Micro and small scale enterprises growth and ownership structure in Hawassa city, Sidama region, Ethiopia

Abstract

The general objective of the study is to examine the nexus between micro and small enterprise growth and ownership structure in Hawassa city, Sidama regional state, Ethiopia. The study used a 4-year dynamic panel data from 2017 to 2020, obtained from Hawassa city trade and industry bureau. Both descriptive statistics and econometric methods have been used to analyze the nexus between growth of micro and small enterprises and ownership structure and other driving factors. System GMM estimation technique was employed to analyze a dynamic panel data model. The result of the study reveals that ownership structure, previous year enterprise growth (persistence of growth) and numbers of permanent employees enhance the growth of micro and small enterprises, while age of enterprises, wage and profit of enterprises appear to be a binding constraint to their growth. This study suggests that enterprises should increase their business partners to enhance their financial capacity and to share managerial skill each other and increase the number of permanent employees.

Keywords:

• micro and small enterprises
• ownership structure
• system GMM
• Hawassa

PUBLIC INTEREST STATEMENT

Growth of micro and small enterprises indicate that the sector is an engine for economic development and creation of job opportunities. This paper examines the nexus between micro and small enterprise growth and ownership structure in Hawassa city, Sidama region, Ethiopia. The study result reveals that persistence of growth, ownership, age, employment, labor quality and profit are determinants of micro and small enterprises in Hawassa city.

1. Introduction

The micro and small-sized enterprise (MSE) sector has emerged as a vibrant and dynamic sector of the Ethiopian economy over the last decade. MSEs not only play a crucial role in providing employment opportunities but also contribute to the socio-economic development of the country, notably in their role as catalysts for the transition to an industrial society. MSEs are viewed as seedbeds for the development of large enterprises (Olana, 2020).

In developing countries, MSEs have also a crucial role because of their potential contributions to improvement of income distribution, employment creation, poverty reduction, industrial development, rural development and export growth (Mamo, 2020).

The Government of Ethiopia is focusing on the micro and small enterprises, basically, because of their contribution to reducing unemployment. The focus stems from the increasing unemployment problem in Ethiopia; and MSEs have a significant role in poverty alleviation and job creation in (Agaje, 2004).

According to the United Nations industrial development organization, Ethiopia has fewer private enterprises in comparison to its population size and the lowest entrepreneurial activity rates’ in Sub-Saharan Africa (Aemiro, 2019).

Ownership structure of firms affects their growth through the degree of risk-taking. The key argument is that sole proprietors are usually risk-averse and more often prefer investing in low-risk items attracting low rates of return compared to partnership. On the other hand, however, partnership firms are risk-takers who can start even risky businesses that attract high rates of return and drive their growth (Munjeyi et al., 2017). This is to mean that the spirit of belongingness and the need to increase earning is very high when the number of owner increases relatively in many business firms (Alemayehu & Gecho, 2016). In addition, benefits associated with the presence of partners may include better capital, functional expertise and a broader range of management experience compared with sole proprietor firms (Papadaki et al., 2002). Therefore, MSSEs owned by partnership may have better growth compared with those privately owned enterprises (Tarfasa et al., 2016).

In the recent past, the growth of micro and small-scale enterprises (MSSEs) has been of great concern mainly due to their contribution to economic growth and employment creation (Daniel & Dereje 2016). However, there are still a large number of workforces employed in the urban informal sector; it has not grown significantly since 2005, and both the level of unemployment and quality of jobs remain a concern, although growth and transformation through the promotion of the sector have been robustly underscored in various development plans of the country (Tarfasa et al. 2016). Additionally, operation and growth of these enterprises have been persistently challenged by numerous factors; even a significant number of enterprises in different parts of the country have collapsed and gone out of operation (Fissiha, 2016; Seyoum et al., 2016). MSEs also have been performing below capacity, and their growth has been severely constrained by a number of factors (Gebreeyesus, 2007). This empirical evidence confirms that although a focus on broad-based growth and transformation through the promotion of MSEs has been considered in various development plans, both the level of unemployment and quality of jobs remain a concern in Ethiopia. Therefore to examine the nexus between MSES growth and ownership structure and identify driving factors, a detailed and regular study at country, regional and firm level is important to provide growth-oriented and sustainable support to the sector (Woldeyohanes, 2014; Abay et al., 2014; Fissiha, 2016; Tarfasa et al. 2016). There are different scholars, who have been conducted on micro and small enterprises. This includes the studies made by (Tefera et al. 2013; Abay et al., 2014; Adem et al., 2014; Feleke, 2015; Debelo et al., 2015; Aynadis and Mohammednur 2014; Leza et al. 2016; Tarfasa et al., 2016; Alemayehu & Gecho, 2016; Fissiha, 2016; Seyoum et al., 2016; Hayelom A. 2020 and Asma B. 2015) to mention a few. However, the aims of most studies were to identify determinants of MSES growth and financial performances. Furthermore, the results of most studies were inconsistent and do not address the nexus between ownership structure and MSSE growth in the city. Moreover, previous studies employed cross-sectional survey using ordinary least squares as an estimation technique. However, given the major research and knowledge gap, this study intends to examine the nexus between MSSE growth and ownership structure using a 4-year dynamic panel data in Hawassa city, Sidama region, Ethiopia. The results of this study were expected to determine the nexus between MSSE growth and ownership structure, identify driving factors of MSSE growth and examine the persistence of growth among existing MSSEs in Hawassa city. The study hypothesis is that there is a growth disparity among micro and small enterprises owned by sole proprietorship and partnership.

The rest of the paper is structured as follows: section 2 deals with literature review. Sections 3 and 4 provide the overall methodology and analysis of results and discussions, respectively. Section 5 offers conclusion and recommendations.

2. Literature review

MSEs have no standard definition. MSEs have been identified differently by various individuals and organizations, such that an enterprise that is considered small and micro in one country is viewed differently in another country. Some common indicators employed in the various definitions include total assets, size of the labor force employed and annual turnover and capital investments (Asma B., 2015).

The Ethiopian micro, small and medium enterprises are business activities that are independently owned and operated, have small share of the market and are managed by the owner and employing five or less employees (Degefu, 2018).

In most fast developing countries, micro and small-scale enterprises by virtue of their size, capital investment and their capacity to generate greater employment have proved powerful proponent effect for economic growth (KINFE, 2019). Hence, several empirical evidences exist to explore what factors determine the growth of MSEs. Navid Hamid (2010) conducted a study on determinants and implications in firm growth in Pakistan. The finding revealed that size, export status, organizational form and quality of human capital affect growth indirectly through the binding constraint, i.e. access to finance.

Khizra (2011) stated that firm age, education of owner, boss attitude, family business, networks, new process, major improvements, market share, on-the-job training and unique know-how are found to significantly and positively increase the probability of firm growth in Gujrat and Sialkot Districts.

Gebreeyesus (2007) conducted a study using learning model of firm growth to investigate some key determinants of success, particularly employment expansion among micro enterprises in six major towns in Ethiopia. The findings indicate that firm’s initial size and age are inversely related to growth providing evidence that smaller and younger firms grow faster than larger and older firms, and the finding is consistent with the learning hypothesis.

Mengesha (2018) conducted on determinants of micro and small enterprise growth in three selected woreda of Gurage Zone, SNNPR, Ethiopia. It is found that there is a strong positive significant correlation between working place factors, management and experience factors, marketing factors, infrastructural factor, financial factors, external environmental factor and growth of MSEs. The existence of favorable business environment has a positive significant contribution to the success of MSEs.

Frequent power interruptions, lack of access to credit, shortage of water, age, marital status and education were important factors affecting growth of both micro and small enterprises. Besides, startup size and growth of the MSEs are negatively related, which means that MSEs that start business larger in size in terms of employment grow slower than their counterparts in Addis Ababa city, Ethiopia (Terfasa et al. 2015).

Degefu (2018) conducted factors that determine the performance of micro and small enterprises in Hawassa, Ethiopia. The study reveals that capital change is determined by age of the respondent, education level of respondents, marital status of respondents, respondents’ involvement in construction sector, respondents’ access to finance, respondents’ access to adequate infrastructure, sex of respondents and government motivation. Hayelom (2020) revealed that initial capital, access to land, access to finance, firm location, sectoral engagement, market linkage and business experience are the determinants of micro and small enterprises in Benishangul-Gumuz Regional State of Ethiopia.

3. Research methodology

3.1. Study area profile

Hawassa is a city in Ethiopia, on the shores of Lake Awassa in the Great Rift Valley. It is 273 km south of Addis Ababa via Bishoftu, 130 km east of Sodo and 75 km north of Dilla. The town serves as the capital of Sidama region. It lies on the Trans-African Highway 4 Cairo-Cape Town and has a latitude and longitude of 7^◦ 3^’ N 38^◦ 28^’ E and an elevation of 1,708 meters above sea level. Hawassa town has 32 kebeles with a total population of 315,267 (Gebere et al., 2021).

3.2. Research design

The types of research employed under this study were descriptive and explanatory research. The descriptive research design was used in order to describe the state of affairs as it exists in the study. Second, the study also explores the relationship between variables with an aim of examining nexus between micro and small enterprise growth and ownership structure. The study also used a combination of qualitative and quantitative research approaches. The quantitative data analysis is presented with the help of tables, percentages, frequencies and figures. Moreover, it measures many characteristics, which are literally quantitative in nature, while qualitative data were narratively analyzed.

3.3. Data type, sources and collection methods

The study employed a 4-year dynamic panel data from secondary source, Hawassa city trade and industry bureau. Furthermore, secondary data were gathered from different documented and published sources including books, journals, government reports, articles, reports of Hawassa city trade and industry bureau, Hawassa OMO and micro finance institution and other publications. The study used a total number of 189 existing micro and small enterprises (N) for the 4-year period (from 2017 to 2020GC) with 756 observations (NT). Since the study is dynamic in nature, it focuses only on existing/surviving micro and small-scale enterprises, and to incorporate only the survived/existing enterprises, the study period is restricted between 2017 and 2020; as the number of years increased, there are a number of enterprises that left the industry. So, to get full data for enterprises, the researcher is obliged to employ only a 4-year data for surviving enterprises in the study periods. Therefore, entered and exited enterprises to the market during the study periods are not incorporated.

3.4. Methods of data analysis

After careful gathering of the appropriate data, the data is analyzed using both inferential statistical and descriptive analysis. Frequency distribution, percentage, measure of central tendencies and measure of variations were used to summarize the data. Examination of the nexus between ownership structure and growth of micro and small enterprise estimation was carried out using econometric methods, particularly system eneralized method of moment. For this analysis, STATA software version 15 is used.

3.5. Model specification

The theoretical basis for this study is the augmented form of the learning model. According to “learning models,” a firm “learns” about its productivity over time-efficient firms’ invest and expand, while less productive ones stay small, shrink or exit. This class of models also predicts that enterprise age and size are both negatively correlated with enterprise growth (Stella et al., 2014).

Evans (1987) methodology is used for MSES growth study and expressed as:

(1) $Growth{}_{it}=ln\left(\frac{\mathrm{\Delta }{Y}_{it}}{Y_{it-1}}\right)=ln\mathrm{\Delta }{Y}_{it}-ln{Y}_{it-1}=\alpha +{\beta }^{\ast }ln{Y}_{it-1}+{\epsilon }_{it}$(1)

where, $growt{h}_{it}$ is the growth of employment for MSES i at period t. $ln{Y}_{it}$ is the level of employment for the MSEs i in period t, $ln{Y}_{it-1}$ being its lag in logarithmic form. ${\epsilon }_{it}$ Is the disturbance term assumed to be normally distributed with mean zero and possibly a non-constant variance of firm i at time t.

Adding last year’s growth (persistence) in equation (1)(1) $Growth{}_{it}=ln\left(\frac{\mathrm{\Delta }{Y}_{it}}{Y_{it-1}}\right)=ln\mathrm{\Delta }{Y}_{it}-ln{Y}_{it-1}=\alpha +{\beta }^{\ast }ln{Y}_{it-1}+{\epsilon }_{it}$(1) , the model can be rewritten as:

(2) $Growt{h}_{it}=\alpha +{\beta }^{\ast }ln{y}_{it-1}+\gamma Growt{h}_{it-1}+{\epsilon }_{it}$(2)

where, $Growt{h}_{it-1}$ allows for persistence of growth. The enterprise growth, function (1), considers the simple dynamic panel data model with the null hypothesis that enterprise growth is random with its size.

(3) $Growt{h}_{it}=\alpha +{\beta }^{\ast }ln{y}_{it-1}+\gamma Growt{h}_{it-1}+F\left({\mathrm{Z}}_{it}\right)+{\epsilon }_{it}$(3)

where $Z_{it}$ denotes other micro and small enterprise growth driving factors such as ownership structure, age, no. of employees, wage, leverage and profit. Except ownership structure, all variables are measured in logs and the hypothesis is all the coefficients are zero, i.e., there is no relationship with MSE growth and its attributes including the past growth or no systematic relationship between the growth of MSE and its attributes.

Combining the time-invariant enterprise-specific effects and time-varying cyclical effects with multiple dynamic panel data model, the final model of interest will be:

(4) $Growt{h}_{it}={\alpha }_i+{\delta }_t+{\beta }^{\ast }ln{Y}_{it-1}+\gamma Growt{h}_{it-1}+F\left(Z_{it}\right)+{\mu }_{it\hspace{0.17em}};{\mu }_{it}=\rho {\mu }_{it-1}+{\epsilon }_{it}$(4)

where ${\alpha }_i$ and ${\delta }_t$ represent individual and time fixed effects, respectively. The unobserved time-invariant enterprise-specific effects, ${\alpha }_i$, implies that there is heterogeneity across enterprises.

According to Roodman (2007), the most advanced estimation technique used for dynamic panel data studies is system generalized method of moment. The model can be developed with advantage of solving the problem of endogeneity, heterogeneity and the estimator’s biasedness which are not solved by other methods (ordinary least square, fixed effect, random effect and differenced generalized method of moment).

4. Results and discussions

4.1. Descriptive analysis

The average growth rate of micro scale enterprise is 1.13%, while that of small-scale enterprise is 1.91%. This shows that small-scale enterprises grow faster than micro scale enterprises. With regard to age, the average age of micro scale enterprise is 22 years, while that of small-scale enterprise is 23 years. This indicates that the average ages of both micro and small enterprises are nearly the same. With regard to ownership structure distribution, 40.2% are owned by sole proprietors, while the remaining 59.8% are owned by partnership. The average growth of enterprises owned by sole proprietors is 1.68%, while it is 1.75 % for enterprises owned by partnership. This shows that the growth rate of enterprises owned by partnership is greater than enterprises owned by sole proprietorship, i.e. enterprises owned by partnership grow faster than enterprises owned by sole proprietorship.

Regarding wage, the average real gross wage is 1681 birr and 1785 birr in sole proprietorship and partnership owned enterprises, respectively. This shows that the wage of labor is higher in enterprises owned by partnership than sole proprietorship. The average wage of labor is 1104 birr and 1944 birr in micro scale and small-scale enterprises, respectively. This indicates the wage of labor is larger in small enterprises than micro enterprises.

With regard to number of employment, the average numbers of employees are 3 and 24 in micro and small enterprises, i.e. the number of employees are larger in small enterprises than micro enterprises and small enterprises create more job opportunities than the micro one. The average numbers of employees in enterprises owned by both sole proprietorship and partnership are nearly similar.

Interims of profit generating, the average profit of small enterprise is 201,479 birr, while it is 100,749 birr. This implies that on average small enterprises realize higher profit than micro scale enterprises; this will induce them to grow faster. Regarding profit in ownership structure type, the average profit is 174,646 birr and 179,115 birr in sole proprietorship and partnership, respectively. This shows that the average profit is larger in enterprises owned by partnership than sole proprietorship.

With respect to leverage, the average ratio of long-term debt to total available capital is 2488 birr and 5074 birr in small and micro scale enterprises, respectively. This implies that the average ratio of debt to total capital is higher in micro scale enterprises than small enterprises, and operational fund of micro enterprises mainly depends on external financial sources than small enterprise one. On ownership structure, the average leverage value is 2479 birr in sole proprietorship, while it is 2395 in partnership. Similarly, the ratio of debt to capital is higher in enterprises owned by sole proprietorship than enterprises owned by partnership.

In attempt to investigate whether micro scale or small-scale enterprises and enterprises owned by sole proprietorship or owned by partnership incur higher expense on establishment of enterprise, on average small-scale enterprises incur an expense of 8364 birr for establishment of enterprise, while micro-scale enterprises incur an initial cost of 12,946 birr for establishment of enterprise. In comparison with sole proprietorship and partnership, on average an established cost of enterprises owned by sole proprietorship is 8217 birr, while for enterprises owned by partnership it is 8418 birr. The result indicates that as the number of partners increased, their capital contribution for the establishment of enterprise also increase and members became involved in risky business.

4.2. Econometrics analysis

Table 3 reveals the results of enterprise growth. In this study, the researcher estimates five models. In the first model, both micro and small enterprises were estimated (see the first column of Table 3), to see the effect of ownership structure on enterprise growth; sole proprietorship and partnership enterprises were separately estimated in the second and third models (see second and third columns of Table 3). Finally, micro and small-scale enterprises were separately estimated in the fourth and fifth models (look fourth and fifth columns of Table 3) for scale comparison purpose. The validity of moment conditions of system GMM estimation method is tested using the Arellano–bond first test (AR (1)) and Sargan test of over-identifying restrictions in the differenced equation. Overall significance of the estimators is tested using the Wald Joint test (W_JS). For Sargan test of over-identifying restrictions, null hypothesis (HO) is: over-identifying restrictions are valid and for Arellano–Bond test, the null hypothesis (HO) is: no autocorrelation.Table 1, 2

Table 1. Description of the model of variable and its measurement

Variables name	Description and measurement of variables	Expected sign
Micro and small-scale enterprise growth	It is a continuous variable. The growth of permanent employment between two consecutive periods.	+ve
Lag of MSES growth	It is a continuous variable. Measured in a lag (previous value) of growth of employment.	+ve
Ownership structure	It is a dummy variable which is categorized ‘0ʹ sole proprietor, ‘1ʹ partnership structure.	+ve
Employment	It is a continuous variable. Measured by the number of permanent employees of the enterprise in a particular year.	+ve
Age	Defined as the number of years of enterprise since its commencement. This is measured in years.	_ve
Profit	It is a continuous variable. Measured by the difference between enterprise revenue and production cost.	+ve
Leverage	Defined as the long-term debt to capitalization ratio, measured by the ratio of debt to available capital.	_ve
Labor quality	It is a continuous variable. Measured in gross real wage.	-ve
Startup capital	Defined as funds for any expenses to be incurred before launching an enterprise.	+ve

Table 2. Descriptive summary statistics of ownership structure

	Ownership structure
	Sole proprietorship									Partnership
Variable name	Mean		St. deviation			Min			Max	Mean		St. deviation		Min		Max
Growth	1.68		3.032			0.034			13.285	1.75		3.02		0.016		14.14
Ownership	0		0			0			0	1		0		1		1
Age	23.7		13.07			8			60	19		12.83		7		64
Employment	18		17.279			1			66	20		17.12		4		69
Labor quality	1681		5877.91			10.64			96,956.	1785		5305		2392		68,330
Leverage	2479		3737.15			2.65			29,220	2395		4617		0.77		39,630
Profit	174,646		375,128			−19,698			499,741	179,115		34,317		−15,501		326,887
Startup capital	8217		12,140			4150			90,126.	8418		12,582		13,775		116,637

Table 3. System GMM result for micro and small-scale enterprise growth and deriving factors

	Whole MSEs	Sole proprietor	Partnership	Micro	Small
VARIABLES	Growth	Growth	Growth	Growth	Growth
Lag of growth	0.0976**	0.240**	−0.0196	0.124	0.140*
	(0.0487)	(0.101)	(0.0549)	(0.185)	(0.0718)
Ownership	22.87***			−0.775	13.05**
	(5.267)			(4.611)	(5.932)
Age	−5.636***	−3.172***	−3.932***	−3.544*	−4.514***
	(0.786)	(1.045)	(0.762)	(2.034)	(1.004)
Employment	0.359**	0.309	0.586***	−0.102	0.888**
	(0.156)	(0.258)	(0.192)	(0.639)	(0.373)
Labor quality	−0.369***	−0.414**	−0.337**	−1.328**	−0.315*
	(0.119)	(0.208)	(0.149)	(0.631)	(0.162)
Leverage	−0.145	−0.532	−0.0598	−0.365	−0.135
	(0.156)	(0.340)	(0.169)	(0.406)	(0.209)
Profit	−0.256**	0.00171	−0.267	0.346	−0.254
	(0.128)	(0.229)	(0.163)	(0.397)	(0.193)
Startup capital	0.0522	0.0428	0.240**	0.157	0.238
	(0.105)	(0.191)	(0.121)	(0.187)	(0.150)
Constant	7.350***	14.12***	12.77***	15.88**	6.052*
	(2.748)	(4.865)	(2.615)	(7.802)	(3.449)
Observations	543	220	323	85	368
Number of enterprise idWald chi2(8)Prob > chi2Sargan testProb > chi2A-B testAR(1)Prob > chi2	18787.990.000012.00054(0.2133)-.97222(0.3309)	7524.000.0011	11254.100.0000	4920.670.0081	14843.120.0000

Standard errors in parentheses

*** p < 0.01, ** p < 0.05, * p < 0.1

In the first model, lag of enterprise growth (dependent variable), ownership structure, age, employment, labor quality and profit of enterprises were statistically significant variables, while leverage and startup capital were insignificant variables of the model. All variables are in logarithm form except the dummy variable, ownership structure.

In the first and second models, persistence of growth (lagged values of the dependent variable) is positively related to MSE growth, while this is not the case for enterprises owned by partnership. This result is in line with evolutionary approach of firm growth; the growth of successful firms should persist over time: there should be a positive serial correlation of growth between consecutive periods and those enterprises that are growing rapidly accumulate experience and hence learn faster than others. This leads to a better competitive position for those already ahead and enables them to move further ahead.

Ownership structure is statistically and positively related to micro and small enterprise growth. Enterprises owned by partnership grow faster than enterprises owned by sole proprietorship. This implies that enterprises which are owned by more than one owner have a higher chance to grow than those enterprises owned and managed by a single owner, and the presence of entrepreneurial teams increases firm’s resources and capabilities, a fact that enhances employment growth indicating that the presence of entrepreneurial teams improves internal decision-making processes leading to higher growth rates.

Age statistically and negatively affects micro and small enterprise growth. Younger enterprise grows faster than the older enterprises, and the result supports the postulate of Jovanovic (1982), i.e. older firms have already learned about their relative efficiency and have adapted their size accordingly no need to grow.

Size (employment) and growth of MSEs and enterprises owned by partnership are positively correlated, indicating that MSEs that start business larger in size in terms of employment grow faster than their counterparts. This finding is oppositely consistent with recent study conducted in Ethiopia, as firms become larger; their rate of growth slows down due to the scale effect (Terfassa et al., 2015).

Wage of labor statistically and negatively affects the growth of micro and small enterprises in general and enterprises owned by sole proprietorship and partnership in particular. This implies as the wage of labor increases, MSE growth falls down, i.e. increment in wage of labor increases cost of enterprises and enterprises required small number of employees; as a result, employment growth of micro and small enterprises falls down. Moreover increment in real wage is a constraint for micro and small enterprise employment growth.

Profit and MSEs are negatively correlated; this indicates that as the profit of the enterprises increases, the growth of micro and small enterprise fall; while this is not the case, the sample is divided into enterprises owned by sole proprietorship and partnership. Profit of the enterprises used reinvestment and replacement of employees via technology. Besides, the withdrawal of enterprise shareholders from their profit is huge, and they are not involved in expansion of plants. This result is consistent with the finding of Dobson and Gerrard (1989).

Startup capital is statistically and positively significant with growth of enterprises owned by partnership only. This implies that enterprises that started their operation with a higher initial investment are more likely to grow than their counterparts which started operation with a relatively smaller initial investment. This result is consistent with the finding of study conducted in Ethiopia (Fissiha, 2016).

5. Conclusion and recommendations

The objective of the study was to determine the nexus between growth of micro and small enterprises and ownership structure in Hawassa city, Sidama region, Ethiopia. With aim of making MSE sector an engine for economic growth and creation of job opportunities, it is relevant to identify driving factors that influence the growth of MSEs generally in Ethiopia and particularly in Hawassa city. This study provides empirical evidence on MSEs based on a sample of 189 micro and small enterprises in Hawassa city. The study employs both descriptive and econometrics method for data analysis. For quantitative analysis, a system GMM estimation technique was employed for a 4-year dynamic panel data. From descriptive statistics, enterprises owned by sole proprietorship grow with 1.68%, while enterprises owned by partnership grow with 1.75%; it can be concluded that to achieve a higher growth, enterprises should be owned and managed by partners or more than one business man. The quantitative analysis reveals lag of dependent variable, ownership structure; age, employment, labor wage, and profit are the main determinants of micro and small enterprise growth. Finally, I recommend that enterprises should increase their business partners to enhance their financial capacity and to share managerial skill with each other. Enterprises should also increase the number of permanent employees. Future research may explore the use and submission of this study in the context of growth of micro and small enterprises as appropriate empirical review. Likewise, future studies should aim at a more empirical and quantitative approach that could explain the effect of power outage on productivity of micro and small-scale enterprises and determine sectoral level growth disparity among enterprises.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Aschale Mekuria Shitaye

Aschale Mekuria is a lecturer at Hawassa University, Ethiopia. His current research interests are industry development issue like manufacturing, construction development, industrial economics and labor economics.