Nigeria: Selected Issues
Author: International Monetary Fund. African Dept.
Publication Date:
01 Apr 2019
eISBN:
9781498306225
Language:
English
Keywords:
ISCR; CR; firm; cost; debt; firms' characteristic; export-oriented firm; diversified firm; firm employment; firm operation; Exports; Credit; Corporate finance; Capacity utilization
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Selected Issues

Abstract

Selected Issues

Macro-Structural Obstacles to Firm Performance: Evidence From 2,640 Firms in Nigeria1

A recent World Bank enterprise survey examines firm characteristics in Nigeria and identifies access to finance as the top constraint to Doing Business. In this context, the objective of this paper is to: (i) study firm characteristics associated with more access to finance and export diversification; and (ii) quantify the impact of these structural obstacles on firm performance. Results suggest that (i) larger and export-oriented firms are about 40 percentage points less likely to report access to finance as a business obstacle, while firms perceiving access to finance as a constraint are, on average, about 10–40 percentage points less likely to be export-oriented diversified firms; and (ii) better access to finance and export diversification can help firm employment —as much as 80 percent higher— and capacity utilization. Results are largely robust to different specifications and estimation methods.

A. Introduction

1. Allowing the private sector to become an engine of growth requires understanding the key constraints to Doing Business. To this end, the latest World Bank Enterprise Survey (WBES)2—conducted in 2014–15 and covering a sample of 2,640 private firms in the manufacturing and services sectors in 19 states in Nigeria—asked firms about various dimensions of the business environment they experience as well as information on individual firm characteristics. Out of a menu of options, around one-third of the interviewed firms cited access to finance as the top business obstacle. This was followed, although to a lesser extent, by electricity and corruption.

2. The objective of this study is two-fold. First, is to study firm characteristics associated with more access to finance and export diversification. Second, is to quantify the impact of access to finance and export diversification on firm performance.

3. Firms with more export diversification have, on average, seen better performance. Survey results suggest that only larger firms have been able to recently invest in improving their research and production methods. Higher employment and capacity growth rates have been observed in firms with some degree of export diversification. Empirical results suggest:

  • The easier the access to credit the more diversified. Larger and export-oriented firms are, on average, about 40 percentage points less likely to report access to finance as a business obstacle, compared to smaller and non-export-oriented firms. Younger, domestic-owned firms with access to finance constraints are associated with less diversified exports. Specifically, firms perceiving access to finance as a constraint are, on average, about 10–40 percentage points less likely to be export-oriented diversified firms. Results shed light on firm characteristics that view access to finance as a constraint, which in turn hinders their efforts at diversifying their exports.

  • Better access to finance and export diversification improves firm performance. Using several model specifications and estimation methods, better access to finance and export diversification are found to be associated with better firm performance, all else equal. For example, results suggest that firms who perceive access to credit as a constraint to their business have, on average, around 80 percent lower employment growth and around 30 percent lower capacity utilization growth, compared to firms where access to finance is not perceived as a constraint. Robustness of results is confirmed using an endogenous treatment regression approach that corrects for potential endogeneity and allows causal interpretation.

B. An Initial Look at the Data

4. The WBES covers a representative sample of formal private sector firms in Nigeria.

According to WB (2015) and WB/IFC (2015), private firms need to satisfy certain criteria to be included in the survey. Firms need to be formally registered, have employees,3 and operate in the manufacturing, retail or other services sectors.4 Firms with 100 percent state ownership are excluded. Firms are chosen through random sampling, stratified by industry, size and region. This led to a sample of 2,640 private firms in Nigeria. Figure 1 shows the geographical distribution, Figure 2 the sectoral distribution and Figure 3 the size distribution of surveyed firms. Most surveyed firms are single ownership (Figure 4), with their sales directed toward the domestic market (Figure 5) and are mostly domestically owned (Figure 6).

Figure 1.
Figure 1.

Surveyed Firms are Distributed over Different Cities

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 2.
Figure 2.

Surveyed Firms are Distributed over Different Sectors

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 3.
Figure 3.

Most Surveyed Firms are Small

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 4.
Figure 4.

Most Surveyed Firms are Sole Ownership

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 5.
Figure 5.

Most Firms Sell to National Markets; Larger Firms Have Bigger Export Shares

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBESNote: Indirect exports are domstic sales to third parties that export
Figure 6.
Figure 6.

Most Firms are Domestically Owned; Larger Firms Report More Foreign Ownership

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES

5. Some other characteristics are emerging from the sample:

  • On average, firms reported around 16 years of operations, ranging from an average low of 14 years in small firms to a high of 27 years in large firms (Figure 7).

  • Specific technical skills are the most important when making hiring decisions, followed by social skills (Figure 8), while skills are not a constraint to hiring women in more than 60 percent of cases (Figure 9).

  • Performance, in terms of growth of employment and capacity utilization, varied across surveyed firms. Micro-sized firms appear to have experienced the highest growth in both their employment and capacity utilization rates (Figure 10).

  • Access to finance is identified as the top obstacle to firm operations in almost one-third of the surveyed firms. Almost 1 out of every 3 of surveyed firms cited access to finance as the top business obstacle (Figure 11). Electricity and corruption came in second and third place. By firm size, access to finance appears to be the top obstacle in micro and small firms, while electricity is more of a binding constraint in medium and large-sized firms (Figure 12).

  • Investments in research and production methods is rather limited. Over 80 percent of surveyed firms did not spend on formal research and development activities over the last 3 years (Figure 13), especially so in smaller firms. Using a measure of export diversification linked to R&D,5 survey implies that firm performance is better in firms with higher export diversification (Figure 14). Regarding production methods, only large firms seem to have marginally invested in improving their underlying methods for production or supply of products (Figures 15 and 16).

Figure 7.
Figure 7.

Firms, on Average, are 16 Years Old

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 8.
Figure 8.

Technical Skills are the Most Important

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 9.
Figure 9.

Skills Is Typically Not a Constraint in Hiring Women

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 10.
Figure 10.

Performance Varied Across Firm Sizes

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 11.
Figure 11.

Access to Finance is the Top Obstacle to Firm Operations

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 12.
Figure 12.

Especially in Smaller Firms

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author;s calculations based on WBES
Figure 13.
Figure 13.

Most Firms Did Not Spend on Research and Development Recently

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 14.
Figure 14.

Better Firm Performance with More Export Diversification

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 15.
Figure 15.

Half of the Sample Only Recently Improved Production Methods

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Source: Author’s calculations based on WBES
Figure 16.
Figure 16.

Especially in Large Firms

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Graphs by sampling firm’s size

C. Empirical Methodology and Results

Access to Finance and Firm Characteristics

6. An ordered logit/probit model is estimated to study which firms perceive access to finance as a constraint to business. The dependent variable is “Access to finance” constructed from the ordinal6 responses to the question: “To what degree is access to finance an obstacle to the current operations of this establishment?”. Responses ranged from “No obstacle” (taking a value of 0) to “Very severe obstacle” (a value of 4). Independent or control variables are firm characteristics, which include export diversification. The estimation of the following ordered logit/probit is done by maximum pseudo-likelihood:

Accesstofinanceist=f(Xist,ExportDiversificationist)

where the dependent variable Accesstofinanceist of firm i in sector s at time t is a function of Xist a set of control variables representing firm characteristics. Accesstofinanceist would range from 0 (“No obstacle” to 4 (very severe obstacle”) in the ordered logit/probit. Firm characteristics, independent variables, come from survey questions covering aspects such as firm age, export status, size, ownership structure, and manager experience and education levels. Importantly, we are also interested in ExportDiversificationist, as defined above. The choice of explanatory variables builds on recent work by Kuntchev et al (2012), EBRD/EIB/WB (2016), Hosny (2017) on a sample of MENA countries, and Hosny (2018) on Egypt.

Results of the model imply:

  • Larger, export-oriented firms are less likely to report access to finance as a business obstacle (table 1, models 1–3). Coefficients attached to firm size (higher value implies larger firm) and export orientation (firms with exports representing 10 percent or more of sales) are negative and statistically significant.7 Results suggest that larger and export-oriented firms, on average, are about 40 percentage points less likely to report access to finance as a constraint compared to smaller and non-export-oriented firms. These types of firms usually have stronger balance sheets and access to collateral, which leads to easier access to credit.

  • Higher manager education levels (models 1 and 3) and foreign firm ownership (model 2) show some evidence of easier access to credit. For example, foreign firms, on average, are around 30 percentage points less likely to report access to finance as a constraint compared to domestic-owned firms, which could possibly be explained by their easier access to foreign sources of credit.

  • Surprisingly, results suggest that export diversification has an inconclusive impact on access to credit. This could be explained by the fact that the statistically significant coefficient on export orientation is already capturing some of this aspect, or by reverse causality—that access to credit influences export diversification rather than the other way around. We return to this point in the section on export diversification below.

Table 1.

Determinants of Access to Finance: Ordered and Binary Logit/Probit

article image
Standard errors in parentheses. Estimation is done using survey weights. Constant and dummies not reported. *** p<0.01, ** p<0.05, * p<0.1

Results largely hold using a binary logit/probit model (table 1, models 4–6). Suppressing the dependent variable into a simpler binary indicator, it would take the value of 1 if responses are “major constraint” or “very severe constraint”, while it takes a value of 0 if the response is “no obstacle”, “minor obstacle” or “moderate obstacle”.8 Binary models allow easier interpretation of results and can be used as first step regressions in treatment-effect estimators as explained below. Firm size, export-orientation and foreign ownership continue to show the same negative and statistically significant correlation with obstacles to accessing credit, while export diversification is not statistically significant. Female managers seem to encounter more obstacles in accessing credit (model 4). Firm age seems to matter in one specification, as younger firms seem to suffer more in accessing credit (model 6).

What Drives Firm’s Export Diversification?

7. An important point is to understand the determinants of export diversification at the firm level. We estimate a binary logit/probit where the dependent variable is the binary export diversification indicator, as defined above. We include typical firm characteristics and access to finance as explanatory variables.

8. Results show that younger, domestic-owned firms with access to finance constraints are associated with less diversified exports (models 7–8). Table (2) shows results using a binary logit and probit, using both the ordered and the binary variable on access to finance defined above. It suggests that younger firms are, on average, 40–75 percentage points less likely to report diversified exports. Foreign ownership also seems to be associated with more diversified exports. Importantly, access to finance is associated with lower export diversification. Specifically, firms perceiving access to finance as a constraint are, on average, about 10–40 percentage points less likely to be export-oriented diversified firms.

Table 2.

Determinants of Export Diversification: Binary Logit/Probit

article image
Standard errors in parentheses. Estimation is done using survey weights.Constant and dummies not reported. *** p<0.01, ** p<0.05, * p<0.1

Access to Finance and Firm Performance

9. Does access to finance affect firm performance? In what follows, the dependent variable is firm performance as in the following specification:

Yist=f(XistAccesstofinanceist,ExportDiversificationist)

where the dependent variable Yist is a measure of firm performance (growth of employment and capacity utilization) of firm i in sector s at time t. Independent variables include Xist a set of control variables representing firm characteristics as identified in the previous section. Our variables of interest are Accesstofinanceist and ExportDiversificationst, defined as in the previous section.

10. The perception of access to finance can be endogenous to firm performance where an unobserved variable affects both firm performance and access to finance. In our context, the objective is to study the effect of access to finance on firm performance. But suppose that a third variable (for instance, political connections) affects both the treatment (perception of access to finance) and the outcome (firm performance), then we have an endogeneity problem.9 As a result of this endogeneity problem, OLS estimates could suffer from a selection bias problem. To address this issue, we use an endogenous treatment-regression model originating from the program evaluation literature that allows the estimation of a linear regression which includes an endogenous binary treatment variable.

11. We use treatment-effects estimators to extract experimental-style causal effects from observed data. In simple terms, the objective is to use non-experimental data to obtain a causal interpretation of the results. To do this in our context, each firm’s probability to receive a binary treatment is estimated (with a probit or logit) as a function of observables, that is firms’ characteristics. Firms with similar probabilities are matched. When firms have similar probabilities, their assignment to the treated group is largely random with respect to the relevant covariates, and thus mimics a controlled experiment, allowing identification of causal effects. Specifically, the estimator compares between treated (firms who perceive access to finance as a business constraint) and control (firms who do not) units and measures the average treatment effect on the outcome (firm performance), conditional on a set of observables (firm characteristics).10 Results are reported in Table 3.

Table 3.

Endogenous Treatment Regression: Firm Performance

article image
Linearized standard errors in parentheses. Estimation is done using survey weights.Constant and dummies not reported. *** p<0.01, ** p<0.05, * p<0.1.

12. Better access to finance can have positive causal effects on firm performance. In all models of Table (3), using both measures of firm performance, the relevant coefficient on access to finance shows the expected sign and is statistically significant. This gives confidence in interpreting the results as casual effects, after controlling for endogeneity. Results imply, for example, that firms who perceive access to credit as a constraint to their business have, on average, around 80 percent lower employment growth and around 30 percent lower capacity utilization growth, compared to firms where access to finance is not perceived as a constraint. Moreover, the coefficient on export diversification is positive and statistically significant. Other results suggest that better firm performance is associated with certain firm characteristics such as ownership structure and firm age.

13. The treatment effects model corrects for endogeneity. In all reported models, the likelihood-ratio test (LR test for independent equations) indicates that we can reject the null hypothesis of no correlation between the treatment-assignment and outcome errors. Furthermore, the estimated correlation between the treatment-assignment errors and the outcome errors, ρ, is positive in all models, indicating that unobservables that raise firm performance tend to occur with unobservables that raise the perception of effect of access to finance on firm operations. This proves the importance of using the treatment effects estimator as it corrects for such endogeneity bias. Model (10) and (12) are preferred as they report lower -LogLikelihood and AIC.

D. Conclusion and Policy Implications

14. The goal of this paper was to understand firm’s characteristics and performance in relation to finance, export diversification, and their characteristics and performance. Empirical results suggest that larger, export-oriented firms are less likely to report access to finance as a constraint, while younger, domestic-owned firms with access to finance constraints are associated with less diversified exports. This highlights the important interconnection between access to finance and firm export diversification. Results hold under different specifications and estimation techniques. Results also indicated that access to finance and export diversification can help firm performance.

15. Increasing access to finance—as also argued in the authorities’ Economic Recovery and Growth Plan (ERGP)—is key for diversification. Hence, the initiatives taken by the government to improve access to credit information and collateral registry are important, as it gives borrowers the legal right to inspect their credit data from credit bureaus, as well as the 2017 Secured Transactions in Movable Assets Act (collateral registry) which enables micro, small and medium enterprises (MSMEs) to obtain credit using movable assets as collateral instead of traditional fixed assets. However, more efforts are needed to ensure banks make full use of the National Collateral Registry and to increase credit registry coverage (which is Nigeria as a percentage of adults stood at 0.1% compared to OECD’s average of 63.7%).

16. Other constraints to access to finance also need to be addressed. Nigeria’s “getting credit” sub-component of the overall ease of doing business index, is among the highest in the world (Figure 17),11 reflecting a supportive legal environment for access to credit, yet there are macro and micro impediments to accessing credit. From a macro perspective, banks’ lending to the private sector is limited by high risk aversion, which along with high-yield risk-free government and CBN bills, are not conducive for lending given fear of credit risk. From a micro perspective, accelerating the implementation of the government’s financial inclusion strategy, including by reforming the regulatory framework and leveraging the potential for mobile payments, would help boost access to credit in more remote areas (see Annex VII).

Figure 17.
Figure 17.

Ease of Doing Business

Citation: IMF Staff Country Reports 2019, 093; 10.5089/9781498306225.002.A006

Sources: World Bank Doing Business Indicators, 2018 Report.

17. Addressing longstanding structural challenges that hamper growth and inhibit economic diversification remains urgent. Beyond efforts to strengthen the business environment through the Presidential Enabling Business Environment Council (PEBEC), overcoming structural constraints requires: increasing public investment efficiency; accelerating the implementation of the Power Sector Recovery Plan; implementing the government’s updated financial inclusion strategy; stepping up efforts to improve education and health outcomes; and strengthening governance, transparency and anti-corruption initiatives. These reforms are in line with the ERGP’s objectives and several reforms already initiated must continue.

References

  • Cameron, A. C., and P. K. Trivedi (2005) “Microeconometrics: Methods and Applications”, New York: Cambridge: University Press.

  • EBRD/EIB/WB (2016) “What’s Holding Back the Private Sector in MENA? Lessons from the Enterprise Survey”, A joint report by The European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB), and the World Bank Group (WBG).

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  • Heckman, J. (1976) “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models”, Annals of Economic and Social Measurement 5: pp. 475–492.

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  • Heckman, J. (1978) “Dummy Endogenous Variables in a Simultaneous Equation System” Econometrica 46: pp. 931–959.

  • Hosny, Amr (2018) “Firm Performance and their Perception of Political Instability in Egypt: Evidence from an Endogenous Treatment Regression Model”, Journal of African Development 20 (2): pp. 61–68.

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  • Hosny, Amr (2017) “Political Stability, Firm Characteristics and Performance: Evidence from 6,083 Private Firms in the Middle East”, Review of Middle East Economics and Finance 13 (1): 1–21.

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  • IMF (2017) “Job-intensive growth through strengthening the business environment”, Selected Issues Paper. IMF Country Report no .17/81. Washington, D.C.: International Monetary Fund.

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  • Kuntchev, Veselin, Rita Ramalho, Jorge Rodríguez-Meza, and Judy S. Yang (2012) “What have we learned from the Enterprise Surveys regarding access to finance by SMEs?”, Enterprise Analysis Unit of the Finance and Private Sector Development, The World Bank Group.

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  • Maddala, G. S. (1983) “Limited-Dependent and Qualitative Variables in Econometrics”, Cambridge: Cambridge University Press.

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  • WB (2009a) “Enterprise Survey and Indicator Surveys: Sampling Methodology”, available at http://www.enterprisesurveys.org/methodology

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  • WB (2015) “Nigeria: Enterprise Survey 2014”, available at http://microdata.worldbank.org/index.php/catalog/2361

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  • Wooldridge, J. M. (2010) “Econometric Analysis of Cross Section and Panel Data”, 2nd ed. Cambridge, MA: MIT Press.

1

Prepared by Amr Hosny (SPR).

2

The full questionnaire, sampling methods, and data are accessible at http://www.enterprisesurveys.org/. WB (2009a; 2009b) outline the general survey methodology, WB (2015) reports the Nigeria enterprise survey questions, and WB/IFC (2015) summarizes Nigeria country responses.

3

Firm size is defined as micro (less than 5 employees), small (5–19), medium (20–99), and large (more than 99 employees).

4

Agriculture, fishing and extractive industries, utilities and some services sectors, (such as financial services, education and healthcare) are not included in the survey.

5

Our measure of export diversification is an interaction variable of each firm’s export orientation (whether exports are above 10 percent of sales) multiplied by a measure of diversification (spending on R&D or improved production methods). Results are broadly robust to altering this definition.

6

An ordinal variable is a variable that is categorical and ordered.

7

As robustness checks, we replace the 10 percent exports dummy (Y/N) with the ordered exports variable, as well as foreign ownership dummy (Y/N) with the ordered foreign ownership variable. Results are largely unchanged.

8

Results are largely similar if we define the new binary variable as taking the value of 1 if responses also include “moderate obstacle”, while taking the value of 0 for “no” and “minor” obstacles only.

9

Similarly, suppose we wish to know the effect of a job training program on employment, and suppose that a third variable (for instance, motivation) affects both the treatment (participation in job program) and the outcome (employment). We have an endogeneity problem since we cannot observe motivation.

10

Estimation is done by MLE. Heckman (1976, 1978) introduced the model, and Maddala (1983) derived the maximum likelihood (MLE) estimator. Cameron and Trivedi (2005) and Wooldridge (2010) introduced the endogenous treatment-effects model.

11

These indicators should be interpreted with caution due to the limited number of respondents, limited geographical coverage, and standardized assumptions on business constraints and information availability. See http://www.doingbusiness.org/methodology for further details on the Doing Business methodology. See IMF (2017) for a recent application of the same database.

Same Series

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Cover IMF Staff Country Reports
Nigeria: Selected Issues
Author: International Monetary Fund. African Dept.
Volume/Issue:
Volume 2019: Issue 093
Publisher:
International Monetary Fund
ISBN:
9781498306225
ISSN:
1934-7685
Pages:
77
DOI:
https://doi.org/10.5089/9781498306225.002

Surveyed Firms are Distributed over Different Cities

Surveyed Firms are Distributed over Different Sectors

Most Surveyed Firms are Small

Most Surveyed Firms are Sole Ownership

Most Firms Sell to National Markets; Larger Firms Have Bigger Export Shares

Most Firms are Domestically Owned; Larger Firms Report More Foreign Ownership

Firms, on Average, are 16 Years Old

Technical Skills are the Most Important

Skills Is Typically Not a Constraint in Hiring Women

Performance Varied Across Firm Sizes

Access to Finance is the Top Obstacle to Firm Operations

Especially in Smaller Firms

Most Firms Did Not Spend on Research and Development Recently

Better Firm Performance with More Export Diversification

Half of the Sample Only Recently Improved Production Methods

Especially in Large Firms

Ease of Doing Business