Dorian Owen and Clayton Weatherston University of Otago EDGES ‘Roads to Riches’ Workshop

1. What Really Matters for Long-term Growth and Development? A Re-Examination of the Deep Determinants of Per Capita Income Dorian Owen and Clayton Weatherston University of Otago EDGES ‘Roads to Riches’ Workshop 15 November 2005

2. Introduction • Average living standards in richest countries 100× those in poorest countries • Recent studies examine (very) parsimonious models to evaluate the overall and relative importance of hypothesized ‘deep’ determinants of economic development • Aims • To argue that much of this literature suffers from problems of ‘model uncertainty’ • To outline an approach for re-examining the role of deep determinants • To present some preliminary results

3. Outline • Brief review of the literature on ‘deep’ determinants of cross-country income levels • Geography versus institutions • Instruments and inference • Criticisms focusing on model uncertainty and evidence of mis-specification • A general-to specific (Gets) approach • Preliminary results • Further work in progress

4. Growth Determinants – the Conventional ‘Production Function’ Approach Aggregate Inputs Production Function Output Physical Capital (K) Y = f(A, K, L, H, …) GDP (Y) Labour (L) Human Capital (H) Technology (A) ‘Proximate determinants’ … but what determines the proximate determinants?

5. Deep Determinants – the Contenders • Geography • Institutions • Protecting property rights • Coordinating/enhancing investment (K, H) • Making governments/rulers accountable • ‘Openness’/Integration • Others – Culture, Ethnic/Linguistic/ Religious Composition • Characteristics: ‘Timescale’ criterion  relative constancy/persistence as a measure of ‘depth’. Not exogenous versus endogenous.

6. Geography Hypothesis • ‘Geography hypothesis’ includes direct and indirect effects • Geography Development • Climate • Ground surface • Geological • Bio-geography • Geography  Institutions  Development • E.g., Acemoglu et al (AER 2001) – high disease environment leads to ‘extractive’ colonies and ‘bad’ institutions, which impede long-term development

7. Institutions Hypothesis • Institutions  Development • “institutions in society … are the underlying determinant of the long-run performance of economies” (North 1990) • ‘Good institutions’: main focus on contract enforcement, protection of property rights, rule of law (‘market-creating’), covering broad cross section of society • Development of institutions: • Legal origin • Endowments: any effect of geography is only via indirect effect on institutions

8. Measures of Deep Determinants • Geographical variables • Latitude, Average mean temperature, % land area within 100km of coast, axis, frost days, etc • Proportion of popn at risk from malaria • Institutional variables • ICRG survey indicators of investors’ risk • World Bank survey assessments of govt effectiveness (including Rule of Law) • Polity IV – constraints on executive Reflect ‘outcomes’ more than durable ‘constraints’, are volatile, and increase with per capita income (Glaeser et al, 2004)

9. Example study:Rodrik et al. (J Econ Growth, 2004) ln y = m + aINS + bINT + gGEO + e1 y = GDP per cap 1995 INS = ‘rule of law’ index INT = ln(nominal trade/nominal GDP) GEO = abs(latitude) • Potentially complicated set of interlinkages • INS and INT potentially endogenous

10. Use of instrumental variables estimation (2SLS) INS = l+ dSM + fln(FR) + jGEO + e2 INT = q+ tSM + sln(FR) + wGEO + e3 SM = ln(settler mortality) ln(FR) = ln(Frankel & Romer measure of constructed trade shares) GEO = abs(latitude) – exogenous regressor in GDP per capita equation

11. Instrumental Variables Estimation requires ‘valid’ instruments: • Instrument relevance – variables in X need to be highly correlated with the endogenous deep determinant, say INS. • Instrument exogeneity – X variables need to be uncorrelated with the model’s error term, e – if not, estimates are inconsistent • Key problem – exogeneity fails if instruments affect income via other channels or are correlated with omitted variables

12. Key Instrument • Acemoglu, Johnson and Robinson(AER, 2001): Europeans adopted different colonisation strategies in different colonies: ‘settler’ versus ‘extractive’ colonies Colonisation mode = f(disease environment) High settler mortality  extractive colonies Low settler mortality  settler colonies (Potential) settler mortality  settlement type  early institutions  current institutions  current economic performance

13. Initial Consensus Primacy of institutions – although geographic conditions affect development (income per capita) they do so only through their impact on the development of institutions • Acemoglu, Johnson & Robinson (AER 2001) • Easterly and Levine (J Monetary Econ 2003) • Rodrik, Subramanian &Trebbi (J Econ Growth 2004) Later studies provide conflicting results • Sachs (NBER WP2003) • Olsson and Hibbs (EER 2005)

14. Model Uncertainty • Brock and Durlauf (2001) critique of cross-country empirical growth literature: • Violations of assumptions required for estimation by OLS and interpretation as a structural model • ‘Open-endedness’ of theories - validity of one causal theory does not imply falsity of another. OK if regressors orthogonal but not with a high degree of collinearity between potential regressors • ‘Model uncertainty’  likely sensitivity of coefficient estimates and t-values to ‘other’ regressors under such conditions

15. Open-endedness of growth theories also has implications for the validity of instrumental variable methods  predetermined variables may not be valid instruments if correlated with omitted variables • Problem – don’t know which variables are relevant, due to open-endedness of theories and range of different feasible mechanisms • Also, parameter heterogeneity in cross-country samples. Cross-section estimates best interpreted as ‘average effects’ - Temple (JEL, 1999) but need to look out for evidence of parameter heterogeneity

18. Replication of Key Existing Studies Key issues apparent in Table: • Choice of regressors (range of proxies) varies • Control for openness – some do, some don’t; other exogenous regressors also vary • Evidence of mis-specification (tests for RESET, normality, hetero) • Parameter constancy • Choice of instruments - Over-identification tests • Not congruent or encompassing – ‘illustrate’ rather than test competing theories

19. Why Use a General-to-Specific (Gets) Approach? • Theory relatively ‘loose’ – admits a wide range of candidate regressors, e.g., different geographical mechanisms, interactions • Model selection important – untested exclusion restrictions. ‘Open-ended theory’ problem • Impressive Monte Carlo results for overall PcGets algorithm • Applicable to cross-section data (Hoover & Perez, Oxford Bulletin 2004)

20. General Unrestricted Model (GUM) • ln(GDP per capita) = f(Const, PhysGeog, Climate, BioGeog, Resources, Institutions, Integration, Culture, e) Vectors of different factors representing PhysGeog, Climate, etc PhysGeog = (Axis, Size, Land100km, Mount) Climate=(MeanTemp, Latitude,TempRange, Frost)

21. BioGeog = (Malfal, Plants, Animals) Resources = (Crop and Mineral dummies) Institutions = (Exprop, ExConst, Plurality) Integration = (YrsOpen) Culture = (EthnicFrac, LingFrac, ReligFrac, Catholic, Muslim)

22. Illustrative OLS Results GUM Const SIZE lc100km EXPROPCATH AXIS MOUNTEXCONST MUSLIM PLANTS LATITUDE PLURAL EthFrac ANIMALS RANGE YRSOPENReligFrac Malfal FROST oil LangFrac MEANTEMP Gets ‘testimation’ Const MOUNTEXPROPCATH MalfalFROSTYRSOPEN oil

23. Coefficient t-value t-prob reliable Constant 6.33030 16.913 0.0000 1.0000 MOUNT -0.01201 -3.187 0.0023 1.0000 Malfal -0.99967 -5.888 0.0000 1.0000 FROST 0.69508 2.755 0.0078 1.0000 EXPROP 0.27445 5.398 0.0000 1.0000 CATH 0.00536 3.098 0.0030 1.0000 YRSOPEN 0.74580 3.286 0.0017 1.0000 oil 0.39362 2.507 0.0149 0.7000 R^2 = 0.84731 Radj^2 = 0.82920 N = 67 FpNull = 0.00000 FpGUM = 0.97713 value prob Chow(34:1) F( 34, 25) 0.7581 0.7764 Chow(61:1) F( 7, 52) 0.6827 0.6859 normality test chi^2( 2) 1.8437 0.3978 hetero test chi^2( 13) 18.3188 0.1458

24. IV estimates – final model Coefficient t-value t-prob reliable Constant 6.54184 0.654 0.0000 1.0000 MOUNT -0.01227 -3.016 0.0038 1.0000 FROST 0.64326 2.177 0.0335 1.0000 CATH 0.00475 2.629 0.0109 1.0000 oil 0.40337 2.510 0.0148 0.7000 Malfal* -1.13708 -5.603 0.0000 1.0000 EXPROP* 0.25820 2.850 0.0060 1.0000 YRSOPEN* 0.70794 2.042 0.0456 1.0000 R^2 = 0.84555 Radj^2 = 0.82722 N = 67 FpNull = 0.00000 FpGUM = 0.99766 Additional instruments: LORGFR, ME, STATEHIST, LSETTMORT, ENGFRAC, EURFRAC, LOGFR; SIZE, AXIS, lc100km, LATITUDE, PLANTS, ANIMALS, RANGE, MEANTEMP, MUSLIM, EthFrac, ReligFrac, LangFrac. Sargan test: chi^2(16) = 13.0364 [0.6701] chi^2( 4) = 7.4498 [0.9636] value prob normality test chi^2( 2) 1.3034 0.5212 hetero test chi^2( 13) 17.9626 0.1589

25. Conclusions and Further Work 1. Model uncertainty and mis-specification (lack of congruence) are problems with existing studies 2. A Gets approach can address these issues 3. Preliminary results suggest that institutions are not all that matters and that geographical variables as well as openness and aspects of culture exert an independent influence on per capita income levels 4. Examining sensitivity of results to variable definition and choice of instruments 5. Ideal would be to select instruments and regressors simultaneously as part of the Gets modelling process (Hendry and Krolzig, EJ 2005)