Impact of COVID-19 on the finance sources of women in the agricultural sector: the case of Kosovo

Shkodra, Jehona; Bajrami, Verlinda Article Impact of COVID-19 on the finance sources of women in the agricultural sector: the case of Kosovo Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Shkodra, Jehona; Bajrami, Verlinda (2022) : Impact of COVID-19 on the finance sources of women in the agricultural sector: the case of Kosovo, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-10, https://doi.org/10.1080/23322039.2022.2085294 This Version is available at: https://hdl.handle.net/10419/303675 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 Impact of COVID-19 on the finance sources of women in the agricultural sector: the case of Kosovo Jehona Shkodra & Verlinda Bajrami To cite this article: Jehona Shkodra & Verlinda Bajrami (2022) Impact of COVID-19 on the finance sources of women in the agricultural sector: the case of Kosovo, Cogent Economics & Finance, 10:1, 2085294, DOI: 10.1080/23322039.2022.2085294 To link to this article: https://doi.org/10.1080/23322039.2022.2085294 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 09 Jun 2022. Submit your article to this journal Article views: 1164 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Impact of COVID-19 on the finance sources of women in the agricultural sector: the case of Kosovo Jehona Shkodra 1 * and Verlinda Bajrami 1 Abstract: The aim of this study is to analyse the level and determinants of women farmers’ access to financial resources in agriculture in Kosovo. The study describes the socio-economic characteristics of women farmers and identifies the socio-economic factors that influence access to financial resources. Primary data were collected from one hundred forty-six (146) women farmers who had access to a source of finance using multistage random sampling. Data were analysed using descriptive statistics, Likert scores, and probit regression estimates. 61.94% of the women had a farm size between 0.2 and 1.0 hectares and had an average farming experience of 8.33 years. The majority, 40.48% of the respondents, had formal education. They had a relatively high number of sources of finance through “family savings” and did not have access to finance through “borrowing from family members”. The probit regression estimation showed that education level, gross annual income, and net worth each exerted a significant positive ABOUT THE AUTHORS Jehona Shkodra is an associate professor at the University of Prishtina, Faculty of Agriculture and Veterinary, Department of Agricultural Economics. She teaches Agricultural Finance, Accounting in Agriculture, Banking and Credit System, Introduction to Economics. Her research interests include agricultural finance, agricultural accounting standards and financial institutions. Her studies have been published in Journal of International Studies, Economics & Sociology, Bulgarian Journal of Agricultural Science, Banks and Bank Systems, Society and Economy, etc. She has participated in some congresses, symposiums and international conferences in: Finland, Istanbul, Malta, Prague, Istanbul, Prishtina, Ljubljana, Thessaloniki, Washington D. C., Zagreb, etc. Is a member of the European Finance Association (EFA), a member of the American Finance Association (AFA) and a member of the councils of the Faculty of Agriculture and Veterinary. Verlinda Bajrami holds an MSc degree in Agricultural Economics at University of Prishtina, Faculty of Agriculture and Veterinary. Some of her areas of interest focused on conducting research to assess rural development and sources of funding for agriculture. PUBLIC INTEREST STATEMENT Securing funding for agriculture is a challenge for both men and women, but women face greater challenges. These challenges are related to women’s role in the family, which often limits the provision of finance and affects their productivity. Women have poorer access to financial services, according to our research, which shows that the main source of finance for them is their savings. Farming experience, education and low equity are also very important indicators of finance. The less these farmers have, the less likely they are to have access to credit. This means that the responsible institutions in the country should develop policies and programs for women farmers with low education, low income and low equity to enable this category to develop their farms. Shkodra & Bajrami, Cogent Economics & Finance (2022), 10: 2085294 https://doi.org/10.1080/23322039.2022.2085294 Page 1 of 10 Received: 12 February 2022 Accepted: 30 May 2022 *Corresponding author: Faculty of Agriculture and Veterinary, Department of Agro-economy, University of Prishtina “Hasan Prishtina”, Pristina, Republic of Kosovo E-mail: [email protected] Reviewing editor: David McMillan, University of Stirling, Stirling, UK 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. influence on the probability. This implies that the relevant institutions in the country should develop policies and programmes for women farmers with low levels of education, income, and net worth. Subjects: Development Studies; Economics; Finance Keywords: Funding sources; women farmers; enterprises; farms JEL classification: E4; E41; E51; F36; G2 1. Introduction Financial support is a key factor for the success of a business, especially for businesses run by women, as most of them do not have their own income and inherit their families’ property and assets. As the agricultural sector is considered one of the most important sectors of the country’s economy, it is very important that this sector and especially women farmers receive adequate financial support. The Republic of Kosovo is suffering from the effects of the closure of COVID −19 and the associated loss of jobs and income (Friedrich Ebert Stiftung, 2020), including income from agricultural products, which is an important financial lifeline for rural households. Agricultural productivity and maximising returns from agricultural products have become critical in the pandemic. Women often have less access to productive resources such as credit and technology, less secure access to land, less savings than men, and less networking opportunities and decisionmaking power (WIEGO, 2020). In this context, sources of finance in agriculture play an important role in survival, or according to Hoang et al. (2022), the financial system plays a central role in promoting a country’s economic growth. Financial support for agriculture is a major challenge for both male and female farmers. A wellfunctioning financial system is an important factor in efficiently channelling funds into the economy to ensure economic development (Shkodra, 2019). These challenges include: high cost of client services in rural areas with low population density, systemic risks in agricultural production, lack of information to assess credit risk for smallholder farmers, lack of farmer organisations, gender bias in institutions and societies, etc. Although both genders face these challenges, women face some particular challenges. These challenges relate to women’s role in the household, which often limits their control over assets and limits their time for productive activities. Their role in the family is often invisible, especially when it comes to their economic and financial contribution. As a result, women have less access to entrepreneurial services. Women are also limited in the diversity of business financing. Regardless of the industry or size of the business, women have only 33 percent of the financial resources available to their male counterparts when they start a business (Riinvest., 2017). Moreover, women entrepreneurs who apply for a loan are significantly less likely to receive one in the year they start their business than their male counterparts in the same industry (De Andrés et al., 2021). Moreover, women use fewer forms of financing. Studies in several countries report that women who start and run businesses mainly use informal forms of financing, such as their own or family savings, household income, inheritance capital and loans. This raises some important questions: What were the socio-economic characteristics of women farmers? From what sources did women in the agricultural sector finance themselves during COVID −19? What socioeconomic factors influence women borrowers’ access to finance in the study area? How did this affect agricultural productivity? We investigate these questions by interviewing 146 women farmers from the municipalities of: Mitrovica, Vushtrri and Skënderaj. Shkodra & Bajrami, Cogent Economics & Finance (2022), 10: 2085294 https://doi.org/10.1080/23322039.2022.2085294 Page 2 of 10 The results of this study can help formulate policies that address the needs of women farmers and ensure their livelihoods in crisis situations, especially in the Republic of Kosovo. 2. Literature review Rural entrepreneurship is an important factor for economic growth and poverty reduction (Bruton et al., 2013; Dwumfour Osei et al., 2020; Naminse & Zhuang, 2018; Naminse et al., 2019; Nik Hussin & Aziz, 2021; Shkodra & Shkodra, 2018; Shkodra et al., 2021) in rural and urban areas. According to Nyoro (2002), lack of working capital and low liquidity limit farmers’ ability to purchase productivity-enhancing inputs such as seeds, fertilisers and pesticides. However, the key factor for the development of these enterprises is financing. From the literature review, it is evident that there are many researches and studies that address the issue of enterprise finance and women-owned enterprises: (Amzad 2009; Alam et al., 2011; Asuming et al., 2018; Constantinidis et al., 2006; Ghosh & Vinod, 2017; Parvin et al., 2012; Saravanabavan et al., 2021; Shah & Mustafa, 2014; Zins & Weill, 2016). This is despite the fact that women play a significant role in agriculture. This is despite the fact that women play a significant role in agriculture. Chowdhury et al. (2018) also studied the financing of women-owned businesses. The study was conducted with women entrepreneurs operating in Bangladesh. The study found that the majority of women-led businesses are micro and small enterprises, which account for nearly 90.8% of the total number of women-led businesses. This is due to the fact that women entrepreneurs have too little capital, which means that they cannot start large businesses but only micro-enterprises. The problem of financing for women entrepreneurs has been addressed by authors Marina Solesvik et al. (2018) in their research. This paper examines women’s entrepreneurship in two post-Soviet countries, Russia and Ukraine. Using an institutional theory, the research aims to examine the entrepreneurial environment, in particular government support programmes and the availability of financial resources, with a particular focus on women entrepreneurs. 3. Methodology To conduct this research, 146 women farmers were interviewed in the Republic of Kosovo, specifically in the municipalities of: Mitrovica, Vushtrri and Skënderaj, were interviewed between April and May 2021. The women farmers were randomly selected from the list of the Farmers’ Register of the Ministry of Agriculture, Forestry and Rural Development. Empirical methods were used to conduct this research. We used primary sources of qualitative and quantitative data (questionnaires) and secondary sources through literature review. Primary data collected include socio-economic variables of women farmers such as: Age, education level, farming experience, farm size, sources of finance, interest rate, grace period, equity and gross annual income. The study was conducted on the sources of finance for women farmers in Kosovo by (i) describing the socio-economic characteristics of women farmers, (ii) identifying women farmers’ access to finance in the study area, and (iii) determining the socio-economic factors that influence women borrowers’ access to finance in the study area. To achieve the objective of this study, descriptive statistics such as mean, frequency distribution tables and percentages were used while Likert scale and probit multiple regression model were employed. The mean value for accessibility X¼∑Fx=N The mean of each item was calculated by multiplying the frequency of positive responses by the corresponding Likert value and dividing the sum by the sum of the number of respondents to the items. This was summarised with the following equation: Shkodra & Bajrami, Cogent Economics & Finance (2022), 10: 2085294 https://doi.org/10.1080/23322039.2022.2085294 Page 3 of 10 X¼∑Fnl=N: Whereby: X = mean score; Σ = summation; F = Frequency or number of respondents who answered positively; nl = Nominal Likert score; N = Number of respondents. The multiple regression model was considered appropriate because access as the dependent variable (Y) takes only one of two possible values (access or no access), i.e. the probability condition 1 or 0. The formula used for the analysis is: Pi y ¼1½ � ¼ FZi½ � (1) Where: Zi ¼β0þβ1X1þμ Yi ¼β1þβ2X2iþ. . . . . . . . . þβkXki þμ(2) Yi * is unobserved, but Yi = 0 if yi* < 0,1 if Yi* ≥ 0 P Yi ¼1ð Þ ¼ P Yi � � 0ð Þ Pðμi�  β1þβ2X2i. . . . . . :. . . :. . . :. . . . . . :βkXki (3) Where: i = 1, 2 . . . 146. Yi = Women farmers who had access to financial resources (dichotomized with mean Likertnominal value; where ≥ 3.0 = access = 1; < 3.0 = no access = 0) β1 = Unknown coefficient value of factors; X1 = Age (years); X2 = level of education (years); X3 = farming experience (years); X4 = farm size; Shkodra & Bajrami, Cogent Economics & Finance (2022), 10: 2085294 https://doi.org/10.1080/23322039.2022.2085294 Page 4 of 10 X5 = interest rate (percentage); X6 = grace period (1 = adequate, 0 = inadequate); X7 = equity (1 = increased, 0 = not increased); X8 = gross annual income (€); μ = error term 4. Results and discussions The socio-economic characteristics of women farmers during COVID −19 are shown in Table 1. The data show that 61% of the women farmers cultivated between 0.2 and 1.0 hectare of land. Overall, 87% of the women farmers farmed less than 2.1 hectares of agricultural land, with an average farm size of 0.7 hectares. This shows the limited opportunities for rural women farmers, which is due to the lower yield leading to the lowest profits from the activity. The data in Table 1 also shows that 52.06% of the women farmers in the study had farming experience between 1 and 10 years. The average year of farming experience was 8.33 years. This shows that most of the women farmers had little farming experience. The less experience the women Table 1. General data of women farmers in the Mitrovica region Variables Frequence Percentage Farm size (hectares) 0.2–1.0 ha 1.1–2.0 ha 2.1–3.0 ha Over 3.0 ha 89 38 18 1 61% 26% 12.32% 0.68% Min = 0.2 Max = 24 Average = 0.7 Experience (years) 0–10 years 11–20 years Over 20 years 76 33 37 52.06% 22.60% 25.34% Min = 1 vit Max = 41 vite Average = 8.33 vite Age 18–40 years old 41–60 years old Over 60 88 42 16 60.27% 28.77% 10.96% Min = 19 Max = 96 Average = 40.2 Level of education (years) 0–4 years 5–8 years 9–12 years Over 12 years 13 46 43 44 8.9% 31.51% 29.45% 30.14% Min = 0 Max = 17 Average = 11 Number of family members 1–5 members 6–10 members over 10 members 58 80 8 39.73% 54.8% 5.47% Min = 3 Max = 15 Average = 6.3 Source: Calculation by the author Shkodra & Bajrami, Cogent Economics & Finance (2022), 10: 2085294 https://doi.org/10.1080/23322039.2022.2085294 Page 5 of 10 farmers have, the less chance they have of obtaining credit. The experience indicator is also due to the age indicator of the women farmers which shows that 60.26% of the women farmers belonged to the age group of 18–40 years. As far as the level of education is concerned, 31.51% of the women farmers had only primary education but a similar number (30.14%) were women farmers with higher education. The average number of members in the families of women farmers was 6.3 members. Table 2 shows the assessment of the extent of financial resources of women farmers divided into four sources (family savings, grants and subsidies, loans and credits from households). We have classified the extent of these resources into five categories, starting with 5 for a very high level of financing and ending with category 1 for a very low level of financing. The data in Table 2 shows that 50.4% of women farmers used money from “family savings” as a source of finance during the period COVID −19. However, 25.05%, 17.20% and 5.5% of women who used family savings as a source of finance during the period COVID −19 were classified as medium, low and very low respectively. Only 1.84% of women who had “family savings” as a source of finance fell into the “very high” category. We see that family savings has a nominal value of 651 and an average value of 4.46, which means that the women farmers in the study had “family savings” as their main source of finance. In terms of “grants and subsidies” as a source of funding, we see that 36.82% of women farmers have “grants and subsidies” as their average source of funding and only 11.24% have no source of funding through “grants and subsidies”. The funding source “grants and subsidies” had a total target score of 651 with an average score of 3.53. This percentage is higher than the average score of 3.0, showing that women farmers are supported by “grants and subsidies”, which ranks second among funding sources for women farmers. The data in the credit category shows that 40.36% of women farmers have poor access to credit to finance themselves. Only 13% of the women farmers had good access to credit. Loan as a source of finance had a nominal value of 446 and a mean value of 3.05, indicating that women farmers in the study had access to finance from financial institutions. On the other hand, “borrowing from family members” is negated as a source of finance during COVID −19 for women farmers as 44.81% of the women farmers had no source of finance through borrowing from family members. The nominal value of this category is 395 with a mean of 2.7, Table 2. Assessment of the extent of funding sources of women farmers Types of funding Very high (5) High (4) Average (3) Low (2) Very low (1) Total The average Family savings 12 1.84% 328 50.4% 163 25.05% 112 17.20% 36 5.5% 651 4.46 Grants - Subsidies 8 1.55% 140 27.13% 190 36.82% 120 23.25% 58 11.24% 516 3.53 Loans 38 8.52% 58 13% 112 25.11% 180 40.36% 58 13% 446 3.05 Borrowing from family members 18 4.56% 63 15.94% 85 21.51% 52 13.16% 177 44.81% 395 2.7 Source: Calculation by the author. Rule 3.0 and above = Access to finance, Rule ˂ 3.0 = No access to finance Shkodra & Bajrami, Cogent Economics & Finance (2022), 10: 2085294 https://doi.org/10.1080/23322039.2022.2085294 Page 6 of 10 which means that this value is below the threshold of 3.0. This shows that the women farmers included in the study have no or limited access to this source of finance. Table 3 shows the binary probit regression estimates for the factors influencing women farmers’ access to sources of finance in the Mitrovica region. Table 3 shows the probit regression used to calculate the factors influencing the sources of funding for women farmers in the Mitrovica region in the period COVID −19. From the statistical model, the log likelihood has the value −105.2278, Chi2 has the value 28.06 and R2 has the value 0.1213. The overall accuracy of the model data is 73.3%. Evaluation of the factors that influenced the sources of finance of women farmers during COVID −19 shows that the coefficients of education level (0.0634), gross annual income (5.31e-02) and equity (0.2121) have significant positive effects on the values of 1.0%, 5.0% and 10.0% probability respectively. The coefficient of the level of education shows a positive significance for the financial sources of women farmers. This shows that women farmers with higher levels of education have better access to sources of finance in the form of grants and loans. This shows that lenders give preference to educated women farmers as they have greater potential to repay the loan. The proportion of gross annual income is statistically significant and has a positive impact on women farmers’ access to sources of finance in the period COVID −19. This implies that women farmers with higher gross annual income have greater potential to access finance, both in the form of loans and grants and subsidies. Net worth is also statistically significant and has a positive effect on access to loans, grants and subsidies. Other factors such as age (−0.1362), farm size (−0.1337) and farming experience (−0.1244) affect access to loans, grants and subsidies. The negative sign of these coefficients indicates that access to loans, grants and subsidies decreases with increasing age of women farmers, farm size and Table 3. Probit estimates of the binary regression of factors influencing women farmers’ access to sources of finance Variables Estimated coefficients Standard error Z raports P> IzI Constant 0.0695 0.4422 0.11 0.442 Age −0.1362* 0.0087 −1.60 0.109 Level of education 0.0634*** 0.0183 2.91 0.004 Experience in agriculture −0.1244* 0.0143 2.41 0.002 Farm size −0.1337 ** 0.7220 −2.12 0.015 Interest 0.1419 0.0540 0.63 0.009 Grace period 0.1419 0.0540 0.63 0.009 Equity 0.2121* 0.2131 1.50 0.311 Gross annual income 5.31e-02** 4.23e-04 2.10 0.020 Log likelihood −105.2278 Chi 2 28.06 R 2 0.1213 Prediction accuracy (%) 73.3 Source: Calculation by the author. ***, **, * Significant at 1.0%, 5.0% and 10.0%. Shkodra & Bajrami, Cogent Economics & Finance (2022), 10: 2085294 https://doi.org/10.1080/23322039.2022.2085294 Page 7 of 10