NIGERIAN ENTERPRISE SECTOR AND POLICY UNCERTAINTY

Ukwueze, Ezebuilo R.¹⁺ --- Asogwa, Henry T.² --- Odo, Augustine C.³

^1,2,3Department of Economics, University of Nigeria, Nsukka, Enugu State, Nigeria

Keywords:Policy uncertainty Political instability Multinomial logit Profitability Enterprise sector Economic environment.

ARTICLE HISTORY: Received:22 June 2017. Revised:5 March 2018.Accepted:9 March 2018. Published:15 March 2018.

Contribution/ Originality:This study contributes in the existing literature. This study is one of very few studies which have investigated policy uncertainty and enterprise sector, and factors affecting it. The paper contributes the first logical analysis, using superior data and methodology to other studies, that policy uncertainty affects Nigerian enterprise sector.

1. INTRODUCTION

Evidence has shown that most of the successes recorded in most economies in the twenty first century have been attributed to the success and growth of the enterprise sectors, as emerging and developed economies have taken the pains to make tough decisions regarding institutions and strong leadership. This link, no doubt could have been responsible for the increasing growth in most developed and emerging economies.

And Nigeria as the one of the largest country in Africa, ranked 7th in the world in terms of population with 173.6 million people as at 2013 has channeled her path in expanding her enterprise sector through her different approaches to industrialization policies which spanned from import substitution policy of 1960, to the Nigerian Indigenization policy of 1972 which aimed at fostering widespread ownership of enterprises among Nigerian citizens. Subsequently, to stimulate non-oil exports and provide a basis for private sector led development, the structural adjustment programme of 1986 was introduced. However, these policies continued further to trade and financial liberalization policy of 1989 to the small and medium industries equity investment scheme introduced by 2000 and the most recent of the Nigeria Industrial Revolution Plan introduced in 2014.

Interestingly, according to the National Bureau of Statistics (NBS) (2014) Nigeria surpassed South Africa as the largest economy in Africa, with a GDP of $ 510 billion in 2013, $190 billion more than the GDP of South Africa. And the statistics further indicated that within the scope, GDP grew by 7.4 percent in 2013 up from 6.5% in 2012 and forecast to achieve an average growth rate of 7.1 percent through 2030.

Nevertheless, several studies have come out plainly to acknowledge the role of the enterprise sector in national and global competiveness, sectoral output growth, huge employment opportunity, improved better living standard, among others (See for example, Yusuf and Dansu (2013)). Not much seems to have changed as the cost of doing business in Nigeria is still very high due to several factors like infrastructural decay, corruption, cost on finance, weak institutions, policy and political uncertainty due to unrest in some part of the country, and capacity to match global competitiveness, to mention but a few.  For instance, the figure below is presented to further, show the decline in deposit banks’ loans to small and medium enterprises from 1992 to 2013 due to the high cost of borrowing; this scared so many private investors, even though government has been making frantic effort to achieve private sector participation in business considering the Nigerian market. 

Figure-1. Deposit Banks loans to SMEs in Nigeria from 1992 to 2013

Source: authors’ computation 2017

The diagram (fig. 1) therefore shows a steady downward trend of the loans from the Nigerian money deposit bank to SMEs. This is likely the reason why the enterprise sector has not been globally competitive so as to attract the support of International Bank for Reconstruction and Development (IBRD), African Development Bank (AfDB), German Development Bank (KFW), French Agency For Development (AFD) and the United kingdom’s Department for International Development (DFID), in partnership with the Nigerian Federal Ministry of Finance (FMoF) for seven years to stimulate economic growth and create jobs for its citizens, by  approving a $500 million credit to fund the Development Finance Project which will facilitate increased access and availability of financing for Micro, Medium and Small-Scale Enterprises (MSME) in agriculture, trade, light-manufacturing, services, and other areas. The clause also include  funding to eligible financial institutions to finance long-term lending to MSMEs, as well as funding to Micro-Finance Banks for on-lending and to expand their outreach (World Bank, 2014).

Therefore, considering the long history of inconsistency in the sound policies developed so far on the Nigerian enterprise sector since civil rule, with very little political will to consolidate on them, it then becomes expedient to ascertain why the enterprise sector has lagged behind all these while. Then, the assertion by World Bank that Nigeria is lagging far behind when compared to other countries is truth. Even when banks are not in a position to give long-term loans, which on its own is an obstacle for growth of enterprises, Nigeria’s micro, small and medium-sized business owners cannot access funds even as the Nigerian banking system is considered to be well capitalized, liquid and profitable, according to financial soundness indicators (WB, 2014). The study therefore, is geared to estimating the impact of policy uncertainty on the Nigerian enterprise sector, and to determine the factors which have influenced the growth of the enterprise sector, using the World Bank enterprise survey data for 2010 given that few researches have been done in this area in Nigeria.

2. REVIEW OF LITERATURE

Due to the complex nature of the enterprise sector, conceptualizing the sector would not have come better except through the Nigeria Central Bank archives (Central Bank of Nigeria (CBN), 2014) which established that an industry with a labour size of not more than 10 workers, or total cost of not more than N1.50 million, including working capital but excluding cost of land can best be described as the micro or cottage industry. However, a small scale enterprise sector according to the CBN  is best described as a sector with a labour size of 11-100 workers or a total cost of not more than N50 million, including working capital but excluding cost of land. The medium scale enterprise sector is a sector with a labour size of between 101-300 workers or a total cost of over N50 million but not more than N200 million, including working capital but excluding cost of land. And then, the large scale enterprise sector as a sector with a labour size of over 300 workers or a total cost of over N200 million, including working capital but excluding cost of land.

Nevertheless, theoretical literature for the purpose of this study is drawn from two theories namely, Baumol’s theory of sales and revenue maximization, and Marris theory of managerial capitalism due to their relevance to the behaviour of firms in microeconomics.

The Baumol’s theory of sales maximization, according to Dwivedi (2014) is one of the most important alternative theories of the firm’s behaviour. The theory as developed by Baumol in 1959 argued that firms are more interested in sales for various reasons; however, maximizing sales remain firms’ ultimate objectives as that has huge likelihood to greater market share and profits in the long run. What to draw from this theory is that most emerging economies have demonstrated their drive in this line as seen in the struggle for African market with all kinds of products.  Albeit, the theory was constrained by time lag mismatch and inconsistency in area of short run sales maximization and long run profit maximization. However, Marris theory of managerial capitalism differed from that of Baumol as considered in the next paragraph.

The theory of Managerial capitalism as developed by Marris in 1964, interestingly, argued that the objective of any manager in a corporate firm is to maximize the firm’s balanced growth subject to managerial and financial constraints. The theory argued that a managerially controlled firm will opt for a higher rate of sales growth than an owner controlled firm such that, profit rate to the owners (that is, the shareholders) will be lower in a managerially controlled firm. This, for Marris, is due to the fact that managerially controlled firms returned their profit to fund growth or diversify further. No doubt most firms have used this theory as bases for diversification.

The relevance of the enterprise sector among emerging economies as shown by several studies have provided evidence of boosting economic growth and creating job opportunities for the poorest people, reducing relative dependence ratio and also fighting poverty level. For instance, Misati (2007) examined the linkages between the informal sector and investment, using data on the informal market activities developed by the Heritage Foundation. And his argument is that since statistics is indicating that formal unemployment is rapidly growing while poverty too is wide spreading, that this has created a drive witnessed in the growth of the informal sector among African states in recent times as more households now resort to small enterprise for better living standard.

In a different context, Driemeier et al. (2010) argued that firms in Africa are however, constrained to their growth due to economic policy uncertainty owing to ‘policy somersault’ and those  statistics show how the enterprise sector is growing rapidly with its effect on poverty reduction. Using Enterprise Survey data demonstrated huge variability as most firms find it difficulty doing business in countries with weak institutions and cases of policy uncertainty and actions like bribes, political connection among others, as these have influenced the firm growth rates in Africa and, which ordinarily would have enhanced the enterprise sector. In line with the argument, again Udah (2010) employed the co-integration and error correction frameworks to establish that government size, inefficiency in government expenditure, poor service delivery, and other factors have not the complemented needed conditions capable of stimulating the enterprise sector development in Nigeria thereby, confirming the findings by Driemeier et al. (2010).

Yusuf and Dansu (2013) in their study established that small and medium scale enterprises (SMEs) are critical to Nigeria’s economic growth and development. Using data generated from fifty (50) SMEs in Lagos State, Nigeria, which was analyzed with Chi-square and descriptive statistics, therefore, argued that the only condition for sustainability of the sector is a favorable macroeconomic environment with strategic risk management and minimum corporate governance standard. Similarly, Anga (2014) further argued that factors like corruption, infrastructure, finance, government policies and marketing of the SMEs have hindered the performance of the SMEs in Nigeria, having employed logistic regression technique to determine that. Nevertheless, the efficiency with which these business enterprises operate in any economy depends on the government policies which form the part and parcel of both microeconomic and macroeconomic environment of an economy. Zhang and Lam (2014) pointed that policy is what set rules that inform firms’ decisions and in the face of policy uncertainty, firms decisions to invest are delayed.  Thus, business enterprises rely on government polices before they make their decisions. They must need clarity in terms of the economic policies as well as effect of the government regulation and taxes. Bernanke (1980) explained this by saying that economic agents normally weigh the benefit of waiting to understand government policies alongside the cost of postponing that investment. He emphasized that because investors gain more when they seek for more information in uncertainty, investments are always delayed and this invariably reduces investment. Rajan and Marwah (1998) referred to this habit of waiting for clarity as “wait and see attitude”. Therefore, the certainty of the government economic policy is like an engine that boosts the growth impact of most business enterprises and their ability to contribute positively to the development of any economy.

However, policy uncertainty as noted by Rodrik (1991) makes the private investors to withhold their investments as they wait for clarity about government policies, and this leads to a reduction in investment. Jeong (2002) also added that policy uncertainty leads to delayed investment because investors will only invest when they have better information about the policy of government. Tobbacka et al. (2014) noted that it also makes both households and firms to cut down on investment and consumption respectively.

The study by Bhagat et al. (2013) showed that policy uncertainty affects negatively firms, industry and the whole economy in general. Their findings revealed that in India, a reduction in economic policy uncertainty increases GDP and fixed investment by 0.56 per cent and 1.36 per cent respectively. On firm level, they found that 10 per cent increase in the policy uncertainty reduces investment by 3 per cent. Similarly, at the industry level, they found that investment would fall by about 2.8 per cent for 10 per cent increase in the economic policy uncertainty. Baker et al. (2013) used frequency of media report about uncertainty in economic policy, the number of federal tax code provisions set to expire, and the extent of forecaster disagreement over future inflation and government purchases as measures of economic policy uncertainty, and found that policy uncertainty has negative effect at both firm level and national level. At the firm level, difference-in-difference estimation technique was employed in which firms were categorized based on their exposure to government contracts. Their findings showed that firms with about 1.2 per cent exposure to government contracts experienced 0.078 percentage point reduction in investment when economic policy uncertainty is doubled, but firms with 90 per cent exposure experience between 0.8 to 5 per cent declined in the investment. They also applied VAR model to national level data which revealed that 102 innovations in policy uncertainty produced the highest negative impact of about 2.5 per cent on firms’ investment in the 14th month and as well led to highest job loss which is about 2.3 million at the end of the 18th months.  Schweitzer and Shane (2011) plotted the relationship between policy uncertainty measure and key events that occurred in the US economy such as the Second Gulf War and 2001 tax cut. The findings further revealed that policy uncertainty was at its peaks in the two periods and such peaks corresponded to sharp fall in the hiring of workers by small firms and their capital expenditure (investment). The study employed different indices of policy uncertainty to confirm that policy uncertainty affects both small firm hiring and capital expenditure negatively. Masayuki (2013) using a regression analysis, investigated negative impact of policy uncertainty on the growth of sales in Japan. Apart from using individual policy uncertainty measure, composite policy uncertainty was also constructed in the study. The findings show that all individual policy uncertainty measures were negative, suggesting that policy uncertainty is detrimental to sales growth. The coefficient of composite uncertainty was found to be negative as well and that 0.6 increase in the composite policy uncertainty reduces sales growth by 1 per cent. Jeong (2002) in his study revealed that policy uncertainty influences negatively investment and output in the long run.

Enterprise sector in Nigeria is emerging and requires friendly policies to cultivate the sector so as to reduce huge socioeconomic burden on government and households. Therefore, this study is timely to fill the literature gap created in this area.

3. METHODOLOGY AND DATA

The analytical framework for the study takes lead from the works of Peng and Nichols (2003) where the authors used the multinomial logistic models to predict adolescent behavioural risk. And it should be noted that the Multinomial logistic regression technique is better for analyzing and estimating hypotheses about correlates between categorical dependent variable and other continuous explanatory variable so as to provide more effective  and reliable way of obtaining estimated probability as indicated by Peng and Nichols (2003). Further too, relevant theories of the qualitative response model have also established that since the Multinomial logistic regression does not make any assumptions of normality, linearity, and homogeneity of variance for the independent variables or even impose certain requirements (Maddala, 1983). Therefore, this study focuses on interpreting estimates from multinomial logit model and subsequently, multivariate analysis as provided in the model. 

3.1. Model Specification.

In the multinomial logit model, coefficients are estimated so as to correspond to each outcome category given

where Y is the predicted probability of the enterprise sector coded with y = 1 for small scale enterprise, y = 2 for medium enterprise and y = 3 for large enterprise sector, and this result to model 2:

Therefore to estimate objective one, which is to analyze the impact of policy uncertainty in the Nigerian enterprise sector, using equation 3 as expressed in a linear model as:

where (tax is tax rate), (cust is customs and trade regulations), (lreg is labour regulation), (licp is licensing and permits), (plcun is policy uncertainty), (plin is political instability), (macv is macroeconomic environment).

In order to estimate objectives two, the study employed a multivariate analysis to determine the factors that have influenced the growth of the Nigerian enterprise sector as provided in equation 4 as:

where; (entr) is enterprise sector, (ns) is national sales, (tel) is telecommunication, (ele) is electricity, (trn) is transportation, (inf) is availability of infrastructure, (wkf) is unskilled workforce, (fin) is access to finance, (cnf) is cost of finance, (inf) is practice of competition in the informal sector,  (po) is power outage, (ma) is management, (acc) is accounting/finance, (tec) product technology, (mkt) is market/trade information, (mat) is cost of raw materials/intermediate goods, (lab) is total cost of labour. 

However, the study employs cross sectional secondary data from the WB (2010) set to attain its objectives. The Enterprise data used is the cross-sectional survey of small and medium enterprise in Nigeria carried out throughout the country in 2010 and has provision for data on the small, medium and large enterprise sectors and is therefore very instrumental to this study. The survey covered samples of small and medium enterprise firms within the 36 states of the country. However, the interest of this study is to analyze several policies that have affected the Nigeria enterprise sector especially with the huge statistics of the sector’s growth.

4. FINDINGS AND DISCUSSION

In order to achieve objective one, which is to analyze the impact of policy uncertainty on the Nigerian enterprise sector, the researchers used Stata 13 software to generate the results from Logistics regression. The results of the multinomial Logistic regression analysis are shown in Table 4.1 below:

Table-4.1. Estimated Results of Logistic regression

Dep. Var. enterprise sector	Coefficient	z-calculated	P>(z)
Small enterprise(base outcome)
Medium enterprise sector
Tax rate	0.3709483	10.68	0.000
Customs and trade regulation	0.11718	2.45	0.014
labour regulation	-0.320821	-6.77	0.000
licensing/permits	-0.1431601	-3.25	0.001
policy uncertainty	-0.05996	-1.78	0.076
political instability	-0.0428733	-1.04	0.299
macroeconomic environment	0.0955361	2.70	0.007
_cons	-1.700047	-14.25	0.000
Large enterprise sector
Tax rate	0.4727886	4.23	0.000
Customs and trade regulation	0.2707803	2.19	0.029
laour regulation	-0.3306326	-2.53	0.011
Licensing/permits	-0.2943806	-2.41	0.016
Policy uncertainty	-0.0602546	-1.51	0.131
Political instability	-0.0067821	-0.06	0.949
Macroeconomic environment	0.1283538	1.35	0.176
-cons	-4.747852	-11.61	0.000
Pseudo R2  = 0.0657
Prob> chi2     =    0.0000
Log likelihood =  -1854.465

Source:author’s computation.

4.1. Medium Enterprise Sector Relative to Small Enterprise Sector

Table 4.1 above shows the results for objective one and the results indicate that not all the a priori signs of our parameters were all met.  The results show that if government were to increase tax rate on the enterprise sector by one unit, the multinomial log-odds for impacting on the medium enterprise sector relative to the small enterprise, the Nigerian enterprise sector would be expected to increase by 0.3709483 units, while holding all other policies in the model constant. In other words, the implication is that for every unit increase in tax rate policy is associated with a 62.9 percent increase in the odds of impacting the medium enterprise sector relative to the small enterprise sector using the 2-t rule of thumb at 5 per cent level of significance.

Similarly, if government were to increase customs and trade regulation on the enterprise sector by one unit, the multinomial log-odds for impacting on the medium enterprise sector relative to the small enterprise, the Nigerian enterprise sector would be expected to increase by 0.11718 units or by 88.3 percent while holding all other policies in the model at 5 per cent level of significance. The result further indicates that the odds ratio for labour regulation indicates that every year, increase in labour regulation in the sector is associated with 132 percent decrease in the odds of impacting on the medium enterprise relative to the small enterprise sector. The results also show that if government were to increase licensing and permits by one unit, the multinomial log-odds for the policy to impact on the medium enterprise sector relative to the small enterprise sector would decrease by 0.1431601 units while holding all other policies in the model constant or perhaps by 114.3 percent.

Policy uncertainty also impacts on the enterprise sector as the results show that if government policy is perceived to be uncertain within one year, the multinomial log-odds for impacting on the medium enterprise sector relative to the small enterprise sector would decrease by 0.0599697 years or reduced by 94 percent, that alone is enough to scare investors away. Likewise, Political instability affects the business climate of the enterprise sector. As the results indicate that if political instability were to increase in one year, the multinomial log-odds for the impact on the medium enterprise sector relative to the small enterprise sector would decrease by 0.0428733 years or 95.7 percent.

In the case of macroeconomic environment, the results show that if macroeconomic environment were to increase by one point, the multinomial log-odds for growing medium enterprise sector relative to the small enterprise sector would be expected to increase by 0.0955361. In other words, every point increase in the macroeconomic environment is associated with a 90.4 percent increase in the odds of impacting on the medium enterprise sector relative to the small enterprise sector.

4.2. Large Enterprise Sector Relative to Small Enterprise Sector

The results show that if government were to increase tax rate on the enterprise sector by one unit, the multinomial log-odds for impacting on the large enterprise sector relative to the small enterprise, the Nigerian enterprise sector would be expected to increase by 0.4727886 units while holding all other policies in the model constant. In other words, the implication is that for every unit increase in tax rate policy is associated with a 52.7 percent increase in the odds of impacting the large enterprise sector relative to the small enterprise sector at 5 per cent level of significant.

Similarly, if government were to increase customs and trade regulation on the enterprise sector by one unit, the multinomial log-odds for impacting on the large enterprise sector relative to the small enterprise, the Nigerian enterprise sector would be expected to increase by 0.2707803 units or by 72.9 percent while holding all other policies in the model constant and is statistically significant at 5 per cent level. The result further indicate that the odds ratio for labour regulation indicate  that every year increase in labour regulation in the sector is associated with  133.1 percent decrease in the odds of impacting on the large enterprise relative to the small enterprise sector. The results also show that if government were to increase licensing and permits by a million, the multinomial log-odds for the policy to impact on the large enterprise  sector relative to the small enterprise sector would decrease by 0.2943806 million while holding all other policies in the model constant or perhaps by 129.4 percent. 

Policy uncertainty also impact on the enterprise sector as the results also show that if government policy is perceived to be uncertain within one year, the multinomial log-odds for impacting on the large enterprise sector relative to the small enterprise sector would decrease by 0.0602546 years while holding all other policies in the model constant or reduced by 106.0 percent that alone is enough to scare investors away. Likewise, Political instability affects the business climate of the enterprise sector. As the results indicate that if political instability were to increase by one year, the multinomial log-odds for the impact on the large enterprise sector relative to the small enterprise sector would decrease by 0.0067821 years or by 100.6 percent.

In the case of macroeconomic environment, the results show that if macroeconomic environment were to increase by one point, the multinomial log-odds for growing large enterprise sector relative to the small enterprise sector would be expected to increase by 0.1283538. In other words, every point increase in the macroeconomic environment is associated with 87.2 percent increase in the odds of impacting on the large enterprise sector relative to the small enterprise sector. This therefore, agrees with the previous studies by Udah (2010) and Driemeier et al. (2010); Yusuf and Dansu (2013) and Anga (2014).  

For objective Two which is to determine the factors that have influenced the growth of the Nigerian enterprise sector, the researchers used Stata 13 software to generate the results from multivariate regression. The results of the multivariate regression analysis are shown in Table 4.2 below:

Table-4.2. Estimated Results of multivariate regression

Dep. Var. enterprise sector	Coefficient	t-calculated	P>(t)
National sales	-0.0181896	-7.64	0.000
Telecommunication	-0.0782395	-6.20	0.000
Electricity	0.0361072	2.56	0.010
Transportation	0.0221234	1.98	0.048
Availability of infrastructure	-0.0033593	-5.07	0.000
Unskilled workforce	0.0102607	2.09	0.037
Access to finance	-0.0076379	-0.68	0.499
Cost of Finance	0.0258333	2.15	0.032
Practice of competitors in the informal sect	0.0190447	1.53	0.126
Power outages	0.0025145	2.03	0.043
management	0.0393428	0.74	0.458
Accounting/Finance	0.0001119	0.00	0.998
Product technology	0.0187712	0.64	0.524
Market/Trade information	-0.0572692	-1.30	0.193
Cost of raw materials/intermediate goods	2.13e-10	3.74	0.000
Total cost of labour	1.04e-09	3.58	0.000
_cons	2.864041	11.40	0.000
R-squared = 0.1759
Prob> F   =    0.0000

Source: author’s computation

Table 4.2 above shows the results of objective two and indicates that not all the a priori signs of our parameters were all met. The results show that a national sales as the result show that a unit increase in national sales leads to 18 percent decrease in the Nigerian enterprise at 5 per cent level of significance. This could be due to the low patronage of the made in Nigeria products as more of the enterprise sector trades on the locally made products.

Interestingly, telecommunication has also demonstrated a negative impact on the enterprise sector as the results show that a unit increase in telecommunication leads to 78 percent decrease in the Nigerian enterprise sector. However, availability of electricity has demonstrated a positive strength on the Nigerian enterprise sector. The result show that unit increase in electricity availability leads to a 36 units increase in the Nigerian enterprise sector at 5 per cent level of significance. Electricity is an important factor retarding enterprise sector given that most small enterprises in Nigeria rely heavily on either private generating plants or government generating plant for their business survival.

However, the Nigerian enterprise sector has also been constrained by decaying level of infrastructures especially on road network. The results show that unit increase in availability of infrastructure has reduced the enterprise sector by 33 percent at 5 per cent level of significant. The deplorable state of the Nigerian roads is enough factor to sabotage distribution channel of items coming from the rural areas to the urban areas and vice versa, as evidenced in the results. In fact, in this area Nigeria has regressed badly. The same goes for unskilled workforce, which constitute artisan and local traders which further make up the enterprise sector. A unit increase in unskilled workforce increase enterprise sector by 10 percent and is statistically significant at 5 per cent level.

Furthermore, the cost of finance for the enterprise sector is yet another determinant factor. As the results show a unit increase in cost of finance increase the Nigeria enterprise sector by 26 percent under 5 per cent level of significance. While the practice of competition in the informal sector is seen as another determining factor to the enterprise sector, a unit increase in the practice of competition in the informal sector will increase the enterprise sector by 19 percent, which shows a statistical significance at 10 per cent level.

Another significant factor is power outage, which shows that a unit increase in power outage has increased the enterprise sector by 25 percent at 5 percent level of significance. This possibly can be attributed to the contribution of the private generating plants as many enterprises patronize the alternative source of power, which at the end is shifted to the final consumer as higher prices. And, this indirectly creates more multiplier effect in the economy by raising prices, which invariably has ripple effects on the national economy. This technically, could answer the reason why the government effort to have stable power has ever been sabotaged as so many seem to be benefiting from the importation of generating plants.

Then the market or trade information factor which ordinarily is supposed to enhance market efficiency due to the effect of information asymmetry theory in microeconomics but this is not the case in Nigeria. As the results show, a unit increase in market or information decreases the enterprise sector by 57 percent. In other words, information symmetry in the commodity market is to be exploited greatly so that the market can operate at high level of efficiency. And this agrees with the outcome of study by Udah (2010) and Driemeier et al. (2010).

And, the cost of raw materials and intermediate goods is part of the determining factor for developing the enterprise sector considering the endowed natural resource within the country. As the results show, a unit increase in the cost of raw materials and intermediate goods increased the enterprise sector by 213 percent, and are statistically significant at 5 percent level of significance. And lastly, the total cost of labour, given the Nigerian huge population figure and the potential it creates for a large market, show that a unit increase in the total cost of labour increase the Nigeria enterprise cost sector by 104 percent and is statistically significant 5 percent level.

And given the coefficient of determination (pseudo R2) which is the summary measure that tells how well the sample regression line fits the data. In other word measures the goodness of fit which is 0.1759. This indicates that 18% of the work is explained by the independent variables given the nature of the data as being a cross sectional data. While the Prob> F of 0.0000 suggests that the overall model is significant hence, the study can conclude that the mode is robust and the results thereof are reliable.

5. CONCLUSION

This Study evaluated the Nigerian enterprise sector and policy uncertainty using the World Bank enterprise survey data for Nigeria 2010. The study therefore, employed the multinomial logistic regression model and the multivariate statistics to specifically analyze the impact of policy uncertainty on the enterprise sector, and the factors that have influenced the growth of the Nigerian enterprise sector. The findings show that tax rate, customs and trade regulations and macroeconomic environment have impacted on the medium and large enterprise sector positively. This by implication shows that with relative to the small enterprise sector that both the medium and large enterprise sector in Nigeria have really had it good as the results indicate, meaning that these policies never influenced them badly, and so, it is most likely that government must have given them strong back up on the premise that the huge market in Nigeria provided by her increasing population.

The indicators in the result further indicated a negative impact on the enterprise sectors even though the results were significant. The results therefore, showed that relative to the small enterprise sector, that labour regulation, licensing and permits, policy uncertainty and political instability had negative impact on the medium and large sector enterprise sector in Nigeria which agreed with the findings of Yusuf and Dansu (2013); Driemeier et al. (2010) and Udah (2010). This goes a long way to show that Nigeria is yet to get it right in area of policy direction and implementation if the government is sincere in partnering with the private sector to sustain economic growth and development and achieve her dreams of vision 2020 economic goals.

The results also show that electricity, unskilled workforce, cost of finance, practice of competitors, power outages and cost of raw materials or intermediate goods have all had positive influence the growth of the Nigeria enterprise sector which, is most probably, the reason for Nigeria’s rating as the one of largest countries in Africa in 2014, and with the description of the “fastest growing economy in the world”. Therefore if with these indicators, the Nigerian enterprise sector is doing very well, then the probability that if proper macroeconomic environment and strong institutions, good leaderships and infrastructures are provided as incentives, they will enhance both public and private sector productivity for economic competitiveness.

Funding: This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

Contributors/Acknowledgement: All authors contributed equally to the conception and design of the study.

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