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[Online paper] 1467-8454.12174 [edited]
Principles of Accounting (Trường Đại học Kinh tế Thành phố Hồ Chí Minh)
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[Online paper] 1467-8454.12174 [edited]
Principles of Accounting (Trường Đại học Kinh tế Thành phố Hồ Chí Minh)
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3. al., 2010). As the world economy's globalisation trajectory becomes increasingly uncertain,
SMEs face pressures to re-evaluate their own value chains, re-examine their investments strate-
gies and re-test their technological innovation activities. These trends have led to substantial
consolidation of SMEs for size and growth, consequently, to significant changes in ownership
structure. However, this literature contains few empirical studies examining the links among
SME performance, economic uncertainty and ownership structure. We contribute to this
nascent literature by examining the effects of the two interrelated dimensions of economic
uncertainty and ownership structure on SME performance.
SMEs account for the largest proportion of business organisations, which are the driving
force of economic development and growth throughout the world. In particular, SMEs contrib-
ute significantly with their share of GDP ranging from 20% to 50% in the majority of the devel-
oping economies (Auzzir et al., 2018) and create over 60% of the employment in the region
(Iqbal & Rahman, 2015). In addition, Organisation for Economic Co-operation and Develop-
ment [OECD] (2017) reported that SMEs in the OECD area are the predominant form of enter-
prise, accounting for approximately 99% of all firms, which provide approximately 70% of the
jobs, on average and are major contributors to value creation, generating between 50 and 60%
of the value added on average. However, they are faced with many barriers in the business pro-
cess such as restrictions in credit access (Ayyagari, Juarros, Martinez Peria, & Singh, 2016) and
developing technology (Hadjimanolis, 1999; Shah, Ashishsanghavi, Poojagundu, Abhikjain, &
Sahiljain, 2017) as well as adapting to uncertainties in new policies that would affect the envi-
ronment where SMEs run their businesses (Wang, Chen, & Huang, 2014). Moreover, small
businesses are sensitive to the changes in economic-related policies compared to other business
because both macroeconomic and firm-specific uncertainties strongly affect the performance of
SMEs (Rashid & Saeed, 2017). While larger organisations are more likely to have organisational
capabilities for project management, smaller firms often lack this capability and, thus, run the
risk of engaging in managerial undertakings without experience (Danneels, 2002).
Economic uncertainty is critical for companies due to its direct impact on not only various
strategic decisions (Madanoglu & Ozdemir, 2019) but also firm-level outcomes (Amore &
Minichilli, 2018). The uncertainty of economic policy creates a poor business environment if
firms cannot maximise their choice to gain profits. Uncertain times affect both firms and con-
sumers; in particular, while firms tend to delay all irreversible and high-cost strategies such as
hiring new employees and making investments, consumers increase their saving accounts by
cutting down on spending (Čižmešija, Loli
c, Sori
c, 2017). When economic uncertainty in a
country increases, firms tend to hold more cash to protect against uncertainty (Demir
Ersan, 2017). Holding more cash will lead to increased costs since fewer of the firms' assets are
used in profitable operations; eventually their performance will reduce. Krol (2017) demon-
strates that uncertainty increases information asymmetry between firms and banks. In terms of
greater uncertainty, banks tend to limit their lending to smaller firms to reduce risk exposure
(Hu Gong, 2019). Consequently, economic uncertainty can shock an SME's investments and
cash holdings, shocking its demand for loans and simultaneously driving commercial banks to
adjust their lending supply (Chi Li, 2017).
Furthermore, firms' uncertainty-performance nexuses differ because of differences in
resource mixes, production methods and management monitoring costs, which could be driven
by differences in firm ownership structures. The policy development of firms depends on the
decisions of managers, but it maintains a problem called the principal-agent problem, which
arises from the differences in aims, interests and preferences between the principal (owner) and
the agent (managers). Previous studies reveal that the influence level of economic uncertainty
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4. varies among different ownership types (e.g., Gulen Ion, 2015; Wang et al., 2014). In particu-
lar, Dang, Fang, and He (2019) find the differences in the effect of economic uncertainty on cor-
porate tax between state firms and non-state firms, for which tax burdens are primarily
significant in state-owned enterprises. Wang et al. (2014) also emphasise the importance of for-
eign ownership in reducing the negative effects of economic uncertainty, particularly since,
unlike state-owned firms, foreign-owned firms tend to have more reactive motilities in eco-
nomic-related policy. A fundamental reason is that the depressing effect of economic uncer-
tainty on performance is more significant in state-owned firms with higher irreversibility in
investing that are more dependent on government public expenditure (Gulen Ion, 2015).
Despite the extensive literature on the difficulty of SMEs to access bank funding (Beck, 2007;
Fowowe, 2017), a comprehensive study on whether the economic uncertainty and ownership
structure enhance or impede the performance of SMEs does not yet exist.
The purpose of this paper is to investigate the relationship between economic uncertainty
and the performance of SMEs. Specifically, we analyse ownership structure affects the uncer-
tainty–performance nexus. The paper contributes to the literature in several ways. First, our
paper is related in spirit to recent studies that provide international evidence about the linkage
between economic uncertainty and the performance of SMEs in both developing countries
(Chi Li, 2017; Gulen Ion, 2015; Wang et al., 2014) and developed countries (Madanoglu
Ozdemir, 2019). In contrast to these studies, which mostly depend on single-country financial
data to analyse this effect, we expand to a broader range of countries with 11 developing coun-
tries and 14 developed countries, which allows us to analyse differences between two groups
since differences in country characteristics also contribute to the impact of the economic uncer-
tainty level on firm decisions (Julio Yook, 2012). In addition, while some related studies
mainly focus on the effect of economic policy uncertainty on enterprise activities, such as firm
investment (Gulen Ion, 2015; Kang, Lee, Ratti, 2014; Wang et al., 2014), investor informa-
tion asymmetry (Nagar, Schoenfeld, Wellman, 2019) and cash holdings (Demir Ersan, 2017),
we directly evaluate the effect of economic uncertainty on firm performance, on which empirical
evidence remains scarce in both developed and developing contexts. Madanoglu and
Ozdemir (2019) also investigate the economic policy uncertainty's response to firm performance;
these authors, however, employ some specific indicators just for applying in cases of the hotel
industry, while our study is popularly used to measure firm performance in many countries and
industries.
Second, we explore the black box relationship between economic uncertainty and firm per-
formance by introducing the interaction terms between ownership types (e.g., state and foreign
ownership) and economic uncertainty. In this way, we explicitly analyse the mediating effect of
ownership types on the relationship between economic uncertainty and firm performance. To
the best of our knowledge, some existing studies tried to combine the interaction between eco-
nomic uncertainty and other factors such as return on invested capital and internal finance
(Wang et al., 2014), as well as sales growth (Kang et al., 2014). While a related study of Wang et
al. (2014) employs interaction terms of dummy non-state enterprises and economic policy
uncertainty focusing on Chinese listed companies, our study attempts to analyse the influence
of ownership structure on the relation between economic uncertainty and the efficiency of
SMEs and we distinguish between the ownership types of government, foreign and private
domestic firms. Although research is increasingly using the economic uncertainty index to
proxy the economic policy effect as the explanatory variables in their regressions (e.g., Dang et
al., 2019; Li Yang, 2015), these studies have only assessed the impact of economic policy
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5. uncertainty on corporate investment and tax burdens, thereby offering a bleak picture of its rel-
ative significance in the performance estimates of SMEs.
As an attempt to fill this void, we examine the effects of ownership structure on the relation
between economic uncertainty and firm performance using data for 27,659 firm-year observa-
tions (4,459 SMEs) in 25 countries over the period 2006–2017. We obtain the following crucial
findings. First, we find that increases in economic uncertainty are negatively associated with
the performance of SMEs. Second, comparing with state- and private-domestic-owned firms,
firms owned by foreign block shareholders acquire a higher firm performance than their non-
foreign counterparts. Finally, firms with foreign ownership are able to better mitigate the nega-
tive effects of economic uncertainty on firm performance.
The remainder of this paper is organised as follows. Section 2 reviews the literature on firm
performance, ownership structure and economic uncertainty. Section 3 presents our measures
of ownership structure and analyses how the ownership structure affects the uncertainty–per-
formance nexus. Section 4 describes the data selection. Section 5 analyses the empirical results
and discusses their implications. Finally, Section 6 concludes the paper with a concise discus-
sion of policy implications.
2 | LITERATURE REVIEW AND HYPOTHESIS
DEVELOPMENT
2.1 | Economic uncertainty and the performance of SMEs
In terms of economic policy uncertainty, scholars have put a great deal of effort into investigat-
ing the linkage between economic uncertainty and firm-level variables in recent years such as
investment (Gulen Ion, 2015; Kang et al., 2014; Liu Zhang, 2019), cash holding (Demir
Ersan, 2017; Phan, Nguyen, Nguyen, Hegde, 2019), stock return (Chiang, 2019; Phan,
Sharma, Tran, 2018), dividend policy (Farooq Ahmed, 2019), the cost of capital (Drobetz,
El Ghoul, Guedhami, Janzen, 2018; Kim, 2019), acquisition (Nguyen Phan, 2017) and earn-
ings management (Yung Root, 2019). In addition, few studies examine the direct impact of
economic uncertainty on firm performance; Madanoglu and Ozdemir (2019) is one of the first
studies demonstrating the negative effect of economic policy uncertainty on firm performance,
in the hotel industry, in particular, which is derived from reducing investments or difficulty in
access to credit under high uncertainty.
From an investment perspective, real options theory implies that during high perceived eco-
nomic uncertainty, firms tend to “wait and see” by delaying investment decisions or cutting
down their investment spending to reduce costly mistakes (Bernanke, 1983; Dixit, Dixit,
Pindyck, 1994). Gulen and Ion (2015) report that when the uncertainty is too large, capital
investment is significantly lower at both the firm and industry levels because firms delay their
real options up to eight quarters into the future until some of the uncertainties could be
resolved. Kim and Kung (2016) document that firms experience a significantly larger reduction
in investment due to using less re-deployable assets to react with uncertainty. Based on the pre-
cautionary saving theory, SMEs are likely to dramatically increase the amount of cash as an
uncertainty hedging instrument instead of spending for both capital investments and hiring
plans (Demir Ersan, 2017; Phan et al., 2019). However, the dark side of holding more cash is
that firms use fewer assets for profitable activities, which directly affects firm performance.
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6. In compliance with the financing perspective, economic uncertainty increases information
asymmetry between investors and managers as well as borrowers and lenders (Çolak,
Durnev, Qian, 2017), leading to a reduction in loans during high uncertainty times. Increased
economic uncertainty not only makes it difficult for firms to access to external financing but
also increases the cost of financing due to a higher risk premiums (Çolak et al., 2017; Kim,
2019). Hu and Gong (2019) find that economic uncertainty diminishes banks' credit growth;
this negative effect is especially greater for larger-sized banks and riskier banks. Under higher
uncertainty, it is likely that commercial banks bear higher credit risk and tend to limit the size
of their loans to prevent losses (Chi Li, 2017), which results in constraints in credit access
from financial institutions. Given that the constraint on bank lending is highly significant dur-
ing the high uncertainty period, firms are thus more likely to cut down their level of investment
spending due to banks' limited financing (Gulen Ion, 2015; Kang et al., 2014; Rashid
Saeed, 2017; Wang et al., 2014).
Despite prior literature providing numerous studies that investigate the effect of economic
uncertainty on firm-level variables in a sample of large firms or listed firms, there is less evi-
dence for the direct effect of economic uncertainty on business performance, and the academic
research is inconclusive about whether risk management activities in SMEs efficiently manage
in the same manner as their counterparts in terms of profitability. Based on a few existing stud-
ies, our expectation is that economic uncertainty tends to reduce firm performance. It can be
seen that financial performance is affected by economic uncertainty in two aspects. First, due to
increasing economic uncertainty leading to an intangible increase in the cost of financing,
SMEs tend to delay their plans and investment to wait for a more suitable time, thus reducing
their efficiency. Second, in terms of consumer expenditure, customers often increase their sav-
ings accounts instead of spending. Two aspects show a shred of strong evidence that firm per-
formance can be affected by the uncertainties in economic-related policies. Given the many
similar results about the impact of economic uncertainty on the performance of SMEs in devel-
oped and developing countries, we now suggest our first hypothesis as follows:
Hypothesis 1 Economic uncertainty is negatively associated with the performance of SMEs.
2.2 | Moderating impact of ownership structure from the perspective
of agency theory
As suggested by agency theory, ownership structure is an important corporate governance
mechanism that helps to limit agency problems arising from the separation of ownership and
control (Jensen Meckling, 1976; Shleifer Vishny, 1986). Ownership structure plays a chief
role in firm growth strategies by their impact on managers' decisions that relate to firm perfor-
mance. The central premise of arguments regarding agency problems arises when firm man-
agers' aims, interests, and preferences are not aligned with those of the firm's owners.
Accordingly, an agency problem directly relates to a firm's decisions, which significantly affects
firm performance. The predictions of the relation between ownership and performance indicate
two main ways to mitigate this problem. First, managers are also large shareholders in a firm
(Denis, Denis, Sarin, 1999; Han Suk, 1998), which would lead to similar interests between
managers and others shareholders. Second, by monitoring manager actions, the board of direc-
tors or shareholders can secure their investments (Berger Bonaccorsi di Patti, 2006). Further-
more, firm strategies differ because of differences in resource mixes, production methods, and
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7. management monitoring costs, which could be driven by differences in firm ownership struc-
tures. Thus, it is important to recognise from the existing literature that the performance of
SMEs will vary based on the ownership types of firm (e.g., foreign, state or domestic
ownership).
The effect of a state-owned shareholder on the uncertainty-performance nexus can be
explained by two main theories, social welfare theory and political theory. Social welfare theory
indicates that state-owned enterprises are created to maximise social welfare instead of focusing
on profit objectives. In addition, the political theory argues that governmental pursuit of politi-
cal intentions (e.g., employment rate, bribes) can reduce public enterprise efficiency (Shleifer
Vishny, 1998). Both theories shape an expectation that state-owned firms tend to be inferior in
performance due to non-profit motivations and resource misallocation for political purposes,
especially in countries with high economic uncertainty (Huang, Jiang, Liu, Zhang, 2011; Lin,
Doan, Doong, 2016).
With this growing literature on ownership structure in developing economies, some studies
go further to compare the corporate performance in different ownership structures but are
inconclusive (see Phan, Daly, Doan, 2018; Wei, Xie, Zhang, 2005; Yu, 2013). Some parts of
the existing literature imply that firms in economies with less stable governments are likely to
be less efficient and more vulnerable to political institutions' outcomes (e.g., Julio Yook, 2012;
Micco, Panizza, Yanez, 2007). Since state-owned enterprises in developing countries strongly
depend on the government policies with more uncertainty (Deng, Morck, Wu, Yeung, 2011),
it is rational to expect that the presence of state ownership is inherently inefficient (Chen,
Chen, Lin, Zhong, 2005). One possible reason is that the agency cost of non-state-owned
firms (e.g., foreign, private domestic ownership) is lower than that of state-owned firms. Huang
et al. (2011) also conclude in the context of emerging countries, state-controlled firms often lack
proper monitoring, and their managers are heavily affected by the respective local government;
in many cases, managers' decisions are largely controlled by state entities including the party
regime, and promotions are based on the loyalty of managers to the government instead of their
abilities. However, a few other papers (e.g.,Phung Hoang, 2013; Sun, Tong, Tong, 2002)
indicate an inverted U-shaped relation between state ownership and firm performance. State
ownership may first contribute to increase the performance of SMEs by its advantages; this
means neither a high nor a low government ownership provide a benefit for firm performance
(Sun et al., 2002).
However, foreign ownership tends to be more beneficial for the performance of SMEs in
developing countries (Carney, Estrin, Liang, Shapiro, 2019). Based on resource-based theory,
the success of foreign-owned firms can be attributed to possessing their tangible assets and
intangible assets, which are difficult or costly for other firms to perform. In addition, agency
theory suggests that the incentives of foreign shareholders are more aligned to obtain an effi-
cient monitoring position. They also tend to invest in fields related to their core business in
which they have more experience to manage. Numerous scholars reveal empirical evidence that
foreign ownership is associated with better performance via monitoring of managers' actions
(Ben-Nasr, 2016; Denis et al., 1999; Gillan Starks, 2003). Bürker, Franco, and Minerva (2013)
indicate that according to the internalisation theory, foreign-owned firms acquire superior
intangible assets coming from the parent company (e.g., technology, managerial skills, produc-
tion method, brand names). Technology is also an important contributor to SMEs in the long
run because direct investment flows from developed countries to developing countries, bringing
with it higher-ranking technology, organisational capital, and access to international capital
markets; this would be the best way to improve firm performance in the host countries (Chari,
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8. Chen, Dominguez, 2012; Hallward-Driemeier, Wallsten, Xu, 2006). Similarly, Wang and
Wang (2015) suggest the positive effect of foreign ownership on firm output for which advan-
tages of both financial funding conditions and credit access can help foreign-owned firms con-
veniently improve their RD actions, sales and market share strategies; they also easily export
their product to break into the overseas market. Recently, Wang et al. (2014) and Yang, Yu,
Zhang, and Zhou (2019) revealed that investment behaviour by state-owned firms is negatively
affected more by economic uncertainty whereas non-state-owned enterprises might benefit
from a firm's investment under uncertainty. A work of Madanoglu and Ozdemir (2019) also
show the positive impact of interaction between economic policy uncertainty and foreign own-
ership on firm performance, indicating that foreign-owned firms' performances are less affected
by economic uncertainty than those of domestic-owned firms. Thus, different ownership types
may be associated, on average, with more negative (positive) effects of state (foreign) ownership
on firm performance.
It is important for economic reformers and regulators to understand how the state (foreign)-
controlled shareholders of SMEs are associated with country-specific uncertainty, which thus
may reduce (enhance) firm performance. Surrounded with unprecedentedly high uncertainty,
economists without considering the ownership structures may face great challenges in under-
standing the origins of economic uncertainty and analysing its causal impacts on real economy.
Based on the predictions of the above-mentioned arguments, the second hypothesis is formu-
lated as follows:
Hypothesis 2a Compared with non-state ownership, an increase of economic uncertainty is
associated with a lower performance level for state-owned SMEs.
Hypothesis 2b Compared with non-foreign ownership, an increase of economic uncertainty is
associated with a higher performance level for foreign-owned SMEs.
3 | METHODOLOGY
3.1 | Measurement of economic uncertainty
Regarding the economic uncertainty measure, the previous studies examining economic uncer-
tainty have used two different databases available to proxy country-specific uncertainty, includ-
ing the news-based uncertainty index of Baker, Bloom, and Davis (2016) and the measures of
macroeconomic uncertainty proposed by Ozturk and Sheng 2018). Since Baker et al. (2016)
indexes are based on the uncertainty-related keyword search on main newspapers, these news-
based indexes often experience large spikes during non-recessionary episodes. More recent stud-
ies have used Ozturk and Sheng (2018) measure, which captures the perceived uncertainty of
market participants and derives two components that are shown to exhibit strikingly different
behaviours (see Berger, Grabert, Kempa, 2017). Because Baker et al. (2016) indexes are lim-
ited to developing countries,1
this study tends to employ Ozturk and Sheng (2018) index for cap-
turing the economic uncertainty of both developing and developed countries for the
appropriate estimation.
Ozturk and Sheng (2018) construct a global measure to evaluate economic uncertainty on a
large set of 45 countries around the world. Economic uncertainty index includes two compo-
nents: common uncertainty and idiosyncratic uncertainty that decompose the volatility of a
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9. typical stock into the market and firm-level volatility, respectively. These country-specific
uncertainties consist of the weighted average of the standardised components of variable-spe-
cific uncertainty measures, which are estimated by the uncertainty of specific variables for eight
economic indicators: GDP, consumption, investment, industrial production, inflation, unem-
ployment rate, short-term and long-term interest rates. Ozturk and Sheng (2018) also suggest
the total uncertainty as a sum of common uncertainty and idiosyncratic uncertainty because it
sends back the uncertainty in the whole economy better than any individual part.2
In this study,
we employ total uncertainty to measure economic uncertainty. Both common and idiosyncratic
uncertainty components vary between 0 and 1, and the sum of these two range between 0 and
2. The higher the total uncertainty index, the more uncertainty in the host country. We use the
average value of economic uncertainty in 12 months for each year and each country (Table 1).
Table 2 reports country-by-country summary statistics for economic uncertainty indexes of
25 countries. The full sample mean is 0.275, accompanying with the standard deviation of 0.281
(Table 3), the statistical data of which deviates from 0.000 to 1.141 within the sample. We find
that the countries that have the highest economic uncertainties are Taiwan and the UK, while
the countries with the lowest uncertainty levels are Thailand and Turkey. The economic envi-
ronment in developing countries (the mean = 0.237) is less volatile than in developed countries
(the mean = 0.315) throughout 2007–2016. This difference is also statistically significant at 1%,
as suggested in Panel C, suggesting a fairly high cross-sectional variation in uncertainty shocks
among countries around the world (Figure 1).
3.2 | Measurement of firm ownership
Following the approach of La Porta et al. (1999), we measure the large block-holder ownership
of a firm. A large block holder is any shareholder owning more than 20% of the shares in a firm.
The 20% threshold employs popularly in many previous studies about firm performance and
firm governance (see also Doan, Lin, and Doong (2018), Nguyen (2011)). Following this
approach, we calculate the percentage of foreign ownership for a firm (Foreigni) by first multi-
plying the share of each shareholder in that firm by the share of that foreign shareholders own
and then summing the resulting products over the shareholders of the firm:
Foreigni =
X
j
i = 1
f jif fj; wheref jif fj 0:2 ð1Þ
where i = 1, …, j indexes firms' shareholders; fji is the share of firm i owned by shareholder j;
and ffj is the share of shareholder j that is owned by a foreign shareholder. The variable Foreigni
stands for the total share of firm i that is owned by large foreign shareholders at the end of each
year. As suggested by La Porta et al. (1999), a firm is classified as foreign ownership if the per-
centage of foreign ownership is at least 20%, which is often sufficient to control a company,
indicated by corporate control literature. The measurements are similar for state ownership
(Statei) and domestic private ownership (Domestici) variables.
Table 2 also shows descriptive statistics for ownership types, including foreign, private
domestic and state block ownership by country. In Panels A and B, the means of the foreign,
private domestic and state ownership of firms in the full sample are 12.2, 58.%, and 0.8%,
respectively. The analyses from panel C suggests that, overall, the mean percentages of both
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10. Table 1 Variables: Descriptions and sources
Variable Description Sources
ROA The ratio of net income to total assets Authors' calculation based
on Orbis
ROE The ratio of net income to total equity Authors' calculation based
on Orbis
Uncertainty The economic uncertainty, defined by the average value of
Ozturk and Sheng (2018) index in 12 months for each
year and each country
Ozturk and Sheng (2018)
State The percentage owned by government shareholders using
a threshold of 20%
Authors' calculation based
on Orbis
Domestic The percentage owned by domestic private shareholders
using a threshold of 20%
Authors' calculation based
on Orbis
Foreign The percentage owned by foreign shareholders using a
threshold of 20%
Authors' calculation based
on Orbis
SO20 A dummy variable that takes a value of one if the
percentage of a firm owned by government block
shareholders is above the threshold of 20%, and zero
otherwise
Authors' calculation
FO20 A dummy variable that takes a value of one if the
percentage of a firm owned by foreign block
shareholders is above the threshold of 20%, and zero
otherwise
Authors' calculation
SO50 A dummy variable that takes a value of one if the
percentage of a firm owned by government block
shareholders is above the threshold of 50%, and zero
otherwise
Authors' calculation
FO50 A dummy variable that takes a value of one if the
percentage of a firm owned by foreign block
shareholders is above the threshold of 50%, and zero
otherwise
Authors' calculation
Firm Age The natural logarithm of the number of years in operation Authors' calculation based
on Orbis
Size The natural logarithm of total assets Authors' calculation based
on Orbis
Sales Growth The growth percentage in reported sales over the period of
2 years
Authors' calculation based
on Orbis
Business Freedom The country-level business freedom index The Heritage Foundation
Control of Corruption The index that captures “the extent to which public power
is exercised for private gain, including both petty and
grand forms of corruption, as well as “capture” of the
state by elites and private interests”
Worldwide Governance
Index developed by
Kaufmann, Kraay, and
Mastruzzi (2011)
Political Stability The index that captures “the likelihood of political
instability and/or politically-motivated violence,
including terrorism”
Worldwide Governance
Index developed by
Kaufmann et al. (2011)
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11. foreign, private domestic and state ownership are lower for developing markets compared to
developed countries. Ownership statistics show a higher concentration of ownership for private
domestic and lower ownership by state investors, which indicates that ownership concentration
in developed countries is higher than in developing countries group. In addition, Table 3 shows
foreign (state) ownership accounting for 15.4% (1.4%) with a threshold of 20% is higher than
those with a threshold of 50% (12.2% and 1.1%, respectively) in the full sample.
3.3 | Basic model
We consider the following baseline model specification:
FirmPerformancei,j,t = α + β1 Uncertaintyj,t + β2 Ownership Structurei,j,t
+ η0
Firm Controlsi,j,t
+ θ0
CountryControls j,t
+ ρ0
Macro Controls j,t
+ Year Dummiest + Country Dummiesj + εi,j,t
ð2Þ
where Firm Performancei,j,t is the performance indicators of firm i in country j in time t. Firm
Performance is defined by the ratio of net income to total assets (ROA); the ratio of net income
to total equity (ROE) is in line with previous studies (Frijns, Dodd, Cimerova, 2016; James,
Wang, Xie, 2018). Despite the fact that it still maintains the limitations (e.g., backwards-
looking), Kay and Mayer (1986) declare that accounting ratios strongly throwback economic
rates of return and effectively contribute for both makers and formulators to make crucial
investment decisions and build a policy. Uncertainty is the economic uncertainty, defined by
the average value of Ozturk and Sheng (2018) index in 12 months for each year and each coun-
try. Ownership Structure stands for firm ownership types, including state ownership (SO20) and
foreign ownership (FO20). SO20 (FO20) is a dummy variable that takes one if a firm is state
(foreign) owned (we define ownership using the 20% threshold),3
(the dummy denoting a pri-
vate domestic bank is excluded).
Firm controls is the vector of firm-level control variables, including Size, Firm Age and Sales
Growth. The usual control variables are Size, which is the natural logarithm of total assets, and
Firm Age, which is calculated by the natural logarithm of the number of years since incorpora-
tion. Since firm productivity is affected by firm size and firm operating process, these variables
are popular in existing papers in the spirit of firm performance (Bennett, Bettis, Gopalan,
Table 1 (Continued)
Variable Description Sources
Inflation The annual inflation rate, GDP deflator for each country World Development
Indicators, The World
Bank
GDP Growth The GDP per capita growth rate, constant 2005 US$ for
each country
World Development
Indicators, The World
Bank
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14. Milbourn, 2017; Frijns et al., 2016; Hauser, 2018). While firm size is significantly associated
with their performance because larger firms may suffer more from agency problems due to
information asymmetry and incentive conflicts, the firm age tends to have a positive impact on
firm performance, as longer operation time indicates better experience in management (Yuan,
Xiao, Zou, 2008). In addition, given that sales growth is likely to have direct impact on firm
efficiency (Bennouri, Chtioui, Nagati, Nekhili, 2018; Frijns et al., 2016; James et al., 2018), we
Table 3 Descriptive statistics of variables
Variables Mean SD Median Minimum Maximum
ROA 0.039 0.094 0.034 −0.945 0.991
ROE 0.102 0.235 0.094 −0.881 0.852
Uncertainty 0.275 0.281 0.232 0.000 1.141
SO 0.008 0.076 0.000 0.000 1.000
DO 0.589 0.397 0.645 0.000 1.000
FO 0.122 0.301 0.000 0.000 1.000
SO20 0.014 0.119 0.000 0.000 1.000
FO20 0.154 0.361 0.000 0.000 1.000
SO50 0.011 0.102 0.000 0.000 1.000
FO50 0.122 0.122 0.000 0.000 1.000
Firm Age 3.022 0.663 3.044 0.000 5.298
Size 10.777 1.315 10.687 7.857 14.061
Sales Growth 0.059 0.338 0.015 −0.678 1.301
Business Freedom 78.545 15.025 83.600 35.500 100
Control of Corruption 0.690 0.885 0.606 −1.087 2.312
Political Stability 0.324 0.807 0.415 −2.500 1.528
Inflation 0.022 0.030 0.015 −0.152 0.227
GDP Growth 0.030 0.031 0.023 −0.052 0.152
Notes: The definitions of the variables are provided in Table 1. The overall sample is an unbalanced panel that
consists of 27,659 firm-year observations (4,459 SMEs), covering the 10-year period from 2007 to 2016.
Economic
Uncertainty
Firm Performance
Ownership
Structure
Control variables
H1
H2
- Firm-level controls
- Macro controls
- Country fixed effects
- Year fixed effects
FIGURE 1 Theoretical
model
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15. also control sale growth effect (Sales Growth) on firm performance by the percentage growth of
sales over the period of two years.
Additionally, Country Controls is a vector of macroeconomic controls. We added Business
Freedom4
as a country-level control variable. The distribution of the business freedom index for
each country is based on a value range between 0 and 100, with 100 indicating the freest busi-
ness environment (Heritage Foundation, 2019). Next, country-level data on national gover-
nance quality indicators such as Control of Corruption and Political Stability, are sourced from
the data of the Worldwide Governance Index (Kaufmann, Kraay, Mastruzzi, 2010). The indi-
cators are shown in standard normal units ranging from −2.5 to +2.5, with −2.5 indicating the
worst national governance quality. The quality of national governance plays a crucial role in
firm performance by a direct effect on firms' successful business running from existing shreds
of evidence (Doan, Lin, Doong, 2020; Nguyen, Locke, Reddy, 2015; Tunyi, Agyei-Boapeah,
Areneke, Agyemang, 2019). We also control for the effects of macroeconomic development
on firm performance, including the following variables: Inflation and GDP Growth. Inflation is
gauged by the annual inflation rate (GDP deflator) and GDP Growth is the growth rate of GDP.
Finally, Countries Dummies is a set of country dummy variables, and Year Dummies is a set of
time dummy variables.5
Note that β1 in Equation (2) measures the impact of economic uncertainty on the perfor-
mance of SMEs. If this economic uncertainty leads to lower SME performance, one would
expectβ1to be negative.
3.4 | The interaction of economic policy uncertainty and ownership
To investigate how the ownership structure affects the relationship between economic uncer-
tainty and firm performance (Hypotheses 2a and 2b), we consider the following model
specification:
FirmPerformancei,j,t = α + β1 Uncertainty j,t + Ownership Structurei,j,t × φ1 + φ2Uncertainty j,t
+ η0
Firm Controlsi,j,t
+ θ0
CountryControls j,t
+ ρ0
Macro Controls j,t
+ Year Dummiest + Country Dummiesj + εi,j,t
ð3Þ
where subindexes i, j, and t stand for the firm, the country and the time, respectively. All vari-
ables are defined in Equation (2), and we continue to impose the sample restrictions by country
and year dummies used in the model specifications. Notably, the coefficients of the interaction
term Uncertaintyj, t × Ownership Structurei, j, t(φ2)will explain whether ownership types of
SMEs incorporated with economic uncertainty enhance or impede firm performance. In other
words, φ2 indicates the differential in economic uncertainty effects between state-, domestic pri-
vate-, and foreign-owned firms. If foreign-owned (or state-owned) firms perform better in miti-
gating the drawback effect of economic uncertainty than non-foreign- (non-state-) owned firms,
the coefficient of the interaction term φ2 would also be expected to be significant and positive.
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16. To control for potential endogeneity problem due to sample self-selection and omitted vari-
ables,6
we estimate all models given in Equations (2) and (3) by using the two-step system-
GMM estimator developed by Blundell and Bond (1998) from the original model of Arellano
and Bover (1995). We employ the two-step GMM with error correction, which results in more
asymptotic efficient estimates than the one-step ones. This methodology allows researchers to
use lagged values of the variables as instruments to mitigate the problem of endogeneity. In the
GMM model, to examine the validity of instrument variables as well as error serial autocorrela-
tion, we further employ two tests. First, the Hansen J test is used to test the overall validity of
the instruments (over-identifying restrictions). The second test gives assurance of the hypothesis
that there is not serial correlation in error in the second order (AR2). Moreover, the potential
multicollinearity concerns are considered by pairwise Pearson's correlation matrix. This estima-
tion approach is also in the line with methodology conducted in recent studies on economic
uncertainty (e.g., Louri Karadima, 2020).
4 | DATA
4.1 | Data sources
This study examines year-end financial statement data from 2007 through 2016 for 25 countries
obtained from the Orbis database developed by Bureau van Dijk. Unconsolidated data were
selected, but when these were not available, we chose consolidated data instead. We follow the
definition of small and medium-sized firms from World Bank, for which firms have either less
than 300 employees or total assets (annual sales) of up to 15 million dollars. For each SME in
the database, state, private domestic and foreign ownership information was hand-collected
from a variety of sources. We first gathered information from the Shareholder Information sec-
tion of the Orbis database. In cases of the data from Orbis's shareholder database are not avail-
able or not enough, we search firm ownership information from additional sources—the Asian
Company Handbook, World Scope Global Disclosure, Moody's International Company Data or
the annual reports provided by individual firm websites.
The initial sample consists of 44,289 firm-year observations in 25 countries, including 11
developing countries and 14 developed countries. The economic development classification fol-
lows World Economic Situation and Prospects reported by the United Nations (UNDESA, 2016).
We start excluding financial and utility firms (firm level SIC codes 6,000–6,999 and 4,900–
4,999) and firms that have available data for fewer than four years. We then drop firm-year
observations with missing values for ownership and performance data. To minimise the effects
of measurement error, we then delete outliers by dropping observed values that lie outside the
1% and 99% quantiles for each considered. As a result, the final sample for the baseline regres-
sion is an unbalanced panel that consists of 27,659 firm-year observations (4,459 SMEs) from 25
countries around the world during 2007–2016.7
In addition, data for the economic uncertainty are collected from Ozturk and Sheng (2018).8
Regarding country-level macroeconomic data, we use the index of business freedom by the Her-
itage Foundation (Heritage Foundation, 2019)9
in this study. Finally, data on national gover-
nance quality indicators (e.g., Control of Corruption and Political Stability) are sourced from the
Worldwide Governance Index (Kaufmann et al., 2010).
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17. 4.2 | Descriptive statistics
Table 2 summarises the descriptive statistics for firm performance, economic uncertainty, and
ownership structure (e.g., state, domestic, foreign ownership) for each country in the sample.
Panel A shows values for developing countries, whereas those in developed countries are repre-
sented in Panel B. During our sample period, the means of ROA and ROE for the whole sample
are 3.9 and 10.2%, respectively. In our sample, the ROA in developing countries (4.1%) is higher
than in developed markets (3.6%). In contrast, the mean is lower than the ROEs between devel-
oping and developed countries (7.3 and 13.6%, respectively). The results in Panel C indicate that
there are differences in average for both ROA and ROE between developing and developed
countries. The differences are statistically significant at the 1% level.
Table 3 provides descriptive statistics for control variables. The Firm Age mean in the whole
sample is approximately 3.022, while the mean and standard deviation of Size are 10.777 and
1.315, respectively. These values for Sales Growth are at 5.9 and 33.8%, respectively, which
ranges from −67.8 to 130.1%. For country-level variables, the overall value of Business Freedom
in our sample is high: its average figure reached 78.545 out of 100 with a minimum of 35.5. For
national governance variables, countries in our sample have an average Control of Corruption
and Political Stability of 0.690 and 0.324, respectively. In addition, this sample reveals that the
mean value of the annual inflation rate is 2.2%, calculated by the GDP deflator, while the coun-
tries' average growth of GDP in our sample is 3.0% per year.
The pairwise correlation values between independent variables are reported in Table 4. The
correlation between Uncertainty and FO20 is negative and statistically significant at the 1%
level, whereas it reports the linkage between Uncertainty and SO20 is statistically insignificant
(negative relation). These correlations indicate that countries with low economic uncertainty
show a greater increase in foreign block shareholders, which means the market stability is a
contributing factor to attract investment from foreign investors. Indeed, as reported by Wang et
al. (2014) and Julio and Yook (2016), the more stable the economic environment, the more for-
eign investment. In addition, the correlation between Uncertainty and Firm Age indicates that
an uncertain environment tends to reduce the number of operation years of businesses. Nota-
bly, countries with lower economic uncertainty show a greater increase in business freedom.
The opposite is true in terms of the linkage between Uncertainty and both Control of Corruption
and Political Stability. The higher the political stability and control of corruption, the higher the
economic uncertainty. In contrast, there are significant negative relations between Inflation and
Uncertainty as well as between GDP Growth and Uncertainty. These results reveal that the econ-
omies that increase Inflation and the GDP Growth rate might reduce economic uncertainty.
5 | EMPIRICAL RESULTS
5.1 | Economic uncertainty, ownership, and firm performance
Table 5 reports the results of our estimations to test Hypothesis . Models 1–6 show the results
for Equation (2) with ROA as a measure of Firm Performance, whereas ROE is presented in
Models 7 through 12. Each group includes the results for three country groups including devel-
oping, developed countries and the full sample. We also include a set of control variables affect-
ing firm performance. Overall, the signs of the coefficient on both ROA and ROE are similar.
We find that economic uncertainty negatively affects the performance of SMEs and that it is
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21. statistically significant in all cases for developing and developed countries. These findings indi-
cate that firms experience lower performance under increasing macroeconomic uncertainty. As
given in Models 1 and (2) for the full sample, the coefficients of Uncertainty are −0.055 and
−0.077, respectively, explaining approximately 0.07% of ROA's reduction in those firms with a
one-unit standard increase in the economic uncertainty index on average. Similar results are
reported in Models 7 and 8 in terms of ROE, indicating that lower economic volatility tends to
enhance firms' efficiency. Based on our analysis in Models 3 and 4, firms in developing econo-
mies experiencing a one-unit increase in Uncertainty could reduce their performance in the
range of 0.136–0.162% in ROA, higher than in developed countries (0.083–0.098%). In the same
vein, in terms of ROE, the results from Models 9 to 12 also indicate the adverse effect of Uncer-
tainty on the performance of SMEs. The estimated coefficients on Uncertainty vary from −0.145
to −0.648 and higher in developing countries. These results are consistent with the results from
Wang et al. (2014), Kang et al. (2014), Gulen and Ion (2015), and Madanoglu and
Ozdemir (2019). This evidence is similar to the view that firms face financial constraints that
are derived from a slowdown in bank loan growth under high uncertainty leading to cuts in
investment spending (Rashid Saeed, 2017), leading to reductions in firm performance. Over-
all, the results from Table 5 support our first hypothesis: increasing economic uncertainty nega-
tively affects the performance of SMEs.
Table 5 also shows our baseline results with the different effects of firm performance across
ownership groups. The findings reveal the different impacts of state-owned firms on perfor-
mance between developing and developed countries. The coefficients on SO20 show that firms
in developing countries with a high level of government ownership tend to have lower firm per-
formance than comparable non-state-owned firms (e.g., foreign- or private domestic-owned
firms). As given in Model 3, the average state-owned business has an ROA that is 0.201% lower
than that of the average private-owned firms. In terms of ROE, compared to non-state firms,
state firms in developing countries bear a 0.344% lower ROE. In contrast, in developed econo-
mies, we find no significant difference in the performance of SMEs between government,
domestic private, and foreign-owned firms. Although the results for the subsample are highly
supported, we cannot confirm this linkage in terms of the full sample for both ROE and ROA.
Indeed, in the full sample (Model 1 of Table 5), we find state ownership negatively affects firm
performance, ROA in particular. However, this effect is quite weak and can be attributed to the
strong effect of firms in developing countries in our sample. These results are consistent with
previous findings that firms with high state ownership tend to be less efficient and less profit-
able than firms with high private ownership in developing countries (Li, McMurray, Sy,
Xue, 2018; Yu, 2013).
Furthermore, the results from Table 5 show the differences in performance between for-
eign-owned firms and their non-foreign counterparts (state- and private domestic-owned firms).
In particular, foreign-owned firms tend to have greater efficiency than comparable private firms
in both developing and developed countries. As reported in Model 2, the coefficient on FO20 is
positive (β = 0.086) and statistically significant at the 1% level (t-statistic = 2.938), indicating
that foreign-owned firms have an ROA that is 0.086% higher than that of non-foreign-owned
firms (state, or private domestic ownership). Notably, the coefficient of the foreign ownership
dummy in developing countries is very large, implying that the differential between the perfor-
mance of foreign- and private-owned firms is more than ten times when compared with that of
developed countries in terms of ROA (the two values in Model 4 and Model 6 of Table 5 are
0.159 and 0.012, respectively). A similar result is found when we consider ROE as a dependent
variable. These above results are consistent with previous studies' suggestions that foreign-
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22. owned firms tend to be more efficient than non-foreign-owned firms in the developing coun-
tries (Dachs Peters, 2014; Meng, Clements, Padgett, 2018) and in developed countries
(Hanousek, Shamshur, Tresl, 2019; Lindemanis, Loze, Pajuste, 2019). The stronger effect
found in developing countries might be attributed to superior technology, export markets, and
management techniques by foreign investors from developed countries (Hallward-Driemeier et
al., 2006).
Table 5 also shows the estimated impacts of control variables. The estimated coefficients of
Firm Age are consistently negative in the developing subsample and positively statistically sig-
nificant in the full sample, which implies that for SMEs, the longer the time in operation, the
lower the profit they gain. This result is in line with Ullah, Wei, and Xie (2014) and Ayyagari et
al. (2016). In contrast, overall, the scope of firms (Size) shows a positive effect on firm perfor-
mance, except for in developed countries, where increasing the value of total assets will lead to
reducing firm performance. Similarly, the growth of sales (Sales Growth) enters a positive effect
on firm performance in most models in both ROA and ROE, in line with previous findings from
Liu, Miletkov, Wei, and Yang (2015), Bennouri et al. (2018) and Hsu, Lin, Chen, and
Huang (2019). Regarding country-level characteristics, SMEs in our sample that run their busi-
nesses in high inflation environments tend to be less efficient than firms in countries that have
low inflation rates; demonstrated by the coefficients of Inflation being negative, and most cases
are statistically significant, consistent with numerous other studies. However, due to the mixed
results about the impact of national governance quality indicators (e.g., Control of Corruption,
Political Stability, Business Freedom) as well as the growth of GDP (GDP Growth), these linkages
are not supported in the scope of this study.
5.2 | The interaction between ownership structure and economic
uncertainty
We are now interested in whether the relationship between Uncertainty and firm performance
will vary depending on the changes in a firm's ownership structure (foreign-owned or state-
owned firms). Table 6 accounts for the interaction terms between Uncertainty and a dummy for
government ownership and a dummy for foreign ownership. The coefficients of the interaction
Uncertainty*SO20 are negative and significant for both full and subsample cases, indicating that
the negative effect of increasing the Uncertainty is significantly stronger for firms with more
state ownership. Specifically, as suggested in Model 1 of Table 5, compared with domestic pri-
vate firms, a one-unit increase in uncertainty is associated with about a 0.052% lower efficiency
level (ROA) for state-owned firms. This result is also similar to interaction coefficients obtained
in developing countries (−0.248) and developed countries (−0.035) and is in line with the find-
ing of Wang et al. (2014), implying that state-owned firms are negatively affected more by
Uncertainty, whereas non-state-owned enterprises might benefit from a firm's investment under
uncertainty. The result is also consistent with Gulen and Ion (2015), who suggest that the nega-
tive impact is stronger in developing countries than in developed economies due to greater
dependence of SMEs on the government in developing countries. Since a same vein is also
explored in Models 7 to 12, when ROE is considered as the firm performance, our result sup-
ports Hypothesis , indicating that, compared with non-state-owned firms, state-owned SMEs
bear a lower performance under increasing economic uncertainty.
Notably, when we take into account the interaction between Uncertainty and the foreign
ownership dummy, we find that the coefficients on Uncertainty*FO20 are positive and
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25. significant in all models. Taking Model 2 of Table 6 as an example, compared with non-foreign-
owned firms (e.g., state-, private domestic-owned firms), foreign ownership firms around the
world tend to have higher efficiency, ROA in particular (β = 0.046, t-statistic = 2.835). In other
words, foreign-owned SMEs can better mitigate the negative impact of economic uncertainty on
their performance than domestic-owned firms. In fact, in Models 4 and 6 of Table 6, the differ-
entials in Uncertainty impact on ROA between average foreign-owned firms and non-foreign-
owned firms are approximately 0.522 and 0.026% for developing and developed countries,
respectively. In addition, the ROEs for foreign-owned firms are higher than those of domestic
firms when experiencing an increase in Uncertainty. For instance, in Models 10 and 12 of
Table 6, the differential in economic uncertainty between average foreign-owned and non-for-
eign-owned firms in developed countries is approximately 0.102%, while in developing coun-
tries the estimated figures yield a difference of approximately 0.645% relative to the benchmark
of non-foreign SMEs, state-owned and private domestic firms in particular. These results are
consistent with Madanoglu and Ozdemir (2019) and Yang et al. (2019). Under uncertainty,
changes in tax policies mainly focus on state-owned firms instead of foreign-owned firms, this
implies foreign-owned firms have fewer tax burdens than other firms (Dang et al., 2019). Their
activities are not only less responsive to changes in the economic environment but also, thanks
to better risk management, they can suffer effects from the uncertainty and pull out of their dif-
ficulties to obtain higher performances (Yang et al., 2019). This finding supports Hypothesis 2b,
strongly indicating that, compared with non-foreign-owned firms, an increase of economic
uncertainty is associated with a higher performance level for foreign-owned SMEs.
5.3 | Robustness tests
In this section, we examine the robustness of our main findings to gauge the reliability of the
results. We now check whether the results are robust to different definitions of ownership. The
change is to consider a 50% share as the threshold. Interestingly, changes in ownership are asso-
ciated with a relevant modification of property rights, which are most likely associated with a
considerable change in the corporate governance (Bürker et al., 2013); these will lead to
enhance or reduce firm performance. By employing different thresholds to identify ownership,
following Nguyen (2011), Bürker et al. (2013) and Doan et al. (2018), we alternatively define
ownership as a large block holder when any shareholder owns more than 50% of the total
shares outstanding. Table 7 shows the robustness test for economic uncertainty and firm perfor-
mance, whereas the effect of interaction between uncertainty and ownership structure on firm
performance is reported in Table 8. Although our sample changes, the main results reported in
Tables 7 and 8 are basically unchanged.
5.4 | Further analysis: The role of cash
The purpose of this section is to test how changes in cash policy affect the main findings. Our
further test has to do with the fact that cautionary saving motive could be a potential channel
through which firm managers may hold more cash. Some parts of the existing literature suggest
that firms are more likely to reduce investment and hold more cash during high period of eco-
nomic uncertainty (Demir Ersan, 2017; Li, 2019; Phan et al., 2019). More specifically,
Li (2019) find a positive linkage between economic uncertainty and firm cash holdings, and this
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32. relationship is stronger in the case of financially constrained firms. Since the investment expen-
diture of profitable projects may reduce when firms hold more cash, it is reasonable to expect
that holding more cash strengthens the negative impact of economic uncertainty on SME per-
formance. To confirm the prediction, we follow the previous studies (Azmat, 2014; Martínez-
Sola, García-Teruel, Martínez-Solano, 2013) and use the cash holding ratio calculated as cash
and cash equivalents divided by total assets and split our sample into two major subsamples:
high cash holding (HIGH) and low cash holding (LOW) groups. In this regard, the group of high
(low) cash holdings is set for firms with the cash holding ratio that is greater (lower) than its
sample median.
Table 9 presents estimate results for the two subsamples of high and low cash holdings.
Consistent with our expectation, the negative effect of economic uncertainty on performance
tends to be higher for firms with high cash holdings. Using ROA as the dependent variable, the
estimated results of high cash holding group (Models 4 and 6) show significant associations (at
1% level) between economic uncertainty and firm performance, whereas the coefficients on
Uncertainty are statistically insignificant for firms with low cash holdings (Models 3 and 5).
Although the result of full sample reports a negative relationship between economic uncer-
tainty and performance for both high and low cash holding firms, the coefficient on Uncertainty
for high cash holding group (β = −0.121, t-statistic = −3.141) exhibits a higher absolute value
compared to that of low cash holding group (β = −0.068, t-statistic = −2.154). This indicates
that the negative effect of uncertainty on firm performance is stronger for firms with high cash
holding policy. The results are also similar in cases using ROE as the dependent variable. The
coefficients on Uncertainty remain statistically significant only in subsamples of high cash hold-
ing group, suggesting that the effect of economic uncertainty on performance is greatly shaped
by cash holding policy of SMEs.
6 | CONCLUSION
One of main challenges for policymakers across the world is the design of effective policies that
deal with the performance of SMEs. These policies are better informed if we can empirically dis-
entangle the relative importance of external policy uncertainty factors to firm ownership. This
paper contributes to the debate by reassessing the relationship between economic uncertainty
and the performance of SMEs. We also evaluate how the ownership structure affects the impact
of economic uncertainty on firm performance by estimating a GMM approach over 10 years
across 25 countries. We first find that economic uncertainty negatively affects the performance
of SMEs. Second, state ownership is associated with firms' lower performance in developing
countries, while foreign-owned firms may obtain a higher performance in both developing and
developed countries. More importantly, the results of the interaction terms explore that state-
owned firms bear lower performance under uncertainty, whereas foreign-owned firms can rise
against the adverse threats from economic uncertainty in both developing and developed coun-
tries, demonstrated by a higher level in their performance.
Assessing the effects of economic uncertainty and the firm ownership structure for the per-
formance of SMEs has direct implications in this debate, especially in the aftermath of the
recent global financial crisis, which increased the prominence of systemic risk. Our study high-
lights the significance for policy makers to consider a change in economic environments before
designing a comprehensive set of appropriate regulations and an ownership supervisory frame-
work that helps maintain the efficiency (and hopefully stability) of SMEs.
132 DOAN ET AL.
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33. ORCID
Anh-Tuan Doan https://orcid.org/0000-0003-2580-3507
ENDNOTES
1
The economic policy uncertainty index by Baker et al. (2016) includes data on over 25 countries and mainly
focuses on developed countries.
2
According to Ozturk and Sheng (2018), the common component is estimated as “the perceived variability of
future aggregate shocks” and the idiosyncratic component is estimated as “the disagreement among professional
forecasters” across three different layers. First, the variable-specific uncertainty is estimated for eight nominal
and real economic indicators. Second, the country-specific uncertainty is measured by the weighted average of
standardized components of variable-specific uncertainty measures. Finally, an index of global uncertainty is
proposed, which is a rather new concept in the literature. As such rich information set of perceived uncertainty
for market participants, this measure of global economic uncertainty is more comprehensive than that of Berger
et al. (2017) and Hirata, Kose, Otrok, and Terrones (2012), but not more comprehensive than that of Ahir,
Bloom, and Furceri (2018). The measure of Ahir et al. (2018) is based on frequency counts of the word uncer-
tainty from the country reports of the Economic Intelligence Unit for 143 countries. However, their measure is a
type of textual analysis and does not include perceived uncertainty from aggregate future shocks.
3
We also use the 50% threshold in our robustness test; refer to Nguyen (2011) and Doan et al. (2018).
4
We use the Heritage Foundation's Index of economic freedom for practical purposes because one of its compo-
nents measures the “Business Freedom.” The index evaluates annually the economic freedom of different coun-
tries based on four main policy categories: rule of law, government size, regulator and efficiency, and open
markets. In addition, business freedom is a dimension of the efficiency of regulations.
5
See Table 1 for the definition of each variable and its sources.
6
We employ data on economic uncertainty from Ozturk and Sheng (2018), which includes monthly measures of
macroeconomic uncertainty covering 45 countries. Since most of countries in our sample are located in Asia, our
model sample may face a problem of self-selection when comparing the effects of economic uncertainty on firm
performance between developed countries and developing countries. In addition, although we used a rich set of
firm-level and country-level characteristics as control variables, endogeneity issues could be at play for some var-
iables due to omitted variables. Using system-GMM is thus appropriate for controlling the potential endogeneity
and the characteristics of the data with a large cross-section and short time series.
7
All the countries and variables do not have data for the entire sample period; hence, we construct unbalanced
panel data sets for both the developed and developing countries.
8
The data are available at monthly frequencies on http://www.american.edu/cas/faculty/sheng.cfm.
9
The data of business freedom can be accessed from http://www.heritage.org/index/.
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134 DOAN ET AL.
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Principles of Accounting (Trường Đại học Kinh tế Thành phố Hồ Chí Minh)
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