Determinants of Land and Labour Market Participation Decisions in Rural Ethiopia

Transcription

1 Journal of African Development 2016, 18(2):73 90 Determinants of Land and Labour Market Participation Decisions in Rural Ethiopia Muna Shifa Abstract This paper attempts to determine the factors influencing farmers land and labour market participation decisions in rural Ethiopia. A multivariate probit estimation technique is used to account for potential interdependencies between land and labour allocation strategies. Results suggest that households that are better endowed with farming resources such as oxen and farming skills are more likely to get access to more land and labour through factor markets, while households that are less endowed with these resources are more likely to rent out their land and participate in off-farm jobs. The complementary nature of land renting out and involvement in off-farm work suggests that policy makers should give due consideration to the development of rural off-farm jobs to reduce poverty in rural areas. Keywords: Agricultural labour Markets, Land rental Markets, off-farm employment, Multivariate probit model, rural Ethiopia JEL Classification: J43, Q15, J46, C35 1. Introduction In an agrarian economy like Ethiopia, where a large share of households are poorly endowed with non-labour assets, access to land, and participation in rural labour markets are important The author is grateful to Prof. Murray Leibbrandt and Prof. Martin Wittenberg for comments that improved the paper. The author gratefully acknowledges Carnegie Foundation for funding her PhD study at University of Cape Town, and the National Research Foundation (NRF) for funding her post-doctoral research. Muna Shifa, Post-doctoral fellow, Southern Africa Labour and Development Research Unit, University of Cape Town, South Africa, shfmhb001@myuct.ac.za.

2 74 Journal of African Development 2016, 18(2):73 90 for poverty reduction. Historically, the state in Ethiopia played an important role in determining the prevalence and characteristics of land rental markets through its organization of the land tenure system. During the socialist era from 1975 to 1991, households were legally restricted from participating in land markets. Land allocation through local officials and family inheritance were the only formal ways of getting access to land (Omiti et al., 2000; Rahmato, 2004). The new land reform in 1995 officially allowed households to rent out their land and to hire in agricultural labour on their farms. However, farmers today are still not allowed to sell or use their land as collateral in credit markets. Thus, the development of land rental markets is a critical factor for equalising the marginal product of land across households with different land and labour endowments. A well-developed rural land and labour markets are important to the development of strategies and policies for lessening food insecurity and poverty especially for female heads of households. Although the incidence of participation in labour and land rental markets has increased following the reform in 1995 (Shifa, 2015), rural land and labour markets are poorly developed in Ethiopia (Ministry of Agriculture and Rural Development [MoARD], 2010). Therefore, it is crucial for policy-makers to understand the factors that affect farmers participation decisions in rural labour and land rental markets. Few studies in Ethiopia have analysed decisions by households concerning participation in land rental markets (e.g. Benin et al., 2005; Deininger et al., 2009; Deininger et al., 2011; Holden et al., 2011; Segers et al., 2010), and decisions concerning participation in off-farm employment (e.g. Bedemo et al., 2013; Bhatta and Årethun, 2013; Woldenhanna and Oskam, 2001). The prevalence of multiple market imperfections in most developing countries suggests that household decisions regarding farm operation are closely related to off-farm employment participation (Binswanger and Rosenzweig, 1986; Pereira and Sumner, 1990). It follows then that any analysis of land and labour market participation decisions in rural areas should take into account the potential interconnection between land and labour allocation decisions. However, the existing studies cited above did not consider the relationship between land and labour market participation decisions in rural Ethiopia. This paper aims to fill this gap by examining the determinants of farm households decisions in rural labour and land rental markets, while explicitly taking into account the interrelationships of land and labour allocation strategies. Analysing land and labour market participation decisions together allows one to derive conclusions about which combinations of land and labour allocation strategies are more likely to be made by a given rural household. For instance, asset-poor households often may rent out their land and then participate in off-farm jobs. These household strategies suggest that policy makers should give due consideration to the development of rural off-farm jobs to reduce poverty in rural areas. The paper is organised as follows: Section 2 provides a brief review of the literature on the determinants of land and labour market participation decisions. Section 3 presents the estimation strategy used. A data description and descriptive statistics are presented in section 4. Section 5 discusses the results. Section 6 concludes with a summary of the main findings. 2. Theoretical Framework It is well recognised that markets for labour, insurance, credit, and output are often highly imperfect in rural economies of developing countries (Binswanger and Rosenzweig, 1986; Otsuka et al., 1992; Skoufias, 1995; Sadoulet, et al., 1998; Behrman, 1999; Carter and Yao, 2002). For a more detailed discussion of this literature see Behrman (1999). The presence of multiple market imperfections in non-land factors such as labour and managerial ability

3 Journal of African Development 2016, 18(2): were suggested as being the rationale for the emergence of tenancy markets in rural areas (Pant, 1983; Skoufias, 1995; Pender and Fafchamps, 2005).The main source of imperfection in labour markets lies in the lack of worker incentives (Binswanger and Rosenzweig, 1986). Given the disutility of work, hired labour has a tendency to shirk whenever the level of effort applied by hired labour cannot be easily observed (Binswanger and Rosenzweig, 1986; Otsuka, 2007). To overcome the incentive problems associated with hired labour, farm operators exercise either strict supervision over the work or design various mechanisms that provide better incentives such as permanent labour contracts (Binswanger and Rosenzweig, 1986). 1 Compared to small-scale farmers, large farm owners, who hire outside labour, can better bear the fixed costs of supervision (Ray, 1998). However, when the variable supervision costs associated with hired labour increase as land size increases, the efficiency of supervision diminishes (Carter and Olinto, 1998; Vranken and Swinnen, 2006). Due to high transaction costs associated with the supervision of hired labour, large-scale producers may opt to supply their land rather than to use hired labour. In contrast to hired labour at large farms, small-scale farmers prefer to utilise family labour by renting in land. This strategy stems from the perception that labour provided from within the family yields higher incentives to work (Eswaran and Kotwal, 1986). Credit market imperfections with liquidity restraints, however, may prevent small-scale farmers as well as landless workers from borrowing in the same way as the better-off households on the on-going market prices (Eswaran and Kotwal, 1986; Pant 1983). Credit-constrained small-scale farmers and landless workers may therefore find it difficult to get access land through land markets and finance their production costs, despite their family labour advantage. The lack of labour diversification in off-farm jobs, which affects the supply of labour to offfarm jobs, is also identified as another source of inefficiency in the labour market (Pereira and Sumner, 1990; Vranken and Swinnen, 2006). The literature on income diversification further suggests that push or pull factors primarily determine the supply of labour to off-farm jobs (Barrett et al, 2001; Ellis, 2000; Reardon, 1997). The push factors include land shortage, risk and seasonality of agricultural production, inadequate farm income, and absence or failure of input and credit markets. The pull factors include increased opportunities and profitability in the non-farm employment sector. However, with a limited number of off-farm jobs, many farm households cannot sell their extra labour to the labour market. Consequently, the marginal product of one s own labour on the farm would be less than the market wage, and this leads to inefficient allocation of resources. 2 Likewise, transaction costs in land rental markets, such as restrictive regulations over land leasing, asymmetric information, contract enforcement, and search and negotiation costs affect farmers participation decisions and the extent of their participation in land rental markets (Skoufias, 1995; Key et al, 2000; Carter and Yao, 2002). Previous studies find that both fixed and variable costs associated with land rental markets restricted farm households efficiency 1 The high cost of supervising hired labour is used to explain the use of permanent farm labour contracts in most agrarian economies because a permanent contract helps the employer to foster reliability and good work ethics from hired workers (Binswanger and Rosenzweig, 1986; Eswaran and Kotwal, 1986).Binswanger and Rosenzweig (1986), however, argued that because of seasonality and synchronic timing of agricultural production, short-term labour contracts are dominant in rural areas, despite their shortcomings. 2 Farm households maximize their utility by allocating their labour such that the marginal return to labour employed on the farm is equal to the marginal return from off-farm employment, and the wage rate for hired labour equalizes with the marginal product of labour on the farm (Skoufias,1995). Market wages and the marginal product of labour on the farm, however, fail to equalize due to market imperfections. For instance, lack of off-farm employment opportunities means the off-farm wage rate is greater than the marginal product of labour on the farm, which can lead to an inefficient allocation of labour.

4 76 Journal of African Development 2016, 18(2):73 90 and ability to adjust their land holdings to the desired optimal holding size (Skoufias, 1995; Deininger et al., 2009; Holden et al., 2011; Pender and Fafchamps, 2005). In contrast to the above studies, which have examined the impact of specific market imperfections, my theoretical model draws from an agricultural household model with multiple market imperfections developed by Vranken and Swinnen (2006). The model assumes the presence of transaction costs in land rental markets, credit market imperfections, moral hazard in hired labour, and rationing in off-farm labour markets. These conditions are considered generally applicable in rural Ethiopia. Consider a farm household with endowments of labour L, land A, some initial wealth W, and other non-tradable inputs and fixed productive assets Z (for example managerial or technical skills). Farm households can derive their income from farming their own farm, supplying land to land-rental markets, and supplying labour to labour markets. Agricultural output is produced according to the following production function, which is increasing, strictly quasi-concave, and twice continuously differentiable: Q 5 f(a,l,x,z) [1] Here, A is the land used by a household, L is the effective labour input on the farm (i.e. family or hired labour or both), X is the amount of purchased inputs (for example seed and fertilizer) used on the farm with price vector P x, and Z is managerial or technical skills. The land used, A, is defined as A 5 A 1 A i 2 A o with A being the land initially owned by the household, A i being the amount of land rented in, and A o is the amount of land rented out. The household allocates its labour endowment, L 5 L f 1 L o 1 l, between leisure (l), on-farm labour (L f ), and off-farm labor (L o ). Given the assumption of moral hazard, family labour can be used to supervise hired labour. Then the effective labour input L is given by: L 5 L f 1 s(a,l f ).L i [2] Here, L f is the family labour devoted to farming, L i is the number of hired labour. The function s(a,l f ) is a supervision function, with 0 s (.) 1, which reflects how nominal labour input is transformed into labour effort. The efficiency of supervision is assumed to be positive, but a concave function of family labour input ( s L 0 and 2 s L f2 0) diminishes as the farm size increases, for a given level of family labour input: s A 0 and 2 s A 2 0. This is because the variable supervision cost associated with hired labour increases as land size increases (Carter and Olinto, 1998; Vranken and Swinnen, 2006). It is also assumed that farm households face binding credit constraints associated with renting in land. Here, access to a loan (B) depends on the amount of land owned 3 and other productive assets. Using the same data set used in this study, Shifa (2015) shows that the percentage of households who received loans for production purposes increased as land size increased. Although land cannot be used as collateral in the formal credit markets in rural Ethiopia, close to 70 percent of the loans (26 percent informal loans and 47 percent formal loans) needed some type of guarantor (Shifa (2015). Thus, it is reasonable to assume that loan access depends on household wealth and land size. Off-farm employment opportunity in rural Ethiopia is institutionally limited by restrictions placed on the movement of labour outside of agriculture. Thus, not all household members will find jobs in rural off-farm employment. Household endowment, household-specific characteristics, or kinship may determine access to the limited off-farm employment opportunities. Thus, it is assumed that the quantity of wage employment is rationed. That quantity rationing imposes a ceiling in the form of L o L o. Furthermore, labour market 3 Land owned refers to land obtained through non-market means (allocated by PA officials or inheritance or both).

5 Journal of African Development 2016, 18(2): imperfections due to lack of off-farm employment opportunities and moral hazard problems associated with hired labour imply that the wage paid to hired labourers (W i ) will be less than the wage that household members can secure from off-farm employment (W o ). Likewise, the assumption of the presence of transaction costs in land rental markets implies that the price of land rented in (r i ) will be higher than the price for land rented out (r o ). The larger the gap between r i and r o means a greater the number of households will decide not to participate in land rental markets (remain Autarky). Finally, output price is normalised to one. Incorporating all of the above conditions, a given household maximises the following utility function, which is an increasing function of income (y) and leisure (l): max U(y,l) L f, L i, L o, l [3] A i, A o, X s.t. P x X 1 r i A i 1 w i L i B(A ) 1 W 1 r o A o 1 w o L o [4] L o L o [5] Where household net income, y, is given by: L 5 L f 1 L o 1 l [6] y 5 f(l, A, X, Z ) 2 P x X 2 r i A i 2 w i L i 1 r o A o 1 w o L o [7] Equation (4) reflects the liquidity constraint, which specifies total expenditure on factor inputs may not exceed the amount of loan at a household s disposal (B(A ), initial wealth (W ) revenue from off-farm employment (w o L o ), and received rental payment (r o A o ). Equation (5) and equation (6) capture constraints in off-farm employment opportunities and time constraints of household members, respectively. Thus, a household must make decisions about the amount of land leased in or leased out, the number of hired labour, the level of purchased inputs, and whether to allocate its labour endowments between working on farm, working off farm (including work related migrations), and leisure time. In sum, the assumption of credit constraint implies that the likelihood of renting in land and hiring labour decreases, while the likelihood of renting out land increases. Similarly, limited access to off-farm jobs implies that farm households will use more labour on their farm, increasing the marginal product of land and the likelihood of land renting in. Therefore, gains from participation in land rental markets are affected by labour market conditions in rural areas (Kung, 2002). Likewise, participation in off-farm jobs in rural areas is contingent on the presence of well-functioning land rental markets and institutional settings that improve the security of farm households tenure (Shi et al, 2007). However, the influence of different factors on farmers land and labour market participation decisions in a given context cannot be determined a priori, and, therefore, needs to be determined empirically.

6 78 Journal of African Development 2016, 18(2): Estimation Strategy In order to estimate the determinates of land and labour market participation decisions of farm households, four separate participation equations are considered: (1) rent out land (Landlord), (2) rent in land (Tenant), (3) supply of household labour to off-farm employment (Off-farm work), and (4) the hiring of outside labour for farming (Hired-labour). A household is considered as a participant in land rental markets if it either leased- in land (Tenant) or leased-out land (Landlord) using either sharecropping and/or cash rental contract arrangements. Similarly, participation in labour markets indicates that a household either supplied labour to the labour market (off-farm work) or hired outside labour (Hired labour). A household is considered as a participant in offfarm employment, if there was at least one family member in the household who participated in any kind of local employment activities outside the family s own farm 4. Likewise, households are considered to have a migrant member, if they have at least one household member aged 15 and above, who left a household in the five years prior to the survey date. The dependent variables are all binary dummy variables that assign 1 if a household participated in a given activity and 0 if it did not participate. Because of the substitutability or complementarity between land and labour market participation decisions, decisions regarding land rental and labour allocation were determined jointly. For this reason, a recursive multivariate probit model is used to simultaneously estimate farmers participation decisions in land and labour markets. The M equation multivariate probit models may be specified as (Cappellari and Jenkins, 2006): y * m 5 X ' mβ m 1 ɛ m [8] y m 5 1 if y * m 0 and 0 otherwise, m = 1,, M E ɛ m x 1,,x M 0 Var ɛ m x 1,,x M 1 E ɛ j,ɛ m x 1,,x M ρ jm (ɛ 1,,ɛ m ) ~ N m 0,R Here, y * m denotes the underlying latent response associated with the m th type of market participation outcome for m 1,.,M; y m denotes the binary response outcome associated with the m th type of market participation outcome, and ɛ m, m 1,,M, are error terms distributed as multivariate normal, each with an expected value of zero, and variance-covariance matrix R, where R has values of 1 on the leading diagonals and correlations ρ jk = ρ kj as off-diagonal elements for j, k=1,, M. The vector X m, denotes a vector of control variables, and β m denotes model parameters to be estimated. When ρ jm is zero it means that the univariate probit models will generate consistent estimates. Assuming multivariate normality of the error terms, model parameters can be estimated in Stata with the multivariate probit model estimator (mvprobit module) proposed by of Cappellari 4 This variable excludes migration to urban areas.

7 Journal of African Development 2016, 18(2): and Jenkins (2003, 2006) or the conditional mixed process estimator (CMP module) proposed by Roodman, (2009). I use the CMP module to estimate the model. The errors ɛ m in each equation are assumed to be orthogonal to the covariates. However, unobserved time-invariant individual heterogeneity such as farming ability may be correlated with some of the regressors in the model. To account for such individual level heterogeneity, a correlated random effects (CRE) procedure is specified to model the unobserved time-invariant individual heterogeneity (Wooldridge, 2010). According to this method, the time-invariant individual effect is a linear function of the exogenous variables in the model. Thus, the model is estimated including time averages for a sub-set of time-varying exogenous variables. The vector of explanatory variables (X m) includes household characteristics such as plot size, family composition, migration decisions, highest education level in a household, participation in local labour sharing arrangements, household head characteristics (age, gender, education), livestock holdings, access to loan, and a tenure security indicator. In addition, year and village dummies are included to capture time and village fixed effects. The same covariates were included in all equations because all participation decisions were assumed interdependent. In order to investigate the relationship between participation in labour and land renting activities, the variables indicating a farmer s decision to rent in land is included in the labour hiring equation. Likewise, the variable indicating households participation in off-farm employment is included in the landlord equation. The proportion of individuals working in off-farm employment in a village is included in the off-farm employment equation as an instrument. We assume this variable directly affects farmers decision to work in off-farm jobs, although it has no direct effect on land renting decisions. Likewise, the proportion of households that participated in land renting activity in a village is included in the land renting equation as an instrument. Regarding migration decisions, we assume that migration decisions are exogenous (pre-determined) in the land renting and off-farm employment decisions. This is because, while participation in land renting and off-farm employment refer to participations within 12 months prior to the survey date, migration decisions refer to migrations that happened within the five years prior to the survey date. 4. Data and Descriptive Statistics The data used in this paper come from the Ethiopia Rural Household survey (ERHS), which is a rich panel dataset. 5 Collection of the panel data started in 1994; a self-weighting sampling technique was used to select 1477 households from 15 Peasant Associations (PAs) located in four regions of the country: Amhara, Oromia, Tigray, and SNNP. Further rounds were conducted in 1995, 1997, 1999, 2004, and Farming systems were used as a stratification base to select the fifteen Peasant Associations. Random sampling was then employed to select households within each PA (Dercon and Hoddinott, 2011). Dercon and Hoddinott (2011) provide the details of the methodology used in the survey. This study uses data from two rounds of the survey (2004 and 2009), because some important variables are not available in the other survey rounds. Table 1 provides descriptive statistics for the variables used in the empirical analysis based on the pooled data (2004 and 2009). An estimated 21 percent of the sample households participated in land rental market as a tenant or landlord at least once during the survey periods. Sharecropping is the dominant contractual arrangement as more than 75 percent of the transactions involves sharecropping. During the same period, 34 percent of the sample 5 The data set is available at:

8 80 Journal of African Development 2016, 18(2):73 90 households hired outside labour on their farms, while 59 percent of them participated in offfarm employment. Likewise, close to 54 percent of the sample households had at least one family member who left a household for various reasons, while only 17 percent of them had at least one family member who left a household for work related reasons. Several factors stood out in examining the data. About 31 percent of the households in the sample were female-headed households. The average land holding size in the sample was 1.62 hectares and only 34 percent of the sample households had a pair of oxen. About 34 percent of the sample households reported that they had lost land because of land redistribution before Government sponsored land redistributions were cited as the main source of tenure insecurity among farmers in rural Ethiopia during this time (Rahmato, 2004; Shifa et al, 2015). However, the incidence of land redistribution has significantly declined in recent years with only 3 percent of the sample households experiencing loss of land due to land redistribution since 1999 (Shifa et al, 2015). Table 1.Variable Descriptions (2004 and 2009, pooled). Variable Mean Std. Dev. Min Max Dependent variables Tenant Landlord Hired labour Off-farm work Control variables Have migrant members (work-related) Have migrant members (for all reasons) Male family labour (number) Female family labour(number) Family member aged <15 (number) Family member aged >65(number) Highest education in a household: primary Highest education in a household: at least high school Female-headed household Household head is literate Household head age (years) Livestock holdings (in TLU) Own a pair of oxen Plot size (hectares) Took loan for production purposes Equb member Participated in labour sharing arrangements Had lost land due land redistributions in the past Source: Own calculations using data from ERHS (2004 and 2009). Note: a male and female labour refers to the number of working age family members. Equb is a rotating saving program. With the exceptions of family size, age, and land and livestock holdings, all the variables are dummy variables (1, if yes).

9 Journal of African Development 2016, 18(2): Table 2 presents summary results indicating household characteristics according to their participation in land rental and labour markets. Table 2 shows that 49 percent of the households that leased out land were female-headed. These households also had on average a smaller number of male family labour, livestock holdings, and only 18 percent of them had a pair of oxen for farming. Likewise, among the sample households who did not participate in both land and labour markets, only 23 percent of them had a pair of oxen for farming, while the figure is 29 percent for households that participated in off-farm employment. In contrast, those households that leased land and hired outside labour were mainly male-headed, and they were better endowed with farming resources such as land and livestock. Table 2. Descriptive statistics by land and labour market participation decisions (mean values). Tenant Landlord Hired labour Off-farm Work Non-participants Have migrant members (work-related) Have migrant members (for all reasons) Male family labour (number) Female family labour(number) Family member aged 15(number) Family member aged 65(number) Highest education in a household: primary Highest education in a household: high school Female-headed household Household head is literate Household head age (years) Livestock holdings (in TLU) Own a pair of oxen Plot size (hectares) Land to labour ratio Took loan for production Equb member Participated in labour sharing arrangements Had lost land due land redistributions Observations , Source: Own calculations using data from ERHS (2004 and 2009). Note: with the exceptions of family size, age, and land and livestock holdings, all the variables are dummy variables (1, if yes). 5. Results and Discussions The estimation results of the multivariate probit analysis are presented in Table A.1 (in the Appendix) and Table 3. Table A.1 presents estimation results without out including the endogenous right hand side variables. The likelihood ratio test of the null hypothesis that

10 82 Journal of African Development 2016, 18(2):73 90 all the correlation coefficients, ρˆ jm, are jointly zero is strongly rejected (with p value 0.000). This justifies the more general specification here than the restrictive single-equation approach. The correlation coefficients between the equation for landlord and the equations for tenant and labour hiring are negative and statistically significant. This formulation takes into consideration the existence of unobserved factors that positively affect land renting in or labour hiring decisions and, which simultaneously reduce the likelihood of land renting out. The correlation coefficient between the error terms of the equation for landlord and the equation for off-farm employment is positive and statistically significant. This correlation indicates the existence of unobserved factors, which simultaneously affect both land renting out and off-farm employment participation decisions positively. Table 3 presents estimation results after including the endogenous variables: participation in land rental markets as a tenant is included in the labour hiring equation, and participation in off-farm employment is included in the landlord equation. Coefficient estimates on the variables indicating household labour allocations to local off-farm employment and migration are positive and statistically significant in the landlord equation. These results suggest that farm households who had at least one family member who participated in local off-farm jobs or who migrated to other work-related places were more likely to rent out their land. The estimated marginal effects indicate that the magnitude of the effect is larger for those households who have family members who participated in local off-farm jobs (21 percent) than those with migrant members (4 percent). This might be partly attributed to the fact that the migration variable in this study mostly captures migrations that happened five years prior to each survey year, which is already reflected in changes in household size. Unpaid family labour is a major source of farm labour supply in rural Ethiopia. More than 80 percent of the sample households reported that they had used family labour in farming. 6 In line with this, the coefficient estimate on male family labour is negative and significant in the landlord and labour hiring equations. Households with less male family labour were more likely to rent out their land, but less likely to hire labour. Likewise, households that had access to labour via labour sharing arrangements were associated with more likelihood of land renting in, but less likelihood of land renting out. In contrast to male labour, the availability of more female labour force in a household did not affect households participation in land rental markets. However, female-headed households were more likely to rent out land, and less likely to rent in land. In addition, more female family labour in a household was associated with more likelihood of participation in off-farm employment. These results are consistent with the fact that in the Ethiopian context, a male labour force is considered more important for farming and to supervise hired labour, while female household members often participate more in unpaid domestic work and in some off-farm employment activities such as collecting firewood and brewing local alcohol. The presence of a family member with higher education (at least primary education) in a household was associated with more likelihood of hiring outside labour and renting in land. These results may suggest that highly educated family members are more likely to migrate to urban areas and earn relatively higher wages. The income from wages (remittance) could be used to finance labour hiring and land renting costs. However, the presence of a family member with higher education does not significantly affect participation in local off-farm employment. One potential explanation for this result would be that local off-farm jobs in rural Ethiopia mainly involve low skill activities (Shifa, 2015). The presence of a pair of oxen in a household and household land holding size are significant factors for decisions concerning land and labour market participations. Compared 6 Results are not shown here.

11 Journal of African Development 2016, 18(2): Table 3. Determinants of Land and Labour Market Participations- a Recursive Multivariate Probit Estimates. Tenant Landlord Hired labour Off-farm work Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Have migrant members (work-related) ** 0.04** (0.10) (0.02) (0.09) (0.02) (0.08) (0.02) (0.08) (0.03) Number of Male family labour *** 0.07*** 0.15*** 0.04*** 0.11* 0.03* (0.06) (0.01) (0.07) (0.02) (0.06) (0.02) (0.06) (0.02) Number of Female family labour *** 0.06*** (0.06) (0.01) (0.07) (0.02) (0.06) (0.02) (0.06) (0.02) Highest education: primary 0.22** 0.04** (0.11) (0.02) (0.09) (0.02) (0.09) (0.02) (0.08) (0.03) highest education: high school *** 0.12*** (0.13) (0.03) (0.11) (0.03) (0.11) (0.03) (0.10) (0.03) Female-headed household 0.66*** 0.13*** 0.32*** 0.07*** (0.11) (0.02) (0.09) (0.02) (0.10) (0.03) (0.07) (0.02) Household head is literate 0.16* 0.03* * 0.04* (0.09) (0.02) (0.08) (0.02) (0.07) (0.02) (0.07) (0.02) Livestock holdings (in TLU) ** 0.01** 0.04* 0.01* (0.02) (0.00) (0.03) (0.01) (0.02) (0.01) (0.02) (0.01) Own a pair of oxen 0.45*** 0.09*** 0.44*** 0.10*** 0.29*** 0.08*** 0.41*** 0.13*** (0.11) (0.02) (0.13) (0.03) (0.10) (0.03) (0.09) (0.03) Took loan for production 0.31*** 0.06*** 0.14* 0.03* 0.27*** 0.08*** (0.09) (0.02) (0.08) (0.02) (0.07) (0.02) (0.07) (0.02) Equb member *** 0.12*** (0.10) (0.02) (0.11) (0.02) (0.09) (0.02) (0.09) (0.03)

12 84 Journal of African Development 2016, 18(2):73 90 Table 3. (continued)/ Tenant Landlord Hired labour Off-farm work Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Participated in labour sharing 0.32*** 0.06*** 0.54*** 0.12*** (0.08) (0.01) (0.08) (0.02) (0.08) (0.02) (0.06) (0.02) Land size is <1 hectares 0.39*** 0.08*** (0.12) (0.02) (0.11) (0.02) (0.12) (0.03) (0.10) (0.03) Land size is > 1 and <=3 hectares 0.75*** 0.15*** 0.40*** 0.09*** * 0.06* (0.12) (0.02) (0.11) (0.02) (0.13) (0.04) (0.09) (0.03) Land size is >3 ha 1.10*** 0.22*** 0.89*** 0.20*** 0.34* 0.10* 0.43*** 0.14*** (0.18) (0.04) (0.15) (0.03) (0.18) (0.05) (0.13) (0.04) Lost land due land redistributions (0.09) (0.02) (0.08) (0.02) (0.08) (0.02) (0.07) (0.02) Off-farm 0.96*** 0.21*** (0.28) (0.07) Tenant (0.42) (0.12) Off-farm self-employment in a village 4.05*** 1.30*** (0.86) (0.27) Off-farm wage employment in a village 2.87*** 0.92*** (0.68) (0.22) Land renting in a village 2.68*** 0.53*** (0.69) (0.14) Source: Own calculations using data from ERHS (2004 and 2009, pooled). Note: Regression includes, household head age and age square, number of old age family members, time averages of time-varying exogenous covariates, time and village dummies. Cluster-robust standard errors in parentheses (Clustered at a household level). N *** p 0.01, ** p 0.05, * p 0.1.

13 Journal of African Development 2016, 18(2): to households with a pair of oxen, those without it were more likely to rent out their land and work off-farm, and less likely to rent in land and hire labour. These findings may imply that relative to land and labour markets, rental markets for oxen are highly imperfect in rural areas. Regarding land size, compared to households with less than 0.5 hectares of land, those households with at least one hectares of land were less likely to rent in land (by percent). Similarly, the probability of participation in off-farm employment is less by 14 percent for households with land size of larger than 3 hectares compared to those with land holding size of less than 0.5 hectares. Given poorly developed off-farm jobs in rural Ethiopia, income from off-farm work is not attractive in comparison to farm income, suggesting that farm households with more land prefer to spend more time on their own farms in order to achieve food selfsufficiency. The coefficient estimate on the variable indicating whether a household took credit for production purposes is positive and significant in the tenant equation, while it is negative and significant in the landlord equation. This result suggests that households that got access to credit were more likely to rent in additional land. Being a member of Equb, which is a rotating saving program is positively associated with more likelihood of participation in off-farm employment. This finding suggests that credit constrained individuals might use the funds as start-up capital to participate in off-farm self-employment activities. The coefficient estimate on the variable indicating household tenure security status, whether a household had lost land due to land reform, or not, is not significant in all factor market participation decisions. This might be consistent with the fact that the incidence of land redistribution in rural Ethiopia significantly decreased in recent years. Therefore, farmers participation decisions in local labour and land rental markets may not be significantly affected by their tenure security status, i.e. fear of losing land due to land reform. Lastly, the coefficients on village dummy variables suggest that there were significant differences in factor prices and other socio-economic variables from one village to another. 6. Conclusion This paper examines the decisions pertaining to participation in land rental and labour markets made by farm households in rural Ethiopia. Estimation results show that households that were well endowed with farming skills and resources were more likely to participate as a tenant in land lease markets, and they were also more likely to hire outside labour. In contrast, households that were less endowed with farming skills such as female-headed households and other farming resources were more likely to rent out their land and find work in off-farm jobs. In this way, land rental markets may enhance allocative efficiency by transferring land from less efficient farmers with fewer resources to farmers that are more efficient. However, given the lack of well-paid off-farm employment opportunities in rural areas and a lack of credit to finance agricultural production, it appears that land renting out by poor households can increase their vulnerability, worsen their poverty, and make their food supply insecure. Therefore, policy makers should give due consideration to the development of rural off-farm jobs to reduce poverty in rural areas.

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16 88 Journal of African Development 2016, 18(2):73 90 Appendix Table A.1. Determinants of Land and Labour Market Participations- Multivariate Probit Estimates Tenant Landlord Hired labour Off-farm Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Have migrant members (work-related) ** 0.04** (0.10) (0.02) (0.09) (0.02) (0.08) (0.02) (0.08) (0.03) Number of Male family labour *** 0.06*** 0.15*** 0.04*** (0.06) (0.01) (0.07) (0.02) (0.06) (0.02) (0.06) (0.02) Number of Female family labour *** 0.06*** (0.06) (0.01) (0.07) (0.01) (0.06) (0.02) (0.06) (0.02) Highest education: primary 0.22** 0.04** (0.11) (0.02) (0.09) (0.02) (0.09) (0.02) (0.08) (0.03) Highest education: high school *** 0.12*** (0.13) (0.03) (0.12) (0.02) (0.11) (0.03) (0.10) (0.03) Female headed household 0.67*** 0.13*** 0.32*** 0.07*** (0.11) (0.02) (0.09) (0.02) (0.08) (0.02) (0.07) (0.02) Household head is literate -0.16* 0.03* * 0.04* 0.16** 0.05** (0.09) (0.02) (0.08) (0.02) (0.07) (0.02) (0.07) (0.02) Livestock holdings (in TLU) * 0.01* 0.04** 0.01** 0.04** 0.01** (0.02) (0.00) (0.03) (0.01) (0.02) (0.01) (0.02) (0.01) Own a pair of oxen 0.43*** 0.08*** 0.61*** 0.13*** 0.29*** 0.08*** 0.42*** 0.14*** (0.11) (0.02) (0.11) (0.02) (0.09) (0.03) (0.09) (0.03)

17 Journal of African Development 2016, 18(2): Table A.1. (continued). Tenant Landlord Hired labour Off-farm Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Took loan for production 0.31*** 0.06*** 0.16* -0.03* 0.27*** 0.08*** (0.08) (0.02) (0.08) (0.02) (0.07) (0.02) (0.07) (0.02) Equb member *** 0.12*** (0.10) (0.02) (0.11) (0.02) (0.09) (0.02) (0.09) (0.03) Land size is 1 hectares 0.36*** 0.07*** (0.12) (0.02) (0.12) (0.02) (0.11) (0.03) (0.09) (0.03) Land size is 1 and 3 hectares 0.74*** 0.14*** 0.36*** 0.07*** 0.20* 0.06* 0.17** 0.06** (0.12) (0.02) (0.12) (0.02) (0.10) (0.03) (0.09) (0.03) Land size is 3 hectares 1.07*** 0.21*** 0.79*** 0.16*** 0.34** 0.10** 0.45*** 0.15*** (0.19) (0.03) (0.16) (0.03) (0.14) (0.04) (0.13) (0.04) Participated in labour sharing 0.32*** 0.06*** 0.54*** 0.11*** * 0.04* (0.08) (0.01) (0.08) (0.02) (0.07) (0.02) (0.06) (0.02) Lost land due land redistributions (0.09) (0.02) (0.08) (0.02) (0.08) (0.02) (0.07) (0.02) Observations 2,387 2,387 2,387 2,387

18 90 Journal of African Development 2016, 18(2):73 90 Table A.1. (continued). Tenant Landlord Hired labour Off-farm Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Coeff. Marginal effs. Error correlation coefficients Rho Std.dev Tenant/ Landlord 0.18*** (0.06) Tenant/Hired Labour 0.27*** (0.05) Tenant/Off-farm 0.04 (0.05) Landlord/Hired labour 0.20*** (0.05) Landlord/Off-farm 0.11** (0.05) Hired labour/ Off-farm 0.06* (0.04) Source: Own calculations using data from ERHS (2004 and 2009, pooled). Note: Regression includes, household head age and age square, number of old age family members, time averages of time-varying exogenous covariates, time and village dummies. Cluster-robust standard errors in parentheses (Clustered at a household level). *** p 0.01, ** p 0.05, * p 0.1.