Profitability of Agricultural Micro and Small-Scale Enterprise in North Wollo Zone,Amhara Regional State,Ethiopia

Abstract

The contribution of micro and small enterprises is limited as the majority are financially constrained and half of them exit from business in 2014/15, Amhara Region. Therefore, this study aimed to analyze the profitability of agricultural micro and small-scale enterprises in North Wollo Zone, Amhara Regional State, Ethiopia. Primary data was collected from 271 sample enterprises. The study employed descriptive and econometrics models for the data analysis. The financial ratio result shows that the return on asset, return on owner equity and net profit margin were 0.1601, 0.2768, and 0.1520 birr, respectively. The result of the probit model estimation shows that six variables, namely enterprise age, manager education level, credit use, input availability, owners’ aspiration, and frequency of extension contact significantly and positively influenced the probability of Micro and Small Enterprises (MSEs) being profitable. The second hurdle model, the truncation model showed that enterprise age, manager education level, record keeping, access to input, and frequency of extension contact significantly affected the extent of agricultural MSEs’ profitability. Therefore, enhancing the knowledge, skill, and aspirations of enterprise owners, improving financial access and outreach, providing financial bookkeeping training and practice, creating reliable integration with input producers, and frequent extension support to enhance the profitability and sustainability of enterprises.

Keywords

enterprise micro-enterprise performance profitability small enterprise

Introduction

Small and Medium Enterprises (SMEs) contribute more than 50% of most African GDP (Muiruri, 2017 ) and 80% of jobs across the continent (Runde et al., 2021). Even though the sector is thought to be marginal and unproductive, its importance in terms of job creation and income is massive (GB news, 2020). It plays a key role in improving nutrition not only by bringing nutritious foods to markets but also through job creation and income generation (FAO, 2018). Agricultural SMEs across sub-Saharan Africa (SSA) bring food to households (CSIS, 2021). Transformational leadership with important knowledge of agribusiness sustainability improves the performance of the sector (Jankelová et al., 2020). The establishment of effective government policies and support in Ethiopia helps youths to engage in productive employment and could discard the stigma of unemployment (Melese, 2018).

The integration of classical firm growth literature into the inclusive finance—enterprise growth discourse improved insight into the trajectories of small enterprise growth in SSA (Eton et al., 2021; Kuada, 2021). Micro Finance Institutions (MFIs) help Micro and Small Enterprises (MSEs) to increase profits, total asset and employment through the expansion and diversification of their operation (Semegn & Bishnoi, 2021). The financing structure affects the profitability of micro and small enterprises. Thus, enterprises financed through retained earnings and personal savings increase their profitability while debt capital significantly reduces profitability (Achola, 2021). Access to credits from MFIs enhances profitability while interest rate and size of business hinder the profitability of SMEs. The positive contributions of microfinance banks via improved access to credit, managerial training, advisory service and growth in the size of business lead to SMEs’ profitability (Babarinde et al., 2019). In addition, informal microfinance sources significantly boost the profitability of enterprise clusters which suggests that informal credit sources support MSEs much more than formal sources (Amakom & Amagwu, 2020).

The financial management practice improves the profitability and growth of SME firms (Musah et al., 2018). The ability of the firm to operate for a longer time depends on a proper tradeoff between the management of investment in long-term and short-term funds (Dinku, 2013). The efficiency of working capital management and internal control (Basir et al., 2021; Benard & Ainomugisha, 2019) like shortening of the cash conversion cycle significantly contributes to the profitability of MSEs (Dinku, 2013). The firms maintain the cash holding level up to a maximum profitability or growth where beyond these levels adding cash would hurt sales growth and profitability (Prijadi & Desiana, 2017). In addition, good financial behaviour such as budgeting, debt management, savings, record keeping and retirement planning contributes to the profitability of MSEs (Ibrahim, 2017).

In a country like Ethiopia with a fast-growing population, proper management and efficient utilization of its workforce is a critical concern (CSA, 2018). Micro and small enterprise has generated a large share of economic growth and employment in Ethiopia. It has a great contribution in reducing unemployment and providing income to those owners and employees of MSEs (Mahmud et al., 2020), and improving the livelihood conditions of target beneficiaries (Bekele, 2017). During 2019/20 alone, 111,547 new MSEs employed about 1.6 million people and received more than Birr 7.7 billion in loans for their operations (NBE, 2020). The annual progress report of GTPII in 2012/13 indicates that over the first 3 years of GTPI implementation, 3.96 million jobs were created over the 3 years (Dom & Vaughan, 2018).

Statement of the Problem

The long-term sustainability of Ethiopian SMEs is substantially hindered by political instability, corruption, and COVID-19 (Abdissa et al., 2022). Ethiopian political uncertainty has a negative and considerable influence on the survival of SMEs (Abdissa et al., 2022). Access to finance remains the leading barrier to the development of MSEs in Ethiopia due to the existence of inadequate loan size, borrowing cost and collateral requirement. In addition, poor infrastructures leads to high worktime loss, reduce productivity, and increased cost of enterprise production (Endris & Kassegn, 2022). Financial constraints reduced the start-up of new businesses, which leads youths to face high cost of financial services (Eton et al., 2021) and struggle with unfair competition and corruptive actions (Mehari & Belay, 2017). Lending to agricultural SMEs is twice as risky as lending to other sectors, and operates at a scale that is too small and yields lower returns, which is the greatest challenge for agricultural SMEs (CSIS, 2021)

The adverse attitude toward MSEs is the main challenge due to lack of knowledge on the potential of MSEs and a preference for paid employment (FMSEDA, 2011). Most graduates of vocational agriculture are less likely to establish a farm/enterprise of their own and the majority want to obtain certificates required for securing employment in paying jobs (M. Francis et al., 2019). According to (Central Statistical Agency (CSA), 2018), 53.8% of unemployed desire to work constrained by the shortage of capital and the other accounting 10.9% did not work due to lack of working place. The politicization of entrepreneurship; weak institutional systems, weak business development services, poor infrastructure, and youth negligence are critical barriers to youth entrepreneurship programs in Ethiopia (Ahmed & Ahmed, 2021; Kebede, 2022). Moreover, Wolday (2015) found that limited access to finance, lack of production and marketing premises, and inadequate market development are the main challenges in expanding and establishing MSEs in Ethiopia.

Firms with a high level of profitability and a low level of growth have a greater chance of subsequently achieving high growth and high profitability than a firm with a high level of growth and a low level of profitability (Rivard, 2014). With this, profitability has a significant effect on the growth of SMEs (Raharja & Kostini, 2019) and lack of enterprise profit appears to be a binding constraint to their growth (Shitaye & Elgammal, 2022). Most Micro, Small and Medium Enterprises (MSMEs) are not profitable and poor utilization of assets, poor cash management practices and difficulty to produce equity returns are the major problems (Mayanja, 2020; Pandey, 2020). MSEs profitability is decreasing as they failed to apply financial statement analysis, made unplanned withdrawals of money for personal use, manage their working capital poorly and faced shortage of finance (Abera et al., 2020).

Micro and small manufacturing enterprises accounting for 60.5% are financially constrained in the Amhara region (Melesse, 2019), and half of them dropout from the business in 2014/15 (Zegeye et al., 2016). Urban agriculture MSEs have registered the lowest mean efficiency (B. A. Abebe & Zemenu, 2021; Ayele, 2021) and created the least employment (10.5%) in Amhara region, while other sectors independently contribute beyond 20% of MSEs employment (EMUDH, 2016). In 2018/19, agricultural MSEs accounted for the lowest number of enterprises (9.12%) in the study area (NWZVEDDO, 2019). Even though agricultural MSEs have played an enormous role in supplying foods and raw materials to agro-processing industries (Daniel & Getaneh, 2016), their contribution to employment and the economy is limited. Thus producing questions on the profitability and growth potential of agricultural MSEs. Empirical evidence revealed that the profitability of MSEs is key to their growth and sustainability (Raharja & Kostini, 2019; Rivard, 2014; Shitaye & Elgammal, 2022). Nevertheless, there are no adequate studies conducted on the profitability of MSEs, particularly in the agriculture sector. Most empirical evidence (Fufa, 2015; Molla, 2016) mainly focused on investigating MSEs’ performance using capital growth and employment size. As a result, this study intends to fill the existing research gap through investigating the profitability of agricultural MSEs in North Wollo Zone, Amhara Regional State, Ethiopia. Understanding the profitability of enterprises helps to evaluate the growth and sustainability of the existing enterprises, and boosts youth aspiration to startup of new ventures. The findings of this study have significant ramifications for scientific knowledge, the academic community, researchers and policy-makers to develop effective policies.

The objective of the study is to analyze the profitability of agricultural micro and small-scale enterprise in North Wollo Zone, Amhara Regional State of Ethiopia.

The study aimed to provide evidence for the following research questions

(1) What are the determinants of agricultural MSEs profitability in the study area?

(2) Does the profitability ratio of the enterprise indicate MSEs’ financial health?

Basic Definitions of Terms Used in the Study

Small and medium enterprises are defined according to size (number of employees), turnover, activity, ownership and legal status (Hussain, 2000). In Ethiopia, FDRE (2016; FMSEDA (2011) define micro and small enterprises.

Enterprise: A system of carrying on a business (Collin, 2006) or a synonym for a business (Statt, 2004).

Micro Enterprises: Enterprises employing up to five persons including enterprise owners and family members, with total assets of not more than ETB 100,000 for the industrial sector (including manufacturing, construction, urban agriculture, and mining) and a total assets of not more than ETB 50,000 for the service sector (retail trade, transport, hotel, tourism, and information technology and maintenance services).

Small Enterprise: Enterprises employing 6 to 30 persons and with a total assets of from ETB 100,001 up to ETB 1,500,000 for the industrial sector and with a total assets of at least ETB 50,001 and up to ETB 500,000 for the service sector.

Profitability: Profitability can be measured through enterprise capital stock from two perspectives. Firstly, from the capital owners’ point of view, the proper measurement of profitability is profitability of equity (ROE). Secondly, since enterprises can mobilize more resources to generate income and profit through borrowing money, instead of equity, we may use profitability of assets (ROA) to measure profitability (Lu et al., 2008).

Research Methodology

Description of Study Area

This research was carried out in Ethiopia’s North Wollo Zone (Figure 1). The capital city of the zone is located 521 km north of Addis Ababa, the country’s capital. The zone is divided into 14 districts and 5 town administrations and covers a total area of 12,172.5 km². The study area is bounded on the south by the South Wollo Zone, on the west by the South Gonder Zone, on the north by the Wag Hemra Zone, on the northeast by the Tigray Region, and on the east by the Mille River. In 2017, the zone’s total population is expected to be 1,824,361, with 913,572 men and 910,789 women. There are 270,686 of these people live in cities, and 1,553,674 live in rural areas (CSA, 2013).

Figure 1.

Study area map.

Types, Sources and Methods of Data Collection

In this study, both qualitative and quantitative data were collected from primary and secondary sources. Primary data was collected from selected agricultural MSEs using semi-structured questionnaires. The study collected relevant information about the enterprise from managers of the enterprise who represent the enterprise as a business entity. The questionnaire was pretested by experts and enterprises outside the study area and necessary modification has been made for final data collection. Finally, the questionnaire designed for the enterprise was translated into the regional language Amharic to make it clear and collect real data from respondents. Furthermore, secondary data was collected from enterprises’ annual financial statements, reports, and business plans and different published and unpublished sources, such as North Wollo Zone enterprise directive office, woreda enterprise development office, NBE, CSA, EEA, reports, and bulletins.

Sampling Procedure and Sample Size

This study solely focused on agricultural MSEs due to the number of enterprises in the sector and the employment opportunity created with them are limited. The desired sample size was selected proportionally from different agricultural MSEs (dairy MSEs, animal production and fattening MSEs, poultry MSEs, fruit and vegetable production MSEs) using a simple random sampling technique. Enterprises established during the survey year were excluded from the sample as those enterprises are startups and may not produce output. The enterprises were stratified into four subsectors and the sample of each subsector was determined by proportional sampling methods, as provided in Table 1.

Table 1.

Sample Size Distribution.

Sector	Total population	Sample
Dairy MSEs	118	38
Animal production and fattening MSEs	450	147
Poultry MSEs	108	35
Fruit and vegetable production MSEs	155	51
Total	831	271

The researchers used (Yamane, 1967) sample determination formula to determine the desired number of samples from the total population

n = \frac{N}{1 + N {(e)}^{2}}

(1)

n = \frac{831}{1 + 831 {(0.05)}^{2}} = 271

Where, n = sample size, N = the total number of agricultural MSEs in North Wollo Zone, and e is the level of precision (i.e., 5%.). Accordingly, out of 831 agricultural MSEs registered in North Wollo Zone, 271 samples were selected.

Methods of Data Analysis

Business performance evaluation methods can be grouped into two categories: traditional methods that are justified only by the analysis of financial indicators and modern ones that combine the company’s financial and non-financial performance information that enables the evaluation of its activity both quantitatively and qualitatively (Narkunienė et al., 2018). Two different indexes of profitability are operating profit ratio and return on total assets (Cozza et al., 2012). Profit and job creation are fundamental outcomes for measuring entrepreneurial performance, especially in the context of developing countries. While profit captures the main monetary outcome of business performance, employment creation is all more socially valuable, in particular, when job opportunities are offered to external workers and not only to family members (OECD, 2017). Financial performance measures such as profitability, liquidity, and solvency ratios are expressed in monetary terms to ensure the business’s financial health and sustainability. Profitability ratios are viewed as a way to identify and measure the ability MSEs to generate a profit (Scarborough, 2012; Warren et al., 2013). Profitability is simply the capacity to make a profit, and a business needs to make a profit to provide a return to the investors and to grow the business. Hence, this study used Return on Assets (ROA), Return on Equity (ROE), and Net Profit Margin (NPM) ratios to examine the profitability performance of MSEs in the study area.

The econometrics model used to analyze the determinants of agricultural MSEs profitability depends on whether the dependent variables are dummy, continuous, or censored at a certain level. Ordinary Least Squares (OLS) is applicable when all enterprises are profitable (have positive returns on assets). However, in reality, SMEs may incur a loss. The double-hurdle model is a more flexible alternative than Tobit and Hickman models assuming a two-step decision is independent. Unlike in the Tobit model, there are no restrictions regarding the elements of explanatory variables in each stage of the double hurdle model. The model estimation involves a probit regression and the truncated regression model to identify factors affecting profitability and the extent of profitability, respectively. The two decisions also have been modelled as sequential, but most studies treat the decisions as separate (Cramer et al., 1995).

A double hurdle model was used to estimate the probability and intensity of agricultural MSEs’ profitability. The dependent variable profitability is measured by return on asset (ROA), which is a limited dependent variable, that is, some observations do not have positive returns on the asset during the survey year. As the likelihood of enterprises’ profitability and extent of profitability are not necessarily made jointly and no selection bias, the Double-hurdle model was chosen over Tobit and Heckman model. The model postulates that households must pass two separate hurdles before they are observed with a positive return on assets (profitable). The first hurdle (probit model) is whether to achieve a positive return on an asset or not, and the second hurdle (truncated regression model) is deciding the level of profitability conditional on the probability of being profitable. The double-hurdle model can be specified as follows:

The first hurdle is the probability of being a profitable equation with a probit model. The model is specified as follows:

Y_{i}^{*} = {X_{i}}^{'} α + ε_{i} ε_{i} ~ N (0, δ^{2})

Y_{i} = {\begin{matrix} 1 if Y_{i}^{*} > 0 \\ 0 if Y_{i}^{*} \leq 0 \end{matrix}}

When $Y_{i}$ is the observed return on assets, and $Y_{i}^{*}$ denotes the latent return on asset. The latent dependent variable that takes the value 1 if MSEs have a positive return on the asset during the survey year, 0 otherwise,

X is a vector of enterprise characteristics and $α$ is a vector of parameters.

In the second hurdle, the truncated regression model was used to analyze factors affecting the level (extent) of MSEs’ profitability. Truncated regression excludes part of the sample observation based on the value of the dependent variable (Wooldridge, 2010). That is, the truncated regression uses observations of enterprises that have a positive return on asset. The level of enterprise profitability is modeled in a truncated regression at zero as follows:

Z_{i}^{*} = {X_{i}}^{'} β + μ_{i} μ_{i} ~ N (0, δ^{2})

Z_{i} = {\begin{matrix} Z_{i}^{*} if Z_{i}^{*} > 0 and Y_{i} = 1 \\ 0 Otherwise \end{matrix}}

Where $Z_{i}$ is the extent of MSEs profitability, which depends on latent variable $Z_{i}^{*}$ being greater than zero and conditional on the probability of profitability $Y_{i}$ . The error terms in the probit and truncated regression models are assumed to be independently and normally distributed.

The log-likelihood functions as the double-hurdle model that nests a univariate probit model and a truncated regression model estimated by(Cragg, 1971):

\begin{matrix} L L_{DoubleHurdle} = \sum_{0} \ln ⌈ 1 - φ (X_{i}^{'} α) Φ (\frac{X_{i}^{'} β}{σ_{i}}) ⌉ \\ + \sum_{+} \ln ⌈ Φ (X_{i}^{'} α) \frac{1}{σ_{i}} ϕ \frac{Y_{i} - X_{i}^{'} β}{σ_{i}} ⌉ \end{matrix}

Where, Ф and φ refer to the standard normal probability and density functions, respectively, Xi′ represent independent variables for the Probit model and the Truncated model, α′ and β′ are the estimated coefficients of the explanatory variables for the probit and the truncated regression models, respectively.

Definitions and Hypotheses of Variables

Dependent Variables

Profitability (ROA): Return on asset ratio is used to measure the profitability of agricultural MSEs during the survey year. The ability to earn profits depends on the assets the company has available for use in its operations, without considering how the assets are financed (Warren et al., 2013).

Independent Variables

The independent variables are those factors affecting the profitability of agricultural SMEs.

Age of the enterprise: The age of enterprises is measured by the number of years over which agricultural MSEs exist in the business until the data was collected. The profitability of enterprises is strongly influenced by their lifecycle stage and the growing process is also determined by the age of the enterprise (Illés et al., 2015). According to Abraham (2018), a long-existing business improves enterprise’s financial performance. Enterprises, which stay for a long period, are expected to achieve economies of scale and generate high returns on the asset. Therefore, this variable is expected to have a positive effect on the profitability of agricultural MSEs.

Number of employees: It is a continuous variable defined as the total number of permanent employees working for the enterprise during the survey year. Empirical findings revealed that human capital is a key in achieving superior performance in both growth and profitability (Rivard, 2014). Besides, efficient use of the proper number of employees improves the financial performance of MSEs (Abraham, 2018). As agricultural MSEs are labour-intensive, the larger number of employees would increase the production, growth, and profitability of the enterprises. Hence, the number of employees is expected to have a positive influence on agricultural MSEs’ profitability.

Enterprise ownership structure: It is a dummy variable measured in terms of whether the enterprise is owned by an association or privately. It takes 1 if the enterprise is owned by a partnership /association/ and 0 otherwise. Concerning ownership structure, there are two opposite views concerning the effects on firm performance. Dagmawit and Yishak (2016); Shibia and Barako, 2017) showed that group ownership positively affects MSEs’ performance due to the ability to raise large capital to undertake the intended tasks of the enterprise, and boost risk-taking capacities (Bhaumik et al., 2017). Nevertheless, Wolday (2015) finding showed that cooperative owned enterprise has registered the lowest performance compared to other forms of ownership due to weak cohesiveness and lack of shared vision. Hence, the ownership structure is expected to have either positive or negative effects on the profitability of agricultural MSEs.

Education level of manager: It is a continuous variable measured in years of formal schooling of the enterprise manager attended. Entrepreneurship education can play a significant role in the establishment and survival of SMMEs (Chimucheka, 2017). Empirical evidence of (Abraham, 2018) suggested that the education level of managers increases the profitability of MSEs. Therefore, the education level of enterprise managers is hypothesized to influence the profitability of agricultural MSEs positively.

Number of training: It is a continuous variable, which refers to the number of formal training enterprises owners or employees have obtained since the startup of the business. Entrepreneurial skills and financial training enhance MSEs’ competitiveness, thereby improving their performance. Vocational training can help MSEs owners reduce technical inefficiency that influences the profitability of MSEs positively (Kelemu, 2018; Tekle et al., 2016). Therefore, the number of training is expected to have a positive effect on the profitability of agricultural MSEs.

Credit use: It is a dummy variable, which takes 1 if the enterprise used credit in their business operation and 0 otherwise. The use of credit in their business might have a positive or negative influence on the profitability of agricultural MSEs. Evidence from Mamo (2022) revealed that enterprises who accessed credit accumulated higher capital, high saving and created higher employment opportunities than enterprises who do not get credit access. Access to finance positively influence MSMEs’ performance (Abraham, 2018; Esubalew & Raghurama, 2020). The negative effect of credit use would be due to the high credit repayment burden in case of business failure. The finds of Achola (2021) revealed that debt capital has a significant negative effect on enterprise profitability. As a result, credit use might influence the profitability of agricultural MSEs either negatively or positively.

Table 2.

Description and Hypothesis of Explanatory Variables.

Variables	Definition of variables	Expected effect
Age of the enterprise	Continuous (years)	+ve
Number of employees	Continuous (number)	+ve
Enterprise ownership structure	Dummy (1 if association or 0 otherwise.)	±ve
Educational level of manger	Continuous (years)	+ve
Number of training	Continuous (number)	+ve
Credit use	Dummy (1 if credit user, 0 otherwise)	±ve
Financial Record-keeping	Dummy (1 if enterprise keeps financial records and 0 otherwise)	+ve
Market access	Dummy (1 if enterprises have secured product market and 0 otherwise)	+ve
Access to input	Dummy (1 if MSEs have sufficient access to inputs and 0 otherwise)	+ve
Access to infrastructure	Dummy (1 if the enterprises have basic infrastructure access and 0 otherwise.)	+ve
Working premise	Dummy (1 if the enterprises have a convenient working premise and 0 otherwise).	+ve
Owners’ aspiration	Dummy (1 if >50% of owners aspire for growth and 0 otherwise).	+ve
Frequency of extension contact	Continuous (number)	+ve

Source. Own summary.

Financial Record-keeping: A dummy variable that takes a value of one if the enterprise keeps a financial record and zero otherwise. Recording the profits and losses help MSE to monitor the progress and to make adjustments to the operations of enterprises. The adoption of a formal record-keeping and financial control system improves MSEs’ performance (Kaleleoul, 2016). Hence, this variable is expected to affect the profitability of agricultural MSEs positively.

Market access: This variable is measured as a dummy variable that takes one if enterprises have a secure product market, and zero otherwise. Availability of secure and sustainable market linkage is one of the decisive factors for the sustainability of MSEs, industry performance (Ebabu Engidaw, 2021) as well as the survival of micro-enterprises (Sohns & Diez, 2019). Therefore, this variable is hypothesized to influence the profitability of MSEs positively.

Access to inputs: A dummy variable that takes a value of one if MSEs have sufficient access to input (raw materials) and zero otherwise. The existence of raw materials nearby the enterprise premise can lower the cost and increase the profitability of the business. The findings of Getnet (2019) indicated that the availability of raw materials influences the development of small and microenterprises. Therefore, it is hypothesized to affect the profitability of agricultural SMEs positively.

Access to infrastructure: It is a dummy variable that takes a value of 1 if the enterprises have basic infrastructure access and 0 otherwise. The availability of infrastructure (water, electricity, transport systems, telecommunication services etc.) influences the productivity and financial performance of agricultural MSEs which are mostly located outside of the main urban centre. The prospect of MSEs growth was high for those MSEs that have sufficient access to infrastructure (Haftom et al., 2014). Empirical findings proved that access infrastructure has a positive effect on MSEs’ profit and growth (Yesuf & Tang, 2020).Therefore, access to infrastructure is expected to influence the profitability of MSEs positively.

Working premises: It is a dummy variable that takes a value of 1 if the enterprises have a convenient working premise and 0 otherwise. The working place is crucial for the successful and sustainable growth of enterprises because it is essential in creating access to resources and markets. Enterprises with secured and convenient land ownership as a working premise have a chance of increasing firm profit (Alene, 2020) and growing faster than their counterparts. The finding of the study indicates that entrepreneurial orientation positively influences venture performance (Mehari & Belay, 2017) and their location determines the growth of MSE in terms of profitability (Fufa, 2015). Hence, access to the working premise is expected to influence the profitability of agricultural MSEs positively.

Enterprise owners’ aspiration: It is a dummy variable that takes a value of 1 if more than half of the enterprise owners are ambitious to realize the growth and sustainability of their business and 0 otherwise. Owners’ aspirations are the key factors for the growth of enterprises as the primary motivation of youths to turn to entrepreneurship is the lack of other options to enter the labour market (Mühlböck et al., 2018). Evidence from Mohammed (2014) confirms that the motivation of operators positively affects the income and growth of MSEs. Therefore, owners’ aspiration is expected to positively determine the profitability of SMEs.

Frequency of extension contact: It is the number of times per year enterprises received technical guidance from extension agents during the survey year. The existence of frequent extension contacts improves the knowledge and information of enterprise operators’, which increases their business performance. Evidence showed that agricultural extension is playing a major role in the transfer of agricultural technologies, and the development of agricultural skills and knowledge (Altalb et al., 2015). Thus, the frequency of extension contact is hypothesized to influence the performance of MSEs positively.

Result and Discussion

Socio-Economic Characteristics of Enterprises

The government of Ethiopia has given priority to the development and employment creation in the manufacturing sector including agriculture, which is a key in solving long-standing food insecurity challenges in the country and the foundation for agro-processing industries. The age of the agricultural MSEs in the study area ranges from 1 to 9 years. From the sample respondents, 8.12% of enterprises startup a year ago during the survey, while the majority of enterprises accounting for 83.76% were aged from 2 to 5 and the other 8.12% were aged more than 5 years old (Table 3). This shows that more than 91.88% of agricultural MSEs are young and less than five years old as there were large government interventions in youth employment creation through MSEs and youth revolving funds released recently.

Table 3.

Financial Ratio of Agricultural MSEs.

Ratios	All MSEs (n = 271)				MSEs ROA > 0 (n = 191)		MSEs ROA ≤ 0 (n = 80)
Ratios	Mean	Std.	Min	Max	Mean	Std.	Mean	Std.
ROA	0.1601	0.1772	–0.5983	0.7384	0.2452	0.1166	–0.0431	0.1237
ROE	0.2768	0.3363	–0.8	2.7425	0.413	0.2981	–0.0483	0.1446
NPM	0.1520	0.1966	–0.7797	0.8478	0.2359	0.1514	–0.0484	0.1385

Source. Own survey (2021).

The mean education level of enterprise managers was 7.5 years of schooling, ranging from 1 to 17 years. About 66.79% of the MSEs managers attained elementary education (from grade 1 to 8), 19.56% attained high school education (grade 11–12), 4.8% attained preparatory and the other 8.86% had a diploma and above certificate level of education. This shows that the majority of entrepreneurs in the agriculture sector are at the elementary education level who are school dropouts and returnees from Arab counties. The number of employees of sampled agricultural MSEs ranges from one to 20 with a mean of 4 employees. The results of the study indicated that most enterprises employed members of the business and extra employment creation for non-members were few as the small size of the enterprise restricted the capacity of enterprises for more employment creation. The result showed shows that a considerable number of enterprises accounting for 40.22% do not obtain a loan from micro-financial institutions. The mean start-up and current capital of agricultural MSEs in the study area were67,319 to 118,127, respectively.

The financial ratios for each agriculture subsector enterprise were computed in Table 4 Below. The result showed that the mean return on asset, return on equity, and net profit margin of agricultural enterprises in the study was 0.1601, 0.2768, and 0.1520 birr per each birr of investment and sales respectively. The minimum ratios, which are indicated by a negative value, represent the financial ratio of non-profitable agricultural enterprises during the survey year.

Table 4.

Socio-Economic Characteristics of Agricultural MSEs.

Variables	Mean	Std. Dev.	Min	Max
Age of MSEs	3.125	1.6098	1	9
Education level of MSEs manager	7.535	3.2916	1	17
Number of employee	4.295	3.8722	1	20
Startup capital (birr)	67319.37	88990.55	200	450000
Current capital (2021)	118127.3	118533.4	2800	600000

Source. Own survey (2021).

Econometrics Result

The study evaluated the performance of agricultural MSEs in North Wollo Zone, Amhara Regional State, Ethiopia. The financial performance of agricultural MSEs is evaluated through profitability, which is measured by the return on assets.

Factors Affecting Enterprise Profitability

In this section, the factors affecting profitability and the extent of profitability estimates are presented with the application of the double hurdle regression model. Model appropriateness tests were performed using the log-likelihood ratio test, Akaike’s Information Criteria (AIC) and Bayesian information criterion (BIC). The result indicates that double hurdle regression is the right model over Tobit regression, with log-likelihood (Γ = 553.92823) higher than the chi-square value (24.99) at 15 degrees of freedom and at 1% significance level. The dependent variable return on asset (ROA) which is used to measure profitability which is limited as some enterprises are profitable while some are not during the survey year 2020/2021. Hence, the first stage of the hurdle model uses the probit model to analyze the probability of enterprises being profitable (positive returns on assets) and the second stage of the hurdle model uses truncated regression to estimate factors affecting the extent of profitability (amount of return on assets) by truncating enterprises that are not profitable during the survey year.

The probit regression model chi-square test indicates that the overall goodness-of-fit of the probit model was statistically significant at 1% probability level. The result of the probit model estimation shows that 6 variables (enterprise age, manager education level, credit use, access to input, owners’ aspiration, and frequency of extension contact) significantly and positively influenced the probability of MSEs being profitable (Table 5). The second hurdle model, the truncation model, was statistically significant at 1% significance level that shows the goodness of fit of the model to explain the effects of the hypothesized variables on the dependent variable (extent of profitability). The truncated regression results revealed that enterprise age, manager education level, record keeping, input access, and frequency of extension contact significantly affect the extent of agricultural MSEs’ profitability.

Table 5.

Double-Hurdle Estimates of Profitability and Level of Profit of Agricultural MSEs.

Variables	Probit regression				Truncated regression
Variables	Coefficient	Std. Err	p>\|z\|	dy/dx	Coefficients	Std. Err.	p>\|z\|
Age of enterprise	0.295***	0.104	.005	0.0324	0.009*	0.005	.084
Manager education level	0.215***	0.08	.007	0.0237	0.017***	0.004	.000
Ownership structure	0.352	0.348	.311	0.0373	0.027	0.02	.175
Number of employee	0.055	0.045	.222	0.006	0.0005	0.003	.868
Number of training	–0.012	0.197	.949	–0.0013	–0.013	0.01	.198
Credit use	1.505***	0.316	.000	0.2270	–0.02	0.022	.361
Record keeping	0.83	0.594	.162	0.0808	0.047**	0.021	.024
Market access	0.424	0.323	.189	0.0531	–0.01	0.019	.589
Access to input	0.613*	0.349	.079	0.0631	0.03*	0.017	.083
Access to infrastructure	0.437	0.301	.147	0.05	–0.008	0.017	.638
Working premise	0.493	0.344	.152	0.0664	0.008	0.025	.752
Owners’ aspiration	0.709**	0.312	.023	0.1072	–0.007	0.024	.783
Extension contact	0.701**	0.291	.016	0.0771	0.02*	0.012	.090
Constant	–5.509	0.864	.000	–	0.019	0.051	.712
Sigma					0.1***	0.006

***

and ** indicates statistically significance at 1%, and 5% level respectively.

Source. Survey (2021).

Age of enterprise: The age of agricultural MSEs positively affects the profitability and extent of profitability at 1% and 10% significance level, respectively. The result indicates that a year increase in the age of agricultural MSEs increases the probability of profitability by 3.24% and returns on asset by 0.9%, respectively. This finding in line with that of firm age has a substantial effect on firm performance (Mallinguh et al., 2020) and an indirect effect on performance through intelligence generation (Doucouré & Diagne, 2020).

Manger educational level: The education level of the enterprise manager positively and significantly affected both the profitability and extent of profitability at 1% significance level. The result shows that a year increase in managers’ education level increases profitability by 2.37% and return on the asset by 1.7%, holding other factors constant. Education widens the scope of perception and enhances an individual’s ability to perform tasks efficiently (Bosire & Etyang, 2003). Transformational leadership, competencies in entrepreneurship and technical expertise obtained through education offer the best chance for business success (Song et al., 2016), marketing performance (Afriyie et al., 2019), and agribusiness financial performance (Abraham, 2018; Jankelová et al., 2020). Managers’ financial literacy has a direct positive significant influence on the performance of SMEs such as promoting efficiency and increasing market share and sales profit (Agyapong & Attram, 2019). The education level of entrepreneurs positively influenced net profit (Gichuki et al., 2014) and SMEs profitability

Credit use: The use of credit positively and significantly affected the probability of profitability at 1% significance level. The result of this study indicates that the use of credit increases the profitability of agricultural MSEs by 22.7%, holding other factors constant. This indicates that credit increases enterprise capital which helps to achieve economies of scale and undertake the required enterprise tasks. The finding aligns with the significant positive relationship between the financial adequacy and profitability of SMEs (Kehinde et al., 2017).

Financial Record Keeping: Financial record-keeping has a significant and positive effect on the extent of enterprise profitability at 5% significance level. The result indicates that keeping enterprise financial records increases agricultural MSEs’ returns on the asset by 4.7%, keeping other factors constant. This is in line with Adeoti and Asabi (2018); Capiña (2021) findings of record-keeping practices help to monitor the overall growth of the business, and foster higher work efficiency The application of a good accounting system (Kehinde et al., 2017) and working capital management has a positive impact on SMEs’ profitability (Benard & Ainomugisha, 2019). In addition, mental budgeting has a significant influence on the financial management of SMEs (Hoque & Ulku, 2017).

Access to input: Access to input (raw materials) has positively and significantly influenced both the profitability and extent of profitability of agricultural MSEs at 10% significance level. The result shows that access to a reliable market increases the profitability of agricultural enterprises by 6.31% and returns on asset by 3%, respectively. The study finding is consistent with that input supply affect the performance of micro and small enterprise in east Amhara (Mengstie, 2016) and significantly influence the profitability of MSEs (A. Abebe, 2018). The high cost of raw materials causes SMEs not to get the optimal level of profit (Massie et al., 2019). Outsourcing primary activities has a significant effect on the organizational profitability of SMEs (Agburu et al., 2017).

Owners’ aspiration: Agricultural enterprise owners’ aspirations significantly and positively influence the profitability of enterprises at 1% significance level. The result shows that enterprise owners’ aspirations in achieving their business growth and sustainability increase the likelihood of enterprise profitability by 10.72%, holding other factors constant. This finding is consistent with Laguir and Den Besten (2016) who founds that entrepreneurial spirit motivation is a cornerstone of MSEs’ growth. Psychological capital and social competence are specifically important resources for entrepreneurial success and enable entrepreneurs to flourish despite the challenges (Baluku et al., 2018).

The result of the study reveals that ownership structure enhances the growth of micro and small enterprises (Shitaye & Elgammal, 2022). Private ownership enables MSEs to develop sustainably (Chen et al., 2022)

Frequency of extension contact: It has a positive and significant effect on both profitability and extent of profitability at 5% and 1% significance level. The result shows that a unit increase in the frequency of extension contact increases profitability by 7.71% and returns to asset by 2%, holding other factors constant. The finding is in line with the findings of Akinnagbe et al. (2013 ) who revealed that agricultural extension services keep users updated with new knowledge and skills to address the emerging challenges (Maulu et al., 2021). Hence, the agricultural extension program improves welfare through an increase in farmers’ income (Danso-Abbeam et al., 2018).

Conclusion and Recommendation

With a fast-growing population in Ethiopia, MSE development has given extensive attention to creating jobs and fostering the economic development of the country. However, the pursuit of entrepreneurship often comes with high stress, multiple obstacles, and high levels of uncertainty regarding outcomes, which limit their contribution to national income, employment, and export performance. The performance of agricultural MSEs in terms of job creation, efficiency and growth is very restrictive compared to other sectors. Despite North Wollo Zone having a high potential for agriculture, the contribution of agricultural MSEs to employment and the economy is limited, which needs empirical evidence to evaluate the profitability factors that hinder the development of MSEs in the agriculture sector. Therefore, this study aimed to investigate the profitability of agricultural MSEs in North Wollo Zone, Amhara Regional State, Ethiopia.

The descriptive results showed that the mean return on asset, return on equity, and net profit margin of agricultural enterprises in the study were 0.1601, 0.2768, and 0.1520 birr per each birr of investment and sales respectively. The result of the probit model estimation shows that age of the enterprise, manager education level, credit use, access to input, owners’ aspiration, and frequency of extension contact significantly and positively influenced the probability of MSEs being profitable. The second hurdle model, the truncation model showed that age of the enterprise, enterprise manager education, record keeping, access to input, and frequency of extension contact significantly influenced the extent of agricultural MSEs’ profitability.

Based on the findings of this study, the following recommendation is provided for the respective concerned body to enhance the profitability and financial performance of agricultural MSEs in the study area as well as national sector development.

Enhancing enterprise knowledge and skills, and owners’ aspirations through skill development training and youth education program are vital for the profitability of the enterprise, thereby developing the sector.

Financial sectors and government should consider unlocking the financial challenges through MSEs targeting loans and improving financial outreach.

Enterprise development offices and lending institutions should provide basic financial skill training and monitor the financial bookkeeping practice of the enterprises.

Creating a reliable input linkage for agricultural MSEs for which their products are perishable and have seasonal production schedules is critical to the development of the sector.

Frequent extension monitoring and advice to enterprises should be intensified.

Research Limitations/Implications

The key limitation of the study is it depends on cross-sectional data obtained from agricultural MSEs. The study was conducted on only MSEs engaged in the agriculture sector which didn’t include firms in the other sector. The finding would not be applicable for generalization at a broader level and to the non-agriculture economic sector. Hence, more evidence is needed through a further comprehensive study using a longitudinal study design, employing both qualitative and quantitative research approaches on MSEs is desirable. More importantly, a study that focuses on dropout enterprises is critical to investigate the problems of MSEs.

Footnotes

Authors’ Contributions

All authors participated in the entire process of the research and write-up. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper received funding from Woldia University

Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code Availability

Not applicable

ORCID iDs

Ebrahim Endris

Andualem Kassegn

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