge University Press.
Burgess, R., & Stern, N. (1993). Taxation and development. American Economic Association, 31.
Chaudhury, K. A. (1997). The price of wealth: Economies and institutions in the Middle East. Cornell University Press.
Collier, P., & Hoeffler, A. (2005). “Democracy and resource rents”, mimeograph. University of Oxford.
Gupta, A. (2007). Determinants of tax revenue efforts in developing countries. IMF Paper 07/184.
Kolstad, I., & Wiig, A. (2009). It’s the rents, stupid! The political economy of the resource curse. Energy Policy, 37.
Medas, P., Ossowski, R., Thomas, T., & Villafuerte, M. (2008). “Managing the Oil Revenue Boom: The Role of Fiscal Institutions”, IMF/World Bank Occasional Paper No.260.
Roos, M. L. (1999). The political economy of the resource curse. World Politics, 51.
Staiger, D., & Stock, J. (1997). Instrumental variables regression with weak instruments. Econometrica, 65.
Tordo, S., Johnston, D., & Johnston, D. (2009). Petroleum exploration and production rights. World Bank Working Paper No.179.
Vito, T. (1992). Structural factors and tax revenue in developing countries: A decade of evidence. In I. Goldin, & L. A. Winters (Eds.), Open economies: Structural adjustment and agriculture. Cambridge University Press.
Vito, T. (2001). Tax policy for developing countries. IMF Economic Issues No.27.
Author information
Authors and Affiliations
Corresponding author
Additional information
The author is greatly indebted to the project supervisor, Prof. Timothy Besley, for relentless support throughout the course of the project. The author has also drawn immense benefits from numerous discussions with Prof. Guy Michaels, who generously made his time available. Many thanks also go out to Prof. Albert Marcet and fellow participants at the undergraduate seminars for comments; to Dimitri Szerman and Tom Cunningham for general discussions, and to Prof. Mark Schankerman for guidance on the weak instruments literature. We are also grateful to Mauricio Villafuerte and Paolo Medas at the IMF Fiscal Affairs Department for providing the data on hydrocarbon revenue and GDP. In addition, the author would like to thank Dr. Judith Shapiro for the opportunity to present in the LSE Undergraduate Research Workshop.
Appendix
Appendix
Rights and permissions
About this article
Cite this article
Jensen, A.D. State-Building in Resource-Rich Economies. Atl Econ J 39, 171–193 (2011). https://doi.org/10.1007/s11293-011-9269-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11293-011-9269-z