Does credit constraint matter for technical efficiency, technological shifts, and profitability of flower growers? An empirical study

Mitra, Sandip; Dipto, Md. Rashid Asef; Ankon, Yasin Ibrahim Article Does credit constraint matter for technical efficiency, technological shifts, and profitability of flower growers? An empirical study Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Mitra, Sandip; Dipto, Md. Rashid Asef; Ankon, Yasin Ibrahim (2024) : Does credit constraint matter for technical efficiency, technological shifts, and profitability of flower growers? An empirical study, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-15, https://doi.org/10.1080/23322039.2024.2399958 This Version is available at: https://hdl.handle.net/10419/321596 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Does credit constraint matter for technical efficiency, technological shifts, and profitability of flower growers? An empirical study Sandip Mitra, Md. Rashid Asef Dipto & Yasin Ibrahim Ankon To cite this article: Sandip Mitra, Md. Rashid Asef Dipto & Yasin Ibrahim Ankon (2024) Does credit constraint matter for technical efficiency, technological shifts, and profitability of flower growers? An empirical study, Cogent Economics & Finance, 12:1, 2399958, DOI: 10.1080/23322039.2024.2399958 To link to this article: https://doi.org/10.1080/23322039.2024.2399958 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 10 Sep 2024. Submit your article to this journal Article views: 621 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Does credit constraint matter for technical efficiency, technological shifts, and profitability of flower growers? An empirical study Sandip Mitra a , Md. Rashid Asef Dipto b and Yasin Ibrahim Ankon c a Department of Agricultural Finance and Cooperatives, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Mymensingh, Bangladesh; b Department of Agricultural Extension and Rural Development, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Mymensingh, Bangladesh; c Department of Agribusiness, Faculty of Agricultural Economics and Rural Development, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Mymensingh, Bangladesh ABSTRACT This study aims to investigate the variations in efficiency, technology gap, and profitability of flower producers depending on their credit constraint position. A total of 160 flower farmers have been selected from Bangladesh by using a multistage sampling technique. Meta-frontier Data Envelopment Analysis (DEA) is employed to estimate the efficiency differences and technological gaps depending on the credit constraint situation. At the same time, the Tobit regression model is used to estimate the factors influencing the meta-technical efficiency of flower farmers. Profitability differences depending on the credit constraint situation are identified using the gross margin and benefit cost ratio. The mean meta-technical efficiency for Marigold farmers is highest when unconstrained (0.73) and lowest when credit is constrained (0.64) relative to the meta-frontier, which indicates output could be increased by 27 and 36%, respectively, without increasing input. On the other side, the efficiency of creditunconstrained rose farmers is slightly higher than that of constrained rose farmers. In addition, credit-constrained marigold farmers achieve the lowest technological gap ratio (0.64) compared to credit-unconstrained farmers. Sociodemographic and farm characteristics such as education, source of seed, land tenure, farm area, and age have a significant positive impact, while earning family members, types of flowers, and credit constraints have a significant negative effect on the technical efficiency of flower farmers. The profitability of credit-unconstrained marigold and rose farmers is higher than that of credit-constrained farmers. Facilitating the loan application process, easing the pre-conditions of loan acceptance, and adjusting the repayment schedule help to remove the credit-constrained situation. IMPACT STATEMENT Bangladesh’s floriculture industry has significant potential, but small-scale farmers face challenges due to limited access to credit. High interest rates and strict collateral requirements hinder their ability to improve technical efficiency, technological gap, and profitability. This study addresses the often-overlooked impact of credit constraints on flower farmers’efficiency, technological gap, and profitability. Farmers with access to credit are more efficient, as timely investment in inputs increases productivity and profitability. In contrast, limited credit access hampers input application, reducing both productivity and technical efficiency, trapping farmers in low-profit cycles, and increasing the technological gap. Researchers interested in the financial constraints in agriculture and their effect on productivity, efficiency, and profitability can gain valuable insights from this study. It also emphasizes the need for policymakers to enhance access to affordable credit by lowering interest rates and easing collateral requirements, which will help boost productivity and support the sustainable growth of Bangladesh’s floriculture sector. ARTICLE HISTORY Received 11 October 2023 Revised 17 July 2024 Accepted 29 August 2024 KEYWORDS Credit constraint; technical efficiency; technological gap; profitability; flower SUBJECTS Finance; Rural Development; Development Studies CONTACT Sandip Mitra [email protected] Department of Agricultural Finance and Cooperatives, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Mymensingh, Bangladesh. ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2399958 https://doi.org/10.1080/23322039.2024.2399958 1. Introduction Globally, the floriculture industry has experienced dramatic change within the past few decades regarding productivity and modern technology adoption in flower farming (Mitra et al., 2022). It has become a lucrative sector because of its contribution to employment generation, livelihood development, and reducing inequality and poverty due to its greater potential for export and revenue earnings from comparably lower capital investments (Tizazu & Workie, 2018). Because of the business opportunities it presents, the competitive dynamic is changing, and production is transferring to developing nations (C¸€ ur€ uk & Alptekin, 2022). Bangladesh has had an enormous change in the floriculture sector over the past few years. Farmers have made a revolution in recent years. The floriculture industry has grown by almost 15% per year, generating Tk 1600 crore in annual turnover in Bangladesh. The flower society has claimed that 95% of production can meet domestic demands and the rest, 5% is exported to China and other countries (Sohel, 2022). Almost 3930 hectares of land have been used for flower cultivation in recent years, whereas only 931 hectares of land were used in FY2009-10 (BBS, 2009–2010; BBS, 2020– 2021). Commercial flower farming requires huge capital investment because of the intensive use of modern inputs which are relatively more expensive (Adesina & Zinnah, 1993; Ahmed & Hasan, 2007; Doss & Morris, 2001; Quagrainie et al., 2010). This industry creates bedding, garden plants, potted flowering plants, cut flowers, and floriculture products, which increases the necessity of modern inputs manifold (Mony et al., 2018). Due to a lack of capital, farmers are unable to afford the increasing expenses of farming. Farmers in developing countries often live below the poverty line and their financial condition cannot afford the massive capital requirement (Reardon & Vosti, 1995). Moreover, the productivity of crops depends on timely input use (Mitra et al., 2019; Begum et al., 2023). Therefore, access to credit is imperative for running an effective, successful, and profitable flower farm (Carlisle et al., 2019). Nevertheless, it appears that the current flow of capital is insufficient for flower farming. In recent years, actual credit disbursement has been slightly higher than targeted but not as much as demanded (Figure 1). Institutional credit in Bangladesh is generally provided by government and private banks and nongovernment organizations (Mehedi et al., 2020). Institutional and non-institutional credit is available for the farmers but not to an extent where it can meet the demand (Mitra et al., 2019). Non-institutional credit is provided in very small amounts for arranging social rituals (marriage, funerals, etc.) but it is not intended to provide big loans for flower growers (Chen, 2003; Feder et al., 1990). Therefore, institutional credit is the key source for flower farming but collateral is the precondition for receiving institutional credit. The farmer uses their farmland, farmhouse, and other durable properties as collateral (Tamura & Tabakis, 2013). However, most farmers do not have such types of assets that can be used as collateral, Figure 1. Actual and targeted credit disbursement in agriculture Source: Bangladesh Bank 2004–2021. 2 S. MITRA ET AL. which leads to a credit constraint situation (Feder et al., 1988). According to earlier studies on agriculture, credit constraints have significant negative effects on farm output, technology adoption, farm investment, and farm profit (Antwi et al., 2017; Engle & Kumar, 2011; Ly & Nguyen, 2014). In most cases, credit-constrained farmers are unable to differentiate between flower production expenses and family consumption expenses, which certainly influence their optimal input uses. Consequently, credit-constrained farmers have less productivity than unconstrained ones and have less profitability, efficiency, and savings (Amanullah et al., 2020; Mitra et al., 2019). Low savings lead to low investment in durable assets. The lack of investment in durable properties reduces the collateral of the farm and further increases the credit constraint problem (Fern andez-Villaverde & Krueger, 2011). It is difficult for a constrained farmer to overcome the vicious circle (Mitra et al., 2019). So, in this study, we are going to answer the following questions: Are there any differences in profitability, efficiency, and technological gaps among flower farmers depending on the credit-constrained situation? This study is carried out in two districts of Bangladesh because these districts contribute more than 80% of the total flower production of Bangladesh and lack of credit access is a crucial problem all over the country (BBS, 2020–21; Mitra et al., 2019). However, several studies have examined the profitability and livelihood effects of floriculture in developing countries. Setu (2018) investigated the influence of flower farming on farmers’livelihoods in the Jhenaidah district of Bangladesh. They found that 76.5% of farmers gained medium livelihood improvement through flower cultivation, while 13.9% had a high impact on flower producers’livelihood. Yeung and Yee (2010) investigated the determinants of consumers purchasing preferences at the flower market and found that distinct packaging, healthy products, special price offers, and free sample tastings have the highest influence on purchasing intention. Raha and Siddika (2004) examined the existing marketing system, marketing cost, and margins of different flowers from different marketing channels and found that flower farmers received 30.75 to 60.42% of the consumer’s taka, while 24.71 to 58.5% were spent as the marketing cost. In addition, Singh et al. (2014), Mou (2012), and Momotaz and Banik (2020) focused mainly on the market price and profitability of different flowers. Mitra et al. (2022) investigated the effects of the COVID-19 pandemic on the satisfaction level of flower farmers, market prices, farm income, profitability, efficiency, and technological shifts of flower farmers in Bangladesh. Kumar et al. (2023) identified the economic viability of different techniques of flower drying along with influencing factors like efficiency, economic feasibility, flower waste management, and sustainability. Chowdhury and Khan (2015) and Laboni et al. (2019) studied the export potentiality of different flowers. The above discussions provide significant shreds of evidence that flower farming is highly profitable, which assists in improving the livelihood of flower farmers. However, the effect of credit constraints on the efficiency, technological gap, and profitability of flower farmers remains ignored. This study is going to enrich the literature regarding the financial access of flower farmers as well as its effect on their technical efficiency, technological gap, and profitability. 2. Methodology 2.1 Study area, sample size, and data collection This study is carried out in the Jashore and Savar districts of Bangladesh, which are well-known for their floriculture. Multi-stage sampling procedures were followed for data collection. In the first stage, the data were collected from two flower-producing districts of Bangladesh, namely, Jashore and Dhaka. These districts capture more than 80% of the total flower production in Bangladesh, which is shown in Figure 2. The total population size of flower farmers is about 7000 (BBC, 2021). In the second stage, upazila-level flower production and farm information were collected from the District Agriculture Officer, and two upazilas (Jhikargacha and Savar) were selected from these two districts based on the production volume. Finally, flower farm lists were collected from the Upazila Agriculture Officer from all upazilas, and a total of 160 flower farms (rose and marigold) were selected randomly, of which 59 farmers were credit constraints and 101 were credit-unconstraint. Rose and marigold farmers have captured more than 80% of the flower market in Bangladesh (BBS, 2020–21). Data has been collected using a pretested questionnaire. The interview schedule includes information on farm and off-farm income COGENT ECONOMICS & FINANCE 3 conditions, the input used and output produced, the market price of flowers, problems faced during flower farming, satisfaction level about input-output quantity, price of flowers, and customers’availability. The face-to-face interview method was applied to collect the data. A few fully trained and experienced graduate students were involved in data collection from the flower farmers. 2.2 Analytical techniques 2.2.1 Identifying the technical efficiency and technological gap differences depending on credit constrained situation Meta-frontier Data Envelopment Analysis (DEA) is used to identify the efficiency and production possibility differences depending on credit-constrained situations (Charnes et al., 1994; Cooper et al., 2002). Two separate production functions are formed for credit-constrained and unconstrained flower farms. These frontiers are known as group frontiers to compare the performance across the farms within each group (Asmild, 2015; Jiang & Sharp, 2015; Mitra et al., 2022). An additional production frontier is designed by pooling both constrained and unconstrained farmers. This frontier is formed, known as the meta frontier (MF), to compare performance across the groups. The output-oriented model is employed to identify the efficiency differences. The output-oriented model can be defined as If group h (here h ¼1, …,2) consists of data on L h farms the linear programming (LP) problem solved for the ith farm can be constructed as follows (Bogetoft & Otto, 2010): Figure 2. Study areas selected in Bangladesh. 4 S. MITRA ET AL. MEi¼1=Maxk,uƟ, such that, –ƟyiþYhk0 xi−Xhk0 (1) k0 where, y i and x i are the M 1 and N 1 vector of outputs and inputs of the ith Decision-Making Units (DMUs), respectively; Y h is the M L h matrix of output quantities for all L h units, and X h is the N L h matrix of input quantities for all L h units; kis an L h vector of weights and Ɵis scalar providing the information on technical efficiency (Managerial Efficiency) of the ith farm. In the case of meta-frontier, the above Linear Programming (LP) is re-run with the same input and output matrices containing data for all DMUs from both groups, L ¼PhLh, that is, MTEi¼1=MaxƟ0,k0Ɵ0, such that −Ɵ0yiþYk00, xi−X0k00, (2) k00, where, y i and x i are the M 1 and N 1 vector of output and input quantities, respectively, for the ith unit: Y’and X’are the M L and N L matrix of output and input quantities, respectively, for all L units; k’is an L 1 vector of weights, and Ɵ’is a scalar. In this approach, both managerial efficiency and meta-technical efficiency (TE) scores can be calculated. Managerial efficiency (ME) is assessed by the distance between each DMU and its group frontier, whereas meta-technical efficiencies (MTE) are determined by the distance between each DMU and the MF. The distance between the DMU’s group frontier and the meta-frontier can also be measured as the technology gap ratio (TGR) for each observation. The equation is as follows: TGRh i¼MTEi MEi (3) The lower difference in production possibilities between group and meta-frontier is indicated when the mean TGR is closer to 1. 2.2.2 Determinants of technical efficiency As the efficiency score relative to the meta-frontier varies from 0 to 1, determinants of technical efficiency cannot be estimated efficiently by using ordinary least squares (Alam, 2011; Kaliba & Engle, 2006; Wooldridge, 2012). Hence, the Tobit regression model is better for this analysis (Jehu-Appiah et al., 2014; Prodhan & Khan, 2018). We used the instrumental variable Tobit regression model because the model incorporated endogeneity. Farmers’collateral is employed as an instrumental variable, while credit constraints are an endogenous variable. Since it is anticipated that collateral will have an indirect impact on the dependent variable (efficiency score) and a direct impact on the endogenous variable (Credit constraints), we have used it as an instrumental variable. The empirical instrumental variable (IV) Tobit regression is as follows: Y¼a0þb1X1þb2X2þb3X3þb4X4þb5X5þb6X6þb7X7þb8X8þb9X9þb10X10 þui(4) where Y ¼Efficiency of flower farmers; X1¼Farm area (decimal); X2¼Age (Years); X3¼Training (in days); X4¼Earning family members (numbers); X5¼Education (Years of schooling); X6¼Source of seed (If own ¼1, Buy ¼0); X7¼Land tenure system (Owned ¼1, Cash tenant¼0); X8¼Credit constrained (If constrained ¼1, unconstrained ¼0); X9¼Types of flower (If Marigold ¼1, Rose ¼0); X10¼Off farm income (If yes ¼1, otherwise ¼0) and u i ¼error term. COGENT ECONOMICS & FINANCE 5 2.2.3 Identify the profitability differences between the credit constraint and unconstrained farmers Besides, profitability differences between Marigold and Rose depending on credit-constrained situations have been identified by using gross return, gross margin, Benefit-Cost Ratio (BCR), and break-even point. We have separately analysed the profitability of marigold and rose because they are annual and perennial flowers, respectively. Total variable costs such as labour, seedlings, fertiliser, insecticides, and harvesting costs of rose and marigold farming may vary due to differences in the production system and the effect of seasonal demand. 2.2.4 Ethical standard The ethical standard was maintained during the research and it was approved by the Research Management Committee (RMC) of Bangabandhu Sheikh Mujibur Rahman Agricultural University. Before each interview, the research purpose and the confidentiality of the data were described to each farmer, and then their verbal consent to provide information voluntarily was taken. The questionnaire content and procedure were properly reviewed by the research team. 3. Results and discussion 3.1 Descriptive statistics of socio-demographic characteristics Credit unconstraint farmers are those who can run their farms without any credit or if they are getting the necessary credit. On the other hand, credit-constraint farmers are those who have not received their demanded credit or do not have access to alternative sources of credit (Dong et al., 2012; Feder et al., 1990; Guirkinger & Boucher, 2008). Table 1 demonstrates the descriptive statistics of the socio-demographic characteristics of rose and marigold farmers. The findings indicate that marigold growers are more credit-constrained than rose growers. Marigold farmers have smaller farms than rose producers. This smaller farm may increase the possibility of a credit-constrained situation. On the other side, rose farmers can use their comparatively larger farmland as collateral to obtain the required loan, which makes their credit unconstrained (Mitra et al., 2019). Additionally, 55% of marigold growers farmed on self-owned land, while 75% of rose farmers farmed on their own farms. Thus, 45% of marigold growers farmed in rented inland and those farmers cannot use their own land as collateral for receiving financing. On the other side, rose farmers can utilise their own land as collateral. The average family size of rose farmers is 5.80, slightly higher than the national average family size (Prodhan & Khan, 2018). The experience of rose growers is greater than that of marigold growers, which may aid in their understanding of the credit application and processing procedures that enable them to have access to credit without restriction. Mitra et al. (2019) and Rusiana et al. found a similar result in the case of agriculture farming. Marigold farmers have more off-farm income compared to their counterparts. The result’s most likely explanation is that, due to their higher profitability from rose farming, they are considerably less active in other off-farm businesses. On the other side, marigold farmers are comparatively less focused on their farming because of their lower profitability. Moreover, Nehring and Table 1. Descriptive statistics of socio-demographic characteristics of flower farmers. Socio-demographic characteristics Rose Marigold Mean Standard Deviation Mean Standard Deviation Credit constraint (Constraint ¼1, Unconstraint ¼0) (Percentage of constraint farmer) 0.24 0.43 0.61 0.49 Age (in years) 41.77 13.50 37.59 11.96 Education (years of schooling) 7.41 4.83 7.52 4.62 Marital status (Single ¼1, Married ¼0) (Percentage of single member) 0.16 0.48 0.26 0.45 Family member 5.68 2.41 4.80 1.23 Male member 2.77 1.20 2.39 0.93 Female member 2.84 1.56 2.35 1.04 Earning member 1.70 0.82 1.55 0.71 Experience (in years) 18.12 8.98 14.00 7.80 Training (in days) 1.29 3.60 2.32 5.21 Farm size (in decimal) 67.12 54 51.56 47 Land tenure (owned ¼1, cash tenant ¼0) (Percentage of owner operated farm) 0.75 0.44 0.55 0.50 Source of seed (own ¼1, buy ¼0) (Percentage of own supplied seed) 0.69 0.46 0.70 0.46 Off-farm income (Yes ¼1, No ¼0) (Percentage of positive response) 0.25 0.44 0.50 0.50 6 S. MITRA ET AL. Fernandez-Cornejo (2005) found that the scale and technical efficiency of farming operations are increased by off-farm revenue. 3.2 Technical efficiency of credit constraint and unconstraint farmers Credit is an important determinant for the commercialization of flower production. If flower growers can run their operations without any sort of credit or if they are getting the necessary credit, they are said to have no credit constraints. On the other hand, if farmers are not provided with enough credit or do not have access to alternative sources of credit, they are considered to be credit-constrained (Dong et al., 2012; Feder et al., 1990; Guirkinger & Boucher, 2008). Credit-constrained farmers find it very complicated to use the optimal amount of inputs and produce more flowers. Table 2 demonstrates the TE and TGR of rose and marigold farmers in Bangladesh. The Marigold farmers obtain the highest mean managerial efficiency score (97%) when he is not constrained. It indicates that output could be increased by 3% without increasing input at the regional level. Under a credit-constrained situation, the mean managerial efficiency is 0.77, which implies that there is room to improve efficiency by 23% if compared to the regional (group) frontier. The same is true for growers of roses, however, growers who have no credit constraints have slightly higher efficiency than growers who have credit constraints. Overall, the regional production of flowers can be improved if all farms can get the necessary credit facilities. To find out how different constrained and unconstrained farmers are in terms of their mean technical efficiency compared to the best practice frontier, meta-technical efficiency scores (MTE) are used. Table 2shows that the mean meta-technical efficiency for Marigold farmers is highest when unconstrained (0.73) and lowest when credit is constrained (0.64) relative to the meta-frontier, which indicates output could be increased by 27% and 36%, respectively, without increasing input. On the other side, the efficiency of credit-unconstrained rose farmers is slightly higher than that of constrained rose farmers. Generally, due to credit constraints flower farmers are unable to use the optimal level of inputs, which severely affects the productivity and efficiency of flower farms (Mitra et al., 2019; Zylberberg, 2013). Mitra et al. (2022) found that the meta-technical efficiency of flower farmers was the highest in normal time and the lowest during the COVID-19 pandemic due to the growing input price, reduced input availability, and a less than optimal amount of inputs. Additionally, the meta-technical efficiency of rose farmers is higher than that of marigold farmers. The most probable explanation of the result is that the profitability of rose farmers is higher than marigolds in the study area. Moreover, farmers tend to cultivate more roses, and their average farm size is also significantly greater. Higher farm size and profitability may encourage financial institutions to extend favourable credit terms to the rose farmers. On the other hand, marigold farmers have lower farm sizes and profitability; these situations discourage financial institutions from extending sufficient credit facilities to marigold farmers. In addition, the off-farm income of marigold farmers is higher than that of rose farmers, which indicates the focus of marigold farmers is slightly diverted from marigold farming, which leads to inefficiency in farming. Table 2 shows the mean technological gap ratio of flower farms in two different scenarios, including a credit constraint situation and an unconstrained situation. 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