Monitoring and analyzing LSLBI in Ethiopia

Background Heated debate on LSLBI often shallow and ill-informed No 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? Opens door to weak governance & RISK that everybody gets a bad deal Governments unable to identify capable good investors No scope to hold bad investors to account or re-assign non-performing leases High levels of risk which financial markets cannot price & insure Lots of investment not realized Only good administrative data will provide answers

Ethiopia’s context Enormous number of investment licenses recorded – 10,600 licenses listed Could be used to learn about entry and exit success & failure; follow-up services & lease pay Would provide an ideal sample frame (potential & operational investments) But list is out of data & basis for awarding a license not clear (lottery ticket) Technical difficulties relating to Large Farm survey Sample frame updating – many investment records held at local level Area measurement Production input and output -> Habe’s presentation for more details Improvements made (in 2 stages) Questionnaire structure – parcel-wise questions Additional questions Updating and linking of sample frame across rounds Potential for yield measurement

Main findings Evolution of large farms and patterns of land use: 95% of large farms Ethiopian owned Slow-down in LSLBI to pre-2007 levels after 2011 Only 55% of the land transferred is actually utilized Top 3 key constraints perceived by investors Technology, manpower, land disputes Only then infrastructure and credit access Contribution to the economy Lease payments of US$ 20 on average Very little permanent employment (1 worker/20-50 ha) Higher yields than smallholders – but also higher inputs So what about productivity (this year’s improved questionnaire will tell)?

Evolution of Commercial Farms

No. of farmsHolding size in haCultivated land in haShare utilized Total 6,612 1,552,262852, By Size (ha) < ,40923, , ,779129, , ,036176, , ,137396, ,97088, > ,211193, By national origin Ethiopian 6,287 1,570,323859, Foreign ,44547, Joint 36 11,9897, By major crop Maize ,99593, Sorghum ,61262, Wheat ,81685, Sesame 2, ,417314, Coffee ,152124, Cotton ,526163, Other ,021165, Levels of Land Utilization

Constraints for operating below full capacity (Top 3) TotalMaizeSorghumWheatSesameCoffeeCottonOtherCrop Cult. <100% of area Experimenting with different crops Lack of manpower Land disputes Land clearing taking time Infrastructure related Lack of credit

Contributions to the economy TotalMaizeSorghumWheatSesameCoffeeCottonOther Lease payment Length of lease reported if yes, years Annual lease fee reported if yes, lease fee (Birr/ha) Other annual payments reported if yes, amount (Birr/ha) Investment Made any investment (y/n) Size of inv. made (Birr/ha) ,35212,80917,2298,8877,92014,68034,006 Share on …. Roads (%) Land clearing Buildings Tractors Has irrigation facility (%) Took any loans last 5 years (%) if yes, amount (Birr/ha) ,26010,31054,25010,97319,83315,83828,169 Employment opportunities Permanent farm workers/ha Temporary workers per/ha

Yield comparison by farm size (Quintal/ha) Farm sizeMaizeSorghumTeffWheatSesameCoffee Smallholder < >

Incidence of Chemical Fertilizer Use by Farm Size Farm sizeMaizeSorghumTeffWheatSesameCoffee Smallholder < >

Spillovers from large to small farms? A key question in the policy debate They could be positive: Technology and markets/credit But could also be negative – market power, exploitation & marginalization of small farmers Unlikely to be the case for employment Having data allows us to give answers to this Start date and location of large farms CSA’s Ag. Sample Survey – 10 years of data (~44,000 parcels per year) DHS – 2000, 2005, 2001 rounds Methodology (to be discussed in more detail in Wed. session) Compare situation before & after start-up of large farms For smallholders growing the same crop or different ones Compute either distance from small to next large farm … or total area of large farming occurring within x km from a small farm

Yield, improved seed use and proximity (Maize)

Yield, chemical fertilizer use and proximity (Wheat)

Main results and implications Key results Technology-based spillover-effects crop specific (still looking at markets) Magnitude is relatively small No employment effects -> not a tool for job creation Policies seem to make a whole lot of difference -> move beyond black/white debate Potential policy relevance in Ethiopia Refine analysis looking at productivity & differentiate effects (gender) Explore scope of collaborating with Investment Agency (Licenses) Look at large farms’ productivity -> Help define ways of attracting investors who do provide local benefits Relevance beyond Ethiopia Similar datasets available for other countries (Moz & Zmb closest) Analysis of these cases would be interesting in its own right These have different policies; comparative analysis to spark meaningful policy dialogue

Conclusions & next steps Governments can benefit from linking/analyzing the data they have (Almost) all the data used here were available in the national statistical system A very small investment can have a very high pay-off And build local capacity to link & analyze these data Administrative data is the only way to make sense of LSLBI Can be complemented by but not substitute for locally sourced information Need to establish data access protocols to allow Governments to use their own data And facilitate a debate that moves beyond black and white Startup cost is high, but so are the benefits Link to other types of admin. data (investment licenses) Considerable opportunities for integration with geopspatial data Global institutions to help overcome collective action problem