Ethnolinguistic Diversity: Origins and Implicationsdevollra/Houston.pdfTheory Empirics Conclusion Appendix Ethnolinguistic Diversity: Origins and Implications Stelios Michalopoulos

IntroductionTheoryEmpirics

ConclusionAppendix

Ethnolinguistic Diversity: Origins and Implications

Stelios Michalopoulos

Department of EconomicsBrown University

February 27, 2008

University of Houston - Department of Economics

Stelios Michalopoulos Ethnolinguistic Diversity: Origins and Implications


ConclusionAppendix

ObjectiveFindingsEthnic Diversity a focal topicThe IdeaA Parallel

Objectives

Provide a theoretical and empirical framework for understanding the

Economic origins of ethnic diversity

Its implications for comparative economic development today



ConclusionAppendix


Objectives

Provide a theoretical and empirical framework for understanding the

Economic origins of ethnic diversity

Its implications for comparative economic development today



ConclusionAppendix


Key Finding

This research shows that:

Diversity in land quality across regions

=)

to the formation and persistence of ethnic diversity



ConclusionAppendix


Additional Findings

Contemporary ethnic diversity displays:

1 A natural component driven by diversity in land quality acrossregions

2 A Man-made component driven by state history

e.g. the European colonization, early economic development etc.



ConclusionAppendix


Additional Findings

Contemporary ethnic diversity displays:

1 A natural component driven by diversity in land quality acrossregions

2 A Man-made component driven by state history

e.g. the European colonization, early economic development etc.



ConclusionAppendix


Why A Theory of Ethnic Diversity?

Long standing subject of research in the realm of socialsciences

Economists have studied:

the impact of ethnic diversity on economic outcomes

the economic performance of ethnic groups themselves



ConclusionAppendix


Previous Research

Ethnic divisions and economic performance across countries

Easterly, Levine (1997)

Fractionalization and public good provision, civil con�ict etc.

Fearon, Alesina et al (2003), Banerjee el al (2006) among others

Inequality across ethnic groups

Loury (1977), Esteban and Ray (2007)

Optimal State formation

Alesina, Spolaore (1997), Alesina et al (2006)



ConclusionAppendix


Previous Research

Ethnic divisions and economic performance across countries

Easterly, Levine (1997)

Fractionalization and public good provision, civil con�ict etc.

Fearon, Alesina et al (2003), Banerjee el al (2006) among others

Inequality across ethnic groups

Loury (1977), Esteban and Ray (2007)

Optimal State formation

Alesina, Spolaore (1997), Alesina et al (2006)



ConclusionAppendix


Interpretation?

However,

the economic origins of ethnic diversity are poorly understood,both empirically and theoretically



ConclusionAppendix


The Forces Behind Ethnic Diversity

Ethnic diversity has deep roots in:

geographic variability

a country�s state history

State history itself may have an independent e¤ect on economicoutcomes

Identifying the foundations of ethnic diversity will improve onthe interpretation of existing �ndings



ConclusionAppendix










ConclusionAppendix










ConclusionAppendix


The Idea

Diversity in land quality across regions =) to the formation andpersistence of ethnic diversity

The main elements:

1 Variation in regional land quality )region speci�c human capital

2 Di¤erences in region speci�c human capital )barrier to population mixing

3 Limited population mixing between regions )emergence of di¤erential ethnic traits



ConclusionAppendix


The Idea


The main elements:






ConclusionAppendix


The Idea


The main elements:






ConclusionAppendix


The Idea


The main elements:





Herders

Farmers

AIsland

QualityLand Farmers

Farmers

BIsland

Herders

Farmers

aEthnicity

bEthnicity

AIsland

QualityLand

DiversityEthnic

Farmers

Farmers

BIsland

Herders

Farmers

aEthnicity

bEthnicity

Farmers

Farmers

EthnicityOne

BIsland AIsland

QualityLand

DiversityEthnic

Variation inLand Quality

Linguistic Diversity

Variation inLand Quality

Linguistic Diversity


ConclusionAppendix


Population Movements and the Spread of Languages

(Pre)Historical Evidence

Families of languages spread over areas which wereproductively relevant for the people speaking these languages

Nomad pastoralists spread where herding was economically optimal

Spread of Altaic language family (Turkish and Mongolian) over theGrasslands of Central Eurasia

Farmers expanded towards regions suitable for agriculture.

Indo-European languages spread from Anatolia, Nature (2003)



ConclusionAppendix


Population Movements and the Spread of Languages

(Pre)Historical Evidence

Families of languages spread over areas which wereproductively relevant for the people speaking these languages

Nomad pastoralists spread where herding was economically optimal

Spread of Altaic language family (Turkish and Mongolian) over theGrasslands of Central Eurasia

Farmers expanded towards regions suitable for agriculture.

Indo-European languages spread from Anatolia, Nature (2003)



ConclusionAppendix


Do humans behave like Darwin�s �nches?

Darwin�s �nches and ethnicities:

an ecological niche ) certain shape of beak

ecologically diverse places ) diversity within �nches

a level of land quality ) speci�c human capital

variation in land quality ) ethnic diversity

Channel: Di¤erences in region speci�c human capital)limits population mixing) increasing ethnic diversity.



ConclusionAppendix











ConclusionAppendix











ConclusionAppendix

The Basic StructureHouseholdsLabor Allocation Across RegionsThe Properties of MigrationThe Formation of Common Ethnicity Over Time

The Basic Structure of the Model

Overlapping-generations economy

t = 0; 1; 2; :::

Single Good

Two regions i and j

Production Inputs: Land, Labor, Region-Speci�c Technology

No population growth



ConclusionAppendix


The Basic Structure of the Model

Overlapping-generations economy

t = 0; 1; 2; :::

Single Good

Two regions i and j

Production Inputs: Land, Labor, Region-Speci�c Technology

No population growth



ConclusionAppendix


The Production of Final Output

Output per worker in region i , y it , is

y it =�z ith

it

� �X=Lit

�1��; � 2 (0; 1)

z it � temporary productivity shock

hit � region i speci�c human capital

Lit � labor employed

X � land



ConclusionAppendix


Households

Individuals are identical and live two-periods

1st Period �Accumulate passively the region speci�c humancapital they are born to.

2nd Period � Inelastically supply the unit time endowment aslabor and consume the earnings

Agents maximize income by working in either region.



ConclusionAppendix


Households







ConclusionAppendix


Households







ConclusionAppendix


The Forces at Work

1 Regional productivity shocks generate an incentive to relocate,�t = z it=z

jt :

2 Relocation entails erosion of knowledge due to di¤erences inregion speci�c human capital, ".



ConclusionAppendix


The Forces at Work

1 Regional productivity shocks generate an incentive to relocate,�t = z it=z

jt :

2 Relocation entails erosion of knowledge due to di¤erences inregion speci�c human capital, ".



ConclusionAppendix


Formation of Common Ethnicity

Conjecture:

the probability, fT ; that individuals from regions i and j sharecommon traits in period T increases in the intensity ofpopulation mixing between the two regions over time

fT =

TPt=1It

T

It =�1 if migration occurs in period t.0 if otherwise



ConclusionAppendix


Properties of Migration


t λ

s λ

ε1

AreaMigration No

j i MM

i j MM

0>→ i j M

0>→ j i M

0

Stylianos

Text Box

λt: ratio of the regional productivity shocks in period t ε: erosion of regional human capital in case or migration


ConclusionAppendix


P1 - The Formation of Common Ethnic Traits

1. The probability, fT ; that regions i and j share common ethnictraits in period T increases in the variance of relativeproductivity shock, �t ;

@fT@var (�t)

> 0



ConclusionAppendix


P2 - The Formation of Common Ethnic Traits

2. The probability, fT ; that regions i and j share common ethnictraits in period T decreases in the size of the erosion, ";

@fT@"

< 0



ConclusionAppendix

Data SourcesCross-Arti�cial Country ResultsPairwise Analysis of Adjacent RegionsCross-Real Country AnalysisThe Colonizing Experience

Ideal Data

Need an index of the transferability of region speci�c humancapital, ";

Ideally:

regional distributions of productive activities in a period ofhuman history when the formation of cultural traits was takingplace



ConclusionAppendix


The Data

Disaggregated level data on the suitability of land foragriculture

Each observation is between 0 and 1, i.e. probability that aparticular grid cell may be cultivated

a) Monthly climate conditions for 1961�1990:

temperature

precipitation

potential sunshine hours.

b) Soil characteristics:

total organic content (soil carbon density)

the nutrient availability.(soil pH)



ConclusionAppendix


The Data

Disaggregated level data on the suitability of land foragriculture

Each observation is between 0 and 1, i.e. probability that aparticular grid cell may be cultivated

a) Monthly climate conditions for 1961�1990:

temperature

precipitation

potential sunshine hours.

b) Soil characteristics:

total organic content (soil carbon density)

the nutrient availability.(soil pH)



ConclusionAppendix


Data Coverage

Global coverage

The resolution is 0.5 degrees, latitude x longitude, averagesize about 55km by 35km.

58920 observations


Global land qualityHigh : 0.999

Low : 0.000


ConclusionAppendix


Distribution of land quality across countries: An example0

24

6D

en

sity

0 .2 5 .5 .7 5 1Qu a lity of La nd

Kerne l d e ns ity es tim a tekde ns ity va lu eRed line: Nepal; Blue line: Greece



ConclusionAppendix


Cross-"Arti�cial Country" Analysis

Unit of analysis: arti�cial countries of 4 degrees latitude by 4degrees longitude

Median land quality observations per arti�cial country: 53

Dependent variable: number of languages spoken today

Source: 15th edition of the Ethnologue linguistics database



ConclusionAppendix


Cross-"Arti�cial Country" Analysis

Unit of analysis: arti�cial countries of 4 degrees latitude by 4degrees longitude

Median land quality observations per arti�cial country: 53

Dependent variable: number of languages spoken today

Source: 15th edition of the Ethnologue linguistics database


Global land qualityHigh : 0.999

Low : 0.000

Regional land quality

0.00 - 0 .15

0.16 - 0 .30

0.31 - 0 .45

0.46 - 0 .72


0.00 - 0 .15

0.16 - 0 .30

0.31 - 0 .45

0.46 - 0 .72


0.00 - 0 .15

0.16 - 0 .30

0.31 - 0 .45

0.46 - 0 .72


0.00 - 0 .15

0.16 - 0 .30

0.31 - 0 .45

0.46 - 0 .72


0.00 - 0 .15

0.16 - 0 .30

0.31 - 0 .45

0.46 - 0 .72


0.00 - 0 .15

0.16 - 0 .30

0.31 - 0 .45

0.46 - 0 .72


0.00 - 0 .15

0.16 - 0 .30

0.31 - 0 .45

0.46 - 0 .72 Number of Languages spoken today in this area: 12


ConclusionAppendix


Arti�cial Countries

Focus on Arti�cial Countries that:

have at least 3 regional observations

each region has population density > 1 person per sq. km



ConclusionAppendix


Results

The support of the distribution of land quality, rangei ;

captures how di¢ cult it is to transfer regional knowledge

Prediction:

Larger spectrum of land qualities ) higher ethnic diversity


Table 1a: Summary Statistics for the Cross-Artificial Country Analysis

statistics #_lang range avg elev_sd areakm2 in_country #_cntry water_area sea_distmean 6.13 0.53 0.40 0.27 98.76 0.48 1.93 1.93 0.49

sd 6.08 0.29 0.27 0.28 60.16 0.50 1.17 2.54 0.57

max 30.00 1.00 0.98 2.17 196.98 1.00 8.00 19.45 2.40

min 1.00 0.00 0.00 0.00 0.61 0.00 1.00 0.00 0.00#_lang : number of languages spoken with at least 1% area coverage within an artificial country;range : spectrum of land qualities within an "artificial country", i.e. the difference in land quality between the region with the highest land quality from that with the lowest; avg : is the average land quality within artificial country;elev_sd : standard deviation of elevation measured in kilometers within artificial country; in_cntry: dummy equals 1 if artificial country's falls as a whole into a single real country; areakm2 : size of each artificial country in thousands of sq. km.#_cntry: number of real countries an artificial country falls into; water_area: area in thousand's of sq km of artificial countryunder water i.e. river or lake; sea_dist: distance of the centroid of an artificial country from the coastline;Data Sources: See Appendix D

Table 1b: The Correlation Matrix for the Cross-Artificial Country Analysis

#_lang range avg elev_sd areakm2 in_country #_cntry water_area sea_dist#_lang 1

range 0.29 1

avg 0.07 0.38 1

elev_sd 0.27 0.20 -0.02 1

areakm2 0.32 0.40 -0.06 0.11 1

in_country -0.27 -0.31 0.01 -0.11 -0.35 1

numb_cntry 0.34 0.32 0.01 0.19 0.36 -0.77 1

water_area 0.04 0.15 -0.13 -0.04 0.37 -0.25 0.18 1

sea_dist 0.02 0.25 -0.11 0.09 0.42 -0.20 0.13 0.26 1#_lang : number of languages spoken with at least 1% area coverage within an artificial country;range : spectrum of land qualities within an "artificial country", i.e. the difference in land quality between the region with the highest land quality from that with the lowest; avg : is the average land quality within artificial country;elev_sd : standard deviation of elevation measured in kilometers within artificial country; in_cntry: dummy equals 1 if artificial country's falls as a whole into a single real country; areakm2 : size of each artificial country in thousands of sq. km.#_cntry: number of real countries an artificial country falls into; water_area: area in thousand's of sq km of artificial countryunder water i.e. river or lake; sea_dist: distance of the centroid of an artificial country from the coastline;Data Sources: See Appendix D

Table 2: Main Specification and Robustness in Cross-Artificial Country Regressions

Dependent Variable: Log Number of Languages Spoken

OLS

Baseline

(1)

range 0.952(4.66)***

Observations 548R-squared 0.09OLS regressions with absolute value of t statistics in parentheses;Standard errors are corrected for spatial correlation following Conley (1999);* significant at 10%; ** significant at 5%; *** significant at 1%;



OLS OLS

BaselineGeographical Controls

(1) (2)

range 0.952 0.488(4.66)*** (2.27)**

avg -0.104(0.50)

areakm2 0.002(1.48)

abs_lat -0.027(5.95)***

elev_sd 0.498(2.18)**

in_country -0.172(1.98)**

#_cntry 0.170(5.33)

sea_dist 0.068(0.64)

water_area 0.017(1.15)

Observations 548 548R-squared 0.09 0.52OLS regressions with absolute value of t statistics in parentheses;Standard errors are corrected for spatial correlation following Conley (1999);* significant at 10%; ** significant at 5%; *** significant at 1%;



OLS OLS OLS


Continental and Country fixed effects

(1) (2) (3)

range 0.952 0.488 0.524(4.66)*** (2.27)** (3.88)***

avg -0.104 -0.184(0.50) (1.13)

areakm2 0.002 0.002(1.48) (1.83)*

abs_lat -0.027 -0.026(5.95)*** (3.17)***

elev_sd 0.498 0.684(2.18)** (3.07)***

in_country -0.172 -0.094(1.98)** (0.95)

#_cntry 0.170 0.161(5.33) (2.47)**

sea_dist 0.068 -0.038(0.64) (0.42)

water_area 0.017 -0.009(1.15) (0.87)

Observations 548 548 548R-squared 0.09 0.52 0.81OLS regressions with absolute value of t statistics in parentheses;Standard errors are corrected for spatial correlation following Conley (1999);* significant at 10%; ** significant at 5%; *** significant at 1%;Specification (3) includes country and continental fixed effects;



OLS OLS OLS OLS



Average Land Quality and Interaction

(1) (2) (3) (4)

range 0.952 0.488 0.524 0.545(4.66)*** (2.27)** (3.88)*** (3.03)***

avg -0.104 -0.184 -0.160(0.50) (1.13) (0.70)

avgxrange -0.059(0.17)

areakm2 0.002 0.002 0.002(1.48) (1.83)* (1.79)*

abs_lat -0.027 -0.026 -0.026(5.95)*** (3.17)*** (3.18)***

elev_sd 0.498 0.684 0.682(2.18)** (3.07)*** (3.02)***

in_country -0.172 -0.094 -0.094(1.98)** (0.95) (0.95)

#_cntry 0.170 0.161 0.161(5.33) (2.47)** (2.45)**

sea_dist 0.068 -0.038 -0.038(0.64) (0.42) (0.42)

water_area 0.017 -0.009 -0.009(1.15) (0.87) (0.88)

Observations 548 548 548 548R-squared 0.09 0.52 0.81 0.81OLS regressions with absolute value of t statistics in parentheses;Standard errors are corrected for spatial correlation following Conley (1999);* significant at 10%; ** significant at 5%; *** significant at 1%;Specifications (3) and (4) include country and continental fixed effects;



OLS OLS OLS OLS OLS




Regions Within Countries

(1) (2) (3) (4) (5)

range 0.952 0.488 0.524 0.545 0.460(4.66)*** (2.27)** (3.88)*** (3.03)*** (2.85)***

avg -0.104 -0.184 -0.160 -0.229(0.50) (1.13) (0.70) (1.15)


areakm2 0.002 0.002 0.002 0.004(1.48) (1.83)* (1.79)* (3.10)***

abs_lat -0.027 -0.026 -0.026 -0.026(5.95)*** (3.17)*** (3.18)*** (2.68)***

elev_sd 0.498 0.684 0.682 1.320(2.18)** (3.07)*** (3.02)*** (4.21)***

in_country -0.172 -0.094 -0.094(1.98)** (0.95) (0.95)

#_cntry 0.170 0.161 0.161(5.33) (2.47)** (2.45)**

sea_dist 0.068 -0.038 -0.038 -0.160(0.64) (0.42) (0.42) (1.37)

water_area 0.017 -0.009 -0.009 -0.013(1.15) (0.87) (0.88) (0.57)

Observations 548 548 548 548 263R-squared 0.09 0.52 0.81 0.81 0.78OLS regressions with absolute value of t statistics in parentheses;Standard errors are corrected for spatial correlation following Conley (1999);* significant at 10%; ** significant at 5%; *** significant at 1%;Specifications (3), (4) and (5) include country and continental fixed effects;



OLS OLS OLS OLS OLS OLS




Regions Within Countries

Tropics and Non-Tropics

(1) (2) (3) (4) (5) (6)

range 0.952 0.488 0.524 0.545 0.460 0.345(4.66)*** (2.27)** (3.88)*** (3.03)*** (2.85)*** (2.84)***

avg -0.104 -0.184 -0.160 -0.229 -0.183(0.50) (1.13) (0.70) (1.15) (1.15)


areakm2 0.002 0.002 0.002 0.004 0.002(1.48) (1.83)* (1.79)* (3.10)*** (1.73)*

abs_lat -0.027 -0.026 -0.026 -0.026 -0.022(5.95)*** (3.17)*** (3.18)*** (2.68)*** (2.56)**

elev_sd 0.498 0.684 0.682 1.320 0.702(2.18)** (3.07)*** (3.02)*** (4.21)*** (3.26)***

in_country -0.172 -0.094 -0.094 -0.101(1.98)** (0.95) (0.95) (1.03)

#_cntry 0.170 0.161 0.161 0.157(5.33) (2.47)** (2.45)** (2.34)**

sea_dist 0.068 -0.038 -0.038 -0.160 -0.029(0.64) (0.42) (0.42) (1.37) (0.37)

water_area 0.017 -0.009 -0.009 -0.013 -0.006(1.15) (0.87) (0.88) (0.57) (0.64)

tropics -0.079(0.54)

tropicsxrange 0.633(2.79)***

Observations 548 548 548 548 263 548R-squared 0.09 0.52 0.81 0.81 0.78 0.82OLS regressions with absolute value of t statistics in parentheses;Standard errors are corrected for spatial correlation following Conley (1999);* significant at 10%; ** significant at 5%; *** significant at 1%;Specifications (3), (4), (5) and (6) include country and continental fixed effects;


ConclusionAppendix


Pairwise Analysis of Contiguous Regions

Unit of analysis: Pairs of adjacent regions

average distance within a regional pair: 50 km.

Total number of unique regional pairs 159358

Index of Ethnic Similarity: Percentage of common languagesspoken within a pair

i.e. number of common languages spoken divided by the total number

within a regional pair



ConclusionAppendix


Pairwise Analysis of Contiguous Regions

Unit of analysis: Pairs of adjacent regions

average distance within a regional pair: 50 km.

Total number of unique regional pairs 159358

Index of Ethnic Similarity: Percentage of common languagesspoken within a pair

i.e. number of common languages spoken divided by the total number

within a regional pair



0.00 - 0 .15

0.16 - 0 .30

0.31 - 0 .45

0.46 - 0 .72

Stylianos

Text Box

The blue arrows point to the neighbors of a region. In this case 8.

Stylianos

Line

Stylianos

Line

Stylianos

Line

Stylianos

Line

Stylianos

Line

Stylianos

Line

Stylianos

Line

Stylianos

Line


ConclusionAppendix


Predictions

The di¤erence in land quality within a pair, lqdi¤ij ; captureshow di¢ cult it is to transfer regional knowledge

Prediction:

Larger di¤erence in land quality ) Lower ethnic similarity


Table 3a: Summary Statistics for the Pairwise Analysis of Adjacent Regions

statistics pct_comlang lqdiff eldiff #_lang sea_dist same_cntry #_body_waters diff_langareamean 0.77 0.08 0.15 2.19 0.70 0.94 4.33 0.26

sd 0.30 0.12 0.25 2.22 0.59 0.24 7.70 0.49

max 1.00 0.99 4.00 67.00 2.68 1.00 176.50 3.11

min 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00pct_comlang : number of common languages divided by the total number of unique languages spoken within a pairof adjacent regions; lqdiff : absolute difference in land quality within a pair of adjacent regions; eldiff : absolutedifference in elevation in km's within a pair of adjacent regions; sea_dist: distance from the coastline in 1000s of km of a regional pair; same_country: dummy equals 1 if a regional pair belongs to the same country; #_body_waters: numberof distinct body waters within a regional pair; #_lang_pair: total number of unique languages spoken within a pair;diff_langarea : difference in area of linguistic coverage between the regional neighbors in 1000 of sq kilometers;

Table 3b: The Correlation Matrix for the Pairwise Analysis of Adjacent Regions

pct_comlang lqdiff eldiff #_lang sea_dist same_cntry #_body_waters diff_langareapct_comlang 1.00

lqdiff -0.15 1.00

eldiff -0.15 0.25 1.00

#_lang -0.60 0.15 0.16 1.00

dist_sea 0.08 0.00 0.02 -0.10 1.00

same_cntry 0.15 -0.08 -0.06 -0.15 0.00 1.00

#_body_waters 0.10 -0.07 -0.14 -0.10 -0.10 0.02 1.00

diff_langarea -0.09 0.00 0.08 0.03 -0.06 -0.07 -0.02 1.00pct_comlang : number of common languages divided by the total number of unique languages spoken within a pairof adjacent regions; lqdiff : absolute difference in land quality within a pair of adjacent regions; eldiff : absolutedifference in elevation in km's within a pair of adjacent regions; sea_dist: distance from the coastline in 1000s of km of a regional pair; same_country: dummy equals 1 if a regional pair belongs to the same country; #_body_waters: numberof distinct body waters within a regional pair; #_lang_pair: total number of unique languages spoken within a pair;diff_langarea : difference in area of linguistic coverage between the regional neighbors in 1000 of sq kilometers;

Table 4: Main Specification and Robustness in the Pairwise Analysis of Adjacent Regions

Dependent Variable: Percentage of Common Languages

OLS

Baseline

(1)

lqdiff -0.163(7.13)***

eldiff -0.120(9.48)***

abs_lat 0.005(20.20)***

elev 0.002(0.37)

land_quality -0.029(1.80)*

sea_dist 0.019(2.51)**

same_country 0.132(14.96)***

#_body_waters -0.006(0.15)

diff_langarea -0.013(2.41)**

constant 0.475(27.05)

R-squared 0.02Observations 159358OLS regressions with absolute value of t statistics in parentheses.Standard errors are corrected for spatial correlation following Conley (1999)* significant at 10%; ** significant at 5%; *** significant at 1%



OLS OLS

Baseline

Country and Continental fixed effects

(1) (2)

lqdiff -0.163 -0.122(7.13)*** (15.66)***

eldiff -0.120 0.095(9.48)*** (22.29)***

abs_lat 0.005 0.003(20.20)*** (16.70)***

elev 0.002 0.001(0.37) (0.50)

land_quality -0.029 -0.027(1.80)* (5.97)***

sea_dist 0.019 0.011(2.51)** (4.83)***

same_country 0.132 0.108(14.96)*** (30.37)***

#_body_waters -0.006 0.0002(0.15) (1.95)*

diff_langarea -0.013 -0.009(2.41)** (4.35)***

constant 0.475 0.368(27.05) (10.97)

R-squared 0.18 0.28Observations 159358 159358OLS regressions with absolute value of t statistics in parentheses; In (1), (3) standard errors are correctedfor spatial autocorrelation following Conley (1999); In (2) standard errors are clustered at the level of eachindividual region; * significant at 10%; ** significant at 5%; *** significant at 1%;(2) includes both continental and country and fixed effects for each region within a pair;



OLS OLS OLS

Baseline


Baseline within India

(1) (2) (3)

lqdiff -0.163 -0.122 -0.172(7.13)*** (15.66)*** (2.13)**

eldiff -0.120 0.095 -0.127(9.48)*** (22.29)*** (3.53)***

abs_lat 0.005 0.003 -0.010(20.20)*** (16.70)*** (2.33)**

elev 0.002 0.001 -0.042(0.37) (0.50) (3.57)***

land_quality -0.029 -0.027 -0.311(1.80)* (5.97)*** (3.70)***

sea_dist 0.019 0.011 0.082(2.51)** (4.83)*** (1.15)

same_country 0.132 0.108(14.96)*** (30.37)***

#_body_waters -0.006 0.0002 0.006(0.15) (1.95)* (5.78)***

diff_langarea -0.013 -0.009 -0.042(2.41)** (4.35)*** (1.46)

constant 0.475 0.368 0.958(27.05) (10.97) (10.41)***

R-squared 0.18 0.28 0.21Observations 159358 159358 4138OLS regressions with absolute value of t statistics in parentheses; In (1), (3) standard errors are correctedfor spatial autocorrelation following Conley (1999); In (2) standard errors are clustered at the level of eachindividual region; * significant at 10%; ** significant at 5%; *** significant at 1%;(2) includes both continental and country and fixed effects for each region within a pair;



OLS OLS OLS OLS

Baseline


Baseline within India

Marginal Effects by Continent

(1) (2) (3) (4)

lqdiff -0.163 -0.122 -0.172 -0.266(7.13)*** (15.66)*** (2.13)** (13.04)**

eldiff -0.120 0.095 -0.127 -0.090(9.48)*** (22.29)*** (3.53)*** (21.19)***

abs_lat 0.005 0.003 -0.010 0.004(20.20)*** (16.70)*** (2.33)** (17.30)***

elev 0.002 0.001 -0.042 -0.001(0.37) (0.50) (3.57)*** (0.55)

land_quality -0.029 -0.027 -0.311 -0.025(1.80)* (5.97)*** (3.70)*** (5.44)***

sea_dist 0.019 0.011 0.082 0.009(2.51)** (4.83)*** (1.15) (3.97)***

same_country 0.132 0.108 0.108(14.96)*** (30.37)*** (30.26)***

#_body_waters -0.006 0.0002 0.006 0.0002(0.15) (1.95)* (5.78)*** (1.70)*

diff_langarea -0.013 -0.009 -0.042 -0.008(2.41)** (4.35)*** (1.46) (4.25)***

lqdiff_asia 0.061(2.54)**

lqdiff_americas 0.287(11.50)***

lqdiff_europe 0.204(8.17)***

lqdiff_pacific -0.275(3.72)***

constant 0.475 0.368 0.958 0.795(27.05)*** (10.97)*** (10.41)*** (7.03)***

R-squared 0.18 0.28 0.21 0.29Observations 159358 159358 4138 159358OLS regressions with absolute value of t statistics in parentheses; In (1), (3) standard errors are correctedfor spatial autocorrelation following Conley (1999); In (2) and (4) standard errors are clustered at the level of eachindividual region; * significant at 10%; ** significant at 5%; *** significant at 1%;(2), (4) include both continental and country and fixed effects for each region within a pair; (5) allows for differentialmarginal effect of the difference in land quality for pairs of different continents; Africa is the omitted continent;pct_comlang : number of common languages divided by the total number of unique languages spoken within a pairof adjacent regions; lqdiff : absolute difference in land quality within a pair of adjacent regions; eldiff : absolutedifference in elevation in km's within a pair of adjacent regions; sea_dist: distance from the coastline in 1000s of km of a regional pair; same_country: dummy equals 1 if a regional pair belongs to the same country; #_body_waters: numberof distinct body waters within a regional pair;diff_langarea : difference in area of linguistic coverage between the regional neighbors in 1000 of sq kilometers;


ConclusionAppendix


Cross-Real Country Analysis

Dependent variable: ethnolinguistic fractionalization (ELF )come from Fearon and Laitin (2003),

an augmented version of the original ELF measure from the Atlas

Narodov Mira (1964).

This index represents for each country the probability that twoindividuals randomly drawn from the overall population belongto di¤erent ethnolinguistic groups.



ConclusionAppendix


Why the distribution of population across regions matters?

Expected fractionalization, E (ELF ); for a pair of places:

E (ELF ) = (1� fT ) 1�

�LiT

LjT+LiT

�2��

LjTLjT+L

jT

�2!# #

Prob. regions i ; j Probability individuals

di¤erent traits drawn from regions i ; j

Unequal distribution of land quality drives inequality in theregional population densities.

Correlation between the gini of land quality and the gini of regional

population density as of 1990 is 0:59



ConclusionAppendix


Why the distribution of population across regions matters?

Expected fractionalization, E (ELF ); for a pair of places:

E (ELF ) = (1� fT ) 1�

�LiT

LjT+LiT

�2��

LjTLjT+L

jT

�2!# #

Prob. regions i ; j Probability individuals

di¤erent traits drawn from regions i ; j

Unequal distribution of land quality drives inequality in theregional population densities.

Correlation between the gini of land quality and the gini of regional

population density as of 1990 is 0:59


Table 6: Main Specification for Cross-Country Analysis

Dependent Variable: ethnolinguistic fractionalization (ELF)

(1)

range 0.203(2.43)**

avg

avgxrange

lqgini

Adj. R-squared 0.03Observations 147Absolute value of t statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%



(1) (2)

range 0.203 0.455(2.43)** (3.55)***

avg 0.076(0.42)

avgxrange -0.659(2.18)**

lqgini

Adj. R-squared 0.04 0.11Observations 147 147Absolute value of t statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%



(1) (2) (3)

range 0.203 0.455 0.889(2.43)** (3.55)*** (5.73)***

avg 0.076 -0.283(0.42) (1.49)

avgxrange -0.659 -1.21(2.18)** (3.90)***

lqgini -0.874(4.45)***

Adj. R-squared 0.03 0.11 0.21Observations 147 147 147Absolute value of t statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

Table 7: Main Specification and Robustness Checks


BaselineContinental fixed effects

(1) (2)

range 0.925 0.641(5.92)*** (4.62)***

avg -0.198 -0.101(0.79) (0.58)

avgxrange -1.359 -0.653(3.85)*** (2.28)**

lqgini -0.91 -0.416(4.30)*** (2.30)**

reg_ssa 0.255(5.73)***

reg_lac -0.102(1.79)*

reg_we -0.172(2.70)***




BaselineAdditional geography

(1) (3)

range 0.925 0.692(5.92)*** (4.71)***

avg -0.198 -0.261(0.79) (1.54)

avgxrange -1.359 -0.834(3.85)*** (2.83)***

lqgini -0.91 -0.742(4.30)*** (3.91)***

abs_latitude -0.005(4.27)***

areakm2 -0.002(1.61)

distcr 0.202(4.14)***

elev_sd 0.037(0.65)




Baseline State History

(1) (5)

range 0.925 0.668(5.92)*** (4.87)***

avg -0.198 0.092(0.79) (0.52)

avgxrange -1.359 -0.765(3.85)*** (2.75)***

lqgini -0.91 -0.508(4.30)*** (2.81)***

abs_latitude -0.004(2.23)**

lpd1500 -0.034(2.03)**

yrentry 0.001(1.86)*

Adj. R-squared 0.21 0.50Observations 147 143Absolute value of t statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%Continental fixed effects and other geographical characteristics are included


ConclusionAppendix


The Role of the Colonizers

Historical evidence:

Introduced own ethnicities, manipulated existing ones

=)

distribution of land quality should explain less of the variationin contemporary ethnic diversity in the colonized world


Table 8: Colonization and Fractionalization


Colonized by Europeans

Non-Colonized by Europeans

(1) (2)

range 0.792 1.09(3.80)*** (4.02)***

avg -0.324 -0.34(1.37) (0.97)

avgxrange -0.892 -1.323(1.83)* (2.79)***

lqgini -0.813 -0.954(3.14)*** (3.16)***

Adj. R-squared 0.16 0.32

Observations 93 47Absolute value of t statistics in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%


ConclusionAppendix


The Role of the Colonizers

Historical evidence:

Drew borders in an arbitrary manner

=)

distribution of land quality should itself be conduciveto higher levels of fractionalization in colonized world


Table 9: Colonization and Natural Fractionalization

nat_ELF

colonized 0.40non-colonized 0.35

Pr(T < t) = 0.04


ConclusionAppendix

Main Contribution

Theoretical insight:

Ethnic groups are bearers of speci�c human capital

Empirical Results

Decompose contemporary ethnic diversity into:

1 Natural component induced by the underlying diversity in landqualities

2 Man-made component re�ecting divergent state histories



ConclusionAppendix

Main Contribution

Theoretical insight:

Ethnic groups are bearers of speci�c human capital

Empirical Results

Decompose contemporary ethnic diversity into:

1 Natural component induced by the underlying diversity in landqualities

2 Man-made component re�ecting divergent state histories



ConclusionAppendix

Applications - Preliminary Findings

Applications:

1. Causal impact of ethnolinguistic diversity on comparativeeconomic development


Table 10: Instrumental Variable Regressions for Economic and Political Variables

Dep. Var: log income per capita,

2002

OLS

(1)

ELF -1.045(0.29)***

avg -0.729(0.42)*

avgxrange 1.299(0.59)**

abs_lat 0.043(0.006)***

eucolony 0.237(0.22)

Observations 137R-squared 0.56Absolute values of standard errors reported in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%


Dep. Var: log income per capita,

2002

OLS 2SLS

(1) (2)

ELF -1.045 -0.147(0.29)*** (0.77)

avg -0.729 -0.364(0.42)* (0.53)

avgxrange 1.299 1.113(0.59)** (0.63)*

abs_lat 0.043 0.051(0.006)*** (0.009)***

eucolony 0.237 0.331(0.22) (0.24)

F-test of excluded instruments

9.97***

Sargan statistic (p-value)

0.55

Observations 137 137R-squared 0.56Absolute values of standard errors reported in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%

Table 11: Instrumental Variable Regressions for Quality of Life Variables

Dep. Var: Access to Clean water 2000

OLS

(1)

ELF -12.204(5.50)**

avg 5.127(8.99)

avgxrange 10.88(12.36)

abs_lat 0.645(0.13)***

eucolony 5.055(4.64)



Dep. Var: Access to Clean water 2000

OLS 2SLS

(1) (2)

ELF -12.204 5.279(5.50)** (16.00)

avg 5.127 12.302(8.99) (10.98)

avgxrange 10.88 7.538(12.36) (13.00)

abs_lat 0.645 0.816(0.13)*** (0.20)***

eucolony 5.055 8.097(4.64) (5.40)


8.64***


0.76



ConclusionAppendix

Applications - Future Research

Applications:

2. Inequality across ethnic groups

natural component:

ethnic speci�c human capital di¤erentially complementary with newtechnologies

man-made component:

di¤erential treatment of ethnic groups by the institutions in place



ConclusionAppendix


Applications:

3. The di¤usion of development across countries

genetic distance may proxy for di¤erences in the distribution of land

endowments, thus, di¤erences in society speci�c human capital



ConclusionAppendix


Applications:

4. Geographic Variability: Ethnic and State Formation.

homogeneous land endowments ) common need for a public good )incentive to politically centralize


AGO

BDIBEN

BFA

BWA

CAFCIV

CMR

CODCOG

ETH

GAB

GHA

GIN

KENLBR

LSO

MDG

MLI

MOZ

MRT

MWI

NAMNER

NGA

RWA

SDN

SEN

SLESOM

TCD

TGO

TZAUGA

ZMB

ZWE-.4

-.20

.2.4

Cen

traliz

atio

n

-.4 -.2 0 .2 .4Variation in Land Quality


ConclusionAppendix

Language Divergence: An ExampleArti�cial Country PlotReal Country PlotGroups in PeruGroups in KenyaIV Econ VarIV Polit VarIV Quality Var

Soil ph and Precipitation



ConclusionAppendix


Example of Language Divergence

Divergence Within the Indo-European Language Group (Science 2003)

7000 years BP: Greek and Armenian diverge

5000 years BP: Italic, Germanic, Celtic, Indo-Iranian familiesdiverge

1750 years BP: Germanic languages split between WestGermanic and North Germanic

North Germanic: Danish and Swedish

West Germanic: German, Dutch, English



ConclusionAppendix











ConclusionAppendix











ConclusionAppendix











ConclusionAppendix


Arti�cial Country Scatterplot


-2-1

01

2Lo

g La

ngua

ges

-1 -.5 0 .5Spectrum of Land Quality

Partial Scatter plot of Spectrum of land quality and log number of languages within “Artificial Countries” controlling for variation in elevation, average land quality and abs latitude.The red dots represent African “Artificial Countries”.


ConclusionAppendix


Real Country Scatterplot


Net relationship between range and ELF:Cross-Country Analysis

AFG

AGO

ALB

ARE

ARGARM

AUS

AUT

AZEBDI

BEL

BEN

BFA

BGDBGR

BIH

BLR

BOL

BRA

BTN

BWA

CAF

CAN

CHECHLCHN

CIVCMRCOD

COG

COL

CRICUB

CZE

DEU

DJI

DNK

DOMDZA

ECU

EGYERI

ESPEST

ETH

FINFRA

GAB GBRGEO

GHA

GIN

GNBGRC

GTM

GUY

HNDHRV

HTIHUN

IDN

IND

IRL

IRN

IRQ

ISL

ISR

ITAJORJPN

KAZ

KEN

KGZ

KHM

KOR

KW T

LAO

LBR

LBY

LKA

LSO

LTU

LVA

MAR

MDA

MDG

MEX

MKDMLI

MMR

MNG

MOZ

MRT

MW I

MYS

NAM

NER

NGA

NIC

NLD

NOR

NPL

NZL

OMN

PAKPANPER

PHLPNG

POL

PRK

PRT

PRY

ROU

RUS

RWA

SAU

SDNSENSLE

SLV

SOMSVK

SVN

SWE

SWZSYR

TCDTGO

THA

TJK

TKM

TUN

TURTZA

UGA

UKR

URYUSA

UZBVEN

VNM

YEM

ZAFZMB

ZW E

-.4

-.2

0.2

.4R

esid

ual L

angu

ages

Spo

ken

-.6 -.4 -.2 0 .2 .4Residua l Range o f Land Q ua lity


ConclusionAppendix


Examples of Language Groups in Peru


Ethnic groups in Peruwith an extent of 25 regions

0 . 2 . 4 . 6 . 8 1S U I T

Y a m i n a h u a

S h i p i b o - C o n ib o

P u n o Q u e c h u a

C e n t r a l A y m a r a

A g u a r u n a


ConclusionAppendix


Examples of Language Groups in Kenya


0 .2 .4 .6 .8 1Land Suitability

Samburu

Orma

Maasai

Kamba

Kalenjin

Gikuyu

Garreh-Ajuran

Distribution of Land Quality within ethnic groups in Kenya

A careful inspection of the box plots reveals that ethnic groups are not randomly dis-

persed across regional land qualities within Kenya. In fact, they seem to cluster in territories of

distinct and homogenous land endowments. The Samburu people, the Orma and the Garreh-

Ajuran are all exclusively located at low levels of land quality where agriculture is almost

impossible to maintain.60 The Samburu are semi-nomadic pastoralists who herd mainly cattle

but also keep sheep, goats and camels, see Pavitt (2001). The Orma are semi-nomadic shep-

herds and the Garreh-Ajuran are semi-nomadic pastoralists. These groups have the human

capital to undertake the productive activities which are optimal for the places in which they

are located. On the other hand, the Gikuyu and the Kalenjin are concentrated in territories

of high land quality and they are mainly engaged in agriculture producing: sorghum, millet,

beans, sweet potatoes, maize, potatoes, cassava, bananas, sugarcane, yams, fruit, tobacco and

60The description of the main productive activities of each ethnic group, unless otherwise noted, comes fromthe entries found in the Ethnologue website, (http://www.ethnologue.com/).

57


ConclusionAppendix


IV Regressions for Economic Variables



Dep. Var: log income per capita growth rate,

1960-2000

OLS

(3)

ELF -1.130(0.58)*

avg 0.145(0.91)


abs_lat 0.04(0.02)***

eucolony -0.902(0.48)*

lngdppc1960 -0.446(0.21)**



Dep. Var: log income per capita growth rate,

1960-2000

OLS 2SLS

(3) (4)

ELF -1.130 0.319(0.58)* (1.99)

avg 0.145 0.82(0.91) (1.26)

avgxrange -0.095 -0.51(1.35) (1.38)

abs_lat 0.04 0.052(0.02)*** (0.02)**

eucolony -0.902 -0.845(0.48)* (0.48)*

lngdppc1960 -0.446 -0.379(0.21)** (0.22)*


4.00**


0.40



ConclusionAppendix


IV Regressions for Political Variables



Dep. Var: Government Effectiveness 1996-2005

OLS

(5)

ELF -0.545(0.27)**

avg -0.61(0.44)

avgxrange 1.086(0.60)*

abs_lat 0.041(0.006)***

eucolony 0.596(0.22)***



Dep. Var: Government Effectiveness 1996-2005

OLS 2SLS

(5) (6)

ELF -0.545 0.051(0.27)** (0.73)

avg -0.61 -0.382(0.44) (0.50)

avgxrange 1.086 0.953(0.60)* (0.62)

abs_lat 0.041 0.046(0.006)*** (0.009)***

eucolony 0.596 0.674(0.22)*** (0.23)***


11.52***


0.56


-


Dep. Var: Corruption 19962005

OLS

(7)

ELF -0.764(0.27)***

avg -0.675(0.44)

avgxrange 0.719(0.60)

abs_lat 0.041(0.006)***

eucolony 0.635(0.22)***


-


Dep. Var: Corruption 19962005

OLS 2SLS

(7) (8)

ELF -0.764 -0.546(0.27)*** (0.70)

avg -0.675 -0.591(0.44) (0.50)

avgxrange 0.719 0.67(0.60) (0.61)

abs_lat 0.041 0.043(0.006)*** (0.009)***

eucolony 0.635 0.664(0.22)*** (0.23)***


11.52***


0.44



ConclusionAppendix


IV Regressions for Quality of Life



Dep. Var: Infant Mortality 2000

OLS

(3)

ELF 50.157(9.99)***

avg 2.743(16.14)

avgxrange -45.364(21.91)**

abs_lat -1.313(0.24)***

eucolony -8.085(8.09)



Dep. Var: Infant Mortality 2000

OLS 2SLS

(3) (4)

ELF 50.157 30.133(9.99)*** (27.65)

avg 2.743 -4.618(16.14) (18.47)

avgxrange -45.364 -40.759(21.91)** (22.64)*

abs_lat -1.313 -1.51(0.24)*** (0.35)***

eucolony -8.085 -10.965(8.09) (8.84)


10.08***


0.89


Documents

Ethnolinguistic Diversity: Origins and Implicationsdevollra/Houston.pdfTheory Empirics Conclusion Appendix Ethnolinguistic Diversity: Origins and Implications Stelios Michalopoulos