WPS7576 Policy Research Working Paper 7576 Large Farm Establishment, Smallholder Productivity, Labor Market Participation, and Resilience Evidence from Ethiopia Daniel Ali Klaus Deininger Anthony Harris Development Research Group Agriculture and Rural Development Team February 2016 Policy Research Working Paper 7576 Abstract Although the nature and magnitude of (positive or nega- growing the same crop, for identification. The results tive) spillovers from large farm establishment are hotly suggest positive spillovers on fertilizer and improved seed debated, most evidence relies on case studies. Ethiopia’s use, yields, and risk coping, but not local job creation, large farms census together with 11 years of nation-wide for some crops, most notably maize. Most spillovers are smallholder surveys allows examination and quantification crop-specific and limited to large farms’ immediate vicin- of spillovers using intertemporal changes in smallhold- ity. The implications for policy and research are drawn out. ers’ proximity and exposure to large farms, generally or This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dali1@worldbank.org, kdeininger@worldbank.org, and aharris3@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Large Farm Establishment, Smallholder Productivity, Labor Market Participation, and Resilience: Evidence from Ethiopia ¶   Daniel Ali, Klaus Deininger, Anthony Harris World Bank, Washington DC dali1@worldbank.org, kdeininger@worldbank.org, aharris3@worldbank.org JEL Classification: O13, Q12, Q15 Keywords: large-scale commercial farms, externalities, productivity, resilience, employment, Ethiopia                                                              ¶ The research leading to this paper would not have been possible without the initiative by Hashim Ahmed, Office of the Prime Minister and Head Economic and Policy Analysis Unit, Government of Ethiopia, the active collaboration by the Ethiopian Central Statistical Agency, especially Biratu Yigezu, Habekristos Beyene, and Ahmed Ebrahim, and support by Alemayehu Ambel, Andrew Goodland, Firew Bekele, Asmelash Haile, Lars Moeller, Gregory Myers, and Lou Scura. Insightful comments from seminar participants at the 2015 Annual Bank Conference on Land and Poverty greatly helped to improve the quality of the paper. Funding from DFID, the German Government and the World Bank’s Strategic Research Program (SRP) is gratefully acknowledged. 1. Introduction The 2007/08 food price spike, together with the recognition that a number of countries are endowed with large amounts of seemingly unoccupied or unclaimed land triggered an enormous increase in private sector demand for agricultural land (Deininger and Byerlee 2011) and, implicitly, water (Rulli et al. 2013) to satisfy seemingly inexhaustible demands for food, fuel, and fiber. Although often described as a ‘land grab’ (Hall 2011; Pearce 2012), this phenomenon, which was most acutely felt in Africa (Anseeuw et al. 2012), also gave rise to expectations of private capital to complement public investment and help make up for decades of underinvestment in agriculture. This, it was hoped, could provide a stepping stone towards more rapid rural development and poverty reduction for countries with ample land resources that had remained heavily dependent on agriculture for growth and poverty reduction (Collier and Dercon 2014). Beyond any direct increments in productivity and value added by large farms compared to earlier land uses, a key argument in this debate revolves around local spillover effects. Critics maintain that, especially if land is made available below its true value, investment promotion policies may attract speculators who fail to benefit locals and generate negative spillovers, e.g. by monopolizing factor markets or encroaching on land or water resources to which they have no right. Supporters believe that, through discovery of agro- ecological suitability and demonstration effects, newly established large farms can provide locals with access to new technology, credit, input, or labor markets and thus generate positive spillovers, similar to other forms of foreign direct investment (FDI). In fact, the argument that public subsides, up to the net present value of the stream of spillover benefits generated, may be justified (Collier and Venables 2012) provides the raison d’etre for agricultural investment promotion agencies all over the globe. In light of the policy relevance of this issue, the marked differences between general FDI and large-scale agricultural investment (Arezki et al. 2015), and the fact that in many African countries the large majority of land-related investment originates with domestic rather than foreign investors, empirical evidence to explore the presence and magnitude of such effects would be highly desirable. Yet, partly due to limited data availability, often justified by the sensitive and potentially controversial nature of such investment, such evidence is currently not available. This limits not only governments’ and investors’ ability to make rational decisions and acquire experience, but may also constrain the availability of resources to the sector, as financial intermediaries have no basis to assess and try to insure the risk associated with such ventures. To show how often widely available survey data can help assess the presence and magnitude of spillovers, we combine data from the smallholder agricultural production survey annually conducted by Ethiopia’s Central Statistical Agency (CSA) in 2003/4-2013/14 with evidence on the evolution of the universe of currently operational large farms over this period from CSA’s large farm survey. GPS coordinates for large farms and smallholder villages allow us to construct, for every village and year, the distance to the next 2   large farm (growing the same crop) or the total or same crop large farm area in concentric circles of 0-25, 25-50, and 50-100 km radius around it. Enormous differences in yield and intensity of input use between smallholders and commercial farms make spillovers plausible while a rapid pace of large farm expansion that resulted in greater proximity between large and small producers provides a source of identification. We focus on maize, wheat, sorghum, and teff, four cereals that are widely grown by large and small farms. Spatial proximity is assumed to be the main channel through which spillovers are transmitted, with plausible mechanisms being learning about new technology, better access to input markets, or increased local labor demand. We find that increased proximity to commercial farms had positive spillovers on input use and yield though the nature and magnitude of spillovers are highly crop-specific. There is strong evidence of significant increases in fertilizer use, yields, and to a lesser extent also use of improved seed in closer proximity to large (maize) farms, closing large pre-existing gaps for maize. The opposite is true for teff where smallholder yields already surpassed large farmers’. For wheat and maize, we find significant increases in yields as a result of establishment of large farms growing the same crop nearby, presumably due to transfer of crop-specific technology. This is consistent with evidence that, for all cereals except sorghum, larger commercial farm areas with the same crop in the proximity increases smallholders’ resilience to drought. Yet, we find no impact of large farms on local labor demand, except possibly on imports of casual labor. To the extent that they do not suffer from omitted variable bias, these results support the notion that large agricultural investment can benefit local farmers. But the properties of such investment, in terms of location, crop choice, etc. matter. Also, with the exception of market access for fertilizer, the magnitude of estimated effects is modest, implying that large farm establishment substitutes neither for provision of public goods and extension to smallholders nor for efforts to make infrastructure available to help integrate them into value chains. Thinking and experimentation on how to forge partnerships and structure contracts so as to fully harness spillovers and create synergies between public efforts to promote rural and agricultural development and responsible private investment in agricultural value chains and land development is likely to have a high payoff. While our methodology can, with adaptations as needed, be applied in other countries where large farm establishment is a policy issue, the results have relevance for policy and research in Ethiopia. From a policy perspective, there appears scope for improved inter-institutional coordination to monitor performance of large farms and their compliance with contracts, together with a review of criteria for land transfers that can harness positive effects and be complementary to public sector efforts. Promising avenues for follow up from a research perspective include (i) exploring how spillover effects vary with commercial farms characteristics such as size, productivity, and operational status; (ii) comparing the size and nature of 3   spillovers from the commercial farms included here to those in flowers or high value vegetables and to those from investments higher up in the agro-processing value chain; and (iii) analyzing effects of large farm establishment on input and output prices as an alternative indicator that could also clarify channels through which some of these effects are transmitted. The paper is structured as follows: Section two motivates by discussing large agricultural investment in Ethiopia and presenting our methodology. Section three introduces the data and provides evidence on differences in performance between large and small farms, changes in smallholder productivity over time, and changes in spatial proximity between small and large farms over time due to new farm establishment. Section four presents results with respect to input use and yields, labor market participation, and resilience to climatic shocks. Section five concludes by summarizing implications for policy and future research. 2. Motivation and methodological considerations There is little doubt that growth of smallholder agriculture is critical for poverty reduction in Ethiopia. Yet, to fully exploit its land endowments and generate spillovers for smallholders, the country also aimed to attract agricultural investors, based on the premise that doing so could help local smallholders. To test for spillovers from such investment, we argue that physical proximity, measured by either the distance between smallholders and the next large farm or total large farm area cultivated in smallholders’ vicinity, can be used as a proxy. Data from 11 years of a national smallholder survey as well as the universe of large farms and their evolution over time can provide a basis for doing so in Ethiopia. 2.1 Large-scale land investment in Ethiopia Ethiopia is one of the poorest countries in Africa and the country’s highlands are among the most densely populated regions in Africa. Land constraints are a key determinant of poverty (Jayne et al. 2014). After 1990, a strategy of market liberalization and agriculture-led industrialization focusing on small-scale producers was adopted. In the past, the country regularly relied on food aid to meet food needs in the face of droughts (Dercon 2004). Yet, investment on land not fully utilized is identified as a strategic priority in the government’s Growth and Transformation Plan. This decision to actively seek out large land-based agricultural investment implied that Ethiopia attracted interest by the global ‘land rush’ debate. Historically experience with large farms in Ethiopia has not been positive: Before 1974, subsidies were used to attract commercial investment for cash crops production in so-called ‘model farms’. But this was often associated with tenant evictions and little employment generation with at best mediocre productive performance.1                                                              1 Although yields were above those by peasants, these farms’ efficiency and contribution to national agricultural output (2%) remained low (Abebe 1990). After the 1974 revolution, most of these were converted into state farms for food production. 4   Supporters of large investment argue that, as most of the land in question is located in the lowlands, capital intensive investment is the only way to bring it to productive use and generate spillovers for smallholders. Critics point to cases of land transfers without proper verification of current occupancy or utilization (Rahmato 2011) and argue that such transfers failed to improve local livelihoods (Rahmato 2014). Yet, with quantitative estimates diverging widely,2 a proper assessment is difficult. In fact, three studies aim to use quantitative evidence for a more representative and rigorous analysis of this issue in the Ethiopian context. A review of a sample of more than 10,600 investment licenses issued by the Agricultural Investment Agency finds that less than 20% of license holders established a farm (UNDP 2013). Moreover, most lack farming experience, a business plan, or regular record maintenance, pointing towards ample scope for improvement. Based on an effort to establish an inventory of and conduct field visits to a sample of farms with more than 1,000 ha established after 2005, Keeley et al. (2014) draw four main conclusions, namely (i) leases cover very large areas of which only parts have been developed; (ii) there are incidences of conflict with existing occupants; (iii) the potential for job generation has not been realized; and (iv) to be effective, government efforts to make lease agreements public, while commendable as a first step, need to be followed by further efforts to improve transparency. Ali et al. (2015) use the census of large farms that is annually conducted by the central statistical agency (CSA) to quantify what had been described qualitatively earlier. Doing so suggests that since the 1990s, about 1.3 mn. ha had been transferred to a total of 6,612 commercial farms,3 most of which cultivated more than 50 ha. The annual rate of new farm establishment dropped from a peak of close to 800 in 2007/08 to some 250 in 2011/13. Also, 95% of land is transferred to Ethiopians or joint ventures rather than foreigners. With an average area of 200 ha (172 ha for Ethiopians and 840 ha for foreigners), this implies that the extent of land transferred to operational farms is well below media reports (Rahmato 2011; Rulli et al. 2013). By respondents’ own estimates, 55% of land transferred remains unutilized, largely due to labor and technology constraints. Less than 20% of farms accessed credit, investments focused on land clearing and machinery, and only 36% made any lease payments. Below we will use these data to explore whether local people were affected by –positive or negative– spillovers from this phenomenon. 2.2 Methodological considerations A presumption of positive economy-wide spillovers from FDI prompted creation of investment promotion agencies worldwide. But the nature of spillovers and the channels through which they materialize will be                                                              2 A report by the Oakland Institute (2011) suggest that “our research shows that approximately 3,619,509 ha of land have been awarded, as of January 2011” (p.18). This is in line with the land matrix (Anseeuw et al. 2012) which reports demand for 3.14 million ha in Ethiopia, second in the world after Mozambique. Yet the most recent revision of this database (as per Aug. 8, 2015) reports that only 1.42 mn. ha had been contracted and production started only on 39,528 ha (see http://www.landmatrix.org/en/get-involved/). 3 Small producers are defined as those with a size below 10 hectares and all farms above this size fall into the ‘commercial’ category. 5   affected by the characteristics of agricultural production (Allen and Lueck 1998) with implications for policies aiming to attract or regulate such investment.4 While employment-intensive agro-processing of high value crops has been shown to improve welfare (Maertens et al. 2011; Minten et al. 2007), impacts of mechanized production of bulk agricultural commodities differ from it in two respects. First, land to labor ratios are higher, making positive labor market effects less and negative land-related impacts, e.g. via displacement, more likely. Second, output is not perishable, reducing the advantage of processing facilities and leading to well-known issues of side-selling (Hueth et al. 2007; Saenger et al. 2013). As both aspects lower the benefits and increase the risks of large production-related investment, studies often find it to lead to ambiguous or even negative effects (German et al. 2013; Schoneveld 2014). Reasons include competition for productive land (Hall 2011), a failure to adhere to required consultation processes (Nolte and Voget- Kleschin 2014), and lack of contract disclosure or independent compliance monitoring (Cotula 2014). We know of only one case study suggesting that large farm establishment benefited neighboring smallholders through positive effects on technology transfer and factor market effects (Adewumi et al. 2013). Given the topic’s importance, more systematic survey-based efforts to assess the direction and size of associated impacts will be desirable. As a first step in this direction, we explore if and to what extent large farm formation in Ethiopia benefited (or harmed) neighboring smallholders by affecting (i) their use of inputs, in particular fertilizer and seed; (ii) temporary or permanent jobs; (iii) crop yields; and (iv) resilience to climatic shocks.5 We assume the main transmission channels to be knowledge transfer or market access. Both rely on physical proximity, so we can use temporal variation in smallholders’ proximity to large farms for identification. If transaction costs are high or knowledge on the potential benefits from use of certain inputs or technologies lacking, smallholders may not use them even if the benefits of doing so would exceed the cost (Key et al. 2000). In this case, large farms who use them may create demonstration effects, act as point of access, and potentially provide links to labor, output, or credit markets, potentially affecting the mean and variance of income. The latter is important, as with climate change the frequency and severity of extreme weather events and associated shocks impairing smallholders’ ability to accumulate assets and escape poverty (Dercon and Christiaensen 2011) is expected to rise. We distinguish ‘generic’ effects (e.g. access to fertilizer or risk coping) from more crop specific ones (e.g. use of improved seeds) that are likely to materialize only if small and large farmers grow the same crop. Spillovers are expected to increase with similarity of technologies used, the size of gaps in yields or input use between large and small producers, and the susceptibility of crops to shocks, in particular moisture stress.                                                              4 One lesson from the literature on extractives is that the regulatory framework is essential to benefits materialize and are equitably shared among stakeholders (Söderholm and Svahn 2015). 5 As will be explained below, most large farmers claim to provide benefits to surrounding smallholders, further justifying this conjecture. 6   While we know of no studies that systematically explore firm-level externalities in agricultural production, a number of studies have done so in an industrial context. Henderson (2003) uses panel data for machinery and high-tech industries to estimate firm-level production functions that allow for scale externalities from other local plants in the same industry and from the diversity of local economic activity, finding strong evidence of information spillovers for high tech industries only and little evidence of benefits from diversity beyond the own industry. Moretti (2004) looks at human capital externalities, finding that in a city spillovers will be larger between industries that are economically closer. Currie et al. (2015) link firm-level with other data to assess the impact of opening or closing of 1,600 U.S. industrial plants in the 1990-2002 period, finding impacts on toxic air emissions, housing values, and the incidence of low birthweight in the vicinity of these plants. Applying this to mines suggests that openings and closings have different effects (Chuhan- Pole et al. 2015) and that mining can empower women (Kotsadam and Tolonen 2015). 2.3 Data sources and definitions Before developing the econometric approach, we discuss data. For large farms, we use the commercial farm survey regularly conducted by Ethiopia’s Central Statistical Agency (CSA). This survey covers a sample of 10-50 ha farms and the universe of operational farms cultivating 50 ha or more. Farm level information is provided on input use, output, and year of establishment. GPS coordinates taken for every field allow us to map all farms above 50 ha that were operational in 2014.6 For any small farm and year, this allows us to compute distance to the nearest large farm or the nearest large farm growing the same crop and the total area cultivated by large farms or devoted to large farm cultivation of a specific crop within a certain radius. Information on smallholders comes from 11 years (2004-14) from CSA’s smallholder survey which had been conducted annually since 1980 by resident enumerators on a sample of some 1,400 kebeles nation- wide. Figure 1 illustrates the location of the kebeles included in the 2013/14 round as well as that of large farms above 50 ha. As sample kebeles were changed only in 2007/8, this provides us with a panel of kebeles in the 2003/4-2006/7 and the 2007/8-20013/14 period. Recovery of kebele codes, properly adjusting for splits, mergers, etc. was, however, possible only for about half the kebeles included in the earlier period, providing us with data from about 500 and 2,000 kebeles before and after 2006/7, respectively. Information on inputs is based on a random sample of 20-40 farmers per kebele, resulting in a coverage of 28,000 to 56,000 farmers per year. Data on yield is based on crop cuts of randomly selected fields in each EA, i.e. not those of the farmers interviewed, limiting the ability to for example estimate production functions. We complement these surveys with two data sources. First, as the smallholder production survey lacks data on labor use, we use data on labor supply at individual level from the 2011/12 and 2013/14 rounds of the                                                              6 Information on the year of establishment is used to reconstruct the inter-temporal evolution of large farms, following Ali et al. (2015). As non- operational farms are not included in CSA’s sample, this implies that our results are valid for operational farms. 7   LSMS-ISA panel to explore labor market effects from large farm establishment.7 Second, to account for inter-temporal variability in climatic conditions, we rely on gridded 0.1’ rainfall data publicly available from NOAA since 1980 to compute long-term mean and standard deviation of precipitation for each pixel. Calculating z-scores and matching them to kebeles then allows us to determine for each year if a kebele experienced drought (z<-1), a flood (1 50 ha, weights are applied to adjust for non-response. 17   Table 2: Productive performance of smallholders vs. commercial farms in different farm size classes Maize Sorghum Teff Wheat Yield (Q/ha) Smallholder 27.07 21.29 13.62 21.85 Commercial farmers 37.691 27.005 8.294 25.578 < 20 ha 42.04 30.88 9.18 41.69 20-50 37.42 24.52 8.79 33.68 50-100 36.89 25.64 8.61 26.11 100-500 39.30 28.21 7.75 24.64 > 500 ha 33.81 29.51 9.90 28.32 Use fertilizer (%) Smallholder farmers 38.76 15.40 65.24 73.18 Commercial farmers 67.73 23.48 73.52 65.25 < 20 ha 52.85 19.48 85.44 65.79 20-50 58.39 20.18 77.00 59.62 50-100 73.79 18.28 61.34 74.22 100-500 84.31 33.18 62.44 64.74 > 500 ha 82.70 44.79 48.01 74.45 Use seed (%) Smallholder farmers 24.92 0.40 4.29 7.17 Commercial farmers 66.81 15.19 69.32 58.19 < 20 ha 51.99 17.58 85.44 65.79 20-50 57.87 18.66 76.23 58.25 50-100 69.57 15.88 58.66 74.22 100-500 86.21 4.21 45.86 40.32 > 500 ha 80.30 42.08 40.93 74.45 Observations Smallholder kebeles 1,368 910 955 634 Commercial farmers 1,659 3,077 826 464 < 20 ha 358 295 291 162 20-50 479 1122 212 109 50-100 351 833 124 28 100-500 382 724 165 128 > 500 ha 89 103 34 37 Source: Own computation from 2013/4 CSA large farm and smallholder farm surveys 18   Table 3: Changes in farm characteristics, input use, and rainfall over time 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Panel A: Overall information Share of households cultivating Maize 0.456 0.564 0.532 0.557 0.575 0.633 0.580 0.573 0.639 0.629 0.594 Teff 0.334 0.378 0.377 0.376 0.423 0.424 0.419 0.424 0.417 0.399 0.414 Sorghum 0.364 0.382 0.401 0.401 0.345 0.378 0.360 0.384 0.380 0.360 0.344 Wheat 0.215 0.252 0.245 0.260 0.277 0.283 0.323 0.296 0.274 0.298 0.290 Input use Area cult. (ha) 0.893 1.004 0.831 0.871 0.955 0.977 1.026 0.958 0.966 0.957 0.959 Use fertilizer 0.200 0.222 0.285 0.267 0.365 0.352 0.405 0.442 0.491 0.532 0.544 Use impr. seed 0.082 0.100 0.117 0.079 0.093 0.108 0.109 0.137 0.174 0.181 0.200 Distance to & area of large farms > 50 ha Dist. to lg. farm 77.7 71.2 59.3 56.2 51.9 46.0 48.6 46.2 42.4 40.9 40.9 # within 25 km 0.37 0.41 0.47 0.60 1.32 1.71 1.81 1.89 2.01 2.20 2.24 # in 25-50 km 0.97 1.17 1.28 1.69 4.06 5.24 5.88 5.79 6.15 6.63 6.79 # in 50-100 km 3.13 4.00 4.73 5.85 17.28 22.41 25.07 25.54 26.84 28.90 29.64 area < 25 km 1.30 1.38 1.93 2.26 4.54 6.15 6.67 6.85 7.00 7.40 7.96 area 25-50 km 3.60 4.20 6.50 7.40 15.40 19.60 22.60 22.90 24.00 24.60 25.60 area 50 - 100 km 18.40 20.60 27.40 31.70 70.00 88.50 98.20 96.80 100.30 104.70 110.00 Rainfall deviation (z-score) 10 0.100 0.114 0.138 0.167 0.259 0.325 0.329 0.341 0.363 0.366 0.363 Area 0.495 0.482 0.599 0.666 2.195 2.374 2.545 3.353 3.298 3.546 4.169 25 50 %>0 0.184 0.303 0.328 0.354 0.500 0.582 0.599 0.605 0.634 0.632 0.629 Area 1.451 1.811 1.961 2.109 6.729 8.282 9.296 10.012 10.369 10.753 11.736 50-100 %>0 0.559 0.757 0.772 0.774 0.819 0.879 0.882 0.875 0.886 0.886 0.885 Area 8.501 9.597 10.124 10.161 24.612 31.889 37.662 38.779 39.865 42.391 47.091 km to lg, farm 89 77 77 73 60 52 51 54 51 51 52 Kebeles (%) 0.536 0.670 0.649 0.655 0.709 0.772 0.720 0.705 0.771 0.760 0.714 Sorghum < 25 %>0 0.075 0.067 0.072 0.158 0.264 0.319 0.328 0.330 0.341 0.359 0.366 Area 2.322 2.065 1.895 1.665 1.204 1.591 2.388 1.981 2.008 2.176 2.936 25 50 %>0 0.162 0.178 0.196 0.258 0.294 0.322 0.355 0.369 0.403 0.424 0.413 Area 2.795 2.741 1.716 1.718 5.132 6.384 9.807 6.175 6.326 6.681 8.117 50-100 %>0 0.412 0.444 0.454 0.525 0.572 0.627 0.703 0.697 0.772 0.775 0.769 Area 7.644 8.197 8.141 7.022 25.550 28.348 39.008 26.066 26.804 28.612 34.261 km to lg, farm 107 105 107 104 101 97 92 89 71 70 70 Kebeles (%) 0.164 0.181 0.197 0.234 0.486 0.509 0.498 0.520 0.516 0.518 0.513 Teff < 25 %>0 0.078 0.099 0.128 0.160 0.276 0.337 0.328 0.394 0.408 0.412 0.405 Area 0.507 0.395 0.263 0.283 0.416 0.589 0.566 0.702 0.746 0.786 0.768 25 50 %>0 0.234 0.317 0.297 0.340 0.398 0.458 0.481 0.524 0.547 0.565 0.568 Area 1.092 0.958 0.963 0.995 2.193 2.478 2.826 3.214 3.336 3.431 3.391 50-100 %>0 0.390 0.475 0.547 0.612 0.739 0.794 0.816 0.809 0.845 0.857 0.861 Area 9.019 6.307 5.046 4.602 5.265 7.761 10.274 10.165 10.521 11.319 11.634 km to lg, farm 112 113 107 98 96 78 75 76 75 68 68 Kebeles (%) 0.158 0.203 0.349 0.366 0.563 0.566 0.545 0.562 0.561 0.546 0.555 Wheat < 25 %>0 0.020 0.017 0.017 0.108 0.242 0.315 0.320 0.364 0.392 0.380 0.365 Area 0.585 0.757 0.512 1.298 2.693 2.890 2.864 3.380 3.607 3.665 3.678 25 50 %>0 0.176 0.237 0.267 0.351 0.379 0.448 0.448 0.474 0.505 0.509 0.504 Area 4.577 5.375 4.051 6.728 6.229 7.670 7.696 8.247 8.989 9.167 9.229 50-100 %>0 0.706 0.678 0.733 0.757 0.757 0.841 0.829 0.853 0.859 0.881 0.876 Area 20.897 23.383 23.138 26.074 24.414 26.736 27.708 26.391 27.940 27.170 28.078 km to lg, farm 80 83 82 75 88 79 80 79 76 64 65 Kebeles (%) 0.105 0.119 0.122 0.144 0.373 0.390 0.405 0.408 0.389 0.409 0.399 Source: Own computation from 2003/4-2013/14 CSA smallholder farm surveys. Note: Area is in 100 ha. Statistics are calculated using the sub-sample of kebeles that are included in the analysis for each of the crops. The criteria for inclusion in the sub-sample are that the kebele is within 150 km of a commercial farm growing the specified crop in 2014 and that more than 10% of households in the kebele grow the crop. 20   Table 5: Estimated impacts of changes in neighboring large farm area/distance on smallholders’ fertilizer use Maize Wheat Sorghum Teff Panel A: Distance measures Distance any farm Distance -0.00165* -8.75e-06 0.00111 0.00222* (0.000854) (0.00206) (0.000677) (0.00118) Distance2 1.24e-05** 5.05e-06 -7.95e-06** -1.66e-05** (5.52e-06) (1.53e-05) (3.36e-06) (7.65e-06) R2 0.375 0.407 0.266 0.494 Dist. same crop Distance -0.00123* -0.000877 0.000460 0.00212** (0.000655) (0.00148) (0.000541) (0.000805) Distance2 5.18e-06* -1.29e-06 -1.50e-06 -8.26e-06*** (2.67e-06) (5.70e-06) (2.40e-06) (2.85e-06) R2 0.375 0.410 0.265 0.494 Panel B: Area measures Area, all farms 0 - 25 km 0.000733*** 0.000424 -0.000189 -0.00105*** (0.000215) (0.000367) (0.000294) (0.000278) 25 - 50 km -0.000432 8.96e-05 -1.59e-05 -0.000518* (0.000339) (0.000234) (8.33e-05) (0.000270) 50 - 100 km 0.000129 0.000182 1.09e-06 -0.000212 (0.000131) (0.000159) (3.24e-05) (0.000147) R2 0.376 0.408 0.265 0.494 Area, same crop 0 - 25 km 0.00119*** 0.000701 -0.000775*** -0.00408** (0.000429) (0.000855) (0.000203) (0.00189) 25 - 50 km -0.000386 0.000221 0.000116* -0.00284** (0.000665) (0.000669) (5.96e-05) (0.00140) 50 - 100 km 0.000165 0.000581 -1.17e-05 -0.000993 (0.000259) (0.000477) (1.86e-05) (0.000988) R2 0.375 0.408 0.265 0.494 No. of obs. (hhs) 170,519 52,885 89,557 92,435 Note: Woreda fixed effects and year trends included throughout. Standard errors clustered at woreda level. 21   Table 6: Estimated impacts of changes in neighboring large farm area/distance on smallholders’ improved seed use Maize Wheat Sorghum Teff Panel A: Distance measures Distance any farm Distance 2.55e-05 0.000176 -0.000125** 0.000159 (0.000656) (0.000445) (4.67e-05) (0.000136) Distance2 2.64e-06 -6.63e-07 6.72e-07*** -3.28e-07 (4.12e-06) (2.69e-06) (1.71e-07) (9.07e-07) R2 0.323 0.071 0.013 0.050 Dist. same crop Distance -7.69e-05 -0.000166 -4.71e-05 -8.92e-05 (0.000544) (0.000318) (3.83e-05) (0.000146) Distance2 2.09e-06 6.22e-08 1.22e-07 1.38e-07 (1.85e-06) (1.28e-06) (1.35e-07) (6.10e-07) R2 0.323 0.071 0.013 0.050 Panel B: Area measures Area, all farms 0 - 25 km 0.000284 -0.000202** 2.20e-05 -2.12e-05 (0.000181) (9.18e-05) (1.56e-05) (5.14e-05) 25 - 50 km -0.000424 -3.70e-06 -1.24e-05 3.40e-05 (0.000292) (9.67e-05) (7.48e-06) (4.42e-05) 50 - 100 km 9.90e-05 -2.83e-05 -3.71e-06 1.15e-05 (0.000116) (4.13e-05) (2.24e-06) (2.63e-05) R2 0.324 0.071 0.013 0.050 Area, same crop 0 - 25 km 0.000926*** -0.000166 2.38e-05 0.000861** (0.000334) (0.000161) (2.63e-05) (0.000342) 25 - 50 km -0.000181 -4.64e-05 -1.48e-05 0.000120 (0.000655) (0.000152) (1.08e-05) (0.000227) 50 - 100 km 0.000567** 5.75e-05 -3.79e-06 -0.000167 (0.000277) (8.11e-05) (2.92e-06) (0.000116) R2 0.324 0.071 0.013 0.050 No. of obs. (hhs) 170,519 52,885 89,557 92,435 Note: Woreda fixed effects and year trends included throughout. Standard errors clustered at woreda level. 22   Table 7: Estimated impacts of changes in neighboring large farm area/distance on smallholders’ yields Maize Wheat Sorghum Teff Panel A: Distance measures Distance any farm Distance -0.00219 0.000202 0.000824 0.00100 (0.00169) (0.00218) (0.00132) (0.00160) Distance2 -1.34e-06 -9.84e-06 -8.64e-06 -3.14e-05* (1.41e-05) (1.88e-05) (9.23e-06) (1.76e-05) R2 0.424 0.363 0.287 0.258 Dist. same crop Distance -0.00339* 0.00144 -0.000178 -0.00142 (0.00178) (0.00181) (0.000857) (0.00121) Distance2 9.81e-06 -1.67e-06 2.54e-06 5.49e-06 (9.19e-06) (5.77e-06) (3.25e-06) (5.36e-06) R2 0.424 0.365 0.287 0.255 Panel B: Area measures Area, all farms 0 - 25 km 0.00110*** 0.00101 0.000119 0.000856 (0.000388) (0.000615) (0.000645) (0.000725) 25 - 50 km -0.000232 -0.000128 0.000103 7.74e-05 (0.000201) (0.000516) (0.000128) (0.000324) 50 - 100 km 3.64e-05 -0.000327 3.29e-05 -9.32e-05 (0.000105) (0.000231) (4.95e-05) (0.000246) R2 0.423 0.365 0.287 0.256 Area, same crop 0 - 25 km 0.00250*** 0.00393*** -0.000447 0.000575 (0.000445) (0.00106) (0.000976) (0.00327) 25 - 50 km 0.000277 0.00205** 0.000125 0.000738 (0.000594) (0.000841) (0.000223) (0.00298) 50 - 100 km -0.000493 0.000574* 7.15e-06 -0.00230** (0.000320) (0.000299) (4.60e-05) (0.00106) R2 0.423 0.367 0.287 0.256 No. obs. (kebeles) 10,768 5,295 6,973 7,767 Note: Woreda fixed effects and year trends included throughout. Standard errors clustered at woreda level. 23   Table 8: Impact of changes in distance to large farms or neighboring large farm area on smallholders’ labor supply Paid work Temp. work General Agric. Panel A: Distance measures Dist. to town -0.000990*** -0.000101 0.000481 (0.000211) (0.000107) (0.000324) Distance -0.000144 -0.000162 -0.000130 (0.000205) (0.000103) (0.000314) Distance2 9.74e-08 6.91e-07 -1.54e-06 (1.46e-06) (7.38e-07) (2.25e-06) No. of obs. 15,738 15,738 15,701 R2 0.110 0.043 0.156 Panel B: Area measures Dist. to town -0.00101*** -0.000120 0.000414 (0.000209) (0.000106) (0.000321) 0 - 25 km 3.34e-05 -3.07e-05 4.84e-05 (7.51e-05) (3.79e-05) (0.000115) 25 - 50 km -5.19e-05 8.32e-06 5.59e-06 (3.95e-05) (1.99e-05) (6.06e-05) 50 - 100 km 6.70e-05*** 1.38e-05 8.64e-05*** (1.93e-05) (9.76e-06) (2.96e-05) No. of obs. (hhs) 15,738 15,738 15,701 R2 0.111 0.043 0.157 Mean of dep. var. Round 1 0.048 0.012 0.081 Round 2 0.041 0.008 0.162 Note: Zone fixed effects and year trends included throughout. Standard errors clustered at zone level. 24   Table 9: Impact of changes in distance to large farms or neighboring large farm area on resilience of smallholders’ yields Maize Wheat Sorghum Teff Any farm Area within 25 km 0.000867 0.000988 -8.46e-05 0.000826 (0.000580) (0.000640) (0.000563) (0.000762) Negative rain shock -0.112*** -0.0308 -0.00978 -0.100*** (z < -1) (0.0417) (0.0317) (0.0382) (0.0338) Rain below normal -0.0337* 0.00737 -0.0196 -0.0354* (0 < z ≤ 1) (0.0188) (0.0211) (0.0223) (0.0190) Positive rain shock 0.0355** -0.0331 0.0378 -0.00894 (1 < z) (0.0153) (0.0330) (0.0255) (0.0169) Area * (z < -1) 0.000377 0.00109 0.00113 0.00124** (0.000607) (0.000818) (0.000834) (0.000607) Area * (0 < z ≤ 1) 0.000681* 2.32e-05 0.000481 -0.000265 (0.000382) (0.000454) (0.000310) (0.000516) Area * (1 < z) -0.000481 0.000391 0.000153 0.000329 (0.000394) (0.000503) (0.000466) (0.000538) R2 0.423 0.364 0.287 0.256 Same crop Area within 25 km 0.00226*** 0.00209** -0.000522 -5.65e-05 (0.000408) (0.000874) (0.000665) (0.00286) Negative rain shock -0.117*** -0.0281 -0.00343 -0.0984*** (z < -1) (0.0414) (0.0301) (0.0366) (0.0331) Rain below normal -0.0347* 0.00911 -0.0181 -0.0350* (0 < z ≤ 1) (0.0196) (0.0206) (0.0220) (0.0188) Positive rain shock 0.0374** -0.0337 0.0363 -0.00759 (1 < z) (0.0157) (0.0319) (0.0247) (0.0158) Area * (z < -1) 0.00284** 0.00136** 0.000353 0.00668* (0.00118) (0.000640) (0.000418) (0.00364) Area * (0 < z ≤ 1) 0.00227*** -0.000606 0.000558 -0.00450 (0.000617) (0.000536) (0.000351) (0.00491) Area * (1 < z) -0.00139** 0.00115*** 0.00177 0.00122 (0.000660) (0.000373) (0.00114) (0.00309) R2 0.424 0.366 0.287 0.255 No. of obs. (kebeles) 10,768 5,295 6,973 7,767 Note: Woreda fixed effects and year trends included throughout. Standard errors clustered at woreda level. 25   Figure 1: Location of large farms and sample kebeles for the smallholder survey 26   Figure 2: Inter-temporal changes in proximity between large and small farm, 2004-2014 .02 2004-2007 2008-2010 .02 .01 .015 .01 .015 Density Density .005 .005 0 0 0 50 100 150 200 250 0 50 100 150 200 250 Distance to nearest large farm above 50h in km Distance to nearest large farm above 50h in km 2011-2012 2013-2014 .02 .02 .01 .015 .01 .015 Density Density .005 .005 0 0 0 50 100 150 200 250 0 50 100 150 200 250 Distance to nearest large farm above 50h in km Distance to nearest large farm above 50h in km 27   Figure 3: Distance from Kebele centroid to nearest commercial farm, by crop 1 1 .8 .8 Prob <= distance Prob <= distance .6 .6 .4 .4 .2 .2 0 0 0 50 100 150 200 250 0 50 100 150 200 250 Distance to nearest maize farm Distance to nearest teff farm 2004 2008 2004 2008 2012 2014 2012 2014 1 1 .8 .8 Prob <= distance Prob <= distance .6 .6 .4 .4 .2 .2 0 0 0 50 100 150 200 250 0 50 100 150 200 250 Distance to nearest sorghum farm Distance to nearest wheat farm 2004 2008 2004 2008 2012 2014 2012 2014 28   Figure 4: Location of commercial maize farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands Legend Kebele boundary 2004 Yield (q/ha) by quintile 1-5 6 - 10 11 - 13 14 - 18 19 - 34 Region boundary Buffer around Maize farm (04) Distance (km) 25 50 100 150 29   Figure 5: Location of commercial maize farms in 2001/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands Legend Kebele boundary 2014 Yield (q/ha) by quintile 3 - 15 16 - 21 22 - 28 29 - 36 37 - 78 Region boundary Buffer around Maize farm (14) Distance (km) 25 50 100 150 30   Figure 6: Location of commercial sorghum farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands Legend Kebele boundary 2004 Yield (q/ha) by quintile 0-5 6-8 9 - 11 12 - 15 16 - 28 Region boundary Buffer around Sorghum farm (04) Distance (km) 25 50 100 150 31   Figure 7: Location of commercial sorghum farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands Legend Kebele boundary 2014 yield6 2 - 12 13 - 17 18 - 22 23 - 28 29 - 83 Region boundary Buffer around Sorghum farm (14) Distance (km) 25 50 100 150 32   Figure 8: Location of commercial teff farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands Legend Kebele boundary 2004 Yield (q/ha) by quintile 0-3 4-5 6-7 8-9 10 - 17 Region boundary Buffer around Teff farm (04) Distance (km) 25 50 100 150 33   Figure 9: Location of commercial teff farms in 2013/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands Legend Kebele boundary 2014 Yield (q/ha) by quintile 1-8 9 - 13 14 - 18 19 - 25 26 - 41 Region boundary Buffer around Teff farm (14) Distance (km) 25 50 100 150 34   Figure 10: Location of commercial wheat farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands Legend Kebele boundary 2004 Yield (q/ha) by quintile 1-5 6-8 9 - 11 12 - 16 17 - 23 Region boundary Buffer around Wheat farm (04) Distance (km) 25 50 100 150 35   Figure 11: Location of commercial wheat farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands Legend Kebele boundary 2014 Yield (q/ha) by quintile 1 - 12 13 - 16 17 - 20 21 - 26 27 - 65 Region boundary Buffer around Wheat farm (14) Distance (km) 25 50 100 150 36   References: Abebe, H. 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