econ 490 – section 011 economics of the poor fall 2011 contact...

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Econ 490 – Section 011 Economics of the Poor

Fall 2011

Contact Information: Siwan Anderson Office: Buchanan Tower 922 (Temporary) e-mail: [email protected] Course Website: www.econ.ubc.ca/asiwan/490hmpg.htm Office hours: Tuesdays/Thursdays 12:30 – 1:30

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BACKGROUND READING

Textbook for Econ 326 (on reserve in Koerner Library) Principles of Econometrics By R.C. Hill, W.E. Griffiths, and G.C. Lim Published by Wiley 2011 Chapter 3 – The Linear Regression Model Chapter 4 – Assumptions of the Linear Regression Model Chapter 7 – The Multiple Regression Model Chapter 14 – Simultaneous Equation Models

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STATA

Using Stata for Principles of Econometrics By L.C. Adkins and R.C. Hill Software Package STATA is available in computer labs in Buchanan. Recommended that you purchase your own copy: Campus-wide special plan for purchasing Place order Pick up on campus

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STATA

http://www.stata.com/order/new/edu/gradplans/cgpcampus-order.html Version: STATA/IC 12 with PDF documentation

Need INTERCOOLED - Small STATA too small

$108 for one year, $75 for 6 months

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COURSE SCHEDULE

Week 2/3: September 13: Lecture 1 -- Snapshot of Poverty September 15: Lecture 2 -- Macro Approach September 20: Lecture 3 – Micro Approach Weeks 3/4: September 22 - 29: No Meetings Office Hours (Tuesdays/Thursday 11:00 – 1:30) Discuss choice of topics and data sets

Week 5: October 4 – 6: Meet in Classroom Student Presentations of topics, key questions, data sets

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Weeks 6 and 7: October 11 – 20: Meet in Computer Lab (B125) Bring data sets and questions

Weeks 8 and 9: October 25 – November 3: No Meetings Office Hours (Tuesdays/Thursday 11:00 – 1:30) Discuss initial results

Week 10: November 8 – 10: Meet in Classroom Student Presentations of initial findings

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Weeks 11 to 13: November 15 – December 1: No Meetings Office Hours (Tuesdays/Thursday 11:00 – 1:30) Discuss final paper

No More Office Hours after December 1 Available for questions via e-mail

Paper Due (submit via e-mail):

December 20, 2011

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COURSE EVALUATION

Student Presentations: 15% Research Paper: 85%

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ECONOMICS OF THE POOR Enormous worldwide disparities in income levels and standards of living Two main questions:

(1) Why do people in some countries live prosperous, healthy lives, while those in others reel under poverty?

(2) What are potential policies which can improve the lives of the

poor?

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MACRO APPROACH “What is responsible for the divergence in living standards that has developed across the world over the last two hundred years?” Alternatively could ask “How did the West grow rich?” Income per capita in sub-Saharan Africa is 1/20th of U.S. income per

captia Specific countries such as Mali and Ethiopia it is 1/35th

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Standard answers: Poor countries don’t save enough Poor countries don’t invest enough in education and skills Poor countries do not adopt the right technologies or organize

production efficiently But why not? New research is concerned with the more fundamental causes: Institutions (humanly devised rules shaping incentives) Geography (exogenous differences of environment) Culture (differences in beliefs, attitudes, preferences)

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Key to understanding the methodology used in economics is to determine the relative importance of these determinants of development Empirical regressions in economics determine which factors are more important – those that are statistically significant Macro approach exploits cross-country variation

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Sources of prosperity (1)

Vast differences in prosperity across countries today.– Income per capita in sub-Saharan Africa on average 1/20th of

U.S. income per capita– In Mali, Democratic Republic of the Congo (Zaire), and

Ethiopia, 1/35th of U.S. income per capita.Why?Standard economic answers:

– Physical capital differences (poor countries don’t save enough)– Human capital differences (poor countries don’t invest enough

in education and skills)– “Technology” differences (poor countries don’t invest enough

in R&D and technology adoption, and don’t organize their production efficiently)

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Sources of prosperity (2)

These are, however, proximate causes of differences in prosperity.

– Why do some countries invest less in physical and human capital?

– Why do some countries fail to adopt new technologies and to organize production efficiency?

The answer to these questions is related to the fundamental causes of differences in prosperity.Potential fundamental causes:

– Institutions (humanly-devised rules shaping incentives)– Geography (exogenous differences of environment)– Culture (differences in beliefs, attitudes and preferences)

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Institutional variation

Big differences in economic and political institutions across countries.– Enforcement of property rights.– Legal systems.– Corruption.– Entry barriers.– Democracy vs. dictatorship.– Constraints on politicians and political elites.– Electoral rules in democracy.

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Economic institutions and economic performance (1)

.

Log

GD

P p

er c

apita

, PP

P, i

n 19

95

Avg. Protection Against Risk of Expropriation, 1985-954 6 8 10

6

8

10

AGO

ARE

ARG

AUS AUTBEL

BFA BGD

BGR

BHR

BHS

BOL

BRABWA

CANCHE

CHL

CHN

CIVCMRCOG

COLCRI

CZE

DNK

DOM DZAECU

EGY

ESP

ETH

FINFRA

GAB

GBR

GHAGIN

GMB

GRC

GTM

GUY

HKG

HND

HTI

HUN

IDN

IND

IRL

IRN

ISLISR

ITA

JAMJOR

JPN

KEN

KOR

KWT

LKA

LUX

MAR

MDG

MEX

MLI

MLT

MNG

MOZ MWI

MYS

NER NGA

NIC

NLDNOR

NZL

OMN

PAK

PAN

PER

PHL

POL

PRT

PRY

QAT

ROM RUS

SAU

SDNSEN

SGP

SLE

SLVSUR

SWE

SYR

TGO

THATTO

TUNTUR

TZA

UGA

URY

USA

VEN

VNM

YEM

ZAF

ZAR ZMB

ZWE

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Economic institutions and economic performance (2)

.

Log

GD

P p

er c

apita

, PP

P, i

n 19

95

Control of Corruption0 .5 1

6

8

10

ARG

ARM

AUSAUTBEL

BFA

BGR

BOL

BRA

CANCHE

CHL

CHN

COL

CZE

DEU DNK

DOMECU

EGY

ESPFIN

FRA GBR

GEO GHA

GRC

HKG

HRV

HUN

IDN

IND

IRLISRITA

JAM JOR

JPN

KAZ

KEN

KOR

LBN

LKA

LTULVAMAR

MDG

MEX

MLIMOZ MWI

MYS

NGA

NLDNOR

NZL

PAK

PAN

PER

PHL

POL

PRT

ROMRUS

SEN

SGP

SVK

SVN

SWE

THA

TUNTUR

TZA

UGA

UKR

URY

USA

VEN

VNM

ZAF

ZMB

ZWE

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Political institutions and economic performance

.

Log

GD

P p

er c

apita

, PP

P, i

n 19

95

Constraint on Exec. 1990s0 2 4 6 8

6

8

10

AGO

ARG

AUSAUT

BDI

BEL

BEN

BFA BGD

BOL

BRABWA

CAF

CANCHE

CHL

CHN

CIV CMRCOG

COL

COM

CRI

DEUDNK

DOMDZA ECU

EGY

ESP

ETH

FIN

FJI

FRA

GAB

GBR

GHAGIN

GMB

GRC

GTM

GUY

HND

HTI

HUN

IDN

IND

IRL

IRN

ISLISRITA

JAMJOR

JPN

KEN

KOR

LKA

LSO

LUX

MAR

MDG

MEX

MLIMOZ

MRT

MUS

MWI

MYS

NERNGA

NIC

NLDNOR

NPL

NZL

OMN

PAK

PAN

PER

PHL

POL

PRT

PRY

RWA

SAU

SDNSEN

SGP

SLE

SLV

SWE

SWZSYR

TCD

TGO

THA TTO

TUNTUR

TZA

UGA

URY

USA

VEN

YEM

ZAF

ZAR ZMB

ZWE

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But institutions are endogenous

Institutions could vary because underlying factors differ across countries.

– Geography, ecology, climate– Culture– Perhaps other factors?

Montesquieu’s story:– Geography determines “human attitudes”– Human attitudes determine both economic performance and political

system.– Institutions potentially influenced by the determinants of income.

Identification problem.– We can learn only a limited amount from correlations and ordinary

least square (OLS) regressions.

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Geography hypothesis: Montesquieu

Montesquieu:– “The heat of the climate can be so excessive that the body

there will be absolutely without strength. So, prostration will pass even to the spirit; no curiosity, no noble enterprise, no generous sentiment; inclinations will all be passive there; laziness there will be happiness,”

– "People are ... more vigorous in cold climates. The inhabitants of warm countries are, like old men, timorous; the people in cold countries are, like young men, brave".

Moreover, Montesquieu argues that lazy people tend to be governed by despots, while vigorous people could be governed in democracies; thus hot climates are conducive to authoritarianism and despotism.

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Geography hypothesis: modern versions

Jared Diamond:– Importance of geographic and ecological differences in

agricultural technology and availability of crops and animals.Jeff Sachs:

– "Economies in tropical ecozones are nearly everywhere poor, while those in temperate ecozones are generally rich" because "Certainparts of the world are geographically favored. Geographical advantages might include access to key natural resources, access to the coastline and sea…, advantageous conditions for agriculture,advantageous conditions for human health."

– "Tropical agriculture faces several problems that lead to reduced productivity of perennial crops in general and of staple food crops in particular" …

– "The burden of infectious disease is similarly higher in the tropics than in the temperate zones"

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Montesquieu’s story?.

Log

GD

P p

er c

apita

, PP

P, i

n 19

95

Latitude0 .2 .4 .6 .8

6

8

10

AFG

DZA

AGO

ARG

ARM

AUS AUT

AZE

BHSBHR

BGD

BRB

BLR

BEL

BLZ

BEN

BTNBOL

BIH

BWABRABGR

BFA

BDI

CMR

CAN

CPVCAF

TCD

CHL

CHN

COL

COMZAR

COG

CRI

CIV

HRV

CZE

DNK

DJI

DMA

DOM

ECU

EGYSLV

EST

ETH

FJI

FINFRA

GAB

GMB

GEO

DEU

GHA

GRC

GRD GTM

GIN

GNB

GUY

HTI

HND

HKG

HUN

ISL

IND

IDN

IRN

IRQ

IRLISR

ITA

JAM

JPN

JOR

KAZ

KEN

KORKWT

LVA

LSO

LBR

LBYLTU

LUX

MDGMWI

MYS

MLI

MLT

MRT

MUSMEX

MDA

MAR

MOZMMR

NAM

NPL

NLD

NZL

NIC

NERNGA

NOR

OMN

PAK

PAN

PNG

PRYPERPHL

POL

PRT

QAT

ROM

RUS

RWA

STP

SAU

SEN

SLE

SGP

SVKSVN

SOM

ZAF

ESP

LKA

KNA

LCA

VCT

SDN

SWZ

SWE

CHE

SYR

TJK

TZA

THA

TGO

TTO

TUN TUR

TKM

UGA

GBR

UKR

ARE

URY

USA

UZB

VEN

VNM

YEM

YUG

ZMB

ZWE

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Empirical pitfalls of correlations and ordinary least square estimates

Montesquieu’s story example of omitted variables bias and identification problem.

– Other omitted factors---human nature, culture, geography---vary across countries and affect economic performance.

– They also are correlated with or have a causal effect on institutions.

– Similar problem affects inferences about geography on income; potentially correlated with omitted variables.

Reverse causality:– Income affects institutions.

Attenuation bias:– Measures of institutions very coarse, poorly correspond to

conceptual measures, creating “errors in variables” problem.

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Need for exogenous variation

Exploit “natural experiments” of history, where some societies that are otherwise similar were affected by historical processes leading to institutional divergence.

– Building towards an “instrument” for institutions (Lecture 3);a source of variation that affects institutions, but has no other effect, independent or working through omitted variables, on income.

Examples of potential natural experiments of history:1. South versus North Korea2. European colonization3. Chinese experience

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The Korean experiment

Korea: economically, culturally and ethnically homogeneous at the end of WWII.If anything, the North more industrialized.“Exogenous” separation of North and South, with radically different political and economic institutions.

– Exogenous in the sense that institutional outcomes not related to the economic, cultural or geographic conditions in North and South.

– Approximating an experiment where similar subjects are “treated”differently.

Big differences in economic and political institutions.– Communism (planned economy) in the North.– Capitalism, albeit with government intervention and early on without

democracy, in the South.Huge differences.

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North and South KoreaGDP per capita

0

2000

4000

6000

8000

10000

12000

14000

1950 1960 1970 1980 1990 1998

South KoreaNorth Korea

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European colonization as a “natural experiment”

After the discovery of the New World and the rounding of the Cape of Good Hope, Europeans dominated many previously diverse societies, and fundamentally affected their social organizations (institutions).Approximating a “natural experiment” because

– Many factors, including geographic, ecological and climatic ones, constant, while big changes in institutions.

– Changes in institutions not a direct function of these factors. – Analogy to a real experiment where similar subjects have

different “treatments”.Consequences?Look at changes in prosperity from before colonization (circa 1500) to today in the former colonies sample.

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Measuring prosperity before national accounts

To answer these questions, we need a measure of prosperity before the modern era.Urbanization is a good proxy for GDP per capita (Bairoch, Kuznets, de Vries).Only societies with agricultural surplus and good transportationnetwork can be urbanized.Urbanization is highly correlated with income per capita today and in the past.And we can construct data on urbanization in the past (Bairoch, de Vries, Eggimann)In addition, use population density as a check.

– Useful also because related to the causal mechanism in Lecture 2.

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Urbanization and income today.

GD

P p

er c

apita

, PP

P, i

n 19

95

Urbanization in 19950 50 100

6

7

8

9

10

AGO

ARG

AUS

BDI

BEN

BFABGD

BHS

BLZ

BOL

BRA

BRB

BWA

CAF

CAN

CHL

CIVCMR COG

COL

COM

CPV

CRI

DMADOMDZAECU

EGY

ERI

FJI

GAB

GHAGIN

GMB

GRDGTM

GUY

HKG

HND

HTI

IDN

IND

JAM

KEN

KNA

LAO

LCA

LKA

LSO

MAR

MDG

MEX

MLIMOZ

MRT

MUS

MWI

MYS

NAM

NERNGA

NIC

NPL

NZL

PAK

PAN

PER

PHLPRY

RWA

SDNSEN

SGP

SLE

SLV SURSWZ

TCD

TGO

TTO

TUN

TZA

UGA

URY

USA

VCT

VEN

VNM

ZAF

ZAR ZMB

ZWE

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Results: until 1500

Persistence is the usual state of the world.– There is “mean reversion” and rise and decline of nations, and

certainly of cities.– But countries that are relatively rich at a point in time tend to

remain relatively rich.The data confirm this persistence.

– After the initial spread of agriculture, there was remarkable persistence in urbanization and population density.

– Largely true from 1000 BC to 1500 AD, and also for subperiods.

– More important, true also in the former colonies sample.

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Reversal since 1500 (1).

GD

P p

er c

apita

, PP

P, i

n 19

95

Urbanization in 15000 5 10 15 20

7

8

9

10

ARG

AUS

BGD

BLZ

BOL

BRA

CAN

CHL

COL CRI

DOM DZAECU

EGY

GTM

GUY

HKG

HND

HTI

IDN

IND

JAM

LAO

LKA

MAR

MEXMYS

NIC

NZL

PAK

PAN

PER

PHLPRY

SGP

SLV

TUN

URY

USA

VEN

VNM

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Reversal since 1500 (2).

GD

P p

er c

apita

, PP

P, i

n 19

95

Log Population Density in 1500-5 0 5

6

7

8

9

10

AGO

ARG

AUS

BDI

BEN

BFA BGD

BHS

BLZ

BOL

BRA

BRB

BWA

CAF

CAN

CHL

CIVCMRCOG

COL

COM

CPV

CRI

DMADOM DZAECU

EGY

ERI

GAB

GHAGIN

GMB

GRDGTM

GUY

HKG

HND

HTI

IDN

IND

JAM

KEN

KNA

LAO

LCA

LKA

LSO

MAR

MDG

MEX

MLIMOZ

MRT

MWI

MYS

NAM

NERNGA

NIC

NPL

NZL

PAK

PAN

PER

PHLPRY

RWA

SDNSEN

SGP

SLE

SLVSURSWZ

TCD

TGO

TTO

TUN

TZA

UGA

URY

USA

VCT

VEN

VNM

ZAF

ZARZMB

ZWE

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When did the reversal happen?

Urbanization in excolonies with low and high urbanization in 1500(averages weighted within each group by population in 1500)

0

5

10

15

20

25

800 1000 1200 1300 1400 1500 1600 1700 1750 1800 1850 1900 1920

low urbanization in 1500 excolonies high urbanization in 1500 excolonies

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The nature of the reversal: industrialization

Industrial Production Per Capita, UK in 1900 = 100(from Bairoch)

0

50

100

150

200

250

300

350

400

1750 1800 1830 1860 1880 1900 1913 1928 1953

US Australia Canada New Zealand Brazil Mexico India

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What’s happening?

Former colonies with high urbanization and population density in 1500 have relatively low GDP per capita today, while those with low initial urbanization and population density have generally prospered.

– But gains in the growing societies not always equally shared. Native Indians and aborigines in the New World have all but disappeared.

(Simple) Geography hypothesis? – It cannot be geographical differences; no change in geography.

Sophisticated geography hypothesis? Certain geographic characteristics that were good in 1500 are now harmful?

– no evidence to support this view; reversal related to industrialization, and no empirical link between geography and industrialization.

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Understanding the patterns from 1500 to 2000

Reversal related to changes in institutions/social organizations.Relatively better institutions “emerged” in places that were previously poor and sparsely settled.

– E.g., compare the United States vs. the Caribbean or Peru.Thus an institutional reversal

– Richer societies ended up with worse institutions.– Europeans introduced relatively good institutions in sparsely-settled

and poor places, and introduced or maintained previously-existing bad institutions in densely-settled and rich places.

E.g.; slavery in the Caribbean, forced labor in South America, tribute systems in Asia, Africa and South America.

Institutions have persisted and affected the evolution of income, especially during the era of industrialization

– why to be discussed more below.

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Institutions matter

Reversal in prosperity resulting from the institutional reversal, combined with persistence in institutions.

– Countries with “better” institutions prosper, while those with “bad” institutions stagnate or decline.

– The reversal also emphasizes that the differences are not only between capitalist and communist systems.

– What matters more is the “type” of capitalism.

But then why different institutions?– And what are “good” and “bad” institutions?

For now, take good institutions to be those that encourage investment in physical, human capital, and in technology, and bad institutions in the opposite

– Are the same institutions always good and bad? Discussed later.

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Are British colonies special?

Popular view going back to Adam Smith and Winston Churchill that British cultural and political influence was beneficial, certainly better than that of Spanish and French influence.Does the evidence support this view?The answer is no.

– The patterns shown above are robust to controlling for the identity of colonial power.

– Similar patterns when we look at only British colonies.

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The Reversal among former British colonies (1)

GD

P pe

r cap

ita, P

PP, i

n 19

95

Urbanization in 15000 5 10 15

7

8

9

10 AUS

BGD

BLZ

CAN

EGYGUY

HKG

IND

JAM

LKA

MYS

NZL

PAK

SGPUSA

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The Reversal among former British colonies (2)

GD

P p

er c

apita

, PP

P, i

n 19

95

Log Population Density in 1500-5 0 5

6

7

8

9

10 AUS

BGD

BHS

BLZ

BRB

BWA

CAN

DMA

EGY

GHAGMB

GRD

GUY

HKG

IND

JAM

KEN

KNA

LCA

LKA

LSO

MWI

MYS

NAM

NGANPL

NZL

PAKSDN

SGP

SLE

SWZ

TTO

UGA

USA

VCT

ZAF

ZMB

ZWE

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The role of culture (1)

Can all this be related to culture?What is culture?

– Culture is a relatively fixed characteristic of a group or nation, affecting beliefs and preferences. Example: religion

Useful distinction between culture and informal institutions. Informal institutions are related to how society shapes incentives, and are related to equilibrium of a given game (typically defined by formal institutions, distribution of income, political power etc.). Informal institutions are not fixed, and change with economic conditions and distribution of power, though they are typically highly persistent.

Culture not useful in understanding the Korean divergence– North and South were culturally homogeneous.

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Possible that the reversal related to culture.– But the growth trajectories of British colonies similarly to

Spanish, Portuguese and French colonies once we control for differences in local conditions.

– Moreover, no econometric evidence that religion matters for understanding the reversal or for long-run growth (to be discussed more in Lecture 3)

– Reversal also not related to the presence of Europeans.Examples of prosperity in Singapore and Hong Kong, where population is now almost entirely non-European, but institutions protect investment.

Overall, no evidence that European values or culture played a special role.

41

The Reversal for colonies with less than 1% Europeans in 1900

.

GD

P p

er c

apita

, PP

P, i

n 19

95

Log Population Density in 1500-2 0 2 4 6

6

7

8

9

BDI

BEN

BFA BGD

BWA

CAF

CIVCMRCOG

EGY

ERI

GAB

GHAGIN

GMBHTI

IDN

IND

KENLAO

LKA

LSO

MDG

MLI

MRT

MYS

NAM

NERNGA

NPL

PAK

RWA

SDNSEN

SLE

SWZ

TCD

TGO

TZA

UGA

VNM

42

The Reversal for colonies with less than 1% of European descent in 1975

.

GD

P p

er c

apita

, PP

P, i

n 19

95

Log Population Density in 1500-2 0 2 4 6

6

7

8

9

10

AGO

BFA BGD

BWA

CIVCMRCOG

DZA

EGY

GAB

GHAGIN

GMB

HKG

HTI

IDN

IND

KEN

LKA

MDG

MLIMWI

MYS

NERNGA

PAKSDNSEN

SGP

SLE

TGO

TUN

TZA

UGA

VNM

ZARZMB

43


The Chinese experience informative about the role of culture versus institutions.

– China, Hong Kong, Singapore and Taiwan many cultural and ethnic similarities.

– While China adopted state planning and communist political institutions, Hong Kong, Singapore and Taiwan followed a capitalist path with relatively well-enforced property rights.

– While Hong Kong, Singapore and Taiwan prospered, China stagnated.

– After the Mao’s death and 1978 reforms, especially the introduction of some basic property rights, changes in economic incentives in China, and now very rapid growth rate.

44

GDP per capita in China, Taiwan, and Hong Kong, 1950-2001

0

5,000

10,000

15,000

20,000

25,000

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

China Taiwan Hong Kong Singapore

Role of culture (4)

106

Revisiting culture and religion (1)

What is the effect of culture?– Even though no comprehensive measures of broad cultural

differences, evidence not favorable for importance of culture.– Proxies for culture: religion, identity of colonizer, presence of

Europeans.Empirical strategy: look at the effect of religion on long-run economic growth once we take differences in institutions into account (that is, estimate the causal effect of institutions simultaneously).Answer: no evidence of any effect of religion (therefore culture) on cross-country differences in income.

– Also recall that no effect of identity of colonizer or direct effect of presence of Europeans.

107

Revisiting culture and religion (2)All former colonies

Dependent variable is log GDP per capita

in 1995

Dependent variable is institutions

Dependent variable is log

GDP per capita in 1995

Dependent variable is institutions

Second stage First Stage Second stage First StageInstitutions 0.96 1.13 (Protection Against Expropriation) (0.16) (0.35)

Percent Catholic 0.006 -0.01(0.01) (0.01)

Percent Muslim -0.002 -0.002(0.01) (0.01)

Percent "Other" -0.011 -0.01(0.01) (0.01)

Percent of European Descent in 1975 -0.008 0.019(0.01) (0.006)

Log Settler Mortality -0.58 -0.40(0.14) (0.15)

R-Squared in First Stage 0.30 0.35Number of Observations 63 63 63 63For religion regressions, base category is percent Protestant.

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MICRO APPROACH

“What policies help to improve the standard of living of the poor in terms of health, education, gender disparities, and access to credit?” Analysis uses individual our household level data In 2000 - U.N. member states agreed to the millennium development goals become a universal framework to eradicate poverty

GRAPH VIEW TABLE VIEW

Percent of population in each consumption group

all countries

Update, June 2011:Data was updated in June 2011 to reflect more recent information on purchasing power parity calculations. Data on website may therefore notmatch identically with what is available in the current (April 2011) edition of Poor Economics: A Radical Rethinking of the Way to Fight GlobalPoverty. Future editions of the book will incorporate these changes. If you have any questions regarding the data, please [email protected].

Data Sources:The data tables incorporate data from the household surveys mentioned above. We also used the Demographic and Health Surveys (DHS) forsupplemental health information for each respective country. DHS data can be downloaded at http://www.measuredhs.com/accesssurveys/

Methodology:We constructed each variable of interest at the household level. Since some of the surveys asked different variants of some of the questions, wetried to define each variable in the most comparable way possible across countries. We normalized all household expenditure variables to a daily percapita level. Using the PPP conversion methodology discussed in the foreword, we converted the local currency units into 2005 world rupees. Wethen applied the daily poverty line cutoffs to each household's total per capita expenditures to classify households into the relevant expenditurecategories. All of the tables display variable means across households within each expenditure group weighted according to the household weightsprovided in most surveys.

DOWNLOAD DATA »

$1 $2 $4 $6 $10% of population within consumption groups

0% 25% 50% 75% 100%

Bangladesh

Brazil

Ecuador

Ghana

Guatemala

India Hyderabad

India Udaipur

Indonesia

Ivory Coast

Mexico

Morocco

Nicaragua

Pakistan

Panama

Papua New Guinea

Peru

South Africa

Tanzania

Timor Leste

ABOUT THE BOOK CHAPTERS TEACHING THE BOOK DATA RESEARCH

WHAT YOU CANDO

Poor Economics | A radical rethinking of the way to fight global poverty http://pooreconomics.com/data/685

1 of 2 16/08/2011 2:42 PM

GRAPH VIEW TABLE VIEW

Percent of population in each consumption group

all countries

Update, June 2011:Data was updated in June 2011 to reflect more recent information on purchasing power parity calculations. Data on website may therefore notmatch identically with what is available in the current (April 2011) edition of Poor Economics: A Radical Rethinking of the Way to Fight GlobalPoverty. Future editions of the book will incorporate these changes. If you have any questions regarding the data, please [email protected].

Data Sources:The data tables incorporate data from the household surveys mentioned above. We also used the Demographic and Health Surveys (DHS) forsupplemental health information for each respective country. DHS data can be downloaded at http://www.measuredhs.com/accesssurveys/

Methodology:We constructed each variable of interest at the household level. Since some of the surveys asked different variants of some of the questions, wetried to define each variable in the most comparable way possible across countries. We normalized all household expenditure variables to a daily percapita level. Using the PPP conversion methodology discussed in the foreword, we converted the local currency units into 2005 world rupees. Wethen applied the daily poverty line cutoffs to each household's total per capita expenditures to classify households into the relevant expenditurecategories. All of the tables display variable means across households within each expenditure group weighted according to the household weightsprovided in most surveys.

DOWNLOAD DATA »

$1 $2 $4 $6 $10

Bangladesh 23.70% 45.70% 24.30% 3.80% 1.00%

Brazil 10.70% 18.80% 26.30% 13.90% 13.70%

Ecuador 1.40% 13.40% 29.90% 20.20% 17.00%

Ghana 33.90% 33.80% 24.10% 4.90% 2.90%

Guatemala 39.70% 25.10% 18.20% 6.20% 5.00%

India Hyderabad 14.30% 55.60% 24.30% 3.20% 1.70%

India Udaipur 66.80% 27.20% 5.10% 0.50% 0.30%

Indonesia 18.10% 36.90% 29.50% 8.20% 4.80%

Ivory Coast 16.10% 34.00% 33.70% 9.30% 4.50%

Mexico 12.90% 21.60% 31.20% 13.60% 11.00%

Morocco 4.00% 24.80% 47.60% 13.20% 7.70%

Nicaragua 14.30% 32.90% 33.30% 11.80% 5.00%

Pakistan 34.00% 44.90% 17.10% 2.30% 1.20%

Panama 5.50% 10.30% 21.10% 17.00% 20.70%

Papua New Guinea 16.10% 25.60% 26.90% 13.20% 9.90%

Peru 7.30% 19.90% 29.80% 17.80% 13.90%

South Africa 11.50% 24.10% 25.40% 10.90% 8.00%

Tanzania 21.70% 37.60% 27.50% 7.70% 3.70%

Timor Leste 18.10% 38.70% 27.60% 9.50% 4.10%

ABOUT THE BOOK CHAPTERS TEACHING THE BOOK DATA RESEARCH

WHAT YOU CANDO

Poor Economics | A radical rethinking of the way to fight global poverty http://pooreconomics.com/data/685?tab=tab-table

1 of 2 16/08/2011 2:42 PM

This translates into

• 30% of the children under 5 in Mali in 2000-2007 had measurable signs of malnutrition (44% in India, 0 in Sweden)

• Under 5 mortality rate in Mali was 217/1000 in 2006 (270 in Sierra Leone, 4 in Norway)

• Life expectancy at birth for males was 52 years in Mali (41 years in Sierra Leone, 79 in Sweden)

• 76% of adults were illiterate in Mali in 2005 (61% in Niger, 0 in Sweden)

In the world

• In 2005, 865 million people lived under a dollar a day at Purchasing power parity: they have the purchasing power of 1 1993 dollar. What does this mean?

• 27 million children every year do not get the essential vaccinations

• 6.5 millon children die every year before their first birthday, mainly of diseases that could have been prevented.

• Half of school-aged children in India cannot read a very easy paragraph (even though most are in school)

17

Goals include:

Achieve universal primary education Girls and children from poor and rural families are least likely to

attend school One child in five who is old enough to attend secondary school is

still enrolled in primary school No consensus on how to achieve this goal

18

Even if we did have perfect enrollment in school – what about the quality?

2/3 of global population that live on less than $3/day still struggling with fundamental education issues no books no buildings outdated curriculum dirt floors child labour

19

MICRO TOPICS - GENDER Secretary General of the United Nations, Kofi Annan: “Achieving gender equality is a prerequisite to achieving other Millennium Development Goals including eliminating poverty, reducing infant mortality, achieving universal education, and eliminating the gender gap in education by 2015.” Statement is based on economic research which demonstrates that: Women are more likely than men to spend income on the health and education of their children How do we promote policies which put income directly into the hands of women?

20

MICRO TOPICS – ACCESS TO CREDIT To get out of the vicious cycle of poverty – the poor need access to credit invest in their own productive activities to reap larger returns invest in the education and skills of their children for future

returns

Basis of borrowing in developed countries – people can borrow based on the notion that they have some form of collateral to back it up assets future earnings

21

How to lend money to poor people with no collateral? Very successful micro-lending institutions

Grameen Bank in Bangladesh founder Dr. Yunus received 2006 Nobel Peace Prize

lends to millions of poor people no ordinary commercial lender

would want as customer

very high repayment rates - significantly higher than conventional lending institutions

key reasons for success --- borrowers form a group in which all borrowers are jointly liable for each other’s loans

How can we transplant a successful lending institution in Bangladesh to elsewhere in the world?

22

Another example related to gender: Amartya Sen (Nobel Laureate) computed that there are 100 million missing women in Asia (China and India). 100 million women should be alive today who are not Many missing due to female infanticide

How does the one child policy in China contribute to these missing women? How does the introduction of gender testing though ultrasounds contribute to these numbers in India? Again want to figure out how to isolate the different contributing factors to pinpoint a key explanatory factor

23

COURSE LECTURES

Lecture 1 - Empirical Snapshot of the Poor

Banerjee, A. and E. Duflo (2007) “Economic lives of the poor” Journal of Economic Perspectives. 21(1), 141-167. Deaton, Angus and Jean Dreze (2009) “Food and Nutrition in India: Facts and Interpretations” Economic and Political Weekly, No 7, Vol XLIV, p. 42-65.

24

Lecture 2 – Macro Approach

Angrist, J., and A. Krueger (2001) “Instrumental variables and the search for identification: from supply and demand to natural experiments”, Journal of Economic Perspectives, 15(4), 69-85. Acemoglu, D., S. Johnson, and J. Robinson (2001) “The colonial origins of comparative development: an empirical investigation”, American Economic Review, December, 1369-1401. Nunn, N. (2008) “The long-term effects of Africa’s slave trades”, Quarterly Journal of Economics, February, 139-176.

25

Lecture 3 - Micro Approach Anderson, S. And D. Ray (2010) “Missing women: Age and disease”, Review of Economic Studies, Volume 77, 1262-1300. Hoddinott, J. and L. Haddad (1995) “Does female income share influence household expenditure? Evidence from Cote d’Ivoire”, Oxford Bulletin of Economics and Statistics, 57 (1), 77-96. Thomas, D. (1990) “Intra-household resource allocation: an inferential approach” Journal of Human Resources, 25(4), 635-664.

DEVELOPMENT DATA SOURCES

Research Institutions:

BREAD:

http://ipl.econ.duke.edu/dthomas/dev_data/index.html

J-PAL:

http://www.povertyactionlab.org/evaluations/data

IPA:

http://www.poverty-action.org/work/data

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HOME | CONTACT ABOUT | FRIENDS | PAPERS| | CONFERENCES | ANNOUNCEMENTS | DATA | SEARCH

Data from Developing Countries

There has been a spectacular increase in the availability andquality of data from developing countries in recent years. Manyof these datasets are either in the public domain or can beobtained at modest cost from the data collection agency. Thispage is intended as a resource to help locate those data. Weprovide links to some of the data that are on-line andexplanations of how to obtain others.

A searchable list of surveys containing information on povertyand health is available here.The International Household Survey Network websitecontains information and questionnaires for many surveys fromdeveloping countries.

Household surveys

Family Life Surveys

Indonesia Family Life Survey (IFLS)

An on-going longitudinal survey of individuals, households,families, communities and facilities, the first wave of the surveywas conducted in 1993/4 and included interviews with 7,224households in 321 communities in 13 provinces in Indonesia.The survey is representative of about 83% of the Indonesianpopulation. The second wave, in 1997/8, was followed by asurvey of 25% of the sample enumeration ares in 1998. IFLS3was conducted in 2000 and IFLS4 was conducted in 2007/08.

The surveys contain retrospective histories about, for example,employment, marriage, fertility and migration over the lifecourse of each respondent. The surveys also include householdconsumption, assets, self-reported health status and a batteryof health measures (including anthropometrics, hemoglobin,blood pressure, lung capacity and time to stand from a sittingposition). In 2007, cholesterol and dry blood spots were added.

Public domain data and documentation are available on theweb .

Malaysian Family Life Surveys (MFLS)

conducted in 1976/7 and 1988 also contain extensive historieson employment, marriage, fertility and migration. Respondentsin the first wave were followed in the subsequent waves; in thesecond wave, a refreshment sample was added. MFLS1(1976/77) and MFLS2 (1988) are in the public domain.

Matlab Health and Social Survey (MHSS)

was conducted in 1996 and covers the same area as the MatlabDemographic Surveillance System. The data are in the publicdomain . A resurvey is underway.

Click here to go to:

Household surveys

Firm-level data

Macro data sources

National statistical offices

B R E A D Bureau for Research in Economic Analysis of Development ::... http://ipl.econ.duke.edu/dthomas/dev_data/index.html

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Mexican Family Life Survey (MxFLS)

is an on-going nationally representative longitudinal survey ofindividuals, households, families and communities. The firstwave was conducted in 2002. The first follow-up was completedin 2005. The second follow-ups was conducted in 2009/10. Inaddition to consumption, income, wealth, employment,marriage and fertility, the survey contains a module on crimeand victimization as well migration histories. Respondents arefollowed if they move and interviewed in their new location.This includes people who move to the U.S. and those that returnto Mexico. Biomarker data are collected and include assets,self-reported health status and a battery of health measuresand dry blood spots. Data from the first two waves collected inMexico are in the public domain .

Guatemalan Survey of Family Health (EGSF)

is a single cross section survey which was was conducted inrural communities in 4 of Guatemala's 22 departments. Thesurvey was fielded in 1995. The data are publicly available .

University of North Carolina Surveys

Cebu Longitudinal Health and Nutrition Surveys

Conducted by a team of researchers from the United States andthe Philippines, the Cebu Longitudinal Health and NutritionSurvey is an ongoing study of a cohort of Filipino women whogave birth between May 1, 1983 and April 30, 1984 and havebeen re-interviewed periodically since then. The data areavailable at UNC.

China Health and Nutrition Survey

The China Health and Nutrition Survey was conducted in 1989and 1991 in 8 provinces in China and provides a wealth ofdetailed information on health and nutrition of adults andchildren including physical examinations. These data areavailable at UNC.

Nang Rong (Thailand) projects

The Nang Rong projects represent a major data collection effortthat was started in 1984 with a census of households in 51villages. The villages were resurveyed in 1988 and again in1994/95. New entrants were interviewed and a subsample ofout migrants were followed. These data are available at UNC.

Russia Longitudinal Monitoring Survey (RLMS)

is an on-going panel survey of households in Russia that beganin 1992. These data are available at UNC.

University of Washington CSDE VietnamResearch Projects

Vietnam Life History Survey (1991)

The Vietnam Life History Survey is a collaboration between theUniversity of Wasthington, the Institute of Sociology and the


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Institute of Social Sciences, in Vietnam. The survey collects datafrom about 100 households in two urban and two rural areas inVietname. The data are available at CSDE at UW.

Vietnam Longitudinal Survey (1995-1998)

The Vietnam Longitudinal Survey is a collaboration betweenProfessor Charles Hirschman, University of Wasthington, theInstitute of Sociology in Vietnam. The survey collects detaileddemographic information from all adult respondents in over1,800 households in one area of Vietnam. The data areavailable at CSDE at UW.

Rural Economic and Demographic Survey (REDS)

The National Council of Applied Economic Research hasbeen surveying households and villages since the late 1960s aspart of REDS. Some of the respondents have been interviewedin several rounds yielding a panel spanning 30 years. The rawdata from the 1969, 1982 and 1999 waves are available onAndrew Foster's web site . Foster provides an overview ofthe files here.

Indian States Data from EOPP, LSE

State-level data from India copiled by the EconomicOrganiasation and Public Policy Programme at the LSE isavailable here. Topics covered include

land reformmedia and political agencylabor regulationquality of lifeeconomic reforms

India Agriculture and Climate Data Set

The database provides district level data on agriculture andclimate in India from 1957/58 through 1986/87. The datasetincludes information on

Area planted, production and farm harvest prices for fivemajor and fifteen minor crops.

Areas under irrigated and high-yielding varieties (HYV) formajor crops.

Data on agricultural inputs, such as, fertilizers, bullocks andtractors - in both quantity and price terms

Agricultural labor, cultivators, wages and factory earnings,rural population and literacy proportion.

Meteorological station level climate data (average climateover 30 year period)

Soil dataThe dataset was compiled by Apurva Sanghi, K.S. Kavi Kumar,and James W. McKinsey, of the World Bank and draws on workby James McKinsey and Robert Evenson of Yale University.For more information, click here . The data and documentationare available here .

National Sample Survey Organization

The National Sample Survey Organisation (NSSO) of Indiahas a long tradition of conducting high quality surveys. NSSOcarries out socio-economic surveys, undertakes field work forthe Annual Survey of Industries and follow-up surveys ofEconomic Census, sample checks on area enumeration and cropestimation surveys and prepares the urban frames useful indrawing of urban samples, besides collection of price data from


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rural and urban sectors.The data are available for purchase on CD.

China Health and Retirement Longitudinal Study(CHARLS)

The China Health and Retirement Longitudinal Study(CHARLS) is patterned after the Health and Retirement Study(HRS) in the US. Pilot data were collected in 2008 in twoprovinces: Zhejiang and Gansu (the richest and poorestprovinces). One person aged 45 and over was randomly chosenin each household with an age eligible person, and they andtheir spouse were interviewed. The sample is representative ofpeople 45 and over in these two provinces in China. This samplecontains data on 1,570 households and just under 2,700individuals. Data are available here. The first nationally-representativa wave of CHARLS will be fielded in 2011 and thesecond in 2013.

Mexican Health and Aging Study

is a prospective longitudinal survey of older adults (born before1951) and their spouses. The first wave was conducted in 2001and interviewed almost 10,000 adults and 5,000 spouses. Thefirst follow-up was completed in 2003. The project is acollaboration of researchers at the Universities of Pennsylvania,Maryland and Wisconsin with INEGI in Mexico. It is directed byBeth Soldo.

SABE (Salud Bienestar Y Envejeveimiento enAmerica Latina y El Caribe)

is a series of comparable cross-national surveys on health andaging organized as a cooperative venture among researchers inArgentina, Barbados, Brazil, Chile, Cuba, Mexico and Uruguay.The goal of the project is to describe health, cognitiveachievement and access to health care among people age 60and older with a special focus on people over 80 years old.Professor Alberto Palloni is the project PI which has been fundedby PAHO and the NIA.

Colombian Familas en Accion

Familias en Accion is a poverty alleviation program inColombia. Data are available here . Evaluation of the programis described at the Center for the Evaluation ofDevelopment Policies at IFS.

Learning and Education Achievement in PunjabSchools

The Learning and Education Achievement in PunjabSchools (LEAPS) Project is a multi-year project initiated byresearchers at Harvard University, Pomona College, and theWorld Bank that attempts to capture and track changes in theeducational universe at the primary level (upto grade 5) in 112villages in Pakistan. The main component of the project is a setof extensive surveys designed & conducted by the LEAPS team,with care being taken to be representative of the various actorsin the educational market.

The data consists of questionnaires administered to all 823primary schools (public, private, NGO) in the 112 villages, toover 800 teachers (with basic information on 5,000 teachers),1800 households, 6000 school children, and achievement tests


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of 12,000 class 3 children in Mathematics, English, and Urdu. Allchildren, households, schools and teachers are matched andthen followed over three additional (annual) rounds of surveys,for a complete 4-year panel.

The first round of data from these surveys & relateddocumentation is now publicly available for researchers at:www.leapsproject.org. The website also provides relatedinformation (questionnaires for all rounds, preliminary papers,and a LEAPS report that highlights findings from the first round).

South African DataFirst Data Archive

DataFirst, a research unit at the University of Cape Town, is aweb portal for South African census and survey data as well asmetadata and all research output based on this data. Thecatalogue of downloadable datasets is here.

Living Standards Measurement Studies (LSMS)

Since 1980, the World Bank has been collecting multi-purposehousehold survey data in several countries under the LivingStandards Measurement Study umbrella. That site containsinformation about the project, lists the countries included in theproject and describes how data may be accessed. For some ofthe surveys, the data are available on the web.

AlbaniaArmenia 1995 Azerbijan Survey of Living Conditions 1995 Bulgaria Integrated Household Survey 1985 Cote d'Ivoire Living Standards SurveyEcuadorGhanaGuyanaJamaica 1988-98KazakhstanKyrgyz RepublicMoroccoNepalNicaraguaPakistan 1991PanamaPapua New GuineaPeru 1985, 1990, 1991Romania 1992 Russian Longitudinal Monitoring Survey

(available from a site at UNC). 1994 South African Integrated Household SurveyTajikistan 1993-1994 Tanzania Human Resource Development

SurveyVietnam 1992-3Venezuela

The Rural Income Generating Activities (RIGA)database

The RIGA project, a collaborative effort of FAO, the World Bankand American University in Washington, DC, aims to promotethe understanding of the roles, relationships and synergies ofon-farm and off-farm income generating activities for ruralhouseholds. Building on existing household living standardssurveys, the project has developed methodologically consistent,internationally comparable income data that are now availablefree of charge from the project's website.


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The database contains cross-country comparable indicators ofhousehold-level income for 26 surveys representing 16countries across Africa, Asia, Eastern Europe and Latin America,making it a valuable resource for researchers and analysts inthe development field. The surveys are both cross-sectional andpanel, and currently run from 1992 through 2005; more surveyswill be added to the database as they become available. Whilethe RIGA project focuses mainly on the analysis of rural issues,the dataset contains information on both urban and ruralincome sources.

Find out more about the RIGA project: http://www.fao.org/es/ESA/riga/

Learn how to access the data: http://www.fao.org/es/ESA/riga/english/form_en.htm

Access the RIGA project publications: http://www.fao.org/es/ESA/riga/english/pubs_en.htm

Jameel Poverty Action Lab (J-PAL)

Descriptions of evaluations conducted by the Abdul Latif JameelPoverty Action Lab are available from the J-PAL evaluationspage. Data underlying these evaluations are available from thesame site.

The International Food Policy Reseach Institute

IFPRI has conducted several very innovative surveys in Africanand Asian countries. Many of these surveys are available forresearch purposes. See their home page and click on datasets.

Townsend Thai Project

and associated Thai databases are described here. TheTownsend Thai project began in 1997 with a relatively largecross-section survey. Annual resurveys have been conductedand a monthly survey was initiated in August 1998.

Agricultural Innovation and ResourceManagement in Ghana

This is an integrated longitudinal farm production andconsumption survey conducted by Christopher Udry and MarkusGoldstein (Yale University). Data may be downloaded fromhere.

Social Networks Project (Kenya and Malawi)

collects longitudinal socio-demographic data in Kenya andMalawi under the direction of Susan Watkins and Jere Behrman.Data are available for downloading here.

South African National Income Dynamics Study(NIDS)

NIDS is a nationally representative panel study that examinesincome, consumption and expenditure of households over timein South. Africa. The baseline survey was conducted in 2008 andthe first follow-up was conducted in 2010. The data will throwlight on matters such as coping strategies deployed in responseto shocks and unexpected events whether negative or positive,such as death in the family or an unemployed relative obtaininga job.


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In addition to income and expenditure dynamics, study themesinclude the determinants of changes in poverty and well-being;household composition and structure; fertility and mortality;migrancy and migrant strategies; labour market participationand economic activity; human capital formation, health andeducation; vulnerability and social capital. See the NIDS webpage for details.

SALDRU Langeberg Survey, South Africa

Langeberge integrated household survey was conducted by aconsortium of South African and American universities alongwith government and non government agencies in South Africa.Data may be requested by sending an email. See their webpage web page for details.

Survey on the Status of Women and Fertility

in five Asian countries collected detailed information on thestatus of women and their husbands in conjunction with fertilitychoices. Data collected in Malaysia, Pakistan, Philippines andThailand in 1993/1994 are available for downloading here.

Center for Data Sharing

is housed at the Economic Growth Center at Yale University anddistributes

Bicol longitudinal surveys, PhilippinesICRISAT India village level studyICRISAT Burkina Faso farm production survey

Mexican Migration Project.

Professor Doug Massey and collaborators have collected severalwaves of surveys on migration from central Mexico with specialsub-samples of Mexicans living in Chicago. The data can beobtained from the MMP. web-site of by contacting KristinEspinosa at the University of Pennsylvania. Her e-mail addressis [email protected].

Latin American Migration Project.

is an extension of the MMP. Mexican Migration Project . Theproject is directed by Professor Doug Massey who, with hiscollaborators, has collected data in Puerto Rico, the DominicanRepublic, Nicaragua, Costa Rica and Peru. Data are availablehere.

Central American Population Project

collects fertility and health surveys carried out in CentralAmerica. Data from Belize, Guatemala, El Salvador, Honduras,Nicaragua, Costa Rica and Panama are included in thecollection.

Tsimane Amazonian Panel Study (TAPS)

TAPS is an annual panel data set covering the period 2002throuh 2006 that follows a native Amazonian horticultural andforaging society experiencing rapid integration to the rest of theworld. The study has been tracking about 1,500 nativeAmazonians in about 250 households of 13 villages along theManiqui River, Department of Beni, Bolivia, and has introducedagricultural development projects. TAPS surveys take place


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every year during June-August. The first five-years of data,2002-2006, are now available to the public in STATA. To requestaccess to the 2002-2006 panel data set and its documentationgo to the following web site: http://people.brandeis.edu/~rgodoy/research/pgs/panel.html or contact RicardoGodoy (781) 736-2784, [email protected]

World Fertility Surveys

The World Fertility Surveys (WFS) were conducted in 41countries during the 1970s and early 1980s. The data are all inthe public domain and available at the Office of PopulationResearch at Princeton University . This is a very good site tofind out about data on fertility including the Chinese In-DepthFertility Surveys.

Countries for which World Fertility Surveys are availableinclude:

Africa

Benin; Cameroon, 1978; Cote d'Ivoire, 1980-81;Egypt, 1980; Ghana, 1979-80; Kenya, 1977-1978;Lesotho, 1977; Mauritania, 1981; Morocco, 1980;Nigeria, 1981-82; Rwanda, 1983; Senegal, 1978;Sudan (North), 1978-79; Tunisia, 1978;

Americas

Colombia, 1976; Costa Rica, 1976; DominicanRepublic, 1975 and 1980; Ecuador, 1979-80;Guyana, 1975; Haiti, 1977; Jamaica, 1975-76;Mexico, 1976-77; Panama, 1975-76; Paraguay,1979; Peru, 1977-78; Trinidad & Tobago, 1977;Venezuela, 1977;

Asia

Bangladesh, 1975-76; Fiji, 1974; Indonesia, 1976;Jordan, 1976; Korea, Republic of, 1974; Malaysia,1974; Nepal, 1976; Pakistan, 1975; Philippines,1978; Sri Lanka, 1975; Syria, 1978; Thailand,1975; Turkey, 1978; Yemen Arab Republic, 1979;

Europe

Portugal, 1979-80;

Demographic and Health Surveys

More recent fertility, mortality and health data are availablefrom Demographic and Health Surveys (DHS) . Nationalwhich is DHS has been collecting national sample surveys ofpopulation and maternal and child health conducted in manydeveloping countries since the 1980s. Data are currentlycollected under the umbrella of the Measure project which isadministered by Macro International. Data have beencollected in four waves:

DHS-I (1986-90)DHS-II (1991-1992)DHS-III (1993-1997)Measure (1998-present)

See the Measure DHS website for a list of countries that havebeen surveyed.


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International Reproductive Health Surveys

The Centers for Disease Control (CDC) assists countriesthroughout the world in the development, implementation andanalysis of national reproductive health surveys.

Africa Household Survey Project

Provides a listing of many household surveys conducted acrossAfrica.

Sticerd (LSE) Fieldwork web-page

which is managed by Markus Goldstein provides links toadditional surveys, questionnaires and survey methodsmaterials. Return to Top of Page,

Firm level sources

African manufacturing sector

Firm level data collected by The World Bank in collaborationwith the Centre for the Study of African Economies, OxfordUniversity, and several Government Statistical Agencies may bedownloaded from this site.

Centre for the Study of African Economies

CSAE faculty have collected firm level data in several Africancountries. Data from Ghana, Ethiopia, Tanzania and also, froma comparative study, in Cameroon, Ghana, Kenya andZimbabwe. are available from the CSAE web-site . Some ofthese data are also available on the World Bank web site.

Return to Top of Page,

Macro data sources

NBER Macro Data

Contains several macro series including Penn-World Tables, Mark 5.6. An international panel,

extract data by country, year and variable. Also called"Summers-Heston" data.

Data on business cycles in the US, NBER Macro HistoryDatabase, manufacturing and imports in the US.

World Bank Research Datasets

This site is maintained by The World Bank and contains countrylevel data on economic growth. Data related to several articlespublished on models of growth are available. These include, forexample:

Barro, Robert J., and Jong-Wha Lee. 1993."International Comparisons of Educational Attainment."Journal of Monetary Economics 32 (3): 363-94.De Long, J. Bradford, and Lawrence Summers.1993. "How Strongly Do Developing Economies Benefitfrom Equipment Investment?" Journal of MonetaryEconomics 32 (3): 395-415.


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Fischer, Stanley. 1993. "The Role of MacroeconomicFactors in Growth." Journal of Monetary Economics 32(3): 485-512King, Robert G., and Ross Levine. 1993. "Finance,Entrepreneurship, and Growth: Theory and Evidence."Journal of Monetary Economics 32 (3): 513-42.Levine, Ross, and David Renelt. 1992. "A SensitivityAnalysis of Cross-Country Growth Regressions."American Economic Review 82 (4): 942-63.

Clik here for a listing of all data available on the World BankResearch Dataset site.

Human Mortality Database

Historical mortality data for 17 countries

Return to Top of Page,

National statistical offices

Many of these offices provide a wealth of information. Censusdata are available from these offices; some household- andfirm-level surveys are public use and may either be downloadedor ordered from their office.

Geohive provides a listing of statistical offices across the globe.

Africa

Botswana

South Africa

October HH Survey Annual labor force surveyIncome and Expenditure Survey 1995, 2000

Asia

India

Census of IndiaNational Council of Applied EconomicResearchNational Sample Survey Organization

Indonesia

SAKERNAS Annual labor force surveySUSENAS Annual consumption survey withspecial module each year

South Korea

Malaysia

Philippines

Taiwan

Latin America


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Argentina

Encuesta Permanente de Hogares (PermanentHousehold Survey) Annual labor force survey of main cities

Bolivia

Brazil

Pesquisa Nacional Amostra Domicilios Annual national income survey with specialmodule each year

Chile

Colombia

Ecuador

Guatemala

Mexico

Peru

Venezuela

This page is maintained by Duke University. Please sendcomments and suggestions about this page including links todata sources to Duncan Thomas. Please address all questionsabout data availability, access and quality to the institutionproviding the data. If no institution is listed, we regret the dataare not supported.

Top | Home | Contact | Search Copyright © 2003 B R E A D All Rights Reserved.


11 of 11 29/08/2011 10:59 AM

Data Collecting Agencies:

DHS:

http://www.measuredhs.com/

World Values Survey:

http://www.worldvaluessurvey.org/

Afro Barometer:

http://www.afrobarometer.org/

Latino Barometer

http://www.latinobarometro.org/latino/latinobarometro.jsp

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AFROBAROMETER SLIDE SHOW

WHAT IS THE AFROBAROMETER?

The Afrobarometer is an independent, nonpartisan research project that measures the social, political, andeconomic atmosphere in Africa.

Afrobarometer surveys are conducted in more that a dozen African countries and are repeated on aregular cycle. Because the instrument asks a standard set of questions, countries can be systematicallycompared. Trends in public attitudes are tracked over time. Results are shared with decision makers,policy advocates, civic educators, journalists, researchers, donors and investors, as well as averageAfricans who wish to become more informed and active citizens. UGANDA ROUND 4.5.2 PRE-ELECTION SURVEY

The Afrobarometer recently conducted a second pre-election survey in Uganda. See the Summary ofResults and a powerpoint presentation of the findings below.

Powerpoint Presentation of Uganda Round 4.5.2 Pre-election Survey Findings

Summary of Results Uganda, 2011 (Round 4.5.2 Pre-election Survey)

UGANDA ROUND 4.5.1 PRE-ELECTION SURVEY

The Afrobarometer recently conducted a pre-election survey in Uganda. See the Summary of Results anda powerpoint presentation of the findings below.

Powerpoint Presentation of Uganda Round 4.5.1 Pre-election Survey Findings

Summary of Results Uganda, 2010 (Round 4.5.1 Pre-election Survey)

Afrobarometer http://www.afrobarometer.org/

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International Organizations:

IFPRI: http://www.ifpri.org/datasets

World Bank: http://data.worldbank.org/data-catalog

OECD: http://www.oecd.org/document/0,3746,en_2649_201185_46462759_1_1_1_1,00.html

UN: http://data.un.org/

WHO: http://www.who.int/research/en/

FAO: http://www.fao.org/corp/statistics/en/

Faculty homepages:

http://rbarro.com/data-sets/

http://econ-www.mit.edu/faculty/acemoglu/data

http://econ-www.mit.edu/faculty/eduflo/social

http://www.economics.harvard.edu/faculty/nunn/data_nunn

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Contact Information

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Papers

Courses

Books

Publications

Selected Presentations

Other Writings

Data ArchiveAcemoglu and Dell(2010)Acemoglu, Johnson,Querubin, and Robinson(2008)Acemoglu and Guerrieri(2008)Acemoglu, Johnson,Robinson, and Yared(2008)Acemoglu and Johnson(2007)Acemoglu and Johnson(2005)Acemoglu, Johnson, andRobinson (2005)Acemoglu, Autor, andLyle (2004)Acemoglu and Linn(2004)Acemoglu, Johnson,Robinson, andThaicharoen (2003)Acemoglu, Johnson, andRobinson (2002)Acemoglu and Ventura(2002)Acemoglu (2002)Acemoglu, Johnson, andRobinson (2001)Acemoglu and Angrist(2001)Acemoglu and Angrist(2000)

Data Archive

Please follow the links below to access the datasets and program files used in a numberof my papers. The links are ordered according to date of publication.

Productivity Differences Between and Within CountriesDaron Acemoglu and Melissa Dellforthcoming, American Economic Journal: Macroeconomics.

When Does Policy Reform Work - The Case of Central Bank IndependenceDaron Acemoglu, Simon Johnson, Pablo Querubin, and James A. RobinsonBrookings Papers on Economic Activity, 2008(1), pp. 351-418.

Capital Deepening and Non-Balanced Economic GrowthDaron Acemoglu and Veronica GuerrieriJournal of Political Economy, 116(3), June 2008: pp. 467-498.

Income and DemocracyDaron Acemoglu, Simon Johnson, James A. Robinson, and Pierre YaredAmerican Economic Review, 98(3), June 2008: pp. 808-42.

Disease and Development: The Effect of Life Expectancy on Economic GrowthDaron Acemoglu and Simon JohnsonJournal of Political Economy 115, December 2007: pp. 925-985.

Unbundling InstitutionsDaron Acemoglu and Simon JohnsonJournal of Political Economy, 113(5), October 2005: pp. 949-995.

The Rise of Europe: Atlantic Trade, Institutional Change and Economic GrowthDaron Acemoglu, Simon Johnson, and James A. RobinsonAmerican Economic Review, 95(3), June 2005: pp. 546-579.

Women, War and Wages: The Effect of Female Labor Supply on the Wage Structure at Mid-CenturyDaron Acemoglu, David Autor, and David LyleJournal of Political Economy, 112(3), June 2004.

Market Size in Innovation: Theory and Evidence from the Pharmaceutical IndustryDaron Acemoglu and Joshua LinnQuarterly Journal of Economics, 119(3), August 2004: pp. 1049–1090.

Institutional Causes, Macroeeconomic Symptoms: Volatility, Crises and GrowthDaron Acemoglu, Simon Johnson, James A. Robinson, and Yunyong ThaicharoenJournal of Monetary Economics, 50, January 2003: pp. 49-123.

Reversal of Fortune: Geography and Institutions in the Making of the Modern World IncomeDistributionDaron Acemoglu, Simon Johnson, and James A. RobinsonQuarterly Journal of Economics, 117, November 2002: pp. 1231-1294.

The World Income DistributionDaron Acemoglu and Jaume VenturaQuarterly Journal of Economics, 117, May 2002: pp 659-694.

Technical Change, Inequality, and The Labor MarketD A l

MIT Department of Economics : Daron Acemoglu : Data Archive http://econ-www.mit.edu/faculty/acemoglu/data

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Journal Websites:

http://www.aeaweb.org/issue.php?journal=APP&volume=3&issue=3

http://www.aeaweb.org/issue.php?doi=10.1257/aer.101.5

http://restud.oxfordjournals.org/content/current

American Economic Journal:Applied Economics

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American Economic Journal: Applied Economics

Vol. 3, No. 3, July 2011

Front Matter (pp. i-vi)

Abstract/Tools | Full-Text Article | Comments (0)

Government Transfers and Political Support (pp. 1-28)

Marco Manacorda, Edward Miguel and Andrea Vigorito

Abstract/Tools | Full-Text Article | Download Data Set | Online Appendix | Comments (0)

Do Value-Added Estimates Add Value? Accounting for Learning Dynamics (pp. 29-54)

Tahir Andrabi, Jishnu Das, Asim Ijaz Khwaja and Tristan Zajonc

Abstract/Tools | Full-Text Article | Download Data Set | Comments (0)

Financial Constraints and Inflated Home Prices during the Real Estate Boom (pp. 55-87)

Itzhak Ben-David


Low-Skilled Immigration and the Labor Supply of Highly Skilled Women (pp. 88-123)

Patricia Cortés and José Tessada


Marrying Up: The Role of Sex Ratio in Assortative Matching (pp. 124-57)

Ran Abramitzky, Adeline Delavande and Luis Vasconcelos


Are High-Quality Schools Enough to Increase Achievement among the Poor? Evidence

from the Harlem Childrenʹs Zone (pp. 158-87)

Will Dobbie and Roland G. Fryer


Subsidizing Vocational Training for Disadvantaged Youth in Colombia: Evidence

from a Randomized Trial (pp. 188-220)

Orazio Attanasio, Adriana Kugler and Costas Meghir


Returns to Local-Area Health Care Spending: Evidence from Health Shocks to Patients

Far from Home (pp. 221-43)

AEAweb Journal Articles Display http://www.aeaweb.org/issue.php?journal=APP&volume=3&issue=3

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econ 490 – section 011 economics of the poor fall 2011 contact...

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