Presentation on theme: "Natural Resources, Energy Supply and Economic Growth: What Does Diversification Achieve? Bankole Fred Olayele."— Presentation transcript:
Natural Resources, Energy Supply and Economic Growth: What Does Diversification Achieve? Bankole Fred Olayele
Background/Motivation Background/Motivation Economic growth depends on economic structure Resource-based western Canadian economy Diversification seen from two perspectives: o Long-term growth strategy that can help mitigate unforeseen problems in the event of structural economic changes? o Costly and unnecessary form of government intervention? 2
Diversification A recurring theme in public policy debates Cure to the “resource curse” challenge? Benefits well known; one puzzle lingers 3 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! 4
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. 5
Diversity Indices Entropy Index (ENT) Hirschman-Herfindahl Index(HHI) Absolute Ogive Index (AGV) Quadratic Ogive Index (QGV) Krugman Index (KRUG) 6 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 7 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 8 a) Agriculture, forestry, fishing, and hunting b) Mining c) Construction d) Manufacturing e) Wholesale trade f) Retail trade g) Transportation, warehousing and utilities h) Finance, insurance, real estate, rental and leasing i) All other services, except public administration j) Government and government enterprises 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 11 Indices Absolute/RelativeReverse OrderUpper BoundLower BoundDecomposability HHI AbsoluteNoUnity1/NYes AGV AbsoluteNoZeroNo QGV AbsoluteNoZeroNo ENT AbsoluteYesNatural log of NZeroYes KRUG RelativeNoZeroNo 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.
Ranking: US States 12 Panel AFive Most Diverse EntropyAbsolute OgiveQuadratic OgiveHHIKrugman WyomingRhode IslandWyoming Missouri AlaskaDelawareNorth Dakota Georgia North DakotaWyomingIowa Minnesota TexasIowaSouth Dakota California Arkansas Oklahoma Oregon Panel B Five Least Diverse Rhode IslandHawaiiMassachusetts Wyoming MassachusettsNew YorkNevadaRhode IslandAlaska HawaiiNew MexicoNew YorkNevadaNorth Dakota New YorkMarylandRhode IslandNew YorkMississippi Nevada FloridaMarylandWest Virginia Highest Index Value1.890.942.070.310.35 Lowest Index Value1.580.670.830.180.04
Ranking: Canadian Provinces 13 EntropyAbsolute OgiveQuadratic OgiveHHIKrugman Saskatchewan Ontario Alberta Manitoba Prince EdwardQuebec New Brunswick Prince EdwardManitobaBritish Columbia Ontario New Brunswick QuebecPrince EdwardNewfoundland Nova Scotia British Columbia Ontario Alberta NewfoundlandQuebec Newfoundland Prince EdwardNewfoundlandBritish Columbia Saskatchewan Nova Scotia Prince Edward Highest Index Value1.940.861.730.270.21 Lowest Index Value1.680.651.120.210.06
Two-Step System GMM Results 15 Dependent VariablelnRGDP Diversity Index UsedHerfindahlAbsolute OgiveQuadratic OgiveEntropyKrugman Log of lagged RGDP0.630**0.943***0.448** 0.720***0.700** [0.260][0.322][0.031][0.252][0.308] Diversity1.2750.0551.382*2.482-0.135 [2.120][1.735][0.803][5.376][0.395] Natural resources0.070-0.0190.070-0.379-0.073 [0.469][0.091][0.092][0.330][0.060] Diversity x natural resources0.007-0.1430.0730.685-0.980 [0.290][0.367][0.157][0.512][0.953] Capital stock-0.264-0.335-0.474**-0.031-0.199 [0.338][0.286][0.221][0.227][0.497] Educational attainment-0.134-0.439-0.076-0.107-0.297 [0.170][0.378][0.240][0.454][0.619] Constant6.1160.1046.1181.4572.087 [6.000][4.046][2.671][1.598][4.238] Year dummiesYes Year fixed effectsNo Number of observations360 Number of jurisdictions60 Specification Tests Number of instruments16 Number of lags used66666 Sargan Test (p value)0.0030.0560.2490.2660.821 Hansen Test (p value)0.0030.0200.2640.0160.672 Arellano-Bond AR(1) ( p-value) 0.3580.4300.0070.2540.561 Arellano-Bond AR(2) ( p-value) 0.7260.1570.0770.3140.019 Note: All estimations based on the Windmeijer’s (2005) finite sample correction to the standard errors.
Alternative Diversity Measures 17 Dependent VariablelnRGDP Diversity Index UsedHerfindahlAbsolute OgiveQuadratic OgiveEntropyKrugman Lagged log GDP0.924***0.633**0.718*** 0.755***0.555** [0.257][0.259][0.266][0.128][0.241] Diversity-1.5671.200*-0.011-2.703**0.549 [1.229][0.615][0.717][1.334][0.348] Natural resources-0.0970.036-0.0140.0680.072 [0.384][0.050][0.070][0.109][0.113] Diversity x natural resources-0.004-0.026-0.056-0.0470.072 [0.218][0.069][0.075][0.157][0.071] Capital stock-1.040**0.256-0.130-0.075-0.016 [0.431][0.276][0.412][0.272][0.222] Educational attainment-1.236**0.028-0.355-0.3280.153 [0.527][0.475][0.559][0.216][0.491] Constant-4.7274.6932.5124.137**5.621 [5.155][3.610][3.862][1.850][3.544] Year dummiesYes Year fixed effectsNo Number of observations360 Number of jurisdictions60 Specification Tests Number of instruments16 Number of lags used66666 Sargan Test (p value)0.2440.1880.0130.1960.426 Hansen Test (p value)0.2700.4790.0290.0580.245 Arellano-Bond Test for AR(1) ( p-value)0.0740.1550.1320.1720.416 Arellano-Bond Test for AR(2) ( p-value)0.8860.0820.0980.0110.000 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. 18
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. 19