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Natural Resources and Economic Diversification: Evidence from the GCC Countries

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Economic Diversification in the Gulf Region, Volume II

Part of the book series: The Political Economy of the Middle East ((PEME))

Abstract

We examine the impact of natural resource rents on diversification in exports, in employment and in value added, covering up to 136 countries from 1962 to 2012. We find a significant negative relationship between resource rents and diversification. The results are heterogeneous across different country groups and resources; the countries of the Arab Gulf are not an exception as they follow the high resource-dependent group. Moreover, we find that the higher the resource dependency, the less likely the country would go into diversification through its development, compared to less resource-dependent countries. Instead, concentration grows rapidly. These results are useful to policymakers in resource-rich countries who should be aware of how their economy is likely to be affected by resource rents.

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Notes

  1. 1.

    Azevedo, João Pedro (2007) AINEQUAL: Stata module to compute measures of inequality .

References

  • Alsharif, N. (2015). Natural Resources and Diversification.

    Google Scholar 

  • Bhattacharyya, S., & Collier, P. (2014). Public Capital in Resource Rich Economies: Is There a Curse? Oxford Economic Papers-New Series, 66(1), 1–24.

    Article  Google Scholar 

  • Cadot, O., Carrère, C., & Strauss-Kahn, V. (2011). Export Diversification: What’s Behind the Hump? Review of Economics and Statistics, 93(2), 590–605.

    Article  Google Scholar 

  • Corden, W. M., & Neary, J. P. (1982). Booming Sector and De-Industrialisation in a Small Open Economy. The Economic Journal, 92, 825–848.

    Article  Google Scholar 

  • Cowell, F. (2011). Measuring Inequality: Oxford University Press.

    Google Scholar 

  • De Benedictis, L., Gallegati, M., & Tamberi, M. (2009). Overall Trade Specialization and Economic Development: Countries Diversify. Review of World Economics, 145(1), 37–55.

    Article  Google Scholar 

  • Easterly, W., Reshef, A., & Schwenkenberg, J. M. (2009). The Power of Exports. World Bank Policy Research Working Paper Series, Vol.

    Google Scholar 

  • Elbadawi, I., & Gelb, A. (2010). Oil, Economic Diversification and Development in the Arab World. Paper presented at the Economic Research Forum Policy Research Report.

    Google Scholar 

  • Esanov, A. (2012). Economic Diversification: Dynamics, Determinants and Policy Implications. Revenue Watch Institute.

    Google Scholar 

  • Flamos, A., Roupas, C. V., & Psarras, J. (2013). GCC Economies Diversification: Still a Myth? Energy Sources, Part B: Economics, Planning, and Policy, 8(4), 360–368. doi:10.1080/15567240903515000.

    Article  Google Scholar 

  • Gelb, A. (2011). Economic Diversification in Resource Rich Countries. In Beyond the Curse: Policies to Harness the Power of Natural Resources (pp. 55–80).

    Google Scholar 

  • Gelb, A., & Grasmann, S. (2010). How Should Oil Exporters Spend Their Rents? Center for Global Development Working Paper No 221.

    Google Scholar 

  • Gylfason, T. (2011). Natural Resource Endowment: A Mixed Blessing? CESifo Working Paper: Resources and Environment Economics No 3353.

    Google Scholar 

  • Harding, T., & Venables, A. J. (2013). The Implications of Natural Resource Exports for Non-resource Trade.

    Google Scholar 

  • Harding, T., & Venables, A. (2016). The Implications of Natural Resource Exports for Nonresource Trade. IMF Economic Review, 64(2), 268–302.

    Article  Google Scholar 

  • Hausmann, R., & Rodrik, D. (2003). Economic Development as Self-Discovery. Journal of Development Economics, 72(2), 603–633.

    Article  Google Scholar 

  • Imbs, J., & Wacziarg, R. (2003). Stages of Diversification. American Economic Review, 93, 63–86.

    Article  Google Scholar 

  • International Labour Office. (2012). Yearbook of Labour Statistics. Geneva: International Labour Office Press.

    Google Scholar 

  • Ismail, K., & Arezki, R. (2010). Boom-Bust Cycle, Asymmetrical Fiscal Response and the Dutch Disease. International Monetary Fund.

    Google Scholar 

  • Koren, M., & Tenreyro, S. (2007). Volatility and Development. The Quarterly Journal of Economics, 243–287.

    Google Scholar 

  • Krugman, P. (1987). The Narrow Moving Band, the Dutch Disease, and the Competitive Consequences of Mrs. Thatcher: Notes on Trade in the Presence of Dynamic Scale Economies. Journal of Development Economics, 27(1), 41–55.

    Article  Google Scholar 

  • Lederman, D., & Maloney, W. F. (2003). Trade Structure and Growth. World Bank Policy Research Working Paper No 3025.

    Google Scholar 

  • McMillan, M. S., & Rodrik, D. (2011). Globalization, Structural Change and Productivity Growth: National Bureau of Economic Research.

    Google Scholar 

  • Moore, W., & Walkes, C. (2010). Does Industrial Concentration Impact on the Relationship Between Policies and Volatility? International Review of Applied Economics, 24(2), 179–202.

    Article  Google Scholar 

  • Papageorgiou, C., & Spatafora, M. N. (2012). Economic Diversification in LICs: Stylized Facts and Macroeconomic Implications. International Monetary Fund.

    Google Scholar 

  • Ross, M. L. (2001). Does Oil Hinder Democracy? World Politics, 53(03), 325–361.

    Article  Google Scholar 

  • Sachs, J. D., & Warner, A. M. (1995). Natural Resource Abundance and Economic Growth: National Bureau of Economic Research.

    Google Scholar 

  • Sachs, J. D., & Warner, A. M. (2001). The Curse of Natural Resources. European Economic Review, 45(4), 827–838.

    Article  Google Scholar 

  • Sala-i-Martin, X., & Subramanian, A. (2003). Addressing the Natural Resource Curse: An Illustration from Nigeria: National Bureau of Economic Research.

    Google Scholar 

  • Stokey, N. L. (1988). Learning by Doing and the Introduction of New Goods. The Journal of Political Economy, 96, 701–717.

    Article  Google Scholar 

  • Tornell, A., & Lane, P. R. (1999). The Voracity Effect. American Economic Review, 89, 22–46.

    Article  Google Scholar 

  • United Nations Industrial Development Organization. (2012). Database on Industrial Statistics. Vienna: United Nations Industrial Development Organization.

    Google Scholar 

  • van der Ploeg, F., & Venables, A. J. (2012). Natural Resource Wealth: The Challenge of Managing a Windfall. Annual Review of Economics, 4, 4, 315-+.

    Google Scholar 

  • WITS. (2013). United Nations COMTRADE Database. World Integrated Trade Solution.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics, University of Sussex, Falmer, UK

    Nouf N. Alsharif

  2. College of Business Administration, Prince Sultan University, Riyadh, Saudi Arabia

    Nouf N. Alsharif

Authors
  1. Nouf N. Alsharif
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Editor information

Editors and Affiliations

  1. Institute of Middle Eastern Studies, King’s College London, London, United Kingdom

    Ashraf Mishrif

  2. Supreme Council for Planning, Muscat, Oman

    Yousuf Al Balushi

Appendix: Data and Additional Tables and Figures

Appendix: Data and Additional Tables and Figures

Employment Data

Sectoral employment data are from the International Labor Office and United Nations Industrial Development Organization (UNIDO 2012). ILO data covers 127 countries, while UNIDO covers 125 countries. The ILO data includes all economic activities at the 1-digit level between 1969 and 2008. Sectoral shares are in percentages. The unbalanced panel has 2369 observations (country-year). The ILO dataset reports employment in different classifications: some countries use the ISIC revision 2, others moved to ISIC revisions 3 and 4 in recent years, and some are using their own national classification. Employment data in the more disaggregated ISICrev3 and ISICrev4 were aggregated to ISICrev2, following Imbs and Wacziarg (2003), Timmer and Vries (2007) and McMillan and Rodrik (2011). If a country reports two revisions, the lower one is used. Official estimates are preferred over labor surveys. Data not following ISIC conventions are dropped. Table 2.1 shows the concordance between ISICrev3 and ISICrev2.

Table 2.1 Classifications between ISICrev2 and ISICrev3a
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ILO data sometimes have sudden big changes in numbers in certain sectors, as countries sometimes change their calculation methods even if the same classification/revision is used. This is taken into consideration in this study, by dropping the observations that report these sudden changes making the panel more harmonized.

Our alternative data source is UNIDO, which covers manufacturing activities only at the 3-digit level of disaggregation (the main 23 industrial sectors) between 1963 and 2010 (INDSTAT2). (INDSTAT4 disaggregates to 4-digit level but only goes back to 1985.) The UNIDO dataset is consistent over the years and did not need adjustment. The unbalanced panel has 3564 employment observations (country-year).

Value Added and Labor Productivity

The UNIDO dataset also provides information on value added per sector, offering an additional measure of sector size and productivity in industrial employment. The value-added dataset covers almost the same period as the employment dataset, although some countries do not report the two sets equally. The unbalanced panel has 3465 value added observations (country-year).

Exports Data

Exports data are from the World Integrated Trade Solution (WITS), which is a collaboration between the World Bank and the United Nations Conference of Trade and Development. The export data covers 133 countries. Data is selected in SITC 1-digit aggregation containing the main ten trade sectors. Values are reported in constant US$1000 with base year 2000. The unbalanced panel has 4575 observations (country-year). The WITS data values are consistent over the years and did not need any adjustment.

Table 2.2 Main differences between the chosen concentration measures
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Diversification Indicators

Computing of these measures is done through Stata.Footnote 1

We calculate diversity for all sectors and for all non-resource sectors. Specifically, in the ILO data, we exclude “mining and quarrying”, and in the WITS exports data, we exclude “crude material, inedible, except fuels”, “mineral fuels, lubricants and related materials” and “commodities not classified according to kind”. The UNIDO data does not cover resource sectors at all.

Table 2.3 shows summary statistics for the diversification measures used in this study. Table 2.4 reports correlation between these measures, which is high. Figure 2.4 shows the historical performance of the diversification using the Gini index in all sectors examined.

Table 2.3 Summary statistics of sectoral concentration indices
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Table 2.4 Correlation matrices of sectoral concentration indices
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Fig. 2.4
figure 4

Gini indices against GDP per capita and the share of resource rents in the GCC countries. Note: Data sources: ILO (2012), WITS (2013) and UNIDO (2012). The graphs show that diversification path takes a U-shaped curve in all countries as shown previously by Imbs and Wacziarg (2003). The red line represents the diversification path in the GCC countries along the development measured by growth in GDP per capita. The graphs show a different trend in the GCC countries, as concentration begins high and remains high despite income increases, especially in the case of exports. The UNIDO manufacturing employment and value added data show a high concentration as well, but the ILO sectoral employment data is not quite specific due to the small number of available figures

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Natural Resources Data

Several natural resources are used in this study: oil, gas, nickel, tin, copper, gold, iron, forest, coal, bauxite, silver, lead and phosphate. Resource rents are from the World Bank Wealth of Nations dataset and cover the period 1970–2008. Aggregate resource rent is calculated as the sum of all reported resources. The World Bank calculates resource rents as: Rents = Unit rent × production

Unit rent = unit price − unit cost

All rents are reported in current US dollars.

The measure for resource rents used in this study is the log of resource rents per capita. Resource rents are available for a wide panel of countries for a long period of time, allowing testing long-term effects on diversification and minimizing the risk of sample selection bias. Normalization by population size, taken from the Penn World Tables, avoids a bias toward large countries. Several resources are aggregated, using data constructed using the same methodology, allowing us to examine the effect of different resource rents on diversification at the same time. This measure has been used by several recent studies (Ross 2006; Bhattacharyya and Collier 2011).

figure a

Structural change (or internal diversification) within manufacturing here is measured by Gini coefficient for the inequality of sector shares in employment. Higher Gini implies concentration and vice versa. The data is sourced from UNIDO

figure b

Structural change (or internal diversification) in manufacturing here is measured by Gini coefficient for the inequality of sector shares in value added. Higher Gini implies concentration and vice versa. The data is sourced from UNIDO

figure c

Structural change (or internal diversification) within non-resource sectors here is measured by Gini coefficient for the inequality of sector shares in employment. Higher Gini implies concentration and vice versa. The data is sourced from ILO

figure d

Note: Aggregate export diversification here is measured by Gini coefficient for the inequality of sector shares in exports. Higher Gini implies concentration and vice versa. The data is sourced from WITS

figure e

Note: Export diversification in the non-resource sector here is measured by Gini coefficient for the inequality of sector shares in exports. Higher Gini implies concentration and vice versa. The data is sourced from WITS

Fig. 2.5
figure 5

Diversification in selected countries. Note: Aggregate structural change (or internal diversification) here is measured by Gini coefficient for the inequality of sector shares in employment. Higher Gini implies diversification concentration and vice versa. Aggregate implies that the figure includes both resource and non-resource sectors. The data is sourced from ILO

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Fig. 2.6
figure 6

Export diversification and resource rents per capita across countries. Note: Countries with higher resource rents per capita also have the highest concentration (Gini) in exports

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Alsharif, N.N. (2018). Natural Resources and Economic Diversification: Evidence from the GCC Countries. In: Mishrif, A., Al Balushi, Y. (eds) Economic Diversification in the Gulf Region, Volume II. The Political Economy of the Middle East. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-10-5786-1_2

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