1. Mining explains substantial long-run differences in health and education outcomes between countries. International evidence shows that higher mining activity is associated with worse health and education indicators over the long run. 2. Doubling the mining share of a country's economy causes health and education outcomes to deteriorate significantly in the long run on average. Specifically, it leads to infant mortality rates being 11% higher, secondary completion rates being 23% lower, and the percentage of people with no education being 75% higher. 3. While some growth is better than no growth, the evidence indicates that "mining growth" tends to lead to worse long-run health and education outcomes than "no growth" on average. Mining does

1. 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

2. Question
What impact does mining have on
health and education outcomes?
2

3. Scope of question
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
3

4. Answer
1.
Mining explains substantial long-run differences in
health & education outcomes between countries
4

5. Answer
1.
2.
Mining explains substantial long-run differences in
health & education outcomes between countries
‘No growth’ is better than ‘mining growth’
5

6. Answer
1.
2.
3.
Mining explains substantial long-run differences in
health & education outcomes between countries
‘No growth’ is better than ‘mining growth’
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

7. Answer
1.
2.
3.
Mining explains substantial long-run differences in
health & education outcomes between countries
‘No growth’ is better than ‘mining growth’
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

8. 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

9. Contributions and motivations
1.
Explicitly considers mining
–
–
2.
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
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

10. Contributions and motivations
1.
Explicitly considers mining
–
–
2.
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
Isolates direct causal impacts of X on Y
–
–
3.
Purges country, time and omitted variable effects with panels
Introduces new time-variant instruments to ‘resource curse’ lit.
Disaggregates social development / human capital
–
Latest (and only causal) papers use composite indices
10

11. Contributions and motivations
1.
Explicitly considers mining
–
–
2.
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
Isolates direct causal impacts of X on Y
–
–
3.
Purges country, time and omitted variable effects with panels
Introduces new time-variant instruments to ‘resource curse’ lit.
Disaggregates social development / human capital
–
4.
Latest (and only causal) papers use composite indices
Contributes to the international evidence on the determinants of longrun 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

12. Why would mining…
12

13. Why would mining have anything to
do with health and education?
?
?
?
13

14. 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

15. 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)
• 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)
15

16. 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)
• 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. 5
Mining and infant deaths
1
2
3
4
SLE
CAF
SOM
AGO
MLI
GNB
TCD
NGA
MOZ
BDI
GIN
GNQ
CIV
BFA
AFG
CMR
LSO
MWI NER
DJISWZ
BEN
MRT
TGO
ZMB
UGA ETH
RWA
PAK
HTI
SDN
STP TMP
KEN
GAB
ZWE IND GHA
MMR SEN
ERI
MDG KHM
BTN
ZAFBOL
NPL
PNGAZE
BGD
NAM
MNG
MAR
GUY
IRQ
DZA
IDNKAZ
GTM
SUR
TUVNIC
BWA
TTO
MHL
DOM
PRY
ECU
HND PHL CPV
GEO
SLB
BRA
PER
MDV
JOR
ARM
TUN
VCT SLV ALB TUR
PAN
JAM COL
BLZ FJI ROM
PLW
SYR
ARG
TON BRB
BGR
RUS
MUS
LKA THA
UKR
SAU
URY GRD DMA
OMN
LVA
BHR KWT
ATG
CRI
MNE
SRB
CHL ARE QAT
BIH
LTU
MYS
HUN
USA
POL
BRN
HRV
MLT
EST CUB
NZL GBR
CAN
ESPGRC
AUS
CZE NLD
CHE IRL
DNK
BEL DEU PRT
AUT
CYP
FRA
ITA
SVN
NOR
LUX LIE FIN
JPN
SMR
SWE
SGP
ISL
-10
-8
-6
-4
Log mining share of value added
limr
-2
0
Fitted values
17

18. 5
Mining and no schooling
MOZ NER
MLI
SLESDN
BDI
RWA MARCAF
NPL
PAK
TGO
MRT
PNG IRQ
BGD
QAT
HTI KHM
GTM IND GHA
NIC
DZA
KEN MWI
MDV SEN
JOR TUNCMR
NAM
BRN
UGA MMR
IDN ECU SAU
GAB
ZMB
SLV
LSO
CHE SWZ
PRT HND TUR BRA
SYR BWA
ZAF
THA
CYP
ARE
MYS
PER
SGP MUS
LKA
COLBOL BHR KWT
BLZ
ITA
ZWE
JAM
GUY
LUX
PAN
BEL DEU IRL HRV
PHLSRB
MNG
PRY
CRI
CHL
CUB NLD ARG
MLT
GRC FJI
ESP
GBR
ROMPOL UKR
ISL FRA URY SWE
TTO
BGR
AUT
TON
RUS
ALB
KAZ
CAN
SVN
AUS
HUN
ARM
FIN LTU
NOR
LVA
EST
JPN
NZL
DNK
0
AFG
BEN
BRB
-5
CZE
-10
-8
-6
-4
Log mining share of value added
llu
-2
0
Fitted values
18

19. Basic equation
Error
ln Yc = α + β ln mining c + γ ln GDPc + δ Xc + ε
Infant / U5 mortality
Years education
No education
Mining valueadded level per
capita
Mining share of
GDP
Non-mining valueadded level per
capita
GDP per capita
Controls
e.g.
latitude
Institutions
Fixed effects
Primary completion
Secondary completion
19

20. Basic equation
Error
ln Yc = α + β ln mining c + γ ln GDPc + δ Xc + ε
Infant / U5 mortality
Years education
No education
Mining valueadded level per
capita
Mining share of
GDP
Non-mining valueadded level per
capita
GDP per capita
Controls
e.g.
latitude
Institutions
Fixed effects
Primary completion
Secondary completion
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’.
20

21. 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
21

22. Addressing potential endogeneity
ln Yc,t = α + β ln mining c,t + … . + εc,t
22

23. Addressing potential endogeneity
ln Yc,t = α + β ln mining c,t + … . + εc,t
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
23

24. Addressing potential endogeneity
ln Yc,t = α + β ln mining c,t + … . + εc,t
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
24

25. 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?
25

26. Empirical approach: extend to panel
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
26

27. 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

28. Recap: Identification strategy and managing conceivable threats
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
28

29. Mining and infant deaths
Dependent variable
Equation
Sample
Log infant mortality (deaths per '000)
(2)
(3)
(4)
(5)
2005
Panel
2005
2005
(1)
2005
Estimator
OLS
IV
IV Between
Log per capita mining level
value-added
0.07***
0.08***
0.11*
(0.02)
(0.02)
(0.06)
-0.64***
(0.06)
0.11***
(0.03)
0.11***
(0.03)
-0.56***
(0.05)
-0.55***
(0.05)
(0.10)
Log real GDP per capita
Excluded F statistic
IV Between
-0.67***
(0.04)
IV
-0.56***
(0.05)
-0.61***
OLS
0.12***
(0.02)
Log mining share of value
added
Log per capita non-mining
level value-added
(6)
Panel
21.99***
51.82***
29

30. 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
30

31. 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
• 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)
31

32. 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
• 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
32

33. 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
33

34. 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
• 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

35. 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
• 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

36. 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

37. Australian GDP growth vs. MFP (Source: Karunaratne, 2010)
37

38. 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
38

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)
39

40. 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

41. In the meantime..
• Standard prescriptions (often not followed!) remain a
good starting point to deal with these issues.
41

42. 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

43. Any questions?
• Thank you for your attention
• Comments are most welcome (it’s a work in progress)
• My contact details are:
43