The development of small and medium enterprises (SMEs) is an essential contributor to economic and social growth in developing economies (Samawi et al., 2016). However, having limited access to finance impedes firm success. The lack of access to financing is one of the most critical obstacles affecting SME performance, competitiveness, and growth (Aldaba, 2011). Lack of access to financing hinders an SME from innovating and acquiring assets to expand its operations. Access to finance is essential to support small businesses' growth (Angeles et al., 2019) by enabling innovation, facilitating investment and expansion to new markets, improving efficiency, and providing employment (Innovations for Poverty Action (IPA), 2015). When financing is maximized, the growth of small businesses is improved (Angeles et al., 2019). Additional capital increases SMEs' opportunities to boost productivity (Krishnaswamy, 2007), while the lack of capital limits small firms' growth opportunities (Angeles et al., 2019). Lack of capital due to credit constraints is the primary obstacle that hinders SMEs from reaching their full potential (Fowowe, 2017). Thus, financial institutions' willingness to support SMEs through financing is critical in overcoming small firms’ growth constraints (Khandker et al., 2013; Kuzilwa, 2005).

Although several studies have analyzed the relationship between firm characteristics and access to finance, a research gap our paper intends to address is whether access to finance through formal channels has different determinants than informal channels. This is important because, like other developing countries, informal financing is a substantial credit provider in the Philippines. Over 38% of borrowers in the Philippines rely on informal sources of credit, despite the associated risks (World Bank Group, 2015).

This paper studied the relationship between firm characteristics and access to finance among SMEs in the Philippines. We examined which firm characteristics were correlated to the predicted probability of being credit-constrained or “quasi-constrained”—i.e., able to borrow from informal sources. Further, we analyzed whether the firm characteristics that were significant in accessing finance solely through formal channels such as banks were also significant when considering informal financing sources such as private moneylenders, family, or friends.

This study used data from 480 SMEs in the Philippines, gathered during the second and third quarters of 2018. Logistic regressions were performed and the marginal effects at the means (MEMs) provided an estimate of the relationship between firm characteristics and the predicted probability that the average SME in the sample is credit-constrained or quasi-constrained. In this paper, we focused on firm size, the previous purchase of fixed assets, and the use of digital accounting and financial management technologies. We studied the relationship between these firm characteristics and access to finance both through formal and informal markets because increased firm size and the previous purchase of fixed assets are typically associated with less credit constraint in the literature, while the digital transformation of accounting and financial management has improved the operation efficiency, productivity, and performance of SMEs (Hoang & Le Dinh, 2019).

The Philippines provides relevant context to develop our study because micro, small, or medium enterprises account for over 99% of all firms, about two-thirds of employment, and a third of gross value added (GVA), serving as the backbone of the economy. Furthermore, our estimates in this paper suggest that 42% of small and 33% of medium enterprises are credit-constrained. In comparison, 47% of both small and medium enterprises are quasi-constrained1 from a sample of 480 SMEs in the National Capital Region (NCR) and Calabarzon region. By identifying which factors enable access to finance in both formal and informal markets, we can evaluate policy options to make financing more inclusive. Alternative financing sources that support projects typically considered too small or risky by traditional banks have experienced rapid growth (World Economic Forum, 2015) in recent years. Many financial technology (fintech) companies enter the market to address the increasing demand for inclusive, fully digital, remotely accessible, and affordable financial services. This is particularly favorable to small businesses and firms operating in remote areas that encounter difficulties accessing traditional financing (OECD, 2018). Digital technologies enable SMEs’ access to credit (Lukonga, 2020) by allowing small businesses to take advantage of innovative digital financial services (OECD, 2018). However, despite the benefits of digital finance that give small businesses greater access to loans, the use of fintech solutions remains limited compared to traditional finance due to the digital and financial literacy requirements to access fintech (Nemoto & Koreen, 2019). Thus, policies that democratize financial services through inclusive finance and financial technologies (fintech) should be promoted to enable the growth of SMEs.

As a preliminary analysis, we ran a binary logistic regression to estimate the relationship between firm characteristics, firm technology, and the probability that an SME i is credit-constrained (1) or quasi-constrained (2). The results of the logistic regressions are presented in Appendix (Table 5) and Average Marginal Effects in Appendix (Table 6). We used the following equations in the logistic regressions:

where β is a vector of coefficients of firmchar, a vector of firm characteristics that include firm size, profit growth rate, employment size, ownership type, industry, firm age, female ownership, administrative region where the firm was located, percentage of employees with a college degree, purchase of fixed assets in the past two years, and the firm’s use of digital technologies for accounting and financial management operations; and ε is the error term.

Absolute values of the estimated logit coefficients (Appendix (Table 5) do not directly express the magnitude of economic impact. Thus, we also present our estimates of Average Marginal Effects (AMEs) in Appendix (Table 6). Marginal effects are one of the more widely-used means of providing an intuitive and meaningful interpretation of the impact of variables in nonlinear models (Williams, 2012) and are especially useful for categorical and binary independent variables. They are also often reported with logistic regression analyses to communicate and quantify how a change in outcome is related to changes in a particular covariate (credit constraint factors) (Cameron & Trivedi, 2010). AMEs use the actual observed values for the variables, making it attractive for some researchers since all the data and not just the means are used in the analysis (Williams, 2012).

Aside from the magnitude, we were also interested in approximating the amount of change in the predicted probability that the average SME in our sample is credit-constrained or quasi-constrained, given a change in a dependent variable such as firm characteristics and firm technology. To this end, we present estimates of Marginal Effects at the Means (MEMs) in Table 3.

MEMs use the means when computing predicted values (Williams, 2012). Use of AMEs over MEMs, and vice-versa are subject to preference, and both allow for meaningful interpretations of coefficient estimates, albeit from different reference points. An argument for preferring MEMs over AEMs is that MEMs can be more representative in analyzing the effects on the average observation (in our case, the average SME) in the sample.

Equation (3) shows the marginal effect of increasing the level of digital software use for accounting and financial management on the predicted probability that an SME i is credit-constrained (3) or quasi-constrained (4). We compute the MEMs using the following equations:

where credconst is a binary variable equal to 1 for credit-constrained SMEs, and 0 otherwise; quasi-const is a binary variable equal to 1 for quasi-constrained SMEs, and 0 otherwise; X is a vector of covariates in the model that include all other firm characteristics held constant at their means, and digiAFM is equal to 1 if the SME i has a moderate to high use of digital software for accounting and financial management, and 0 if the SME does not, or at most has limited use of digital software for accounting and financial management.

To find the relationship of changes in firm characteristics on the predicted probability that the average SME in our sample is credit-constrained or quasi-constrained, we report the marginal effects at the means (MEMs). Our analysis of MEMs allowed us to quantify the impact of changes in the covariates on the predicted probability that the average SME in our sample is credit-constrained. We identified the average SME in our sample by holding all firm characteristics at their mean values. The covariates we focused on were firm characteristics and the SME’s adoption of digital technologies for accounting and financial management. For our analysis of MEMs, the estimated marginal effects show how the predicted probabilities of being credit-constrained or quasi-constrained change as the covariates change. We are particularly interested in the marginal effect of an SME’s increased use of digital software in accounting and financial management operations.

The estimated marginal effects coefficients allowed us to determine how the predicted probability of being credit-constrained or quasi-constrained changes for the average SME in our sample as it increases its use of digital technologies for accounting and financial management operations, holding firm characteristics at their mean values. Compared to SMEs that are credit-constrained, respondents were classified as quasi-constrained if they obtained loans from informal sources. For purposes of this analysis, we defined informal sources as any of the following: the business owner or one of the owners, family or relative of the business owner/one of the owners, moneylenders (“five-six loan sharks”10), and other sources apart from banks or financial institutions such as cooperatives, savings, and loan associations and lending companies.

We present the marginal effects at the mean estimation results in Table 3 below. There are two major findings from the results. First, firm characteristics (asset size and fixed assets purchase) that matter in accessing finance through formal channels are not significant when accounting for informal sources. In other words, the factors that affect an SME’s predicted probability of being credit-constrained may be different from the factors that affect the probability of being quasi-constrained. Second, the increased use of digital software for accounting and financial management operations is associated with a lower predicted probability of being credit-constrained and quasi-constrained.

Column 1 in Table 3 shows that the predicted probability of being credit-constrained is 11.6 percentage points lower for medium-sized enterprises, relative to small enterprises, holding firm characteristics at their means. This result is consistent with Aldaba (2011), whose study finds that smaller SMEs in the Philippines had a more challenging experience accessing external financing, primarily due to having limited records, collateral, and inadequate financial statements. An enterprise’s ability to access finance is typically correlated with firm size. Smaller companies generally encounter more difficulties accessing finance. Smaller firms usually have fewer collateral options, a riskier profile, lower accounting and financial management capacity, and higher informality rates (OECD et al., 2019). Besides having less access to external finance, SMEs face higher risk premiums and higher transaction costs than larger firms (Beck & Demirguc-Kunt, 2006). Cross-country studies show that as firm size increases, the probability of being credit constrained decreases (Kuntchev et al., 2013). In the Philippines, SMEs, especially the smaller firms, have faced difficulties accessing credit due to limited acceptable collateral, inadequate financial statements, and incomplete financial records (Aldaba, 2011).

Also consistent with Aldaba (2011), we find that having purchased fixed assets, which can be used as collateral and is also a positive signal of creditworthiness to creditors, has an inverse association with the probability of being credit-constrained. Particularly, the predicted probability of being credit-constrained is about 8.1 percentage points lower for SMEs that have purchased fixed assets in the past two years, relative to those that did not purchase fixed assets. Previous asset purchase records have also been linked to increased access to finance. A firm’s prior acquisition of assets can be indicative of its credit history. Quartey et al. (2017) identified a firm’s credit information as a determinant of access to finance. To reduce asymmetric information and assess their clients’ ability to repay loans, banks and other formal financial institutions typically require collateral (Love et al., 2013). While firms have traditionally used fixed assets as collateral, moveable assets such as equipment have recently been gaining increased use as a form of collateral, especially for small businesses that lack fixed assets. Love et al. (2013) found that introducing movable collateral registries increases firms' access to bank finance, especially for smaller firms. Nonetheless, the recent acquisition of assets, whether fixed or moveable, may increase the likelihood that a firm can access finance from formal institutions.

Also, we find that an SME’s increased adoption of technology, represented by its level of digital software use for accounting and financial management operations, is inversely associated with the predicted probability that an SME is credit-constrained and quasi-constrained. For average SMEs in our sample, those with moderate to high use of digital accounting and financial management software are 15.6 percentage points less likely to be credit-constrained and 12.4 percentage points less likely to be quasi-constrained than those with limited or no use of digital software for accounting and financial management. This result is also consistent with Aldaba (2011), which identified having inadequate financial statements as one of the barriers to accessing finance. Using digital software for accounting and financial management may make it easier to oversee and generate reports on a firm’s finances. Increased use of digital processes also improves a firm’s ability to organize its records, such as financial statements. Thus, having a higher level of digital accounting and financial management use could make the firm more attractive to external creditors, especially if the creditor requires the firm's financial statements, as do most banks and other lending institutions in the formal sector. A firm’s adoption of new technologies, particularly in accounting and financial management, is also a determinant of its ability to access formal credit markets. Increased digital technologies adoption has an inverse association with the predicted probability of being credit constrained. In addition to firm size, Peñaloza (2015) identified the firm's technological capacity as a key factor in determining a firm’s access to credit markets primarily because of competitive advantages brought about by increased use of technology. Using digital software for accounting and financial management also streamlines firms' record-keeping process, enabling them to meet the requirement of records to access credit from formal financial institutions.

Comparing column 1 with column 2 in Table 3, the estimation results indicate that some of the firm characteristics that affect the probability of being credit-constrained may not significantly affect the probability of being quasi-constrained. Firm size, past purchase of fixed assets, and a moderate to high level of digital software use for accounting and financial management operations each have a statistically significant inverse relationship with the predicted probability that an SME is credit-constrained. Among the firm characteristics that were significant in determining access to formal finance, only the use of digital software for accounting and financial management has a statistically significant inverse relationship with the predicted probability that an SME is quasi-constrained in informal channels. For the average SME in our sample, higher use of digital accounting and financial management software is associated with a lower predicted probability of being credit-constrained and quasi-constrained.

In short, access to informal finance may have different determinants than formal finance, although the results of our estimates show that the use of digital technologies in accounting and financial management are both significantly correlated to less credit constraint in both formal and informal markets.

Access to finance is essential in enabling and maximizing small businesses' growth (Angeles et al., 2019). Having additional capital allows entrepreneurs to innovate, expand to new markets, improve efficiency, and provide employment (Innovations for Poverty Action (IPA), 2015). While the lack of capital limits small firms' growth opportunities (Angeles et al., 2019), additional capital increases small businesses' potential to boost productivity (Krishnaswamy, 2007).

The lack of capital due to credit constraints is the primary obstacle that hinders SMEs from reaching their full potential (Fowowe, 2017). Thus, financial institutions' willingness to support SMEs through financing is critical to overcoming small firms’ growth constraints (Khandker et al., 2013; Kuzilwa, 2005).

Our study contributes to the literature on SME finance by distinguishing between the types of credit constraints small businesses face – whether they cannot obtain loans from formal sources (credit-constrained) or can obtain loans but only from informal channels (quasi-constrained). This distinction is especially relevant for SMEs in developing countries where informal channels are an important source of financing for small businesses. Moreover, our study also shows that access to finance in formal channels has different determinants than informal channels, although the use of digital technologies for accounting and financial management is positively associated with increased access to finance in both formal and informal markets. This result is important because although access to finance in formal channels has different determinants than informal channels, the use of digital technologies for financial management may help mitigate credit constraints for SMEs in both formal and informal markets.

In this paper, we defined credit constraints and identified the characteristics of credit-constrained and quasi-constrained SMEs in the Philippines using data from a sample of 480 firms in the NCR and Calabarzon regions. We were particularly interested in the relationship between firm characteristics, including its level of technology use in accounting and financial management operations and the probability of being credit-constrained and quasi-constrained.

We distinguished between being credit-constrained and quasi-constrained, where SMEs that obtained loans from informal sources are considered quasi-constrained. Comparing the estimates of marginal effects at the means from logistic regressions, we show that while firm size and past purchase of fixed assets are associated with a lower predicted probability of being credit-constrained, they have no statistically significant relationship with the probability of being quasi-constrained when involving informal sources. In addition, our estimates also revealed that high use of digital software for accounting and financial management has a statistically significant inverse relationship with the predicted probability of being credit-constrained or quasi-constrained.

Many financial technology (fintech) companies enter the market to address the increasing demand for inclusive, fully digital, remotely accessible, and cheap financial services. This is particularly relevant for small businesses and firms operating in rural and remote areas that find it difficult to access the formal financial sector (OECD, 2018). Digital technologies enable SMEs’ access to credit (Lukonga, 2020) by allowing small businesses to take advantage of innovative digital financial services (OECD, 2018). Alternative financing sources have seen rapid growth in both developing and developed economies as they enable investment projects typically considered too small or too risky by traditional banks (World Economic Forum, 2015). Despite the social and economic benefits of fintech in increasing access to finance, innovative digital financial solutions remain limited compared to traditional finance. This is because digital and financial literacy are requirements for SMEs to access fintech and other digital financial solutions that give them greater access to loans (Nemoto & Koreen, 2019). Policies that democratize financial services through inclusive finance and financial technologies (fintech) should be promoted, in addition to digital financial literacy, to enable the growth of SMEs.

This work was supported by the Konrad Adenauer Stiftung (KAS) (Funder Identifier: http://dx.doi.org/10.13039/501100004079) through grant under project number S20250000V for 2020. All views expressed herein are solely of the authors and do not necessarily reflect the views of KAS or the Asian Institute of Management.