What Really Matters for Long-term Growth and Development?

A Re-Examination of the Deep

Determinants of Per Capita Income

Dorian Owen and Clayton WeatherstonUniversity of Otago

EDGES ‘Roads to Riches’ Workshop 15 November 2005

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

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

Growth Determinants – the Conventional ‘Production Function’ Approach

AggregateInputs 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?

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.

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

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

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)

Example study:Rodrik et al. (J Econ Growth, 2004)

ln y = + INS + INT + GEO +

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

• Use of instrumental variables estimation (2SLS)

INS = + SM + ln(FR) + GEO +

INT = + SM + ln(FR) + GEO +

SM = ln(settler mortality)

ln(FR) = ln(Frankel & Romer measure of constructed trade shares)

GEO = abs(latitude) – exogenous regressor in GDP per capita equation

• 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, – if not, estimates are inconsistent

– Key problem – exogeneity fails if instruments affect income via other channels or are correlated with omitted variables

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

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 WP 2003)– Olsson and Hibbs (EER 2005)

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

• 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

Study Institutions

variable

Trade Variable

Geog

Variable

Mis-spec tests

HJ (1999)

GADP

(EngFrac EurFrac Latitude)

YrsOpen

(lnFR)

Excluded ×√×××

AJR (2001)

Exprop

(Settmort)

Excluded Latitude MeanTemp Humidity

Malfal

×√××S

EL (2003)

Instit Dev

(Settmort Latitude Landlock)

YrsOpen Excluded ×√√√√

Study Institutions

variable

Trade Variable

Geog

Variable

Mis-spec tests

Sachs (2003)

Rule of Law

(Settmort

KGPTemp)

Excluded % popn close to coast

Malfal

(ME)

×××√S

RST (2004)

Rule of Law

(Settmort)

Trade % of GDP

(lnFR)

Latitude ×√×√×

OH (2005)

Political

environment

Excluded Bio- and Geo-Conditions

×√××S

Replication of Key Existing StudiesKey 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

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)

General Unrestricted Model (GUM)• ln(GDP per capita) = f(Const, PhysGeog,

Climate, BioGeog, Resources, Institutions,

Integration, Culture, )

Vectors of different factors representing PhysGeog, Climate, etc

PhysGeog = (Axis, Size, Land100km, Mount)

Climate =(MeanTemp, Latitude,TempRange,

Frost)

BioGeog = (Malfal, Plants, Animals)

Resources = (Crop and Mineral dummies)

Institutions = (Exprop, ExConst, Plurality)

Integration = (YrsOpen)

Culture = (EthnicFrac, LingFrac, ReligFrac,

Catholic, Muslim)

Illustrative OLS Results

GUM ConstSIZE lc100km EXPROP CATHAXIS MOUNT EXCONST MUSLIMPLANTS LATITUDE PLURAL EthFracANIMALS RANGE YRSOPENReligFrac

Malfal FROST oil LangFracMEANTEMP

Gets ‘testimation’

Const MOUNT EXPROP CATH Malfal FROST YRSOPEN

oil

Coefficient t-value t-prob reliable

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

IV estimates – final model Coefficient t-value t-prob reliableConstant 6.54184 0.654 0.0000 1.0000MOUNT -0.01227 -3.016 0.0038 1.0000FROST 0.64326 2.177 0.0335 1.0000CATH 0.00475 2.629 0.0109 1.0000oil 0.40337 2.510 0.0148 0.7000Malfal* -1.13708 -5.603 0.0000 1.0000EXPROP* 0.25820 2.850 0.0060 1.0000YRSOPEN* 0.70794 2.042 0.0456 1.0000R^2 = 0.84555 Radj^2 = 0.82722N = 67 FpNull = 0.00000 FpGUM = 0.99766Additional 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 probnormality test chi^2( 2) 1.3034 0.5212hetero test chi^2( 13) 17.9626 0.1589

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)