2.1. Data
The study employed the World Bank Enterprise Survey (WBES) which is collected from business enterprises operating in 13 SSA countries. The WBES was collected from manufacturing and service in every region of the world including SSA countries. Even though the WBES covers different themes related business environment, the data utilized in this study relates to firms’ perceptions related to doing their business, the relative significance of various constraints to firms’ business operations which are mainly under the infrastructure and services theme of the survey.
The WBES provides an array of economic data on more than 140,000 firms in more than 141 countries worldwide. The data used in this study is, however, restricted to selected firms operating in 13 SSA countries .These countries were selected based on the number of firms included in the survey and the year the survey was conducted. Accordingly, this study considered only countries for which the survey was conducted after the year 2010 and countries for which data on at least 100 firms are available after cleaning for missing information.
Combining firm data for 13 SSA countries selected for this study yields 5129 observations. However, data analysis was made with 3594 observations after cleaning the dataset for missing values and outliers.
The main advantage of using the WBES is that the survey uses standardized survey instruments and the same sampling methodologies across countries. This minimizes measurement error and yields data that are comparable across different economies. This is important to capture cross-country variation in the business climate and its impact on firm performance.
2.2. Variables and Descriptive Statistics
Alternative definitions of credit constraints are used and discussed in this section.
a) Perception approach
In the perception approach to credit constraint, firms are asked to rate the degree to which lack of access to finance is an obstacle to doing their business (Beck and Demirguc-Kunt, 2006; Asiedu et al., 2013). In the WBES, firms are given a categorized choice from no obstacle to a very severe obstacle. Following the approach in Hansen and Rand (2014) and Asiedu et al (2013), two versions of credit constraint variables are constructed from a firm’s response to this question. The first is a categorical variable–constraint– which takes a value ranging from 0 to 4 in which higher value implies that the firm is more credit constraint. The second is a dummy variable–constrainta– which equals 1 if the firm has reported access to finance is a moderate, major and very severe constraint to doing its business and zero otherwise (details are reported in Table A.1 in the annex).
Table 1
Description and Definition of Variables
Variable
|
Description
|
Mean
|
Std. Dev.
|
Obs.
|
Outage(lnH)
|
Outage time in days/year
|
1.51
|
1.46
|
3488
|
Gow
|
Generator ownership
|
0.64
|
0.48
|
3591
|
Gsh
|
Share of electricity from self-generation
|
0.33
|
0.28
|
2230
|
lnAge
|
Age of a firm (years)
|
2.51
|
0.703
|
3505
|
PID
|
Power intensity dummy
|
0.55
|
0.49
|
3594
|
Ownership
|
Percentage of firms owned by foreigners
|
0.16
|
0.36
|
3594
|
Export
|
Percentage of firms engaged in export
|
0.15
|
0.36
|
3594
|
Constraint
|
Finance as obstacle to doing business
|
1.93
|
1.30
|
3570
|
Constrainta
|
1 if the firm is credit constraint
|
0.37
|
0.48
|
3594
|
Constraint1
|
1 if the firm is credit constraint
|
0.47
|
0.49
|
3055
|
Constraint2
|
1 if the firm is credit constraint
|
0.58
|
0.49
|
3594
|
The variable constraint is the firm’s response to the question “to what degree lack of access to finance is an obstacle to doing your business”. This a categorical variable taking a value ranging from 0 to 4. The variable “constraint_a” is dummy variable version of the variable “constraint” in which firms are classified as credit constrained if they have responded to the above question as a moderate, major, and severe constraint. While variables constraint_1 and constraint_2 are the alternative definitions of credit constraint defined in alternative b and c, respectively.
b) Credit application information
Based on the credit application information, firms are classified as credit constrained or not based on whether they have applied for a loan and the stated reasons for not applying. In the spirit of Bigsten et al (2003), and Hansen and Rand (2014), a firm is classified as credit constrained–constraint1– if: (i) the firm has applied for a loan and was denied, (ii) did not apply for a loan due to reasons such as ‘‘application procedures was complex’’, ‘‘collateral requirements were too high’’, or ‘‘possible loan size and maturity were insufficient’’. If a firm did not apply for a loan because it does not need one or applied for a loan and were approved, the firm is classified as unconstrained (see Table A.2 for details).
c) Use of financial services
Some studies (Aterido et al, 2013; Muravyev et al, 2009) use the firm’s use of formal financial services as an indicator of credit constraint. According to this approach, firms that use formal financial services are classified as credit unconstrained while firms that do not use formal financial intuitions are classified as credit constrained. Following the same logic, this study also classifies firms that use formal financial institutions as credit unconstrained and others as credit constrained.
Table (2) classifies firms in the sample as credit constraint or not according to the three definitions the credit constraint given above. Using the first and third definitions, about 59% of firms are credit constrained while 47% of firms are credit-constrained based on the direct credit application information. The credit application information criterion resulted in a relatively less percentage of credit constrained firms compared to the other two.
The classification of firms as credit-constrained and unconstrained by firm size shows that a relatively higher percentage of large firms are credit unconstrained while a large share of small firms were found to be credit constrained. This shows that large firms are more likely to have access to external funds to finance their operations and hence less credit constrained than small firms.
Table 2
Classification of Firms by alternative definition of credit Constraint
Definition
|
Constrained
|
Unconstrained
|
Total
|
Perception approach
|
2119 (59)
|
1475(41)
|
3594
|
Credit application information
|
1446 (47)
|
1609 (53)
|
3055
|
Use of formal financial institutions
|
2113 (59)
|
1481(41)
|
3594
|
Firm Size
|
Small
|
Medium
|
Large
|
Percentage of Constrained
|
62.90
|
56.94
|
48.51
|
Percentage of Unconstrained
|
37.10
|
43.06
|
51.04
|
Figures in brackets are percentages. The perception approach is used to classify firms as credit-constrained and unconstrained. |
Outage time ( lnH )
The variable outage time utilized in the study is computed from the reported frequency and duration of power interruptions that a firm faces in a month. A monthly outage time is obtained by multiplying the frequency of power outages with its duration and then it is converted into yearly data assuming the same outage frequencies and duration throughout the year. The outage time–the number of days a firm is without power supply from the public grid–also measures the reliability of power supply.
Furthermore, a correlation between different definitions of credit constraint and the firm’s decision to invest in self-generation is examined and the result is reported in Table 3. The correlation matrix shows a meaningful result in which all measures of credit constraint are negatively correlated with both firm’s decision to invest and the volume of investment a firm wishes to invest. On the other hand, a power outage is positively correlated with both firm’s decision to invest and volume of investment which implies that unreliable power supply induces firms to invest in private substitutes. Moreover, the Table shows that positive and significant correlation between the alternative definitions of credit constraints which imply the consistency of the alternative measures of credit constraint used.
Table 3
Variables
|
Gow
|
Gsh
|
Constraint
|
Constrainta
|
Constraint1
|
Outage(ln)
|
Gow
|
1
|
|
|
|
|
|
Gsh
|
0.571***
|
1
|
|
|
|
|
Constraint
|
-0.109***
|
-0.103***
|
1
|
|
|
|
Constrainta
|
-0.105**
|
-0.093***
|
0.847***
|
1
|
|
|
Constraint1
|
-0.061***
|
-0.034**
|
0.338**
|
0.296**
|
1
|
|
Outage(ln)
|
0.216***
|
0.506***
|
0.106**
|
-0.114***
|
0.028
|
1
|
Constraint- is the perception approach to credit constraint definition and takes value from 0 to 4 with higher value implies more credit constraint, constrainta is the binary version of the variable “Constraint” and takes the value of one if a firm reported access to finance is moderate, major and severe constraints to doing business.Constraint1 is the credit application information definition of credit constraint and takes 1 if the firm is credit-constrained and 0 otherwise. Outages are the total power interruption in days a firm faces in a year.
2.3. Model specification
The methodology used in this paper is based on a theoretical model of a firm’s investment decision by Abdisa (2020) where a similar approach was used in estimating the firm’s investment decision. According to the approach in Abdisa (2020), all costs of investment in self-generation are weighted against the expected future benefits. This is based on the Net Present Value (NPV) approach to investment decisions and a firm undertakes an investment with a positive NPV.
In order to examine the role of access to finance in a firm’s investment decision, we included financial constraints in the cost component of the firm’s NPV computation. The implication is that a high financial barrier increases a firm’s borrowing cost which worsens the NPV of the investment. Based on the NPV of the investment, a firm decides whether to invest in self-generation; and how much to invest. The first question is a binary outcome which can be modeled by a standard probit model. The second question is the volume of investment which is left-censored at zero. To address this, two-part and Heckman selection models are employed. More formally, the models are stated below.
A firm invests in self-generation if the NPV of the investment is positive. However, we observe whether the firm has invested in self-generation or not. Assuming unobserved latent variable y* that establishes the following linear relationship between the relevant variables.
y * = αxi + ui (2)
where xi is a vector of explanatory variables, α is the associated parameters to be estimated, ui is a normally distributed error term with mean zero and variance σui2. The observed variable y, is related to the latent variable y* as follows:
Determinants of a firm’s incentive to invest in self-generation are estimated by probit model as indicated above. In the second part, linear regression model is used only for estimating a positive value. Thus, the two-part model for yi following the approach stated in Cameron and Trivedi (2005) is given by:
Where y denotes the volume of investment, d is a binary indicator such that d = 1 if y > 0 and d = 0 if y = 0. When y = 0 we observe only Pr(d = 0). For those with y > 0, let f(y ⁄ d = 1) be the conditional density of y.
The two-part model has some flexibility and computational simplicity by assuming that the two parts– the decision to invest and the volume of investment– are independent. But firms with positive investments are not randomly selected from the population. This may result in second stage regression to suffer from selection bias (Cameron and Trivedi (2005). To allow for the possible dependency between the equations, the selection model of Heckman (1979) is also used.
The main interest in equations (5a) and (5b) is to identify the causal effect of credit constraints on investment decisions. However, there is a potential reverse causality in the model because firms with poor investment opportunities are more likely to be credit constrained. Following the approach in (Petersen and Rajan, 1994 and Garcia-Posada, M. 2018), we implemented different strategies to tackle this identification challenge. First, traditional determinants of firm investment opportunities such as firm size and firm age are included as control variables. Second, country and industry dummies are included to control for the country and industry specific investment opportunities. Third, the perceived financial obstacles, rather than actual financing constraints are used as an alternative definition of credit constraint as a robustness check for the result obtained.
However, including these variables may not perfectly control for a firm’s investment opportunities. Thus, as a final strategy to tackle the potential reverse causality in the model, the study uses an instrumental variable to isolate the exogenous part of credit constraints. Following the logic of Beck and Demirguc-Kunt (2006) and Fowowe (2017), banking regulatory and supervisory structure are used as IV for the credit constraint variable in this study . Specifically, the average tenure of bank supervisors and an index of overall supervisory independence from both banks and politicians are used as an instrument for credit constraint. It is expected that bank regulation and supervision will influence a firm’s access to finance but do not have a direct impact on firm performance.