Pre-industrial InequalitiesPre-industrial Inequalities

Branko MilanovicWorld Bank Training Poverty and Inequality Analysis

CourseMarch 3, 2011

Questions

● Is inequality caused by the Industrial Revolution?

Or, has inequality been pretty much the same before and after?

● Is inequality in poor pre-industrial economies today pretty much the same as in ancient pre-industrial economies?

● Was inequality augmented by colonization?

● Have some parts of the world always had different levels of inequality than others?

Constraints on the Elite in Ancient Pre-Industrial Societies

● Fact: Ancient pre-industrial societies had average income levels usually twice, but sometimes 4-5 times, the subsistence level.

● Fact: Low average income, combined with the requirement that few fall below subsistence, meant that the elite’s surplus (and thus inequality) could not be very large.

● Query: What happened when average income and the potential surplus rose? Did the poor subsistence workers get any the added surplus or did the elite grab it all?

A New Measure: the Inequality Possibility Frontier

• Divide society into 2 groups: people with subsistence income and elite (fraction ε of total population) that shares the surplus equally among themselves.

• There is no overlap between the two classes, and no inequality within each.

• Then, the Gini simplifies to:

jiij ppyyG )1

• Per capita income of the elite is:

)]1([1)1(

s

N

sNNyh

where N=total population, μ=overall mean income, s=subsistence.

• Per capita income of people is s; and respective population shares are ε and (1-ε).

• Substituting all of this into Gini gives

)(1

)1(1

* sssG

If, for simplicity, we express μ as so many (α) subsistence minimums, the Gini becomes

)1(1

)1(1

*

s

sG

IMPORTANT: The expression gives the maximum Gini compatible with mean income of αs; ε fraction of the elite, and no inequality among either elite or people.

When ε tends to 0 (one Mobutu), G* = (α-1)/α. With α=1, G*=0; α=2, G*=0.5; if α=100 (like in the US today), G*=0.99.

Introduction of inequality among the elite does not affect the maximum Gini.

1*

0

limG

Other interpretations• This is the maximum inequality which may exist

at a given income level when the entire surplus income is appropriated by (at the extreme) one individual.

• The size of the overall income (the pie) limits the level of measured inequality (measured by the synthetic measures like the Gini where all incomes matter).

• It is a new and realistic generalization of the Gini index since it requires that the society be sustainable.

New Measurement of Inequality

• The ratio between the actual Gini and the maximum Gini (a point on the IPF) is the inequality extraction ratio.

• The inequality extraction ratio shows what percentage of maximum feasible inequality an elite is able (or wishes) to extract = ratio A/B (next slide).

0

20

40

60

80

100

Average income as multiple of subsistence minimum (alpha)

Max

imu

m f

easi

ble

ineq

ual

ity

(G*)

The locus of maximum inequalities is “inequality possibility frontier”

Note: Vertical axis shows maximum possible Gini attainable with a given α.

A

B

How are we going to study “ancient inequalities”

• There are no household survey data, but..• There are social tables akin to King’s 1688 table.• We shall use mostly the social tables that have

already been produced or the data that can allow us to produce such tables (in some cases from professional censuses). Plus Ottoman censuses of settlements (2 cases)

• Inequality (Gini) calculated from such tables assumes that (i) all members of a group have the same income, and (ii) groups are non-overlapping (i.e, all members of an upper group have higher incomes than all members of a lower group). This is our lower-bound Gini1.

• We relax assumptions (i) by calculating maximum feasible inequality within the income ranges of the groups (thus allowing for an estimate of within-group inequality). But we have to keep (ii) although we know that there are members of (say) nobility who may have lower income than some merchants. This is our upper-bound Gini2.

• The ratio between Gini2/G* estimates inequality extraction ratio for a given country.

What countries do we include?

• Wherever we could find a social (class) table with estimated mean class income and population shares.

• We set time limits: for the developed world, 1810; for the rest, 1929 (with India 1947 as an exception).

• Difficult decision to decide what is a country: an officially distinct territory with autonomous or foreign government (the latter is a colony).

• We do not include cities (Jerez, Paris, Amsterdam for which data exist).

• This leaves us with 30 data points, ranging from Rome 14 to India 1947.

• Four data points from England (1230, 1688, 1759, 1801) and three from Holland though (1561, 1732, 1808)

• Number of social classes mostly in double digits except in Nueva España and China (3 classes only), Moghul India (4) and England 1290 (7). Median number of classes = 20, but Tuscany (1427) almost 10,000 households, Levant (1596) 1415 settlements.

• Does number of classes matter? Sensitivity analysis suggests Not (see below).

• Estimated per capita incomes in 1990 $PPP almost all from Maddison; if not, use the ratio between the estimated mean LC income and estimated subsistence (α) and price the latter at $PPP 300 (Byzantium paper)

• In the sample, α ranges from 1.6 to 6.7 (based on a subsistence minimum of $PPP 300).

An example of a social table: France 1788

Social Group Population

(in 000)

Per capita income (livres

per annum)

Population %

Nobles and Clergy 540 724.1 1.9

Bourgeoisie 2160 724.1 7.7

Shopkeepers and artisans 3240 150.0 11.6

Workers (non agricultural) 1500 66.7 5.4

Servants (non agricultural) 1080 92.6 3.9

Small scale farmers 5250 64.6 18.8

Large scale farmers 2250 219.6 8.0

Agricultural day laborers and servants

10150 39.4 36.3

Mixed workers 1800 75.0 6.4

Total 27970 143.3 100

Source: Morrisson and Snyder (2000)

Country/territory Source of data Year Number of social classes

Population (in 000)

Estimated GDI per capita

Roman Empire Social tables 14 11 55000 633

Byzantium Social tables 1000 8 15000 533

England Social tables 1290 7 4300 639

Tuscany Household survey

1427 9,780 38 978

South Serbia (w/o foreign)

Census of settlements

1455 615 80 443

Holland Tax census dwelling rents

1561 10 983 1129

Levant Census of settlements

1596 1,415 237 974

England and Wales Social tables 1688 31 5700 1418

Holland Tax census dwelling rents

1732 10 2023 2035

Moghul India Social tables 1750 4 182000 530

Old Castille Income census 1752 33 1980 745

England and Wales Social tables 1759 56 6463 1759

Data Sources, Estimated Demographic Indicators and GDI Per Capita…(Contd.)Data Sources, Estimated Demographic Indicators and GDI Per Capita…(Contd.)

……Data Sources, Estimated Demographic Indicators and GDI Per Capita Data Sources, Estimated Demographic Indicators and GDI Per Capita

Country/territory Source of data Year Number of social classes

Population (in 000)

Estimated GDI per capita

France Social tables 1788 8 27970 1135

Nueva España Social tables 1790 3 4500 755

England and Wales Social tables 1801-3 44 9277 2006

Bihar (India) Monthly census of expenditures

1807 10 3362 533

Netherlands Dwelling rents 1808 20 2100 1800

Kingdom of Naples Tax census dwelling rents

1811 12 5000 637

Chile Professional census

1861 32 1702 1295

Brazil Professional census

1872 813 10167 721

Peru Social tables 1876 9 2469 653

China Social tables 1880 3 377500 540

Java Social tables 1880 32 20300 661

Japan Tax records 1886 21 38622 916

Java (w/o foreign) Social tables 1924 12 34984 909

Siam Social tables 1929 21 11607 793

British India Social tables 1947 8 346000 617

Notes: GDI per capita is expressed in 1990 Geary-Khamis PPP dollars (equivalent to those used by Maddison 2003 and 2004).

18th century included countries

19th century included countries

12 countries before the French revolution, 18 countries after…

No social tables for the United States (!), Russia, Africa (except Kenya and Maghreb)

… but more may be coming

American colonies 1776/1800 (Lindert and Williamson working on it)

Czarist Russia (Mironov)PolandMehmet Ali’s EgyptMore Ottoman deftersMadagascarAudiencia de Quito

Kingdom of Naples around 1810

Map of Levant 1596-97 (yellow areas included)

Country/territory/

year

Gini1 Gini2 Maximum feasible Gini with s=300

Actual Gini as % of the maximum

Roman Empire 14 36.4 39.4 52.6 75

Byzantium 1000 41.0 41.1 43.7 94

England and Wales 1290 35.3 36.7 53.0 69

Tuscany 1427 46.1 69.3 67

South Serbia (w/o foreign) 1455

19.1 20.9 32.2 65

Holland 1561 56.0 73.4 76

Levant (w/o foreign) 1596 39.8 69.1 67

England and Wales 1688 44.9 45.0 78.8 57

Holland 1732 61.0 61.1 85.2 72

Moghul India 1750 38.5 48.9 43.4 113

Old Castille 1752 52.3 52.5 59.7 88

England and Wales 1759 45.9 45.9 82.9 55

France 1788 54.6 55.9 73.5 76

Inequality MeasuresInequality Measures

Inequality MeasuresInequality Measures

Country/territory/

year

Gini1 Gini2 Maximum feasible Gini with s=300

Extraction ratio:

Actual Gini as % of the maximum

Nueva España 1790 63.5 62.0 105

England/Wales 1801-3 51.2 51.5 85.0 61

Bihar (India) 1807 32.8 33.5 43.7 77

Netherlands 1808 56.3 57.0 83.3 68

Naples 1811 28.1 28.4 52.9 54

Chile 1861 63.6 63.7 76.8 83

Brazil 1872 38.7 43.3 58.3 74

Peru 1876 41.3 42.2 54.0 78

China 1880 23.9 24.5 44.4 55

Java 1880 38.9 39.7 54.6 78

Japan 1886 39.5 67.2 59

Java 1924 31.8 32.1 66.9 48

Siam 1927 48.4 48.5 62.1 78

British India 1947 48.0 49.7 51.3 97

Estimated Gini Coefficients and the Inequality Possibility Frontier

Note: The IPF is constructed on the assumption that s=$PPP300. Estimated Ginis are Ginis2 unless only Gini1 is available

0

10

20

30

40

50

60

70

80

90

0 300 600 900 1200 1500 1800 2100 2400

GDI per capita (in 1990 $PPP)

Gin

i in

dex

Serbia 1455

China 1880

Naples 1811

England 1290

India 1750

Byzant 1000

Rome 14

Peru 1876Brazil 1872

Java 1880

India 1947Old Castille 1752

Siam 1929

England 1688

France 1788

Chile 1861

Netherlands. 1808

England 1759

Holland 1732

England 1801

Bihar 1807 Java 1924

Nueva España 1790

Holland 1561

Florence 1427

Japan 1886Levant 1596

Kenya 1914

Kenya 1927Maghreb 1880

IPF

• At α<3, Ginis range from 25 to low 60s and are clustered around the IPF. These countries “extract” quite a large share (on average ~ 80% of maximum inequality).

• With higher mean income, as the IPF becomes higher, Gini does not rise to the same extent, and the extraction ratio goes down.

• This is true when we compare ancient and modern societies, but true within ancient as well as within modern (application of IPF methodology to the contemporary societies; see below)

• All countries with the extraction ratio around 100% were colonies: Moghul India 1750 (112%), Nueva España 1790 (105%), Maghreb 1880 and Kenya 1927 (100%), Kenya 1914 (96%). 4 different colonizers.

• For the ancient, if α<3, the median Gini is 42 and median extraction 78% (n=18). If α>3, the median Gini is 49 and median extraction 64% (n=12). Ho of ↓ extraction accepted (p=0.999), Ho of ↑Gini accepted (p=0.972; Kuznets).

• Thus, Gini alone is not a sufficient measure of inequality.

• A Gini of (say) 40 in Rome and in the US does not mean the same thing. In Rome, that Gini extracts 75 percent of maximum inequality, in the US less than 40 percent.

Ginis and the Inequality Possibility Frontier for the Ancient Society Sample and Selected Modern Societies

Note: Modern societies are drawn with hollow circles. IPF drawn on the assumption of s=$PPP 300 per capita per year. Horizontal axis in logs.

TZA

MYS

BRA

USA

SWE

ZAF

CHN

KENCON

IND

2040

6080

100

Gin

i

1000 2000 5000 10000 20000GDI per capita

Inequality extraction ratio for the ancient and the “same” modern societies

Based on the subsistence minimum = $PPP300.

KEN

IND IDNSRB

CHN

PER

BRA

THA

TUR

MEX CHL

ESPITAENG

FRANDLJPN

020

40

60

80

100

120

inequalit

y ext

ract

ion r

atio

1000 2000 5000 10000 20000gdp per capita in 1990 ppp

All but one, colonies!

Highlight colonies’ extraction ratio

KEN

IND

BIH

KENIND

JAV

DZA

NES

JAV

020

4060

8010

0G

ini

500 1000 1500 2000 2500 3000GDI per capita in 1990 PPP dollars

Distribution of the extraction ratio across three types of society

modern preindustrial non-colonies

preindustrial colonies

.00

5.0

1.0

15

.02

.02

5.0

3d

ensi

ty fu

nct

ion

20 40 60 80 100 120extraction ratio

Use Figure25.do file (bottom graph)

Relationship between GDI per capita and extraction ratio for ancient societies only

Note: 95 percent confidence interval

South Serbia

Kenya

India-Moghul

Bihar

Byzantium

China

KenyaIndia-British

Roman Empire

Kingdom of Naples

Eng1290

Peru

Java1880

Maghreb

Brazil

Old Castiille

Nueva España

Siam

Java1924

JapanLevant

Florence

Hol1561France

Chile

Eng1688Eng1759

Netherlands

Eng1801

Hol1732

40

60

80

100

120

inequalit

y ext

ract

ion r

atio

6 7 8ln GDI per capita

Can we try to explain determinants of ancient inequalities and extraction ratio?

• Paucity of data points (30 in total) and possible explanatory variables

• However, we have some: income per capita (Kuznetsian relationship), urbanization rate, population density, dummy for being a colony

Gini determinantsFirst cut Is Asia different? Drop 2 Javas

Ln GDI pc 360.5*** 366.7*** 360.2***

(Ln GDI pc)2 -25.0*** -25.5*** -25.0***

Urbanization 0.349* 0.354* 0.353*

Pop. density -0.105*** -0.100*** -0.107*

Colony 12.63*** 12.93*** 12,41***

Asia -1.28

No foreign -9.59 -9.97 -9.26

No. of groups -0.009 -0.01 -0.01

Tax survey -4.86 -4.85 -4.85

Adjusted R2 (N) 0.75 (28) 0.73 (28) 0.73 (26)

And the extraction ratio…

Parsimonious Add pop density Drop 2 Javas

Ln GDI pc -20.92** -6.48 -6.45

Urbanization 0.677* 0.229 0.236

Pop. density -0.188*** -0.200**

Colony 16.12** 25.52*** 25.35***

No foreign -25.28** -39.20*** -39.23***

Adjusted R2 (N) 0.34 (28) 0.65 (28) 0.60 (26)

Drawing together Gini and the extraction ratio

• Kuznets quadratic relationship relatively strong for Gini, but income negatively associated with the extraction ratio (as we saw before)

• Asynchronism in the behavior of the Gini and extraction ratio as societies get richer: Gini at first ↑, but the extraction ratio ↓ throughout

• Population density puts downward pressure on both Gini and the extraction ratio. The effect on the latter particularly strong—so much that both urbanization and income lose significance

• Colony very significant: adds 12-13 Gini points, and twice as many extraction points throughout

• Controls for different types of surveys and number of social groups not significant

Other implications• Asia (absence of economies of scale in the cultivation of

rice) does not appear to have been more equal in Gini terms; population density more important (although high population might have been made possible by the absence of extreme inequality)

• No causality can be proven. • 2 possibilities: (i) less extractive regimes –however they

might have arisen-- allow population to increase; (ii) greater population density ---however it happened-- threatens the rulers more so the extraction ratio goes down (Campante and Do). Think why Louis XIV moved from Louvre to Versailles.

• Most likely both effects operate and impossible to disentangle them

• IMP: Why and how population density limits elite’s predatory power?

Other implications (cont.)

• Re. Engelman-Sokoloff Ho: If Western Europe was as unequal as Latin America, why were the trajectories of the two so very different in 19th-20th century?

• W. European mean Gini (1500<year<1810; 8 obs) = LA mean Gini (4 obs) in 19th century = 53. But Europe’s extraction ratio 70% vs. LA 85%.

• Their Ho should be recouched in extraction, not Gini terms

Two propositions• Proposition 1. While the estimated Ginis for pre-

industrial societies fall in the same range as inequality levels observed today, ancient inequality was much greater when expressed in terms of the maximum feasible inequality.

• Proposition 2. Under conditions of economic growth, particularly in poor or middle-income societies, constant inequality reflects great restraints on exploitation because the inequality extraction ratio is falling. The reverse is true during periods of economic decline (e.g., Russia under Yeltsin).

Global inequality and poverty

• If we take all 12 countries within years 1750 and 1880, we have 583 income groups representing incomes of almost 650 million people.

• Over that period, average world population was around 900 billion.

• These LC incomes are converted into $PPP (Geary-Khamis, 1990)

• What is inequality among world citizens, and poverty rate?

• Gini for these individuals is 38.2. This is only about a half of global Gini today (70 with the new $PPP data; 65 with the old $PPP data).

• The poverty headcount (with the PL=$PPP410) is 85 percent. Crucially depends on China.

Global inequality then and now

0

10

20

30

40

50

60

70

1750-1880

1820 1870 2005

1820, 1870 from Bourguignon and Morrisson, 2005 from Milanovic

MLW data

Global poverty then and now(much more dependent on the assumption re. income of the poor in

China than inequality calculations)

0

10

20

30

40

50

60

70

80

90

1750-1880

1820 1870 2005

1820, 1870 from Bourguignon and Morrisson, 2005 from Chen and Ravallion

MLW data

Who were the people with the highest incomes then?

• European colonizers in Java: about 2,500 people had per capita incomes in excess of $PPP90 100,000.

• Also a few hundred people in England 1759 and the Netherlands 1808. (English top income group in 1801-3 is broader.)

• Incidentally, the rich British in 1947 India had an average per capita income in excess of $PPP90 50,000 which would place them in the 2nd richest percentile in the US today.

• Little wonder colonies were good for colonizers!

An added dimension: the share of top 1%

• Recent work (Piketty etc) implies that there is a strong correlation between the top 1% (and fewer) income share and inequality.

• Is it true in ancient societies?• Caveat: these are not true distributions of people

or families but of social classes.• Estimate the top share using Pareto interpolation

(assumes Pareto distribution at the top).

Top 1% share in total income (in %)

The cut-off point (in terms of mean income)

Gini coefficient

Byzantium 1000 30.6 3.7 41.0

Chile 1861 25.7 11.8 63.7

China 1880 21.3 5.6 24.5

Nueva España 1790 21.1 9.8 63.5

Japan 1886 19.1 39.5

Netherlands 1808 18.1 9.8 57.0

France 1788 16.8 6.9 55.9

Rome 14 16.1 12.4 39.4

England 1801 8.9 6.2 51.5

England 1688 8.7 6.1 45.0

Old Castille 1752 7.0 6.2 52.5

Siam 1929 6.7 5.1 48.5

Average ancient 14.6 7.4 45.4

Average modern counterparts 8.6 5.4 42.1

Chile 2000 14.6 7.9 54.6

UK 1999 7.0 4.3 37.4

India 2004 5.2 4.2 32.6

Estimated top of income distribution: ancient and modern counterpartsEstimated top of income distribution: ancient and modern counterparts

Weak correlation (ρ=0.45) between Gini and top 1% income share

twoway scatter top_percent gini if sample==1, msize(vlarge) mlabel( country)

ROM

BYZ

ENGITA

SRB

SYR

ENG

HOLIND

OCA

ENG

FRA

NES

ENG

BIH

NLD

NAP

CHL

BRA

PER

JAV

CHN

JPN

JAV

THA

IND

01

02

03

0sh

are

of

top

1%

20 30 40 50 60gini2

Top five percentiles of income distribution in Rome 14, Byzantium 1000, and England 1688

Note: All data points except for the top 1 percent are empirical. The top 1 percent share is derived using Pareto interpolation.

Byzantium

Roman E England 1801-3

010

20

30

40

cum

ul. incom

e s

hare

1 2 3 4 5 6top percentile

Embourgeoisement of England: increasing share of top 5% and declining share of top 1%

1801

1688

1759

01

02

03

04

0cu

mu

l. in

com

e s

ha

re

0 2 4 6 8cumulative top perc

Based on per capita transformation of King, Massie and Colquhoun social tables

Third proposition (re: the top shares)

● Fact: The share of the top percentile in ancient societies is not tightly connected with overall inequality in contrast with modern societies.

● Proposition 3. What drove ancient inequality was not the top share, but rather the size of the income gap between average income (y) and the average income of poor (w) = y/w.

Figure 8. Gini versus the y/w Ratio in an Ancient Sample of Twelve

Nueva Espana 1790

Byz 1000

China 1880

Naples 1811

Rome 14Brazil 1872

India 1750

India 1947

Castille 1752 England 1759

England 1688

England 1801-3

0

10

20

30

40

50

60

70

0.00 1.00 2.00 3.00 4.00 5.00 6.00

Average Economy-wide Income versus Income of Rural Labor (y/w))

Gin

i Co

effi

cien

t

Five take-away observations● Measured annual inequality is not very different in pre-

industrial societies today than it was in ancient societies.● New measure of inequality: maximum inequality

compatible with preservation of a society: the inequality possibility frontier.

● The extraction ratio – how much of potential inequality was converted into actual – was much bigger in ancient societies.

● In contrast with modern societies, the top 1% share was not correlated with overall inequality in ancient societies. But the gap between elite or average income and poor people’s incomes was correlated with overall inequality.

● Can we contrast “equal” societies with a very small and very rich elite (Oriental despotism) vs. those with a more “graduated” (diversified) income structure?

Moving to the present: the use of the IPF and extraction ratio

• Maximum Gini: a new upper bound on the Gini such a society is sustainable in the long-run.

• More realistic Gini.

• Extraction ratio: reflection both of the level of development and rapacity of the elites (or their ability to appropriate the surplus).

The extraction ratio and GDI per capita (year 2002)

ALB

ARG

ARM

AUSAUTBEL

BENBFA

BGD

BGRBIHBLR

BOL

BRA

CANCHE

CHL

CHN

CHN-RCHN-U

CIVCMR

COGCOL

COM

CPV

CRI

CZE DEU

DNK

DOMECU

EGY ESPEST

ETH

FINFRA

GAB

GBRGEO

GIN

GNB

GRC

GTM

HKG

HND

HRV

HTI

HUN

IDN

IDN-R

IDN-UIND

IND-R

IND-U

IRL

IRN

ISRITA

JAM

JOR

JPN

KAZKGZ

KHM

KOR

LAOLKA

LTU LUX

LVA

MAR

MDA

MDG

MEX

MKD

MLI MOZ

MRT

MWI

MYS

NER

NGA

NIC

NLDNOR

NPL

PAK

PANPER

PHL

POL

PRY

ROMRUS

SEN

SGP

SLE

SLV

SVK SVNSWE

SYR

TCD

THATJK

TUR

TZA

UGA

UKR

URY-U

USAUZB

VENVNM

YUG

ZAF

ZAR

ZMB

20

40

60

80

100

ext

ract

ion r

atio

6 7 8 9 10 11ln gdpppp

gini/gini_max*100 Fitted values

Gini and GDI per capita (year 2002)

ALB

ARG

ARM

AUS

AUTBEL

BENBFA

BGD

BGRBIHBLR

BOL BRA

CANCHE

CHL

CHN

CHN-RCHN-U

CIV CMR

COG

COL

COM

CPV

CRI

CZEDEU

DNK

DOMECU

EGYESP

EST

ETH FINFRA

GAB

GBRGEO

GIN

GNB GRC

GTM

HKG

HND

HRV

HTI

HUN

IDN

IDN-R

IDN-UIND

IND-R

IND-U IRL

IRN

ISRITA

JAM

JOR

JPN

KAZKGZ

KHM

KOR

LAO

LKA

LTU LUX

LVA

MAR

MDA

MDG

MEX

MKDMLI

MOZ

MRTMWI

MYS

NERNGA

NIC

NLDNOR

NPL

PAK

PANPER

PHL

POL

PRY

ROM

RUS

SENSGP

SLE

SLV

SVK SVN SWE

SYRTCD THA

TJK

TUR

TZA

UGA

UKR

URY-U

USA

UZB

VEN

VNM

YUG

ZAF

ZAR ZMB

20

40

60

80

6 7 8 9 10 11lngdpppp

gini Fitted values

Using ineq_frontier.do file

Probability of civil war (1990-97) as function of inequality or extraction ratio in the period 1970-1990

Mean HBS income (ln)

-0.319

(0.002)

-0.238

(0.000)

-0.410

(0.000)

-0.321

(0.000)

Gini (in %) 0.0004

(0.82)

0.0015

(0.49)

Extraction ratio (in %) 0.0075

(0.00)

0.012

(0.000)

Democracy(Polity2) 0.056

(0.000)

0.058

(0.000)

Ethnolinguistic fract. 0.765

(0.000)

0.675

(0.000)

Pseudo R2 0.042 0.046 0.097 0.104

No. of obs 427 427 381 381

Civil war = “within-war” variable from CoW project; my gdppppreg.dta file; weighted probit probit civil_warCoW Giniall lngdpppp if year>1970 & year<1990 [w=hhh]