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This study examines the relationship between economic diversification and economic growth in resource-based economies. It analyzes data from Western Canada and the United States, using different diversity indices and econometric models to understand the impact of diversification on economic outcomes.
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Natural Resources, Energy Supply and Economic Growth:What Does Diversification Achieve? Bankole Fred Olayele
Background/Motivation • Economic growth depends on economic structure • Resource-based western Canadian economy • Diversification seen from two perspectives: • Long-term growth strategy that can help mitigate unforeseen problems in the event of structural economic changes? • Costly and unnecessary form of government intervention?
Diversification • A recurring theme in public policy debates • Cure to the “resource curse” challenge? • Benefits well known; one puzzle lingers • Puzzle: Diversification helps some to succeed where others fail! • Inconclusive empirical evidence on the resource-diversity-growth nexus.
Why Canada and the US? • Similar, yet distinct, resource endowments, technology, demographics and institutions. • Two federations with two or more orders of government acting directly.; flexible regional economic policy making. • Ideal for panel data analysis!
Methodology No single explicit framework • Different measures and concepts ; depends largely on the theoretical foundation explored. • Popular models include: ec. dev. theory, portfolio theory, regional business cycle theory, trade models, location theory, economic base theory and input-output analysis. Our approach • Compare regional employment distribution across industries with the national average. • Sectoral composition of national employment is dynamic; this defines the limits of diversification. • A region’s employment is taken to be more specialized (or less diversified) than that of the “parent” nation.
Diversity Indices • Entropy Index (ENT) • Hirschman-Herfindahl Index(HHI) • Absolute Ogive Index (AGV) • Quadratic Ogive Index (QGV) • Krugman Index (KRUG) Notes: 1. Indices highly sensitive to the number of industries used. 2. Four and six broad categories for goods- and services-producing sectors. 3. Strategy helps achieve greater data aggregation. 4. Also overcomes missing data issues.
Variables/Data • Variables • Economic growth: per capita real GDP • Natural resources/energy supply: mining as a share of GDP • Economic diversity: five diversity indices • Human capital stock: educational attainment • Physical capital stock: gross capital formation under PIM • Employment data • Labour Force Survey (Statistics Canada) • Current Population Survey (Bureau of Labour Statistics) • GDP/EXR data • Regional Economic Accounts (US Bureau of Economic Analysis) • Provincial Economic Accounts (Statistics Canada) • Rates and Statistics (Bank of Canada) • Summary • Annual panel data set; eight three-year intervals from 1987 to 2010 • All 60 jurisdictions;1987-97 based on SIC,1998-2010 based on NAICS.
Sectors of Interest Notes: The mining sector comprises of establishments primarily engaged in extracting naturally occurring minerals. These can be solids, such as coal and ores; liquids, such as crude petroleum; and gases, such as natural gas. Natural resource-energy supply nexus needs further clarification. An alternative variable less prone to productivity biases is sectoral GDP distribution. However, GDP itself is likely susceptible to measurement errors and exchange rate biases.
Properties of Indices Notes: The reference level for absolute measures is the equal distribution of employment shares across all industries; relative specialization measures are based on the average economic structure of the jurisdictions. For HHI, the index increases with the degree of specialization, and reaches its upper limit of 1 when a region is specialized in only one industry. In that case, the lowest level of specialization is indicated by 1/N i.e. the lowest degree of specialization indicated by an equiproportional employment share for each industry. Successively higher values of the indices imply successively lower degrees of diversity; the only exception to this rule being the Entropy index.
Two-Step System GMM Results Note: All estimations based on the Windmeijer’s (2005) finite sample correction to the standard errors.
Alternative Diversity Measures Notes: All estimations based on the Windmeijer’s (2005) finite sample correction to the standard errors. We also model all five diversity indices as strictly exogenous, IV-style, regressors.
Conclusions • All five indices are quite arbitrary because both the absolute and relative specialization measures are arbitrary. • Results suggest the growth-promoting stance of economic diversity. • The GMM framework does not allow us to test the resource curse proposition. Same with the interactive effect of diversity on resources. • Jurisdictions with KRUG value less than 0.209 will suffer from the curse; those above will not. • Conclusion qualified due to endogeneity not addressed by the fixed effects technique employed.
Future Work • Spatial autocorrelation effects among regions would be critical in explaining any link between diversity and growth. • Pede (2013) concludes that spatial econometrics provides a framework for the true factors at the origin of spillovers to be modeled by geographical distance. • Future work will consider applying spatial econometric techniques. • Among other things, this strategy will add robustness by offering a basis for comparison with the few DPD-based studies out there.
