mining, deaths and dropouts international evidence on the long-run health and education effects of...

Mining, deaths and dropouts International evidence on the long-run health and

education effects of mining

4 November 2013 Crawford Ph. D conference

Ryan Edwards Ph. D candidate Arndt-Corden Department of Economics Crawford School of Public Policy

Panel: Paul Burke (Chair), Robert Sparrow and Budy Resosudarmo

Question

What impact does mining have on health and education outcomes?

2

Scope of question

3

What impact does mining have on health and education outcomes?

Effect of X on Y

Holding all else constant

‘Average treatment effect’ (ATE/LATE)

The mining sector and mining growth

Relative to other sectors and non-mining growth

In the long run

Across / between countries

Answer

1. Mining explains substantial long-run differences in health & education outcomes between countries

4

Answer


2. ‘No growth’ is better than ‘mining growth’

5

Answer



3. In the long run, on average, doubling the mining share of the economy causes the:

- infant death rate to be 11 % higher

- secondary completion rate to be 23 % lower

- % of people with no education to be 75 % higher

6

Answer



3. In the long run, on average, doubling the mining share of the economy causes the:

- infant death rate to be 11 % higher

- secondary completion rate to be 23 % lower

- % of people with no education to be 75 % higher

(Caveat: ATE and impact heterogeneity)

7

Contributions and motivations 1. Explicitly considers mining

– Mining has not explicitly been considered in any cross-country econometric research on the ‘resource curse’ or human capital

– Only channel for ‘point’ resources to have effects

8




2. Isolates direct causal impacts of X on Y

– Purges country, time and omitted variable effects with panels

– Introduces new time-variant instruments to ‘resource curse’ lit.

9







3. Disaggregates social development / human capital

– Latest (and only causal) papers use composite indices

10







3. Disaggregates social development / human capital

– Latest (and only causal) papers use composite indices

4. Contributes to the international evidence on the determinants of long-run prosperity and development

– Human development is a long-run phenomena

– More external validity and useful to identify trends

– Useful starting point to develop theory and mechanisms for micro studies

11

Why would mining…

12

Why would mining have anything to do with health and education?

13

?

?

?

Many potential channels: net impact ambiguous

• Positive channels – Income / wealth effects (e.g. van der Ploeg, 2011)

– Endogeneity of human capital (e.g. Easterly, 2001)

– Strengthened fiscal position (e.g. Emerson, 1982; Arezi et al, 2011)

– Spill-overs (Kaplinsky, 2011) and private local investment (MCA, 2012)

14






• Negative channels – Low returns to skills, education and knowledge (e.g. Gylfason, 2001)

– ‘Dutch Disease’ (e.g. Corden & Neary, 1982; Sachs & Warner, 2001)

– Endogenous institutions and conditionality (Mehlum et al, 2006)

– Uncertainty and volatility (van der Ploeg, 2011; Carmiganani, 2013)

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• Negative channels – Low returns to skills, education and knowledge (e.g. Gylfason, 2001)

– ‘Dutch Disease’ (e.g. Corden & Neary, 1982; Sachs & Warner, 2001)

– Endogenous institutions and conditionality (Mehlum et al, 2006)

– Uncertainty and volatility (van der Ploeg, 2011; Carmiganani, 2013)

• Today, I am only interested in casual effects of X on Y – ‘Black box’ impact evaluation approach (ATE / LATE only)

– Mechanisms, ‘why’ and impact hetero. are 2ndary questions, for future research

16

17

Mining and infant deaths

AFG

AGO

ALB

ARE

ARG

ARM

ATG

AUSAUT

AZE

BDI

BEL

BENBFA

BGD

BGR

BHR

BIH

BLZ

BOL

BRA

BRB

BRN

BTN

BWA

CAF

CANCHE

CHL

CIV CMR

COL

CPV

CRI

CUB

CYPCZE

DEU

DJI

DMA

DNK

DOM

DZA

ECU

ERI

ESPEST

ETH

FIN

FJI

FRA

GAB

GBR

GEO

GHA

GINGNB

GNQ

GRC

GRD

GTMGUY

HND

HRV

HTI

HUN

IDN

IND

IRL

IRQ

ISL

ITA

JAMJOR

JPN

KAZ

KENKHM

KWT

LIE

LKA

LSO

LTU

LUX

LVA

MAR

MDG

MDV

MHL

MLI

MLT

MMR

MNE

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NERNGA

NIC

NLD

NOR

NPL

NZL

OMN

PAK

PANPER

PHL

PLW

PNG

POL

PRT

PRY

QAT

ROM

RUS

RWA

SAU

SDNSEN

SGP

SLB

SLE

SLV

SMR

SOM

SRB

STP

SUR

SVN

SWE

SWZ

SYR

TCD

TGO

THA

TMP

TON

TTO

TUNTUR

TUV

UGA

UKRURY

USA

VCT

ZAF

ZMB

ZWE

12

34

5

Log in

fant

mort

alit

y ra

te (

per

'000

death

s)

-10 -8 -6 -4 -2 0Log mining share of value added

limr Fitted values

Mining and no schooling

18

AFG

ALB

ARE

ARG

ARMAUS

AUT

BDI

BEL

BENBGD

BGR

BHRBLZ BOLBRA

BRB

BRNBWA

CAF

CAN

CHE

CHL

CMR

COL

CRICUB

CYP

CZE

DEU

DNK

DZAECU

ESP

EST

FIN

FJI

FRA

GAB

GBR

GHA

GRC

GTM

GUY

HND

HRV

HTI

HUN

IDN

IND

IRL

IRQ

ISL

ITA JAM

JOR

JPN

KAZ

KENKHM

KWTLKA

LSO

LTU

LUX

LVA

MAR

MDV

MLI

MLT

MMR

MNG

MOZ

MRT

MUS

MWI

MYS

NAM

NER

NIC

NLD

NOR

NPL

NZL

PAK

PANPER

PHL

PNG

POL

PRT

PRY

QAT

ROM

RUS

RWA

SAU

SDN

SEN

SGP

SLE

SLV

SRB

SVN

SWE

SWZ SYR

TGO

THA

TONTTO

TUN

TUR

UGA

UKRURY

ZAFZMB

ZWE

-50

5

Log p

erc

enta

ge o

f popuatio

n w

ith n

o s

choolin

g

-10 -8 -6 -4 -2 0Log mining share of value added

llu Fitted values

Basic equation

ln Yc = α + β ln mining c+ γ ln GDPc + δ Xc+ ε

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Infant / U5 mortality

Years education

No education

Primary completion

Secondary completion

Mining value-

added level per capita

Mining share of

GDP

Non-mining value-


GDP per capita

Error

Controls

e.g. latitude

Institutions Fixed effects

Basic equation

ln Yc = α + β ln mining c+ γ ln GDPc + δ Xc+ ε

20

Infant / U5 mortality

Years education

No education

Primary completion

Secondary completion

Mining value-


Mining share of

GDP

Non-mining value-


GDP per capita

Error

Controls

e.g. latitude

Institutions Fixed effects

log β coefficient interpretation: • Long-run health and elasticity of mining in the economy,

holding all else constant; and • Effectively a long-run equilibrium ‘impact estimate’.

Data

• Sample – 99-151 countries, 1970 – 2008, up to 6630 observations

• Mining value-added – United Nations Environmental Accounts, 1992 – 2008

– Plus: mining + utilities: United Nations National Accounts, 1970 - 2010

• Health and education indicators – World Development Indicators (WDI) and Barro and Lee (2010)

• GDP, institutions, latitude and other controls – Penn World Tables (2013)

– Sala-i-Martin et al (2004), Brunschweiller and Bulte (2008)

– World Development Indicators, World Governance Indicators, Resource Governance Index, Corruption Perceptions Index

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Addressing potential endogeneity

ln Yc,t = α + β ln mining c,t+ … . + εc,t

22



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Why might endogeneity be a threat? (Estimates will be biased and inconsistent with

OLS etcetera under endogeneity)

1. Mining value-added is determined by

initial capabilities. E.g. exploration abilities, ability in other sectors

2. Mining value-added is a product of

domestic decisions. E.g. industrial policy, trade policy, firms and individuals



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Why might endogeneity be a threat? (Estimates will be biased and inconsistent with

OLS etcetera under endogeneity)

1. Mining value-added is determined by

initial capabilities. E.g. exploration abilities, ability in other sectors

2. Mining value-added is a product of

domestic decisions. E.g. industrial policy, trade policy, firms and individuals

Need to instrument mining (IV estimates will be biased but consistent)

1. Initial country reserves / SS assets - Time invariant - Source: Norman, 2009; World Bank

2. Time variant country reserves - Only for oil and gas - Less country coverage - Source: US Energy Information

Administration, 2013

3. Commodity prices - Time and country variant country

weighted commodity price index - Source: Burke and Leigh, 2010

Empirical approach: Cross section first

Cross-section estimators:

1. Ordinary least squares (OLS) (inconsistent and biased)

2. Instrumental variable estimator (IVE) (consistent; opp. bias) – Instrumented with initial reserves; all use ‘ivreg2’

3. Panel ‘between’ IVE – Instrumented with initial reserves

– Confirms OLS/IVE results over many years by averaging out

• A cross section exploiting between-country variation has a natural long run interpretation – Primary result: long run elasticities and marginal effects

• Omitted, unobserved and other country-specific factors?

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Estimators:

1. Fixed effects (LSDV/within) (inconsistent and inefficient)

2. Fixed effects (FE) IV (consistent, but both inefficient) – Instrumented by the commodity price index OR reserves

3. FE generalised method of moments (GMM) (efficient) – Instrumented by the commodity price index and both reserves

4. System-GMM (efficient) – Instrumented by system of lags and lagged differences, using ‘xtabond’

• Controls for country/time-specific factors

• Minimises omitted / unobserved variable biases

• Efficient estimation under endogeneity

Empirical approach: extend to panel

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Rationale behind this dual approach

• Identification rests on holding all everything except for mining constant and finding valid instruments to remove endogeneity

• Many cross-country results do not ‘survive’ fixed effects

• Results consistent in magnitude and significance between cross-section and panel data imply that: – Time-specific effects are not an issue;

– Country specific effects are not an issue;

– Omitted variables are not an issue;

– Unobserved variables are not an issue; and

Primary parsimonious cross-section IV specification is sufficient to obtain consistent and unbiased parameter estimates (simpler, better)

27

Recap: Identification strategy and managing conceivable threats

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Threats Solutions

Sample is not random. Selection bias is unavoidable

Use IVEs and control for as much as possible

Time-specific effects (‘year selection bias’)

Use between and panel estimators

Small sample bias and IV inconsistency

Use a large sample of countries (excluded none). Panel approach. Rich and non-resource countries important for control group.

Endogeneity of mining IVs and reduced form / conditional equations. Local average

treatment effect

Weak instruments Strong by Stock-Yogo critical values and over-identification tests.

Exclusion restriction Commodity prices exogenous. Commodities pass through mining

to development. A-R and overid. tests.

Omitted and unobserved variables Country and time fixed effects and system-GMM

Mining and infant deaths

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Dependent variable Log infant mortality (deaths per '000)

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

Sample 2005 2005 Panel 2005 2005 Panel

Estimator OLS IV IV Between OLS IV IV Between

Log per capita mining level

value-added

0.07*** 0.08*** 0.11*

(0.02) (0.02) (0.06)

Log mining share of value

added

0.12*** 0.11*** 0.11***

(0.02) (0.03) (0.03)

Log per capita non-mining

level value-added

-0.61*** -0.64*** -0.67***

(0.04) (0.06) (0.10)

Log real GDP per capita -0.56*** -0.56*** -0.55***

(0.05) (0.05) (0.05)

Excluded F statistic

21.99*** 51.82***

Results are similar if I use

• Any of the estimators discussed so far – OLS, IVE, panel BE and IVE BE; panel FE, FE IVE, and SGMM

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• Different variables – Resources: rents share of GDP and point resource exports (WDI)

– Dependent variables: U5MR, life expectancy, WDI education data

– Instruments: disaggregated Norman, WB sub-soil assets and natural capital, interaction instruments (e.g. index * Norman or reserves)

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• Different variables – Resources: rents share of GDP and point resource exports (WDI)

– Dependent variables: U5MR, life expectancy, WDI education data

– Instruments: disaggregated Norman, WB sub-soil assets and natural capital, interaction instruments (e.g. index * Norman or reserves)

• Different functional forms – 1st differences and growth rates over 20 years, cross-section

– 1st differences and growth rates over 12 / 20 years, t = 4; 2 panel FE

– level-level, level-log, log-level

– Different level panels intervals (e.g. 5 and 10 year intervals

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Does this result fit the ‘real world’? Example: Health in Papua New Guinea

• Compare PNG’s IMR reduction to other countries

• Mining rose from 19% (1994) to 30% (2008) of GDP

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• Prediction: This ~50% increase should correspond to around a 5.5 per cent increase in IMR, holding all else constant, or an 0.4% increase p.a. (Hint: all else was not constant!)

34




• Prediction: This ~50% increase should correspond to around a 5.5 per cent increase in IMR, holding all else constant, or an 0.4% increase p.a. (Hint: all else was not constant!)

• Actual? – East Asia and Pacific average IMR decrease: - 4% p.a.

– World average IMR decrease: - 1.6% p.a.

– PNG IMR decrease: - 1% p.a

– Difference with world: + 0.6

– Estimates seems highly plausible, in this case

35

Issues to be resolved and ongoing work

• Impact heterogeneity – ATE overestimates impacts for resource rich advanced countries

(e.g. Australia and Norway)

– ATE underestimates impacts for developing countries (e.g. Papua New Guinea and Nigeria)

– Global effect (ATE) is robust

• controlling for Africa, regions, country FE and institutions

• using interaction terms

• Possible application to human capital more broadly (e.g. TFP, R&D)

• Further work needed to explain divergent experiences within a cross country framework

36

Australian GDP growth vs. MFP (Source: Karunaratne, 2010)

37

Issues to be resolved and ongoing work

• Mechanisms – First examination used same model as discussed, for simplicity and to

avoid identification issues

– Investment in health (consistently negative)

– Investment in education (insignificant or positive)

– Institutions (consistently negative)

– Gini coefficient (insignificant)

– Growth in other sectors, productivity, others (?)

• Within country spatial and dynamic analysis – Household surveys, administrative data and qualitative mining data

– Papua New Guinea, Indonesia, and Australia

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Final remarks

• Absence of evidence of non-monetary development impacts to date is absolutely telling

– Income and wealth effects from mining growth on human capital are, on average, non-existent

– Human capital effects undermine long-run growth and development prospects net of this (2/3 of HDI, most of MPI)

39

Final remarks

• Absence of evidence of non-monetary development impacts to date is absolutely telling

– Income and wealth effects from mining growth on human capital are, on average, non-existent

– Human capital effects undermine long-run growth and development prospects net of this (2/3 of HDI, most of MPI)

• May need to rethink any mining-based human development strategy

– Specific policy recommendations require further understanding of SR / LR mechanisms and within-country dynamics - each is likely to be different

40

In the meantime..

• Standard prescriptions (often not followed!) remain a good starting point to deal with these issues.

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In the meantime..

• Standard prescriptions (often not followed!) remain a good starting point to deal with these issues. E.g.

– Stably invest resource revenues in public HK (WB/IMF)

– Encourage micro-diversity and value-adding (UNIDO, 2011)

– Strengthen institutions / minimise rent-seeking opportunities (Extractive Industries Transparency Initiative, Publish What you Pay, Natural Resource Charter)

– Smooth macroeconomic volatility (van Der Ploeg, 2011)

– Ensure a broad tax and transfer system (IMF)

– Avoid high levels of inequality (Carmignani, 2013)

42

Any questions?

• Thank you for your attention

• Comments are most welcome (it’s a work in progress)

• My contact details are:

43

mining, deaths and dropouts international evidence on the long-run health and education effects of...

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long run

health education outcomes