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Go Back       IAR Journal of Business Management | IAR J Bus Mng, 2020; 1(1): 20-26. | Volume:1 Issue:1 ( June 10, 2020 ) : NA
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I AR Journal of Business Management
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Frequency : Bi-Monthly
Language : English
Origin : Kenya
Website : https://www.iarconsortium.org/journal-info/IARJBM

Original Research Article


Business Regulations and Shadow Economy In Nigeria: Is There Any Significant Relationship?


Article History

Received: 25.04.2020

Revision: 24. 05.2020

Accepted: 04. 06.2020

Published: 10. 06.2020

Author Details

Emmanuel O. Okon

Authors Affiliations

1Dept of Economics, Kogi State University Kogi State, Nigeria


Corresponding Author*

Ibrahim Baba

How to Cite the Article:

Emmanuel O. Okon,(2020); Business Regulations and Shadow Economy In Nigeria: Is There Any Significant Relationship?, IAR J Bus Mng, 1(1), 20-26.

Copyright @ 2020: This is an open-access article distributed under the terms of the Creative Commons Attribution license which permits unrestricted use, distribution, and reproduction in any medium for non commercial use (NonCommercial, or CC-BY-NC) provided the original author and source are credited.



Abstract: The paper empirically investigates the nexus between business regulations and shadow economy in Nigeria using vector autoregressive (VAR) model and covering the period 1996 to 2018. The ground for selecting the VAR model is to reveal the direct consequences of explanatory variables on the explained variable. The corroboration from the VAR reveals that growth in the shadow economy of the preceding period has been adapting well to the present level. However, the statistically insignificant negative relationship between the quality of institutions (proxy by the index for Rule of Law (LOGRULP)) and shadow economy implies that the quality of institutions is not significant in diminishing shadow economy in Nigeria. Furthermore, a 1% increase in the regulation quality escalates the present level of shadow economy by 0.148340%. As such, range of policy measures to contend against shadow economy should be vigorous. That is, it should accommodate a comprehensive option of applications and models, which can be utilized in diverse status and conditions as they appear.


Keywords: Business Regulations, Shadow Economy, Stationarity, Vector Autoregressive Model, Nigeria.


INTRODUCTION

Economics is human activity that tries to meet the needs of the population, which can be dedicated to primary (such as agriculture, animal husbandry and mining) activities, secondary or industrial or tertiary or services (Wiki Didactic, 2013). The economy is predicated on the creation of goods and services and the demand for consumption or use, thus giving rise to exchange and circulation of money. Profit-making ventures in the economy are regulated by the government. According to Samuelson (1985), regulations are made up of government rules and law discharged to amend or direct the running of economic activities. The economic activities could be regulated through legislation, fiscal and administrative regulations. Individuals and companies devoted to this motive are expected to be registered, as well as their employees, and pay taxes. As stated by IGI Global (2019), all economic activities operating within the official legal framework that are paying taxes on all generated incomes is called formal economy.


Apart from the formal economy regulated and controlled, there is the informal economy which is a maltifarious set of economic activities, enterprises, jobs, and workers that are not regulated or safeguarded by the government (Wiego, 2019). The notion basically applied to self-employment in small unregistered enterprises. It has been enlarged to incorporate wage employment in unprotected jobs. The informal economy has a tendency to be discredited as “illegal”, “underground”, “hidden”, “black market”, “black economy” or “grey market”. It is often called the "shadow economy" and characterized as illegitimate or unscrupulous activity (Wiego, 2019).


However, extreme regulations in the aboveground economy may impel individuals and businessmen to operate underground so as to get things done more successfully and decisively. In this regard, it may be useful to note Hernando de Soto’s findings about the problems which unrestrained regulations could bring (Yin, 2004). In the ‘Mystery of


Capital’, de Soto has documented how infuriating it was to secure real estate’s legal rights in some countries (de Soto, 2001). In the Philippines for example, it entails 168 incomprehensible steps involving 53 public and private agencies. In Egypt, it takes 77 complex actions involving 31 public and private agencies. In Peru, it took a year or more to start a legal business costing, in government fees, 31 times the minimum monthly wage. Cutting officialism thus appears to be the workable perspective to curtail the complication (Yin, 2004). In Nigeria, simple operations like business registration, issuance of permits and renewal of Nigerian passports, which take less than five days, and even performed by post in some other countries, take months. Clearance of goods and other formalities that are done within 48 hours in some neighboring countries take 28-30 days in Nigerian ports due to the high level of corruption and needless multiplication of security agencies (Proshare, 2005).


Against this scenery, this paper empirically explores the impact of regulation on the shadow economy in Nigeria. Studies on Nigeria such as Elijah and Uffort2007; Ihendinihu and Ochonma 2010; Ariyo and Bekoe 2012; Ogbuabor and Malaolu 2013; Ihendinihu 2013; and Nmesirionye and Ihendinihu, 2016, regarding shadow economy did not address the issue of regulations. As such, findings from this study can supply details on streamlining regulatory frameworks, making market-friendly policies, ameliorating the general ease of doing business and making determined efforts to deal with growing shadow economy in the country.

Nigeria is Africa’s second substantial market and a doorway to the African business focal point with a population of over 170 million, a consumer market of over $400 billion yearly and a middle class of 25 million people (NSACC, 2016). Nigeria is the number one producer of 8 products in the world and has about 44 solid minerals scattered all over the 36 states of the federation. In the past five 5years, Nigeria has an average growth rate of 7% and vigorous macro-economic environment (NSACC, 2016). The Gross Domestic Product in Nigeria hiked from N80.3 trillion in 2013 to above N100 trillion by the first quarter of 2014 supplanting that of South Africa whereby becoming the largest/biggest economy in Africa (NSACC, 2016). In 2015, Nigeria recorded a Gross Domestic Product of about $594.257 billion (NSACC, 2016).

Nigeria, Africa’s topmost economy is a country with prospects of emerging as one of the leading economies in the globe, but one of the difficulties it faces is the poor index on its “Ease of Doing Business”. In the Ease of Doing Business Index, countries are rated from 1st to 189th, and Nigeria is presently ranked 169th, with only a small number of other African nations with severe economic climates for investors, most of which are nowhere near the size and producing power of the Nigerian economy and some of which have suffered years of protracted national dispute (NSACC, 2016).

According to Andersentax (2018), throughout the years, the problem of regulatory infraction has become more disturbing as companies go on to dish out billions of naira to sort out fines enforced for numerous regulatory breaches. In 2015, the Nigerian Communications Commission (NCC) enforced a fine of $5.2 billion (although negotiated downwardly) on a leading telecommunications company. This penalty resulted in the substitution of more than a few cardinal members of the Board of Directors of the company. Similarly, the Central Bank of Nigeria (CBN) fined four banks for numerous regulatory breaches in November 2016 – one of the banks was fined ₦4 billion (Andersentax, 2018).

The rest of the paper is structured as follows. Sections 2 provides the methodology. Section 3 presents the result and discussion. Finally, Section 4 gives summary, conclusion and recommendation.


METHODOLOGY

In examining the impact of business regulations on shadow economy in Nigeria, the data were taken from the GlobalEconomy.com portal, Index Mundi portal, Knoema.com and covers the period 1996 to 2018. The data were converted to their logarithms to account for non-linear properties and heteroscedasticity. The dependent variable was shadow economy as percent of total annual GDP while the independent variable was business regulation. However, in assessing the impact of regulation, it was important to consider the quality and the quantity of regulations and institutions because they have influences. Therefore, the quality of institutions (proxy by the index for Rule of Law which captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence) and the regulatory quality (proxy by the index of regulatory quality which captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development) are used to explore the impact of regulation on the shadow economy.


To check for stationarity, Augmented Dickey-Fuller (ADF) test was applied to avoid spurious results. The test is used to determine whether a unit root is present in an autoregressive model. Thereafter, a Vector autoregression (VAR) model was applied. The main idea of this model is that the value of a variable at a time point depends linearly on the value of different variables at previous instants of time.


RESULT AND DISCUSSION

Table 1: Augmented Dickey-Fuller Test Result

Variable

Statistics

Critical Values

Statistics

Critical Values

With intercept

1%

5%

10%

With trend and intercept

1%

5%

10%

Level Form

RULP

-1.284981

-3.769597

-3.004861

-2.642242

-3.072298

-4.532598

-3.673616

-3.277364

REQP

-2.263115

-3.769597

-3.004861

-2.642242

-2.343963

-4.440739

-3.632896

-3.254671

SHA

-2.436149

-3.769597

-3.004861

-2.642242

0.790296

-4.467895

-3.644963

-3.261452

First Difference Form

RULP

-4.020732*

-3.788030

-3.012363

-2.646119

-3.913527**

-4.467895

-3.644963

-3.261452

REQP

-5.187539*

-3.788030

-3.012363

-2.646119

-5.061696*

-4.467895

-3.644963

-3.261452

SHA

-6.909451*

-3.788030

-3.012363

-2.646119

-7.611078*

-4.467895

-3.644963

-3.261452

Source: Author’s computation using eviews software 9

Note: *, ** and *** imply statistical significance at 1%, 5% and 10% levels respectively.

The Augmented Dickey-Fuller test was used to test for the existence of unit roots and to determine the order of integration of the variables. In applying the ADF test, the maximum lag length of 4 was determined automatically (i.e., the number of lagged dependent variables that was included in the model in order to correct for the presence of serial correlation) and Schwarz Info Criterion was automatically selected. This unit root tests was first performed on all the series in levels and first difference in order to determine the univariate properties of the data. Investigating the stationary properties of the variables, ADF test was further carried out with an intercept term and with intercept term with trend to test the presence of a unit root.

In Table 1, the results show that all the variables were non stationary in level form because the ADF test statistic of the level form of the variables are less than their respective critical values whether with intercept or with trend and intercept. This means that they all have the unit root problems and hence they suffer from instability problem in the short run.

But in first difference form, both intercept term and intercept term and trend, the statistic values significant. So, the null hypothesis of non-stationarity is rejected, i.e., the data series are stationary at first difference form. Technically speaking, the series are I(1) (integrated of order one). Under this scenario, there is need to test for co-integration. As such, Philips-Ouliaris (1990) residual-based unit root test was deployed. In essence, a test of the null hypothesis of no co-integration against the alternative of co-integration corresponds to a unit root test of the null of non-stationarity against the alternative of stationarity. Table 2 shows the long-run variance and one-sided long run variance using a Bartlett kernel and New-West fixed based bandwidth. More importantly, the test statistics show that, the Phillips-Ouliaris tau-statistic (t-statistic) and normalized autocorrelation coefficient (the z-statistic) both accept the null hypothesis of no co-integration. Based on this result, a short-run (Vector autoregressive model) model was applied. Vector autoregression (VAR) is a stochastic process model used to capture the linear interdependencies among multiple time series. VAR models generalize the univariate autoregressive model (AR model) by allowing for more than one evolving variable. All variables in a VAR enter the model in the same way: each variable has an equation explaining its evolution based on its own lagged values, the lagged values of the other model variables, and an error term. VAR modeling does not require as much knowledge about the forces influencing a variable as do structural models with simultaneous equations: The only prior knowledge required is a list of variables which can be hypothesized to affect each other intertemporally.

Table 2: Phillips-Ouliaris- Cointegration Test Result



Value

Prob.*


Phillips-Ouliaris tau-statistic

-4.407940

 0.3345


Phillips-Ouliaris z-statistic

-18.59213

 0.5053







*MacKinnon (1996) p-values.



Warning: p-values may not be accurate for fewer than 25 observations.

Source: Author’s computation using eviews software 9



Concerning the optimum lag length, the sequential modified LR test, the final prediction error (FPE) test, Akaike information criterion (AIC) test, Schwarz information criterion (SIC) test and Hannan Quinn (HQ) information criterion were employed at 5 percent level of significance to carry out the selection. SC and HQ criteria both indicated zero lag order while FPE and AIC criteria indicated a lag order of one. However, a lag order of one was eventually chosen for the VAR.


Table 3: VAR Lag Order Selection Criteria








 Lag

LogL

LR

FPE

AIC

SC

HQ















0

 57.65156

NA*

 1.10e-06

-5.204910

 -5.055693*

 -5.172526*

1

 66.80751

 14.82391

  1.10e-06*

 -5.219762*

-4.622892

-5.090226

* indicates lag order selected by the criterion Source: Author’s computation using eviews software 9

LR: sequential modified LR test statistic (each test at 5% level)

FPE: Final prediction error

AIC: Akaike information criterion

SC: Schwarz information criterion

HQ: Hannan-Quinn information criterion

Table 4a: Vector Autoregression Estimates


LOGUDA

LOGREQP

LOGRULP









LOGSHA(-1)

 0.449875

 0.452338

 0.312260


 (0.19086)

 (0.54120)

 (0.28619)


[ 2.35707]

[ 0.83580]

[ 1.09110]





LOGREQP(-1)

 0.148340

 0.499385

 0.055195


 (0.08197)

 (0.23243)

 (0.12291)


[ 1.80968]

[ 2.14852]

[ 0.44907]





LOGRULP(-1)

-0.093040

 0.059285

 0.727685


 (0.10963)

 (0.31087)

 (0.16439)


[-0.84864]

[ 0.19070]

[ 4.42655]





C

 2.243635

-1.896387

-1.221361


 (0.77190)

 (2.18877)

 (1.15743)


[ 2.90665]

[-0.86642]

[-1.05524]









 R-squared

 0.467678

 0.393946

 0.662158

 Adj. R-squared

 0.378958

 0.292937

 0.605851

 Sum sq. resids

 0.057541

 0.462655

 0.129373

 S.E. equation

 0.056540

 0.160322

 0.084779

 F-statistic

 5.271371

 3.900112

 11.75978

 Log likelihood

 34.19266

 11.26333

 25.28040

 Akaike AIC

-2.744787

-0.660303

-1.934582

 Schwarz SC

-2.546416

-0.461931

-1.736211

 Mean dependent

 4.019389

-0.133634

 0.136999

 S.D. dependent

 0.071745

 0.190662

 0.135038









 Determinant resid covariance (dof adj.)

 5.20E-07


 Determinant resid covariance

 2.85E-07


 Log likelihood

 72.12682


 Akaike information criterion

-5.466074


 Schwarz criterion

-4.870960


Source: Author’s computation using eviews software 9






A 3-variable unrestricted VAR model was actually estimated (as shown in Table 4a ) but because of explaining the relationship of interest (i.e., showing the direct effects of explanatory variables on the explained variable – shadow economy), focus was on equation 1 where the past value of endogenous variable, shadow economy (LOGSHA) has the expected sign and it is significant in determining its own current value (0.449875%). This indicates that growth in the shadow economy of the previous period has been adjusting well to the current level. According to Medina and Schneider (2018) shadow economy (percent of GDP) average value for Nigeria during the period 1991 to 2015 was 56.66 percent with a minumum of 50.64 percent in 2014 and a maximum of 66.61 percent in 1994. Nonetheless, in 2016, the shadow economy in Nigeria represented 48.37 per cent of GDP (approximately N 49.67 trillion), according to Association of Chartered Certified Accountants (Okechukwu, 2017). According to the IMF, the Nigerian informal sector accounted for ~65% of Nigeria’s 2017 GDP (BOI, 2018).


Table 4b: Vector Autoregression Estimates


Coefficient

Std. Error

t-Statistic

Prob.  











C(1)

0.449875

0.190862

2.357066

0.0221

C(2)

-0.093040

0.109634

-0.848644

0.3998

C(3)

0.148340

0.081970

1.809680

0.0759

C(4)

2.243635

0.771898

2.906646

0.0053
















Determinant residual covariance

2.85E-07


















Equation: LOGSHA = C(1)*LOGUDA(-1) + C(2)*LOGRULP(-1) + C(3)

        *LOGREQP(-1) + C(4)



Observations: 22



R-squared

0.467678

    Mean dependent var

4.019389

Adjusted R-squared

0.378958

    S.D. dependent var

0.071745

S.E. of regression

0.056540

    Sum squared resid

0.057541

Durbin-Watson stat

2.252142





Source: Author’s computation using eviews software 9


A closer examination of Table 4b shows that a 1% increase in rule of law (proxy for quality of institution) decreases shadow economy by 0.093040% in Nigeria. Thus, the a priori expectation is fulfilled by this result. Invariably, the fall in the shadow economy’s overall share of Nigeria’s GDP is a very positive sign that efforts to curb its impact have been implemented in recent years (Okechukwu, 2017). However, the statistically insignificant negative relationship between the quality of institutions (proxy by the index for Rule of Law (LOGRULP)) and shadow economy means that the quality of institutions is not significant in reducing shadow economy and should not be included in the model. This is in support of empirical survey evidence that fines and punishment (i.e., rule of law) do not have significant influence on the shadow economy (Andreoni, Erard and Feinstein (1998), Pedersen (2003), Feld and Larsen (2005), Feld and Schneider (2010).


Table 4b also shows that the regulatory quality (LOGREQP) has a statistically significant positive effect on current level of shadow economy in Nigeria. This implies that a 1% increase in the regulation quality increases the current level of shadow economy (LOGSHA) by 0.148340%. This result aligns with Johnson, Kaufmann, and Shleifer (1997), Johnson, Kaufmann, and Zoido-Lobatón (1998), Friedman, Johnson, Kaufmann, and Zoido-Lobaton (2000), Kucera and Roncolato (2008) and Schneider (2011) that countries that are more heavily regulated tend to have a higher share of the shadow economy in total GDP. Especially the enforcement and not the overall extent of regulation – mostly not enforced – is the key factor for the burden levied on firms and individuals, inducing them to operate in the shadow economy.


The overall goodness of fit shows that 0.467678% variation in shadow economy (LOGSHA) is caused by the variations in the previous values of shadow economy (LOGSHA) , regulatory quality (LOGREQP), and quality of institutions (proxy by the index for rule of law (LOGRULP)). The Durbin-Watson (DW) test statistic (2.252142) shows the absence of serial correlation between the error terms in the model.



SUMMARY, CONCLUSION AND

RECOMMENDATION

This study examines the nexus between business regulations and shadow economy in Nigeria using Vector autoregressive (VAR) model. The rationale for selecting the VAR model is to show the direct effects of explanatory variables on the explained variable. The evidences from the VAR show that growth in the shadow economy of the previous period has been adjusting well to the current level. However, the statistically insignificant negative relationship between the quality of institutions (proxy by the index for Rule of Law (LOGRULP)) and shadow economy means that the quality of institutions is not significant in reducing shadow economy in Nigeria. Furthermore, a 1% increase in the regulation quality increases the current level of shadow economy by 0.148340%.


In conclusion, government formulates many rules and regulations that direct businesses. However, convictions are heterogeneous on how much and what kind of regulations either support or hamper businesses. Certainly, many businesses prosper and others can suffer as a result of complex regulations and codes. When businesses suffer, they are bound to go into the shadow. Thus, in drawing up policies that can directly battle the shadow economy, the policymakers should be aware that the enticements only impel unofficial activities from the shadow into the official economy and not cause them to halt functioning absolutely or be executed with more social waste as the business conceals itself even further. Furthermore, range of policy packages to fight shadow economy should be sturdy. That is, it should contain a wide choice of applications and models, which can be used in various situations and circumstances as they appear. Above all, encouraging institutional strengthening is needed. One way is by adequately financing regulatory bodies so that they do not become ineffectual, rent seek, and unscrupulous, thus, resulting to greater underground economic activity.


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