Modern Industries, Pollution and Agricultural Productivity: Evidence from Mining in Ghana

1. Modern Industries, Pollution and Agricultural Productivity: Evidence from Mining in Ghana Fernando Aragon (SFU) (joint with Juan Pablo Rud, Royal Holloway) CEA Conference May 2013

2. Main issue Negative spillovers of modern industries • What is the effect of modern industries on agricultural production? In this paper: • Case of gold mining in Ghana • Modern, capital-intensive industry • Severe concerns of pollution • Near fertile rural area (cacao)

3. Why is this relevant? • Effect of pollution on agriculture not explored • Literature focuses on effects on human health • Biological evidence that pollution affects crops • Spillovers of modern industries • Thought in terms of input competition • Other non-input negative spillovers (e.g. pollution) neglected • Economic policy • Private and social costs • Compensation and mitigation

4. What do we do? • Main idea: pollution may affect crop yields • Non-input channels  residual productivity

5. What do we do? • Estimate an agricultural production function • Effect of mine activities on total factor productivity • Empirical strategy • Repeated cross sections of HH surveys • D-i-D: expansion of mining x exposure to mines (distance) • Endog. inputs: IV and imperfect IV (partial identification)

6. Main findings • Reduction of agricultural productivity • 40 % decrease between 1998-2005, near mines • But, no change in input use nor prices. • Results consistent with pollution channel • Satellite imagery: increase in air pollutants (NO2) • Increase in rural poverty

7. Unsolved issues • Cannot measure pollution directly (not available) • Effect on residual productivity  does pollution affect quality of inputs (land, water) or crops’ health? • Large scale and artisanal mines overlap cannot separate source

8. Outline • Background • Methods • Results

9. Background – Gold mining in Ghana • Important industry in Ghana • 97% of mineral revenue, 45% of total exports, 12% of fiscal revenue. • Modern, large scale, capital intensive • 96% large scale, rest artisanal/galamsey • Mostly foreign owned, exports all production as raw material.

10. Background – Gold mining in Ghana • Located in fertile agricultural land • Ashanti gold belt: Western, Ashanti and Central regions. • Cocoa producing regions • Negative spillovers • Population displacement • Environmental pollution: anecdotal and scientific evidence • Significant increase in late 1990s • We exploit this source of variation.

12. Why would mining affect agriculture? • Input competition channel • Demand-Supply  Increase in price of local inputs

13. Why would mining affect agriculture? • Input competition channel • Demand-Supply  Increase in price of local inputs • Mining has potential to pollute: air, water and soil • Industry-specific pollutants  cyanide, acid drainages, heavy metals • Similar to small city or power plant  emissions from heavy machinery (air pollutants) • Biological evidence • Exposure to air pollutants from burning fossil fuels  reduction in yields 30-60%, more susceptible to diseases. • Heavy metals in water and soil  vegetation stunted or dwarfed (Environment Canada 2009)

14. Analytical framework • Production function: F(A, Labor, Land) • Consumer-producer household choose inputs to maximize HH utility.

15. Analytical framework • With perfect input markets: • input demand is function of: prices and A  endogeneity of inputs problem • If farmers cannot buy/sell inputs • Input demand constrained by HH endowments • Extreme case: Input demand = input endowment • Use endowments as instruments for input use.

16. Analytical framework • Two possible channels for mining to affect agricultural output (and HH income) • Change on input prices  change in input use • Pollution  change on A • We can isolate effect on A, by estimating the production function i.e. conditioning output on input use

17. Methods – empirical implementation • Assume Cobb – Douglas, • y, m, l : log of agricultural output, land and labor A is function of: • Svt = measure of exposure to mine activity • Farmer characteristics Zi: age and literacy, land ownership, place of birth • District, year fixed effects , dummy prox. each mine

18. Methods - Data • Household data • Ghana Living Standard Surveys (GLSS): GLSS 4 (1998-99) and GLSS5 (2005-06), repeated cross sections • Input and output (farming households) • Real output calculated using local agric prices. • Poverty (all HHs) • Geographical coordinates of Enumeration Areas • Distance to mining areas (GIS)

19. Methods – empirical implementation • Two issues: • Endogeneity of mining activity: mining areas may be systematically different. • Endogeneityof input choice

20. Methods - solutions • Difference in difference: • Treated and control group defined by proximity to mine • “mining” area = within 20 km of an active mine • Treatment (continuous) : cumulative gold production • Svt = cumulative gold production within 20 km

23. Results – Mining and Agricultural Productivity Go to: Crop yields Go to: First Stage

24. Methods - solution • Use of instrumental variables: • Endowments as instruments of input use (with imperfect input markets) • But input endowments may be correlated to error term • Use an imperfect IV strategy (Nevo and Rosen, 2012) • If correlation between instrument and error is weaker than for the instrumented variable and • The sign of that correlation is the same  Bounds of parameter values, i.e., partial identification

25. Results – Mining and Agricultural Productivity Go to: Crop yields Go to: First Stage

26. Results – Mining and Agricultural Productivity Go to: Crop yields Go to: First Stage

27. Increase of one S.D in gold production  reduction of 30% in residual productivity • Between 1998/99 and 2005  40% decrease. • Too large? Consistent with biological evidence: 30-60% decrease in yields of crops exposed to polluted urban air.

28. Role of distance

29. Robustness checks • No evidence of compositional change • Farmer’s observables • Agricultural practices • Robust to alternative specifications • Parsimonious vs saturated • Similar for locals and immigrants • Placebo test (future mines) • CES production fct

30. Is this pollution? • No ground measures of pollution  satellite imagery (cross section only, 2005) • Detect NO2  air pollutant linked to fuel combustion , toxic & precursor of tropospheric ozone

31. Is this pollution?

33. Competition for inputs? • Mine demanding more labor / reducing supply of land  • Increase in input prices, reduction in demand for inputs.

35. Lack of effect of mining on input demand?, but productivity declined… • Consistent with imperfect input markets (inflexible inputs)

36. Measures of living standards - poverty

37. Increase in rural poverty (both farmers and non-farmers) • But nothing on urban poverty

38. Final remarks • Expansion of mining associated to • Significant reduction in agricultural productivity • Deterioration of living standards for rural population • Seems to be driven by pollution instead of competition for inputs • Important crowding out effect of modern industries • Significant spillovers and re-distributive effects • Local farmers lose, rest of country may gain • Disregard for these spillovers over-estimate net benefits of sector

40. Results – Mining and Crop Yields Back

41. First stage

42. Robustness – compositional changes

43. Robustness – alternative specifications

44. CES prod function