Technical efficiency impact of microfinance on small scale resettled sugar cane farmers in Zimbabwe

Matsvai, Simion; Mushunje, Abbissynia; Tatsvarei, Simbarashe Article Technical efficiency impact of microfinance on small scale resettled sugar cane farmers in Zimbabwe Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Matsvai, Simion; Mushunje, Abbissynia; Tatsvarei, Simbarashe (2022) : Technical efficiency impact of microfinance on small scale resettled sugar cane farmers in Zimbabwe, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-23, https://doi.org/10.1080/23322039.2021.2017599 This Version is available at: https://hdl.handle.net/10419/303552 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Technical efficiency impact of microfinance on small scale resettled sugar cane farmers in Zimbabwe Simion Matsvai, Abbissynia Mushunje & Simbarashe Tatsvarei To cite this article: Simion Matsvai, Abbissynia Mushunje & Simbarashe Tatsvarei (2022) Technical efficiency impact of microfinance on small scale resettled sugar cane farmers in Zimbabwe, Cogent Economics & Finance, 10:1, 2017599, DOI: 10.1080/23322039.2021.2017599 To link to this article: https://doi.org/10.1080/23322039.2021.2017599 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 05 Jan 2022. Submit your article to this journal Article views: 2401 View related articles View Crossmark data Citing articles: 5 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Technical efficiency impact of microfinance on small scale resettled sugar cane farmers in Zimbabwe Simion Matsvai 1 *, Abbissynia Mushunje 2 and Simbarashe Tatsvarei 3 Abstract: The main objective of the study was to investigate the impact of microfinance on smallholder resettled sugarcane farmers’ productivity and technical efficiency. The study evaluated the impact of microfinance on technical efficiency of resettled sugarcane smallholders as well as the determinants of their technical efficiency. The study used Transcendental Logarithmic (Translog) Stochastic Frontier Analysis. Data from a household level survey of 2018 was collected using questionnaires in a multi-stage sampling technique. The hypothesis tests confirmed the adequacy of Translog SFA frontier over Cobb–Douglas together with the appropriateness of SFA over OLS. The results revealed that both microfinance and intensity of participation significantly improve technical efficiency. Extension services, secondary education, tertiary education, experience, and farming assets were among statistically significant determinants of observed variation in technical efficiency. Estimated technical efficiency scores from the truncated normal distribution model with heteroscedasticity and exogenous determinants were on average 64.4% and Simion Matsvai ABOUT THE AUTHOR Simion Matsvai ([email protected]) is a PhD holder in Agriculture Economics from the University of Fort Hare (South Africa) and a lecturer at Great Zimbabwe University (Department of Economics) with interdisciplinary research interests in Agriculture economics, Productivity & Efficiency Analysis, Microeconometrics, Development Economics, Financial Economics, International and Applied Economics. His career goal is to contribute positively to the above disciplines of Economics. PUBLIC INTEREST STATEMENT This study concerns the impact of microfinance (microcredit) on technical efficiency of A2 smallholder resettled sugarcane farmers. Technical efficiency is critical aspect in agricultural total factor productivity and output growth in agrobased economies. Smallholder farmers gained significance after land reform, but their productivity significantly declined likely due to existing technical inefficiencies. This study contributes to the current debate through pointing smallholder farmers’ productivity strategies, highlighting key policy interventions (complementary agricultural finance mechanisms) since smallholders hardly access formal credit due to lack collateral (assets and title deeds). Homogeneity of challenges faced by smallholder farmers makes the findings applicable to various agribusiness specialisations. Sugarcane output responds positively to the traditional production factors. Efficiency therefore could be improved through government and private sector interventions (in promoting access to production factors including agricultural finance and ensuring better and more reliable agricultural extension services which will to restore Zimbabwe’ breadbasket of Africa status). Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 1 of 23 Received 4 February 2020 Accepted 8 December 2021 *Corresponding author: Simion Matsvai, Department of Economics, Munhumutapa School of Commerce, Great Zimbabwe University, Box 1235, Masvingo, Zimbabwe, +263778131731. E-mail[email protected] Reviewing editor: Raoul Fani Djomo Choumbou, University of Buea, CAMEROON Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. 33.6% for treatment and control groups, respectively. Bank participants were more efficient (65.4%) than MFIs participants (63.3%). The results confirmed that microfinance promote efficient utilization of agricultural inputs. Policy suggestions include expansion and sufficient disbursement of microfinance. Subjects: Development Studies; Sustainable Development; Rural Development; Economics and Development; Economics; Environmental Economics Keywords: microfinance; technical efficiency; resettled sugarcane farmers 1. Introduction Increasing agricultural production and productivity in Zimbabwe requires timely and adequate supply of agricultural inputs including agricultural finance. Smallholder farmers need financial support to meet the expenses on various agricultural activities (Wadud, 2013). A large number of smallholder farmers in Zimbabwe are dependent on microfinance of different forms. As marginal and smallholder farmers have little to no access to mainstream financing mechanisms, microfinance provides smallholder resettled farmer with timeous access to factors of production and technology adoption. Access to and participation in microfinance is crucial for smallholder agricultural productivity growth. Appropriate amounts and quality of agricultural microfinance (microcredit) are crucial for realising the full potential of agriculture as a profitable economic activity (Wadud, 2013). Agricultural production is strongly conditioned by the fact that inputs are transformed into outputs with considerable time lags (Conning & Udry, 2005), causing rural household to struggle in balancing their budgets during the off-season. With limited access to finance, balancing the budget within a season becomes a binding constraint to agricultural productivity growth. Binding liquidity constraints result in suboptimal input combinations by farmers, thereby restraining optimum production choices. With the majority of smallholder farmers lacking direct access to formal financial system in Zimbabwe, microfinance (microcredit) becomes their next best alternative. With strategies to revitalize and restore the agriculture contribution to GDP in Zimbabwe involving Operation Maguta, 1 presidential input schemes (mainly seed and fertilizer), command agriculture, 2 and Pfumvudza. 3 Much effort is directed towards food security directly linked crops such as maize and other small grains. Sugarcane is generally considered to have an indirect link to food security though it has and is receiving little to no government attention, though critical since many smallholder resettled sugarcane farmers solely depend on it as a source of livelihoods. The Zimbabwe vision 2030 (attaining middle income country status) can only come to be if all corners of the agricultural sector receive significant attention they deserve. The full potential of sugarcane on employment creation, export promotion, import substitution, and food and energy security will therefore be fully realised if sufficient funding is directed towards the smallholder resettled farmers. The terms “Microfinance” and “microcredit” are often used interchangeably though they are not precisely the same. Microfinance (which entails financial inclusion—micro-savings, microinsurance, financial literacy, and management training and money transfer services) is wider than microcredit (though microcredit is the critical pillar of microfinance). The above misconception of microfinance was also narrowed to microcredit by Christen et al. (2003) and Microfinance Gateway (2008). For the sake of this study, microfinance was proxied to microcredit. Productivity is the growth in output not accounted for by changes in factor inputs, measured as the ratio between output(s) and input(s), while farm productivity is the ratio of output (s) to input (s) (Coelli, Rao, O’Donnell, and Battese, 2005). This study is mainly centred on Technical Efficiency Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 2 of 23 (TE). Technical efficiency (TE 4 ) is the ability of a decision-making unity (DMU 5 ) to attain the best production from a given set of inputs (output-increasing). It may also be defined as the measure of the ability to use the minimum feasible amount of inputs to produce a certain level of output (input-saving). Koopmans (1951) and Cooper et al. (2007) posited that a DMU is fully technical efficient if, and only if, it is not possible to increase inputs or output without making some inputs or output worse off. Debreu (1951) defined TE as one minus maximum equiproportionate reduction (expansion) in all inputs (output) that still allows the production process to continue. Efficiency measurement relies on the specification of a production frontier, which represents the maximum potential output produced from a given input vector (S. C. Kumbhakar & Lovell, 2000). Smallholder resettled A2 6 sugarcane farmers contribute immensely towards the national productivity through utilising previously underutilised and unutilised land. Growth in sugarcane production increases the chances of producing biofuels. This therefore contributes significantly towards sustainable development and poverty reduction through various environmental and economic advantages over fossil fuels. The benefits of biofuels to the Zimbabwean economy may include but not limited to enhanced energy security, improved trade balance by reducing oil imports (import substitution), creation of new export opportunities (export promotion), and the potential to help tackling climate change through reduced emissions of greenhouse gases and other air contaminants. Growth in sugarcane production therefore enhance energy security. Also, strategically supporting sugarcane production can significantly contribute towards reducing unemployment in Zimbabwe because its production is labour intensive. Land redistribution can be an effective tool in fighting poverty and promoting agricultural productivity growth and ensuring food security (World Bank, 2006). However, empirical evidence linking land redistribution, microfinance (microcredit), agricultural productivity (technical efficiency) in Zimbabwe is still scanty. Also, studies which compare technical efficiency impacts of microfinance from different sources (MFIs versus Bank microfinance) are still limited. In addition, previous studies did not consider the possible influence of random shocks like measurement errors and other noises in the data Battese and Coelli 1995) due to the use of nonparametric methods-DEA 7 as done in Tahir and Tahrim (2015). Non-parametric approaches result in overstatement of TE. For those who used parametric approaches, there is a strong bias towards the less flexible Cobb–Douglas stochastic frontier which is also flawed by considering factors of production as substitutes rather than complements. This study therefore implored the more flexible Translog Stochastic Frontier (with increased number of estimated parameters (cross-elasticities)). The aims of the study are to examine the impact of microfinance on TE together with its determinants on smallholder resettled farmers in Zimbabwe. In achieving set objectives, answers to the following pertinent questions will be provided: Could it be that smallholder resettled sugarcane farmers who participate in microfinance are more efficient than those who do not participate?, Could it be that farmers who participate in microfinance through MFIs are more efficient than those who participate through the mainstream financial institutions? What are the determinants of TE for smallholder resettled sugarcane farmers? From a policy perspective, answers to these questions are very important given the decreasing agricultural production in Zimbabwe and the results are applicable to other smallholder cash crop production (tobacco, soya beans, and cotton). The answers will help in the development of comprehensive and complementary microfinance and land resettlement policies despite smallholder farmers’ varying socioeconomic and agro-ecological settings. All stakeholder smallholder farmer intervention strategies and schemes will be aided by the findings through the development of productive lending schemes. The rest of the study is organized into the following sections, literature review (section 2), sampling, definition and measurement of technical efficiency, research methodology (section 3), presentation of results, interpretation and discussion (section 4), then conclusions and policy recommendations (section 5). Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 3 of 23 2. Literature review Several studies have been carried out to investigate the link between agricultural credit and productivity in both developing and developed countries. These studies have used different approaches in the domain of cross sectional and panel data and also of nonparametric and parametric approaches. Of the studies that have used parametric approaches, very few have applied the flexible Translog Stochastic Frontier model-TLSFM. 8 Of the very few that have used the Translog model, on distributional assumptions, very few have applied the Truncated normal distribution Technical Inefficiency model that caters for Heteroskedasticity and exogenous determinants. What follows is a snapshot of some of the studies that have been carried out within the domain of this study. Wadud (2013) examined the impact of microcredit on farm performance, output and food security using farm level survey data of northern Bangladesh on 682 (450 treatment and 232 control) farms. Cobb–Douglas stochastic frontier model and data envelopment analysis along with inefficiency effects model were used. Inefficiency effects model revealed that microcredit, experience, and education help farmers to efficiently utilise inputs. Level of technical efficiency of participants was on an average, higher than the control group. Suggested policies include the expansion, timely, and fair distribution of microcredit to small farmers. Ambali (2013) examined the effect of microcredit on technical efficiency of rural farm households in Egba in Nigeria using a multistage sampling procedure to select 160 farmers. The stochastic frontier production function was used. Results revealed that farm output increases with farm size and labour. The inefficiency model revealed age, farming experience, education, household size, and credit to decrease technical inefficiency. The mean technical efficiency of 69% implied that there still exist room for improvement in technical efficiency. Policy suggestions included the reinforcement of farmers’ education and increased awareness of microcredit benefits on farmers’ productivity. Anang et al. (2016) examined the impact of agricultural microcredit on technical efficiency of smallholder rice farmers in Northern Ghana using farm household survey data. A stochastic frontier production function was used. Micro-credit-participating households had TE of 63% compared to 61.7% percent for non-participants. Conventional farm inputs revealed significant effects on rice production except labour and capital. The significant determinants of inefficiency included microcredit, age, sex, educational status, distance to the nearest market, herd ownership, access to irrigation, and specialisation in rice production. Recommendations were that microcredit should be availed to farmers. Dessale (2019) analysed the level of TE of smallholder wheat producers and identified the determinants of TE in Ethiopia. Cobb–Douglas Stochastic Frontier Model was used. Wheat output was positively and significantly influenced by area, fertilizer, labour and number of oxen. Estimated average TE was 82%. The estimated inefficiency parameters showed that age, education, improved seed, training, and credit were negative and significant determinants of technical inefficiency. Bangwayo-Skeete et al. (2010) examined whether Zimbabwe’s Fast Track Land Reform Programme (FTLRP) farms are more technically efficient than the traditional communal farms. The study used data from FTLRP 9 beneficiaries and a control group of unsuccessful communal applicants. To account for possible systematic selection into FTLRP, they employed a probit selection equation and estimated a corrected Cobb–Douglas stochastic frontier function model. Inefficiency model estimates revealed that FTLRP beneficiaries were more technically efficient than the traditional communal farmers. The average efficiency for the FTLRP beneficiaries was 24% higher than that of communal farmers group. Dube and Guveya (2014) investigated the technical efficiency of smallholder out-grower tea farmers of Chipinge in Zimbabwe. DEA was used on 50 smallholder outgrower farmers. The Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 4 of 23 estimates of technical efficiency ranged from 37% to 100%, while the mean technical efficiency was 79% suggesting 21 % tea output loss due to inefficiency. Experience, area cultivated, fertiliser, extension services, and extent of farm commercialization significantly affected TE. There have been generalisations of the assessments of the smallholder farmers’ technical efficiency in considering agricultural production of many crops limiting relevance and applicability of the studies given the heterogeneous demands of various crops (maize, cotton, tobacco, and cotton) and nothing yet in line with smallholder A2 resettled sugarcane farmers. It has therefore become imperative to fill the absurd gap on the both scarcity of the literature on the impact of microfinance on smallholder (A2/commercial) resettled farmers and the methodological shortcomings of nonparametric and less flexible parametric approaches. The study also wish to provide pointers on the critical routes to take when crafting microfinance, agriculture finance, and land resettlement policies in Zimbabwe. 2.1. Population, sampling frame, sampling procedure, and data collection The target population of the study were the smallholder A2 resettled sugarcane farmers. Only farmers who specialise in sugarcane farming were selected. Again, only FTLRP beneficiaries in the sugarcane growing areas of Chiredzi resettlement schemes (Hippo Valley, Mkwasine and Triangle) constituted the target population. Chiredzi in Masvingo province of Zimbabwe was the study area. It is one of the few areas where farmers gained medium sized farms for smallholder farming purposes. Also, Chiredzi surrounding areas constitute the total commercial sugarcane production in Zimbabwe hence it was selected under this study. Farmers resettled under the A2 smallholder commercial/outgrower schemes who specialises in sugarcane growing were selected since they share common socio-economic characteristics. Over and above being FTLRP beneficiaries, bank (Agribank 10 ) and MFI (Getbucks) beneficiaries of microfinance (microcredit) were considered together with non-beneficiary smallholder A2 resettled sugarcane farmers. A multi-stage sampling technique was employed. First stage involved the stratification of the Chiredzi resettlement area into the three resettlement schemes namely Mkwasine, Hippo valley, and Triangle. Second stage followed the purposive selection of microfinance participants (microborrowers) with the assistance of the baseline survey data from the microfinance service providers. Third, farmers in each stratum were randomly selected and interviewed using a structured and researcher administered questionnaire. Both microfinance beneficiaries from MFI (Getbucks) and bank (Agribank) together with non-beneficiaries of microfinance in the selected areas were interviewed. Non-participants included both unsuccessful applicants and those who never applied. Farmers of the three resettlement areas constituted a sample of 370 smallholder resettled farmers. Stratified random sampling procedure was selected for its ability to provide a better representation of the target population by ensuring that every subgroup within the total sample is properly represented, thereby providing better coverage of the population since the researcher have control over the sub-categories. Keeping in mind the objectives and methodology of the study, a detailed questionnaire was prepared for data collection. A pre-tested, structured, and comprehensive questionnaire was designed with the aim of gathering relevant, reliable, and valid data. The questionnaire included questions about household characteristics microfinance (microcredit) participation, input (land, labour, fertiliser, seed and irrigation), and output information of farm activities and their prices. 2.2. Efficiency measurement—parametric versus nonparametric approaches In the literature, there are two widely used methods of measuring technical efficiency: the nonparametric Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). The choice between parametric and nonparametric depends on production frontier specification and treatment of measurement errors. Non-parametric method has no parameters to be Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 5 of 23 estimated and therefore does not account for measurement or sampling errors (Kathuria et al., 2013). Non-parametric techniques make no attempt to distinguish stochastic noise from technical inefficiency and notably the Stochastic Frontier Analysis (SFA) takes care of that. In other words, all deviations from the frontier are considered inefficiency in DEA, whereas this is decomposed into inefficiency and random errors in SFA (Battese & Coelli, 1995; Dorfman & Koop, 2005; Färe et al., 1990). Through econometric estimation, a statistical error term is added to account for deviations from the frontier independent of inefficiency (S. Kumbhakar & Wang, 2015). The assumption of considering all deviations from the frontier as inefficiency becomes very difficult gives the inherent variability of agriculture production in developing countries due to exogenous shocks (Battese & Coelli, 1995), which include factors like weather shocks triggered by climate change in Zimbabwe such as droughts, cyclones (cyclone Elini and cyclone Gloria that reached Zimbabwe in the year 2000 and cyclone Idai of 2019), floods, pests, and diseases (army worms of 2016/2017). Since the data used in this study is obtained from responses of farmers who base on mental accounting (inability to do proper accounting (low levels of education and lack of accounting knowledge)), SFA (accounts for data noises) is reasonably preferred to DEA because it measure efficiency in the presence of statistical noise. 2.3. Measurement of technical efficiency and the theoretical model Technical efficiency measurement can be either output or input oriented. Technical efficiency that relate to the ability to minimize inputs given output (Input-Oriented) and that which relates to the ability to maximize output from given input vector (Output-Oriented; S. C. Kumbhakar & Lovell, 2000). In the output-oriented approach, interest is by how much output could be expanded from a given vector of inputs, which is output-shortfall. In the case of single output-multiple inputs, the efficiency index is that of the output divided by summed weighted inputs according to Färe et al. (1994). The best practice or frontier function shows the ability of a DMU to produce maximum attainable output from a given mix of inputs (Islam 2011). Debreu (1951) and Farrell (1957) described a feasible outputinput vector technically efficient if, and only if, no increase in output or decrease in input is feasible. It follows then that technical inefficiency occurs if a producer is producing below the frontier. Building from early theoretical work by Debreu (1951), Farrell (1957), Aigner et al. (1977), Meeusen and van Den Broeck (1977), and Battese and Corra (1977), in triple simultaneous and independent papers, introduced models that allowed technical inefficiency alongside a stochastic or random shock. Introducing the random shock as an error term in (5.1) produced a stochastic production frontier expressed as: yi¼f xi;βð Þexp vi ð ÞTEi(1) where vi is a two-sided error term which captures individual specific noise. Therefore, TE in a stochastic frontier specification for a cross sectional data set becomes: TEi¼yi f xi;βð Þexp vi ð Þ (2) For this study, we have a cross sectional data set hence the need to specify technical efficiency in a cross sectional stochastic frontier model form that is: TEi¼yi f xi;βð Þexp vi ð Þ ¼exp ui ð Þ (3) Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 6 of 23 where i¼1;. . . . . . :; T represents time period. 2.4. Functional forms of the stochastic frontier production models In this section, the discussion is based on two commonly used frontier functions which are the Cobb–Douglas Production Function enroute to the Translog Stochastic Frontier Production Function. 2.4.1. The Cobb–Douglas stochastic production frontier model-functional form According to Greene (2010), the key assumptions underlying a Cobb–Douglas production function are that of positive and diminishing marginal products to inputs, constant returns to scale, and competitive product and inputs markets. The perfectly competitive factor and output markets assumption is self-defeating (factor and output markets for sugarcane are not perfectly competitive) due to the overregulated agriculture sector markets like sugarcane production in Zimbabwe. The stochastic frontier production function is founded on the neoclassical production function: Y¼f K;Lð Þ (4) Changes in K (capital) and L (labour) lead to changes in output (Y). However, output may change without changes in inputs and this constitutes technical progress (Grosskopf, 1993) as: Y¼Af K;Lð Þ:(5) where A represents all the exogenous variables that account for change in output with capital and labour being constant; therefore, A is the level of technical efficiency/inefficiency, which (allows) prohibits an entity from producing its maximum feasible output. Equation (5) can then be is expressed as Y¼f K;Lð ÞTE:(6) Aigner et al. (1977), Meeusen and van Den Broeck (1977), and Battese and Corra (1977) added evi, to equation (6) to develop the stochastic frontier production function, which captures the typical measurement errors and statistical noise in regression translating into: Yt¼F Kt;Lt ð ÞTEtevt:(7) where vt is a two-sided independently and identically distributed (iid) error term with mean zero and variance, δ2 v, thus vtN0;δ2 v  �. Statistical operationalization of the TE term gives: TEt¼eut:(8) where ut measures the output distance to the frontier and follows a non-negative truncated normal distribution with mean zero and variance δ2 u, that is uiNþ0;δ2 u  �. Substituting equation (8) into equation (7) and expressing it in a multiplicative Cobb–Douglas production function yields: Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 7 of 23 and irrigation (β11, β12, β13, β15, and β45). On another note, labour squared, labour and fertilizer and chemicals and irrigation (β22, β24, and β55) had significant but, negative coefficients meaning these inputs can be viewed as substitutes in sugarcane production. The other seven own and cross elasticities (β33,β44,β14;β23, β25, β34, and β35) had positive and significant influence on sugarcane output. Therefore, these inputs can be considered as complements in production economic theory. The majority of the cross elasticities exhibited the existence of general interdependent/complementary nature of inputs. Agriculture inputs should not always be treated as competing/substitutes. 4.5. Technical inefficiency model The focus of the analysis was to provide an empirical analysis of the determinants of productivity and inefficiency gaps among A2 smallholder resettled sugarcane farmers in Zimbabwe. Having knowledge that farmers were technically inefficient is not sufficient until the sources of technical inefficiency are identified. Thus, the study further investigated farm and farmer-specific attributes that impact on smallholder resettled farmers’ TE. This was therefore estimated using the Truncated Normal model with Heteroskedasticity and Exogenous determinants (Wang (2002) model as in S. Kumbhakar and Wang (2015)). The model was selected to cater for homoscedasticity in a truncated normal model according to Battese and Coelli (1995). The model also caters for exogenous determinants according to KGMHLBC. Combined in the Wang (2002) model, the selected model allows a further estimation of the marginal effects. Given the simultaneous estimation of the translog SFA and the prediction of technical efficiency scores, the technical efficiency scores were then simultaneously regressed against a set of independent variables (Technical inefficiency determinants). From Table 5, given that the dependant variable is technical inefficiency, a positive sign of the coefficient shows the negative effect of that variable on TE. Microfinance participation, magnitude of participation, household farming assets, age, secondary education, tertiary education, off-farm income, and extension visits/services were found to be negative and significantly responsible for TE variations amongst the smallholder resettled sugarcane farmers. The results further showed that, former estate worker was a positive and significant determinant of TE variation. Microfinance participation was found to be a negative and statistically significant determinant of technical inefficiency that is by participating in microfinance (microcredit), farmers increase their TE levels. On average, a one percent increase in microfinance participation result in a 9.7% increase in TE as indicated by the marginal effects of the mean (musigmas). Participating in microfinance also increases the certainty of technical TE 5.3% as indicated by the marginal effects of the usigmas. The results, therefore, were found to be consistent with the empirical works of Islam et al. (2011), Asefa (2012), Wadud (2013), and Sarker et al. (2016) who also found positive relationship between TE and microfinance (microcredit). The magnitude of participation (MF) representing the amount borrowed proved to be a positive and significant determinant of TE. From the results, a percentage increase in the magnitude of participation (amount borrowed) simultaneously increases, on average, the level of TE (by 3.22%) and the certainty of TE (by 3.5%). As the amount borrowed increases, the level of TE increases as hypothesised. In other words, participating with smaller amounts does not significantly increase technical efficiency. The results therefore conformed to the findings of Wadud (2013); and Dessale (2019) who found a positive relationship between microfinance (microcredit) and TE. Household farming assets (HFA) had a negative and statistically significant coefficient meaning it helps in explaining TE variations among the smallholder resettled sugarcane farmers. Thus, a percentage increase in the ownership of farming assets simultaneously increases, on average, the level of TE for the smallholder resettled sugarcane farmers by (8.77%) and the certainty of the TE (by 3.4%). This is because Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 14 of 23 being in ownership of such assets reduces the time lags, inconveniences, and production lags associated with hiring equipment (timeous transportation of inputs and output) thereby increasing TE. Following the treatment and behaviour of age (of farmer) as in Asefa (2012) guided by Wittenberg (2010), age was squared (for optimal age) and used in conjunction with age. Both proved to significantly (positively at 5% and negatively at 10% levels of significance, respectively) explain TE variation among smallholder A2 resettled sugarcane farmers despite the null hypothesis of negative impact on TE. A percentage increase in the farmer’s age result in the farmer’s TE increasing (due to farming experience, knowledge, skills and physical capability). However, after a farmer reaches a certain age interval specifically at 67 years for this study, age tended to have negative influence on TE symbolising an inverted u-shaped (nonmonotonic) relationship between age and TE. The findings were consistent to Asefa (2012) who also found an inverted u-shaped relationship between age and TE (though at a different age turning point). Household head’s highest education qualification was decomposed into no schooling, primary education, secondary education, and tertiary education. The results revealed that secondary and tertiary education has negative and statistically significant coefficients in explaining TE variations amongst the smallholder resettled sugarcane farmers. Secondary and tertiary education qualifications aids farmers to produce higher output from efficiently utilising the existing recourses. A percentage increase in a farmer’s education result in TE efficiency increases. For instance, improvement from primary to secondary education increases average TE (by 6.1%) and also increases the certainty of the TE (by 0.2%). Improvement from primary education qualification to tertiary education Table 5. Determinants of technical inefficiency Variable/Regressor Description Marginal musigmas Marginal usigmas MFI Microfinance Participation −0.097*** −0.053*** MF Microcredit (amount) −0.032*** −0.035* SEX Gender 0.034 −0.006 HFA Household Farming Assets −0.088*** −0.034* AGE Age 0.010 0.002** AGE^2 Age Squared −0.0001* −0.0001*** Eductn 2 Primary Education of Farmer −0.007 0.007 Eductn 3 Secondary Education −0.061*** −0.002** Eductn 4 Tertiary Education −0.093*** −0.012*** HHS Household Size −0.008 0.001 HHSS Household Size Squared −.01744 −.00051 EXP Experience of farmer −0.015*** −0.002*** FEW Former Estate worker −0.022*** −0.003** NOF Nature of Farming 0.090 −0.008 OFY Off Farm Income 0.040*** 0.004*** EXT Extension Visits −0.046* 0.001*** MFA Association Membership −0.013 0.005 MS Marital Status 0.0126 −0.00003 Authors’ computations from STATA for 2018 field survey data, Note: ***, **,* indicate significant at 1% (P < 0.01), 5% (P < 0.05), and 10% (P < 0.10) levels respectively. Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 15 of 23 qualification increases TE (by 9.3%) and reduces the uncertainty of technical inefficiency (by 1.24%). The results were consistent with Haji and Tegegne (2018); and Dessale (2019). Experience was also found to be a negative and statistically significant determinant of technical inefficiency. The findings indicated that as experience of the smallholder farmer increases, the level of TE increases. Experienced farmers are more efficient than inexperienced farmers. A percentage increase in a farmer’s experience simultaneously increases on average; TE (by 1.52%) and the certainty of TE (by 0.22%). The results (positive relationship between farming experience and TE) conforms to Wadud (2013); and Prasanna and Lakmali (2016). Likewise, former estate worker (FEW) exhibited a negative and statistically significant relationship with technical inefficiency. Allocating land to former sugar estate workers simultaneously increases the level of TE (by 0.29%) and the certainty of TE (by 0.3%). The positive influence of former estate workers on TE is via experience earned during the period worked for the Estates (Tongaat Hullets). For efficient utilisation of sugarcane farming resources, land for sugarcane farming should be allocated to estate workers where their estate working experience will be valid and utilised on their own farms meaning their working experience in the same environment counts. Extension visits also have a negative and statistically significant relationship with technical inefficiency. As extension visits per sugarcane growing season increases, TE increases. A percentage increase in extension visits simultaneously increases TE (by 4.61%) and the certainty of TE (by 0.11%). This was found to be consistent with Mango et al.et al (2015), Dube and Guveya (2014) and Haji and Tegegne (2018) who also found a positive relationship between extension visits and TE for farmers. However, off-farm income was found to be positive and statistically significant (not as hypothesised). Increases in off-farm income result in an increase in technical inefficiency due to increase off-farm activities (dedication and sugarcane farming value reduced). A percentage increase in off-farm income simultaneously decreases the level of TE (on average by 4%) and the certainty of TE (by 0.4%). These findings are consistent to Haji and Tegegne (2018) and Asefa (2012) who found off-farm income to negatively affect TE. This might be due to the fact that growth in off-farm income emanates from increases in off-farm activities hence there will be diversion of attention to the off-farm activities at the expense of sugarcane farming activities. 4.6. Conclusion Smallholder A2 resettled sugarcane farmers under their existing levels of technology are not technically efficient implying a wide variation of output below frontier output. There is vast potential for increasing TE with proper allocation of their existing resources. Microfinance plays a significant role in increasing the TE of the smallholder resettled sugarcane farmers. Microfinance improves the existing levels of input use (technical efficiency) and decreases the existing levels of input wastage (technical inefficiency). Furthermore, the microfinance (microcredit) provided by the formal financial institutions (Banks) is slightly more efficient enhancing than microfinance provided by semi-formal institutions (MFIs). Sugarcane production in Zimbabwe can be increased if integrated development efforts such as agriculture financing mechanisms (microfinance (microcredit)) are embraced. For agro-based economies like Zimbabwe, microfinance therefore promotes inclusive economic growth. This will be through promoting the participation of the previously marginalised (smallholder farmers) in the economic development discourse (inclusive growth). 4.7. Policy recommendations (1) Connected to the foregoing findings of the study, policy makers should make efforts in strengthening financial institutions like MFIs. Banking institutions should also be encouraged to establish microfinance units to serve the marginalised smallholder farmers niche. (2) Farmers are generally recommended to participate in microfinance (microcredit) initiatives and further recommended to take advantage of available microfinance service providers (Banks and MFIs) for better and maximum utilisation of resources. Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 16 of 23 (3) Specialised farming and farm budgeting is also recommended. (4) The government also encouraged (through the Reserve Bank of Zimbabwe) should constantly review the maximum thresholds for microfinance taking cognisance of the dynamic macroeconomic trends (inflation and exchange rate dynamics). 4.8. Implications/recommendations for future land resettlement programmes Microfinance—Given the significance of both microfinance and the size and magnitude of participation, there should be plant/crop specific complementary financing mechanisms before or on the onset of any land redistribution exercise. Extension services—Prior land resettlement exercises, the government (through the Ministries of agriculture and Higher and tertiary education) should avail extension support mechanisms (train and provide adequate extension workers) for the newly resettled farmers. Household farming assets (HFA)—Land resettlement exercises by the government should give first priority to farmers who own farming assets or who are capable of purchasing and or vary land size accordingly. Experience and former estate worker—Resettlement exercises should also prioritize former farm workers given that their experience gained as a farm worker counts on TE. Age—Households/potential farmers over and closer to 67 years of age should not be prioritized since they will be over and closer their maximum level of productivity and TE. Therefore the young and middle aged should be given the first priority and or this can be catered for through land sizes. Education—Secondary and Tertiary educational qualifications of farmers/potential farmers should be the minimum requirement for land allocations and or be catered for through varying land sizes (increasing land size with educational qualifications). Funding The authors received no direct funding for this research. Author details Simion Matsvai 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-3283-323X Abbissynia Mushunje 2 Simbarashe Tatsvarei 3 1 Department of Economics, Great Zimbabwe University, Box 1235, Masvingo, Zimbabwe. 2 Department of Agricultural Economics and Extension, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa. 3 Department of Agricultural Economics, University of Zimbabwe, Box137, Mt Pleasant Harare, Zimbabwe. Citation information Cite this article as: Technical efficiency impact of microfinance on small scale resettled sugar cane farmers in Zimbabwe, Simion Matsvai, Abbissynia Mushunje & Simbarashe Tatsvarei, Cogent Economics & Finance (2022), 10: 2017599. Notes 1. Operation Maguta means bumper harvest and it was a programme launched in 2005 earmarked to boost food security and was spearheaded by the Joint Operations Command (JOC) comprising the army, police, prisons and the intelligence. 2. Command agriculture is a Zimbabwean agricultural scheme of 2016 aimed at ensuring food selfsufficiency. 3. Pfumvudza is a climate smart conservation agriculture aimed at boosting food security in Zimbabwe. 4. Technical Efficiency. 5. Decision Making Unit in which refer to farmer/ household/farm in relation to this study. 6. Smallholder commercial farmers’ scheme of the FTLRP with slightly bigger pieces of land than the A1 scheme and it involves land allocations greater than 5 ha per household. This is the type of land allocation scheme carried out in sugarcane producing areas around Chiredzi in Zimbabwe. 7. Data Envelopment Analysis which is a nonparametric approach to the measurement of efficiency. 8. Transcendental Logarithmic Stochastic Frontier Model commonly referred to as the Translog Stochastic Frontier Model. 9. Fast Track Land Reform Programme which involved the redistribution of land through the smallholder farming (A1), smallholder commercial (A2) and the large-scale commercial farm allocation schemes. 10. Agricultural Development Bank of Zimbabwe formed to provide agricultural finance to the Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 17 of 23 agribusiness sector through both long-term and short-term (microcredit) finance. 11. The estimation of TE incorporating the submissions of Kumbhakar, Ghosh, and McGuckin (1991); Reifschneider and Stevenson (1991); Huang and Liu (1994); and Battese and Coelli (1995). 12. Ordinary Least squares estimation technique. Disclosure statement No potential conflict of interest was reported by the author(s). References The Microfinance gateway, (2008). Aigner, D. J., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. 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Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 19 of 23 Appendix 1 Summarized Results Variable/ Regressor Parameter Coefficient Std Err z P>|z| Marginal musigmas Constant δ08.275 2.392 3.46 0.000*** MFI δ1−0.115 0.032 −3.64 0.000*** −0.097 MF δ2−0.002 0.001 −2.68 0.007*** −0.032 SEX δ30.040 0.025 1.57 0.117 0.034 HFA δ4−0.098 0.026 −3.78 0.000*** −0.088 AGE δ50.079 0.074 1.06 0.289 0.010 AGE^2 δ6−0.0001 0.0001 −1.66 0.098* −0.0001 Eductn 2 δ7−0.024 0.037 −0.661 0.588 −0.007 Eductn 3 δ8−0.046 0.010 −4.64 0.000*** −0.061 Eductn 4 δ9−0.072 0.009 −7.93 0.000*** −0.093 HHS δ10 −0.054 0.060 −0.889 0.591 −0.008 HHSS δ11 −1.682 2.05 0.82 0.412 0.002 EXP δ11 −0.023 0.005 −4.75 0.000*** −0.015 FEW δ12 −0.012 0.004 −3.07 0.001*** −0.022 NOF δ13 0.090 0.069 1.30644 0.207 0.090 OFY δ14 0.040 0.020 1.99 0.007*** 0.040 EXT δ15 −0.038 0.024 −1.61 0.090* −0.046 MFA δ16 −0.015 0.026 −0.60 0.548 −0.013 MS δ17 0.020 0.020 1.08 0.282 0.0126 usigmas Constant δ0−1.135 0.216 −2.43 0.009** MFI δ1−0.183 0.061 −2.88 0.002*** −0.053 MF δ2−0.0001 0.00006 −2.00 0.045* −0.035 SEX δ3−0.268 0.239 −1.12 0.261 −0.006 HFA δ4−0.532 0.238 −2.23 0.026* −0.034 AGE δ51.258951 0.669 1.88 0.009** 0.002 AGE2 δ6−0.024 0.006 −3.69 0.000*** −0.00001 Eductn 2 δ70.294 0.340 0.86 0.402 0.007 Eductn 3 δ8−0.041 0.018 −2.32 0.018** −0.002 Eductn 4 δ9−0.362 0.091 −3.97 0.000*** −0.012 HHS δ10 0.317 0.335 0.95 0.342 0.001 HHSS δ11 0.371 0.2948 −1.34 0.379 .00051 EXP δ11 −0.344 0.195 −3.51 0.000*** −0.002 (Continued) Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 20 of 23 Appendix 2 Stata Results Marginal Effects and Exogenous Determinants of Technical Inefficiency estimates . sf_predict, bc(bc_w) marginal The following is the marginal effect on unconditional E(u). The average marginal effect of MFI on uncond E(u) is −.0970158 (see MFI_M). The average marginal effect of MF on uncond E(u) is −.0321612 (see MF_M). The average marginal effect of SEX on uncond E(u) is .03417428 (see SEX_M). The average marginal effect of HFA on uncond E(u) is −.0876534 (see HFA_M). The average marginal effect of AGE on uncond E(u) is −.0102832 (see AGE_M). The average marginal effect of AGE2 on uncond E(u) is −.00010449 (see AGE2_M). The average marginal effect of Edctn2 on uncond E(u) is −.0066044 (see Edctn2_M). The average marginal effect of Edctn3 on uncond E(u) is −.0605580 (see Edctn3_M). The average marginal effect of Edctn4 on uncond E(u) is −.09343946 (see Edctn4_M). The average marginal effect of HHS on uncond E(u) is −.00770858 (see HHS_M). The average marginal effect of HHSS on uncond E(u) is −.01743677 (see HHSS_M). The average marginal effect of EXP on uncond E(u) is −.01512843 (see EXP_M). The average marginal effect of FEW on uncond E(u) is −.02220299 (see FEW_M). Variable/ Regressor Parameter Coefficient Std Err z P>|z| Marginal FEW δ12 −0.178 0.086 −2.06 0.040** −0.003 NOF δ13 −0.128 0.277 −0.46 0.649 −0.008 OFY δ14 0.109 0.029 3.77 0.000*** 0.004 EXT δ15 −0.350 0.121 −2.88 0.005*** 0.001 MFA δ16 0.203 0.257 0.72 0.471 0.005 MS δ17 0.061 0.105 0.58 0.561 −0.00003 Vsigmas −32 9.795 −3.27 0.001*** Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 21 of 23 The average marginal effect of NOF on uncond E(u) is .08971299 (see NOF_M). The average marginal effect of OFY on uncond E(u) is .03998709 (see OFY_M). The average marginal effect of EXT on uncond E(u) is −.006046 (see EXT_M). The average marginal effect of MFA on uncond E(u) is −.01281996 (see MFA_M). The average marginal effect of MS on uncond E(u) is .01262517 (see MS_M). The following is the marginal effect on uncond V(u). The average marginal effect of MFI on uncond V(u) is −.0528961 (see MFI_V). The average marginal effect of MF on uncond V(u) is −.0345063 (see MF_V). The average marginal effect of SEX on uncond V(u) is −.0061868 (see SEX_V). The average marginal effect of HFA on uncond V(u) is −.0337769 (see HFA_V). The average marginal effect of AGE on uncond V(u) is −.00241315 (see AGE_V). The average marginal effect of AGE2 on uncond V(u) is .00001511 (see AGE2_V). The average marginal effect of Edctn2 on uncond V(u) is .00736751 (see Edctn2_V). The average marginal effect of Edctn3 on uncond V(u) is −.00145136 (see Edctn3_V). The average marginal effect of Edctn4 on uncond V(u) is −.01237477 (see Edctn4_V). The average marginal effect of HHS on uncond V(u) is .00104193 (see HHS_V). The average marginal effect of HHSS on uncond V(u) is −.0005041 (see HHS_V). The average marginal effect of EXP on uncond V(u) is −.00223782 (see EXP_V). The average marginal effect of FEW on uncond V(u) is −.00293835 (see FEW_V). The average marginal effect of NOF on uncond V(u) is −.00825001 (see NOF_V). The average marginal effect of OFY on uncond V(u) is −.00364343 (see OFY_V). The average marginal effect of EXT on uncond V(u) is −.00104075 (see EXT_V). The average marginal effect of MFA on uncond V(u) is .00504112 (see MFA_V). Matsvai et al., Cogent Economics & Finance (2022), 10: 2017599 https://doi.org/10.1080/23322039.2021.2017599 Page 22 of 23 The average marginal effect of MS on uncond V(u) is −.00003525 (see MS_V). © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. You are free to: Share — copy and redistribute the material in any medium or format. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. 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