Vol.:(0123456789)

SN Bus Econ (2021) 1:66https://doi.org/10.1007/s43546-021-00071-8

ORIGINAL ARTICLE

Corruption and firm growth: evidence from Nigeria

Philip Akrofi Atitianti1  · James Chukwubudom Chikelu2

Received: 7 December 2020 / Accepted: 1 April 2021 / Published online: 26 April 2021 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021

AbstractWe assess the impact of corruption on firm growth using the World Bank Enterprise Survey (WBES) data on Nigerian firms for the periods 2006 and 2008. We meas-ure corruption as firms’ informal payments to get things done faster and bypass due process. Firm growth is measured as sales growth of firms. To address endogeneity issues and the possibility of measurement error, we use industry averages of firms’ informal payments as an instrument in a Two-Stage Least Squares (2SLS) estima-tion. The results we obtain for both 2006 and 2008 confirm that informal payments substantially hinder firm growth in Nigeria. The results are robust and still hold after controlling for city variation and omitting outliers. The findings suggest that in the quest to improve firm growth, measures that focus on implementing frameworks that shorten the duration required for documentation and other firm-related services pro-vided by the government would discourage informal payments and thus, improve firms’ sales growth.

Keywords Nigeria · Corruption · Firm growth · Sales growth · Informal payment · Instrumental variable

JEL Classification C26 · D22 · L25

Introduction

There are numerous reports on the various forms of bureaucratic corruption and its concomitant effects. The corruption-growth nexus has been one of the most debated themes both in academia and among policymakers. Documented evi-dence shows that corruption is one of the most significant impediments to growth

* Philip Akrofi Atitianti p.akrofi@live.com

James Chukwubudom Chikelu jc_chikelu@outlook.com

1 School of Economics, Zhongnan University of Economics and Law, Wuhan, China2 Shenzhen, China

SN Bus Econ (2021) 1:6666 Page 2 of 18

and development; accounting for the poor economic performance of countries (d’Agostino et al. 2016; Gyimah-Brempong and de Gyimah-Brempong 2006; Hope 2000; Mauro 1995; Méndez and Sepúlveda 2006; Méon and Sekkat 2005).

Nigeria stands out as one of such countries where corruption is plaguing the nations’ progress. Since 1999 when Nigeria returned to a democratic system of gov-ernance, successive administrations have implemented various anti-corruption poli-cies in a bid to curb the ever-increasing rate of corruption but with little progress. A look at Fig. 1 and Fig. 2 seems to buttress the publicly held opinion about corruption and growth, at least for the case of Nigeria. The trend of the Corruption Perceptions Index (CPI)1 for the period 2000–2018 is generally upward and peaked at 2.8 in 2016; suggesting that despite the fight against corruption, Nigeria still ranks among the highly corrupt countries in the world. Poverty, family demand for financial sup-port, opportunity to make money are some of the factors that may compel officials to indulge in corrupt practices; however, Brownsberger (1983) argues that in try-ing to identify the causes of corruption in Nigeria, we must not only focus on the external conditions but also consider the “inner changes” that fuel corrupt behavior. In that light, Brownsberger advances two reasons for the corrupt behavior seen in

010

2030

40G

DP

Gro

wth

(%

)

2000 2005 2010 2015 2020Year

Fig. 1 Trend of Annual GDP Growth and Corruption Perception Index. Source: Authors’ calculations. Data source: World Development Indicators (WDI)

1 Published annually by Transparency International, ranking countries by the level of perceived corrup-tion in the public sector. The CPI ranks countries on a scale of 100 to 0, 100 for “very clean” and 0 for “highly corrupt”.

SN Bus Econ (2021) 1:66 Page 3 of 18 66

Nigeria; first, most civil servants have been corrupted inwardly, weakened by the disparities in living standards. Secondly, the disintegration of political administra-tion in the country allows people to pursue their selfish interests. These factors are central to the corruption bane which has been detrimental to Nigeria’s development. Over the period covered in Fig. 1, Nigeria’s annual Gross Domestic Product (GDP) growth rate has been declining in the face of rising corruption. Consequently, the general trend of the GDP growth rate and CPI offers a quite convincing proposition to believe that the presence of corruption hampers Nigeria’s economic growth and development. However, the perceived negative effect of corruption on growth can only be substantiated by empirical evidence, of which there is a dearth, especially for the case of Nigeria.

Intuitively, the diversion of resources by firms in the form of bribe payments will lead to a shortage of available funds for investment, hence reducing firms’ capacity to grow and expand. Interestingly, this isn’t the only proposition; some studies provide contrary empirical evidence to argue that making these bribe payments can stir firm growth (Méon and Weill 2010). Such positives emanat-ing from corruption is usually the case where firms are deliberately constrained by bureaucratic holdups so that illegal payments help to facilitate their business transactions. Firms are also compelled to make bribe payments when dealing with unavoidable public officials whose directives influence the firms’ business opera-tions (Svensson 2003). A considerable number of studies adopting varying meas-ures of corruption also provide evidence of the positive effects corruption has on

11.

52

2.5

3C

orru

ptio

n P

erce

ptio

n In

dex

2000 2005 2010 2015 2020Year

Fig. 2 Trend of Corruption Perception Index. Source: Authors’ calculations. Data source: Transparency International

SN Bus Econ (2021) 1:6666 Page 4 of 18

firm growth (Ayaydın and Hayaloglu 2014; Wang and You 2012) and to a larger extent economic growth (Heckelman and Powell 2010; Méndez and Sepúlveda 2006). Consequently, there seems to be a lack of consensus on the effect that cor-ruption has on firm growth; this has also been compounded by the methodologi-cal concerns characterizing some earlier studies on corruption. It is, therefore, essential that in seeking to understand the relationship between corruption and firm growth, the feasibility of the estimation strategy is not overlooked. This study, therefore, tries to empirically analyze how bureaucratic corruption affects the growth of firms, drawing evidence from Nigeria, Africa’s largest economy.

In assessing the impact of corruption on firm growth, our paper makes some significant contributions to literature. First, about novelty, this paper, to the best of our knowledge, is the first to use firm-level data to assess the extent to which bribe payments constrain Nigerian firms’ growth. The existing studies on cor-ruption in Nigeria have largely focused on its impact on other economic indica-tors, largely to the neglect of analyzing corruption’s impact on firm growth (see Adewuyi and Emmanuel 2018; Ojeka et al. 2019). This paper contributes to help-ing address this prevailing gap in corruption literature. Also, we use a compre-hensive and reliable firm-level dataset obtained from the World Bank Enterprise Surveys (WBES). One main advantage of using the WBES data is that the method of data collection significantly rules out the issue of respondent reticence, espe-cially on corruption information; thus, making it an ideal dataset for this study. Further, our study adds to the limited studies on the relationship between growth and corruption at the firm level. Corruption-growth nexus has been researched to some extent, however, most of the existing studies on this relationship have been conducted at the macro-level; only a few studies investigate this relationship at the firm level. The study of corruption at the firm level is particularly necessary. Fisman and Svensson (2007) argue that macro-level study of corruption does not allow us to ascertain the specific effects of corruption at the micro-level; study-ing corruption at the macro level only does not provide sufficient evidence on the state of corruption and its effects at the micro-level. Svensson (2003) shows that though corruption may be damaging to growth at the macro level, a positive correlation can exist between bribe payments and firm growth; thus, highlighting the need for analysis of the corruption-growth nexus at the firm level. Finally, focusing on Nigeria in this study is desired. Nigeria has over the years, consist-ently ranked among the most corrupt countries in the world. Thus, the findings of this study will be influential to policy-formulation targeted at reducing corruption incidence in Nigeria.

We use the WBES data on Nigerian firms for the period 2006 and 2008 to conduct our analysis. We measure corruption as informal payments firms make to bypass due processes to get things done faster. To examine the causal relationship between cor-ruption and firm growth, we make use of instrumental variable (IV) estimation using the location and industry averages of informal payments as instrumental variables in a Two-Stage Least Squares (2SLS) estimation. To show the economic and statisti-cal significance of our IV estimates, we also report Ordinary Least Squares (OLS) estimates and compare them to the 2SLS estimates. In both estimation methods for both years, our estimates reveal that informal payments have a significantly negative

SN Bus Econ (2021) 1:66 Page 5 of 18 66

impact on firm growth, thus confirming that rising levels of informal payments ham-per firm growth.

The remainder of the paper is as follows; “Review of related literature” presents a review of earlier studies on corruption-growth nexus; “Methodology, estimation technique, data” captures the methodology, estimation techniques and data. We pre-sent our results and discuss the same in “Results and discussion” and finally con-clude the paper in “Conclusion”.

Review of related literature

Theoretical literature

Theory on the economic impact of corruption interestingly postulates that corrup-tion can have both desired and undesired outcomes and should not only be assessed morally as evil.2 The bi-directional (positive or negative) impact of corruption has been identified as largely a result of institutional quality variations across econo-mies. Two hypotheses have subsequently evolved in relation to the impact of cor-ruption in the light of institutional quality; the “grease the wheels” hypothesis and “sand the wheels” hypothesis.

The “grease the wheels” versus “sand the wheels” hypothesis

The “grease the wheels” hypothesis simply suggests that corruption is beneficial in countries characterized by aspects of poor institutions or ineffective governance, but remains harmful elsewhere. In an economy characterized by bureaucratic inefficien-cies such as delays and lengthy queues which impair firm activities, corruption can help firms circumvent these inefficiencies. For instance, through bribes, firms can influence bureaucrats to speed up the otherwise slow process required to establish a new firm in a country (Leys 1965) and reduce waiting time in queues (Lui 1985). Civil servants who engage in bribery transactions could improve their livelihood with the perks they receive, especially when they receive low wages (Bayley 1966; Leys 1965). Thus, the “grease the wheel” hypothesis advocates that in an atmos-phere of poor and ineffective institutions, corruption provides avenues to bypass such inefficiencies thereby making corruption growth-enhancing (Friedrich 1972; Huntington 1968; Leff 1964; Nye 1967).

On the other hand, the “sand the wheels” hypothesis argues contrarily, pos-tulating that in the presence of poor institutions, costs associated with corruption manifests or may be exacerbated (Méon and Weill 2010). In an economy with poor institutions, corrupt officials can intentionally slow processes or cause delays to induce bribe payments (Myrdal 1968). They can also deliberately create distortions in the economy to preserve their illicit revenue extraction (Kurer 1993) or enforce

2 Studies that adopt the moralistic approach to corruption include Leys (1965) and Nye (1967).

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additional conditions masqueraded as public policies to stimulate conditions for bribery transactions (Pecorino 1992). Literature on the corruption-growth nexus adopts the development of human capital as the harbinger of growth, arguing that to bypass such inefficient institutions, firms divert resources that could be invested in human capital and other aspects of the firm to fund the required bribe demands. The result is decreased human capital accumulation and in effect, a lowered rate of growth. In providing supporting evidence for this claim, Ehrlich and Lui (1999) assert that in countries where the development of human capital is low, corruption tends to have a higher adverse effect on growth; leading to a rising rate of corruption (Rose-Ackerman 1997). In agreement, Kaufmann (1998) and Kaufmann and Wei (2000) explain that in economies where the level of corruption is high, managers and executives expend a large proportion of their time negotiating with government officials more than they do on productive activities for the firm. Aidt (2003) stresses that the argument in support of the non-zero impact of corruption relies on knotty assumptions. Their claim assumes that government officials do not care about bribes but care for the maximization of output; nobody spends valuable resources seeking out people to bribe, and on keeping the deal secret, all corrupt contracts are enforce-able and spending potential tax revenue on corrupt public officers do not result in an excess burden on taxation. Nevertheless, in a second-best world where avenues for getting bribes abound, the impact of corruption turns out to be non-zero as prevent-ing public officials from corrupt practices require close monitoring, which costs a lot (Acemoglu and Verdier 1998; Klitgaard 1988).

Empirical literature

Mauro (1995) is one of the first to carry out an empirical study assessing the effect of corruption on growth; using data on the subjective indices of corruption for a cross-section of countries and adopting ethnolinguistic fractionalization as an instru-ment, Mauro finds that corruption affects growth by reducing the level of invest-ment. Building on Mauro’s work, Mo (2001), and Pellegrini and Gerlagh (2004) find that corruption also deters growth through political stability, human capital, and trade openness. Both studies adopt the estimation strategy demonstrated in Barro (1991), and Levine and Renelt (1992); regressing the mean growth rate on a set of controls in a cross-sectional analysis. These studies similarly conclude that regress-ing economic growth on corruption without adding the mediums (openness, politi-cal stability, investment, and human capital) results in corruption having a signifi-cantly negative effect on growth. However, adding the mediums renders corruption insignificant. These studies, therefore, conclude that corruption has no direct effect on economic growth rather, the effect is indirect, reducing the rate of human capital accumulation, limiting the degree of openness, and reducing investment levels and political stability.

Using a cross-sectional analysis, Méon and Weill (2010) trace a direct association between growth and corruption. Méon and Weill treat corruption as an independent distortion, which adds to the already existing distortions formed by poor govern-ance. To estimate this distortion, the authors introduce an interaction term between

SN Bus Econ (2021) 1:66 Page 7 of 18 66

corruption and the quality of governance in their model. They find that as corruption levels increase, poor quality of governance reduces the rate of economic growth, while improved governance quality lowers corruption’s effect on economic growth. Bardhan (1997), however, points out some flaws in this approach, stating that both distortions of corruption and poor governance are part of a complex system of cor-ruption; hence should not be segregated. Moreover, though their study focuses on bureaucratic corruption, the measures of corruption3 used apply to aggregate cor-ruption—judicial, political, and bureaucratic and thus raises concerns about the reli-ability of the findings.

In addressing some of the shortcomings of the cross-sectional studies, Ehrlich and Lui (1999) and Méndez and Sepúlveda (2006) introduce panel fixed-effects analysis which sufficiently deals with endogeneity issues associated with corruption studies. From a sample of 68 countries, Ehrlich and Lui (1999) find that corrup-tion instead affects the level of GDP but does not have a significant effect on the rate of economic growth. Méndez and Sepúlveda (2006) observe that the interaction between growth and corruption is negative and nonlinear in countries with a reason-able amount of freedom. They report the relationship to be statistically insignificant in countries that are not free. Neither of these studies, however, takes into considera-tion the endogeneity between corruption and investment.

The differing state of institutions across economies means that the impact of cor-ruption will differ across economies. Thus, despite the forgoing studies that record a negative relationship between corruption and growth, other studies find quite nuanced evidence. For example, Neeman et  al. (2008) find corruption to be neg-atively related to growth only in countries with high levels of financial openness; this trend is not observed for countries with low levels of financial openness. The theoretical hypotheses of corruption, either greasing or sanding the wheels, high-lights the significance of institutional quality to the corruption-growth nexus. An inverse relationship is witnessed in the corruption-growth nexus in economies with relatively better institutions; the effect, however, reverses when the institutions are weak (Aidt et al. 2008; Méon and Weill 2010); adding to that, corruption has been identified as growth-enhancing at the early stages of economic development and growth-inhibiting as development continuously rises (Méndez and Sepúlveda 2006). Interestingly, we observe much evidence on the positive association between cor-ruption and growth in what Wedeman (2003) refers to as the “East Asian paradox”; where countries like China, Thailand, South Korea, and Indonesia have grown their economies persistently in the presence of high rates of corruption. Rock and Bonnett (2004) also find that the corruption-growth nexus is negative in developing coun-tries; especially the relatively small countries, but positive in newly industrialized East Asian countries.

Moving away from macro-level analysis, we focus on studies assessing the cor-ruption-growth nexus using micro-level data from firms across different countries. Macro-level evidence is not necessarily a perfect reflection of what transpires at

3 Méon and Weill (2010) measure corruption using Transparency International’s Corruption Perception Index and World Bank’s Control of Corruption.

SN Bus Econ (2021) 1:6666 Page 8 of 18

sub-macro-levels; hence the need to consider micro-level relationships. Svensson (2003), studying a cross-section of Ugandan firms, finds that though macro-level evidence points to an inverse relationship between growth and corruption, there is still evidence that the growth of firms is positively correlated with bribe payments. In China, the amount of time (measured in days) management spends negotiating with government officials is positively correlated with firms’ sales growth, and that external funding improves the performance of firms (Hallward‐Driemeier et  al. 2006). Similarly, Wang and You (2012) using firm-level data to make sense of the East Asian paradox observe that corruption contributes to firm growth. However, they point out that a positive effect of corruption on growth only holds in the pres-ence of weak financial markets, but the reverse is the case in developed financial markets.

In contrast, other firm-level studies identify an inverse association between firm growth and corruption. For instance, Fisman and Svensson (2007) employ a unique dataset to study how bribe payments and taxation affect the growth of a cross-sec-tion of firms in Uganda. Using industry location averages to cater for endogeneity concerns and measurement error, their result shows that both bribe payments and taxation exert a negative impact on firm growth; the impact of bribe payments on growth being about three times that of taxes. Their findings corroborate that of Gaviria (2002) who assesses the effect of corruption and crime on employment growth, sales, and investment; and whether the payment of bribes helps clear some bureaucratic bottlenecks faced by firms. The findings indicate that crime and cor-ruption negatively affect firm growth and that payment of bribes is positively cor-related with bureaucratic bottlenecks. Overall, the study concludes that corruption and crime impede firms’ competitiveness and corruption is not likely to improve anything.

Building on the earlier reviewed studies, this paper aims to analyze how cor-ruption affects growth, using data collected by the World Bank Enterprise Survey (WBES) on Nigerian firms in different industries. The review of the available litera-ture presented above shows that few studies have investigated how corruption affects growth in Nigeria, about a handful of cross-country studies on corruption include Nigeria. Again, such studies that have Nigeria in their sample are macro-level stud-ies. Fisman and Svensson (2007) opine that studying corruption at a cross-country level will not allow us to ascertain how corruption truly affects growth at the firm level, thereby raising doubts about the established adverse effect of corruption at a micro-level. Nigeria is the largest economy in Africa and ranks as one of the most corrupt countries in the world.4 The nation has not lived up to expectations given the abundant resources at her disposal. It, therefore, begs the question; could it be that the growth rate in Nigeria is being hampered by corruption? This is what this paper seeks to assess.

4 Nigeria ranked 149 out 180 economies in the 2020 corruption perception index. https:// www. trans paren cy. org/ en/ count ries/ niger ia.

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Methodology, estimation technique, data

Methodology

To assess the impact of corruption on firm growth in Nigeria, we specify an ordi-nary least squares (OLS) model as follows;

where Growthijk represents the sales growth of firm i in city j and industry k. infpmt is the percentage of firm sales paid as bribes to government officials to get things done faster and bypass due process. The term X′ is a vector of observable factors (covariates) that affect firm growth, comprising; tax which is the percentage of a firm’s sales income paid as taxes; trade is a dummy which takes the value 1 if a firm engages in international trade or 0 for domestic trade; infrac and burcy are ordinal variables measuring the extent to which infrastructure and bureaucracy, respectively constrain a firm’s operations. The term ln_age is the logarithm of the firm’s age; manexp is the number of years of experience the firm manager has; ln_sales is the logarithm of the previous year’s sales of a particular firm. � represents the vector of unknown parameters associated with each of the covariates; �j represents the city fixed effects; �ijk is the error term.

Infrastructure captured as infrac in our estimations is a composite score of how transport, electricity and telecommunication serve as obstacles to firm performance. We compute this as the average score of firm managers’ responses on how transport, electricity, and telecommunication each constrains firm performance. Likewise, bureaucracy captured as burcy is the average score of firm managers’ responses on how labour regulation, customs, business licensing, and tax administration each con-strain firm performance. Prior to taking the averages, the responses for each measure ranges from 0 (no obstacle) to 4 (severe obstacle).

Estimation technique

It must be noted that managers of non-performing firms are more likely to engage in the “blaming strategy” overly citing certain issues as the reason for their non-performing firms. Such managers are more likely to overstate their informal pay-ments and also rate infrastructure and bureaucracy as significant constraints to their firms’ performance, leading to endogeneity issues. OLS estimations, however, do not provide consistent estimates; to obtain consistent estimates of the effect of infor-mal payment on firm growth, we adopt an instrumental variable (IV) for use in a two-stage least squares estimation (2SLS). The IV we use is the industry averages of informal payments. Though managers of non-performing firms are more likely to misreport, the likelihood that all managers in the same industry will misreport is very low; hence we calculate the average of all informal payments in the same indus-try for use as IV. The three conditions guaranteeing the validity of an IV are satisfied for the instrument. Industry averages as an IV is relevant since it significantly has a positive correlation with the endogenous informal payments. Instrument exogeneity

(1)Growthijk = �1infpmtijk + X�ijk� + �j + �ijk,

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is likewise justified because we do not have sufficient reason to believe the instru-ment is correlated with the error term. The exclusion restriction assumption is also satisfied because industry averages affect firm growth only through informal pay-ments. Consequently, we base our analysis on the results from the 2SLS estimation.

Following Fisman and Svensson (2007), we measure firm growth as the logged difference of a firm’s total sales in the current year, relative to three years earlier as;

For each current year, the WBES data contains sales records of firms 3 years ago making it feasible to calculate frim growth despite the use of cross-sectional data.

Measuring corruption is difficult; no corruption measure is without issues. Most studies on corruption make use of either the perception of corruption by respond-ents or bribe payments as measures for corruption (see Brazys et al. 2017; Fisman and Svensson 2007; Isaksson and Kotsadam 2018; Knutsen et al. 2017). We like-wise measure corruption using bribe payments. In collecting the WBES data, firm managers were asked two corruption-related questions. The first was their percep-tion of corruption. The second focused on firms’ involvement in corruption through informal payments. Firm managers were asked the percentage of the firm’s sales paid out as a bribe to bypass due processes and get things done faster. More often than not, reticence becomes a challenge to gathering accurate information on brib-ery. How sure are we that the information reported by managers are right? It is for such concerns that the WBES is one of the most credible corruption data sources. Since the WBES are carried out independently without any governmental affilia-tions, the confidentiality of reported information is assured. Firm managers are also at liberty to either respond or refrain from responding to questions on bribery. Due to this liberty, we have sufficient reason to believe that firms that choose to report do so rightly since those who have concerns may rather refrain from answering than providing wrong information. Moreover, Clausen et al. (2010) show that only about 13.1% percent of the respondents are likely to be reticent, using a variant of random sampling technique for the WBES conducted in Nigeria. To obtain the percentage of firms engaged in informal payments, we code the informal payments of firms into a dummy variable which assumes 1 if a firm spends a portion of its sales income on bribe payment and 0 otherwise. However, for our regressions, we make use of the actual percentage figures of informal payments.

Data

The data we used for this study is obtained from the World Bank Enterprise Survey (WBES). The WBES provides comprehensive firm-level data on a wide range of business environment variables and has been a prioritized data source for a con-siderable number of studies on the business environment (see Ayyagari et al. 2008; Bah and Fang 2015; Commander and Svejnar 2010). We use the data collected on Nigerian firms in 2006 and 2008. Only a few firms in our dataset were surveyed in

(2)Growth2006 = (log(sales in 2006) − log(sales in 2003))∕3,

(3)Growth2008 = (log(sales in 2008) − log(sales in 2005))∕3.

SN Bus Econ (2021) 1:66 Page 11 of 18 66

both years, the larger portion of the firms are only captured in either year; hence we treat the data as two sets of cross-sectional data. Table 1 presents summary statistics of the data.

From Table  2, we observe that some firms in our data recorded negative sales growth in both years’ data. About 37% of the firms in the 2006 sample engaged in informal payments while about 53% engaged in informal payments in the 2008 sample.

Results and discussion

Table 3 presents the regression output of the impact of corruption on firm growth. Column (1) of both years reports the estimates of the solo impact of informal pay-ment on the growth of firms without controls. In both columns, informal payment does not have any significant effect although the estimates bear the expected negative sign. In columns (2) of both years, additional variables are controlled for; the results, however, do not show significant variation from those in column (1). Informal

Table 1 Summary statistics for the year 2006. Source: Authors’ calculations. Data source: WBES

Informal payment is made a dummy in order to find out the percent-age of firms indulging or not indulging in any forms of bribe pay-ments. However, we make use of the actual percentages of informal payments in our estimations, thus allowing us to compute the loca-tion and industry averages

Variable Obs Mean Std. dev Min Max

2006 dataManager’s Experience 1507 10.79 6.633 1 43Trade 1507 0.014 0.117 0 1Sales 2003 (log) 1507 15.43 1.712 10.82 22.03Growth 1507 0.118 0.106 − 0.534 0.980Log of age 1506 3.036 0.268 2.708 4.263Tax 1507 0.699 0.321 0 1Informal payment 1507 0.373 0.484 0 1Infrastructure 1507 1.791 0.713 0 4Bureaucracy 1507 0.879 0.709 0 42008 dataManager’s Experience 1768 12.54 10.53 1 30Sales 2005 (log) 1581 15.82 1.485 11.00 22.97Growth 1581 0.065 0.070 − 0.348 0.756Trade 1768 0.009 0.095 0 1Infrastructure 1768 2.093 0.740 0 4Bureaucracy 1768 1.042 0.648 0 4Informal payment 1768 0.531 0.499 0 1Age (log) 1768 3.094 0.349 2.485 4.710Tax 1768 0.692 0.255 0 1

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Tabl

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f cor

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estim

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NN

Y

SN Bus Econ (2021) 1:66 Page 13 of 18 66

Tabl

e 3

Rob

ustn

ess c

heck

: im

pact

of c

orru

ptio

n on

firm

gro

wth

Loca

tion

aver

ages

are

use

d as

an

instr

umen

t in

colu

mn

(1);

indu

stry

aver

ages

in c

olum

n (2

), an

d lo

catio

n an

d in

dustr

y av

erag

es in

col

umn

(3).

Robu

st st

anda

rd e

rror

s ar

e in

par

enth

eses

. ***

, **,

* in

dica

tes s

igni

fican

ce a

t 1%

, 5%

and

10%

, res

pect

ivel

y

Varia

ble

IV E

stim

ates

of t

he y

ear 2

006

IV E

stim

ates

for t

he y

ear 2

008

(1)

(2)

(3)

(1)

(2)

(3)

Info

rmal

pay

men

t−

0.00

9 (0

.009

)−

0.04

1**

(0.0

20)

− 0.

015*

(0.0

08)

− 0.

003*

** (0

.001

)−

0.03

0**

(0.0

12)

− 0.

004*

** (0

.001

)Ta

x−

0.02

9***

(0.0

08)

− 0.

013

(0.0

11)

− 0.

029*

** (0

.008

)−

0.02

2***

(0.0

07)

− 0.

016

(0.0

13)

− 0.

018*

** (0

.007

)Tr

ade

0.03

0 (0

.016

)0.

016

(0.0

20)

0.02

8* (0

.016

)0.

046*

** (0

.012

)0.

052*

(0.0

29)

0.04

3***

(0.0

13)

Infr

astru

ctur

e−

0.00

1 (0

.004

)0.

008

(0.0

06)

0.00

1 (0

.004

)−

0.00

2 (0

.003

)0.

002

(0.0

06)

− 0.

002

(0.0

03)

Bur

eauc

racy

0.01

0* (0

.005

)0.

016*

* (0

.007

)0.

011*

* (0

.005

)0.

007*

* (0

.003

)−

0.00

9 (0

.009

)0.

008*

** (0

.003

)Lo

g of

age

− 0.

013

(0.0

13)

− 0.

019

(0.0

17)

− 0.

012

(0.0

14)

− 0.

007

(0.0

06)

− 0.

008

(0.0

11)

− 0.

009

(0.0

07)

Man

ager

’s e

xper

ienc

e0.

001

(0.0

01)

0.00

1 (0

.001

)0.

001*

* (0

.000

5)0.

0002

(0.0

002)

0.00

1 (0

.001

)0.

0003

(0.0

003)

Log

of p

revi

ous y

ears

’ sal

es−

0.02

5***

(0.0

02)

− 0.

023*

** (0

.002

)−

0.02

5***

(0.0

02)

− 0.

013*

** (0

.002

)−

0.00

9* (0

.005

)−

0.01

3***

(0.0

02)

Con

stan

t0.

572*

** (0

.044

)0.

539*

** (0

.053

)0.

562*

** (0

.045

)0.

320*

** (0

.033

)−

0.00

6***

(0.0

05)

0.32

2***

(0.0

34)

Obs

erva

tions

1311

1311

1311

1584

1584

1584

R-sq

uare

d0.

196

0.15

30.

133

0.09

4C

ity fi

xed

effec

tsN

YN

NY

NIn

dustr

y fix

ed e

ffect

sY

NN

YN

N

SN Bus Econ (2021) 1:6666 Page 14 of 18

payment is still negative and insignificant in both columns. For the 2006 data, tax is negatively correlated as expected, exhibiting a strong significance at 1% significance level. The log of sales 2003 is also strongly significant but shows a negative correla-tion with sales growth. For the 2008 data, informal payment remains insignificant, while trade, manager’s experience, and log of sales 2006 are all significant.

The results in columns (1) and (2) of both years do not present any significant evidence of the impacts of corruption on the sales growth of firms. Noting the cred-ible endogeneity concerns of our data, OLS estimation fails to produce consistent and unbiased estimates. We, therefore, follow Fisman and Svensson (2007) and esti-mate an instrumental variable (IV) regression using industry averages of informal payment as an instrument for informal payment in the regression model, controlling also for city fixed effects. The results of the IV regression are presented in column (3) of both years.

We observe a substantial difference between estimates of the first two columns and the IV estimates in column (3). We draw the conclusions of this study from the IV estimates since they are consistent and unbiased, compared to the OLS estimates. In both years, informal payment is negative and strongly significant, howbeit the magnitude of impact in 2008 is higher. Holding other factors constant, a 10 unit increase in informal payments leads to a 0.26 unit decrease in firm growth in 2006 and 2.42 units decrease in firm growth in 2008. In effect, the results confirm the negative effect of informal payment on firm growth; indicating that informal pay-ments hinder Nigerian firms’ sales growth. Our results, therefore, corroborate that of Graviria (2002) who also find evidence of the negative effect of corruption on sales growth in Latin America and that of Fisman and Svensson (2007) who find similar evidence in Uganda.

Considering the dynamics of the impact of corruption, some arguments can be raised to explain the results we obtained. First, one channel through which informal payments can affect sales growth is marketing and advertisement. Studies show that marketing and advertisement are significant determinants of firms’ sales (Assmus et al. 1984; Ataman et al. 2010; Ayanwale et al. 2005; Meunier-FitzHugh and Piercy 2007). Hence, higher investment by firms in marketing strategies and advertise-ment draws higher sales. However, investment in marketing and advertisement can be limited by informal payments. Firms’ resources are limited, therefore, the more resources are used for one purpose, the fewer resources available for other purposes. Consequently, firms follow a prioritized order to determine where resources ought to be channeled to. Since firms’ resources are limited, higher amounts of informal pay-ments mean fewer resources would be available for other purposes such as market-ing and advertisement. Allocating insufficient resources to marketing and advertise-ment will limit the firms’ ability to reach customers with their products and services and consequently limit the sales firms can make.

Another channel through which informal payments can hinder sales growth is research and development (R&D). Research and development positively impact sales (Barge-Gil and Lopez 2015) and is a significant determinant of firms’ ability to innovate and introduce new products (Artz et al. 2010; Barge-Gil and Lopez 2015); also, firms that innovate experience improved sales growth (Artz et al. 2010; Choi and Williams 2014). Thus, these studies provide evidence to confirm that investment

SN Bus Econ (2021) 1:66 Page 15 of 18 66

in R&D increases firms’ sales growth. However, as explained earlier, the more firms allocate resources to make informal payments, the fewer resources available to be invested elsewhere. Consequently, diverting resources to make informal payments limits firms’ ability to invest in R&D and thus, hinders innovation and sales growth. In a competitive market, firms that do not invest in R&D to innovate are likely to lose market share to other competitors that might be doing so.

In the light of government policy measures, the results suggest that to boost the growth of firms in Nigeria, sufficient attention would have to be focused on imple-menting measures that discourage firms from making informal payments. Since some corrupt officials can deliberately cause delays or distortions to extract bribes, another remedy could be the adoption of structures that dissolve the opportunities these corrupt officials use to extract informal payments from firms. With such meas-ures in place, firms can channel resources that would have been used for informal payments to other aspects of the firms’ operational activities and this could boost growth.

Robustness check

To check the robustness of our results, we drop outliers from our sample and re-estimate the model. The presence of outliers could lead to either overestimation or underestimation of the effect and may cast doubts on the obtained findings. Finding out the responsiveness of our estimate to the absence of extreme values would be a feasible check for robustness (Fisman and Svensson 2007). To identify outliers in our data, we observe the boxplots of the variables and classify those outside the minimum and maximum whiskers of the boxplot as outliers. We consequently drop 60 and 111 observations 2006 and 2008, respectively.

Table 3 presents the robustness check results of the 2SLS. For both years, column (1) reports the effect with location averages as the instrument. Column (2) reports the effect with industry averages as an instrument and column (3) reports the effect with both instruments; we control for the appropriate fixed effects among the first two columns of each year such that the validity of the instruments is not violated.

We find that the effects without outliers are larger relative to the initial effect with the presence of outliers. For all three columns and in both years, the effects of informal payment are negative, thus, aligning with our prior expectations. The mag-nitude of significance, however, varies across the columns. For the year 2006, we find that informal payment is insignificant in column (1) where location averages are used as an instrument; however, in columns (2) and (3), informal payment attains significance at 5% and 10% respectively. We recorded a highly statistically signifi-cant effect in 2008; informal payment is strongly significant in columns (1) and (3) and drops in significance in column (2) to 5% significance level. Thus, overall, the robustness check similarly provides evidence of the negative effect of informal pay-ments on firm growth.

We find that the statistical significance of the negative impact of corruption on firm growth is stable across both the main finding and the robustness check and thus, confirms that informal payments hinder firms’ sales growth.

SN Bus Econ (2021) 1:6666 Page 16 of 18

Conclusion

This study assesses the corruption-firm growth nexus among Nigerian firms using comprehensive WBES firm-level data for 2006 and 2008. We measure corruption as informal payments by firms to government officials to get things done faster and bypass due process; firm growth is measured as the sales growth of firms. To deal with endogeneity issues and the possibility of measurement error, we make use of the industry averages of informal payments as instruments in a two-stage least squares (2SLS) estimation. The results we obtain for both 2006 and 2008 confirm that corruption negatively impacts firm growth in Nigeria. The results are robust and still hold after controlling for city variation and omitting outliers. Overall, the results conclude that firms’ informal payments significantly hinder their sales growth. The finding thus corroborates that of earlier studies that assert that corruption adversely affects firm growth (Fisman and Svensson 2007; Gaviria 2002).

The finding is informative for policy considerations. Firms are more likely to make informal payments to get things done faster when it takes a relatively pro-longed time to get things done by following the formal procedures. Hence, poli-cies that aim at instituting a relatively shorter period required for documentation and other forms of service provision needed by these firms would likely deter firms from indulging in informal payments. It must also be noted that corrupt officials can intentionally lengthen or delay procedures so that they can extract bribes; curbing this would require efficient monitoring, strict accountability as well as bringing per-petrators to book to serve as a deterrent to others.

Funding No funding was received for this research.

Code availability Codes can be made available upon request.

Declarations

Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest or competing interests.

Availability of data and material Data is available upon request.

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