GEOGRAPHIC AND INSTITUTIONAL DETERMINANTS OF REAL INCOME: A SPATIO-TEMPORAL SIMULTANEOUS EQUATION APPROACH

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1 GEOGRAPHIC AND INTITUTIONAL DETERMINANT OF REAL INCOME: A PATIO-TEMPORAL IMULTANEOU EQUATION APPROACH Guyslain K. Ngeleza 1 Raymond J.G.M. Florax 2,3 William A. Masters 2 1 Mississippi tate Universy, tarkville, UA 2 Purdue Universy, West Lafayette, UA 3 Vrije Universe, Amsterdam, The Netherlands ngeleza@agecon.msstate.edu 1

2 Introduction: Income per capa distribution Developed country-african country Developed country-asian country Developed country-outh American country Data PWT 6.2 Facts In 196, developed countries on average 5 times richer than Africa In 2 developed are 8 times richer than Africa But also variation across regions whin Africa N B: The vertical axis represents the ratio of the average income per capa for developed country relative to developing country in a given region. 2

3 Africa Income per capa distribution North Africa West Africa Central Africa East Africa outh Africa Data PWT 6.2 Facts income per capa significantly increased in North Africa and outhern Africa 1 stagnant in West and East Africa falling in central Africa N B: The vertical axis represents the ratio of the average income per capa for developed country relative to developing country in a given region. 3

4 Determinants of income per capa Instutions North, Acemoglu et al. focus: property rights, market infrastructure, price incentives policy implication: adopt right instutions, convergence Geography achs, Diamond focus: location, local technology Policy implication: overcoming geography comes at a cost location-specific technology transportation public health 4

5 Determinants of Africa's poor performance External condions (Bloom and achs) legacy of centuries of slave trading and colonial rule and manipulation of African polics during the cold war ocial condions (Easterly and Levine) ethnic divisions, religious diversy and low levels of social capal Trade specialization (Temple) heavy dependence on a small number of primary exports, wh declines and volatily in terms of trade Low level of urbanization (Acemoglu et al) average 16% in 196 average 35% in 22 5

6 Central question National policies and instutions interacting wh trade, urbanization corruption, civil liberty, rule of law geography, transportation opportunies, and agriculture Neighborhood effects characteristics of places spillovers among neighbors Hypotheses spillovers among neighbors and shared characteristics of places could help better explain how instutions, trade and urbanization affect income 6

7 Challenges in testing these hypotheses: Endogeney correlation between instutions and income could be due to reverse causaly, or to omted variables that affect both of them Rodrik, ubramanian and Trebbi (24), Easterly and Levine (23) and Acemoglu, Johnson and Robinson (21). patial correlation interactions among neighbors such as trade or migration geographic clustering could be due to spatially correlated attributes misspecification 7

8 A new spatial analysis of economic growth To account for both spatial effects and endogeney apply new estimators (Kelejian and Prucha 24) use panel data in simultaneous equations (Masters and achs 21) To distinguish between kinds of neighborhood effects spillovers among neighbors shared characteristics 8

9 Data : standard sources Eq.# and Variable Description (all 5-yr. averages, centered on 196, ) ource 1. Agric. output net farm output/r capa (thousands of 2 International dollars) FAOtat 2. Infant mortaly deaths in first year, per thousand live births WHO 3. Instutional qual. average of six ICRG indexes, linked to two Freedom House indexes ICRGFH 4. Urbanization percentage of the population living in urban areas FAOtat 5. Trade exports plus imports as a fraction of GDP PWT Income real GDP per capa (thousands of 2 PPP dollars) PWT Agricultural land land used in agriculture (thousands of hectares) FAOtat 1. Land qualy farmland in high soil fertily classifications (percent) UDA 1. Frost land receiving 5 frost-days/month in winter (share) IPCC 1. Rainfall average precipation over the country s land mass (mm/yr) IPCC 2. Malaria ecological index of malaria transmissibily Kiszewski et al. 4. Catholic estimated percentages of the population wh the specified religion Barro and Lee 4. Protestant estimated percentages of the population wh the specified religion Barro and Lee 4. Muslim estimated percentages of the population wh the specified religion Barro and Lee 5. Coastal percent of a country s land whin 1 km of sea or navigable river achs 6. Population Millions PWT Time dummies five-year averages around 1965, 197,, 2; omted is 196.

10 Data : Overview Coverage and time frame 3 countries pooled dataset arranged in five year averages centered around 196, 1965, 197,, 2 Weights matrix inverse distance matrix (W) cut-off point 2 miles, standardized n = 3, 33% nonzero, min/max links 2/4, average 1 patial Variables patial lags (weighted average of the values of neighbors) 1

11 Creating the Weight Matrix W ij = 1/ d 1/ d 2,1 3,1 1/ d 1/ d 1,2 3,2 1/ d 1/ d 1,3 2,3 where w ij = 1/ d ij if < otherwise d represents the distance between country i ij and country jmeasured from the center. d ij 2miles

12 tandardized Weight Matrix (see Bell and Bockstael 2) ij = 2,1 3,1 1,2 3,2 1,3 2,3 Where ij = d ij 3 j = 1 1 d ij

13 patial lag variable for income per person Weights Matrix Income per person Income spatial lags variable (Wpci) = 3,2 2,2 3,1 1,2 2,3 3,2 2,1 1,2 1,3 3,2 1,2 2,2 3,2 2,2 1,2 3,2 3,1 2,3 2,1 1,3 1,2 y y y y y y y y y

14 Unequal world income distribution 2 year 2 constant PPP U$ strong geographic cluster dramatically low in Africa W pci pci 2 Moran scatterplot Gradient of the trend is the coefficient of spatial correlation 14 strong cluster

15 Income variation across Africa 2 year 2 constant PPP U$ moderate clustering high in North and outh Africa moderate in coastal regions W pci2 4 2 moderate clustering pci2 15-4

16 World Agricultural Output 2 dramatically low Africa high North America Europe Australia W agout agout 2 patial autocorrelation low, but posive 16

17 Agricultural Output Across Africa 2 high Egypt outh Africa Tunisia Uganda lowest D. R. Congo W agout agout2 patial autocorrelation low, but posive 17

18 World Trade hare 2 patterns less obvious highest ingapore Guyana Malaysia Ireland W trade trade 2 no significant spatial clustering in trade 18

19 Trade hare Across Africa 2 Patterns less obvious highest Angola Gambia Tunisia enegal W.trade trade no significant spatial clustering in trade 19

20 World Infant Mortaly Rate 2 highest India Pakistan Iraq ub-aharan Africa low North America Europe Australasia W imort imort (/) cluster Africa, Asia 2

21 Infant Mortaly Rate Across Africa 2 Highest Central Africa low North Africa outh Africa W.imort imort strong cluster in central Africa 21

22 World Instutions Qualy 2 Lowest Africa Asia highest North America Northern Europe and Australasia intermediate zone outhern Europe W inqual inqual two clusters (/) industrial ( / ) rest of the world 22

23 Instutional Qualy Across Africa 2 highest outh Africa W inqual2 4 2 Moderate clusters inqual2 23-4

24 World Urbanization 2 Uniform 5% for most countries But low in Africa and outheast Asia W urban urban strong clusters for / and / 24

25 Urbanization 2 High North Africa outh Africa W urban2 4 2 urban strong clusters in east Africa

26 26 A simultaneous-equations model ( ) t t i t i t i i i t t i i i trade urbanization instqual schooling imrate agoutput a income population coastal income a trade coastal agouput income a urbanization pctmus pctprot pctcath imrate income a instqual malaria income a imrate malaria rainfall frost landqual agland a agoutput (6) (5) (4) (3) (2) 1 ε δ ε δ ε δ ε δ ε δ ε δ = = = = = =

27 Econometric method Issues endogeney: system approach spatial dynamics: ARAR ARAR single equation lag and error correlation y = ρwy X ε, ε = λwε μ system of m equations Y = YP YΓ U = UΛ E XB U Estimators Feasible G3L estimator (Kelejian and Prucha 24) estimate equations wh 2L choice of instruments obtain 2L residuals and use GM to determine λ perform Cochrane-Orcutt transformation, apply feasible G2L use feasible G3L to obtain full information results 27

28 Empirical results (i) exploratory results (OL) Variables ignificance is indicated by ***, ** and * for the 1, 5, and 1 per cent level, respectively LM H H lag 1 : ρ = : Agricultural output y test : = ρwy Infant mortaly rate X ε Instutional qualy Urbanization Trade Income LM-error ** 3.284**.468 Robust LM-error ** 2.564* ** LM-lag 1.654* 3.93** * 4.819** ** Robust LM-lag *** 3.86** ** ** LM H H 1 Error : λ = test : ε = λwε u : All variables display spatial dependence no clear spatial-error for agricultural output 28

29 Empirical results (ii) Regression output, system estimation, 3L not allowing for spatial spillovers Variables Agricultural output Infant mortaly rate Instutional qualy Urbanization Trade Income Agricultural output.41***.43*** Agricultural land.12** Land qualy.21* Frost.16 Rainfall.164*** Malaria.4**.38*** Infant mortaly rate.637*** 1.37*** Income.337***.146***.551***.624*** Instutional qualy.166 trade.312*** Catholic.6*** Protestant.1 Muslim.2 Urbanization Coastal.35***.13* Population.79*** ignificance is indicated by ***, ** and * for the 1, 5, and 1 per cent level, respectively.57*** comparison purposes account for endogeney no spatial dependency system context 29

30 Empirical results (iii) Regression output, system estimation, full information estimator for the ARAR Variables W agricultural output Agricultural output.837*** Infant mortaly rate W infant mortaly.4 W instutional qualy Instutional qualy.821*** W urbanization.51*** W trade Urbanization Trade Income.16 W income.196*** ignificance is indicated by ***, ** and * for the 1, 5, and 1 per cent level, respectively account for endogeney and neighborhood effects ARAR system context controlling observed variables 3

31 Empirical results (iv) Regression output, system estimation, full information estimator for the ARAR Variables Agricultural output Infant mortaly rate Instutional qualy Urbanization Trade Income Agricultural output.186***.112** Agricultural land.122** Land qualy.4 Frost.26 Rainfall.55 Malaria.54**.68*** Infant mortaly rate.188**.753*** Income.257*** ***.211** Instutional qualy.613*** Trade.12* Catholic.1 Protestant.3** Muslim.1 Urbanization.536*** Coastal.35***.16 Population.128*** ignificance is indicated by ***, ** and * for the 1, 5, and 1 percent level, respectively controlling unobserved variables coefficients are of smaller values several interesting correlations 31

32 ummary determinants of country income whin Africa. instutions first Instutions shaped by external condions and social condions geography first geography influences both trade and urbanization Novelty of the paper : correct omted variable due to spillovers among neighboring countries correct misspecification of error structure due shared characteristics of places In doing so, we found significant spatial lags among most endogenous variables support for both geographic and instutional claims 32

33 Implications Good instutions needed poorest regions in Africa Investment where technologies are location-specific policy transportation sector public health Implications of spatial analysis for growth theory ignificant lags suggest that determinants of economic growth should not be examined in isolation spatial method rigorously applied produces better results 33

34 Questions