Abubakar S. Garba, Ph.D., Department of Business Admin & Management, School of Management Studies, Kano State Polytechnic, Nigeria, This email address is being protected from spambots. You need JavaScript enabled to view it.
Fariastut Djafar, Ph.D., Department of Economics, Faculty of Economics and Business, Universit Malaysia Sarawak, Malaysia, This email address is being protected from spambots. You need JavaScript enabled to view it.
Shazali Abu Mansor, Prof., Department of Economics, Faculty of Economics and Business, Universit Malaysia Sarawak, Malaysia.


The objectve of this paper is to examine the influence of poverty, unemployment and GDP on entrepreneurship. Time series data for 31 years was collected from various ofcial sources for the analysis. Vector autoregressive (VAR) framework was adopted to systematcally capture the rich dynamic of multple tme series. Other tests conducted were unit root test, Johansen and Juselius (1990) co-integraton test, Granger causality and dynamic model analysis beyond the sample. It was found that poverty and GDP influence entrepreneurship negatvely, while unemployment influences entrepreneurship positvely. The paper reveals the presence of both opportunity and necessity driven entrepreneurs in the country. There is a need for the government to revisit the existng policy on micro, small and medium enterprises (MSMEs) to adequately address the problem of the poor and unemployed by availing them with the opportunity to engage in entrepreneurship. Future study should consider mitgatng the effect of frequent entry and exit from entrepreneurship in their data to correctly predict the effect of entrepreneurship on the economy.

Keywords: entrepreneurship development, poverty, unemployment, GDP.


The potental of entrepreneurs to harness the necessary resources and create vibrant economy is increasingly being recognized in developing countries. The signifcance of micro, small and medium enterprises (MSMEs) has been recognized globally in terms of productvity and compettveness of the economies. MSMEs are the primary source of job creaton, nurturing ground for entrepreneurial capabilites, innovatveness as well as providing managerial competency for private sector development. MSMEs play a key role in developing countries that are characterized with high level of unemployment and poverty.

The potental of entrepreneurs to harness the necessary resources and create vibrant economy is increasingly being recognized in developing countries. The signifcance of micro, small and medium enterprises (MSMEs) has been recognized globally in terms of productvity and compettveness of the economies. MSMEs are the primary source of job creaton, nurturing ground for entrepreneurial capabilites, innovatveness as well as providing managerial competency for private sector development. MSMEs play a key role in developing countries that are characterized with high level of unemployment and poverty.

There is a great opportunity for entrepreneurship in the country and entrepreneurial actvity has the potentals of addressing the incessant problem of poverty and unemployment. Entrepreneurship development requires more than a policy pronouncement, but acton must be taken to provide a conducive business environment partcularly for the micro and small business to emerge and prosper. It is noted that there is no previous study that examines the influence of GDP, unemployment and poverty on entrepreneurship at the same tme. Generally, there is paucity of studies that examine the influence of GDP and poverty on entrepreneurship. Therefore, this paper atempts to fll this research gap by examining the influence of poverty, unemployment and GDP simultaneously on entrepreneurship in which no previous study does so in Nigeria context. The objectve of this paper is to examine the influence of poverty, unemployment and GDP on entrepreneurship.

Literature review

Entrepreneurship plays an important role in boostng productvity, increasing competton and innovaton, creatng employment and economic prosperity (Ritche and Lam, 2006). Entrepreneurship is synonymous with business start up or creaton of new organizaton (Keister, 2005). There are several factors influencing entrepreneurship in both developed and developing countries. The nature and dynamics of entrepreneurship is dependent on the country’s level of economic development. The patern and type of entrepreneurs are based on how socio-economic variables affect entrepreneurship in the country. The refugee/push and Schumpeterian/pull effect hypotheses provide the basic understanding of the relatonship between entrepreneurship, poverty, unemployment and GDP (Audretsch et al., 2001). The relatonship between entrepreneurship and the macroeconomics variables are discussed as follows:

Entrepreneurship and Poverty: The long term analysis of economic and social development, partcularly on poverty reducton, is very important in discussing any developmental issues (Szirmai, 2005). Poverty reducton should also be the ultmate goal of all development endeavors (Akoum, 2008). Poor people are motvated to engage in micro and small scale business to sustain their lives and possibly get out of poverty. They can make a difference by turning themselves into entrepreneurs to productvely and economically contribute to the society. Poor economic conditons lead to higher entrepreneurial actvites in many developing countries than in the developed countries. However, the frequent and high entry and exit is noted among people who have started business out of necessity.

Rosa, Kodithuwakkub and Balunya (2006) in Uganda and Sri Lanka found that poverty signifcantly influences entrepreneurial actvity. Mulira, Namatovu and Dawa (2011) in Uganda reveal negatve and signifcant relatonship between poverty and entrepreneurship. Block and Sandner (2009) and Wanger (2005) in Germany and Verheul, Thurik, Hessel and Zwan (2010) in 27 European countries and the US discovered that there were more opportunity than necessity entrepreneurs in these countries.

Entrepreneurship becomes inevitably the last opton partcularly for the poor in an economy where employment opportunites are not readily available. The poor can be creatve and exert high impact through radical innovatons. The idea of creatve destructon is built on dynamic, deliberate entrepreneurial effort to change market structures and make use of proft opportunites that exists. It is interestng to fnd whether high rate of entrepreneurial actvity due to necessity could be translated into economic growth or not. This may depend on the situaton and level of economic development of a partcular country in which the entrepreneurs exist. Both opportunity and necessity entrepreneurs can be found in both developed and developing countries.

Entrepreneurship and Unemployment: There are an increasing number of studies on the relatonship between unemployment and entrepreneurship. Most of the previous studies used cross sectonal or longitudinal data at micro level and tme series data at macro level (Meager, 1992). The entry into entrepreneurship by unemployed people has atracted the atenton of many researchers and policy makers. The propensity to start a business because of unemployment is very important to public policy (Audretsch and Jin, 1994). That is why many governments in both developed and developing countries are encouraging and supportng unemployed people to start up business. Evan and Leighton (1990) in the US studied small businesses started and operated by both unemployed and employed workers. It was discovered that entry into entrepreneurship was higher for the unemployed than for the employed people. The relatonship between entrepreneurship and unemployment is not clear but empirical studies revealed two ways of relatonships. One strand of the studies confrmed that unemployment stmulates entrepreneurial actvity which is referred to as refugee effect, while the other body of the literature confrms that high entrepreneurial actvity influences reducton of unemployment which is known as Schumpeterian effect.

Unemployment is positvely related to new frm start ups in 23 OECD countries (Audretsch et al., 2001). Other studies found positve influence of unemployment on entrepreneurship (Reynolds, Storey and Westhead, 1994; Evans and Leighton, 1989 and Highfeld and Smiley, 1987). While Garof (1994) in UK, Audretsch and Fritsch (1994) in Germany indicate that unemployment is negatvely related to new frm start up. Audretsch and Thurik, (2000) believe that new business could possibly generate employment thereby cutng down the rate of unemployment. Hamilton (1989) and Faria, Cuestas and Mourelle (2010) suggest that the relatonship between entrepreneurship and unemployment can be bidirectonal and non linear. Carree (2002) in the US found that there is no signifcant relatonship between the variables.

In another dimension, entrepreneurial actvity reduces unemployment and could have positve effect on economic performance in different ways. Stel et al. (2007) and Audretsch et al. (2001) atempted to reconcile this ambiguous relatonship using data from 23 OECD countries between 1974 and 1998. Phehn-Dujowich (2012) in the US discovered that the unemployment has Granger causal effect on entrepreneurship. Storey (1991) provided an explanaton which looks like a consensus on the relatonship between unemployment and entrepreneurship.

Entrepreneurship and GDP: The link between entrepreneurship and GDP can be traced to Schumpeter’s work which highlights the role of entrepreneurs in creatng disequilibrium through the process of creatve destructon (new combinaton). Schumpeterian entrepreneurs are productve, innovatve and opportunity seekers (Sexton and Kasarda, 1992). In a period of high economic growth there will be proliferaton of opportunity entrepreneurs, who make high impact and promote economic development (Mojica-Howell, Whitaker, Gebremedhin and Schaeffer, 2012 and Jones-Evans, Brooksbank and Aaron, 2006). High level of GDP may lead to increasing economic prosperity which in turn affects consumpton and investment (Hartog, Parker, Stel and Thurik, 2010). The increase in consumer demand and services due to economic prosperity will create opportunites for entrepreneurs (Audretsch and Keithbach, 2004). On the other side, low GDP creates necessity entrepreneurs who start up business because of poor economic conditon characterized by limited optons for wage employment due to low demand for goods and services. This situaton reflects the ‘push / recession hypotheses’.

Some previous studies atempted to investgate two directonal relatonships between GDP and entrepreneurship (Thurik, Carree, Stel and Audretsch, 2008 and Mojica-Howell et al., 2012). Other studies try to examine the influence of economic growth on entrepreneurship (Storey, 2003). Entrepreneurship is likely to be endogenous where high level of GDP has a strong incentve for opportunity based business start up. It was found in the US by Phehn- Dujowich (2012) that economic growth causes entrepreneurship (Granger causality). Hartog et al. (2010) found evidence of long run equilibrium relaton between entrepreneurship (business ownership) and economic growth measured by per capita income.

The relatonship between necessity entrepreneurship and GDP is likely to be negatve for developing countries and positve for developed natons. Koster and Rai (2008) discovered that in India the increase in GDP does not go with the decreasing rate of entrepreneurial actvity as expected in the Global Entrepreneurship Monitor (GEM) model. Their result shows a weak positve relatonship between economic growth and entrepreneurship in least developed regions. Stel, Carree and Thurik (2004) in GEM countries found that there is a signifcant linear effect between total entrepreneurial actvites (TEA) and GDP growth. They also discovered a signifcant nonlinear effect which shows a negatve effect in relatvely poor countries and positve effect in relatvely rich countries. Salgado-Banda (2005) in 22 OECD countries found both positve and negatve relatonship using two different measures of entrepreneurship. GEM research work represents one most important analysis and source of data for global entrepreneurial actvity and partcularly provides a link between entrepreneurship and economic growth (UNCTAD, 2004). GEM believed that the traditonal view on GDP and economic compettveness neglected the role of entrepreneurship (new and small business) in the economy.

The developing countries are assumed to have a high number of necessity entrepreneurship because of the unbearable conditon and the need to survive (Koster and Rai, 2008). Opportunity entrepreneurship tends to pick up as the economy improves when people consider it safe to abandon self employment for wage employment. The prevalence of opportunity and necessity can be depicted in a U shaped curve which is termed as U shaped curve hypothesis (Bosma et al., 2008; Koster & Rai, 2008; Wennerkers et al., 2005). In the early stage of development there will be a higher rate of business creaton, but as the country’s GDP per capita increases there will be a decrease in the rate of business creaton. ON the other hand, in the later stage the relatonship tends to be positve, which means increase in GDP per capita causes increase in the rate of new business creaton.

Data and method

This secton deals with the methodology. It explains the model specifcaton, defning and measuring variables and method of data analysis as follows; Econometrics model specifcaton:

LENTt = βo + β1LPOVt+ β2LUEMt + β3LGDPt + µt

LENT = logarithm of entrepreneurship
LPOV = logarithm of poverty
LUEM = logarithm of unemployment
LGDP = logarithm of GDP
β = Parameter
µ = error term

Defning and measuring variables

Entrepreneurship: New business creaton is used as aproxy for entrepreneurship as adopted by the previous studies (Wang, 2006; Sternberg and Wennekers, 2005; and Lafuente and Driga, 2007). It is defned as the number of micro and small business created annually in the country. The data was collected from Corporate Affairs Commission (CAC), Nigeria.

Poverty: The Natonal Bureau of Statstcs adopted World Bank internatonal poverty threshold of $1.25 per day for Sub-Saharan Africa for measuring poverty in absolute term. Therefore any person with income below this ofcial threshold is considered to be poor. In this case the number of poor people was used as a measure of poverty. The limitaton of this measure is that in Nigeria consumer survey on poverty is not conducted on a yearly basis. Data for other years was obtained based on annual poverty projecton. The data was obtained from Natonal Bureau of Statstcs for 31 years.

Unemployment: Unemployment is defned as a situaton where someone of working age would like to be in full tme employment, but is unable to get a job. In this paper the number of registered unemployment was used. The data for the registered unemployed was obtained from Federal Ministry of Labour and Productvity, Nigeria. The limitaton of this data is that many unemployed people may not be included in the study because they did not register as unemployed in the Ministry.

Gross domestc product: It is basically the amount of goods and services produced in a country over a specifc period of tme. In this paper absolute value of real GDP was used as a measure for the period between 1980 and 2010. The data for real GDP was collected from Central Bank of Nigeria (CBN).

Data analysis

Vector autoregressive (VAR) framework is used to provide a systematc way of capturing rich dynamic in multple tme series. It is useful in data descripton, forecastng, structural inference and policy analysis (Stock and Watson, 2001; and Gujarat and Porter, 2009). The tests conducted were unit root (augmented Dickey-Fuller and Phillips-Perron), Johansen and Juselius (1990) co-integraton, error correcton model (ECM) and Granger causality, variance decompositons and impulse response functon. The Vector error correcton model (VECM) is represented by the following equatons in which each variable become endogenous;

ENTt = α0 + δ0 ENTt-1+ δ1 GDPt-1 + δ2 POVt-1 + δ3 UEMt-1+ λ0 ECTt-1+ et
GDPt = α0 + δ0 GDPt-1+ δ1 ENTt-1 + δ2 POVt-1 + δ3 UEMt-1+ λ0 ECTt-1+ et
POVt = α0 + δ0 POVt-1+ δ1 ENTt-1 + δ2 GDPt-1 + δ3 UEMt-1+ λ0 ECTt-1+ et
UEMt = α0 + δ0 UEMt-1+ δ1 ENTt-1 + δ2 GDPt-1 + δ3 POVt-1+ λ0 ECTt-1+ et

Where ECT is the error correcton term measuring the speed of adjustment to the long run equilibrium, and α, δ, λ are estmated parameters

Results and discussion

Unit root test results

In Table 1 and 2 the results of Augmented Dickey fuller (ADF) and Phillips Perron (PP) unit root tests are shown respectvely. The results indicate that the null hypotheses of presence of a unit root or non-statonarity in both methods cannot be rejected at level form, but it can be rejected afer frst differencing at 1% level of signifcance.

Table 1. Unit root test - Augmented Dickey-Fuller (ADF)
VariableLevelFirst Difference
  Intercept Intercept with trend Intercept Intercept with trend
LENT -2.1361(0) -2.9055(0) -5.5600(0)*** -5.4656(0)***
LPOV -1.2808(1) -2.2534(1) -5.7494(0)*** -5.7140(0)***
LUEM -0.3635(0) -2.6438(0) -5.7646(0)*** -5.6316(0)***
LEG 1.1616(0) -1.3405(1) -8.5310(0)*** -8.5952(0)***
Note:*** denote statstcal signifcance at 1% level. The critcal value of ADF can be found in Mackinnon (1996). The optmum lag length in the test was selected automatcally based on Schwarz Informaton criterion. Lag selecton fgures are shown in ( ) . In ADF, null hypothesis indicatng presence of unit root was examined against alternatve for statonarity. LENT is a natural log of ENT, LPOV is a natural log of POV, LUEM is a natural log of UEM, LGDP is a natural log of GDP.
Table 2. Unit root test - Phillips-Perron (PP)
VariableLevelFirst Difference
  Intercept Intercept with trend Intercept Intercept with trend
LENT -1.8383(5) -2.8715(3) -7.8855(16)*** -9.3802(18)***
LPOV -1.4925(2) -2.5001(2) -5.7400(2)*** -5.7095(1)***
LUEM -0.2637(6) -2.6758(1) -5.9593(7)*** -5.7995(7)***
LEG 0.8177(2) -2.6758(3) -8.3547(1)*** -8.3250(2)***
Note:*** denote statstcal signifcance 1% level. The critcal value of PP can be found in Mackinnon (1996). The optmum lag length in the test was selected automatcally based on Newey-West estmator using lag selected by Bartlet kernel informaton criterion. Lag selecton fgures are shown in ( ). In PP null hypothesis indicatng presence of unit root was examined against alternatve for statonarity. LENT is a natural log of ENT,LPOV is a natural log of POV, LUEM is a natural log of UEM, LGDP is a natural log of GDP.

The results from the two testng procedures clearly show that the variables are I(1) integrated order of 1. It is stated that most of the macroeconomic variables are I(1) process (Gujarat and Porter, 2009; Bahmani-Osokoee,1995). Based on these results and having the same order of integraton, it is found suitable to proceed to co-integraton test to examine the long run relatonship among variables.

Co-integraton and hypothesis testng results

The result of Johansen cointegraton test is presented in the Table 4 and the selecton of lag length performed using Schwarz informaton criterion (SIC) can be seen in Table 3.

Table 3. Lag selecton based on multvariate SIC
0 3.290467
1 -2.627118*
2 -1.652972
Note: SC refers to Schwarz Informaton Criterion. Asterisk * denotes the optmum lag selected for VAR estmaton in Eviews

From Table 4 panel a the co-integraton result reveals that the null hypotheses that states no co-integratng vector (r=0) is rejected in both max eigen value and trace tests, therefore alternatve hypothesis is accepted indicatng 1 co-integratng vector. This means that the variables in the system share a common trend and move toward one directon in the long run. The result in Table 4 panel B shows normalized co-integratng vector. The coefcients indicate the long run elastcity of the variables. It can be seen that poverty (LPOV) has negatve effect on entrepreneurship (LENT). This means that keeping other variables constant, any increase in poverty will decrease the rate entrepreneurship by .13% points.

Table 4. Co-integraton and hypothesis testng result
HoHaMax eigen value95% CVTrace95% CV
Panel A: Johansen multvariate test
r = 0 r = 1 28.9815** 27.5843 48.9196** 47.8561
r ≤ 1 r = 2 15.0945 21.1316 19.9381 29.7970
r ≤ 2 r = 3 4.7803 14.2646 4.8435 15.4947
r ≤ 3 r = 4 0.0631 3.8414 0.0631 3.8414
Panel B: Normalizing the co-integratng vector
    -1.000 0.1346 0.9603 -0.2093
Notes: r indicates number of co-integratng relatonships. Asterisks ( **) indicate 5% level of signifcance.

The negatve effect of poverty on entrepreneurship supports the fndings of Rosa, et al. (2006) in Uganda and Sri Lanka, Mulira et al. (2011) in Uganda, Block and Sandner (2009) and Wagner (2005) in Germany and Verheul et al. (2010) in 27 European countries and the US. This result reveals the existence of opportunity entrepreneurship as poverty cannot stmulate most of the poor to engage in entrepreneurial actvites. The fnding also reflects pull/prosperity effect which points that people decide to enter into entrepreneurship because of the existng opportunity rather than poverty.

Since about 70% of Nigerian populaton are poor, they probably lack resources to enable them to meet their basic needs and engage in entrepreneurial actvity. Various government regimes in the past have atempted to promote entrepreneurship in order to address the problem of poverty through enactment policies and programs such as Natonal Poverty Eradicaton Program (NAPEP), Poverty Alleviaton Program (PAP), Family Economic Advancement Program (FEAP) and Family Support Program (FSP). The negatve relatonship between entrepreneurship and poverty is a clear indicaton that these policies and programs did not make signifcant impact on entrepreneurship to reduce poverty. Moreover, one of the important means through which the millennium development goal of halving poverty can be atained is to empower the poor people to massively engage in entrepreneurship, otherwise the MGDs target for 2015 will remain elusive.

It also appears that unemployment (LUEM) influences entrepreneurship positvely. The result shows that any increase in unemployment will increase the rate of entrepreneurship by .96% points holding other variables constant (see table 4). This indicates that as unemployment is increasing, the rate of entrepreneurship is also increasing. This result corroborates the fndings of Yamawaki (1990) in Japan, Audretsch et al. (2001) in 23 OECD countries, Highfeld and Smiley (1987) and Evan and Leighton (1989) in US, Ritsila and Tervo (2002) in Finland, and Reynolds et al. (1994) in France, Germany, Ireland, Italy, Sweden, UK and US. The result also indicates that people in the country become entrepreneurs because of threat of unemployment. This reflects the existence of refugee effect/push hypothesis in which unemployed persons are motvated to start up their own business because there is no prospect of getng paid jobs due to macroeconomic instability or depressed market conditon (Storey, 1991).

The rate of unemployment is high (23.9% in 2011) in Nigeria, therefore the unemployed can have only two optons either to start up their business or keep searching for employment opportunites elsewhere. However, the decision in this regard is dependent on the relatve payoff in the environment. People in the country can exercise their latent potentals to form new business as the unemployment rate is increasing (Hamilton, 1989). The dimension of entry into entrepreneurship varies between unemployed and employed people. Evan and Leighton (1990) in the US found that entry into entrepreneurship is high among unemployed than those who are already employed. Although unemployed are motvated to start business because of lack of paid job, they have different mission on how they want to promote their business. The rate of business start up by the unemployed could be accelerated based on the conditons and other environmental factors in the country. The extent to which unemployment influences the rate of entrepreneurship is very crucial in the realm of public policy (Audretsch and Jin, 1994).

The government of Nigeria also came up with various policies and programs to support unemployed persons to become entrepreneurs. These include the formaton of Natonal Directorate for Employment (NDE) in 1986 which is saddled with the responsibility of training and supportng the unemployed to become self reliant by startng their own business. The recent introducton of entrepreneurship courses in all tertary insttutons across the country which is aimed at providing necessary training and business skills to the students is another complementary effort to address the problem of youth unemployment. This is partcularly designed to relieve the graduates from the problem of unavailable vacancies in the labor market.

The result further reveals that GDP (LGDP) affects entrepreneurship negatvely. It indicates that any increase in economic growth will reduce entrepreneurship entry by about .21% points. This result reflects the lef hand side of U curve shaped hypothesis for developing countries and is supported by the fndings of Carree, Stel,Thurik and Wennekers (2002), Stel et al. (2004), Wennekers et al. (2005) Naude, et al. (2012) and Acs (2007) in GEM countries and Koster and Rai (2008) in India. From the previous studies the relatonship between entrepreneurship and GDP is more likely to be positve for developed countries and negatve for developing countries (Acs, 2007 and Acs, Desai and Hessels, 2008).

In developing countries at the inital stage where people face poor economic conditons such as low income and high unemployment they may not have other opton than to engage in entrepreneurial actvity as a means of survival. There will be a proliferaton of many necessity entrepreneurs at this stage, but with the improvement of country’s economic conditons these necessity entrepreneurs will decline their interest in entrepreneurial actvity leaving only opportunity entrepreneurs. This negatve relatonship is an indicaton that as country’s GDP is increasing the rate of necessity entrepreneurship is decreasing. The reason is that the necessity entrepreneurs may not have necessary interest and enthusiasm to cope with the intense competton generated in the market and harsh business environment in Nigeria. They would fnd that it is easier to look for paid employment rather than contnuing with their business or startng a new one again.

Short run Granger causality and VECM results

Sequel to the detecton of co-integraton relaton, the proper VAR framework that studies the dynamic relatonship between variables must include error correcton term (ECT). Thus, VECM provides a way to examine both short run and long run causal relatonship among variables in the model. The result of Granger causality among the variables is presented in the Table 5.

Table 5. VECM and short run Granger causality result
X2 –StatstcsECT
Dependent Variables∆LENT∆LPOV∆LUEM∆LGDPCoefcientt-statstc
∆LENT - 4.9881(0.026)** 2.0555(0.152) 1.6682(0.197) -0.1467 -1.5652
∆LPOV 0.0809(0.776) - 6.1019(0.014)** 11.768(0.001)*** -0.1836*** -5.2393
∆LUEM 0.3588(0.549) 2.0593(0.151) - 0.3350(0.563) -0.1082 -0.7852
∆LGDP 0.0203(0.887) 0.9732(0.324) 1.4282(0.232) - -0.0388** -2.4226
Note: The VAR was based on 1year lag structure and a constant. ***, **, * indicates statstcal signifcance at 1%, 5%, 10% level respectvely. Figures in parenthesis ( ) are p- value.

The result shows that LPOV and LGDP equaton have ECT that is statstcally signifcant which indicates that these variables are responsible for the short run adjustment to bring back the system to long run equilibrium. Without any innovaton due to LPOV in the short run, the speed of adjustment will be 18% per year which indicates that system needs about 6 years to revert to the long run equilibrium. The results from Granger causality test in Table 5 show direct and indirect short run causality among the variables. Poverty directly Granger caused entrepreneurship. Unemployment and GDP indirectly caused entrepreneurship through poverty. Both the direct and indirect causality found reflect the existence of refugee/shop keeper’s effect. This fnding can contribute to the argument on whether entrepreneurship is relevant and necessary under the present economic conditon in which unemployment and poverty is high and GDP is steadily increasing in the country.

The level of unemployment in a country causes people to live without income and accounts for a situaton when they cannot afford basic needs and wants. Hence they become poor and necessitated to pursue entrepreneurial actvity. High level of unemployment beyond certain critcal level does not necessarily induce people to become entrepreneurs in a country (Hamilton, 1989). The indirect causality from GDP to entrepreneurship through poverty indicates that low GDP due low economic actvity and consumer demand causes poverty which consequently pushes poor people to engage in entrepreneurial actvity. This situaton creates necessity entrepreneurs who will make litle impact to the economy. They may exit from entrepreneurship as soon as the situaton improves because they were not motvated by opportunity in market.

The diagnostc test results are presented in table 6 which indicate that the model is robust and satsfactorily proven. The estmated residuals have followed normal distributon patern, the residual are serially uncorrelated, there is no problem of misspecifcaton and there is evidence of homoscedestcity of variance. Moreover, the recursive parameter estmate of CUSUM and CUSUM of square tests are presented in Figures 1 and 2 respectvely (see Appendix 1). The tests indicate that the model is relatvely stable as the cumulatve values fall within the two standard deviatons boundaries at 5% level of signifcance.

Table 6. Diagnostc test
1.050 0.349 0.998 0.858 0.905
(0.365) (0.907) (0.327) (0.651) (0.538)
Note: AR and ARCH are the Lagrange multplier tests for serial autocorrelaton and ARCH effect respectvely. RESET refers to Ramsey Reset specifcaton test. JB is the Jarque Bera statstcs for residual normality test and White refers to White general heteroscedestcity test. Figures in parenthesis are p- value.

Variance decompositons (VDCs)

The variance decompositons gauged the strength of the causal relatonship among all the variables in the system. This dynamic analysis beyond the sample strengthened the empirical evidence from the earlier Granger causality analyses. Table 7 shows the decompositons of the forecast error variances of each variable in the system up to 50 years. The analysis can be summarized in the following manner; frst, the result indicates that LUEM is the most exogenous variable in the system with only 9% of its forecast error variance being explained by the other variables. Secondly, LPOV is the most interactve variable in the system, about 93% of its forecast error variance is explained by LENT (72%), LUEM (12%) and LGDP (8%). Therefore poverty is most endogenous variable and this strengthens the evidence of causality running from unemployment and economic growth to poverty. Thirdly, the changes in LENT happen largely as a result of movement in LUEM. The effect of LUEM on LENT is increasing as the tme horizon (years) is also increasing.

Generalized impulse response functons (GIRFs)

The system as earlier mentoned has four dimensional variables. Therefore 12 possible scenarios of GIFRs are presented for all the variables afer disregarding each variable’s own shock. The Figure 3 (Appendix 2) shows the visual illustratons of the GIRFs up to 50 years. In most of the result the variables exhibit rapid responses to the shocks, they move fast untl afer 5 years when they become stable. Moreover, LENT respond negatvely due to shock in LPOV that indicate the existence of negatve relatonship between them. LPOV respond positvely due to shock in LUEM and respond negatvely due to shock in LGDP.

Table 7. Generalized variance decompositons (VDCs)
Percentage of forecast variance explained due to innovaton:
    Relatve variance in          
Years 1 ∆LENT 100.000 0.000 0.000 0.000 0.000
  2   84.301 0.143 15.098 0.457 15.699
  10   68.011 0.965 30.779 0.245 31.989
  30   65.841 0.971 33.016 0.172 34.159
  50   65.392 0.972 33.478 0.157 34.608
    Relatve variance in          
  1 ∆LPOV 0.358 99.642 0.000 0.000 0.358
  2   11.937 83.354 1.303 3.406 16.646
  10   63.211 18.002 12.267 6.521 81.998
  30   71.139 8.567 12.410 7.884 91.433
  50   72.600 6.828 12.436 8.136 93.172
    Relatve variance in          
  1 ∆LUEM 2.247 2.400 95.353 0.000 4.647
  2   3.012 1.407 95.481 0.100 4.519
  10   1.178 5.803 92.231 0.788 7.769
  30   0.795 7.248 90.808 1.149 9.192
  50   0.713 7.561 90.499 1.228 9.501
    Relatve variance in          
  1 ∆LGDP 6.586 0.062 3.996 89.356 10.644
  2   14.499 0.318 3.155 82.028 17.972
  10   28.836 4.423 0.348 66.394 33.606
  30   30.196 4.896 0.137 64.770 35.230
  50   30.426 4.976 0.102 64.496 35.504
Note: Figures in frst column is horizons in years. The column in bold represents the impact of each variable’s own shock. The last column provides the percentages of forecast error variances of each variable explained by the other variables. All fgures in table are rounded to 3 decimal places.


The interest in this study came as a result of the observed dwindling socioeconomic conditons in Nigeria and aqueston whether entrepreneurship could be relevant and necessary in addressing myriad socio-economic problems. Therefore the paper examines the influence of poverty, unemployment and GDP on entrepreneurship. The existence of a long run relatonship among entrepreneurship, poverty, unemployment and GDP has been found and the Granger causality result shows that poverty directly causes entrepreneurship, while unemployment and GDP indirectly cause entrepreneurship entry. The causality from unemployment to entrepreneurship entry strengthens the evidence of refugee/shop keepers’ effect which means unemployment causes people to engage in entrepreneurship in Nigeria. It is discovered that poverty and GDP influence entrepreneurship negatvely which indicates that the existng entrepreneurs are likely to be an opportunity entrepreneurs and supports Schumpeterian/prosperity effect hypothesis. The negatve influence of poverty on entrepreneurship is not antcipated as poverty is expected to increase entrepreneurship in the country, but it is found that poverty cannot stmulate entrepreneurship.

The positve influence of unemployment on entrepreneurship is an indicaton of the presence of necessity entrepreneurs and it supports push/ refugee effect hypothesis. Therefore, the paper reveals the presence of both necessity and opportunity entrepreneurship in the country. Necessity entrepreneurship could create job and income in the short run, thereby reducing the social problem. Unemployed and poor people ofen have feelings of dissatsfacton about their entrepreneurial involvement which may result in their exit from entrepreneurship as soon as they get an alternatve paid job. Opportunity entrepreneurs are innovatve individuals who create disequilibrium in the economy. The prevalence of this type of entrepreneurs in a country may result in more innovatons, high competton and economic growth in both short and long run. Schumpeterian entrepreneurs are opportunity driven, productve and high impact individuals who are carrying out new combinatons (innovaton) and contribute towards economic development. The paper contributes signifcantly in providing useful informaton to various stakeholders for effectve policy formulaton towards entrepreneurship development.

The paper also contributes to the theory and literature of entrepreneurship in the Nigeria context. The Schumpeter’s theory of economic development is based on the assumpton that entrepreneurs are innovatve and can stmulate GDP. Entrepreneurship may not necessarily drive and stmulate the desired GDP if it is driven by necessity. It is also expected theoretcally that increase in poverty will automatcally increase the rate of entrepreneurship entry. The unexpected negatve relatonship between entrepreneurship and poverty shows that poverty may not necessarily cause people to engage in entrepreneurship because of probable lack of start-up capital.

Practcal implicatons and directon for further research

There is a need for the government to revisit the existng policy on micro, small and medium enterprises (MSMEs) to adequately address the problem of the poor and unemployed in order to avail them with the opportunity to engage in entrepreneurship. There will be an increase in the rate of crime and other social vices where majority of poor and unemployed people are lef without employment or incentves to partake in entrepreneurial actvites. Lack of necessary infrastructure could affect the performance of business, income and subsequently lead to a closure of the enterprises. Therefore the government should place high priority in boostng electricity generaton and supply so as to reduce the cost of operaton and make the business environment more compettve, conducive and friendly for entrepreneurial actvity.

The paper focuses on some selected macroeconomic variables in examining their influence on entrepreneurship in Nigeria. Measuring entrepreneurship at the aggregate level is a difcult and complex task. Using new business creaton as a proxy may not always be appropriate because sometmes it is not easy to distnguish between legal and illegal business actvity. The total number of micro and small businesses registered (as business name) annually was used without fltering or removing the number of those businesses that ceased to exist. There is no ofcial record of those registered businesses that stopped operatng as micro or small businesses over the years. Many businesses were not included in the study because they did not register with the government agency as such their number will not be reflected in the list of new business created in the country.

This study is limited in scope but provides sufcient evidence on the factors that influence entrepreneurship in Nigeria. In future, similar study should mitgate the effect of frequent entry and exit from entrepreneurship in the data and effort should be made to flter and consider those with genuine business interest in order to correctly predict the effect of entrepreneurship on the economy. The rate of new business creaton varies according to sectors and industries from year to year, therefore there is need to look at individual sector on how entrepreneurship is affected rather than taking analysis on the whole sectors of the economy.


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Appendix 1

Figure 1. CUSUM Test
Figure 2. CUSUM OF SQUARE Test

Appendix 2

Figure 3. Generalized impulse response functons (IRFs)