language policy and economic development: delving into the...

Language Policy and Economic Development:Delving Into the Black Box of Fundamental

Institutions

David Laitin (Stanford University)Rajesh Ramachandran (Goethe University)

Noah Rosenberg (Stanford University)

SSDEV 2013July 16th, 2013

David Laitin, Rajesh Ramachandran and Noah Rosenberg

Motivation and question

I One of the defining feature dividing “developed” from“developing” states -choice of official language.

I “Developed” states - official language belongs to (majority)indigenous group/s in the country.

I “Developing” states official language belongs to no indigenousgroup in the country and distant.

I Literature highlights importance of institutions for growth:I However those identified are equilibrium outcomes (rule of law,

quality of govt., protection against expropriation) rather thanfundamental causes.

I In this paper try explore if language institutions are afundamental source for growth and development.


Current approach

I Construct a measure that captures distance from andexposure to the official language.

I Show that our empirical measure is robustly correlated withvariety of indicators of development.

I Theoretical channel - Argue that language choices imposevarying costs of participation depending on languagerepertoire of linguistic groups and hence:

I Determines who has access to power, wealth and prestige insociety.

I Provide numerous historical examples supporting the aboveclaim.

I In a cross-country regression framework, estimate impact ofdistance and exposure to language policy on GDP per capita.


Construction of measure


Measure of distance and exposure

I Language choices determines cost of participation.I Cost of participation for group i dependent on:

I Distance of group i from official language o.I Distance of other groups (i 6= j) from official language (the

exposure effect)

I Operationalize the measure using language family trees fromEthnologue.


Distance measure and example

I Distance between any two languages i and j defined as:dij =

1− ( # of common nodes between i and j12 (# of nodes for language i+# of nodes for language j)

)λ

I λ is a parameter that determines how fast the distancedeclines as the number of shared branches increases.

I Example Bawaen and IndonesianI Both belong to the Austronesian Language Family.I Share 3 common nodes.I Bawean 5 nodes; Indonesian 7 nodesI dij = 1− ( 36 )

λ

I Example Spanish and IndonesianI Different language families - dij = 0.

tree


Country level measure

I Consider all language groups comprising at least 1% ofpopulation (data from Fearon (2003))

I Calculate distance from official language (dio) for eachlinguistic group i in the country.

I Official language is the language in which the first organic lawsor constitution has been written.

I Alternatively language of secondary education and highercourts.

I The average distance from the official language for anycountry i is calculated as:Di = ∑j=1 nPijdjo


Reduced from evidence

DOM

KOR

JPN

CYP

HRV

HTI

MUS

PRT

ITA

EGY

LAO

URY

BIH

IRLNORSGP

BGD

ISRDNKAUS

BRA

CHN

JAMTUN

POL

SVNARG

COLCUB

ALB

BEL

VEN

JOR

CRICZE

GRC

SWE

YEM

DEU

UKR

CAN

BLR

ARM

NLD

NIC

CHEAUTGBRFIN

KHM

LBYCHLLBNPAN

RUS

AZEVNM

USA

ESP

HND

LTU

SYR

FRA

SLV

HUN

BGR

TKM

ROU

MNG

DZA

SAU

SVK

NZL

MRTUZB

TUR

NPL

MKDMAR

MDA

THA

OMN

LVA

IRQIDN

TTO

GEO

BHR

TJK

MEX

KGZ

EST

IRNGUY

MYS

KWT

BTN

ECUGTMPER

KAZ BOL

PAKSDN

LKA

INDCMR

ARE

ZAFFJI NAM

SLE

GAB

DJIZWE

LBR

BWA

TZA

GNB

PHL

NGAZMB

NERMDGRWAMLI

SENAGO

BDIMWIMOZBEN

ERICAFBFA

LSOCIV

GINGHATGO

COGPRY

ETH

UGATCD

SWZ

COD

KENGMB

46

810

12Lo

g GDP

per c

apita

2000

US c

onsta

nt $

0 .2 .4 .6 .8 1average distance from official language

Figure: Log GDP per capita in 2000 US constant dollars and averagedistance from official language


Reduced form evidence on channel

CHN

BGD

PRTPRKITADOMHRV

HTI

NORBRA

MUS

AUS

LAO

URY

CYP

JPN

SGP

DNKISRKOR

JAM

POL

SVNARGCUB

COLALBBELVENCRICZE

GRC

SWE

DEU

UKR

CAN

BLR

ARMNLD

NICCHEAUT

GBRFINCHL

PAN

RUS

AZE

VNM

USA

ESP

HNDLTU

FRASLV

BGR

TKM

ROUMNG

DZA

SVK

NZL

MRT

TUR

NPL

MKD

MMR

MDATHA

LVA

IDN

TTO

GEOBHR

TJK

MEX

KGZ

ESTIRN

GUY

MYS

AFG

BTN

ECU

GTM

PER

KAZ

BOL

LKA

IND

CMRZAF

FJI

NAMSLE

GAB

DJI

ZWE

LBR

TZABFASWZ

ETHLSO

KENZMB

RWA

CAF

PRY

NGA

MWIGNB

TGO

GIN

GHABEN

AGO

TCD

MOZ

MDGSENBDIGMBMLINER

COG

COD

CIV

UGA

0.2

.4.6

.81

Prop

ortion

spea

king o

fficial

lang

uage


Figure: Proprtion speaking official language and average distance fromofficial language

Sources: Ethnologue, Leclerc (2011), Albaugh (2012), La Francaphonie(2007), Crystal(2003)

I In fact in more than 25 African countries less than 20% speakthe official language - Average less than 10%.


Link between indicators of interest and average distancefrom official language


Reduced form evidence on effect on educational indicators

CYP

AUS

PRTCHN

KOR

EGY

BGD

HRVITA

JPN

HTI

DNK

DOM

LAO

BRA

SGPURY

ISRNOR

MUS

IRL

JAM

TUN

POL

SVN

ARG

COL

CUBALBBEL

VEN

JORCRI

CZE

GRC

SWE

YEM

DEU

UKR

CAN

ARMNLD

NIC

CHEAUTGBR

FIN

KHM

LBY

CHLPAN

RUS

VNM

USA

ESP

HND

LTU

SYR

FRA

SLV

HUN

BGRROU

MNG

TWN

DZA

SAU

SVK

NZL

MRT

TUR

NPLMMRMAR

THA

LVA

IRQIDN

TTOBHRTJK

MEX

KGZ

EST

IRNGUY

MYS

KWT

AFG

ECU

GTM

PER

KAZ

BOL

PAK

SDN

LKA

IND

CMR

AREZAF

FJI

NAM

SLE

GABZWE

LBR

BWA

TZA

MLI

TGO

BEN

PHL

UGA

CAFRWAGMB

MOZ

CIV

PRY

COG

BDI

SWZ

NER

KEN

COD

MWI

LSOZMB

SEN

GHA

0.00

5.00

10.00

15.00

Avg.

years

of Sc

hooli

ng


Panel A

MUSCHN

BRA

ISRITA

DOM

URYNOR

EGY

CYPSGP

HRVKOR

BGD

AUSDNK

PRT

JPN

LAOHTI

IRLJAM

TUN

POLSVNARGCOLCUBALBBEL

VEN

JOR

CRICZEGRCSWE

YEM

DEUUKRCANARMNLD

NIC

CHE

AUTGBRFIN

KHM

LBY

CHLPAN

RUS

VNM

USAESP

HND

LTU

SYR

FRAHUN

SLV

BGRROUMNGTWN

DZA

SAU

SVKNZL

MRT

TUR

NPL

MMR

MAR

THA

LVA

IRQ

IDN

TTO

BHR

TJK

MEX

KGZEST

IRN

GUY

MYSKWT

AFG

ECU

GTM

PER

KAZ

BOL

PAK

SDN

LKA

IND

CMR

AREZAF

FJI

NAM

SLE

GAB

ZWE

LBR

BWA

TZA

MOZ

SENMWI

PHL

KEN

NER

BDI

MLI

CIV

BEN

LSO

GHA

COG

SWZ

RWA

GMB

UGA

TGO

PRY

COD

ZMB

CAF

0.00

20.00

40.00

60.00

80.00

% of

pop.

with n

o sch

oolin

g0 .2 .4 .6 .8 1

average distance from official language

Panel B

Figure: Educational indicators and average distance from official language


Distance from official language and effects on education

I Most poignant example Sub-Saharan Africa:I No country, except Ethiopia and Tanzania, provides the entire

span of primary schooling in a local language.I At the same time the lowest levels of educational attainment

in the world, despite huge increase in resources allocated(Devarajan and Fengler 2013)

I Studies from developed world (finding little effects of mothertongue provision) might be inapplicable to the developingworld:

I Exposure effect very different.I Supply side constraints - eg. Teacher language skills

I Namibia - 98% teachers had inadequate English skills.I Tanzania - 89% teachers had inadequate English skills.


Language and health

I Disease prevention activities and accessing medical care ofteninvolve the use of various forms of interpersonalcommunication and mass media.

I Report of institute of medicine of United States (2003)analyzing racial and ethnic disparities in health finds:

I Language barriers and limited English proficiency were a majorsystem-level determinant of poor health outcomes.

I Led to the first Ferderal policy (2000) of language in healthcare.

I Martinez (2007) finds language an important cause fornegative health outcomes for Lationos.

I Similar findings by Health Canada (2001)I Striking lack of basic preventive care in developing countries

(Madajewicz et al. 2007, Jalan Somanathan 2008, Dupas2011, Cohen et al. 2011).

I Role of lack of information has been highlighted - specific roleof language barriers remains unstudied.


Average distance from official language and health

SOM

EGY

LAO

KORBRA

PRK

BGD

BIHCHNURYDOM

HTI

JAMTUNARGCUBCOLALBVENJORCRIGRCDEUBLRARMNIC

KHM

LBYCHLLBNPANAZE

VNM

USAESP

HNDSYRSLVBGRMNGDZASAU

MRT

UZBTUR

NPL

MKD

MMR

MARMDATHAOMNIRQ

IDN

GEO

TJK

MEXKGZIRNGUYMYS

KWT

AFG

BTNECU

GTMPERKAZBOL

PAKSDN

LKA

IND

CMRZAF

NAM

SLEDJI

ZWE

LBR

BWATZA

NER

BFA

SENMOZ

NGA

AGOGMBPHLCIV

GHA

PRY

TCD

RWA

GINCAF

GNB

SWZ

ZMB

ETH

COG

MDG

TGOKENLSO

BDI

BENMWI

MLI

UGA

010

2030

4050

Malnu

trition

prev

alenc

e


Panel A

BGD

BIH

SGPDNKJPN

HTI

KOR

MUS

ISRNORHRVBRA

CYP

EGYPRK

IRL

CHNURYDOM

LAO

PRTAUS

SOM

ITA

JAMTUNPOLSVNARGCOL

CUBALBBEL

VENJOR

CRICZEGRCSWEDEU

UKR

CAN

BLRARM

NLD

NIC

CHEAUTGBRFIN

KHM

LBYCHL

LBNPAN

RUSAZEVNMUSAESP

HNDLTUSYRFRA

HUNSLVBGR

TKMMNGDZASAUSVK

NZL

MRT

UZBTUR

NPL

MKD

MMR

MARMDATHAOMNLVAIRQIDNTTO

GEOBHR

TJK

MEX

KGZESTIRNGUYMYSKWT

AFG

BTN

ECUGTMPER

KAZBOLPAKSDN

LKA

IND

CMR

ARE

ZAF

FJI

NAM

SLE

GABDJI

ZWE

LBRBWATZAGMB

NGACIVAGOGNBBDITCDBFAGIN

SEN

MOZNER

LSO

RWA

ZMB

ERI

SWZ

PHL

UGABEN

GHA

KEN

PRY

MDG

ETH

MLI

TGOCOGMWICAF

4050

6070

80Lif

e Exp

ectan

cy at

birth


Panel B

LAO

JPN

BGDCHNHTIHRVITABIH

EGYDOM

DNK

MUS

ISRURYCYPKORAUSIRLPRTBRANOR

SGPJAMTUNPOLSVNARGCOL

CUB

ALB

BEL

VEN

JORCRICZE

GRCSWE

YEM

DEU

UKR

CAN

BLRARM

NLDNICCHEAUTGBRFINKHM

LBY

CHLLBNPANRUSAZEVNM

USA

ESPHNDLTUSYR

FRA

HUNSLVBGR

TKM

MNGDZASAU

SVKNZL

MRTUZBTURNPLMKD

MMR

MAR

MDA

THAOMN

LVAIRQ

IDNTTO

GEO

BHRTJKMEXKGZESTIRNGUY

MYSKWT

AFG

BTN

ECUGTMPERKAZ

BOLPAK

SDNLKAINDCMR

ARE

ZAF

FJI

NAM

SLE

GAB

DJI

LBR

BWATZAMLI

MDG

UGAPRYBFARWA

NERSWZMOZMWITGO

PHL

NGA

LSO

ZMBGNBCAFGHATCDKENERICIVGINBENCOGAGOGMBETH

BDI

SEN

05

1015

20He

alth E

xp. a

s % of

GDP


Panel C

BGDBIHBRA

IRL

SGPCYP

DNK

EGY

KOR

NOR

MUS

PRT

URYHTICHN

ITA

DOM

JPN

LAO

ISRHRV

AUS

JAMTUNPOL

SVN

ARGCOLCUBALB

BEL

VENJORCRICZEGRCSWE

YEM

DEU

UKR

CAN

BLRARM

NLD

NIC

CHEAUTGBRFIN

KHMLBYCHLLBNPANRUSAZEVNM

USA

ESP

HNDLTUSYR

FRA

SLV

HUNBGRTKMMNGDZASAUSVKNZL

MRTUZBTURNPLMKDMMRMARMDATHAOMNLVAIRQIDN

TTOGEO

BHRTJKMEXKGZ

ESTIRNGUYMYSKWT

AFGBTNECUGTMPERKAZBOLPAKSDNLKAINDCMR

AREZAF

FJI NAM SLEGABDJILBRBWATZAGNBNGAMWITGOGMBMOZBENZMBKENSWZETHPHLERICOGPRYGINMDGRWASENMLIGHANERUGACAFTCDAGOBDIBFALSOCIV0

2000

4000

6000

8000

Per c

apita

healt

h Exp

. in 20

05 PP

P doll

ars0 .2 .4 .6 .8 1

average distance from official language

Panel D

Figure: Health indicators and expenditure and average distance fromofficial language


Historical examples of role of language policy

I Tool for elite manipulation leading to conflict - Sri Lanka(1956 decree)

I Tool to preserve privileges - Austro-Hungarian Empire (1899decree)

I Tool to impose new barriers to mobility - United Pakistan(1947 law)

I How gains might not materialize due to changes in onlyeducation - Algeria (1967 law)


Language policy and final effects on majority

LAO

DOM

BGD

CHN

EGY

BRA

BIHURYHRV

HTI

JAMTUNPOLSVN

ARG

COL

ALB

VEN

JORCRIUKRBLR

ARM

NIC

KHM

CHL

PAN

RUS

AZE

VNMHND

LTUSYRHUN

SLV

BGRSVK

MRT

TUR

NPL

MKD

MARMDA

THALVA

IDN

GEOTJK

MEX

KGZ

ESTMYS

BTN

ECU

GTM

PER

KAZ

BOL

PAK

LKACMR

ZAFFJI

NAM

SLE

DJI

TZA

MWIMOZ

MLI

ETH

RWA

SEN

AGO

BEN

UGA

SWZ

GMB

GNB

TCDNGA

PHLCIV

PRY

MDG

GIN

LSO

ZMB

BFA

CAF

020

4060

80Pr

oport

ion of

peop

le livi

ng un

der $

1.25


Figure: Proportion of population living below $1.25 a day and averagedistance from official language


Empirical methodology


The method

I We run the reduced form regression given by:

I Log GDP per Capitai =α ∗ Average Dist. From Official Languagei + Bi ∗ Xi + εi

I Xi refers to a vector of controls.

I Robust standard errors

I Clustering at country level does not change results


Table: Effect of average distance on GDP per capita

Dependent variable - Log GDP per capita in 2000 US constant dollars(1) (2) (3) (4) (5) (6) (7) (8) (9)

Average Distance from Official Language -2.494*** -2.665*** -2.366*** -2.266*** -2.121*** -1.949*** -1.854*** -1.470*** -1.492***(0.227) (0.390) (0.428) (0.361) (0.409) (0.456) (0.425) (0.480) (0.479)

Ethno-Linguistic Fractionalization -0.0293 1.134** 1.168*** 1.209*** 1.117** 0.914* 0.589 0.956*(0.523) (0.544) (0.409) (0.429) (0.510) (0.492) (0.547) (0.543)

Avg. Protection against 0.540*** 0.516*** 0.532*** 0.499*** 0.470*** 0.372*** 0.359***Expropriation risk (0.0519) (0.0640) (0.0612) (0.0648) (0.0588) (0.0758) (0.0787)

Tempreature Controls Yes Yes Yes Yes Yes Yes

Humidity Controls Yes Yes Yes Yes Yes Yes

Soil Quality Yes Yes Yes Yes Yes

Landlocked Dummy Yes Yes Yes Yes Yes

Natural Resources Yes Yes Yes Yes

Legal Origin Dummies Yes Yes Yes

Continent Dummies Yes Yes

Size of Largest Ethnic Group Yes

Observations 149 117 102 99 99 99 99 99 99R-squared 0.387 0.477 0.754 0.797 0.822 0.841 0.884 0.900 0.903

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1


Residual analysis

GAB

COGJPNISR

ETH

BWA

MWI YEM

SGP

BGD

TURCOL

JORMAR

TTO

PHLUGA DEU

MEX

GUY

GMB ITA

HTI ARG

CHN

SLV

ESP

MOZ

FRA

TZA

BRA

IRL

URYNAM

NLDBOL DZA CANTHA

SWEZWENGA

VEN AUTEGYKENGHA

TGO

AGO

GRC

NOR

PRY KOR GBR

IDN FINBGR CHEVNMIND LKA

BFA SDN

MLI

HUN

PERSLE

PAN

LBR

HND

ECUCMR

MYS

POL PRT

BELDNK

SYR

GIN

PAK

ZMB TUNSENMDG CRIJAM

GTMMNGGNB USANIC LBYNER CHL AUSNZLCIV DOMZAF

-20

24

Jack

-knife

d res

iduals

4 6 8 10 12Fitted values

Figure: Plot of Jack-knifed residuals and fitted values


Table: Effect of ELF on GDP per capita controling for averagedistance from official language

Dependent variable - Log GDP per capita in 2000 US constant dollars(1) (2) (3)

Ethno-Linguistic Fractionalization -2.934*** -1.545*** 1.134**(0.333) (0.383) (0.544)

Avg. Protection against 0.615*** 0.540***Expropriation risk (0.0579) (0.0519)

Average Distance from Official Language -2.366***(0.428)

Observations 146 110 102R-squared 0.291 0.672 0.754

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1


ELF and link to language policy

I Show how ethno-linguistically fractionalized polities likely tochoose languages belonging to no indigenous group.

I Rationale - nation building, avoiding conflict.

I Ethnically neutral policies however might be not neutral whenlooked from class perspective.

I Path-dependence implies former colonial language choices.

I Is negative effect of ELF found in the literature an artifact of thepositive correlation between distance and ELF?


Methodological concerns

I Omitted variable bias

I Apply AET (2005) methodology

I Results suggest selection on unobservables would need to be 1.7 to2 times stronger than selection on observables.

I Endogeneity

I Instrument - use genetic distance of indigenous population frompopulation to which official language belongs.

I Does it still meet exclusion restriction?


Other work in progress

I Compliment with analysis at the sub-national level

I Compare South vs. North India?

I Hindi belongs to Indo-European family; South Indian languages tothe Dravidian family.

I Other suggestions?

I Why sub-optimal langauge policy?

I An elite based capture model.

I Compare average incomes of top 1% or 0.05% - slope with averagedistance should be zero or turn positive?


THANK YOU


Figure: Family Tree

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