Do Mining Windfalls Stimulate Local

Development?

Evidence From Tanzania

Anthony Mveyange

Development Economics Research Group (DECRG)The World Bank Group

July 13, 2016

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Tanzania...the land of minerals

Source: Tanzania mineral audit agency report

Mineral reserves figures:Gold > 2000 tonnes; Diamond > 50 mil ounces; Nickel > 200 mil tonnesCopper > 12 mil ounces; Iron ore > 100 mil tonnes; Tanzanite > 10 mil ounces

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Mining sector reforms in perspective⇒ Prior to 1997 mining sector was predominantly stated owned (i.e.STAMICO)⇒ 1992 Foreign Exchange (FE) Act⇒ Two key reforms:

1 1998 mining act (also revised the FE Act)

⇒ Royalty calculation → Net smelter returnI 5% Diamonds & raw gemstonesI 3% Other minerals (including gold)I 0% cut and polished gemstones

⇒ 100% foreign ownership⇒ Unrestricted profit & capital repatriation⇒ Import duty waivers & tax exemptions⇒ In 2006 → $200,000 to local communities⇒ Waiver on local procurement of goods and services

2 2010 mining act revisions

⇒ Royalty calculation → Gross revenueI 5% Diamonds, uranium & raw gemstonesI 4% Other minerals (including gold)I 1% cut and polished gemstonesI 3% Salt, industrial and building materials

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Mining Sector’s FDI

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Mineral Rents % of GDP

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Mineral Revenues % of Domestic Revenues

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A glance of the literatureThe Resource Curse Debate, (Van der Ploeg, JEL, 2011):

1 Pessimistic View ⇒ Natural Resources is a curseF Dutch disease, commodity price volatility, rent grabbing,

corruption, conflict, & enclave economies2 Optimistic View ⇒ Natural Resources is blessing

F Institutions, & developed financial systems

Recent evidence ⇒ contradictingPositive⇒ Gold mines increase local real income, consumption, prices, and reducepoverty in Peru (Ar̊agon and Rud, 2013).⇒ Gold mines increase local female employment in service sector(Kotsadamen and Tolonen, 2013).Negative⇒ Local resource windfalls add little public goods & living standards(Casseli and Michaels, 2013).⇒ Gold mines reduce agricultural productivity in Ghana (Ar̊agon and Rud,2012).

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Rationale & Data1 Impacts of mining reforms in developing countries⇒ Scant evidence exists⇒ More so on localized effects

2 Tanzania is an interesting case study⇒ Two successive policy reforms in 15 years time⇒ Informing policy debates on the on-going natural gasexplorations⇒ No quantitative study to evaluate the impact of reforms

Data1 Mineral data: Tanzania mineral audit reports

2 USGS historical mineral prices data

3 Night Light Data - NOAA, US Defense Ministry ⇒ Local economic activities

4 NASA-SEDAC population data⇒ Population estimates 1990 - 2015

5 ACCLED conflict data

6 District councils financial data (PO-RALG)

7 Tanzania National Panel Survey data

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The Empirical Strategy: DID Estimator

Yd ,t = α′Post id ,t + β′Mine jd ,t + γ′(Mine jd ,t× Postid ,t)

+τ ′Xd ,t + Yeart + FEd + νd ,t(1)

WhereY is local economic activities ⇒ Night-time light intensity

d is district

t is (1996–2007) & (2008–2013)

i = 0, 1 with 0 and 1 being the pre– and post-treatmentperiods, respectively

j = 0, 1 with 0 and 1 being control and treatment groups,respectively.

X is vector of controls⇒ Rainfall, conflict, darkness (unlit grid cells)

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Identification: Parallel trends – 1998 Act

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Identification: Parallel trends – 2010 revised Act

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Main result: Zero impact on local development

Light per capita growth: 1996-2007 Light per capita growth: 2008-2013

(1) (2) (3) (4) (5) (6) (7) (8)

Post 1998 x Active Mine 0.002 -0.006 -0.011 -0.009[0.079] [0.081] [0.113] [0.115]

Post 2010 x Active Mine 0.175∗ 0.170 0.402∗∗ 0.369[0.101] [0.105] [0.188] [0.219]

No Mine x Year 1997 0.015 0.010[0.134] [0.137]

No Mine x Year 2009 -0.722∗ -0.691∗

[0.378] [0.395]

Log(Prices) -0.011 0.134[0.032] [0.346]

Year FE Yes Yes Yes Yes Yes Yes Yes YesDistrict FE Yes Yes Yes Yes Yes Yes Yes YesRainfall No Yes Yes Yes No Yes Yes YesConflict No Yes Yes Yes No Yes Yes YesUnlit pixels No Yes Yes Yes No Yes Yes Yes

N 1050 1050 1050 1042 566 566 566 560R2 0.219 0.222 0.222 0.222 0.301 0.301 0.304 0.304Dep. Variable [mean] -0.034 -0.034 -0.034 -0.034 0.185 0.185 0.185 0.185

Standard errors in brackets∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

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So where did the revenues go?The Missing Money...

Yd ,t = λ′Mine jd ,t + θ′Xd ,t + Yeart + FEd + εd ,t (2)

The Missing Living Standard & Welfare...

Yh,d ,t = α′Mine jh,d ,t + β′Xh,d ,t + Yeart + FEh + εh,d ,t (3)

WhereY is revenues, expenditure, and households consumption and welfare measures

h is household

d is district

t is Years

j = 0, 1 with 0 and 1 being control and treatment groups, respectively.

X is vector of controls

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The missing money...revenues vs. expenditures:

2010–2013

log(Rev) log(Exp) log(Rev cap) log(Exp cap)

(1) (2) (3) (4) (5) (6) (7) (8)

Mine 0.80∗∗∗ 0.76∗∗∗ -0.25∗∗∗ -0.22∗∗∗ 0.22∗∗∗ 0.18∗∗∗ -0.25∗∗∗ -0.22∗∗∗

[0.09] [0.09] [0.05] [0.06] [0.05] [0.06] [0.05] [0.06]

Rev [A>B, 1=Yes] 0.21 -0.51 0.07 -0.59[0.17] [0.34] [0.15] [0.35]

Exp [A<B, 1=Yes] 0.03 -0.52 -0.04 -0.58[0.10] [0.35] [0.11] [0.35]

Rev x Exp dummy 0.25∗ 0.54 0.34∗∗ 0.61∗

[0.13] [0.34] [0.13] [0.35]

Year FE Yes Yes Yes Yes Yes Yes Yes Yes

District FE Yes Yes Yes Yes Yes Yes Yes Yes

N 399 399 399 399 395 395 395 395R2 0.93 0.93 0.83 0.85 0.88 0.89 0.73 0.77

Standard errors in brackets∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

⇒ District with mining activities collected more revenues but spentless!

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The missing money...sectoral expenditures:

2010–2013

log(Educ) log(Agric) log(Health) log(Water) log(Roads)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Mine -0.5∗∗∗ -0.4∗∗∗ -2.3∗∗∗ -1.8∗∗∗ -1.9∗∗∗ -1.9∗∗∗ -2.6∗∗∗ -2.7∗∗∗ -0.3∗∗ -0.6∗∗

[0.0] [0.1] [0.1] [0.2] [0.2] [0.2] [0.1] [0.2] [0.1] [0.2]Rev 0.1∗∗ 0.6 0.2 0.2 0.8∗

[0.0] [0.4] [0.6] [0.7] [0.4]FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

N 395 395 370 370 361 361 368 368 370 370R2 0.8 0.8 0.7 0.8 0.5 0.5 0.6 0.6 0.6 0.6

Standard errors in brackets∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

⇒ District with mining activities spent less on key sectors fodevelopment!⇒ Echoes Bomani report (2008) on infrastructures development!!

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The missing money...other expenditures:

2010–2013

log(Emoluments) log(Charges) log(Recurrent) log(Other–recur) log(Devpt)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Mine -0.60∗∗∗ -0.46∗∗∗ 0.44∗∗∗ 0.44∗∗∗ -0.37∗∗∗ -0.16∗ 0.51∗∗∗ 0.52∗∗∗ 0.02 0.01[0.03] [0.05] [0.05] [0.05] [0.02] [0.08] [0.03] [0.04] [0.10] [0.11]

Rev -0.02 -0.16 0.03 0.18 -0.15[0.13] [0.37] [0.05] [0.23] [0.44]

FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

N 395 395 395 395 395 395 395 395 385 385R2 0.84 0.84 0.76 0.79 0.84 0.85 0.80 0.82 0.58 0.61

Standard errors in brackets∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

⇒ District with mining activities spent more on charges(unclassified) and other recurrent spending!!

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The missing living standards & welfare...

% employment Log(Expenditure) % schooling Average assets Number of rooms

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Mine 0.005 0.002 0.107 0.109 -0.007 -0.007 -0.361 -0.370 -0.052 -0.058[0.037] [0.038] [0.094] [0.093] [0.026] [0.026] [0.300] [0.303] [0.200] [0.202]

Household FE Yes Yes Yes Yes Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes Yes Yes Yes Yes YesTemperature No Yes No Yes No Yes No Yes No YesRainfall No Yes No Yes No Yes No Yes No Yes

N 8527 8527 8689 8689 8732 8732 8733 8733 8733 8733R2 0.540 0.540 0.783 0.783 0.825 0.825 0.726 0.726 0.697 0.697

Standard errors in brackets∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

⇒ No welfare impacts on households located in district with miningactivities!!

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Lessons

1 No effect on local economic activities⇒ Judge Bomani report (2008)

I Local procurement of goods and services...I Infrastructure development...I Integration of mining into local economy...

2 Despite collecting more revenues districts spent less overall andacross key developmental sectors

3 More spending was directed towards other charges & otherrecurrent spending

I Loopholes for corruption...?→ CAG reports on district councils

4 No impacts on household welfare!

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Thank you !!

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