Credit Access and the Firm–Government Connection: Is There Any Link?

Abstract

Access to credit for businesses is an unresolved issue, especially in developing countries and transition economies. There has been a lot of research exploring factors affecting firms’ credit accessibility. Particularly, factors related to borrowers and lenders are always placed under consideration. However, besides those factors, institutional elements could also play an important role in guiding companies’ operations. In countries where the economy lacks transparency and low-level development is limited, informal institutional factors can have potential impacts. In this paper, we focus on exploring the relationship between firm–government links and credit access, thereby offering managerial implications through utilizing cross-sectional data sets at the firm level, with an initial sample of 26,849 observations from 38 countries at different levels of development around the world. The results show a positive correlation of firm–government connection with credit access. Moreover, this relationship may vary depending on the market in which the business primarily operates. Specifically, firms working internationally are less influenced by links with governments and tend to rely more on their own characteristics and conditions.

1. Introduction

Credit access plays a significant role in the progress of firms all over the world. It is widely believed to have a positive impact on growth through assisting firms to overcome liquidity problems, promoting entrepreneurial and start-up activities, exploiting better investment opportunities and therefore improving the economy’s capital allocation (Ayyagari et al. 2011; Beck and Demirgüç-Kunt 2008; Claessens and Laeven 2004; Love 2003). However, despite the policy changes in many countries, the problem of limited access to finance still exists and is considered an “unfinished agenda” (Beck and Demirgüç-Kunt 2008). Several studies have been carried out to explore internal factors and firms’ characteristics that determine their credit accessibility (Andrieu et al. 2018; Chaudhuri et al. 2020; Fowowe 2017; Stefani and Vacca 2015); the impact of external factors from regional, local, structural and social aspects has been less of a concern.

Among the external factors, the connection between firms and government is one to put into consideration. Michelson (2006) suggested that understanding the role of government connections is an approach to better understanding legal systems and policy mechanisms, which can result in better borrowing opportunities from formal sources like bank lending (Bonnet et al. 2016; Michelson 2006). Creating an association with higher authority is highly likely to accelerate the reciprocal trust for firms and governments, therefore increasing the likelihood of performing better, gaining more credit externally and decreasing constraints (Banerji et al. 2018). In this paper, we aim to investigate the relationship between firm–government connection and credit access in various firms in the world, and if there is a link, we study how it could differ from the main market in which firms mostly operate. To do that, this paper utilizes the latest cross-sectional firm-level data from the Business Environment and Enterprise Performance Survey sixth wave (BEEPS-VI), taken by the European Bank for Reconstruction and Development (ERBD), EIB (European Investment Bank) and the World Bank Group (WBG) between 2018 and 2020 from 28,000 firms in 41 countries across Eastern Europe, Central Asia, Middle East and North Africa. The reason underlying the use of this database is that the countries in these areas are mostly transitional and developing economies, which always deal with unfair competition from the shadow economy, are highly finance-constrained, and face corruption hurdles (Nikolova et al. 2012; Schaffartzik et al. 2014). From that perspective, firm–government connections could be an informal social capital factor that can equip firms with competitive advantages.

Based on these considerations, we focused on understanding the relation of firm–government connections to firms’ credit accessibility. Through this, the paper aims to develop a better understanding of the impact of government connections and different levels of connection for firms working in different markets. With these objectives, the paper contributes to the literature by confirming the presence of ties between regulatory entities and firms. Research on different types of firms and the markets in which firms work could also pave the way to offering better managerial implications regarding how to support both domestic and foreign firms to access finance, especially as regards bank lending. Understanding this informal mechanism could help firms to be more unconstrained in the credit market.

The rest of the paper is organized as follows: Section 2 focuses on a literature review related to credit access and government connections, and then develops a hypothesis. Section 3 demonstrates the research design, data collection and methodology. Section 4 analyzes the main findings of the paper. Section 5 concludes with several discussions.

2. Literature Review

2.1. Overview on Credit Access

Different from the standard markets in which commodity delivery happens simultaneously and aims at the selling and buying of homogenous commodities, credit markets focus on giving credit in the form of both money and other goods, and promising the receipt of future repayment (Jaffee and Stiglitz 1990). The market operates with two main participants, borrowers and lenders; the cost of credit is normally the interest rate on loanable funds, and access to credit is simply defined as the ability to approach the credit service (Zeller et al. 1998). Notably, in that market, the number of fund demanders is likely to exceed the number of fund suppliers. Moreover, together with the existence of imperfect information and uncertainties, borrowers often have to face hurdles such as higher interest rates, larger requirements of collateral and net worth, or cumbersome loan application procedures, and therefore fail to obtain the full amounts of loans they need. The situation is normally referred to as credit rationing or credit constraint; subsequently, credit access can also be measured as the amount of money borrowers can acquire from lenders (Boucher et al. 2009; Diagne and Zeller 2001). In short, from that viewpoint, the process related to access to credit can be briefly explained as the involvement of borrowers in the credit market, and lenders’ decisions to accept credit requests and determine the amount of credit that borrowers are able to obtain. Another approach to understanding the term could be through the framework of supply and demand and the use/non-use of credit. Claessens (2006) and Beck et al. (2009) distinguished non-users as those who are involuntarily (rejected by the fund suppliers) and voluntarily excluded (no need or self-constrained) from financial services (Beck et al. 2009; Claessens 2006). Goods demanders who willingly disqualify themselves from accessing credit due to their fear of rejection can also be called discouraged borrowers (Kon and Storey 2003). Figure 1 represents the relationship between firm’s decisions to use credit and their credit accessibility, which is synthesized from the above literature.

It is undeniable that whatever the methodology and hypothesis underlying the notion, credit access still appears to be essential, with several impacts on socio-economic aspects. Various researchers have confirmed that through accessing credit, more effective wealth distribution, poverty alleviation and inequality reduction could be achieved (Beck et al. 2009; Yadav and Sharma 2015). Markedly, even when the over-borrowing situation of bad borrowers can have several negative effects, credit constraints are considered one of the most significant obstacles causing persistent growth (Coeurdacier et al. 2015; Getachew 2016; Petrick 2004; Rajan and Zingales 2003). As a result, deeper evaluations of credit access are required to make the financial system more inclusive for every participant within the whole economy.

In recent research on firm contexts, various studies have focused on explaining the factors affecting credit access. The modern view focuses on analyzing the characteristics of both the supply and the demand side, and how it can have impacts on the loan results (Linh et al. 2020). The factors affecting firms’ credit accessibility are demonstrated in Figure 2.

On the borrowers’ side, entrepreneurial factors such as firm size, firm age, technical capability, innovation level and capital structure are always under evaluation. Besides this, demographic elements related to firms’ owners and managers are also of concern. These can include the managers’ gender, age, experience and educational level. On the suppliers’ side, lending decisions can be derived from a blend of various components, such as the lenders’ characteristics, the risk assessment process and lending requirements. Notably, the impacts of these variables on the credit access of firms can be varied based on different their sectors and locations. This also means that external factors could play a significant role in determining the firms’ credit accessibility.

External factors such as the interactions of firm and the institutional environment wherein firms work are believed to have a notable effect on business activities (Acemoglu and Robinson 2008; Alonso and Garcimartín 2013; Banerji et al. 2018; Dollar and Kraay 2003). According to institutional theory, the interaction environment can define the way people and firms behave and operate, as it can define the “rules of the game”, which can define, assist and enforce what is accepted widely and socially (Chaudhry et al. 2018; Krasniqi and Williams 2020; North 1990; Webb et al. 2020). It must be noted that these rules exist in two forms: (1) formal regulations and legislation systems, and (2) informal institutional ones that derive from social beliefs, norms and values (Helmke and Levitsky 2004; Ogunsade and Obembe 2016; Siqueira et al. 2016). While formal frameworks are set up to operate, manage, monitor and set standards officially, informal ones are unwritten, or are put into any codified rules while being deeply rooted in society, and tend to change slowly (Williams and Vorley 2015). Studies on formal and informal institutional impacts show the different effects on firms’ profitability and efficiency between countries. Business activities in more legitimate environments are normally seen to perform better, as they could enjoy a preferable environment of operation (Kistruck et al. 2015; La Porta and Shleifer 2014), while the existence of off-the-record influences play specific roles in developing countries. Due to the high level of diversity in norms, social values, beliefs, trust and other social factors, informal institutional elements should be paid due attention.

2.2. Government Connections and Impacts on Firms’ Business Actitivies

As mentioned above, external factors from the regional, local, political and institutional environment have to be placed under consideration. They even come in observable forms, such as codified frameworks and regulations, or show unobservable characteristics like beliefs and social norms. These elements tend to shape firms’ behaviors and operating activities. In transition economies, formal rules tend to undergo frequent modifications due to policy instability (Dang et al. 2020; Smallbone and Welter 2012), which requires firms working therein to adapt flexibly. Under these circumstances, informal institutional factors appear to be of importance, which include poor legislature systems, the low quality of regulatory bodies and the high levels of corruption (Alence 2004; Ault 2016). The relationship between businesses, governments and politics often receives a lot of attention from researchers. From the firm’s side, research on this topic has focused on two aspects: (1) explaining the existence of this relationship, and (2) describing how that relationship can affect business operations.

As regards the first angle, numerous studies confirm the significant value of governments’ relationships and political capital with firms. A recent study by Wang et al. (2019), focusing on China in the period 2004–2014, shows that visits by government authorities have positive implications in terms of reducing asymmetric information, bringing unusual positive returns to the stocks of these companies, and improving the business governance efficiency (Wang et al. 2019). This result is consistent with those of previous studies, such as those of (Li et al. 2008; Zhang et al. 2014; Lin et al. 2015). Some studies also show the preferential treatment of businesses with government relationships, according to which both the firm and government can achieve specific sets of purposes and benefits (Li et al. 2008; Lin et al. 2015; Zhang et al. 2014). Specifically, lenders are pressured to give preferential loans to businesses at the request of politicians, according to research on 90,000 businesses in Pakistan during 1996–2002 (Khwaja and Mian 2005). These firms are also able to set up contracts with governments, and achieve regulatory support, and they are consequently more likely to meet their objectives (Brown and Huang 2020; Marquis and Qian 2014; Wang and Qian 2011).

In terms of the second aspect, political connections can benefit firms via a buffering effect, which involves better knowledge derived from working with higher-level authorities, legitimacy, involvement in policymaking activities and protection against unfavorable interference, resulting in better economic results (Banerji et al. 2018; Lester et al. 2008). In developing and transition economies, due to the higher level of asymmetric information, firms are highly likely to lack acknowledgement and creditability, especially when participating as borrowers in credit market (Berger and Udell 1998). According to the study of Qi and Nguyen (2021), when firms gain “insider knowledge” of the whole system through connection with the government, they are highly likely to be more confident in accessing bank loans, thus exhibiting a lower likelihood of being credit-discouraged (Qi and Nguyen 2021). From the above considerations, we propose the first hypothesis, as follows:

Hypothesis 1 (H1).

Firm–government connection has a positive relation with credit access.

Notably, studies on the relationship between companies and governments are often conducted in developing countries and transition economies. When formal institutional factors are still loose, informal factors can play a prominent role in determining the behavior of businesses, as well as the behavior of related entities (Krasniqi 2007). However, in the current context of extensive globalization, besides internal domestic influences, external powers from foreign countries also need to be considered. When businesses participate in import–export activities and diverse investment activities on a global scale, the relationship between the firm and the government is highly likely to be affected. Besides this, some researchers have also tried to clarify the differences in the effects of institutional ties on domestic versus foreign fims. In detail, foreign firms are less likely to take advantage of this special connection due to a lack of understanding of social, political and institutional factors, and therefore may be treated less preferentially, while this factor appears to be of crucial importance to domestic firms. Using the databases of 280 firms working in the Chinese market, Li et al. (2008) showed a monotonic and inverted U-shape association between government connections and the performance of domestics and foreign firms. In explaining these interesting results, the levels of competition and structural uncertainty were considered. When foreign firms are faced with greater rivalry and insecurity from outside markets, their political ties are less likely to support them anymore (Li et al. 2008: Do managerial ties in china always produce value).

Although several studies have examined the influence of firms’ performance, institutional environment and motives in emerging countries on their capacity to expand abroad (Luo and Tung 2007; Wu and Chen 2014), few papers have focused on explaining the difference between the markets in which the firms are operating and institutional forces. Given the above-mentioned issues, we hypothesize that relationships vary depending on the market in which the firms work. Hypothesis 2 can be outlined as follows:

Hypothesis 2 (H2).

For firms working in different markets, the impact of firm–government relations could vary to different degrees.

3. Materials and Methods

3.1. Research Design and Data Collection

Centered on the above considerations, this study aims to develop an understanding of the connection between credit access and firm–government relations, with a broader scope looking at various countries around the world. To do this, the authors employed cross-sectional firm-level data from the Business Environment and Enterprise Performance Survey wave 6th (BEEPS-VI), taken by ERBD-EIB-WBG for the period of 2018–2020 from across Eastern Europe, Central Asia, Middle East and North Africa. The dataset consists of survey results for 28,000 enterprises working in a variety of industries in 41 countries. In this research, we control the three fixed effects of country, year and sector. These specifications prevent us from excluding variables specific to a country or industry. After cleaning the data, only 38 countries with a total of 26,849 observations were placed under consideration, with the areas considered highlighted in red (for developed countries) and blue (for developing countries) in Figure 3.

The list of countries is given in

Table 1. List of countries and diversification based on level of development.

Developed Countries	Developing Countries
Italy	Transitional economies	Other
Cyprus	Albania	Jordan
Greece	Bosnia and Herzegovina	Egypt
Romania	Montenegro	Lebanon
Czech Rep.	North Macedonia	Mongolia
Croatia	Serbia	Morocco
Estonia	Armenia	Tunisia
Slovak Rep.	Azerbaijan	Turkey
Slovenia	Belarus
Bulgaria	Georgia
Portugal	Kyrgyz Rep.
Lithuania	Kazakhstan
Latvia	Moldova
Hungary	Russia
Poland	Tajikistan
	Ukraine
	Uzbekistan

(Source: UN 2020).

. These countries are divided into different groups, with different levels of development based on the specifications of the UN (2020).

We employed the measurement approach set out by the UN (2020) to better differentiation between developed and developing countries. According to this, there are 15 developed economies and 23 developing ones. Among the developing countries, 16 are in transition. Transitional economies became prevalent following the collapse of the Soviet Union, and the phrase is commonly understood as referring to a scheme of economic and structural reform from a government-centered command mechanism to a market-oriented apparatus. In these countries, establishing connections with the government is necessary for business activities, and therefore firms working therein tend to actively create and maintain their governmental links (Jiang and Kim 2015; Li et al. 2008).

3.2. Empirical Models

As mentioned in the above-cited literature, a firm’s credit access is affected by several factors, including the firm’s internal characteristics and financial conditions, external factors related to the markets in which they work, the countries they operate in, and the relationship between themselves and the government. In this paper, we focused on firms’ applications for credit and how their activities determined credit access. We used both logit and normal regression to identify the determinants of credit access at the firm level with control of fixed effects for country, sector and year. The main econometric model is as follows:

$$\begin{gathered} {Credit access}_i=\alpha +\beta {government connect}_i+\gamma {firm characteristics}_i+\delta {external factor}_i+ \\ FE\left(country,sector,year\right)+\epsilon \end{gathered}$$

(1)

where the credit access of firm i is the dependent variable, and the government connection i is an independent dummy variable standing for the firm–government connection of each firm. The firm’s characteristic i relates to a set of control variables on the firm side, while external factor i is related to the country and market in which the firm works. We also included the fixed effect factors of country, sector and year so as to better protect the rationale of the model from omitted elements that are particular to a specific country or sector. The above equation also aimed at clarifying whether firm–government connections can determine the credit access of firms. If this relation was found to exist, we made further regressions using different contextual markets so as to better capture the differences in the effects of their relations with regulatory authorities.

3.3. Variables Descriptives

In evaluating credit access, we followed the methods in the literature, and used the three main questions of the survey. Commencing with question K16 “Referring again to the last fiscal year, did this establishment apply for any lines of credit or loans?”, the cases of credit applicants and non-applicants were ascertained. The results of loan applications were then expounded through question K20 “Referring to this most recent application for a line of credit or loan, what was the outcome of the application?” Following the framework outlined by Boucher et al. (2009), firms with a limited borrowing amount are regarded as being rationed. As a result, in this paper, only acceptance for the full amount was used to determine credit access.

Recognizing the importance of the financial access of firms, this article also studies enterprise characteristics, and the operating environment factors that affect this issue are included in the research model. First, we considered factors related to the establishment form of firms. Specifically, firms were divided into three main types: foreign, domestic and state, based on the percentages of ownership by foreigners, domestic individuals and governments or states, respectively. As the firms in the survey were based in different nations, and the indicators of firm size vary between countries, we measured them by the number of permanent full-time employees in the firm at the end of that fiscal year. To evaluate firms’ capabilities and operations, “lnsales” (natural logarithm of last fiscal year’ sales) and “firm_growth” (the increase in firms’ sales over the previous 3-year period) were used. Whether firms were checked and verified by external auditors or not was also used (via the variable named “audited”) to better assess firms’ transparency. The indebtedness of a firm can be inferred by observing whether that firm had a credit line from a financial institution (credit_line), and purchased materials and services on credit (purchased_credit), or not. “Innovation” represents the presence of innovation in firms’ products or services within a 3-year period. “Main_market” refers to the main market in which the firms’ products and services were traded, while “developed” indicates the level of development of the countries where the firms work. Besides this, elements related to the gender and experience of the firm’s manager (“female_mang” and “manager_exp”, correspondingly) are also addressed in the model.

Sticking to the main objectives, the important variable focused on in this model is the relationship between the business and the government. This element was assessed through question J6a, “Over the last year, has this establishment secured or attempted to secure a government contract?” We are highly aware that evaluating government connections can be done through various measurements, meaning establishing unique definitions is not easy. The rationale behind our evaluation is that when firms successfully secure a contract with a government, they become better equipped in terms of experience and knowledge regarding how to deal with regulatory officials and authorities, what procedures to undertake, and which crucial mechanisms to follow. During their time working with a government, businesses have opportunities to better understand regulatory processes. This approach enables us to apprehend the aspects underlying political and governmental connections with firms.

All variables and their modes of measurement are represented in

Table 2. Variable summary.

Variable	Description	Measurement
credit_access	Firm’s credit access	=1 if firms applied and got full amount of money needed =0 otherwise
governconnect	Firm–government connection	=1 if firms secured government contract(s) =0 otherwise
foreign_firm	Firms were established as foreign-owned	=1 if the percentage owned by foreigners equals or is more than 50% =0 otherwise
private_firm	Firms were established as private-owned	=1 if the percentage owned by domestic individuals or companies equals or is more than 50% =0 otherwise
state_firm	Firms were established as state-owned	=1 if the percentage owned by governments or states equals or is more than 50% =0 otherwise
female_mang	Female managers	=1 if there are females among managers and owners =0 otherwise
manager_exp	The experience of managers	Measured in years
lnsales	The ln of firms’ sale
firm_growth	The growth of firms	=1 if firms’ sales increased =0 otherwise
firm_size	The size of firms	=1 if the number or permanent full-time employees in the firm equals or exceeds 100 =0 otherwise
innovation	The state when firms experienced innovation	=1 if firm introduced new or improved products and services during the last three years =0 otherwise
audited	The state when firms are audited	=1 if firms have annual financial statement checked and certified by external auditors =0 otherwise
purchased_credit	The percentage of total annual purchases of inputs in credit	%
credit_line	The existing credit line	=1 if firms have a line of credit with a financial institutions
main_market	The main market where firms operate	=1 if the main market of the firm’s main products is international =0 otherwise
developed	The status of the country where firms work	=1 if the countries that the firm works in are developed =0 otherwise

.

4. Main Findings

4.1. Descriptives Statistic

Table 3. Descriptive statistics.

All Data						Access		No Access
Variable	Obs	Mean	Std. Dev.	Min	Max	Mean	Std. Dev.	Mean	Std. Dev.
credit_access	9563	0.4504	0.4976	0	1
governconnect	26,576	0.1621	0.3686	0	1	0.2360	0.4247	0.1472	0.3543
main_market	26,722	0.1229	0.3284	0	1	0.2004	0.4003	0.0719	0.2584
foreign_firm	26,495	0.0644	0.2454	0	1	0.066	0.2486	0.0343	0.1819
private_firm	26,503	0.9110	0.28467	0	1	0.9103	0.2858	0.9343	0.2477
state_firm	26,513	0.0081	0.0895	0	1	0.0146	0.1199	0.0038	0.0617
female_mang	26,592	0.2973	0.4570	0	1	0.3386	0.4733	0.2893	0.4535
manager_exp	26,195	20.3906	16.8349	1	70	22.1620	32.6978	19.2404	11.0484
lnsales	24,160	16.3315	2.8585	7.6009	30.6755	17.3030	2.9272	16.0904	2.7769
firm_growth	25,740	0.5361	0.4987	0	1	0.5995	0.4900	0.4817	0.4997
innovation	26,677	0.2442	0.4296	0	1	0.3821328	0.4859	0.1738	0.3789
audited	26,380	0.4003	0.4899	0	1	0.5057417	0.5000	0.3370	0.4727
purchased_credit	24,430	30.3431	35.1561	0	100	42.31046	37.0768	27.6911	30.8691
developed	26,849	0.34872	0.4766	0	1	0.40065	0.4901	0.2346	0.4238
credit_line	26,354	0.35463	0.4784	0	1	0.9019334	0.2974	0.2547	0.4357
firmsize	26,669	0.2016	0.4012	0	1	0.3034965	0.4598	0.1442252	0.3513516

(Source: Author’s synthesis).

demonstrates the descriptive statistics of all variables. We take the number of observations, means, standard deviation and min–max value of each variable into account in the model. To better evaluate the differences between access cases and non-access ones, we further divide the results into two sub-groups to compare their values. Within the chosen sample, comprising 26,849 observations, only 4308 firms have access to credit and 5255 are being constrained for several reasons, related to both lenders’ requirements and themselves. To gain a clear picture of the story, we also investigate the differences in the means and standard deviations of variables based on the credit accessibility of the firm. Particularly, the coefficients of most of the variables are observed to be higher when firms have access to credit, except for private domestic firms.

Remarkably, the values of the social relations between governments and firms in cases wherein the firms have credit access show a higher mean than when the firms have no access, with coefficients for the two scenarios of 0.2360 and 0.1472, respectively. This implies that the relationship between firm–government ties and credit access tends to be stronger when the credit is accepted, which is consistent with Hypothesis 1. We further evaluate these links in the context of different markets to yield better assessments and avoid the biases that arise when putting all firms with dissimilar contexts into the same pooled source. The comparison results are displayed in

Table 4. Summary of credit access, main markets and connections with government.

Main Market	Local and National Markets				International Markets
Access to Credit Status	Have Access		No Access		Have Access		No Access
	Freq.	Percent	Freq.	Percent	Freq.	Percent	Freq.	Percent
Having government connection	885	25.92	723	14.97	122	14.20	44	11.73
Not having government connection	2.529	74.08	4108	85.03	737	85.80	331	88.27

. It is notable that in both markets, the number of firms with credit access is obviously lower than that of those without. What is more, firms for whom the main working market is domestic are likely to have access to credit when having government connections, while they are constrained more when no relationship with a government pertains. In contrast, for international firms, the proportion of those with access to credit tends to be higher, even when they do not have government bonds, compared to those entities without access. These findings indicate that the impact of a government connection could be weaker than was thought, and other characteristics should be taken into consideration, which confirms Hypothesis 2.

4.2. Multivariate Analysis

4.2.1. Determinants of Credit Access

According to the regression results presented in

Table 5. Regression results for firm’s credit access.

	OLS Regression		Logit Regression
	Robust		Robust
Credit_Access	Coefficient	std. err.	Coefficient	std. err.
governconnect	0.0209365 **	0.0111234	0.175585 **	0.0929263
main_market	0.0466474 ***	0.0128938	0.3834697 ***	0.1106174
foreign_firm	0.0159856	0.0263647	0.2512683	0.2570128
private_firm	0.0091816	0.0209206	0.1834811	0.220174
state_firm	0.0083895	0.0561211	−0.0088763	0.452529
female_mang	0.0097508	0.0090418	0.0705454	0.0770195
manager_exp	0.000412 ***	0.0001215	0.0070458 **	0.0033259
lnsales	0.0278627 ***	0.0029388	0.2295181 ***	0.0251442
firm_growth	0.0196083 **	0.008581	0.2008557 ***	0.0720461
innovation	0.0741038 ***	0.010082	0.5756328 ***	0.0825376
audited	0.043598 ***	0.0099411	0.4162117 ***	0.0802769
purchased_credit	0.0009542 ***	0.00014	0.0077883 ***	0.0011769
developed	0.2394206 ***	0.053525	1.738563 ***	0.5616032
credit_line	0.5165336 ***	0.0109331	3.08098 ***	0.0862904
firmsize	−0.0004478	0.0127644	−0.0368867	0.1091499
Country FE	YES		YES
Sector FE	YES		YES
Year FE	YES		YES
Observations	7744		7743
R-squared	0.5326
Pseudo R2			0.4697

**: Significant at 5% level. ***: Significant at 1% level.

, the development level of countries wherein firms operate and the main market wherein the firms work, as well as the experience of managers, the firm’s sales and growth, the level of innovation, the state of being audited by external auditors, and the indebtedness status (including having a current credit line from financial institutions, and having purchased inputs, materials and services on credit) showed significant positive correlations. This implies that firms with a higher level of transparency (related to the verification of external auditors), better growth (through increasing sales), improved or new lines of products and services (innovation) and an existing credit line (credit line and purchase on credit) will have a greater chance to access credit. These factors serve as indicators of the credibility and prospects of a firm, and therefore can eliminate to some extent the problem of asymmetric information between borrowers and lenders in the credit market. Firms who already have a credit line or have purchased credit before can indicate their ability to pay back, and sometimes can be referred to “good borrowers”, as they have already met the requirements of financial institutions. Managers’ experiences tend to have a significant positive correlation with the ability of their firm to access credit. The results are consistent with those of various studies on the determinants of credit access, such as Hewa Wellalage et al. (2020), Chaudhuri et al. (2020) and Khan (2022).

4.2.2. Credit Access and Firm–Government Connections

As per Table 5, a government connection has a positive relation with credit access, which is consistent with the hypothesis mentioned above. It is notable that the main markets in which firms work show a positive correlation with firms’ credit access (with significant level 1%). However, the behaviors of firms working in local and national markets can be different to those of firms working at the international scale. As such, pooling both domestic and international markets together tends to make the results biased. Under the circumstances, we ran Equation (1) again with respect to domestic and international markets. The results are shown on

Table 6. Multivariate regression results for different main markets.

Panel A.	OLS Regression		OLS Regression
	Domestic Market		International Market
	Robust		Robust
Credit_Access	Coefficient	std. err.	Coefficient	std. err.
governconnect	0.0267812 **	0.0119485	−0.0408491	0.0311004
foreign_firm	0.0050628	0.030186	0.0294626	0.0823499
private_firm	0.0151669	0.0215814	−0.0092703	0.083758
state_firm	−0.0042807	0.0586561	0.1360075	0.1814738
female_mang	0.0112706	0.0100185	−0.0055056	0.0215929
manager_exp	0.0003896 ***	0.0001056	0.0010589	0.0009832
lnsales	0.0271619 ***	0.0031463	0.0358212 ***	0.0087486
firm_growth	0.0173928 **	0.0093961	0.0368617 *	0.0220943
innovation	0.0770364 ***	0.0113427	0.055905 **	0.0216531
audited	0.0440751 ***	0.0108464	0.0385322	0.0262339
purchased_credit	0.0009667 ***	0.0001565	0.0009426 ***	0.0003284
developed	0.2436328 ***	0.0554467	0.1725976	0.1865976
credit_line	0.5139784 ***	0.0116147	0.5340571 ***	0.0337033
firmsize	−0.0021074	0.0147374	−0.008441	0.0277787
Country FE	YES		YES
Sector FE	YES		YES
Year FE	YES		YES
Observations	6685		1059
R-squared	0.5062		0.5478
Panel b.	Logit Regression		Logit Regression
	Domestic Market		International Market
	Robust		Robust
Credit_Access	Coefficient	std. err.	Coefficient	std. err.
governconnect	0.23124 **	0.0978012	−0.4962416	0.3464139
foreign_firm	0.1628892	0.2801423	0.5210463	1.46363
private_firm	0.2715771	0.2303876	0.0217728	1.409156
state_firm	−0.1127508	0.4499458	1.941971	2.517831
female_mang	0.0887728	0.0832094	−0.0947777	0.2408591
manager_exp	0.0062877	0.0035817	0.0142179	0.010714
lnsales	0.2241249 ***	0.0266486	0.4289459 ***	0.1003604
firm_growth	0.1843081 **	0.077589	0.4244306 *	0.2249413
innovation	0.5735981 ***	0.0893924	0.6260676 ***	0.2410605
audited	0.4228991 ***	0.0861838	0.4314003 *	0.2596691
purchased_credit	0.0076551 ***	0.0012758	0.0112053 ***	0.0034869
developed	1.854543 ***	0.5784227	−0.9608744	0.8673186
credit_line	3.103038 ***	0.09259	3.486382 ***	0.3181397
firmsize	−0.0693225	0.1220315	−0.1211175	0.3271965
Country FE	YES		YES
Sector FE	YES		YES
Year FE	YES		YES
Observations	6684		1013
Pseudo R2	0.4553		0.5228

*: Significant at 10% level. **: Significant at 5% level. ***: Significant at 1% level.

, Panel a (for OLS regression) and Panel b (for Logit regression). Remarkably, the correlation of government connection in a domestic market is significantly positive with credit access, while an adverse, insignificant outcome is observed for the international market, which further affirms the existence of a relationship between firms and governments. Besides this, the regression results are also consistent with those in the research of Hawawini et al. (2004), confirming country-based factors could have a weaker effect on firms operating globally (Hawawini et al. 2004). For firms with more operations at the international scale, firm-specific characteristics such as sales, growth and indebtedness are significant driving forces in relation to accessing credit. Michelacci and Silva (2007) indicated that indigenous companies are more likely to exploit their social capital, networks and connections in their operations compared to their foreign counterparts, and therefore, the impact of the firm–government linkage will be reduced (Michelacci and Silva 2007).

We also assessed the interaction between the presence of a government connection and the main markets in which the firms work through normal OLS regression, with the results outlined in

Table 7. Regression with interaction variable between credit access and main market.

	OLS Regression
	Robust
Credit_Access	Coefficient	std. err.
1.governconnect	0.025259 **	0.0117818
1.main_market	0.0527146 ***	0.0137319
governconnect#main_market
1 1	−0.0404767	0.031968
foreign_firm	0.015928	0.0263334
private_firm	0.0092789	0.0209106
state_firm	0.0102553	0.0560889
female_mang	0.0099103	0.0090467
manager_exp	0.0004106 ***	0.0001203
lnsales	0.0278481 ***	0.0029382
firm_growth	0.0195862 **	0.0085818
innovation	0.0741059 ***	0.0100815
audited	0.0434484 ***	0.0099457
purchased_credit	0.0009518 ***	0.00014
developed	0.2397157 ***	0.0535644
credit_line	0.5164188 ***	0.010933
firmsize	−0.0005874	0.012765
Country FE	YES
Sector FE	YES
Year FE	YES
Observations	7744
R-squared	0.5237

**: Significant at 5% level. ***: Significant at 1% level.

. The findings show a positive correlation of credit access with government relations and the market. But for the interaction variable of “govern_connect” and “main_market, the result is negative and insignificant. This could imply that the firm–government relation does not guarantee greater credit accessibility for firms working internationally. In this case, such firm-specific and managerial characteristics remain significant at 1%, which again confirms the greater impacts of these variables for firms working globally.

Overall, through multivariate regressions, the positive relationship between a firm–government connection and credit access is confirmed. It should be noted that this factor is related to social capital, besides being an important dynamic related to the business itself and the environmental context in which the business operates. The influence of this relationship tends to decline as companies expand their operations and the markets in which they operate to the international level. In these markets, the relationship between business and government is no longer statistically significant, implying that individual business factors have greater explanatory significance.

4.3. Other Measurements of Firm–Government Connections

We have also employed another method of evaluating firm–government connections to ensure robustness. Based on the same dataset, we utilize the question J2, “In a typical week over the last year, what percentage of total senior management’s time was spent on dealing with requirements imposed by government regulations?” The time referred to here can be either face-to-face or via indirect modes such as paperwork; senior managers could also learn about the regulatory rules, laws and codified requirements. This is highly likely to enhance a firm’s ability to understand the process, thus improving their knowledge of how to get things done. This new variable is named “governtime”.

We performed normal regression and logit regression with the following equation:

$$\begin{gathered} {Credit access}_i=\alpha +\beta {governtime}_i+\gamma {firm characteristics}_i+\delta {external factor}_i+ \\ FE\left(country,sector,year\right)+\epsilon \end{gathered}$$

(2)

Table 8. Regression results on credit access and time firms spend on dealing with government regulations.

	OLS Regression		Logit Regression
	Robust		Robust
Credit_Access	Coefficient	std. err.	Coefficient	std. err.
governtime	0.0010712 ***	0.0002723	0.0086052 ***	0.0021329
main_market	0.0442196 ***	0.0132324	0.3622347 ***	0.1142047
foreign_firm	0.0097245	0.0269333	0.2181048	0.271316
private_firm	0.0097709	0.0213095	0.206033	0.2340198
state_firm	0.0011414	0.0577336	−0.0302601	0.4734311
female_mang	0.0074014	0.0094331	0.0470922	0.080178
manager_exp	0.000389 ***	0.0001059	0.0065362 **	0.0034349
lnsales	0.0278536 ***	0.0030599	0.2285698 ***	0.0262615
firm_growth	0.0163446 **	0.0088689	0.176225 **	0.0747214
innovation	0.072864 ***	0.0104599	0.5584937 ***	0.0855745
audited	0.0427718 ***	0.0103555	0.4068815 ***	0.0842671
purchased_credit	0.0010746 ***	0.0001461	0.00877 ***	0.001231
developed	0.226572 ***	0.0534017	1.552112 ***	0.5625899
credit_line	0.5150807 ***	0.0113429	3.073989 ***	0.0890883
firmsize	−0.0048306	0.0133411	−0.0753502	0.1142516
Country FE	YES		YES
Sector FE	YES		YES
Year FE	YES		YES
Observations	7229		7228
R-squared	0.5240
Pseudo R2			0.4705

**: Significant at 5% level. ***: Significant at 1% level.

shows the regression results yielded by Equation (2). The correlation of firm characteristics with external factors remained robust compared to the regression results from Equation (1). A significantly positive coefficient is also seen for “governtime” at the 1% level, indicating the positive relationship between this factor and a firm’s credit accessibility. Once again, this result is consistent with our hypothesis. The result is reasonable. According to the behavioral principle developed by Skinner (1950), behavior can stem from learned responses, and can be easily adjusted via the relationship with certain elements of the external environment (Skinner 1950). Such mechanisms explain our findings. When the time they spend focusing on government regulations increases, senior managers can gain more important information about the governing mechanisms, and create links with specific authorities, therefore creating stimuli for their operations as well as increasing the likelihood of getting credit.

To better capture the relationship between credit access and time spent on government regulations, we also considered the different main markets in which firms operate to derive the regression results shown in

Table 9. Regression for different markets.

Panel A.	OLS Regression		OLS Regression
	Domestic Market		International Market
	Robust		Robust
Credit_Access	Coefficient	std. err.	Coefficient	std. err.
governtime	0.0010977 ***	0.0002928	0.000217	0.0007153
foreign_firm	−0.0081349	0.0307118	0.0660552	0.0961314
private_firm	0.0099332	0.0218897	0.0247695	0.099099
state_firm	−0.0196096	0.0605905	0.151136	0.1901056
female_mang	0.0084567	0.0104469	−0.0036933	0.0223523
manager_exp	0.0003651 ***	0.0000891	0.0014239	0.0010042
lnsales	0.0276639 ***	0.0032736	0.0302289 ***	0.0096093
firm_growth	0.0140902	0.0097094	0.0405349 *	0.0228511
innovation	0.0758878 ***	0.0117864	0.0550385 **	0.0224324
audited	0.0447164 ***	0.0113031	0.0284062	0.0269001
purchased_credit	0.0011081 ***	0.000163	0.0009858 ***	0.0003482
developed	0.2293676 ***	0.0552663	0.1832202	0.192256
credit_line	0.5123492 ***	0.0120375	0.5355719 ***	0.0361078
firmsize	−0.0066067	0.0154379	−0.0021057	0.0295608
Country FE	YES		YES
Sector FE	YES		YES
Year FE	YES		YES
Observations	6249		980
R Squared	0.5058		0.5475
Panel b.	Logit Regression		Logit Regression
	Domestic market		International market
	Robust		Robust
Credit_Access	Coefficient	std. err.	Coefficient	std. err.
governtime	0.0089248 ***	0.0022317	−0.0006286	0.0084678
foreign_firm	0.0460699	0.2917169	0.963692	1.788953
private_firm	0.2301966	0.2413958	0.3923589	1.734228
state_firm	−0.2169917	0.4689108	2.143092	2.729872
female_mang	0.0621899	0.0864926	−0.0407682	0.2504103
manager_exp	0.0055742	0.0036927	0.0175061	0.0109155
lnsales	0.227224 ***	0.0278339	0.364095 ***	0.1066143
firm_growth	0.1622806 **	0.0804869	0.5213513 **	0.2361899
innovation	0.5579072 ***	0.0927952	0.6209382 **	0.2438208
audited	0.430503 ***	0.0903385	0.3112077	0.2759973
purchased_credit	0.0087521 ***	0.0013301	0.0115493 ***	0.0036521
developed	1.630155 ***	0.5827996	−0.8844369	0.8847935
credit_line	3.089671 ***	0.0950989	3.514702 ***	0.3426694
firmsize	−0.1087144	0.1281895	−0.0407205	0.3405306
Country FE	YES		YES
Sector FE	YES		YES
Year FE	YES		YES
Observations	6245		937
Pseudo R2	0.4551		0.5245

*: Significant at 10% level. **: Significant at 5% level. ***: Significant at 1% level.

. Notably, the coefficients from both normal regression and logit regression for “governtime” are significant at the 1% level for firms whose main markets are local and national. The coefficients for “governtime” are mixed (in both + and −) but generally insignificantly, with firms operating in the international context showing higher values. These results are consistent with what was hypothesized and the regression results of Equation (1).

In summary, regardless of the main markets in which the firms work, factors related to firm-specific characteristics remain robust after various tests. For companies mainly operating in international markets, such factors as “lnsales”, “firm_growth”, “innovation”, “purchased_credit” and “credit_line” tend to be higher than these values for firms with a domestic context. This could be explained as follows: when firms cannot rely on the cultural understanding of governments and other familiar environmental factors, they can only depend on themselves and their current situation of operation to survive.

5. Conclusions

This study used the cross-sectional firm-level database of BEEPS VI from 2018 to 2020, with an initial sample of 26,849 observations taken from 38 countries. This research has focused on analyzing and once again affirming the positive relationship of business factors, such as growth in sales, transparency achieved through auditing activities, the existence of formal debt related to borrowing from credit institutions and informal debt from credit purchases, the level of innovation, as well as the characteristics of the business manager (specifically the number of years of experience), with access to finance.

To examine the effects of government connections on the credit access of firms, we have also extended the research in the literature by employing two main modes of measurement, and exploring how the informal institutional factor affects firms in different markets. Positive coefficients were found in regression studies performed across the entire dataset, suggesting the existence of this effect. When researching more deeply into the contextual markets, we find that this coefficient remains robust and is stronger for businesses operating mainly on a local or domestic scale. For businesses operating abroad, the correlation between this variable and firm accessibility is not found. In explaining the results, we can see that the majority of countries in our sample are in the developing or transitional phase, whereby the whole market still has to cope with several weaknesses, such as limited infrastructure and low transparency. In such a phase, informal institutional factors can make it easier for businesses to carry out their preferred activities. In addition, businesses undertaking domestic operations can take advantage of relationships with the government to gain a better understanding of processes, regulations and laws, and therefore more easily pursue actions related to access to credit. For firms working in foreign markets, the obstacles related to the presence of outsiders could be higher, such as higher levels of competition, the lack of understanding of market sentiments, unfamiliarity and even discrimination; therefore, firms can only rely on themselves, instead of being able to exploit the domestic government and managerial ties. The results are also made more robust through different modes of measurement applied to the government connections.

The above results have important managerial implications in terms of helping businesses in developing countries and transitional economies access loans more easily. Developing appropriate policies, strengthening legal guidance, and strengthening the relationship between businesses and the government through bilateral and multilateral forums can help businesses gain a better understanding of the market and the procedures to follow, and thereby achieve greater access to capital that will benefit production and development activities. The combination of direct policies with support from governments can be enhanced in developing countries to better increase firms’ credit accessibility. In fact, the operating environment and relationship with the government are also impacted by a combination of many other factors, specifically corruption, bribery, and trust issues. In this article, we only study the geographical aspect of business operations, and we believe that future research on other social aspects should be included to offer a more comprehensive view of the issue of access to credit, thus enabling more comprehensive development not only for the businesses but also for the countries wherein those companies operate.