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The purpose of this paper is to measure technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from actual inefficiency using comprehensive household-level panel data.

This paper estimates technical efficiency based on the true random-effects stochastic production frontier estimator with a Mundlak adjustment. By utilising comprehensive panel data with 4,694 observations from 39 districts of four major maize-producing regions in Ethiopia, the author measures technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from technical inefficiency. By using competing stochastic production frontier estimators, the author provides insights into the influence of farm heterogeneity on measuring farm efficiency and the subsequent impact on the ranking of farmers based on their efficiency scores.

The study results indicate that ignoring unobservable farmer heterogeneity leads to a downwards bias of technical efficiency estimates with a consequent effect on the ranking of farmers based on their efficiency scores. The mean technical efficiency score implied that about a 34% increase in maize productivity can be achieved with the current input use and technology in Ethiopia. The key determinants of the technical inefficiency of maize farmers are the age, gender and formal education level of the household head, household size, income, livestock ownership, and participation in off-farm activities.

While the findings of this study are critical for informing policy on improving agricultural production and productivity, a few important things are worth considering in terms of the generalisability of the findings. First, the study relied on secondary data, so only a snapshot of environmental factors was accounted for in the empirical estimations. Second, there could be other sources of unmeasured potential sources of heterogeneity caused by persistent technical inefficiency and endogeneity of inputs. Third, the study is limited to one country. Therefore, future research should extend the analysis to ensure the generalisability of the empirical findings regarding the extent to which unmeasured potential sources of heterogeneity caused by persistent technical inefficiency, endogeneity of inputs and other unobservable country-specific features – such as geographical differences.

This paper contributes to the literature on agricultural productivity and efficiency by providing new evidence on the influence of unobservable heterogeneity in a farm efficiency analysis. While agricultural production is characterised by heterogeneous production conditions, the influence of unobservable farm heterogeneity has generally been ignored in technical efficiency estimations, particularly in the context of smallholder farming. The value of this paper comes from disentailing producer-specific random heterogeneity from the actual inefficiency.

The purpose of this paper is to provide a unified theoretical framework that explains farm capital structure choice.

The framework accommodates different credit access scenarios and heterogeneous risk profiles of borrowers. It recognizes that the costs of capital are endogenously determined, reflecting the degree of credit risk and accessibility to credit markets. Based on the proposed model and the comparative statics derived thereof, the paper empirically tests the impacts of different factors on capital structure choice.

Based on the theoretical framework, the paper derived the impacts of different factors on capital structure choice using comparative statics. Results suggest that the potential determinants of capital structure have varying effects at different ranges of leverage. Empirical evidence supports the theoretical model.

Despite all of previous work on various aspects of farm capital structure choice, a framework that encompasses each of the different assumptions and scenarios is still lacking. The theoretical model integrates credit risk models and accommodates endogenous cost of capital, providing a comprehensive framework for studying farm capital structure choice and its determinants. The results provide insights that could help policy makers and lenders develop effective instruments to manage, monitor, and influence the financial leverage of farms at different quantiles of debt ratio.

Within the framework of potential efforts and strategies to employment generation for young people in Africa in general and Ethiopia in particular, the agricultural sector is increasingly considered as an important sector and a valuable means for poverty reduction, the promotion of economic development, and youth's economic independence. Renewed hope is placed on the sector to offer sustainable livelihood prospects for the rural youth. Yet, the success and sustainability of the sector require a proper understanding of how households allocate youth labor time in the sector and whether agricultural labor supply is responsive to economic incentives such as shadow wages. Using gender- and age-specific plot-level panel data, we systematically analyze the impacts of shadow wages of each household member on youth agricultural labor supply across types of farms. The results indicate that agricultural shadow wages matter for the youth's labor supply in the sector, but the impact differs for male and female youth. We also show that trends and patterns of youth labor supply vary across gender and whether they work on their own farm, and so do their labor returns. The results are consistent after controlling for individual heterogeneity and instrumenting for possible endogeneity. Taking into account the intensity of youth's actual involvement in the family farm, own farm or off-farm work instead of their stated intentions, the results challenge the presumption that youth are abandoning agriculture, at least in agricultural potential areas of Ethiopia. Instead, the frequent narrative of youth disengaging from agriculture may be a result of methodological flaws or data limitations. The findings suggest that it is necessary to invest in agricultural development to enhance labor productivity and employability of young people in agriculture.

The purpose of this paper is to examine the impact of agricultural cooperative membership on farmers’ decisions to adopt integrated pest management (IPM) technology and to estimate the impact of IPM adoption on farm economic performance.

An endogenous switching probit model that addresses the sample selection bias issue arising from both observed and unobserved factors is used to estimate the survey data from a sample of 481 apple households in China. A treatment effects model is employed to estimate the impact of IPM adoption on apple yields, net returns and agricultural income. In order to address the potential endogeneity associated with off-farm work variable in estimating both cooperative membership choice specification and IPM adoption specifications, a control function approach is used.

The empirical results show that cooperative membership exerts a positive and significant impact on the adoption of IPM technology. In particular, farmers’ IPM adoption decision is significantly associated with household and farm-level characteristics (e.g. education, farm size and price knowledge). IPM adoption has a positive and statistically significant impact on apple yields, net returns and agricultural income.

The findings indicate that agricultural cooperatives can be a transmission route in the efforts to proliferate the adoption and diffusion of IPM technology, and increased IPM adoption tends to improve the economic performance of farm households.

Despite the widespread evidence of health and environmental benefits associated with IPM technology, the adoption rate of this technology remains significantly low. This paper provides a first attempt by investigating to what extent and how agricultural cooperative membership affects IPM adoption and how IPM adoption influences farm economic performance.

The purpose of this paper is to examine the factors (including conservation payments) that influence household decisions to participate in off-farm work and estimate the impact of participation on household welfare under the auspices of the Grain for Green (GfG) program.

The authors used survey data from 225 farm households on the Loess Plateau and addressed the possible sample selection and endogeneity problems by employing a jointly estimated endogenous switching regression (ESR) model.

The findings of this paper are as follows: off-farm participation is positively related to households’ educational attainment and negatively related to their land resource endowment and the presence of children; participation in off-farm work exerts positive effects on household income and per capita household income, but negative effects on farm productivity; and conservation payments show no significant impact on off-farm participation, no significant impact on any of the three household welfare indicators for off-farm non-participant households, but a significantly negative impact for off-farm participant households.

This paper makes two contributions. First, the authors address the selection bias and endogeneity problem of GfG participating households by employing the ESR method and explicitly estimating the treatment effects of off-farm participation on their household welfare. Neglecting these problems leads to biased estimates and misleading policy implications. Second, this analysis stresses the important role of government in reducing market or institutional failure and other barriers that impede farmers’ efficient allocation choices instead of compensating households for conserving sloping land, shedding new light on the most effective policy options to achieve the program’s goals.

The purpose of this paper is to investigate the relationship between poverty and technical efficiency (TE) of paddy farmers in presence of their heterogeneous selling behaviours. This paper explains how such behavioural heterogeneity affects this relationship in south-western Bangladesh.

Translog production frontier model was used to estimate TE since it fitted the data set better. On the other hand, poverty indices were constructed by using P-α method. Then, multinomial logit models examined the existence of heterogeneous selling behaviours. It revealed adequate evidences in favour of behavioural heterogeneity. Finally, the authors employed a series of two stage instrumental variable regression models to relate poverty and TE with and without considering the behavioural heterogeneity.

The study finds that around 18, 39 and 44 per cent of households exhibit autarkic, non-wholesaling and wholesaling behaviour, respectively. Market failure due to transaction cost and credit constraints leads to emergence of such heterogeneity. Across these heterogeneous behaviours, impact of improving TE on poverty status significantly differs. Without controlling behavioural heterogeneity, TE significantly improves the poverty status of the rural farm households. However, scenario is changed after controlling this heterogeneity. After behavioural segregations, TE improves poverty status only for wholesalers. In contrary to prior expectation, it worsens the poverty situation for both autarkic and non-wholesaling households. Simultaneous failure in both credit and product market for these households might be the plausible reason behind this heterodox finding. Credit market failure compels these households to borrow from local money lenders with costlier terms. This effort might improve their TE. But, product market failure makes their additional production due to improved TE unsold. Thus, repayment of credit directly reduces their consumption expenditure. Therefore, an effort to improve TE might increase prevalence and depth of poverty when market failure exists. Henceforth, the improvement of TE appears as an effective policy instrument only when households exhibit wholesaling behaviour.

The earlier studies show the relationship between TE and poverty status but did not account behavioural heterogeneity. The authors attempt to overcome this shortcoming and show how market failure induced behavioural heterogeneity affects the effectiveness of TE on improving poverty status of farm households.

This study aims at exploring the choices of livelihood strategies amongst the rural youth and how these choices relate to food security and income poverty.

The study used data from the 2016/17 wave of Integrated Household Living Condition Survey, with a sample size of 1,050 rural youths. Statistical and econometrics methods including descriptive statistics and the Multinomial Endogenous Treatment Effect (METE) model were used to analyse the data.

Livelihood choices were grouped into five categories, namely agriculture, non-farm wage employment, agriculture plus non-farm wage, agriculture plus self-employment and agriculture plus non-farm wage plus self-employment. The estimates from METE indicate that the youths' choice of non-farm wage, agriculture plus non-farm wage and agriculture plus self-employment contributes substantially to household food security improvement and poverty reduction. These findings show that agriculture is necessary but not a sufficient livelihood strategy to sustain the rural youth's contribution to youth's household welfare. The rural youth will pursue agriculture as a reliable source of livelihood not only for food self-sufficiency, but also for ensuring adequate return to labour.

This paper extends single choice analysis to multiple choices impact analysis, which has the advantage of accounting for selection bias due to both observed and unobserved heterogeneity. This paper assesses the differential impact of the choice of single as well as multiple livelihood strategies.

Productivity and production risks affect the use of agricultural production practices and inputs, particularly in developing countries. This paper aims to investigate the effects of adopting drought-tolerant maize varieties (DTMVs) on farm productivity, yield variance and downside risk exposure of maize growing households of Zambia.

The study uses household survey data collected from 11 maize producing districts of Eastern, Southern and Copperbelt provinces of Zambia using a structured questionnaire. The Antle’s flexible moment-based approach was used in specifying, estimating and testing a stochastic production function. The study further applied an endogenous switching regression model to control for both observable and unobservable sources of bias.

The study revealed that DTMV adoption increases maize yield by 15 per cent and reduces the risk of crop failure: reducing yield variance by 38 per cent and exposure to downside risk by 36 per cent.

This study establishes the benefits of DTMV adoption in Zambia with regards to productivity, yield stability and downside risk in the face of climate change. Results from this study underscore the need for more concerted efforts to scale-out DTMVs for both maize productivity enhancement and for risk mitigation against weather shocks.

The objective of this study is to investigate the simultaneity between farm couples’ decisions on labor allocation and production efficiency. Using an unbalanced panel data set of Norwegian farm households (1989–2008), we estimate off-farm labor supply of married farm couples and farm efficiency in a three-equation system of jointly determined endogenous variables. We address the issue of latent heterogeneity between households. We solve the problem by two-stage OLS and GLS estimation where state dependence is accounted for in the reduced form equations. We compare the results against simpler model specifications where we suppress censoring of off-farm labor hours and endogeneity of regressors, respectively. In the reduced form specification, a considerably large number of parameters are statistically significant. Davidson–McKinnon test of exogeneity confirms that both operator and spouse's off-farm labor supply should be treated as endogenous in estimating farming efficiency. The parameter estimates seem robust across model specifications. Off-farm labor supply of farm operators and spouses is jointly determined. Off-farm work by farm operator and spouses positively affects farming efficiency. Farming efficiency increases with operator's age, farm size, agricultural subsidises, and share of current investment to total farm capital stock.

The purpose of this paper is to investigate how credit access affects the welfare of households and sheds light on how household characteristics influence the decision to take credit and the efficiency in credit use.

This study uses data from the fourth round of the Ethiopian Rural Household Survey conducted in 2009, and examines factors that determine the decision to take credit and the effect of such decision on household welfare. The household welfare variable is measured by the food security indicator and total food expenditure. The study employs endogenous Regime Switching model to account for endogeneity in access to credit and self-selection bias in the decision to participate in credit.

The result from the kernel distribution shows households with access to credit have more consumption expenditure than those without access to credit. The ordinary least square regression shows that access to credit increases total consumption by 12 percent without considering self-selection bias. Participation in non-farm activity increases the demand for credit by 17 percent. Land holding, household size, and participation in saving associations increase the probability of getting credit by 5, 11, and 20 percent, respectively. Access to credit appears to have a positive impact on food security in both actual and counterfactual cases for the current credit receivers.

This study provides a thorough analysis of the impacts of access to credit on household welfare in Ethiopia. The study contributes to the debate on the link between access to credit and household welfare and provides valuable input for policy makers.