Nurullah Gür et al., Firm Size and Financing Behavior during the COVID-19 Pandemic: Evidence from SMEs in Istanbul (2023)
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Research paper by Nurullah Gur, Mehmet Babacan, Ahmet Faruk Aysan and Selim Suleyman, MPRA Paper No. 116300, 2023, on how small and medium-size enterprises in Istanbul managed their financing during the COVID-19 pandemic. It uses a survey conducted in May–June 2021, with data provided through the Istanbul Chamber of Commerce, to compare firms by size. The authors find that micro and small firms borrowed more from relatives and friends, that micro firms were less likely to restructure loans, borrow from banks or inject capital and cut costs more, and that micro and small firms applied for bank loans less often and reported more difficulty accessing credit.
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Munich Personal RePEc Archive
Firm Size and Financing Behavior during
the COVID-19 Pandemic: Evidence from
SMEs in Istanbul
Aysan, Ahmet Faruk and Babacan, Mehmet and Gür,
Nurullah and Süleyman, Selim
2023
Online at https://mpra.ub.uni-muenchen.de/116300/
MPRA Paper No. 116300, posted 13 Feb 2023 07:27 UTC
Firm Size and Financing Behavior during the COVID-19 Pandemic: Evidence from
SMEs in Istanbul
Nurullah Gur1, Mehmet Babacan2, Ahmet Faruk Aysan3, and Selim Suleyman4
Abstract
This paper examines how small and medium-size enterprises (SMEs) in Istanbul managed their
financial needs during the COVID-19 pandemic. A unique survey was conducted in May–June
2021 to analyze the effect of the pandemic on financial conditions and access to finance. The paper
maps the differences between firms in terms of their financing conditions and behavior based on
their size during the pandemic. The novel data set helps to conceptualize the impact of the COVID-
19 pandemic on SMEs. The paper makes a contribution to the literature through using a large
number of variables related to firms’ financial conditions and opportunities (e.g., credit
restructuring, debt postponing, capital injection). The paper hypothesizes that SMEs are less likely
than large firms to access formal finance opportunities, but they tend to rely more on informal
financing. The empirical findings suggest that, during the pandemic, micro and small firms tend
to borrow more from their acquaintances, such as relatives and friends. Micro firms are less likely
to restructure their outstanding loans, borrow from banks, or inject capital. Furthermore, micro
firms tend to cut their costs more to avoid further difficulty in their financial positions. Micro and
small firms tend to apply for bank loans less than large firms, while medium-size firms are more
likely to apply. Micro and small firms are more inclined to report difficulty in accessing credit.
Keywords: COVID-19, emerging markets, finance, small and medium-size enterprises
(SMEs)
JEL Classifications: D22, G21, H81
Acknowledgments
We thank Şekib Avdagiç and Nihat Alayoğlu at the Istanbul Chamber of Commerce for their
support and making the survey data available.
Declaration of Competing Interest
None of the authors has any financial and personal relationships with other people or organizations
that could inappropriately influence (bias) our work.
1 Corresponding author: Istanbul Medipol University, School of Business, stanbul Medipol University School of
Business, Göztepe Mahallesi Atatürk Caddesi No: 40 Beykoz-İstanbul Turkey, e-mail: ngur@medipol.edu.tr. ORCID:
0000-0001-7640-6726
2 Istanbul Medipol University, School of Business, e-mail: mbabacan@medipol.edu.tr, ORCID: 0000-0003-4705-6549
3 Hamad bin Khalifa University, e-mail: aaysan@hbku.edu.qa, ORCID: 0000-0001-7363-0116
4 Istanbul Chamber of Commerce, e-mail: selim.suleyman@ito.org.tr.
1
Firm Size and Financing Behavior during COVID-19 Pandemic: Evidence from SMEs
in Istanbul
Abstract
This paper examines how small and medium-size enterprises (SMEs) in Istanbul managed their
financial needs during the COVID-19 pandemic. A unique survey was conducted in May–June
2021 to analyze the effect of the pandemic on financial conditions and access to finance. The paper
maps the differences between firms in terms of their financing conditions and behavior based on
their size during the pandemic. The novel data set helps to conceptualize the impact of the COVID-
19 pandemic on SMEs. The paper makes a contribution to the literature through using a large
number of variables related to firms’ financial conditions and opportunities (e.g., credit
restructuring, debt postponing, capital injection). The paper hypothesizes that SMEs are less likely
than large firms to access formal finance opportunities, but they tend to rely more on informal
financing. The empirical findings suggest that, during the pandemic, micro and small firms tend
to borrow more from their acquaintances, such as relatives and friends. Micro firms are less likely
to restructure their outstanding loans, borrow from banks, or inject capital. Furthermore, micro
firms tend to cut their costs more to avoid further difficulty in their financial positions. Micro and
small firms tend to apply for bank loans less than large firms, while medium-size firms are more
likely to apply. Micro and small firms are more inclined to report difficulty in accessing credit.
Keywords: COVID-19, emerging markets, finance, small and medium-size enterprises
(SMEs)
JEL Classifications: D22, G21, H81
2
1. Introduction
The COVID-19 outbreak turned into one of the most widely spread pandemics in human history.
As of October 2022, more than 600 million cases had been recorded since the end of January 2020,
with over 6.5 million deaths worldwide (World Health Organization, 2022). Beyond the human
losses and the significant challenge for health systems, it has caused severe negative social and
economic results in many countries. The COVID-19 pandemic rapidly led to one of the most
formidable economic crises in the past century and the worst economic downturn since the Great
Depression in the 1920s (IMF, 2020; World Bank, 2021), however, these negative effects had a
greater impact on emerging markets (EMs).
Firms experienced negative economic effects because of the worldwide disruptions caused by the
pandemic through various channels. Sluggish demand, supply chain disruptions, and rising
production costs put significant pressure on firms. These supply- and demand-side problems made
it very difficult for firms to carry out their daily business activities or to take advantage of new
investment opportunities, even though access to external finance was even more vital for firms
than usual during the pandemic.
Because of some economic and political constraints, not every country can offer the same degree
of generous support to its firms. Advanced economies are usually better equipped and thus can
weather the negative impact of the COVID-19 shock, especially through direct support, whereas
EMs face more difficulty in extending rescue packages to households as well as small and medium-
size enterprises (SMEs). Theoretical and empirical studies both point out that SMEs have greater
challenges in accessing finance during economic and financial crises, when the level of uncertainty
and volatility rises (Carbo-Valverde et al., 2016; Demirgüç-Kunt et al. 2020; Stiglitz & Weiss,
1981). Although firms are supported in a coordinated manner through expansionary monetary and
3
fiscal policy instruments, business problems, such as credit contraction and bankruptcy, are still
widespread for SMEs (Gourinchas et al., 2021).
Limited fiscal room for maneuver and macroeconomic weakness have forced developing countries
to support domestic firms at a relatively modest rate and extent during the pandemic, worsening
the financial conditions of SMEs. In that regard, it is worth investigating how SMEs differ from
large firms in meeting their financing needs during the pandemic, especially in developing
countries.
Using a unique firm-level survey, this paper examines how SMEs in Istanbul, which is the
economic center of Türkiye, a vibrant EM, have met their financial needs during the pandemic.
Türkiye was the only G-20 country other than China to achieve positive GDP growth in 2020, with
growth of 1.9 percent. In 2021, after a significant period of recovery, the country posted a growth
rate of 11.4 percent. However, at the same time, differences have continued in both industrial and
firm-level performance, as some of them have not recovered at such a rapid pace. For instance, the
services sector shrank by 5.6 percent in 2020, though it grew by 21.1 percent in 2021, and, despite
signs of better performance in 2021, financial and insurance activities contracted by 9 percent in
2021. During the pandemic, SMEs showed some resilience as the supply chains mostly remained
intact, though access to finance remains an issue, and higher borrowing costs were detrimental to
SMEs’ business performance, especially in 2021.
COVID-19 has been, by far, the most severe public health threat in Türkiye’s modern history, with
the number of cases exceeding 5,250,000 , and deaths totaling nearly 48,000 from March 11, 2020,
when the first case was recorded, and June 2, 2021, which is the endpoint of our survey. To prevent
the health crisis from turning into an economic crisis, the Turkish government initiated
multidimensional policies, including a wide range of credit lines in support of businesses that were
4
affected by the measures intended for the protection of public health. Credit channels such as the
credit guarantee fund were mostly extended to SMEs via private and public banks over the period
covered by our survey-based analysis.
An immediate stimulus package of TRY 100 billion (USD10 billion) called the Economic Stability
Shield, consisting of 21 measures, was implemented for the Central Bank of the Republic of
Turkey (CBRT), and the Banking Regulation and Supervision Agency (BRSA) took bold steps to
maintain liquidity in the banking sector as well as preserving financial stability while becoming
more flexible. On March 17, 2020, the CBRT cut the policy rate by 100 basis points (bps), to 9.75
percent from 10.75, and then held two consecutive meetings in a month at which it extended
needed liquidity to banks through direct injections of repurchase (repo) auctions with maturity
terms of up to 91 days in addition to the regular one-week repo auctions. Asset-backed securities
and mortgage-backed securities were included in the collateral pool. At the same time, the central
bank reduced the foreign exchange (FX) reserve requirement ratio by 500 bps for banks that satisfy
the real credit growth criteria (Apergis et al., 2022). As of March 20, 2020, the maximum maturity
for rediscount credits was extended from 120 days to 240 days for short-term credit utilization and
up to 720 days for longer-term credit utilization, and TRY-denominated rediscount credits for
exports were offered an overall limit of TRY 60 billion. Also, the open market operations (OMO)
conditions were relaxed, and the ratio of the OMO portfolio to the central bank’s balance was
increased to 10 percent from the previous 5 percent. Commercial banks postponed loan repayment
for businesses and households.
On the fiscal front, the government postponed tax payments and social security insurance
payments as well as deferring income tax filing and payment for 1.9 million people. The lowest
retirement pension was increased to TRY 1,500 and a lump-sum amount of TRY 1,000 was given
5
to more than 2 million people, along with some TRY 20 billion cash in salary support and TRY 7
billion in minimum wage support.
As of August 2021, fiscal and financial support since March 2020 totaled around TRY 705.5
billion (10.6% of Türkiye’s gross domestic product [GDP]). Credit totaled around TRY 528.5
billion, of which some TRY 270 billion came from the public banks. Other than newly extended
and restructured credit lines along with monetary support through the unemployment insurance
fund and social solidarity fund, direct government payments comprised TRY 108.7 billion for the
same period—an increase of almost 2.1 percent of GDP, facilitated via direct transfers and tax
reductions, exemptions, or amnesty. The labor force has been preserved through short-term
working allowances and a ban on firing, from April 2020 to June 30, 2021.
The Turkish government extended massive credit during the COVID-19 outbreak, but SMEs have
found it harder to benefit from both direct and indirect support, especially with respect to credit.
Moreover, they faced additional challenges due to macroeconomic volatility, such as rapid
depreciation of the domestic currency, a worsening inflation outlook, rising interest rates, and
increased uncertainty, as second and even third waves of pandemic hit the country. However, all
these judgments require firm-level data and empirical scrutiny. Therefore, this paper fill the gap
in the literature by exploring these issues empirically with a novel firm-level dataset.
Overall, this paper contributes to two strands of literature. Some studies analyze the difficulties
faced by firms, especially SMEs, during the COVID-19 pandemic, using survey-based methods
(see Bartik et al., 2020; Cowling et al., 2020; Ferrando & Ganoulis, 2020; Guerrero-Amezaga et
al., 2022). Thus, our paper first adds to this recently emerging empirical literature by addressing
the financing channels of firms of different sizes. Second, empirical studies that have increased in
number in the past decade reveal that SMEs have more difficulty in accessing external finance in
6
times of crisis (see Block et al., 2018; Carbo-Valverde et al., 2016; Cowling et al., 2012; Demirgüç-
Kunt et al., 2020; McGuinness & Togan, 2016); our paper provides new evidence by analyzing
the effect of the COVID-19 pandemic, which was a combination of a public health and an
economic crisis. In addition, our novel dataset enables us to conceptualize the impact of the
COVID-19 pandemic on SMEs better in a less studied EM context. As a contribution to the
literature, this paper considers and uses more variables related to financial conditions and
opportunities for firms (e.g., credit restructuring, debt postponement, capital injection).
Our empirical results can be summarized as follows. Not surprisingly, during the COVID-19
pandemic, both micro and small firms tend to borrow more from their acquaintances, such as
relatives and friends, to access finance. Micro enterprises differ further in their financing
opportunities and preferences. They are less likely to restructure their outstanding loans, obtain
bank loans, and inject capital and tend to cut costs more to avoid further difficulty in their financial
position. Although micro and small firms both apply for bank loans less than large firms, medium-
size firms are more likely to use them. Micro and small firms are more inclined to report that they
faced difficulty in accessing bank credit during the pandemic, such as extension of the loan
approval period, application rejection, and higher collateral required than in normal conditions.
The structure of the paper is as follows. Section 2 presents the theoretical background and the
literature review. Section 3 outlines data, variables, and empirical methodology. Section 4 presents
our main results and robustness checks. Section 5 offers some concluding remarks.
2. Theoretical Background, Literature Review, and Hypotheses
7
Among the most significant obstacles to the growth of firms in the business environment is
difficulty in accessing finance (Beck et al., 2005; Gur, 2012). In a theoretical paper, Cabral and
Mata (2003) show that financial constraints cause underinvestment by small firms, which prevents
them from reaching their optimal firm size. Financial development helps to expand the pool of
financial resources, keeps collateral requirements at reasonable levels, reduces the cost of
borrowing, extends loan maturity, introduces alternative financing models to the credit
mechanism, helps in managing financial risks, and enables firms and sectors to grow more quickly
through more investment, exportation, and innovation (Hsu et al., 2014; Levine, 2005; Levine et
al., 2008; Manova, 2013; Rajan & Zingales, 1998). In theory, a well-functioning financial system
contributes to the emergence of a Schumpeterian growth dynamic by increasing and diversifying
much-needed financing opportunities for promising young firms and entrepreneurs so that they
can challenge incumbent firms (Aghion et al., 2005; Laeven et al., 2015).
Although access to finance is vital, many firms, especially SMEs, still have difficulty in obtaining
external finance. In times of increased uncertainty and market disruption, it becomes even more
difficult for firms to find resources to finance their daily business operations and investment
(Besley, 1994; Bordo et al., 2016; Gozgor et al., 2019; Stiglitz & Weiss, 1981). Natural disasters
(Berg & Schrader, 2012; McDermott et al., 2014), sudden geopolitical events (Khawaja & Mian,
2008), and crises cause external financing opportunities to dry up and create bottlenecks for firms.
In such periods, banks are usually reluctant to lend to SMEs, as high uncertainty reduces their pool
of qualified borrowers. When the credit dries up, capital market instrument can be used as an
alternative. However, because capital markets are shallow in many EMs, SMEs operating in these
countries have very little access to capital market instruments. Firms disappointed with the
8
financial markets might turn to informal financing channels, such as trade credit and borrowing
from friends and relatives (Allen et al., 2005, 2019; Petersen & Rajan, 1994).
The COVID-19 pandemic affected firms’ access to finance and their financing preferences because
of shrinking demand and supply-side issues, on the one hand, and its impact on the risk perception
and resource allocation preferences of financial markets, on the other. In such a chaotic
environment, with overlapping massive risks and uncertainty shocks, firms are expected to use
different financial opportunities to survive. It is not surprising that firms of different sizes try to
meet their financing needs in different ways based on their firm-level
risks/characteristics/opportunities (e.g., credit score, the availability of collateral, the choice
between arm’s-length and relationship-based finance, shareholding structure), or macroeconomic
conditions. The literature provides us with important clues and evidence in this regard.
As the level of risk and uncertainty increases during economic slowdowns and crises, banks charge
higher interest rates and require more collateral to compensate for the additional risk and
uncertainty associated with investing in small firms (Cowling et al., 2012). In times of crisis,
because their internal financing opportunities are narrowing, SMEs tend to turn to external
financing opportunities, such as credit and equity financing. Cowling et al. (2012: 781) summarize
this situation briefly as follows: “younger and smaller firms are more likely to require outside
financing as their internal resources (owner’s wealth, retained profits and cashflow) are unable to
finance new investment and growth opportunities fully. These general problems can be
exacerbated in periods of low demand when cash flows tighten and profits decline, and business
survival requires an injection of capital to maintain liquidity until demand grows again.” The
increasing credit crunch that SMEs have faced during the COVID-19 period might have other
causes as well. Berrospide et al. (2021) state that “buffer-constrained” banks have reduced loan
9
commitments to the SMEs more, and they are more likely to end pre-existing loan agreements
with such firms than with “buffer-unconstrained” banks. Lenders’ low ability to mitigate the risks
of the COVID-19 outbreak put additional pressure on limiting the provision of loans for all but the
lowest-risk borrowers, that is, large firms. As mentioned in the World Development Report (2022),
if financial markets continue to foster such a cycle, a widespread reduction in credit to SMEs, in
particular, micro enterprises, might lead to large-scale market exits.
Firms might refrain from applying for external financing out of fear of rejection during crises,
when information asymmetry increases (for a theoretical discussion, see Kon & Storey, 2003).
When risks and uncertainty are high, demand for credit may decline not only because of the
“discouraged borrowers” effect but also for different reasons and motivations. Under volatile
economic conditions, firms might also prefer to reduce their demand for credit or shorten their
debt maturity as long-term financial contracts and credits with covenants reduce the financial
flexibility of firms (for a theoretical discussion, see Brunnermeier & Oehmke, 2013). As firms’
expectations for the future fall in such periods, their demand for loans for investment, if not for
working capital, might decline.
Table 1 summarizes the findings in the empirical literature on changes in SMEs' access to credit
and financing conditions during an economic crisis. Cowling et al. (2012) use firm-level survey
data to examine how loan supply and demand conditions differed for firms of different sizes and
characteristics in the UK during the global financial crisis (GFC). Their results reveal that the
credit supply was directed mostly at large firms. Among SMEs, micro firms had more difficulty
in accessing credit than others. Six months after the GFC began (February 2009), 10 percent of
small firms had their loan applications rejected, and credit rationing hit its peak. A year later, in
parallel with relaxation in the banking sector with the decrease of uncertainty in the market, the
10
rejection rate decreased to 4.8 percent. Cowling et al. (2016) show that the discouraged borrower
effect was not significant among SMEs in the UK during the GFC. Popov and Udell (2012) show
that, in 16 developing countries in Central and Eastern Europe, SMEs working with wounded
banks, which suffered damage caused by the GFC more because of declining bank capital and
asset losses, experienced more difficulty in accessing credit during the crisis. De Young et al.
(2015) find that SMEs were more likely than large firms to face credit rationing in the US during
the GFC, except that some community banks with a strong lender–borrower relationship increased
their credit supply to SMEs. Using a firm-level database that includes 75 countries and covers the
period 2004–2011, Demirgüç-Kunt et al. (2020) investigate the effect of the GFC on evolution in
firms’ capital structure. Their empirical results reveal that firm leverage and debt maturity declined
significantly after the GFC. These adverse financial effects are experienced more by SMEs,
especially unlisted firms, and they are more pronounced in countries with less efficient legal
systems and less developed financial markets.
Table 1. A Brief Summary of the Empirical Literature
Article Country or Group of Type of Shock Main Findings
Countries
Cowling et al. (2012) UK Global Financial Crisis Micro-sized firms faced
more difficulties in
accessing credit. Loans
directed more towards
large firms
Cowling et al. (2016) UK Global Financial Crisis There is no significant
evidence on ‘discouraged
borrower’ hypothesis for
SMEs
Carbo´-Valverde et al. Spain Global Financial Crisis The dependency of credit-
(2016) constrained SMEs on trade
credits increased during
the crisis.
McGuninness and Hogan Ireland Global Financial Crisis Trade credit became more
(2014) important for SMEs that
were less liquid, highly
dependent on short-term
bank finance, and with
more intangible assets
11
Andrieu et al. (2018) 11 European countries Global Financial Crisis Bank loans and
commercial loans were
complementary for SMEs
Demirgüç-Kunt et al. 75 Countries Global Financial Crisis Firm leverage and debt
(2020) maturity declined more in
SMEs, especially non-listed
ones, after the crisis
Apedo-Amah et al. (2020) 56 countries COVID-19 Small firms were more
financially constrained
Bartik et al. (2020) USA COVID-19 Small firms suffered more
from cash shortages.
Ferrando and Ganoulis 12 Euro area countries COVID-19 Expectations of both
(2020) SMEs and large firms for
bank loans deteriorated.
Micro and small firms had
more positive conditional
expectations for trade
credits.
Liu et al. (2022) China COVID-19 SMEs decreased their loan
demand significantly in
Hubei, the starting point of
the pandemic, compared
to SMEs in other
provinces.
Cowling et al. (2021) UK COVID-19 Micro and small-sized
firms demanded loan
relatively more than others.
As discussed above, informal financing methods, such as trade credits and loans from family and
friends, are expected to come into play in a substitute or complementary manner, helping
companies survive when formal financing channels offered by financial institutions dry up. The
empirical findings also indicate that SMEs benefit from informal financing resources during
periods of high risk, uncertainty, and volatility. Carbó-Valverde et al. (2016) find that the
dependence of credit-constrained SMEs on trade credits increased during periods of financial crisis
in Spain. Using firm-level data on 34 countries during the period 1990 to 2011, Levine et al. (2018)
find that liquidity-dependent firms in high-trust countries receive more trade credit and perform
better in terms of profit and employment levels during banking crises than similar firms in low-
trust economies, indicating that trade credit as an informal finance option compensates for the
credit crunch during a banking crisis. Using panel data methods, McGuinness and Hogan (2014)
12
examine how the bank loan and trade credit composition of Irish SMEs changed during the GFC.
Their empirical findings reveal that, despite contraction in net trade credit, such as in bank loans,
trade credit became more important for financially “vulnerable” SMEs, which were less liquid,
highly dependent on short-term bank finance, and had higher levels of intangible assets, during the
GFC. These results prove that trade credit acted as a substitute for bank loans for Irish SMEs during
the GFC. In a firm-level survey-based empirical paper, however, Andrieu et al. (2018) show that
bank loans and commercial loans were complementary, rather than substitutes, for SMEs in 11
European countries from 2009 to 2014, when the effects of the GFC were felt quite heavily.
Informal financing methods for SMEs are not limited to trade credits. Some empirical papers also
show that financially constrained SMEs in EMs, such as China and Vietnam, borrow from the
family and friends of the firm owner to meet their financial needs (Allen et al., 2019; Nguyen et
al., 2022).
The number of studies in the newly emerging literature examining how firms’ financing
expectations, preferences, and conditions changed after the COVID-19 pandemic has rapidly
increased. Using firm-level survey data from the end of February to early April 2020 in the euro
area, Ferrando and Ganoulis (2020) show that, although the expectations of both SMEs and large
firms regarding bank loans and credit lines worsened during the pandemic, micro and small firms,
in particular, had more positive conditional expectations about trade credits. Song et al. (2021) use
loan-level data from China to investigate the effect of the pandemic on bank loans for small firms.
They find that banks were more generous to small firms by giving more credits at lower cost and
shorter maturity in places that were harder hit by the COVID-19 pandemic. These results indicate
that expansionary monetary policy encourages banks to lend, thus keeping SMEs afloat. Liu et al.
(2022) find that demand for loans by SMEs decreased significantly after the pandemic emerged in
13
the Hubei Province, which is considered the origin of the pandemic, unlike SMEs in other
provinces. This decline in credit applications is more notable among non-state-owned enterprises
and those without prior bank relationships. Cowling et al. (2021) found that micro and small firms
had the highest loan demand in the UK during the first two quarters of the COVID-19 outbreak.
The loan rejection rate among small firms did not differ significantly compared to the pre-
pandemic period. Another interesting finding shows that micro firms were more likely to receive
government-guaranteed loans. The COVID-19 crisis has had a negative effect on the financial
conditions in the real sector by narrowing not only the credit mechanism but also other alternative
formal financing opportunities. For example, Brown et al. (2020) show that entrepreneurial finance
deals for startups declined significantly in the United Kingdom during the pandemic.
It is important to stress that variations might occur among SMEs regarding access to finance and
other financial conditions. For example, access to formal finance is expected to be more
problematic for micro and small firms than for large and medium-size firms (Beck et al., 2006;
Beck et al., 2013). Therefore, micro and small firms might resort to informal financing channels
more. With few exceptions (see, e.g., Cowling et al., 2012; Ferrando and Ganoulis, 2020), the
empirical literature generally examines the effect of firm size on access to finance without
examining micro and small firms separately. In our analysis, we divide SMEs into three categories:
micro, small, and medium-size, to determine whether the results differ among SMEs.
Drawing on theory and the empirical literature, we propose the following hypotheses regarding
the financial conditions and preferences of SMEs during the COVID-19 pandemic:
Hypothesis 1: SMEs are less likely than large firms to access formal finance opportunities. Micro
and small firms have even less access.
14
Hypothesis 2: SMEs tend to rely on informal financing to meet their financing needs more than
large firms. Micro and small companies are more dependent on informal finance than medium-
size firms.
Hypothesis 3: SMEs tend to apply for credit less than large firms. Among credit applicants, SMEs
are more likely to experience difficulty.
3. Data and Methodology
Since the outbreak of the COVID-19 pandemic, some essential data, such as the results in the
World Bank’s World Enterprise Survey, have become even more important for capturing the
effects on SMEs. Various case studies and surveys from different countries have also been
instrumentalized, including those from China to the US and European countries. Surveys,
therefore, are important data sources for understanding the real impact of the pandemic at the firm
level. Using the World Bank Group’s Business Pulse Survey on 51 countries in six regions during
the first wave of the pandemic, Apedo-Amah et al. (2020) show in particular that small firms were
cash trapped and liquidity concerns were more severe in terms of cash buffers as well as other
financial constraints. Similarly, Bartik et al. (2020) find that small businesses were financially
more fragile because of COVID-19-related disruptions. Thus, understanding the financial issues
and coping strategies by SMEs to access finance is a key component of the surveys during the
COVID-19 pandemic.
One of the most recent surveys by the European Union’s (EU) Everywhere International SMEs
(EIS) project, initially implemented before the pandemic, was extended to capture the impact of
the COVID-19 outbreak on SMEs’ internationalization in three channels: disruption in global
15
supply chains, the rising role of digitization in SMEs’ internationalization to cope with the
pandemic, and the general resilience of the international business ecosystem. The survey results
demonstrate the impact channels of the crisis on the firms, particularly SMEs, their perception of
policy measures as well as coping strategies. Furthermore, throughout the SME Envoys Network
subgroup surveys, EU countries focus on the search for a more flexible yet diversified set of
solutions to liquidity shortages as well as challenges in access to sustained finance.
The survey data collected for this study were made possible thanks to cooperation with the Istanbul
Chamber of Commerce (ICOC), which has more than 600,000 member firms, of which around
420,000 are actively engaged in ICOC business activities. We were able to reach around 100,000
of them in the ICOC’s database online and asked them to respond to our survey; 6,030 firms
partially responded and 3,493 fully responded to the survey. Considering that the possible effects
of COVID-19 on the firm size preferences of entrepreneurs when establishing new companies
would bias our results, in this paper, we include only firms that were established before the
pandemic. As a result, our paper analyzes the survey responses from 2,983 firms.
The data gathered through a jointly designed survey carried out among the ICOC member firms is
unique in the Turkish and even the emerging market economies context because the ICOC is the
best representative of Türkiye’s largest industrial and commercial city, Istanbul, which is home to
more than 16 million people, corresponding to around 20 percent of the country’s total population.
Istanbul generates more than 30 percent of Türkiye’s GDP, some 54 percent and 51 percent of the
country’s imports and exports, respectively; more than 40 percent of the country’s tourism
revenue; more than 20 percent of the labor force; and 45 percent of the tax revenue. Istanbul also
comprises some 25 percent of consumer expenditure as well as around 15 percent of the central
government investment expenditure.
16
The survey was conducted and finalized between May 11 and June 2, 2021, the period of the most
severe waves of COVID-19 in Türkiye. During this period, the country experienced long-lasting
closures by public utilities, including transportation, bans on international and interurban travel,
strictly regulated and limited business activities, and tight health restrictions, followed by a gradual
opening in social and economic life.
Firms that are members of the chamber are predominantly SMEs, as the survey results reflect. The
survey is designed to capture the firms’ assessment of the economic impact of the COVID-19
pandemic and the precautionary measures taken and the effect on their own performance of
economic policies implemented by the government. Questions aimed to measure the economic
impact of the COVID-19 pandemic while also asking about the coping strategies by the respondent
firms. Survey questions were intended to reveal how the firms perceived their current status
concerning supply- and demand-side factors as well as future expectations on overall business
conditions.
To capture the financial conditions and opportunities for firms, the survey poses questions about
how firms met their financial needs during the pandemic and their experience in accessing credit.
The focal point of this study, therefore, is to assess the presence of any financial problems, firms’
approaches to problems, and financing methods/channels. These questions were used to construct
the dependent variables. Table 2 describes the dependent variables in detail. Although 39.73
percent of the firms secured new banking credit, 37.95 percent reduced their operating costs. Not
surprisingly, the share of firms that obtained the much-needed cash from their acquaintances is
also relatively high (36.47%), making informal finance one of the most used methods of accessing
finance. At least 19.28 percent of the firms deferred their outstanding debt, with the approval of
their creditors; around 13 percent restructure old loans, and 6.2 percent raised their own equity
17
through capital injection. Interestingly, 13.14 percent of the firms said that they were not in
financial straits during the pandemic, and 66.4 percent of the respondents who applied for new
banking credit stated that they encountered various difficulties during the application process.
Table 2. Description of Dependent Variables
Variable Name Survey Question/Description Specification
No Cash Problem Through which channels did you meet your financing Table 4
needs as a company during the pandemic period? Table 5
Table 6
Answer: Did not experience any cash flow problems.
Yes / No
A dummy variable equals to 1 if the answer is ‘Yes’, 0
otherwise
Credit Restructuring Through which channels did you meet your financing Table 4
needs as a company during the pandemic period? Table 5
Table 6
Answer: Restructured our old bank loan
Yes / No
A dummy variable equals to 1 if the answer is ‘Yes’, 0
otherwise
Debt Postponing Through which channels did you meet your financing Table 4
needs as a company during the pandemic period? Table 5
Table 6
Answer: Agreed with the lenders and postponed our
debt Yes / No
A dummy variable equals to 1 if the answer is ‘Yes’, 0
otherwise
Informal Credit Through which channels did you meet your financing Table 4
needs as a company during the pandemic period? Table 5
Table 6
Answer: Borrowed from acquaintance
Yes / No
A dummy variable equals to 1 if the answer is ‘Yes’, 0
otherwise
New Banking Credit Through which channels did you meet your financing Table 4
needs as a company during the pandemic period? Table 5
Table 6
Answer: Getting a new bank loan
Yes / No
A dummy variable equals to 1 if the answer is ‘Yes’, 0
otherwise
Capital Injection Through which channels did you meet your financing Table 4
needs as a company during the pandemic period? Table 5
Table 6
Answer: Increased our company's equity through capital
injection Yes / No
A dummy variable equals to 1 if the answer is ‘Yes’, 0
otherwise
Cost Reductions Through which channels did you meet your financing Table 4
needs as a company during the pandemic period? Table 5
Table 6
Answer: Reduced operation costs
Yes / No
A dummy variable equals to 1 if the answer is ‘Yes’, 0
otherwise
Credit Application Did you experience any difficulties (extension of your Table 7
loan approval, rejection of your application, request for
collateral beyond normal conditions, etc.) in the approval
of your loan application during the pandemic period?
Answer: Did not apply for a loan
A dummy variable equals to 1 if the answer is “Did not
apply for a loan”, 0 otherwise
Credit Application Difficulty Did you experience any difficulties (extension of your Table 7
loan approval, rejection of your application, request for
collateral beyond normal conditions, etc.) in the approval
of your loan application during the pandemic period?
Answer: Yes/No (Among applicants)
18
A dummy variable equals to 1 if the answer is “Yes”, 0
otherwise
Table 3 Description of Independent Variables
Variable Name Survey Question/Description Specification
Micro Number of employees at the end of 2019 Table 4
Table 5
Answer: 1-9; 10-49; 50-249; 250+ Table 6
Table 7
A dummy variable equals to 1 if the answer is ‘1-9’, 0
otherwise
Small Number of employees at the end of 2019 Table 4
Table 5
Answer: 1-9; 10-49; 50-249; 250+ Table 6
Table 7
A dummy variable equals to 1 if the answer is ’10-49’, 0
otherwise
Medium Number of employees at the end of 2019 Table 4
Table 5
Answer: 1-9; 10-49; 50-249; 250+ Table 6
Table 7
A dummy variable equals to 1 if the answer is ’50-249, 0
otherwise
Firm age How long has your company been operating? Table 4
Table 5
Firm age = (2021 – Year of Establishment) Table 6
Table 7
Natural logarithm of firm age
Export Does the company export? Table 4
Table 5
Answer: Yes / No Table 6
Table 7
A dummy variable equals to 1 if the answer is ‘Yes’, 0
otherwise
Joint-stock Type of the company Table 4
Table 5
Answer: Joint-stock, Unlimited liability, Limited partnership, Table 6
Limited company, Cooperative, Table 7
A dummy variable equals to 1 if the answer is ‘Joint-stock’, 0
otherwise
Unlimited liability Type of the company Table 4
Table 5
Answer: Joint-stock, Unlimited liability, Limited partnership, Table 6
Limited company, Cooperative, Table 7
A dummy variable equals to 1 if the answer is ‘Unlimited
liability’, 0 otherwise
Limited partnership Type of the company Table 4
Table 5
Answer: Joint-stock, Unlimited liability, Limited partnership, Table 6
Limited company, Cooperative, Table 7
A dummy variable equals to 1 if the answer is ‘Limited
partnership’, 0 otherwise
Cooperative Type of the company Table 5
Table 6
Answer: Joint-stock, Unlimited liability, Limited partnership, Table 7
Limited company, Cooperative,
A dummy variable equals to 1 if the answer is ‘Cooperative’, 0
otherwise
Manufacturing Main field of activity Table 5
Table 6
Answer: Manufacturing, Construction, Commerce, Services Table 7
A dummy variable equals to 1 if the answer is
“Manufacturing”, 0 otherwise
Construction Main field of activity Table 5
Table 6
Answer: Manufacturing, Construction, Commerce, Services Table 7
A dummy variable equals to 1 if the answer is “Construction”,
0 otherwise
Commerce Main field of activity Table 5
Table 6
Answer: Manufacturing, Construction, Commerce, Services Table 7
A dummy variable equals to 1 if the answer is “Commerce”, 0
otherwise
19
Covid-effect “How much has your company and business environment Table 6
been affected by the COVID-19 outbreak?” Table 7
Answer: 1 ‘very positively affected’ to 5 ‘very negatively
affected’.
Layoff-Lockdowns Has your company had to suspend its activities due to the Table 6
restrictive measures taken during the epidemic? Table 7
Answer: Yes / No
A dummy variable equals to 1 if the answer is ‘Yes’, 0
otherwise
Following the previous literature, as empirical specifications, we include various independent
variables to control for firm characteristics, such as firm age, legal status (limited or partnership),
and industrial sector. Table 4 gives detailed information about the independent variables. The basic
descriptive statistics related to the survey reveal that around 43.98 percent of the respondent firms
operate in the services, followed by some 27.66 percent in commercial business and 22.9 percent
in the manufacturing with respect to their core businesses. Construction firms make up 5.46
percent of our sample. With regard to the type of company, the firms are overwhelmingly limited
liability companies (70.1 percent) and stock corporations (22.06 percent), whereas collective or
limited partnership companies constitute a small portion of our sample (4.12% and 3.49%,
respectively). Finally, cooperatives make up only 0.23 percent of the respondent firms. The firms
are mainly SMEs, among which 67.01 percent are micro enterprises. Small enterprises make up
around 23.63 percent of the sample, and 7.11 percent consider themselves medium-size
enterprises. Large firms comprise only 2.25 percent of the sample.
To test our main argument regarding the effect of firm size on firms’ financial conditions and
behavior during COVID-19, we use the following main regression:
𝐹𝑖𝑛𝑎𝑛𝑐𝑒𝑖 = 𝛼 + 𝛽1 𝑀𝑖𝑐𝑟𝑜𝑖 + 𝛽2 𝑆𝑚𝑎𝑙𝑙𝑖 + 𝛽3 𝑀𝑒𝑑𝑖𝑢𝑚𝑖 + 𝛾𝐹𝑖𝑟𝑚𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖′ + 𝛿𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 +
𝜃𝐷𝑖𝑠𝑡𝑟𝑖𝑐𝑡 + 𝜀𝑖 (1)
20
where Finance is a binary dependent variable for the financial behavior and conditions of firm i.
Micro, Small, and Medium are dummy variables that take a value of 1 for a micro (small or
medium-size) firm, and 0 otherwise. Firmcontrol denotes a vector of firm characteristics. Industry
and District are sets of dummy variables for industries and districts to control for industry- and
district-fixed effects. To correct for any potential correlation of individual errors at the industry
level, standard errors are adjusted for clustering at the industry level in all regressions.
We use a logit model to estimate Equation (1). Using the ordinary least squares (OLS) technique
to estimate a model with a binary dependent variable can produce inefficient estimators (Greene,
2008; Wooldridge, 2009). First, the error term depends on the value of the independent variables,
meaning that results might suffer from heteroskedasticity. Second, the error term is not normally
distributed, implying that the tests of the hypotheses might be invalid. Third, the predicted
probabilities produced by OLS can be less than 0 and greater than 1, meaning that the basic law of
probability might be violated. Therefore, to achieve more reliable results, our regressions are
estimated using a logit model. One alternative model is to use a probit model, which yields similar
results.5
4. Empirical Results
The results in Table 4 come from the most parsimonious specification, which does not include any
control variables other than firm-size dummies. Column (1) shows that micro and small firms are
more likely to experience financial problems during the COVID-19 pandemic. The estimated
5
To conserve space, we provide only the logit results, but the probit results are available from the authors upon request.
21
coefficients of micro and small firms are statistically significant at the 1 percent level. The results
indicate that, unlike large firms, medium-size companies experiencing financial problems do not
experience financial conditions that significantly differ statistically. The results in Columns (2),
(5), and (6) show that micro firms are less likely to restructure their debt, obtain new bank debt,
and inject capital into their firm than large firms. These effects are highly significant for micro
firms. Column (4) shows that micro and small firms are more likely to borrow money from their
acquaintances, such as friends and relatives, to meet their financial needs. Column (7)
demonstrates no significant differences across firms of different sizes in terms of cost reduction to
meet financial needs. The initial results also show that although medium-size firms are more likely
to obtain new bank credit, they have a lower probability than large firms of postponing debt
repayment and obtaining informal credit. These effects are statistically significant at conventional
rates.6
6
In Table 4, the pseudo R2 values are low because we do not add any firm-level characteristics and industry-level
controls into the specification. When we include a large amount of control variables to the model, as seen in other
tables (Tables 5, 6, and 7), R2 increases significantly and becomes consistent with that in other articles (see Beck et
al., 2005, 2013). Because it is more difficult to control all relevant variables in micro-level empirical studies, R2 is
generally lower than in macro studies.
22
Table 5. Main Results without any Control
No Cash Credit Debt Informal New Capital Cost
Problem Restructuring Postponing Credit Banking Injection Reductions
Credit
(1) (2) (3) (4) (5) (6) (7)
Micro -1.291*** -0.463** 0.426 1.153*** -0.506*** -0.788*** 0.311
(0.286) (0.188) (0.476) (0.169) (0.161) (0.240) (0.218)
Small -0.837** -0.038 0.390 0.666*** -0.069 -0.256 0.295
(0.392) (0.346) (0.306) (0.111) (0.105) (0.620) (0.197)
Medium -0.043 -0.276 -0.954** -2.700*** 0.407*** -0.362 0.056
(0.662) (0.313) (0.383) (0.851) (0.052) (0.510) (0.206)
No of obs. 2,983 2,983 2,983 2,983 2,983 2,983 2,983
Pseudo R2 0.029 0.006 0.005 0.030 0.010 0.011 0.001
Note: Standard errors are clustered at the industry level. ***, ** and * denote statistically significant at the 1 percent, 5
percent, and 10 percent, respectively. Finance is a binary type of dependent variable on financial behaviour and conditions
of firm i. Micro, Small, and Medium are dummy variables that take the value 1 if a firm is micro (small or medium) and 0
otherwise.
Next, in Table 5, we add some firm characteristics, such as age, whether the firm is an exporter,
and the type of ownership, along with industry dummies, and district dummies to check whether
our results are robust to controlling for other relevant and standard control variables used in similar
firm-level empirical studies. Then, Table 6 shows the empirical results after the perceived and real
effects of the COVID-19 pandemic on firms are added. The question “How much has your
company and business environment been affected by the COVID-19 outbreak?” is used to measure
the perceived COVID-19 effect (Covid-effect). This is a Likert-type question, with responses
scored from 1 (affected very positively) to 5 (affected very negatively). To check the real effect of
COVID-19 on firms, we add a dummy variable (Layoff-Lockdowns) and pose the following
question: “Has your company had to suspend its activities due to the restrictive measures taken
during the epidemic?,” to which the response can be 1 (yes) or 0 (no). Including all these variables
in the regression causes minor changes in the main results. In the specification with the COVID-
19 impact, firm size loses its significance in determining the probability of having cash problems.
With respect to cost reductions, the estimated coefficient on Small gains some significance,
23
implying that small firms are more inclined to reduce costs to create some additional space for
financial needs. Other results about firm size are the same as the initial results.
Table 5. Controlling for Firm and Industry Characteristics, and District Fixed Effects
No Cash Credit Debt Informal New Capital Cost
Problem Restructuring Postponing Credit Banking Injection Reductions
Credit
(1) (2) (3) (4) (5) (6) (7)
Micro -0.822*** -0.467** 0.165 0.782*** -0.625*** -0.759*** 0.381**
(0.293) (0.210) (0.475) (0.147) (0.160) (0.256) (0.178)
Small -0.550 -0.021 0.255 0.427*** -0102 -0.263 0.309
(0.404) (0.345) (0.341) (0.107) (0.089) (0.589) (0.196)
Medium -0.118 -0.267 -0.867* -2.636*** 0.411*** -0.383 0.061
(0.710) (0.276) (0.452) (0.983) (0.065) (0.543) (0.238)
Firm age 0.228** 0.070 -0.237*** -0.270*** -0.024 -0.196*** 0.030
(0.085) (0.072) (0.031) (0.040) (0.026) (0.074) (0.072)
Export 0.449*** -0.074 -0.196*** -0.208* -0.191** 0.220*** -0.052
(0.107) (0.126) (0.054) (0.109) (0.067) (0.075) (0.111)
Joint-stock 0.449*** -0.112 -0.134 -0.555*** -0.026 0.170** -0.061**
(0.091) (0.079) (0.128) (0.100) (0.053) (0.076) (0.027)
Unlimited liability -0.453*** 0.508** 0.182 -0.073 0.555*** -1.296*** -0.240
(0.161) (0.247) (0.233) (0.067) (0.143) (0.368) (0.160)
Limited -0.628 -0.382*** -0.418 -0.132 0.379* -0.640 0.134
partnership (0.400) (0.073) (0.400) (0.083) (0.230) (0.714) (0.225)
Cooperative 1.370 - - - -1.111 - -0.350*
(0.870) (1.072) (0.210)
Manufacturing -0.156 -0.089 -0.029 0.093 0.210*** 0.079 -0.210***
(0.099) (0.070) (0.075) (0.080) (0.035) (0.080) (0.047)
Construction -0.457*** 0.210*** 0.192*** 0.470*** -0.197*** 0.066* 0.092***
(0.030) (0.013) (0.061) (0.036) (0.043) (0.035) (0.023)
Commerce 0.200*** -0.168*** -0.093 -0.038 0.092** 0.182*** -0.394***
(0.051) (0.048) (0.057) (0.032) (0.044) (0.031) (0.018)
District Fixed YES YES YES YES YES YES YES
Effect
No of obs. 2,970 2,975 2,971 2,975 2,982 2,883 2,892
Pseudo R2 0.076 0.021 0.031 0.068 0.024 0.050 0.019
Note: Standard errors are clustered at the industry level. ***, ** and * denote statistically significant at the 1 percent, 5
percent and 10 percent, respectively. Finance is a binary type of dependent variable on financial behaviour and conditions
of firm i. Micro, Small, and Medium are dummy variables that take the value 1 if a firm is micro (small or medium) and 0
otherwise. Firmcontrol’ denotes for a vector of firms’ characteristics. Industry and District are sets of dummy variables for
industries and district to control for industry and district fixed effects.
24
The results in Table 6 indicate that being a micro firm reduces the probability of credit
restructuring, new bank credit, and capital injection by 6.4, 17.2, and 3.4 percent, respectively, and
increases the likelihood of reporting a cost reduction during the pandemic by 8.5 percent compared
over that of large firms. The estimated coefficient of Micro (Small) in Column (4) of Table 6 shows
that being a micro (small) firm increases the probability of borrowing money from an acquaintance
by 14.1 (8.4) percent over that of a large firm. However, being a medium-size firm decreases the
probability of credit from acquaintances by 57 percent over that of a large firm. In Column (5) of
Table 5, medium-size firms are more likely than large firms to obtain new banking credit during
the pandemic by 10.6 percent. Empirical findings are largely consistent with H1 and H2. At this
point, micro firms are negatively differentiated in terms of access to formal finance channels,
whereas medium-size companies are positively differentiated.
Table 6. Controlling for COVID-19 impact
No Cash Credit Debt Informal New Capital Cost
Problem Restructuring Postponing Credit Banking Injection Reductions
Credit
(1) (2) (3) (4) (5) (6) (7)
Micro -0.437 -0.629*** 0.022 0.648*** -0.722*** -0.699*** 0.362**
(0.362) (0.207) (0.458) (0.142) (0.155) (0.252) (0.165)
Small -0.479 -0.085 0.194 0.378*** -0.139 -0.246 0.303
(0.473) (0.337) (0.348) (0.128) (0.103) (0.588) (0.192)
Medium -0.253 -0.231 -0.829* -2.584** 0.445*** -0.408 0..065
(0.753) (0.247) (0.500) (1.074) (0.039) (0.513) (0.244)
Covid-effect 1.009*** -0.534*** -0.394*** -0.491*** -0.324*** 0.303** -0.170***
(0.057) (0.048) (0.106) (0.055) (0.019) (0.121) (0.041)
Layoff-Lockdowns -1.009*** 0.313 0.428*** 0.494*** 0.061 -0.005 -0.124
(0.188) (0.222) (0.156) (0.024) (0.150) (0.200) (0.111)
Firm age 0.190* 0.110* -0.210*** -0.245*** -0.007 -0.214*** 0.035
(0.095) (0.063) (0.034) (0.048) (0.028) (0.072) (0.070)
Export 0.319*** 0.148 -0.122*** -0.121 -0.148** 0.178* -0.042*
(0.092) (0.124) (0.046) (0.136) (0.043) (0.100) (0.107)
Joint-stock 0.402*** -0.049 -0.100 -0.537*** 0.006 0.133* -0.050*
(0.124) (0.090) (0.136) (0.101) (0.050) (0.077) (0.029)
25
Unlimited liability -0.854*** 0.530** 0.185 -0.055 0.589*** -1.370*** -0.225
(0.133) (0.247) (0.245) (0.090) (0.163) (0.406) (0.162)
Limited -0.399 -0.420*** -0.464 -0.190* 0.361 -0.612 0.132
partnership (0.410) (0.090) (0.400) (0.103) (0.232) (0.720) (0.222)
Cooperative 1.363** - - - -1.092 - -0.337*
(0.525) (0.940) (0.180)
Manufacturing -0.309*** -0.012 0.034 0.174** 0.262*** 0.043 -0.188***
(0.072) (0.068) (0.084) (0.085) (0.037) (0.105) (0.046)
Construction -0.464*** 0.190*** 0.167*** 0.457*** -0.207*** 0.047 0.100***
(0.079) (0.021) (0.070) (0.040) (0.039) (0.035) (0.028)
Commerce 0.187*** -0.116*** -0.069 -0.007 0.129** 0.148** -0.371***
(0.030) (0.059) (0.056) (0.030) (0.056) (0.058) (0.020)
District Fixed YES YES YES YES YES YES YES
Effects
No of obs. 2,970 2,975 2,971 2,975 2,982 2,883 2,892
Pseudo R2 0.232 0.046 0.052 0.101 0.035 0.058 0.021
Note: Standard errors are clustered at the industry level. ***, ** and * denote statistically significant at the 1 percent, 5
percent and 10 percent, respectively. Finance is a binary type of dependent variable on financial behaviour and conditions
of firm i. Micro, Small, and Medium are dummy variables that take the value 1 if a firm is micro (small or medium) and 0
otherwise. Firmcontrol’ denotes for a vector of firms’ characteristics. Industry and District are sets of dummy variables for
industries and district to control for industry and district fixed effects.
Tables 5 and 6 have interesting and important results on firm- and industry-based impacts. The
results reveal that older firms and exporters are less likely to experience cash problems during the
pandemic. Although firms in commercial and manufacturing sectors report fewer cash problems
than those in the services, the probability of having a cash problem is higher for construction firms.
Manufacturers are more likely than firms in the service sector to access both formal and informal
financing channels. Because construction companies have more problems in accessing both formal
and informal finance, they have a greater tendency to postpone repayment of their debt, restructure
loans, and reduce their costs than firms in the service sector. Commercial firms are more inclined
to secure credit from formal and informal channels and to inject capital, but they are less likely to
restructure outstanding loans and reduce their costs. Exporters are less likely to postpone
repayment of debt and obtain new bank credit but more likely to inject capital. Türkiye’s export
26
performance gained momentum after the first wave of the COVID-19 pandemic, so it is not
surprising that exporters tend to prefer options such as delaying repayment of debt and bank credit
less and strengthen their capital structure with export revenue. Firms at which the COVID-19
pandemic negatively affected business operations and temporarily suspended activity during the
lockdowns are more likely to report cash problems, delay debt repayment, and receive funding
from acquaintances. Firms that perceive the effects of COVID-19 on firms positively tend to inject
capital and use trade credit more than others. These firms are less likely to restructure their debt
and reduce their costs to meet their financial needs during the pandemic.
To test H3, first, we regress firm size dummies and other control variables on a binary dependent
variable that takes a value of 1 if firms report that they applied for bank credit during the pandemic;
then, among credit applicants, we use another binary dependent variable that takes a value 1 if
applicant firms report any difficulty (e.g., extension of the loan approval period, rejection of the
application, request for more collateral beyond normal conditions) in accessing credit. These
results are shown in Table 7. The results in Column (3) show that although micro and small firms
are less likely than large firms to apply for bank credit, medium-size firms are more eager to do
so. The estimated coefficients are statistically significant and economically meaningful. Being a
micro (small) firm reduces the likelihood of applying for bank credit by 20.2 percent (4.9 percent)
of that of large firms. However, being a medium-size firm increases this probability by 22.4
percent. The results in Column (6) indicate that micro and small firms are more likely than large
firms to report difficulty in applying for bank credit by 11.7 and 10.1 percent, respectively.
Table 7. Results for Credit Applications
Credit Application Credit Application Difficulty
(1) (2) (3) (4) (5) (6)
Micro -0.478*** -0.697*** -0.865*** 0.958*** 0.717*** 0.521**
(0.084) (0.025) (0.053) (0.284) (0.275) (0.240)
27
Small -0.030 -0.139** -0.210*** 0.644*** 0.507*** 0.450***
(0.036) (0.064) (0.074) (0.137) (0.129) (0.153)
Medium 0.833** 0.905** 0.958*** -0.346 -0.273 -0.255
(0.390) (0.373) (0.309) (0.241) (0.250) (0.260)
Covid-effect -0.371*** -0.622***
(0.057) (0.029)
Layoff-Lockdowns 0.234 0.466***
(0.232) (0.057)
Firm age -0.207*** -0.187*** -0.319*** -0.285***
(0.034) (0.031) (0.023) (0.029)
Export -0.199 -0.140 -0.109** 0.015
(0.147) (0.115) (0.030) (0.039)
Joint-stock -0.080 -0.045 -0.091 -0.002
(0.070) (0.068) (0.076) (0.108)
Unlimited liability 0.386*** 0.432*** -0.216 -0.129
(0.112) (0.143) (0.222) (0.217)
Limited partnership 0.449*** 0.417** -0.568* -0.581*
(0.199) (0.200) (0.327) (0.342)
Cooperative - - - -
Manufacturing 0.281*** 0.344*** -0.087 0.046
(0.070) (0.066) (0.080) (0.091)
Construction 0.259*** 0.247*** 0.249*** 0.403***
(0.023) (0.028) (0.050) (0.065)
Commerce 0.033 0.065*** -0.254*** -0.200**
(0.026) (0.020) (0.034) (0.025)
District Fixed NO YES YES NO YES YES
Effect
No of obs. 2,983 2,975 2,975 1,839 1,837 1,837
Pseudo R2 0.011 0.033 0.052 0.020 0.059 0.107
Note: Standard errors are clustered at the industry level. ***, ** and * denote statistically significant at the 1 percent, 5
percent and 10 percent, respectively. Finance is a binary type of dependent variable on financial behaviour and conditions
of firm i. Micro, Small, and Medium are dummy variables that take the value 1 if a firm is micro (small or medium) and 0
otherwise. Firmcontrol’ denotes for a vector of firms’ characteristics. Industry and District are sets of dummy variables for
industries and district to control for industry and district fixed effects.
The Turkish economy showed unique resilience and rapid recovery after the first two waves of the
COVID-19 pandemic. The increase in exports to Europe and the US as a result of the tight supply
chains in Asia and the increase in transportation costs from Asia to Europe and the US, as well as
28
high domestic demand, thanks to the expansion in consumer loans, increased the profitability of
large manufacturing firms to record levels. Because the Borsa Istanbul (Istanbul Stock Exchange)
had many new domestic investors during the pandemic, it broke some records, which was
financially beneficial for many large firms, enabling them to afford the increasing production and
transportation costs and to engage in new investment. Therefore, many large firms may have
unexpectedly decreased dependence on bank loans during that period. However, medium-size
companies that wanted to take advantage of the economic opportunities presented by the reopening
of the economy after the waves of the pandemic applied for more loans to expand business. At the
same time, micro and small firms borrowed more from acquaintances in order to meet financing
needs that arose during the pandemic.
5. Discussions
Economic shocks can have a more negative impact on SMEs than large firms, damaging
business/market dynamism cyclically and structurally. In this respect, the COVID-19 pandemic,
an external shock that damaged economic activity through multiple channels, was a critical test
for SMEs. As in many crisis periods, during COVID-19 access to finance was one of the most
challenging factors for SMEs. Financial difficulties are often more decisive for SMEs in EM,
where financial markets are relatively shallow. This article empirically examines the differences
in the financial conditions and behavior of firms of different sizes during the COVID-19 pandemic
in Istanbul, which is both a regional business hub and the economic heart of Türkiye, an emerging
market with critical importance for the global economy.
This paper hypothesizes that although SMEs are less likely to access finance from banks and other
financial institutions, they are more likely to rely on informal finance to survive and float their
29
business during the various waves of the pandemic. In addition, this paper also hypothesizes that
SMEs are discouraged when applying for credit and face more difficulty than large firms in credit
applications.
Not surprisingly, in line with previous literature, the results show that micro and small firms are
less likely to receive new bank credit, but this effect is significant only for micro firms. Our
contribution to the literature is the use of more information about the financial conditions and
behavior of firms through surveying them about their conditions, such as debt restructuring,
delaying debt repayment, injecting capital, and reducing costs during the pandemic. Our empirical
results show that micro firms tend to restructure loans and inject capital significantly less than
large firms. Consistent with the previous literature, our empirical results conclude that micro and
small firms meet their financial needs by borrowing funds from acquaintances more than large
firms.
According to the empirical results, which differ from the literature in some respects, compared to
large firms, medium-size firms did not experience significant difficulty in accessing credit during
the pandemic. On the contrary, they are more inclined to obtain more credit. This situation has two
different cause. First, the Turkish government preferred to provide more credit through public
banks, rather than using fiscal policy tools to mitigate the effects of the pandemic on the real sector.
This credit was primarily targeted at SMEs. Medium-size firms with a longer or better credit
history and more collateral opportunities among SMEs had more access to this targeted credit. The
second reason might be related to the emerging commercial opportunities for medium-size firms
and the corresponding need for increases in capacity. During COVID-19, they gained new export
opportunities, especially to Europe and the US, because of the supply disruptions and logistics cost
increases, especially in East Asia. Medium-size firms might have applied for and accessed more
30
credit to finance their new investment, as they increased their ability to satisfy the increasing export
orders and the need for more domestic products in the domestic market (Delgado et al., 2022). Our
empirical results confirm this potential explanation.
Medium-size firms tend to apply for credit more, but, compared to large firms, do not face
significant difficulty in their credit applications. At the same time, the results confirm “the
discouraged borrower” hypothesis for micro and small firms. They are significantly less inclined
to apply for credit (Disli et al., 2022). In addition, among credit applicants, they face more
difficulty.
6. Conclusion
SMEs are the backbone of the real economy. When economic conditions and the business
environment are too suffocating for SMEs, the competition that triggers innovation is eroded,
fewer jobs are created, and the wheels of economic growth slow down. Although SMEs play such
a critical role in the economy, crisis periods can be extremely challenging for SMEs, especially in
financing channels. The COVID-19 pandemic, in which different demand- and supply-side shocks
hit the real economy simultaneously, was even more arduous for SMEs. EMs, where financial
markets are shallower, monetary and fiscal policies have less room for maneuver, and
macroeconomic stability is weaker, pose additional challenges to SMEs in such a period.
Understanding the financial conditions and behavior of SMEs is vital for EMs if they are to cope
with destructive economic shocks more resiliently. The paper lays out the differences in financing
conditions and behavior among firms based on their size during the COVID-19 pandemic in an
EM. We collected survey data from Turkish firms that are members of the ICOC, one of the biggest
in the broader Middle Eastern and North African region, as well as among its European peers. Our
31
empirical results show that micro and small firms face more financial problems and have less
access to formal financing channels than large firms. As a reflection of this situation, micro and
small firms finance their business operations through informal financing. They tend to borrow
more from their acquaintances, rather than from formal credit channels in the banking industry.
But medium-size firms have not experienced significant difficulty in accessing formal financing
opportunities. They are more inclined than large firms to apply for and take out bank loans.
The empirical results offer policy makers clarity about these issues. Policy makers should focus
even more on the financial restrictions on micro firms during the COVID-19 pandemic.
Undoubtedly, integrating SMEs into the formal banking sector on a regular basis and improving
their transparency help economies remain resilient in challenging times. Additionally, the findings
highlight the importance of informal lending channels for micro firms and show policy makers
and businesses the value of enhancing the social capital of SMEs. The findings also point out
market opportunities for banks and other companies in the financial sector while revealing that
micro firms still have untapped potential. Technological resources from fintech could help banks
reach a larger audience. In sum, the banking industry has the potential to benefit from fintech
resources (Nanaeva et. al., 2021) and reach a wider group of SMEs with low operating costs and
contribute to resilience and social well-being.
The outbreak of COVID-19 was a period in which the credit mechanism was kept alive thanks to
the decline in interest rates and credit guarantees. In terms of the size of the support differentiated
this period from the 2008 GFC. Because the real sector received generous support through
monetary and fiscal policy channels, medium-size firms with high collateral and a good credit
history did not experience more difficulty than large firms in accessing finance. Therefore, when
the credit mechanism is supported by both monetary and fiscal policies, it would be better for
32
policy makers to monitor not only access to credit but also whether credit is directed to the right
sectors. Policy makers should implement macroprudential policies to ensure that credit goes to
medium-size firms in promising sectors with export potential.
This paper has certain limitations. First, the survey was only performed during the pandemic period
and has not been repeated. No prior survey was conducted with similar questions. Hence, it is
difficult to compare the results over time. Moreover, during the pandemic, many policy measures
were implemented over a short period, which also limits the interpretations about which policies
worked better than others. This period featured not only with high global uncertainty but also high
domestic uncertainty mostly due to increasing inflation and exchange rate volatility. It is difficult
to capture both global and domestic uncertainty in a single study. These limitations require future
research.
In future research, similar surveys need to be repeated over time to capture the behaviors of SMEs
better. Policies implemented by the government during the pandemic could be assessed in terms
of their implications for SMEs. In particular, which kinds of policies adopted by the government
were more effective for different types of firms matters for their employment implications.
Another future study could be about the financialization of SMEs during the COVID-19 period.
The policy measures implemented during COVID-19 might have even led to further
financialization of SMEs and could pose greater challenges in the future. Another dimension could
be an analysis of the resistance to change at SMEs (Alperen et al., 2023). Certainly, in terms of
finding solutions to SME financing, one could also investigate the role of fintech companies (Unal
et al., 2022). This paper is a good basis for future studies and will be useful for policy makers and
the SMEs to help them become more resilient to future shocks.
33
References
Aghion, P., Howitt P., & Mayer-Foulkes, D. (2005). The effect of financial development on
convergence: Theory and evidence. Quarterly Journal of Economics, 120(1), 173-222.
https://doi.org/10.1162/0033553053327515.
Allen, F., Qian, J., & Qian, M. (2005). Law, finance, and economic growth in China. Journal of
Financial Economics, 77(1), 57-116. https://doi.org/10.1016/j.jfineco.2004.06.010.
Allen, F., Chakrabarti, R., De, S., & Qian, M. (2012). Financing firms in India. Journal of
Financial Intermediation, 21(3), 409-445. https://doi.org/10.1016/j.jfi.2012.01.003.
Allen, F., Qian, M., & Xie, J. (2019). Understanding informal financing. Journal of Financial
Intermediation, 39, 19-33. https://doi.org/10.1016/j.jfi.2018.06.004.
Andrieu, G., Staglianò, R., & Van Der Zwan, P. (2018). Bank debt and trade credit for SMEs in
Europe: firm-, industry-, and country-level determinants. Small Business Economics,
51(1), 245-264. https://doi.org/10.1007/s11187-017-9926-y.
Apedo-Amah, M.C., Avdiu, B., Cirera, X., Cruz, M., Davies, E., Grover, A., Iacovone, L., Kilinc,
U., Medvedev, D., Maduko, F.O., Poupakis, S., Torres, J., & Tran, T.T. (2020).
Unmasking the impact of COVID-19 on businesses: Firm level evidence from across the
world. World Bank Policy Research Working Paper, No. 9434. Washington, DC.
Apergis, N., Aysan, A. F., & Bakkar, Y. (2022). Borrower-and lender-based macroprudential
policies: What works best against bank systemic risk? Journal of International Financial
Markets, Institutions and Money, 80, 101648.
Bartik, A.W., Bertrand, M., & Cullen, Z. (2020). The impact of COVID-19 on small business
outcomes and expectations. Proceedings of the National Academy of Sciences of the
34
United States of America (PNAS), 117(30), 17656–17666.
https://doi.org/10.1073/pnas.2006991117.
Beck, T., Demirgüç‐Kunt, A., & Maksimovic, V. (2005). Financial and legal constraints to growth:
does firm size matter? Journal of Finance, 60(1), 137-177. https://doi.org/10.1111/j.1540-
6261.2005.00727.x.
Beck, T., & Demirguc-Kunt, A. (2006). Small and medium-size enterprises: Access to finance as
a growth constraint. Journal of Banking & Finance, 30(11), 2931-2943.
https://doi.org/10.1016/j.jbankfin.2006.05.009.
Beck, T., Demirgüç-Kunt, A. & Singer, D. (2013). Is small beautiful? Financial structure, size and
access to finance. World Development, 52, 19-33.
https://doi.org/10.1016/j.worlddev.2013.05.014.
Berg, G., & Schrader, J. (2012). Access to credit, natural disasters, and relationship lending.
Journal of Financial Intermediation, 21(4), 549-568.
https://doi.org/10.1016/j.jfi.2012.05.003.
Berrospide, J.M., Gupta, A., & Matthew, S.P. (2021). Un-used bank capital buyers and credit
supply shocks at SMEs during the pandemic. Finance and Economics Discussion Series
2021-043, Washington: Board of Governors of the Federal Reserve System.
Besley, T. (1994). How do market failures justify interventions in rural credit markets? World
Bank Research Observer, 9(1), 27-47. https://doi.org/10.1093/wbro/9.1.27.
Bordo, M.D., Duca, J.V., & Koch, C. (2016). Economic policy uncertainty and the credit channel:
Aggregate and bank level US evidence over several decades. Journal of Financial
Stability, 26, 90-106. https://doi.org/10.1016/j.jfs.2016.07.002.
35
Brown, R., Rocha, A., & Cowling, M. (2020). Financing entrepreneurship in times of crisis:
exploring the impact of COVID-19 on the market for entrepreneurial finance in the United
Kingdom. International Small Business Journal, 38(5), 380-390.
https://doi.org/10.1177/0266242620937464.
Brunnermeier, M.K., & Oehmke, M. (2013). The maturity rat race. Journal of Finance, 68(2), 483-
521. https://doi.org/10.1111/jofi.12005.
Cabral, L., & Mata, J. (2003). On the evolution of the firm size distribution: Facts and theory.
American Economic Review, 93(4), 1075-1090.
https://doi.org/1257/000282803769206205.
Carbo‐Valverde, S., Rodriguez‐Fernandez, F., & Udell, G.F. (2016). Trade credit, the financial
crisis, and SME access to finance. Journal of Money, Credit and Banking, 48(1), 113-143.
https://doi.org/10.1111/jmcb.12292
Cowling, M., Liu, W., & Calabrese, R. (2021). Has previous loan rejection scarred firms from
applying for loans during Covid-19? Small Business Economics, 1-24.
https://doi.org/10.1007/s11187-021-00586-2
Cowling, M., Liu, W., & Ledger, A. (2012). Small business financing in the UK before and during
the current financial crisis. International Small Business Journal, 30(7), 778-800.
https://doi.org/10.1177/0266242611435516
Cowling, M., Liu, W., Minniti, M., & Zhang, N. (2016). UK credit and discouragement during the
GFC. Small Business Economics, 47(4), 1049-1074. https://doi.org/10.1007/s11187-016-
9745-6.
36
Delgado, J. L. A., Demirbaş, D., & Aysan, A. F. (2022). Old but resilient story: Impact of
decentralization on social welfare. Journal of Risk and Financial Management, 15(12),
584.
Demirgüç-Kunt, A., Peria, M.S.M., & Tressel, T. (2020). The global financial crisis and the capital
structure of firms: Was the impact more severe among SMEs and non-listed firms?.
Journal of Corporate Finance, 60, 101514.
https://doi.org/10.1016/j.jcorpfin.2019.101514.
Deyoung, R., Gron, A., Torna, G., & Winton, A. (2015). Risk overhang and loan portfolio
decisions: Small business loan supply before and during the financial crisis. Journal of
Finance, 70(6), 2451-2488. https://doi.org/10.1111/jofi.12356.
Disli, M., Aysan, A. F., & Abdelsalam, O. (2022). Favoring the small and the plenty: Islamic
banking for MSMEs. Economic Systems, 101051.
Ferrando, A., & Ganoulis, I. (2020). Firms’ expectations on access to finance at the early stages
of the Covid-19 pandemic. European Central Bank Working Paper Series, No. 2446.
Gozgor, G., Demir, E., Belas, J., & Yeşilyurt, S. (2019). Does economic uncertainty affect
domestic credits? An empirical investigation. Journal of International Financial Markets,
Institutions and Money, 63, 101147. https://doi.org/10.1016/j.intfin.2019.101147.
Gur, N. (2012). Financial constraints, quality of institutions and firm size: What do perceptions
tell us? Eurasian Economic Review, 2(2), 17-36. https://doi.org/10.14208/BF03353835.
Hsu, P.H., Tian, X., & Xu, Y. (2014). Financial development and innovation: Cross-country
evidence. Journal of Financial Economics, 112(1), 116-135.
https://doi.org/10.1016/j.jfineco.2013.12.002.
37
Khwaja, A.I., & Mian, A. (2008). Tracing the impact of bank liquidity shocks: Evidence from an
emerging market. American Economic Review, 98(4), https://doi.org/1413-42.
10.1257/aer.98.4.1413.
Kon, Y., & Storey, D.J. (2003). A theory of discouraged borrowers. Small Business Economics,
21(1): 37-49. https://doi.org/10.1023/A:1024447603600.
Laeven, L., Levine, R., & Michalopoulos, S. (2015). Financial innovation and endogenous growth.
Journal of Financial Intermediation, 24(1), 1-24.
https://doi.org/10.1016/j.jfi.2014.04.001.
Levine, R. (2005). Finance and growth: Theory and evidence. In P. Aghion, & S. Durlauf (Eds.),
Handbook of Economic Growth (pp. 865–934). Amsterdam: North-Holland.
Levine, R., Lin, C., & Xie, W. (2018). Corporate resilience to banking crises: The roles of trust
and trade credit. Journal of Financial and Quantitative Analysis, 53(4), 1441-1477.
https://doi.org/10.1017/S0022109018000224.
Liu, Y., Zhang, Y., Fang, H., & Chen, X. (2022). SMEs’ line of credit under the COVID-19:
evidence from China. Small Business Economics, 58, 807–828.
https://doi.org/10.1007/s11187-021-00474-9 .
Manisaligil, A., Gölgeci, İ., Bakker, A. B., Faruk Aysan, A., Babacan, M., & Gür, N. (2023).
Understanding change in disruptive contexts: The role of the time paradox and locus of
control. Journal of Business Research, 156, 113491.
https://doi.org/10.1016/j.jbusres.2022.113491
Manova, K. (2013). Credit constraints, heterogeneous firms, and international trade. Review of
Economic Studies, 80(2), 711-744. https://doi.org/10.1093/restud/rds036.
38
McDermott, T.K., Barry, F., & Tol, R.S. (2014). Disasters and development: Natural disasters,
credit constraints, and economic growth. Oxford Economic Papers, 66(3), 750-773.
https://doi.org/10.1093/oep/gpt034.
McGuinness, G., & Hogan, T. (2016). Bank credit and trade credit: Evidence from SMEs over the
financial crisis. International Small Business Journal, 34(4), 412-445.
https://doi.org/10.1177/0266242614558314.
Nanaeva, Z., Aysan, A. F., & Shirazi, N. S. (2021). Open banking in Europe: The effect of the
Revised Payment Services Directive on Solarisbank and Insha. Journal of Payments
Strategy & Systems, 15(4), 432-444.
Nguyen, H.T., Nguyen, T.T., Dang, X.L.P., & Nguyen, H.M. (2022). Informal financing choice in
SMEs: Do the types of formal credit constraints matter? Journal of Small Business &
Entrepreneurship, 34(3), 313-332. https://doi.org/10.1080/08276331.2019.1692441.
Petersen, M.A., & Rajan, R.G. (1994). The benefits of lending relationships: Evidence from small
business data. Journal of Finance, 49(1), 3-37. https://doi.org/10.1111/j.1540-
6261.1994.tb04418.x.
Popov, A., & Udell, G.F. (2012). Cross-border banking, credit access, and the financial crisis.
Journal of International Economics, 87(1), 147-161.
https://doi.org/10.1016/j.jinteco.2012.01.008.
Rajan, R., & Zingales, L. (1998). Financial development and growth. American Economic Review,
88(3), 559-586.
Song, Q., Du, J., & Wu, Y. (2021). Bank loans for small businesses in times of Covid-19: Evidence
from China. Emerging Markets Finance and Trade, 57(6), 1652-1661.
https://doi.org/10.1080/1540496X.2021.1900820.
39
Stiglitz, J.E., & Weiss, A. (1981). Credit rationing in markets with imperfect information.
American Economic Review, 71(3), 393-410.
Unal, I. M., & Aysan, A. F. (2022). Fintech, digitalization, and blockchain in Islamic finance:
Retrospective investigation. FinTech, 1(4), 388-398.
World Development Report (2022). Finance for an equitable recovery. World Bank Group.
Washington.
World Health Organization. (2022). Coronavirus (COVID-19) Dashboard. Available at
https://covid19.who.int.
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