Domestic trade regulation and growth of wholesale and retail firms; evidence from Kenya

Abstract

The aim of this paper was to assess the effect of Kenya’s domestic trade regulations on the growth of wholesale and retail firms in Kenya. To achieve the study objective, the existing domestic trade regulatory framework was reviewed, and policy gaps were identified. Further, a cross-sectional dataset from the World Bank enterprise survey 2018 was used for empirical analysis as it contains regulatory variables that influence the growth of wholesale and retail trade firms. The Tobit model was used for regression analysis. The study established that business registration regulations, licensing regulations, firm size, use of mobile money, business websites, membership in a trade association, and training of employees support firms’ ability to grow and therefore create jobs. The study recommended that there is a need to develop a framework that will coordinate both national and county governments in the implementation of business registration reforms provided under the Business Registration Act of 2015 and other reforms provided for ease of doing business in the country. Further, there is a need to reduce business licensing obstacles across the counties by simplifying business license application procedures, conditions, and requirements. Finally, there is a need to fast-track the implementation of the MSE Act 2012 and the MSE regulations 2019 on trade associations, as well as to assess their contribution to the wholesale and retail trade sectors’ self-regulation since their enactment and, if necessary, to revise them.

Keywords:

• domestic trade
• regulatory framework
• growth
• wholesale
• retail trade

1. Introduction

The regulatory environment in which domestic trade takes place is important in influencing the growth capabilities or collapse of the businesses in the sector (Khan, 2014; Luiz, 2011). Among the Organization for Economic Cooperation and Development (OECD) countries, the intricacy of the regulatory framework continues to be a key hindrance to domestic trade activities. While significant improvement has been made, challenges, including those associated with tangled license and permit procedures, still affect domestic trade (OECD, 2019).

In Kenya’s economy, domestic trade which comprises wholesale and retail firms is a very significant sector in providing employment opportunities, facilitating the distribution of goods and services, and the country’s GDP contribution. In addition, the sector plays a major role in the growth and development of the economy due to its linkages with other sectors by providing readily available markets for products and services to consumers (National trade policy, 2017).

The Kenya Constitution of 2010 devolved the functions of development and regulation of domestic trade to the county governments. Consequently, devolution of this function has brought a lot of interest to county governments, especially in terms of raising revenue, and has become the major source of county revenue and also a job creation sector at the county level. That said, the need to provide a supportive regulatory framework cannot be underscored as a study on the county’s business environment by the Kenya Institute for Public Policy Research and Analysis (KIPPRA) found that the business regulatory framework has the potential to influence the growth of businesses (KIPPRA, 2019).

Despite devolving the regulation function of domestic trade to county governments, firms in the sector experience multiple regulators as national government agencies still regulate the sector in terms of business registration and taxation, while the county governments also impose levies or other fees which vary across different counties. Further, firms have to obtain permits and licenses from other technical national government regulatory agencies depending on the type of trading the firm is engaged in (National trade policy, 2017). Statistics from Kenya’s economic survey reports (2011–2022) indicate that the wholesale and retail firms have been experiencing some growth shrinkage for a period of 10 years¹ and their contribution to GDP over the period was 7.5 percent below the projected GDP target of 10 percent.

Notably, the sector’s regulation is hampered by an insufficient regulatory coordination framework between national government agencies and county governments, leaving the sector with multiple uncoordinated regulators, resulting in overregulation. As a result, the growth of wholesale and retail firms has been compromised, leading to the collapse of major retail brands. Others are struggling to remain afloat while the foreign-owned wholesale and retail outlets are grappling with how to exit the Kenyan market.

However, despite the regulatory challenges, the Kenya Vision 2030 aspirations for the wholesale and retail trade sector should not become a pipe dream. The sector needs to contribute to the improvement of livelihoods through job creation, which is a major concern for the government. Thus, to achieve the aspirations of the country’s vision of 2030, this study is expected to proffer a remedy to the regulatory challenges that hinder the growth of the wholesale and retail sectors.

As such, this paper aims to assess the effect of regulations, which include business registration, licensing and permits, taxation, and access to access to credit² on the growth of domestic trade. Further, the study controls for non-regulatory factors that affect the growth of the sector by examining indicators that were found to constrain business by the Kenya MSME survey in 2016 as control variables. The indicators include the use of mobile money, the gender of the business owner, the size of the business, the accessibility of a website, trade associations (self-regulation), and employee training. Policy recommendations are drawn to enhance the growth of firms in wholesale and retail trade.

2. Domestic trade regulatory framework

Kenya’s constitution lays the foundation for trade policies and development. The regulation of wholesale and retail trade was devolved to county governments by the (Constitution, 2010) fourth schedule, section (7), part (2). Consequently, the county governments are mandated, among other things, to carry out the function of trade development and regulation, which includes trade licensing (excluding regulation of professions), markets, fair trade practices, local tourism, and cooperative societies. Other functions that have an impact on wholesale and retail trade, agriculture (particularly marketing and licensing of food businesses), public entertainment, and county road transportation are also delegated.

A review of the County Government Act 2012 shows that despite the regulation of domestic trade being devolved, there was no legislation enacted to guide the regulation of the sector across all the counties, and each county develops its trade regulations. To facilitate sector regulation, county governments developed a trade licensing act that guides business regulation in the counties. Notably, not all county governments have developed the Trade Licensing Act.

Finally, it is evident from the review of wholesale and retail firms’ regulation framework that the sector is regulated by both the county governments and the national government. Table 1 shows the classification of regulations on wholesale and retail trade by the county governments, national government, and other trade sector regulators. It is evident that the sector has multiple regulators, constraining the growth of firms in wholesale and retail trade.

Table 1. Regulators in the domestic trade sector

County governments Regulatory framework
Trade license/permits	County governments
Cess (Infrastructure maintenance fees)	County governments
Outdoor advertising (Motor vehicle branding)	County governments
Distribution licenses	County governments
Fair-trade practices in the county include inspection, investigation, and prosecution of offenses arising from the infringement of the weights and measures	County Weights & Measures office
National government Regulatory framework
Registration of Business/Companies	BRS-National government
Taxation	KRA- National government
Trade finance/credit access	National treasury, Central bank of Kenya- National government
Promotion of trade/business associations to enhance self-regulation	Micro and Small & enterprise Authority- National government
Other agencies that regulate the activities of the wholesale and retail trade
Carrying out inspection and enforcement of counterfeiting and counterfeit products	Anti-Counterfeit Agency
Quality of products and processes	Kenya Bureau of Standard (KEBS)
During mergers and acquisitions and also during pricing as they have the buyer power which they control	Competition Authority of Kenya
Where retailers operate delis and bakeries.	Tourism Fund
Where the retailers dispense milk. Retailers have to acquire licenses from them.	Kenya Dairy Board
Regulate distribution and sale of pest control products	Pest Control Products Board
Where retailers deal with seeds	Kenya Plant Health Inspectorate Service (KEPHIS)

Source: Review of various regulatory frameworks for wholesale & retail trade

2.1. Selected reforms in regulatory framework related to wholesale & retail trade

The government has taken policy initiatives to improve the business environment. A few of the reforms that have been adopted that support domestic trade include the adoption of i-Tax, an online platform for filing and paying taxes, and the simplification of the VAT schedule to reduce the time spent submitting taxes. In 2015, the business registration reforms were enacted, which included the e-citizen portal, which is an online platform for registering company names, businesses, and partnerships, as well as making business registration changes through the online platform. Furthermore, to improve the ease of doing business in Kenya, the Business Law (Amendment) (No 2) of 2020 was enacted. This amendment contained various acts whose amendments are meant to support the ease of doing business in the country. Table 2 presents selected regulatory framework reform efforts by the government to support wholesale and retail trade.

Table 2. Selected regulatory reforms in domestic trade

Policy Variable	Regulatory Framework	Year of Reform	Reforms	Expected outcome
Registration of business	Business Registration Service Act, 2015	2015	Online registering and making changes to business names, companies, and partnerships among others	To encourage formalizationReduce the time it takes to register a business to an average of 3–5 days
	Companies Act, 2015	2015	The e-citizen platform replaced the manual registration and filing of paperwork with an online registration system among others	To encourage the incorporation of businesses.
	Business Laws (Amendment) (No 2), 2020	2020	Several Acts were amended to support ease of doing business including Companies Act, No 17 of 2015.	To improve the ease of doing business in Kenya.
Licensing & Permits	The County Licensing (Uniform Procedures) Bill, 2019; Senate bill 32	2020	The Senate has not yet enacted the bill, and each county now sets its license fees under the County Finance Bill.	If passed, business licensing across counties will be standardized.
Taxation	Filing and paying of taxes(KRA)	2013	Implementing i-Tax, an online platform for filing and paying taxes and standard levies, as well as simplifying the VAT schedule to reduce tax filing time.	To encourage tax complianceTo reduce the tax compliance burden due
Credit access	Finance Act 2019	2019	The interest rate cap was abolished.	To improve access to credit for SMEs and other

Source: Review of various Kenya regulatory frameworks for wholesale & retail trade

3. Related work

Most emerging countries have a large number of informal firms. This is a major source of concern for policymakers, especially as the informality of business grows. According to Levenson and Maloney (1998), the majority of enterprises in developing countries choose to begin their operations as informal until they perform well and flourish, at which point they join the formal sector. First, decreased tax collection is related to the informal business sector, which limits the government’s ability to meet its financial responsibilities and provide public services.

Using panel data, Klapper and Love (2011) found that the implementation of business registration reforms with fewer procedures resulted in a significant increase in the number of new firms that are registered. Their research also discovered that when several business regulatory indicators are improved, the likelihood of new business registration increases. This suggests that economies with weaker regulatory frameworks may require more extensive reforms to increase the rate of business registration.

According to the OECD (2016), government licensing of firms is prevalent in both emerging and developed countries, with two conflicting goals: regulating the industry or generating revenue for the government. Licensing, on the other hand, gives data about business activity that can be used for regulatory purposes. In most economies, business licensing has been the responsibility of local governments to remedy market failures that are largely local in nature, so that local citizens benefit from regulations.

The findings of Olawale and Garwe (2010) revealed that licensing impedes SMEs’ growth. Mullainathan and Scnabl’s (2010) study of the effects of licensing reforms on procedures in Lima, Peru found that reform implementation enhanced business licensing of informal enterprises. Moore and Rose (1998) found that more significant efforts are being made to reform business licensing by removing regulations that aren’t achieving their intended purpose.

The study by Atawodi and Ojeka (2012) established that the tax policy used in an economy is determined by the government’s objectives. The use of a special tax preference is an incentive that encourages small business entry and growth. Furthermore, it was established that the rate of tax compliance among small businesses is a challenge to the implementation of tax policy because of the excessive tax burden placed on small businesses due to their limited resources (Pope & Abdul-Jabbar, 2008).

According to the findings of a study by Fagbemi et al. (2010) and Tomlin (2008), small businesses in developing countries are often non-compliant with tax rules. The resources that could have been used to pay taxes are instead diverted to reinvestment and future growth. Because of their low turnover, the majority of small firms alter their status and end up avoiding taxes. Further, the extraordinarily strict tax system and tax enforcement burden small businesses and affect their growth (Masato, 2009).

Multiple taxation hurts firms regardless of their size or gross returns, according to the findings of Adebisi and Gbegi (2013); Oboh et al. (2013). Using correlation analysis, they discovered that there is a significant relationship between multiple tax practices and taxpayers’ noncompliance attitude in Nigeria. Furthermore, Masud et al. (2014) using correlation analysis of the effects of the tax rate on compliance in Africa established a significant negative correlation between the tax rate and tax compliance, showing a negative effect. According to this study, countries should have an average tax rate to improve tax compliance, promote industrial development, and encourage business growth.

In addition, the size of the organization was established by Storey (1994) to influence the growth of the firm. Further, Storey (1994) revealed that the cost of compliance with the regulatory framework is much higher for smaller firms than for large firms. This was also supported by the findings of Schaffer and Weder (2001), which established that small firms had more challenges than large firms in terms of complying with taxes and regulations and anti-competitive regulations. Their study concluded that the growth of a firm is a decreasing function of its size. The policy implication is that policies that are geared towards the growth of small firms should be differentiated and made simpler to build a supportive ground for growth.

On the other hand, Clement and Wong (2004) established that the growth of the firm was affected by radically changing technology and innovation demands. As such, the findings of Nyaga and Okonga (2014) found that mobile money has made a significant contribution to the SME sector. This is an indication that embracing mobile money technology by firms increases access to financial resources, which constrains smaller firms and acts as an impediment to growth. Further, Fossen and Sorgner’s (2018) on the use of technology and entrepreneurship found that technological adoption in the world of entrepreneurship does not always result in unemployment because labor markets have adjusted to technological change and workers are becoming more entrepreneurial because of their use of technology. Wong and Aspinwall (2004); Arinaitwe () established that the human capacity to adopt new technology and its application is crucial to increasing business productivity and is a major success factor for the growth of small enterprises. Despite the government’s support in developing countries, limited technological capability in businesses has made it difficult for SMEs to grow.

The findings of Jenkins (2006) established that providing skills and entrepreneurial training to business owners supports the growth of a business. Using the Tobit model, Mulwa and Gichana (2018) conducted a study in Kenya and found that enterprises with formal training programs are more efficient than those without. A study conducted by Abdel et al. (2010); Alattar et al. (2009) revealed that knowledge of financial accounting is a major factor that is lacking among owners of retail businesses. This is coupled with the deficiency of capacity for financial planning. This study asserted that some business managers within the SME sector have little skills in accounting and finance matters, and those in possession of little financial planning abilities do not maximally use that knowledge to extract information for decision-making from the financial statements.

Further, a study conducted by the World Bank (2015) established that over 50% of SMEs globally do not have adequate capital, which is considered the main hindrance to their growth. This position is even worse in many developing economies because of high collateral obligations (Beck & Demirguc-Kunt, 2006). Enhancing regulations for access to bank credit could be crucial to overcoming these barriers. From this study, it is evident that the government needs to implement several targeted policies to support small and medium enterprises, for instance, subsidized credit and providing start-up grants.

The findings of Karanja et al. (2014) established that the majority of entrepreneurs lack access to credit due to complicated lending procedures that are rigid and collateral requirements that do not attract small entrepreneurs. As such, according to a survey done by the Ministry of Labour and Social Protection on informal sector skills and occupations (2020), the majority of informal sector businesses prefer family and personal funds as their primary source of start-up capital. Similarly, Bondinuba (2012) established that the main challenges making it difficult for SMEs to access credit consist of credit policy regulation, strict collateral requirements, and a lack of institutional capacity by the majority of players in the SME sector

3.1. Theoretical framework

This study is underpinned by the theory of the growth of a firm, which posits that the growth of a firm depends on the ability a firm to interact and cope with its internal and external environment. This theory underscores the factors that drive firm growth as well as those that constrain it. According to Penrose’s (1959) growth theory of the firm, firms are a collection of both internal and external resources that help the firm achieve its competitive advantage and ensure its survival. The theory suggests that, in the long run, as the firm continues to operate, both internal and external resources can limit growth even if its size continues to expand. The ability to integrate the external environment, which includes the regulatory framework, into the firm’s internal competence determines how the firm will position itself for continued growth to a large extent. Based on this theoretical framework, we come up with an analytical model as in (4.1) in which the growth of the wholesale and retail trade sector is a function of the regulatory framework and control variables.

(1) $\left(\begin{array}{c}Growthof\\ WholesaleandRetail\\ firm\end{array}\right)=f\left(\begin{array}{c}Regulatory\\ framework\end{array},\begin{array}{c}Control\\ variables\end{array}\right)$(1)

4. Empirical equation, data, and data sources

We estimate equation (2)(2) $Growthofwholesaleandretailfirm={\beta }_0+{\beta }_{i}Regulatoryframework+{\beta }_{j}Controlvariables+\mu$(2) using the left-censored Tobit model. The censoring is placed at zero to examine the effect of the regulatory framework among firms with an expansion in employment. This estimation is, however, reinforced by the multinomial logistic regression, which looks at the dependent variable as categorical with “1” for firms that experienced shrinkage in employment creation, “2” for firms that experienced no change in employment creation, and “3” among firms that experienced an expansion in employment creation. Further, where the censored observations are few, the Tobit estimates closely mirror the OLS estimates.

(2) $Growthofwholesaleandretailfirm={\beta }_0+{\beta }_{i}Regulatoryframework+{\beta }_{j}Controlvariables+\mu$(2)

Where $\hspace{0.17em}{\beta }_0$ is the constant, ${\beta }_{\mathrm{i}}$ is a vector of variables on the regulatory framework, and ${\beta }_{\mathrm{j}}$ is a vector of control variables and $\mu$ is a stochastic error term measuring the effect of other variables that influence the growth of the wholesale and retail sector but are unobservable in the 2018 WBES dataset. The specific variables comprising the regulatory framework include registration of firms; informal competition; operating licenses and permits; obstacles in obtaining licenses and permits; tax inspections; tax rates; tax administration; and access to credit lines. The control variables include the use of mobile money, the gender of the firm owner, the size of the firm, the availability of a website, trade associations (self-regulation), and employee training.

We utilize data from the World Bank enterprise survey (WBES, 2018), which was conducted in Kenya between May 2018 and January 2019. The sample for this data was selected through a stratified sampling technique. The sampling frame was obtained from the census of business enterprises done by the Kenya National Bureau of Statistics (KNBS 2012). The data is cross-sectional and the latest enterprise survey dataset in Kenya was collected from a sample of 1001 enterprises, of which 248 are wholesale and retail firms. However, we note that WBES 2018 data is limited in terms of questions of interest in the regulatory framework, and firms with less than 5 employees were not captured by the survey. The WBES (2018) survey data captured Kenya’s regulatory framework indicators, which were identified by the World Bank’s ease of doing business. All these indicators are tied to Kenya’s formal trade regulatory framework, and they affect the growth of the country’s domestic trade sector.

From the summary statistics in Table 3, positive values indicate firms with positive employment growth; negative values indicate firms with negative employment growth (shrinkage); while zero values indicate firms that have had no change in employment creation.

Table 3. Summary of descriptive statistics

Variable	Obs	Mean	Std. Dev.	Min	Max
Dependent Variable
Firms with negative employment growth (shrinkage)	44 Negative	−10.73	17.12	−87	−1
Firms with no employment growth	72 No growth	0	0	0	0
Firms with positive employment growth	132 Positive	17.05	50.49	1	500
Regulatory Variables
Registration of business	245	.93	.26	0	1
Registered businesses compete with unregistered	244	.83	.38	0	1
License applications	247	.67	.47	0	1
License obstacle to firm operations	243	.65	.48	0	1
Tax inspection	247	.62	.49	0	1
Tax rates	244	.75	.42	0	1
Tax administration	245	.68	.47	0	1
Credit_1	244	.35	.48	0	1
Control Variables
Mobile_money	248	.77	.42	0	1
Firm size
1-Small	248	.57	.50	0	1
2-Medium	248	.35	.48	0	1
3-Large	248	.09	.28	0	1
Ownership	245	.43	.50	0	1
Trade association	242	.32	.49	0	1
Website	247	.40	.49	0	1
Training of employees	246	.34	.47	0	1

Source: Authors’ computation based on WBES 2018

4.1. Diagnostic test

To ensure the variables used in the empirical estimations meet the requirements of the neoclassical regression requirements, we tested for normality of the continuous variables, multicollinearity, and heteroscedasticity tests were conducted. The Shapiro-Wilk normality test was used to determine whether the continuous variables have a normal distribution at a 5% level of significance. The findings revealed that continuous variables had a p-value (p = 0.00), implying that the variables were normally distributed. The Variance Inflation Factor (VIF) was used to see if multicollinearity was a significant issue affecting the analysis. If the obtained mean VIF value is less than 10, then it would be concluded that multicollinearity is not a major problem affecting the analysis. The obtained mean VIF was 3.88, and it was concluded that multicollinearity is not a major problem. Heteroscedasticity occurs when the variance of the error terms is non-constant across observations. The coefficients obtained in the presence of heteroscedasticity are unbiased, but inference, which entails hypothesis testing, is inefficient. The study controlled for heteroscedasticity at a 5% level of significance by testing whether the residuals from the Tobit estimator were normally distributed. The p-value obtained was p = 0.05 and the conclusion was made that heteroscedasticity was not a major problem for the analysis.

5. Results and discussions

The study utilized a left-censored Tobit model to estimate the effect of Kenya’s regulatory framework on the growth of the wholesale and retail sectors. For robustness, the Multinomial Logit estimator was also employed and the results from the two models were compared. The Tobit model was a good fit as shown by the p-value (p = .00, χ² = 52.09) while the Multinomial logit model was a good fit as shown by the p-value (p = 0.00, χ² = 55.52).

Further, the results in table 4 show that business registration, mobile money, firm size, availability of the firm’s website, and employee training were statistically significant both in the Left censored Tobit model and the Multinomial Logit. Licensing obstacles were statistically significant only in the Tobit model, while trade association (self-regulation) was statistically significant only in the Multinomial logit. However, variables such as gender of the firm owner, firms’ competition, tax inspection, tax rates, tax administration, and access to credit were not statistically significant in both the Tobit model and multinomial logit.

Table 4. Estimation results

Growth of Wholesale & retail sector	Left-censored Tobit		Multinomial Logit
			Shrinkage		Expansion
	Coefficient	SE βi	Coefficient	SE βi	Coefficient	SE βi
Registration of business	−31.45	14.41**(−2.17)	.94	1.21(−.04)	.23	.19***(−1.80)
Registered businesses compete with unregistered	4.89	10.77(0.42)	2.22	1.45(1.22)	1.41	.66(.75)
Licence applications	−1.13	9.94(−0.14)	.48	.23(−1.52)	.75	.29(−.76)
Licence obstacle to firm operations	−21.24	3.10**(−2.14)	1.25	.72(.39)	.85	.37(−.37)
Tax inspection	6.01	8.33(0.72)	.53	.24(−1.40)	1.17	.43(.44)
Tax rates	−.77	11.08(−0.07)	.64	.42(−.67)	.52	.25(−1.39)
Tax administration	9.92	10.57(0.94)	1.71	1.02(.90)	1.62	.71(1.09)
Credit_1	2.09	8.23(0.25)	1.59	.74(1.02)	1.24	.45(.59)
Mobile money	26.55	10.03**(2.65)	2.79	1.44**(1.99)	3.63	1.44***(3.25)
2-Medium	−7.2	8.79(−0.82)	3.25	1.57**(2.45)	1.03	.40(.07)
3-Large	65.07	15.27**(4.26)	1.75	1.75(.56)	1.19	.83(.25)
Ownership	−8.29	7.89(−1.05)	2.03	.93(1.55)	1.02	.36(.06)
Trade Association	−3.93	8.81(−0.45)	.72	.34(−.69)	.41	.15**(−2.40)
Website	19.89	8.37**(2.38)	1.20	.58(.37)	2.37	.89**(2.30)
Training employees	14.41	8.71***(1.66)	.37	.20**(−1.88)	.82	.31(−.51)
Constant	−14.80	20.24(−0.73)	.15	.23(−1.22)	2.91	2.93(1.06)
var (e. growth)	2471.69	335.78
No. of observations	220
Uncensored	115
Left censored	105
Right censored	0
LR chi2(23)	52.09
Prob > chi2	0.00
Pseudo R2	0.04

The ** and *** mean statistically significant at the 5% and 10% levels of significance respectively.

Source: Authors’ computation based on WBES 2018.

5.1. Regulatory variables

a) Business registration

Business registration regulation was statistically significant at 5% with a negative coefficient (−31.45) in the Tobit model. We found that every business that undergoes formal registration when beginning operations is less likely to create jobs than if it had begun operations informally. Using the Multinomial Logit, firms that were formally registered when they started operations were.23 times less likely to create employment compared to those that started operations as informal entities. The results from the two models on the effect of business registration on employment creation by wholesale and retail firms are in agreement. This finding could be linked to the cost of business formalization as well as additional costs that the firm incurs once it becomes a formal organization. This finding is consistent with the findings of Levenson and Maloney (1998), who found that the majority of firms in developing countries choose to start their business operations as informal until they perform well and grow, at which point they join the formal economy.

b) Licensing and permits

We found that obstacles to obtaining operating licenses/permits were significant in the Tobit model at the 5% level of significance, with a corresponding negative coefficient of (−21.24). This means that obstacles related to obtaining licenses and permits are likely to reduce a firm’s ability to create jobs. This finding is consistent with the findings of Olawale and Garwe (2010), who discovered that licensing is an obstacle to the growth of SMEs. Notably, to conduct business, every wholesale and retail firm is required to obtain an operating license or permit from the county government. This is per the County Government Act of 2012 and the various county trading licensing Acts.

5.2. Control variables

a) Mobile money

The use of mobile money in financial transactions was statistically significant at 5% with a positive coefficient of 26.55 from the Tobit model. This finding indicates that using mobile money by firms in financial transactions is likely to create jobs. From the multinomial logit estimator, firms that had reported shrinkage in employment creation were 2.79 times more likely to create employment if they used mobile money platforms for financial transactions compared to those that did not. Further, firms that had reported growth in employment creation were 3.63 times more likely to create employment if they used mobile money platforms for financial transactions compared to those that did not. This finding is consistent with the findings of Nyaga and Okonga (2014), who found that mobile money has made a significant contribution to the SME sector.

b) Firm size

Using the Tobit estimator, large firms were found to be more likely to create new jobs (65.07) compared to small firms. However, from the Multinomial Logit estimator, firms were 3.25 times more likely to create new jobs if they were medium-sized compared to what would happen if they were small enterprises. Furthermore, Storey (1994) established that the size of the firm influences its growth because the cost of compliance with the regulatory framework is much higher for smaller firms than for medium-sized and large firms.

c) Trade association/Self-regulation

Membership in a trade association was statistically significant at a 5% level of significance. Firms that had reported growth in employment creation were 0.41 times less likely to create new jobs if they were members of trade associations compared to what would happen if they were not. This agrees with KEPSA (2021) that there is a need to strengthen self-regulation in the sector. Currently, the sector’s business membership association is regulated under the Micro and Small Enterprise Act of 2012, which is relevant to this finding as the majority of firms in wholesale and retail trade are MSMEs. This finding is consistent with Morrell and Bettcher (2013), who found that business associations are crucial in advocating for institutional and legal reforms. Furthermore, business associations have been linked to advocacy initiatives that require greater transparency and accountability when dealing with suppliers, as well as advocating for simplified procedures to make doing business in the country easier.

d) Website

From the Tobit estimator, firms were more likely to create new jobs (19.89) if they had a website compared to those without a website. Using the multinomial logit estimator, firms which had reported growth in employment were 2.37 times more likely to create new jobs if they had a website compared to what would happen if they did not. This finding agrees with Fossen and Sorgner’s (2018) findings, which found that technological adoption in the world of entrepreneurship does not always result in unemployment because labor markets have adjusted to technological change, and workers are becoming more entrepreneurial as a result of their use of technology.

e) Employee training

Using the Tobit estimator, firms were more likely to create new jobs (14.41) if they were undertaking on-the-job training for permanent employees compared to what would happen if they were not. However, using the multinomial logit estimator, firms which had reported a shrinkage in employment creation were .37 times less likely to shed jobs if they undertook on-the-job training for permanent employees compared to what would happen if they did not. This is supported by the findings of Mulwa and Gichana (2018), who conducted a study in Kenya and found that enterprises with formal training programs are more efficient than those without. This shows that a firm that trains its employees is likely to increase its ability to create jobs.

6. Conclusions

From empirical estimations, we conclude that business registration, which represents the formalization of firms in wholesale and retail firms, reduces the firm’s ability to create jobs. As such, every business that undergoes formal business registration when beginning its trading activities is less likely to create jobs than when it would have begun trading informally. As such, informal firms create more employment than formal businesses due to the cost associated with formalization. It is evident that obstacles related to obtaining operating licenses and permits are likely to reduce a firm’s ability to create jobs. Further, the use of mobile money in financial transactions and having websites, which are both forms of technology, has the potential to enhance the growth of wholesale and retail firms and create more jobs, especially in the 21^st century. In addition, the size of a firm determines the growth of a firm, where medium and large firms are seen to have the capacity to create more jobs than small firms.

Notably, there are few firms in the wholesale and retail sectors that are members of a formal business association, which was statistically significant enough to influence the growth of the sector. The low membership goes against the spirit of Section 3(e) of the MSE Act 2012, which encourages the formation of representative associations and the registration of business associations.

6.1. Policy recommendations

1. To operate a formal wholesale and retail firm, business registration is a key requirement. That said, there is a need to develop a framework that will coordinate both National and County governments in the implementation of business registration reforms provided under the Business Registration Act of 2015 and other reforms provided for ease of doing business in the country.

2. A wholesale or retail firm cannot operate without a county government trading license/permit. As a result, there is a need to reduce business licensing obstacles across the counties by simplifying business license application procedures, conditions, and requirements.

3. To strengthen self-regulation and encourage membership in trade associations in the sector, there is a need to fast-track the implementation of the MSE Act 2012 and the MSE regulations 2019 on trade associations, as well as to assess their contribution to the wholesale and retail trade sectors self-regulation in the since their enactment and, if necessary, to revise them. Future research can also be carried out in this area and establish whether the trade associations have been able to influence policy and existing gaps in self-regulation in the wholesale and retail sectors.

Disclosure statement

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

Additional information

Funding

The author received no direct funding for this research.

Notes

1. The Kenya economic survey reports from the Kenya National Bureau of Statistics for the years from 2011–2020 indicate that there is shrinkage in terms of GDP contribution and growth of wholesale and retail trade.

2. World Bank ease of doing business 2020 identifies registration of business requirements, licensing requirements, taxation among others as regulatory frameworks that constraints doing business in Kenya.