spillover effects of large- scale commercial farms in ethiopia daniel ali, klaus deininger and...

Spillover Effects of Large-Scale Commercial

Farms in EthiopiaDaniel Ali, Klaus Deininger and Anthony Harris

(World Bank – DECAR)

Background

• Heated debate on LSLBI often shallow and ill-informed• Limited data on availability of land, actual transfers or use

• Even basic questions cannot be answered• How much land has been transferred? What % is actually used/abandoned? • Is use in line with investment plans?• Do large farms provide opportunities for local communities?

• Using administrative data collected by Central Statistical Agency in Ethiopia we estimate spillovers from commercial farms

Measuring spillovers to small-holders• Benefits from commercial farms to local small-holders key part of

debate on large farms• Positive: employment, technology transfer, marketing, access to inputs• Negative: displacement and conflict, water resources, etc.

• Investors often receive land on the understanding they will create local employment and improve outcomes for local farmers• Valuable for policy makers to assess whether this happens

Two possible channels: Employment & Technology• Entry of commercial farms in an area could improve access to inputs,

demonstrate technology or provide source of employment• Value in good relationships with local communities• We test this by looking at the relationship between proximity to

commercial farms and yields, improved seed use, chemical fertilizer use, and employment

Outline

• Context• Data & descriptive statistics• Empirical identification• Results

Context

• Evolution of large farms and patterns of land use:• Slowed after 2011• Only 55% of land is utilized • 95% of large farms Ethiopian owned

• Contribution to the economy• Very little permanent employment: 1 worker/20 ha (1 worker /50ha for

maize)• Land clearing

Evolution of Commercial Farms

Evolution of Commercial Farms

Evolution of Commercial Farms

Evolution of Commercial Farms

Evolution of Commercial Farms

Evolution of Commercial Farms

Evolution of Commercial Farms

Evolution of Commercial Farms

No. of farms Holding size in ha Cultivated land in ha Share utilizedTotal 6,612 1,552,262 852,936 0.55By Size (ha) < 20 392 45,409 23,921 0.53 20-50 1,031 252,779 129,940 0.51 50-100 2,224 329,036 176,438 0.54 100-500 2,292 682,137 396,692 0.58 500-1000 243 155,970 88,106 0.56 >1000 162 303,211 193,712 0.64By national origin Ethiopian 6,287 1,570,323 859,211 0.55 Foreign 134 80,445 47,677 0.59 Joint 36 11,989 7,087 0.59By major crop Maize 885 165,995 93,493 0.56 Sorghum 843 117,612 62,316 0.53 Wheat 242 118,816 85,419 0.72 Sesame 2,494 607,417 314,268 0.52 Coffee 977 209,152 124,579 0.60 Cotton 373 263,526 163,442 0.62 Other 797 286,021 165,293 0.58

Level of Land Utilization – Commercial farms

Yield comparison by farm size (Quintal/ha)

Farm size Maize Sorghum Teff Wheat Sesame Coffee

Smallholder 28.16 22.06 14 21.63 7.82 7.6

< 20 42.04 30.88 9.18 41.69 13.94 4.24

20-50 37.42 24.52 8.79 33.68 10.74 8.43

50-100 36.89 25.64 8.61 26.11 8.46 6.38

100-500 39.3 28.21 7.75 24.64 9.77 6.37

> 500 33.81 29.51 9.9 28.32 10.84 7.02

Data (1)

• CSA Agricultural Sample Survey - 10 years of data on small-holder’s agricultural practices and yields (~44,000 parcels per year) • CSA Commercial Farm Survey – 4 years of data on commercial farms,

including start dates and location • Demographic and Health Survey – 2000, 2005 and 2011 rounds

include information on individual employment status

Data (2)

• Link individual observations across rounds in DHS and AGSS using geographic location• Link start date of farm to the year of the survey• Using commercial farm location to measure distance between

location in AGSS/DHS and commercial farms• Create kebele-level panel with measure of proximity by crop,

changing over time

Descriptive (1) – Yields (Q/ha)

Descriptive (2) – Inputs

Descriptive (3) – Proximity (km)

Yield, improved seed use and proximity (Maize)

Yield, chemical fertilizer use and proximity (Wheat)

Estimating yield/technology spillovers• Problem: Commercial farm’s decision location relates to area

characteristics that also influence small-holder outcomes• Control for fixed characteristics of a region• Focus on changes in the distance to the nearest farm growing same

crops (distance)• Estimate effect on yield (kebele level), chemical fertilizer and

improved seeds (individual level)• Equation for estimating yield and input use spillovers:

Regression results (yield/technology)

Effect of Distance to nearest farm growing: Yield (kebele level)Chemical fertilizer use

(HH Level)Improved seed use (HH

Level)Coffee -0.000282 -0.000230 0.000232

(0.00451) (0.000335) (0.000196)Maize -0.112*** -0.00171** -0.00102*

(0.0282) (0.000790) (0.000513)Sorghum -0.0110 0.000506 -0.000240

(0.0202) (0.000408) (0.000198)Teff -0.0257* 0.000672 -0.000213

(0.0148) (0.000653) (0.000211)

Wheat -0.0753 -3.79e-05 -0.000812**(0.0903) (0.00145) (0.000346)

Observations 18,729 436,575 436,575R-squared 0.460 0.355 0.306Year FE Y Y YWoreda FE Y Y YStandard errors are clustered at the Zone level*** p<0.01, ** p<0.05, * p<0.1

Increasing distance to nearest farm from 25km to 100km, decreases likelihood of using chem. fert. By 17%. Same change decreases likelihood of using Improved seeds for wheat or maize by 10%.

Estimating employment effects

• “Active farm” group: individuals living within 15km or 25km of a commercial farm that is active prior to the year of the DHS survey• Comparison group: individuals living with 15km or 25km of a

commercial farm that will open at some point after the year of the DHS survey• Compare likelihood of employment for individuals ()• Equation for estimating employment effects:

Employment generation from commercial farms – (active farms compared to future farms)

Dependent variable: Individual does Paid work, {1,0}

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

Effect of active farm growing CROP, {1,0} 0.0269 0.0219 0.158*** 0.0125 -0.111 -0.0178 0.246** 0.0449 0.126** 0.0749

(0.0484) (0.0344) (0.0570) (0.0475) (0.0857) (0.0399) (0.102) (0.0701) (0.0481) (0.0485)

Observations 4,327 7,783 2,528 4,237 1,841 4,485 1,178 2,289 2,700 4,651

R-squared 0.229 0.195 0.208 0.179 0.225 0.212 0.408 0.335 0.084 0.169Share of DHS clusters with active farm in 00, 05, 11 .67 .71 .69 .69 .76 .72 .73 .78 .75 .73

Crop Maize Maize Sorghum Sorghum Wheat Wheat Coffee Coffee Sesame Sesame

Distance band (km) 15 25 15 25 15 25 15 25 15 25

Standard errors are clustered at the DHS cluster level. Controls include: head status, age, age2, education, female, hhsize, religion

Fixed effects for survey year and month as well as area fixed effects (defined as 100x100 grid)

*** p<0.01, ** p<0.05, * p<0.1

Proximity to cities and paid work (DHS) – check validity of measure

Conclusions

1. Employment effects are low• Little job creation – large farms will not replace small holders, but depends on

crop• Commercial farms report 1 permanent worker / 20 ha• Effects dissipates with distance

2. Evidence of technology spillovers, improved yields and improved access to inputs for some crops• Identifying potential mechanisms for increased yields• Greater use of improved seeds or chemical fertilizer may be channel for effect

on yields

Conclusions & next steps

• Administrative data is the only way to make sense of LSLBI• Drawing on and improving upon existing data sources can get you a

long way• Highly relevant policy questions for government can be addressed:• Small improvements to CSA Commercial Farm survey we find evidence of

technology spillovers and assess employment effects of commercial farms

• We hope to expand this work to other countries facing high demand for LSLBI