48699 Land rental in Ethiopia: Marshallian inefficiency or factor market imperfections and tenure insecurity as binding constraints? Klaus Deininger,*1 Daniel Ayalew*, Tekie Alemu# * World Bank, Washington DC #Department of Economics, Addis Ababa University, Addis Ababa October, 2006 1 1818 H St. NW, Washington DC, 20433; Tel 202 4730430, fax 202 522 1151, email kdeininger@worldbank.org. We would like to thank the Economics Departments of Addis Ababa University and Gothenburg University for giving us access to the data and Stein Holden, Gunnar Kohlin, Esther Mwangi, Kejiro Otsuka, Frank Place, Xiaobo Zhang, and seminar participants at IFPRI and ILRI for insightful comments and suggestions. Funding from the DFID-World Bank collaborative program on land policy is gratefully acknowledged. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, its Board of Executive Directors, or the countries they represent. Land rental in Ethiopia: Marshallian inefficiency or factor market imperfections and tenure insecurity as binding constraints? Abstract: Although a large theoretical literature discusses the possible inefficiency of sharecropping contracts, empirical evidence on this phenomenon has been ambiguous at best. Household level fixed-effect estimates from about 8,500 plots operated by households who own and sharecrop land in the Ethiopian highlands provide no support for a Marshallian inefficiency. At the same time, a factor adjustment model suggests that the extent to which rental markets allow households to attain their desired operational holding size is extremely limited. Our analysis points towards factor market imperfections (no rental for oxen), lack of alternative employment opportunities, and tenure insecurity as possible reasons underlying such behavior, suggesting that, rather than worrying almost exclusively about Marshallian inefficiency, greater attention to the policy framework within which land rental markets operate may be warranted. 1. Introduction In situations where the ownership distribution of land is different from the optimal operational structure, mechanisms -in many cases rental markets- to transfer this factor to its most productive use will have a key role to increase total production and ensure economic efficiency. Historically, when most of the land was cultivated under traditional technology and non-agricultural labor markets were virtually non- existent, the majority of such land transactions occurred between large landlords, often absentees, and small tenants with few alternative opportunities, essentially compensating for a skewed distribution of land ownership. Increasing complexity of the agricultural production process implies that imperfections in other markets which are frequent in rural areas of developing countries will affect the nature and direction of rental contracts. Moreover, with economic development, a number of other disequilibrating factors such as changes in the demographic structure, non-agricultural demand for land, and an array of other reasons for land transactions -from producers' desire to take advantage of opportunities for off-farm employment and temporary migration to differential agricultural ability- have started to complement, and in many cases substitute for, the "traditional" model of land leasing from large to small landlords (Sadoulet et al. 2001). As a result, land lease markets have become quite active even in countries, such as China or Vietnam, where, in view of a highly egalitarian ownership distribution of land, the traditional model of land leasing would otherwise lead one to expect limited levels of land rental activity. As in many developing countries land is as a key productive asset and source of income, it is not only the level of land rental activity but also the productive use of the land thus transferred that is of relevance. In fact, economists have long been concerned about the efficiency implications of rental market operations with a view towards identifying policy options that, by helping improve the efficiency of outcomes, have the potential to make everybody better off. A key motivation for doing so has been that, in addition to the limited investment incentives conveyed by short-term rental contracts, share tenants will, in any given season, receive only part of their marginal product and thus have limited incentives to supply effort. A 1 large number of theoretical and empirical studies have aimed to identify conditions -mainly in terms of other market imperfections- under which sharecropping may be a rational strategy and to quantify the productivity implications, in a given production period or in terms of longer-term investment, of such an arrangement as compared to owner-cultivation. While such analysis can provide useful insights and has led to a number of policy interventions, it takes the renting decision as given and makes few predictions about the extent to which rental markets' response to exogenous changes is optimal in the sense that it fully utilizes available opportunities. Exploring this issue is of policy relevance because, as discussed earlier, the incidence of exogenous changes to which land markets need to respond has sharply increased and is likely to become of even greater importance with economic development. More importantly, the transaction costs that need to be incurred in renting out land may well affect land owners' decisions on whether to, e.g., adopt new technology or take up off-farm employment and could thus have implications for growth in the non-farm economy. Studies of this issue in Ethiopia yield contradictory results; while some argue that land markets will, at best, facilitate partial adjustment (Teklu and Lemi 2004, Holden and Ghebru 2006) others are unable to reject the hypothesis of friction-less adjustment in rental markets (Pender and Fafchamps 2006). In this paper, we use a large panel data set from Ethiopia data that is significantly more detailed than what had been available in other studies to explore the efficiency of sharecropping and the extent to which land markets in this country help farmers to capitalize on existing opportunities. Ethiopia is of interest because, with a scant endowment of land that is under significant threat of degradation and high levels of climatic risk, making the most productive use of available land resources is important to escape the threat of starvation and dependence on food-aid. At the same time, it is difficult to imagine sustained growth of rural incomes in this country without a significant transfer of labor out of the agricultural sector, something which will be contingent on well-functioning land rental markets. To assess the efficiency of contractual arrangements, we make use of the fact that we have a sizeable sample of households who cultivate owned and sharecropped land simultaneously to estimate a production function and input demand functions using household fixed effects. For the latter, we use a friction model (Rosett 1959, Skoufias 1995) to assess whether land rental markets allow households to attain their desired level of cultivated land irrespectively of their endowment, or if they do not to find out about the magnitude of the friction involved. We fail to find evidence for inefficiency of sharecropping contracts. At the same time, our results point not only towards the existence of large amounts of friction that prevents land owners to use rental markets to adjust to their optimum size but also the fact that such friction reduces productivity by preventing productive producers from gaining access to additional land. 2 The paper is organized as follows. Section two discusses features of the rural economy in Ethiopia to motivate the study and provide the background for some of the analytical hypothesis. It also introduces the conceptual framework for testing the efficiency of sharecropping and the extent to which rental markets facilitate optimum adjustment. Section three describes the sample and uses descriptive statistics from our data to generate descriptive statistics at household and plot levels. Section four presents results from the econometric analysis and discuses potential implications. Section five concludes by relating our results to the literature and highlighting areas for future research. 2. Background and conceptual framework To motivate our analysis, we highlight key features of land tenure in Ethiopia, in particular the relative scarcity of land and the high risk associated with agricultural production, the fact that land is state owned and relatively equally distributed. We note that, despite recent efforts to increase tenure security by demarcating individual land holdings in a very participatory manner, land policy remains ambiguous and show how this may limit individuals' and households' desire and ability to participate in non-agricultural activities. This is followed by a discussion of the conceptual framework. 2.1 Distinguishing features of Ethiopian land tenure Prior to 1975, land in Ethiopia was concentrated in the hands of absentee landlords, tenure was highly insecure, arbitrary evictions posed a serious threat, and many lands were severely underutilized. The land tenure system was characterized by great inequality which, through its negative impact on production and investment, not only affected productivity but was also considered to have been the most important cause of political grievances that eventually led to the overthrow of the imperial regime. The Marxist government, soon after ousting the imperial regime, transferred ownership of all rural land to the state for distribution of use rights to cultivators through local peasant associations (PAs) and then embarked on massive collectivization of peasant agriculture. However, contrary to the case of China (Dong 1996), the collectivization program was not associated with high levels of infrastructure investment, and as a result, most of the agricultural land remained rainfed and subject to degradation and soil erosion (Kebede 2002). Transferability of land through lease, sale, exchange, or mortgage, among others, was prohibited and inheritance was possible only within immediate family members. In spite of these restrictions, farm households were involved in informal rental arrangements ­ in the form of share or fixed rent tenancy ­ at the very risk of losing land allocated to them by the PAs. The ability to use land was contingent on proof of permanent physical residence, thereby preventing migration from rural areas. More importantly, tenure security was undermined by the PAs' and other authorities' ability to redistribute land, an ability that was in some cases used for political ends (Ege 1997). 3 Although it committed to a free-market philosophy and enacted a number of new pieces of legislation, the government that took power in 1991, did not fundamentally alter the ambiguity surrounding many aspects of land policy. This is illustrated by the fact that the right of every Ethiopian who wants to engage in agriculture to receive a piece of land for free is anchored in the 1995 Constitution. While it is recognized that this provision may conflict with other goals such as the desire for greater tenure security and well- functioning land rental markets, a 1997 federal proclamation, formulated to set out the overall legal framework, essentially transferred responsibility for enacting laws regarding the nature of land rights, their transferability, and matters of land taxation to regional governments to (FDRE 1997).1 Given their relevance for the topic at hand, we briefly review regional policies stance with respect to administrative land redistribution and land rental markets. Despite repeated public announcements against land redistribution, regional positions are not at all clear- cut even beyond the use of eminent domain for establishment of irrigation with due compensation for land and improvements which is clearly enunciated in the Southern Nation, Nationalities and Peoples Region (SNNPR) Amhara, and Oromia (ANRS 2000; ONRS 2002; SNNPR 2003). Proclamations in Amhara and SNNPR explicitly provide for other types of redistribution but make them the responsibility of local communities and require that they be supported by research that it will not lead to land fragmentation and does not adversely affect land productivity. No clear statement on redistribution is included in the Tigray proclamation (TNRS 1997). Proclamations in all regions highlight that the rights of persons who obtain a non-agricultural job or are absent from the village longer than a certain period will fall back to the village. While households' use rights now include the rights to lease land to a third party, i.e. rental is officially allowed (Pender and Fafchamps 2006), regions impose various restrictions on the extent of land that can be leased or the duration of contacts (Nega et al. 2003, Beyene 2004). Oromia and SNNPR allow farmers to rent out up to 50% of their holding and stipulate maximum contract terms of 3 years for traditional and 15 years for modern technology (ONRS 2002; SNNPR 2003). Current policy in Amhara, the region where our data was collected, is more progressive as a recently passed proclamation allows leasing of land for up to 25 years irrespective of the technology used (ANRS 2005). As all our data is from before 2005, it will not reflect changes brought about by this new development but suffice it to say that even after its passage, large amounts of uncertainty remain. For example, a clause that would have allowed mortgaging was removed from the draft proclamation before its enactment. More importantly, regulation is silent on whether long-term rental contracts entered into by a person who subsequently finds a non- farm job will have to be honored or not. 1A new federal rural land administration and use proclamation was enacted in 2005, partly to establish some consistency. Although its relevance for the analysis reported here is limited as our data were all collected before its enactment, it does not appear to clarify many of the provisions that had remained ambiguous in earlier legislation. 4 The hypothesis that legal provisions lack clarity is supported by our data and other surveys. On average, 47% of the households in our data expect to gain or lose land due to administrative land redistribution or reallocation in the next five years. This could limit both investment incentives (Deininger and Jin 2006) and the propensity to rent out land could be perceived as a signal that the land is no longer needed. Without judicial institutions to interpret regulations or adjudicate in case of dispute, it is not surprising to find land conflict to be frequent and bureaucratic discretion relating to land matters pervasive (Rahmato 2003). To reduce such conflicts and respond to widespread tenure insecurity, the government has recently launched an ambitious program of land certification.2 This program has made rapid progress, covering more than 6 mn. households by late 2005 in a highly participatory and low-cost process that includes delineation of borders and issuance of land use certificates in the name of husband and spouse. Evidence on farmers' willingness to pay for land registration, e.g. 84% in a sample from Amhara (Solomon 2004) suggests that this is indeed a step in the right direction. However, unless land ownership and the ways in which it can be exercised (including land transfers) are defined more clearly and the scope for interference is limited, the impact of such initiatives may well be significantly below the potential. 2.2. Testing the efficiency of sharecropping In a world of perfect information, complete markets and zero transaction costs, the distribution of land ownership will affect welfare but will not matter for efficiency as everybody will operate their optimum farm size. However, agricultural production is risky and outcomes depend on the level of technology and producers' ability. In addition to non-agricultural options which will affect potential tenants' reservation utility, labor and credit market imperfections as well as the transaction costs associated with transferring land will affect the outcomes that can be achieved in land rental markets and a key question is whether, taking the ownership distribution of land as given, rental markets will achieve socially optimal outcomes, and to identify the factors that will affect their ability to do so. By varying the share and a fixed payment to the tenant, land owners who wish to rent can achieve any combination of contractual forms from a wage labor over a share to a fixed rent contract. While all contracts will lead to equivalent outcomes if output is certain and tenants' effort can be enforced (Cheung 1969), relaxation of this assumption gives way to a number of scenarios. If effort can not be monitored and agents are risk neutral, only the fixed rent contract is optimal. The reason is that, in all other cases, equalizing the marginal disutility of effort to their marginal benefit will lead tenants to exert less than the socially optimal amount of effort, thus resulting in lower total 2"In order to protect the user rights of farmers, their land holdings should be registered and provided with certificate of user rights. In this regard, a guarantee may be given to the effect that land will not be re-divided for a period ranging from 20-30 years. Some regional states have already started this aspect of the land use policy and it is a step in the right direction." (FDRE, 2002:p.53; italics added). 5 production. The optimum outcome will require a trade-off between the risk-reducing properties of the fixed-wage contract, under which the tenant's residual risk is zero, and the incentive effects of the fixed- rent contract, which would result in optimal effort supply but no insurance. Second, limited tenant wealth has a similar effect because in case of a negative shock tenants with insufficient wealth are likely to default on rent payments. This implies that landlords will tend to enter into fixed-rent contracts only with tenants who are wealthy enough to pay the rent under all possible output realizations, implying that poorer tenants will be offered only a share contract (Shetty 1988). Finally, a dynamic setting opens up a number of additional perspectives, in addition to the scope for using the repeated game context and the threat of eviction to reduce the efficiency losses of sharecropping. A rental contract that provides tenants with adequate incentives to maximize production in any given time period may lead to overexploitation of the land if (dis)investment is considered, implying that a share contract with lower-powered incentives and possibly compensation may be more appropriate (Ray 2005). A large literature has focused on testing the extent of inefficiency involved in sharecropping contracts, although with often mixed results (Otsuka and Hayami 1988). One of the key problems of cross-sectional estimates is that the estimated `inefficiency' of sharecropping is due to unobserved household characteristics that affect contract choice rather than the contractual structure per se (Binswanger et al. 1993).3 Use of within-household variation suggests that, in India, share tenancy associated is associated with an average a loss of productivity of 16% (Shaban 1987) although part of the losses may have been policy-induced as in the study setting administrative measures outlawing fixed rent contracts were in effect. More recent studies using household fixed effects cast doubt on the inefficiency of sharecropping (Jacoby and Mansuri 2006), suggesting that agents' choice of contractual arrangements is rational given the constraints faced in a given situation and that the scope for government to bring about more effective outcomes may be limited. Although possibly limited by a rather small number of observations, studies from Ethiopia do not lend strong support to the hypothesis of Marshallian inefficiency either but instead suggest that farmers apply the same amount of inputs on land under informal and less secure contracts (rented, sharecropped and borrowed) and on lands formally allocated by peasant associations (Gavian and Ehui 1999, Pender and Fafchamps 2006). In fact, recent evidence suggests that, possibly due to eviction threats, sharecropped plots may be more productive than owner operated ones (Kassie and Babigumira 2006). To control for household-specific characteristics that vary systematically across owned and sharecropped plots (Bell 1977, Shaban 1987), we use the presence of a large number of households who own and sharecrop land at the same time to apply a methodology that compares yields and input intensities on owned and sharecropped plots for the same household. Formally, we estimate 3If households entering into sharecropping contracts are poorer or have less access to capital than own-cultivators or fixed renters, what shows up in a cross sectional regression as an "impact" of sharecropping may well be the result of these factors rather than the specific form of the contract. 6 yhi = shi + xhi +Vvt + uh +hi (1) where yhi denotes the value of either crop output or of variable inputs (family labor use per hectare, pair of oxen days per hectare, or quantity of chemical fertilizer use) per hectare by household h on plot i, shi is a dummy variable that equals one if the plot is owner-cultivated and 0 if the plot is sharecropped, and xhi is a vector of exogenous plot characteristics such as soil quality and topography (measured by several dummy variables), a dummy variable whether the plot is irrigated or not, and the number of years the plot has been possessed by the current user. Vvt is a vector of time-varying district dummies that capture season- and community specific effects, uh captures the impact of all observed and unobserved household specific variables such as managerial ability, credit market access, or risk aversion that affect production decisions on all plots cultivated by the household equally, and hi is plot specific unobserved error term assumed to be identically and independently distributed with mean zero and finite variance. In addition to yields, the main variable of interest, our empirical analysis focuses on use of family labor, draft power, and chemical fertilizer as the three most important inputs. The fact that all households use family labor allows estimation of a fixed effects model for this input whereas presence of non-trivial fraction of zero observations leads us to choose a random effects tobit model for oxen and fertilizer. In this framework, the key variable of interest is the estimated coefficient on the ownership dummy, , which measures differences in yield or input intensity between owner-cultivated and sharecropped plots. A positive and significant coefficient would point towards existence of Marshallian inefficiency, i.e. differences that are due to the form of contract. 2.3 Optimality of adjustment Irrespectively of whether sharecropping is indeed associated with a Marshallian inefficiency, it will be important to explore whether, with land rental incurring significant transaction costs (e.g. for search of partners and contract enforcement) and imperfections in markets for labor and draught animals, land rental will allow households to attain their `optimum' land size (Bliss and Stern 1982, Bell and Sussangkarn 1988). Studies from Ethiopia take evidence on the failure of rental markets to equalize factor ratios as a sign of only partial adjustment (Teklu and Lemi 2004, Holden and Ghebru 2006), although their methodology does not allow to quantify the size of inefficiency. Surprisingly, the only study that formally tests factor adjustment, albeit based on a very small sample that is limited to households renting out land, is unable to reject the hypothesis of land markets facilitating full and frictionless adjustment to the desired land size (Pender and Fafchamps 2006). To test whether this is the case, let k be the amount of net land leased which is itself a function of the difference between desired cultivated area (DCA), defined as the amount of land that matches the 7 household's resource endowment in the absence of friction in the tenancy market, and total owned area (K).4 Letting h be an the adjustment function which depends on the size and type of transaction costs in the rental market (Skoufias 1995), this relationship can be written as k = h(DCA - K )= h( f (O,L) - K ), (2) where, by assumption, DCA is a function of the household's endowment with oxen power O and family labor L. With the adjustment function h being non-decreasing in (DCA ­ K) and h(0)=0, i.e., a household that owns its desired cultivated area will not participate in the tenancy market, and f non-decreasing in both of its arguments, a first-order Taylor series expansion of equation (2) yields k = (h' fO )O + (h' fL )L - h'K + C, (3) where h' is the slope the adjustment function (i.e. the first derivative with respect to (DCA ­ K)), fO and fL are partial derivatives of the desired cultivated area function with respect to oxen and family labor endowments of the household, respectively, and C a constant.5 Distinguishing three types of observations, i.e. positive, zero, and negative net land leased-in, an econometric model that accounts for these three observations, and that allows for asymmetry is given by -n + nZi +i if i < n - nZi ki = 0 if n - nZi i p - pZi (4) - p + pZi +i if i > p - pZi where subscripts n and p denote the rental market participation status of household i with negative net leased-in area and positive net leased-in area, respectively; the parameters n and p include the constant term in (3) and the unobserved transaction costs that relate the latent to the observed variable; Zi is a vector of household level variables; is a vector of coefficients that combine the slope the adjustment function and the marginal responses of desired cultivated area to the underlying input endowment; and i is the unobserved household specific error term. Household level variables which we expect to determine a household's participation in the tenancy market and its desired amount of net leased-in area and which are included in Zi. They encompass the demographic structure of the household (number of male adults, number of female adults, number of children, and age and sex of the head of the household), key assets (number of bulls and oxen and other livestock owned, number of rooms in dwellings), the area of land and its quality, and the literacy level of the head of the household as a proxy for human capital 4For graphical exposition of some of the possible forms of the adjustment function, h, see Bliss and Stern (1982) and Skoufias (1985). 5Using this basic model, Bliss and Stern (1982) applied ordinary least-squares to obtain regression estimators by pooling participants and non- participants together. This approach has been criticized as it ignores the non-linear nature of the participation decision in the tenancy market due to the presence of transaction costs and the possibility of asymmetries across the two sides of the market (Bell and Sussangkarn 1988, Skoufias 1995). Such asymmetries may arise from differences in the slope of the adjustment function and/or from differences in the marginal responses of desired cultivated area to the household's endowment of draft power and family labor as can be seen from equation (3). 8 endowments. In addition, we also estimate a version of this equation that includes a measure of agricultural ability as well as social capital variables, in particular whether or not the household belongs to a village-level burial association, at the household-level. This formulation implicitly assumes that households' endowments with land, labor and oxen are exogenously determined, a reasonable assumption in our context as endowments of land use rights have been assigned by the village some time ago and land can not be sold, oxen markets are highly imperfect, and instantaneous adjustments in family size are impossible. District dummies and a time trend are included to capture differences in social and infrastructure endowments that affect agricultural productivity, prices, access to markets, and time- varying factors that affect rental market functioning. The parameters of the model are estimated using maximum likelihood (ML) under the assumption of an identically, independently, and normally distributed error term with mean zero and constant variance 2 (Skoufias 1995). The ML estimator is consistent and asymptotically efficient. However, since the model to be estimated is based on the first-order Taylor expansion, it is not possible to distinguish the threshold values related to transaction costs from the intercept terms and less weight should be given to the magnitude of the estimated intercept terms in interpreting results. The econometric model in (4) can be used to test a range of hypotheses with respect to the role of transaction costs and market imperfections in explaining the performance of the land rental market. Firstly, ascertaining that the coefficients of land area owned equal -1 would provide evidence for efficient and complete adjustment towards desired cultivated area through the land rental market whereas its rejection would suggest that there is significant friction that prevents full adjustment in this market (Skoufias 1995). Second, we can test the symmetry of the adjustment function for households who rent in land and rent out land through a straightforward test of the equality of the coefficients of land area owned across the two sides of the rental market as well as the symmetry of all or subset of the estimated coefficients of the two sides of the rental market. Finally, we can make inferences regarding non- tradability of other inputs by testing whether the estimated coefficients of these inputs are statistically different from zero (Pender and Fafchamps 2006). While land rental can affect overall efficiency through the choice of specific contractual forms adopted in rental markets may reduce productive efficiency, a potentially more important channel through which rental can enhance productivity is by transferring land from less to more productive producers. To test whether this is the case in our data, we use the fact that we have detailed information on production inputs and outputs for 3 seasons that differ from each other in terms of rainfall, prices, and other opportunities. Assuming that the key determinant of overall output is farmers' managerial ability, that differences in terms of soil quality are directly controlled for, and that other time invariant factors such as infrastructure 9 and market access are accounted for by district dummies, household fixed effects from a panel production function regression reflect heterogeneity in farming skills (Lanjouw 1999, Deininger and Jin 2005). We thus run a household fixed effect Cobb-Douglas crop production function (see appendix table 1 for results) to recover a measure of households' ability and include it in the above adjustment cost regression. The literature on institutional economics has long highlighted that social capital and associated networks can have an important role in reducing the cost associated with economically beneficial transactions. Examples include that the size of a person's network will make it easier to identify potential transaction partners6 and that being part of a network may reduce the cost of enforcing contracts or monitoring the other party's performance (Sadoulet et al. 1997). These could be of great relevance in our case. Unfortunately, the closet measure to a network available from our data, which we include in the respective regression, relates to whether or not the household participated in a village level burial association (`iddir'). 3. Data and descriptive statistics The panel data point towards a comparatively high level of land rental activity, together with an almost exclusive reliance on sharecropping and the vast majority of transactions occurring between relatives. Supply of land to the rental market is concentrated among female-headed households who lack sufficient draught power, suggesting that, contrary to what is observed in other countries, land rental actually contributes towards concentration of operational holdings as compared to land ownership. 3.1 Data and household level evidence We use data from three rounds of a longitudinal survey of rural households in the Amhara region of the Ethiopian highlands that comprise production information during the main agricultural season (meher, i.e. Sept.-Feb.) of the 1999, 2001, and 2004 agricultural years. In each of these periods, the survey, which was implemented by the Department of Economics of Addis Ababa University in collaboration with the Department of Economics of Gothenburg University, covers about 1520 randomly selected households from 12 villages (kebeles) in 6 districts (woredas) in South Wollo and East Gojam zones of the Amhara region. Among others, data for every year include detailed information on household resource endowments, crop production inputs and outputs at the plot level, demographic characteristics of households and their participation in land, labor and credit markets. In the last round, additional modules, including among others detailed information on household consumption and rental contracts, were added. 6This can be beneficial economically in a number of settings, e.g. by improving the scope for co-insurance if agents are subject to random but uncorrelated shocks (Fafchamps and Lund 2003), if there are economies of scale in utilizing fixed investment as in marketing (Fafchamps and Minten 2002) or if, as in labor markets, the surplus to be derived from a particular transaction depends on the quality of the match between the two partners. 10 Table 1 provides household level information for the whole sample, and separately for those leasing out, leasing in, and remaining in autarky. A first observation of interest is that land markets are very active with almost half the households (46%) participating in rental markets and only 54% remaining in autarky. The fact that our sample is relatively balanced (20% leasing out land and 26% leasing in) also highlights that the vast majority of transactions are between households in the same village, contrary to what is often observed in other samples.7 A second observation of interest is that, with 40%, a significant share of those renting out (compared to 12% among those in autarky and 3% among those renting in) are female headed. The fact that more than 70% of all female headed households rent out land implies that one important function of land rental is to transfer from resource poor female-headed households to resource rich male- headed ones. Third, with no significant differences in area owned between those renting-in and renting- out land and those in autarky (1.04, 1.06, and 1.11 hectares, respectively), the operation of land markets leads to a concentration of operational land holdings compared to the land ownership structure, differently to what is observed in most other countries. In fact, the Gini coefficient is only 0.40 for land ownership but 0.47 for operational holdings. While this is low compared to the high levels of inequality found in Latin American countries, it is higher than in China where rental markets also equalize the operational land distribution.8 Finally, while one would expect to find those renting in land to have better access to family labor, the rather surprising finding that renters also have significantly higher endowments of human and physical capitals (literacy and education levels), contrary to most other countries where those engage in non-agricultural activities suggests that there may be barriers to entry into non-farm activities. 3.2 Plot level characteristics Information on output and input as well as source of traction and, for rented ones, the type of transaction partner for the 23,215 plots -out of which slightly more than one third (8,562) belong to owner-cum- sharecropper households who both own and lease in land- is provided in table 2.9 The first 4 columns are based on the whole sample while columns 5-8 refer to the sub-sample of owner-cum-sharecroppers where column 8 indicates statistical significance of the difference in means between owned and sharecropped plots. The table illustrates that, with more than 90% of rented plots transferred under share- rather than fixed rent, sharecropping is the predominant contractual arrangement. Panel 1 also illustrates that most transfers were limited to close kin; 60% of plots were exchanged between relatives or in-laws, 30% 7Almost all the households involved in the land rental market exclusively participated in either renting-out or -in land with less then 1% of households reporting to simultaneously lease out and lease in land. 8The Gini coefficient for the ownership distribution of land is 0.92 for Venezuela and 0.87 for Brazil (Deininger and Squire 1998) compared to a Gini of 0.36 for the distribution of use rights, which was further reduced through transfers, in three provinces of China (Deininger and Jin 2005). 9Given the small number of plots transferred under fixed rent arrangements and the fact that these are not used in our subsequent plot-level analysis, descriptive statistics for these are not reported separately but can be obtained from the authors upon request. 11 between neighbors or members of the same village association,10 and only 10% between unrelated individuals. About 21% of plots are transacted in rental markets, a share that is significantly lower than that of households involved in rental transactions due to the fact that rental transactions normally only involve part of a household's endowment. With close to 50%, the share of output accruing to landlords is comparatively high. Although detailed data was not collected on the nature and formality of contracts, the mean leased-in plot in the sample had been in the possession of the current tenant for about 4 years, consistent with an average duration of contracts of contracts of 3 years as reported in the third round data. While there are minor differences in crop output per hectare and plot characteristics, few of them are statistically significant (panel 2). For the overall sample and for owner-cum sharecroppers, output values are slightly lower on shared plots as compared to owned ones but this may be due to the fact that the latter are of somewhat lower quality. The use of fertilizer, manure, improved seeds, and male and female family labor appear to be higher on owned compared to shared plots. At the same time, the incidence of hiring labor, but not the actual number of days spent, is higher on shared as compared to owned plots (panel 3). Although traditional technology requires a pair of oxen, only about 60% of plots are cultivated with own oxen and the considerably higher share of own oxen on shared in plots points suggests that, as noted earlier, shortage of oxen could indeed be a key reason for households to engage in land rental. The lack of rental markets for oxen can be attributed to synchronic timing of activities and moral hazard that could lead renters to over-use and under-feed animals. Thus, the main source of traction power for about 30 percent of cultivated plots was either oxen-sharing (mekenajo) or exchange of labor for oxen services. 4. Econometric results Results from econometric estimation demonstrate that, even in an environment where the observed outcomes from operation of rental markets are fully efficient, significant barriers to entry or friction in their operation could imply that whatever adjustment is brought about through the operation of such markets falls short of the potential. The hypothesis of sharecropping leading to inefficient use of resources is strongly rejected although we do find evidence of lower investment on such plots. This pales, however, in comparison to the magnitude of friction that would, according to our estimates, prevent rental markets from attaining a first best allocation of resources. 4.1 Is there evidence of Marshallian inefficiency? 10Village associations consist of self-help groups and networks such as agricultural mutual aid groups, rotating savings and credit associations, rotating feast groups, burial associations, etc. that are formed among persons of common kin, neighborhood or faith. 12 Table 3 reports results from the household fixed effect estimates of the yield equation at the plot level.11 While land quality attributes and the coefficients for observed inputs are highly significant, the coefficient on the ownership dummy, which is the variable of main interest, is indistinguishable from zero in all the specifications. This leads us to strongly reject the hypothesis of a Marshallian inefficiency, i.e. lower efficiency on sharecropped as compared to owned plots by the same household. A possible reason for this is that monitoring of tenants is relatively easy due to the fact that land rental is concentrated among close relatives or neighbors and virtually all of the landlords reside within the village. To complement this, table 4 reports results from fixed effect regressions for labor input, suggesting that the input of male and total family labor on owned plots is higher by 13 and 16 points, respectively, than on sharecropped ones. A possible explanation is that, as it results in improvements affecting production in subsequent periods, such labor input should more properly be considered as an investment. Examples would be the removal of stones or more intensive weeding to prevent accumulation of weed seeds on own plots. Of course, lack of local labor demand is likely to increase households' propensity to use family labor in this way even if the benefit from doing so may be rather modest.12 Qualitatively similar conclusions emerge from random effect tobit regression for fertilizer application (columns 2 and 3) which imply that, conditional on fertilizer use, owned plots receive about 31% more fertilizer than sharecropped ones. As P-fertilizers improve soil fertility in the longer term (McCullum 1991), this can be understood as a longer-term investment. This interpretation is supported by household fixed effect regressions for soil fertility investments (bunding, mulching, green manure application, etc.) which highlight that such investment is significantly more likely to be undertaken on own compared to rented plots (not reported). While more intensive application of labor and fertilizer on owned plots would work against a Marshallian inefficiency in any given production cycle, it clearly suggests that, most likely due to the difficulty of implementing rental contracts that provide optimum investment incentives, rental will still be associated with a dynamic inefficiency. By contrast, and consistent with the notion that the intensity of soil preparation does not have longer-lasting effects, the regressions provide no evidence for a significant difference in application of oxen between owned and sharecropped plots. 4.2 Do land rental markets capitalize fully on existing opportunities? The failure to reject the hypothesis that production on sharecropped plots is as efficient as it is on owned ones could still be consistent with the fact that there is considerable rationing in rental markets, i.e. that high transaction costs prevent producers from adjusting to their optimum holding size. ML estimates of 11To compute values, median prices at the village level were used. Lack of price information forced us to exclude about 100 plots from each round allotted for fodder, eucalyptus, and similar crops from the analysis. 12Lack of information on the stock of soil fertility investments makes it impossible to assess the impact of such activity on production that would be needed to derive such an estimate. 13 the parameters for intensity of participation in tenancy market as presented in table 5 facilitate a number of insights.13 Most importantly, noting that the coefficient of owned land area corresponds to the slope of the adjustment function in (2), the positive (negative) and significant coefficients of owned area in the leasing out (in) equations suggest that, once households' endowments with fixed factors are accounted for, land markets facilitate an adjustment towards households' desired level of cultivated area. Figures in table 6 illustrate that both the hypotheses of the symmetry of the coefficients on both sides of the market and, more importantly, full adjustment, i.e. the equality of these coefficients to 1 or -1 is strongly rejected. While suppliers appear to be slightly less constrained than those demanding land through the market, estimated coefficients suggest that, on average, farmers are able to realize only 22% of the desired amount of land leased, points towards large amounts of friction in the land lease market. This is in marked contrast to results by other studies in Ethiopia which -based on a much smaller sample (161 observations) in which only leasing in was observed- were unable to reject the hypothesis of perfect adjustment (Pender and Fafchamps 2006). To put this figure into perspective, note that in India where a large number of policies impose de jure restrictions on land rental (Nagarajan et al. 2006), the corresponding coefficient was 78% (Skoufias 1995). This suggests not only that these policies were in practice often honored in the breach but also highlights the surprisingly large magnitude of friction in the case of Ethiopia. We interpret this as an indication that the transaction costs of land rental markets, many of which are likely to be related to the policy environment as discussed earlier, prevent participation by a large number of potential participants. Even for those able to overcome the obstacles to entry, the level of adjustment afforded by such markets is far from the desired one. A second finding of interest relates to the signs and significance levels of the coefficients on other factors, all of which should equal zero under the assumption of perfect markets.14 The coefficient on ownership of draft animals is highly significant and positive (negative) on the demand (supply) side of the market, suggesting that households with more (less) animals are likely to lease-in (out) more (less) land in line with the hypothesis that land leasing decisions help to improve utilization of households' imperfectly tradable endowment with oxen. The extent of land transactions is also significantly affected by female headship and the number of male adult members and dependents in the expected way, due in part to the fact that -even if females may participate in other activities such as weeding and harvesting- ox ploughing is a male task and that it will be difficult for females to obtain the necessary male labor (plus oxen) input through the market. The value of other livestock and number of rooms in the household, which can be 13The dependent variable is the amount of net land leased-in (i.e. leased in minus leased out) in hectares. To facilitate interpretation, estimated coefficients in the lease-out equation (except for the district dummies, the time trend and the intercept term) are multiplied by -1. A positive (negative) coefficient in the leasing-out column can thus be interpreted as an increase (decrease) in the amount of land leased-out in response to a positive change in a given explanatory variable. 14Note that it is difficult to disentangle the effect of the adjustment function from the marginal responsiveness of the desired cultivated area to the key inputs. 14 interpreted as a proxy for wealth and access to credit and working capital, is positive (negative) in the lease in (out) equation, pointing towards credit market imperfections as another factor that may prevent households from leasing in land. While the regressions suggest that land markets transfer land from older to younger households and thus substitute for administrative redistribution of land, households' literacy level is not significant. This suggests that, in contrast to other countries where off-farm labor markets are often found to be a driving factor underlying land market activity, higher levels of education do not increase the propensity of renting out land in our sample. Finally, intercept terms and the district dummies are significantly different from zero (table 6), pointing towards differences in the extent of land rental market functioning across villages. Although the coefficient on our social capital variable is highly significant in the rent-out but not in the rent-in equation (not reported), its high correlation with woreda dummies implies that its significance disappears once these are suppressed. Although this is consistent with the notion that higher levels of social capital reduce the barriers to renting out land, we conclude that the quality of the (binary) variable is too poor and that better data will be needed to draw more far-reaching conclusions. Results for the farming ability variables are reported in the last two columns of table 5. The failure to find a statistically significant effect of ability on leasing-out decisions is consistent with the descriptive evidence which suggests that, rather than ability, imperfections in input markets are a key reason for households to supply land to the rental market. At the same time, households with higher levels of ability are likely to rent-in more land, implying that by providing greater land access to those with higher levels of ability, rental markets still make a significant contribution towards enhancing productive efficiency. Thus, contrary to what has traditionally been the focus of the literature, it appears that it is not the form of the contract but the extent to which participation in rental markets is possible, which has far-reaching implications for the productivity of land use. 5. Conclusion and implications for future research Study of land rental markets in Ethiopia is of interest not only because of the relevance of such markets for non-agricultural development but also because the country is characterized by an equal allocation of land but high levels of production risk that lead to sharecropping as the most prevalent contractual form. Our results suggest that those who are able to overcome the barriers to market entry are able to choose contractual arrangements that do not suffer from static inefficiency. At the same time, transaction costs imply that many more are rationed out from rental market participation or, even if they are able to participate, are not able to use rental markets to attain their optimum operational land holding. Compared to other countries where similar studies have been undertaken, the magnitude of friction estimated here is very large. This is of relevance because, in rural areas all over the world, land markets that can adjust 15 flexibly to the changes brought about by new technology, greater integration with marketing and processing, and better off-farm opportunities, will affect the extent to which rural producers will be able to take advantage of these new opportunities. For example, lack of clarity in the policy as to whether land will be lost if the owner takes up non-agricultural employment may constitute a strong impediment to out- migration from rural areas (Rahmato 2003). Although they do not allow us to unambiguously identify the source of such friction, our results are not inconsistent with this interpretation. We fail to find support for the hypothesis that Marshallian inefficiency reduces the productivity of land use for plots that are sharecropped. At the same time, there is strong evidence that, by affecting participation in land rental markets and the amount of land transacted in these, friction brought about by policy-induced transaction costs has considerable productivity impact. This suggests that complementing the emphasis on sharecropping that has long prevailed in the academic literature with more in-depth discussion of factors that prevent full use of land rental markets could yield useful results. This could provide a basis for policy suggestions that are more policy relevant than the often futile efforts to restrict the scope for sharecropping as a "feudal" production relation that has often been justified with reference to the literature on sharecropping. In conducting such analysis it would be useful to assess factors that prevent households from entering into land rental market transactions at all or supplying or demanding certain amounts of land (including renting out all of their land); contracting with certain types of partners (e.g. close kin); or restricting the duration of contracts. To the extent that large part of the observed friction in Ethiopia's land rental markets is likely to be policy-induced, more detailed analysis with the goal of identifying factors that prevent adjustment to households' desired level of operated land through rental markets could be of great policy relevance. If a national sample were available, the fact that, as noted earlier, policies are formulated at the regional level and, at least on paper, have undergone considerable change over time could help separate the impacts of policy from those of other relevant factors. Indeed, our data suggest that, during the period over concern, households' perception of the likelihood of receiving or losing land through administrative redistribution has undergone significant changes. Combining `objective' legislative provisions with households' subjective perceptions of their land rights could thus provide insights on how awareness of new legislation spreads through the population and the way in which it affects economic outcomes. Such analysis which could for example, help to determine whether better dissemination of existing legislation or improvements in the legal framework would be most effective, is left for future research. 16 Table 1: Household level descriptive statistics Variable Total By land market participation status Leased-out Autarky Leased-in Household characteristics Female head dummy (%) 15.37 39.91 *** 11.82 3.29 *** Head literate (%) 41.31 29.13 *** 41.24 51.14 *** Age of head in years 48.38 51.61 *** 48.71 45.11 *** No. of rooms in dwellings 1.95 1.74 *** 1.95 2.13 *** Owned area in ha 1.08 1.04 1.11 1.06 Number of adult males 1.49 1.04 *** 1.57 1.70 *** Number of adult females 1.46 1.33 *** 1.51 1.46 * No. of dependents (<15 or >60) 2.72 2.26 *** 2.74 3.05 *** Agricultural production Net land leased-in 0.01 -0.68 *** 0.00 0.60 *** Cultivates any land (%) 63.3 Owned cultivated area in ha 0.94 0.36 *** 1.10 1.05 Total cultivated area in ha 1.09 0.36 *** 1.11 1.65 *** Share of good quality land (%) 38.43 36.67 * 39.44 37.72 Owns bulls or oxen (%) 67.3 23.7 *** 72.4 91.2 *** No. of bulls/oxen (for owners) 1.19 0.39 *** 1.21 1.78 *** Value of other livestock in (`000) Birr 1.20 0.57 *** 1.20 1.69 *** Distribution over districts (Woredas) Woreda1 (Machakel), East Gojam (%) 15.44 13.07 12.56 23.38 Woreda2 (Gozamin), East Gojam (%) 15.91 16.86 16.64 13.61 Woreda3 (Enemay), East Gojam (%) 15.98 21.33 12.26 19.54 Woreda4 (Tehuledere), South Wollo (%) 20.57 19.04 23.16 16.35 Woreda5 (Tenta), South Wollo (%) 15.96 13.53 20.03 9.32 Woreda6 (Habu), South Wollo (%) 16.14 16.17 15.34 17.81 Number of observations 4268 872 2301 1095 Source: Own computation from AAU/UG Amhara Panel Survey Unit of observation is a household in a given year. Except for negligible attrition (less than 3% between rounds), this implies that the same household contributes three observations. Stars indicate that means for the lease-out and lease in group, respectively, are significantly different from those for the group remaining in autarky. * significant at 10%; ** significant at 5%; *** significant at 1%. 17 Table 2: Plot characteristics for different samples Total sample Owner-cum-sharecroppers All Own cult. Shared in Shared out All Own cult. Shared in Relation w. partner (TP) TP relative (%) 48.42 49.10 49.84 48.90 48.90 TP in-law (%) 10.65 11.02 11.19 10.64 10.64 TP neighbor (%) 17.41 18.47 18.43 15.48 15.48 TP member of VA (%) 12.55 10.97 10.64 14.12 14.12 TP unrelated (%) 10.97 10.45 9.90 10.86 10.86 Harvest share received (%) 51.11 53.06 49.18 53.29 53.29 Output & plot characteristics Crop output/ha (Birr) 2753.71 2788.92 2579.31 2868.60 2973.47 2480.64 Plot size in ha 0.24 0.24 0.28 0.27 0.26 0.25 0.28 *** Years of possession 16.34 17.72 4.01 17.91 14.06 16.81 3.89 *** Good soil quality (%) 38.03 38.91 35.40 33.86 36.24 36.66 34.71 Medium soil quality (%) 40.85 40.66 40.51 42.98 40.96 41.19 40.11 Poor soil quality (%) 20.98 20.28 24.09 23.02 22.67 21.99 25.18 *** Flat land (%) 69.28 68.56 73.44 69.92 68.75 67.52 73.27 *** Gently sloped (%) 26.74 27.47 23.31 25.35 27.33 28.27 23.87 *** Steeply sloped (%) 3.86 3.84 3.18 4.64 3.91 4.20 2.85 *** Irrigated (%) 3.58 3.93 2.34 2.23 3.34 3.74 1.87 *** Variable input use Used fertilizer (%) 34.06 33.85 33.38 40.33 41.54 35.84 *** Fertilizer used per ha (kg) 88.04 88.71 81.34 100.86 106.49 81.50 *** Used manure (%) 27.29 30.29 6.39 25.84 31.13 6.26 *** Manure used per ha (kg) 4030.46 4454.27 394.71 4174.73 5073.04 366.80 *** Used improved seed (%) 3.62 3.81 2.16 3.87 4.36 2.03 *** Male family labor/ha (d) 172.62 178.36 135.68 178.72 189.69 138.15 *** Fem. family labor/ha (d) 88.48 92.91 58.83 90.53 97.96 63.01 *** Total family labor/ha (d) 261.10 271.27 194.51 269.25 287.65 201.16 *** Used hired labor (%) 14.52 13.53 20.19 17.41 16.10 22.28 *** Hired labor/ha (d) 75.66 82.93 45.82 50.98 52.95 45.73 Source of traction Own pair of oxen (%) 58.18 56.30 69.88 67.34 66.76 69.48 ** Oxen exchange for labor (%) 15.52 16.73 7.19 10.93 12.05 6.78 *** Oxen sharing (%) 19.24 19.29 20.13 18.85 18.26 21.01 *** Gift/support (oxen party) (%) 5.26 5.72 2.17 2.00 1.95 2.17 Oxen rental (%)1 1.29 1.22 0.53 0.70 0.76 0.50 Hoe (%) 0.80 0.92 0.13 0.39 0.49 0.07 ** Pair of oxen days per ha 52.27 51.71 56.20 56.47 56.82 55.21 Number of observations 23215 18292 2234 2307 8562 6740 1822 Source: Own computation from AAU/UG Amhara Panel Survey Significance levels reported for t-tests of the equality of the means between owned and shared-in plots for the sub-sample of owner-cum-sharecroppers. * significant at 10%; ** significant at 5%; *** significant at 1% 1Rental includes provision of animals by the sharecropping partner or other arrangements. 18 Table 3: Determinants of value of crop output per ha for owner-cum-sharecroppers: Household fixed effects estimates Excluding input use All variables Ownership dummy 0.032 -0.044 (0.96) (1.54) Number of years possessed -0.002 0.000 (0.96) (0.25) Male family labor per ha (log) 0.409*** (21.82) Female family labor per ha (log) 0.051*** (3.44) Hired labor per ha (log) 0.044** (2.02) Pair of oxen days per ha (log) 0.214*** (14.82) Chemical fertilizer kg/ha (log) 0.139*** (8.43) Manure in kg per ha (log) 0.099*** (6.90) Dummy female family labora 0.164*** (2.77) Dummy hired labora 0.020 (0.28) Dummy oxen labora 0.594*** (7.78) Dummy chemical fertilizera 0.402*** (5.11) Dummy manurea 0.796*** (6.93) Good soil quality 0.143*** 0.118*** (4.25) (4.17) Medium soil quality 0.114*** 0.097*** (3.60) (3.66) Flat land 0.057 0.047 (0.93) (0.93) Gently sloped land 0.074 0.065 (1.19) (1.27) Irrigated land 0.209*** 0.088 (3.14) (1.59) Constant 7.076*** 3.038*** (105.03) (19.56) Observations 8562 8562 Number of households 606 606 R-squared 0.05 0.35 Absolute value of t statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. a The value of the dummy is 1 if the input is not used, and the value is 0 if the input is used. Time-varying district (woreda) dummies included throughout but not reported. 19 Table 4: Determinants of input intensity per ha for owner-cum-sharecroppers Variable Family Family Pair of oxen days per ha ­ Quantity of fertilizer per ha ­ male labor labor per Random effect tobit Random effect tobit per ha (log) ha (log) Coef. Marginal effect Coef. Marginal effect (dlny/dx) (dlny/dx) Ownership dummy 0.126*** 0.155*** 3.586 0.031 53.889*** 0.309*** (4.70) (5.76) (0.98) (0.98) (5.06) (5.05) Number of years possessed -0.002* -0.002 -0.264* -0.002* -1.019** -0.006** (1.73) (1.38) (1.91) (1.91) (2.34) (2.33) Good soil quality 0.036 0.064** 4.483 0.039 -28.427*** -0.163*** (1.33) (2.39) (1.24) (1.24) (2.78) (2.78) Medium soil quality 0.020 0.039 5.467 0.047 -20.020** -0.115** (0.78) (1.51) (1.59) (1.59) (2.09) (2.09) Flat land 0.026 0.043 4.993 0.043 -17.082 -0.098 (0.53) (0.87) (0.75) (0.75) (0.92) (0.92) Gently sloped land 0.018 0.033 5.999 0.052 -25.490 -0.146 (0.37) (0.67) (0.87) (0.87) (1.35) (1.35) Irrigated land 0.213*** 0.213*** 14.564** 0.126** -26.154 -0.150 (4.04) (4.02) (1.96) (1.96) (1.13) (1.13) Constant 4.373*** 4.659*** 49.487*** -94.213*** (81.85) (86.57) (6.62) (4.44) Observations 8562 8562 8562 8562 Number of households 606 606 606 606 R-squared 0.11 0.11 Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. Note: The reported marginal effects for the oxen and fertilizer regressions are provided in the form of dlny/dx such that we can interpret the coefficient of the ownership dummy similar to that of the labor intensity regressions as the percentage deviations of input intensities on owner-cultivated plots relative to sharecropped plots. Time-varying district (woreda) dummies are included, but not reported. 20 Table 5: Determinants of net land leased-in: Maximum likelihood estimates Simple friction model Ability included Variable Leased-out Leased-in Leased-out Leased-in Owned area (ha) 0.266*** -0.182*** 0.270*** -0.186*** (4.66) (3.46) (4.57) (3.45) Proportion of good soil quality -0.060 -0.008 -0.078 -0.010 (0.96) (0.14) (1.12) (0.17) Number of dependents -0.053*** 0.065*** -0.043** 0.064*** (2.64) (3.46) (2.02) (3.34) Number of adult male -0.081** 0.070** -0.088** 0.070** (2.56) (2.40) (2.55) (2.36) Number of adult female -0.001 -0.043 0.006 -0.046 (0.03) (1.25) (0.19) (1.29) Number of bulls and oxen -0.622*** 0.284*** -0.613*** 0.279*** (9.15) (4.25) (8.83) (4.21) Value of other livestock owned × 10-3 (Birr) -0.091*** 0.035** -0.082*** 0.035** (2.94) (1.99) (2.61) (1.99) Number of rooms of the household -0.026 0.073*** -0.039 0.079*** (0.90) (2.65) (1.21) (2.75) Age of household head (years) 0.011*** -0.011*** 0.012*** -0.011*** (6.00) (5.20) (5.86) (5.04) Female headed household 0.615*** -0.652*** 0.606*** -0.637*** (7.42) (5.86) (6.84) (5.55) Household head can read and write 0.027 0.001 0.022 -0.002 (0.47) (0.03) (0.35) (0.03) Farm ability -0.070 0.124** (1.31) (2.00) Threshold effect and constant parameters Woreda2 -0.269** 0.298*** -0.282** 0.304*** (2.52) (3.25) (2.54) (3.23) Woreda3 -0.272*** -0.190* -0.269** -0.135 (2.62) (1.94) (2.35) (1.28) Woreda4 -0.784*** 0.321*** -0.875*** 0.315** (6.07) (2.68) (6.18) (2.57) Woreda5 -0.982*** 0.238** -1.068*** 0.272** (7.34) (2.08) (7.33) (2.30) Woreda6 -0.462*** -0.074 -0.463*** -0.019 (4.00) (0.69) (3.70) (0.16) Time trend 0.098*** 0.134*** 0.096*** 0.138*** (3.43) (4.49) (3.04) (4.50) Constant -0.608*** 0.380*** -0.709*** 0.367** (3.53) (2.67) (3.75) (2.51) 1.011*** 1.045*** (9.50) (9.38) Log likelihood -4659.95 -4467.17 Wald chi2(23) 146.51*** 136.85*** Observations 4268 4119 Robust z statistics adjusted for clustering in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% Coefficients for leasing-out (except constant and dummies) are multiplied by -1 for ease of interpretation. The discrepancy in the total number of observations is mainly because about 137 households leasing-out land were pure landlords over the three periods, and the rest is due to missing values in the value of crop output or its determinants. This leads to some missing values in the household fixed effects (a proxy variable for farming ability) obtained from the regression estimates given in appendix table 1. 21 Table 6: Wald tests of Equality of coefficients on opposite sides of the land lease market Hypothesis tested (using estimated coefficients given in the first two columns of table 5) Wald statistic chi2(r) Symmetry of all coefficients (except woreda dummies), r=11 33.87*** Symmetry of subset coefficients (resource endowment: labor, land, oxen, assets), r=8 33.42*** Owned area 4.05** Owned area=-1, r=2 246.55*** Good soil quality 0.77 Dependants 0.24 Male adult 0.08 Female adult 1.08 Oxen 22.25*** Other livestock 2.79* Rooms 1.91 Age 0.11 Female headship 0.11 Literacy 0.17 Equality of intercept terms Woreda1a 0.91 Woreda2 0.81 Woreda3 9.07*** Woreda4 6.53*** Woreda5 16.94*** Woreda6 9.97*** * significant at 10%; ** significant at 5%; *** significant at 1% aWoreda1 is the district included in the constant term (the reference category). The intercept term for the other districts is, therefore, the sum of the coefficients of the respective district dummy and the constant term. 22 Appendix table 1: Determinants of value of crop output for all plots: Household fixed effects estimates Value of crop output (log) Plot size (log) 0.218*** (21.34) Male family labor (log) 0.353*** (25.29) Female family labor (log) 0.015 (1.36) Hired labor (log) 0.028 (1.51) Pair of oxen days (log) 0.145*** (13.02) Chemical fertilizer kg/ha (log) 0.164*** (12.70) Manure in kg (log) 0.052*** (4.35) Dummy female family labora -0.061* (1.81) Dummy hired labora -0.112*** (3.03) Dummy oxen labora -0.195*** (4.58) Dummy chemical fertilizera 0.195*** (4.53) Dummy manurea 0.321*** (4.45) Number of years possessed -0.000 (0.28) Good soil quality 0.136*** (6.36) Medium soil quality 0.119*** (5.98) Flat land 0.018 (0.48) Gently sloped land 0.033 (0.86) Irrigated land 0.118*** (2.79) Constant 3.917*** (37.17) No. of observations 17104 No of households 1509 R2 0.30 Absolute value of t statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% a The value of the dummy is 1 if the input is not used, and the value is 0 if the input is used. 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