The conservation versus production trade-off: does livestock intensification increase deforestation? Evidence from the Brazilian Amazon Petterson Molina Vale

Agenda of Puzzle 2. Recent contributions to the debate 3. Policy relevance 4. My contribution 5. Results 6. Policy discussion

prod (+) def (-) prod (+) def (+) What role do policies that enhance cattle productivity play in reducing deforestation? More productive farms produce more in the same grazing area, thus reducing demand for cleared land More productive farms eventually increase demand for cleared land and displace less productive ones 3

Avery Cohn et al. (2014) - PNAS 4

Examples of 165  Indonesia’s “agricultural involution” (Clifford Geerz, 1963)  Brazil’s soya expansion (Richards, Walker and Arima, 2014; Brown et al., 2005; Macedo et al., 2012)

A growing literature 6  In theory, Jevons paradox is possible both at the regional and global levels (Hertel, 2012; Villoria et al., 2014)  Many recent contributions, including Arima et al. (2011); Sa, Palmer and di Falco (2012); Marchand (2012); Ceddia et al. (2013); Havlik et al. (2013, 2014); Strassburg et al. (2014)

Policy relevance 7  DEFRA: “Low carbon agriculture and avoided deforestation to reduce poverty in Brazil”, , £ 25 MM  Brazilian government: “low carbon agriculture programme”, agricultural year, R$ 4.5 bn  Brazil’s agricultural research and extension agencies: cattle intensification programmes Embrapa Boas Práticas Agropecuárias and Emater Programa Balde Cheio

This paper 8 ×Macro perspective: second round effects depend largely on elasticity of demand for food Micro perspective: second round effects that propagate across space – “indirect land use effect of intensification” Q: Does livestock intensification increase deforestation? H0: Intensification in consolidated areas pushes traditional cattle ranching to the frontier through land markets. H1: Intensification in consolidated areas keeps farmers from migrating to frontier areas. of 16

9 Dependent variableDeforestation per Km2 in frontier areas (DEFif) Independent variable Productivity of cattle in consolidated areas (R$ / ha / year) (PRic) Relevant covariates Gate price of beef, cattle herd in frontier areas, land titling, protected areas, environmental legislation, enforcement of environmental legislation Units of observation756 municipalities PopulationBrazilian Amazon Time period1996, 2006 Model

10 (based on Rodrigues et al., 2009)

Example 1: Aripuanã (MT) of 1611

Example 2: Matinha (MA) 12

13 1) 221 municipalities dropped for not having data on deforestation / productivity RegionN∆ productivity (96-06) Pre-frontier %** Frontier %** Consolidated %*** Total % Productivity of cattle ranching (R$ / ha / year), Results

of 1614 Results

15 Results  First difference model: regress deforestation ( ) in frontier areas on cattle ranching productivity in consolidated areas ( )  Intensification in consolidated municipalities is associated with less deforestation in neighbouring frontier municipalities, less deforestation in neighbouring consolidated municipalities, and no outcome in pre- frontier areas  a one SD increase in productivity growth implies a 30% of one SD decrease in the change in deforestation

Conclusion of 1616  Evidence points to land-sparing effect: negative leakage of deforestation  Land use intensification policy likely to be win-win  Jevons paradox in agriculture likely to be true in some conditions and under some circumstances, but possibly not in this case  Thank you very much!

APPENDIX 17

18 Deforestation x productivity in consolidated areas Dependent variable: ln(change in deforestation) ( ), frontier (first differences) (1)(2)(3)(4) ∆W1prod 1.675***-2.591***-4.058***-3.724*** W1prod96 —-2.709***-2.691***-2.774*** ∆W1price ——-4.201** 15,390 ∆W2cattle — ——9.68e-06 Year 3.749***4.374***6.343** Other controls no yes Observations 65 R-squared *** p<0.01, ** p<0.05, * p<0.1

19  Regress deforestation ( ) in frontier areas on cattle ranching productivity in consolidated areas ( ), controlling for beef prices in consolidated areas ( ), cattle herds in frontier areas ( ) and other covariates ( ).

20  First-difference removes time-invariant correlates. This controls for climatic and fixed environmental conditions, as well as legal constraints.  The relevant time-varying variable that intermediates treatment and outcome is migration, not controlled for.  Control for baseline year to account for global difference in deforestation.  Results are unchanged without fixed effects / year dummy.  Results unchanged for other timeframes in dependent variable ( )  Results non-significant for a timeframe that atecedes treatment ( )

Arima et al (2011) 21 Dependent variable: deforestation in frontier municipalities Independent variable: change in soya area in consolidated areas Y = a+bX+cWX+u W = inverse- distance weights matrix

22  Cluster municipalities into 3 groups (pre-frontier, frontier and consolidated) according to deforestation extent and activity ( ) (Rodrigues et al, 2009).  Pre-frontier: deforestation extent and activity low  Frontier, the boom: deforestation extent low, activity high  Consolidated: deforestation extent  I use the dependent variable to classify municipalities, and this is inevitable as the only true indicator of whether a municipality is in either of those categories is deforestation.  Use three alternative classifications with the same results.  I find a significant overall effect even when I ignore the classification and use deforestation anywhere as the dependent variable

Variables 23  Deforestation (Km2) (INPE)  Output: total value of livestock production, including bovine, bubaline and other types of stock animals (R$ 2000) (IBGE)  Total pasture area, both natural and planted (km2) (IBGE)  Gate price of beef (R$ 2000) (varios sources, own calculation)  Total cattle herd (heads) (IBGE)  Share of land with full land title (IBGE)  State protected areas (Ministry of Environment)  Mandatory legal reserve (%) (Forest Code)  Total environmental fines / municipality’s agricultural output (Ministry of Environment)

Weights matrix: k-nearest neighbours 24

Example 1: Soya to cattle spillover R$ 100 / ha / year R$ 150 / ha / year 25

Example 1: Soya to cattle spillover R$ 100 / ha / year R$ 150 / ha / year Agricultural frontier 26

Example 2: cattle to cattle spillover R$ 50 / ha / year R$ 100 / ha / year 27

Example 2: cattle to cattle spillover R$ 50 / ha / year R$ 100 / ha / year Agricultural frontier 28