Political environment, employee tenure security and firm performance in middle-income economies

Abstract

Purpose

The study investigates the effect of political instability and employee tenure security on the performance of firms in middle-income economies (MIEs) after controlling for the influence of corruption, international quality certification, external auditor services and firm age. It examines whether ownership and sector effects matter in the explored relationships.

Design/methodology/approach

The study adopts the generalized method of moments estimator and collects firm-level cross-sectional data from 77 MIEs.

Findings

The evidence shows that political uncertainty, employee tenure security and firm age negatively impact firm performance. Also, external quality assurance mainly improves firm performance. Additionally, foreign-owned firms benefit from corruption more than their domestic counterparts. Moreover, there are ownership and sector effects in the firm performance drivers.

Practical implications

The findings suggest the need for MIE firm managers to implement policies and programs to improve permanent employees' efficiency, commitment and honesty. Policy makers and political actors must work toward a stable political environment in MIEs. The policy must also focus on at least minimizing corruption.

Originality/value

The study shows the contributions of employee tenure security, political instability and corruption to the performance of MIE firms. It documents sector and ownership effects in the factors influencing firm performance.

Citation

Boamah, N.A., Ofori-Yeboah, F. and Appiah, K.O. (2023), "Political environment, employee tenure security and firm performance in middle-income economies", Journal of Economics and Development, Vol. 25 No. 3, pp. 226-243. https://doi.org/10.1108/JED-06-2022-0105

1. Introduction

The political setting and employee tenure insecurity may impact firm performance (e.g. Farooq et al., 2021; Rodríguez-Ruiz et al., 2021; Moric et al., 2021). An unstable political setting weakens the rule of law, contract enforcement processes, investment flow and business expansion, thus undermining labor activities, firm performance and economic growth (Alexandre et al., 2022, Boamah, 2021; Farooq et al., 2021; Giacomelli and Menon, 2017). Similarly, firms' productivity and product innovation are constrained by employee tenure uncertainty (Rodríguez-Ruiz et al., 2021; Lisi and Malo, 2017). Successful firms facilitate innovation, employment growth, poverty reduction and investments in an economy (Matekenya and Moyo, 2022; Younas and Rehman, 2021; Lazer, 2016). Improved firm performance, therefore, has consequences for the success of middle-income economies' (MIEs) economic policies.

However, political instability may undermine labor activities, firm performance and economic growth (Alexandre et al., 2022). Existing evidence supports this notion (see Khafaga and Albagoury, 2022; Ashraf, 2022), highlighting that stable institutions affect poverty reduction. Rashid et al. (2022), Feng et al. (2021), El-Bassiouny and Letmathe (2020) and Rezgallah et al. (2019) suggest that political uncertainty increases corruption, decreases investments, undermines governance structures and impairs firm performance. Additionally, employee tenure security (ETS) is critical for innovation, productivity growth and employees' attitude, which influence firms' performance (Rodríguez-Ruiz et al., 2021; de Jong et al., 2019). Moric et al. (2021) observe that temporary employment undermines firms' financial performance but improves innovation. Contrarily, Duan et al. (2021) argue that temporary employment improves firms' outcomes. Also, Koen et al. (2020) note that permanent employees perform better when their job security is threatened. Thus, temporary employment may positively impact performance by driving permanent workers to improve performance.

Prior studies have explored the influence of political instability and ETS on credit delivery, cost of capital and firm performance. Despite this, the relative importance of the political setting and employee tenure in MIE firms' performance remains an empirical question. Also, the role of ownership and sector effects in the political setting, employee tenure and firms' performance nexus has been least explored. This study attempts to fill a void by exploring these issues for firms pooled from 77 MIEs. The investigation will enhance our understanding of the success factors for MIE firms. The pooling enables us to collect a large enough sample that diversifies across many MIEs, thereby controlling for potential small sample problems associated with country-specific studies. The study shows that firm performance decreases as political uncertainty and ETS increase. ETS hurts the performance of domestic-owned firms more than foreign-owned firms. Also, political uncertainty harms manufacturing firms' performance more than service firms.

The rest of the paper proceeds as follows. Section 2 reviews the related literature. Section 3 discusses the data and methodology of the study. The results and results discussion are presented in sections 4 and 5, respectively. Section 6 concludes.

2. Literature review and hypothesis

2.1 Resource-based view/market-based view/social exchange theory

The resource-based view (RBV), social exchange theory (SET) and market-based view (MBV) are the theoretical perspectives used to explore whether the political environment and ETS influence firm performance. Following the reasoning of Penrose (1959), Rumelt (1984), Wernerfelt (1984) and Barney (1991), the RBV is based on two assumptions, namely (1) resources are heterogeneously distributed among firms and (2) resources are imperfectly mobile. These assumptions underscore differences in firm resource endowments, thereby allowing for a resource-based competitive advantage. The RBV-driven research argues that a firm's sustained competitive advantage is enhanced by selecting and developing valuable, rare and costly resources, including tangible and intangible resources, that are difficult to imitate and exploit by their competitors (see Wernerfelt, 1984; Peteraf, 1993; Barney, 1991). Thus, how a company combines its technical, human capital and other resources accounts for its performance (Penrose, 1959). Thus, a firm that considers employees an essential resource will secure their services to guarantee job security. Afterward, ergonomics maximize employees' capabilities and knowledge (Mahoney and Pandain, 1992). These, in turn, promote the firm's sustained competitive advantage and, ultimately, enhance performance in the short term (Barney, 1991). Thus, resources such as quality and innovative employees are critical to the firm's growth, profitability and survival. Therefore, firms that rely on permanent employees are likely to outperform those relying on temporary employees. The skills and capabilities of permanent employees are more valuable to achieving a firm's basic strategic goals (i.e. growth, profitability and survival) than environmental factors. The RBV postulates a positive relationship between ETS and firm performance.

The SET prescribes the employer–employee relationship as a social exchange (Aryee et al., 2002; Homans, 1958). It emphasizes that the latter reciprocates voluntary ‘actions’ initiated by the former to further their job security or otherwise (Blau, 1964) and suggests that sustained performance depends on the employee's commitment. The employee's commitment is related to his trust, gratitude and personal obligation to the employer and his firm (Aryee et al., 2002; Haas and Deseran, 1981). Thus, the SET postulates that the relationships between employees and the firm influence employee performance, implying that short-term relationships, including temporary employees, do not build employees' loyalty and commitment to advancing the firm's performance.

The MBV propagated by Porter (1979) and Tallman (1991) postulates that factors beyond the firm's control may affect its growth and performance. Thus, a country's political environment is critical to its firms' success. Unstable political environments typified by electoral violence, political conflict and disregard for democratic rules and principles may disrupt economic activities and undermine firm performance.

2.2 Political uncertainty and firm performance

Political instability may impact firm growth and employment creation. For instance, Matta et al. (2018) argue that political instability after the Arab Spring hindered the growth of Tunisian small, exporting, hospitality and tourism firms. In addition to intense international competition, exporting firms incur a higher cost of production in an era of political unrest. Prolonged higher production cost suggests a decline in efficiency and export capacity. However, Bahri et al.’s (2021) study shows that political instability and corruption jointly influence exports positively. This suggests that Small and Medium Enterprises (SMEs) are incentivized to explore foreign markets in an environment of political instability and high corruption incidence. Similarly, Farooq et al. (2021) observe that firms seek credible and quality information in a politically unstable environment by recruiting reputable external auditors. This signals improvement in information disclosure as firms endeavor to win investor confidence and minimize their cost of capital. Thus, political instability leads to improvement in information disclosure.

Montes and Nogueira (2022) investigate the effect of political instability on investment and business confidence. They observe increased investor sentiments and a decline in investments as political uncertainty rises. Okafor and Calderon (2022) examine the relationship between political tension and the performance of Nigerian manufacturing firms. They suggest that political uncertainty disrupts economic activity and negatively impacts firm performance. Kouzez (2023) studies the relationship between the political environment and bank performance. Kouzez argues that a high level of political risk negatively influences banks' performance. Also, political risk impacts smaller banks more than larger ones. Alexandre et al. (2022) observe that a democratic environment affects labor productivity negatively. This suggests that democratic settings may undermine firm performance via a decline in labor productivity. Alexandre et al. suggest a significant positive effect of economic freedom and government stability on firm performance.

Recent evidence by Garedow (2022) shows a negative effect of political uncertainty on firm productivity and growth in developing countries. The author shows that the level of democracy and democratic accountability impact long-term economic performance negatively, whereas political violence and instability adversely influence economic performance in the short run. Garedow argues that democracy constrains investments and firm performance as a result of dysfunctional state institutions, inadequate checks and balances on politicians' and public officials' actions, and weak democratic institutions which promote rent seek.

We hypothesize that

H1a.

Political instability may influence firm performance negatively.

H1b.

Political instability may influence firm performance negatively, regardless of ownership.

H1c.

Political instability may affect firm performance negatively, regardless of the firm's sector.

2.3 Employee tenure security and firm performance

ETS significantly influences the quality of job outcomes (Van Vuuren, 2020). Recently, Moric et al. (2021) found that temporary workers improve firms' innovation output, although they decrease firms' financial performance. Temporary workers provide quick access to the needed knowledge and specialized skills, enhancing innovation. They, however, demand higher compensation due to job insecurity, resulting in higher labor costs and a decline in firms' financial performance. Their findings suggest that tenure security improves firm performance. Also, Roca-Puig et al. (2015) observe a concave relationship between temporary contracts and the gross operating margin of firms and a negative linear relationship between labor productivity and temporary contracts. They argue that although temporary employment saves labor costs, it reduces productivity among permanent staff. Thus, temporary employment increases gross operating margin but reduces overall labor productivity.

Rodriguez-Grutierrez (2007) observes that a rise in temporary employees decreases firms' output but increases the total production cost of manufacturing firms. Similarly, Rodríguez-Ruiz et al. (2021) argue that increasing temporary staff dampens firms' innovation output due to little incentive to be innovative. Also, ETS facilitates the development and enhancement of knowledge, promoting innovation in firms. In a related study, Qian and Wang (2021) analyze the impact of temporary employment on the financial performance of Chinese firms. The authors find a nonlinear relationship between full-time temporary workers and performance—also, a surge in temporary workers results in declining financial performance for firms pursuing innovative strategies. In a study of Korean listed firms, Lim and Mali (2023) observe that permanent employment contracts improve firms' performance more than temporary contracts. Similarly, Lisi and Malo (2017) contend that temporary employment results in a high level of workers' rotation, which disrupts the efficiency and productivity of firms. Also, temporary employment impairs firms' productivity, especially in the skills sector.

Hence, we hypothesize that

H2a.

ETS may affect firm performance positively.

H2b.

ETS may affect firm performance positively, regardless of ownership.

H2c.

ETS may affect firm performance positively, regardless of the firm's sector.

3. Methods

3.1 Data and data sources

We obtained the studied data from the World Bank Enterprise Survey database. The database contains firm-level survey data across various countries. We gathered firm-level cross-sectional data for 2010, 2013, 2016 and 2019. We selected data at three-year intervals, which enabled us to have a representative sample over the recent period. We sampled only years with data for at least 18 countries. This enabled adequate representation of MIEs in the study's sample for each year, making the study's findings applicable to a broader context of MIEs. The final sample consists of firms selected across 77 MIEs [1]. The data used for the study are yearly cross-sectional firm-level data. The sample consists of 11,703, 23,363, 8,162 and 16,267 observations for 2010, 2013, 2016 and 2019. Thus, the sample has sufficient cross-sectional observations for each sampled year to conduct a cross-sectional analysis. Data on the real annual sales growth (RASG), percentage of firms choosing political instability as their biggest obstacle (PINSTO), percentage of firms expected to give gifts to public officials to get things done (GIFPO), percentage of firms experiencing at least one bribe payment request (BRIBE), percentage of firms with internationally recognized quality certification (QUAC), percentage of firms with annual financial statement reviewed by an external auditor (EAUD), proportion of permanent workers out of all workers (PMWP) and age of the firm in years (AGEF) were collected from the database.

3.2 Descriptive statistics and correlations

The descriptive statistics of the data are recorded in Table 1. The mean (standard deviation) of the RASG for 2010, 2013, 2016 and 2019 are, respectively, 3.29 (26.11), −2.45 (23.22), 0.133 (22.31) and 0.90 (21.33). The evidence shows that MIE firms experienced a decline in annual sales growth in 2013. On a risk-adjusted basis, MIE firms performed better in 2010 (0.126), followed by 2019 (0.042), 2016 (0.006) and 2013 (−0.106). Between 2010 and 2019, firms' risk-adjusted performance decreased by −0.084. However, the performance has been steadily improving recently, increasing by 0.148 between 2013 and 2019. The spread between the risk-adjusted performance of the best and least-performing years is 0.232, which is economically meaningful. This suggests a wide disparity in the performance of MIE firms over time that variations in the domestic and global economic conditions across the years may drive.

Furthermore, the annual averages of GIFPO and BRIBE are in the range of 11.1% (2019)–19.7% (2016) and 8.2% (2019)–16.9% (2013), correspondingly. The results indicate a monotonic increase in GIFPO between 2010 and 2016. That is, bribe inducement of public officers increased continuously from 2010 to 2016 but declined over the 2016 to 2019 era. Although BRIBE declined from 2013 to 2016, it increased moderately between 2016 and 2019. The excess of bribe inducements over bribe requests by public officers ranges from 2.8–2.9%. The GIFPO and BRIBE evidence shows a high incidence of business-related corruption in MIEs. The finding agrees with Saddiq and Abu-Bakar (2020) and Park and Khanoi (2017).

The mean of PINSTO ranges from 8.4% (2010) to 20.2% (2013). This indicates increasing political instability concerns from 2010 to 2013 (an increase of 11.8%); however, concerns decreased continuously from 2013 to 2019. The 2019 concerns exceed that of 2010 by 2.8%, which is economically meaningful. The significant jump in PINSTO between 2010 and 2013 may result from political conflicts and unrest, such as the 2010–2013 Arab Spring. The PINSTO evidence disagrees with Campos and Gassebner (2013). Also, the average PMWP ranges from 84.6% (2013) to 90.8% (2010 and 2016). The evidence suggests a relatively high ETS. This may improve firm performance. The PMWP decreased from 2010 to 2013 by 6.2%, which may be the outcome of the rising PINSTO over the same period. The PMWP evidence disagrees with those of Wickramasinghe and Chandrasekara (2011).

Table 1 indicates that the ranges of the average QUAC, EAUD and AGE are 15.55% (2016)–21.9% (2010), 36.4% (2019)–59.8% (2010) and 16.7 years (2013)–22.7 years (2010), correspondingly. The evidence shows that few MIE firms have QUAC. The QUAC decreased continuously from 2010 to 2016 but increased from 2016 to 2019. The findings conform to Aamer et al. (2021). Also, excluding 2019, between 50% and 60% of MIE firms had EAUD – this may enhance their performance. The EAUD finding is consistent with Jusoh and Ahmad (2013). However, the decline in EAUD in 2019 (36.4%) may undermine firm performance. Table 1 further shows that MIE firms are relatively young. Our evidence shows that all the variables are non-normally distributed.

We examine the correlations between the variables and record the results in Table 2, which shows that the correlations between RASG and all the other variables are negative, excluding its correlation with GIFPO, QUAC and EAUD. This suggests sales growth declines with political uncertainties, bribe incidence, permanent employment and age. These findings corroborate those of Matta et al. (2018) and Martins et al. (2020) but contradict those of Moric et al. (2021) and Roca-Puig et al. (2015). Our results indicate that GIFT correlates negatively with all the variables but not RASG and BRIBE. Also, aside from EAUD, PINSTO and GIFPO, BRIBE negatively correlates with all the variables. Thus, an increase in corruption coincides with a decrease in permanent employment and the acquisition of an internationally recognized quality certification. Also, a rise in gift payments and bribe incidence coincides with a decline and a rise in external auditor engagement. Also, corruption increases with political uncertainty.

Additionally, younger firms are primarily involved in corrupt practices than matured ones. The findings suggest that firms particularly younger ones rely mostly on corrupt practices than external quality assurance. Such firms may be unable to acquire external quality assurance.

Table 2 indicates a positive correlation between PINSTO and QUAC, EAUD, PMWP and AGE. Also, both QUAC and EAUD have negative correlations with PMWP and positive correlations with AGE. In addition, QUAC and EAUD are positively correlated, whereas AGE and PMWP are negatively correlated. The findings indicate that political uncertainty is associated with increased external auditor engagement. External auditors likely instill investor confidence in firms in an unstable political setting. The evidence corroborates the study by Farooq et al. (2021). Also, external quality assurance is associated with a decrease in permanent employment. Possibly, external assurance enables firms to reduce overstaffing and improve labor productivity. The finding agrees with the correlation between RASG and PMWP, RSAG and EAUD, and RASG and QUAC. This finding is inconsistent with the evidence from Roca-Puig et al. (2015). The absolute correlations range from 0.001 to 0.375, indicating that multicollinearity minimally influences the study's results.

3.3 Econometric specification

The study employs the cross-sectional Equation (1) to investigate the drivers of firm performance in MIEs for each of the sampled years. We propose Equation (1) based on evidence from prior studies. The model suggests that political instability (see, e.g. Farooq et al., 2021; Montes and Nogueira, 2022), ETS (see, e.g. Van Vuuren, 2020; Moric et al., 2021), firm ownership (see, e.g. Boamah et al., 2023; Greenaway et al., 2014) and sector variations (see, e.g. Lagesh et al., 2018; Mukherjee, 2018) influence MIE firms' performance. It controls the potential influence of corruption, international quality certification, external auditor services and firm age on firm performance, as prior studies suggest their relevance (see, e.g. Shibia and Barako, 2017; Gebreeyesus, 2015; Liu et al., 2021). Equation (1) is estimated using the generalized method of moments (GMM) estimator. The GMM estimation technique helps in controlling for potential endogeneity problems. As observed by Zhao et al. (2021), Dalwai et al. (2021) and Sarpong-Kumankoma (2021), the GMM technique controls for potential endogeneity, unobserved heterogeneity, dependent variable persistence and heteroskedasticity problems. The GMM approach is thus appropriate for the study.

(1)PERFi,j=α0+β1PINSTi,j+β2CINDi,j+β3CBREQi,j+β4ETSi,j+β5EEASi,j+β6IQCTi,j+β7AGEFLi,j+β8DUMAi,j+β9DUMOi,j+εi,j

PERF=firm performance,a=intercept,β=coefficients,PINST=political instability,

CIND=gift to induce public officers,CBREQ=bribery request by public officers,

ETS=employee tenure security,IQCT=international quality certification,

EEAS=engagement of external auditor services,AGEFL=log⁡offirm′s age in years

DUMA=indicator variable,which takes the value of 1 for manufacturing firms and 0

for service firms,

DUMO=indicator variable,which takes the value of 1 for domestically owned firms and 0

for foreign firms,

ε=errorterm. The i and j are, respectively, country and firm indicators.

Our proxies for firm performance (PERF), political instability (PINST), the gift to induce public officers (CIND), bribery request by public officers (CBREQ), ETS, international quality certification (IQCT) and the engagement of external auditor services (EEAS) are, respectively, RASG, PINSTO, GIFPO, BRIBE, PMWP, QUAC and EAUD. Further details of the variables are presented in Table 3.

4. Results

Table 4 presents the results of estimating various restricted Equation (1) versions. Panels A, B, C and D demonstrate the 2010, 2013, 2016 and 2019 results, respectively. The results show that PINST negatively influences firm performance across all examined years. The coefficients were insignificant only in 2016. The PINST evidence shows that political instability impairs MIE firms' performance. Political uncertainty limits firms' ability to attract new investment and/or financing, decreases the demand for products or compels firms to reduce production due to high uncertainty and risk. The PINST evidence agrees with Matta et al. (2018) but disagrees with El-Bassiouny and Letmathe (2020).

The results indicate a negative loading of ETS on performance, exempting 2013. The ETS coefficients are significant, excluding those of 2016. The ETS evidence shows that increasing ETS leads to a decrease in firms' performance. The observed negative effect of ETS on performance is intriguing. Likely, labor productivity declines with ETS. Such labor inefficiencies may result from overstaffing and, thus, a redundant labor force. It may also be that the recruitment policies of MIE firms may not be driven by growth opportunities but by other factors such as political influence and nepotism. Such effects may be high in state- and domestically owned firms. The ETS evidence may also be related to employee work ethics, commitment, honesty and supervision quality. The ETS evidence appears inconsistent with Lisi and Malo (2017), Moric et al. (2021) and Rodríguez-Gutiérrez (2007) but syncs with Kimya (2019).

In addition, Table 4 shows that manufacturing firms outperformed service firms in 2010 and 2019; however, service firms had superior performance in the 2013 and 2016 periods. Table 4 further shows that domestic-owned firms underperformed foreign-owned firms aside from 2016. The performance variations between foreign and domestic firms are significant, excluding 2016. The consistently improved performance of foreign firms relative to domestic firms may be due to variations in labor efficiency and attitude to work, employment policy, response to political instability and participation in corruption. These issues are explored further in Section 4.1. The findings infer firm ownership and sector effects in MIE firms' performance. The ownership influence is in agreement with Greenaway et al. (2014) and Nakano and Nguyen (2013) but disagrees with Asiedu and Freeman (2009). Similarly, the sector influence concurs with Lagesh et al. (2018) and Mukherjee (2018).

Table 4 provides evidence that, excluding 2016, CIND loads positively on firm performance. The CIND coefficients are significant except in 2013. Additionally, the CBREQ coefficients are all significant, excluding those of 2019. The CBREQ coefficients are positive for the 2010 and 2016 eras but negative for the 2013 and 2019 periods. Excluding 2010, the signs of the CIND and CBREQ coefficients are opposite. The result suggests that the net gain of corruption on firm performance on average depends on the relative strength of the CIND and CBREQ effects and could potentially be zero or negative. Thus, the impact of corruption on firm performance is complex.

The negative CIND and CBREQ effects corroborate the findings of Sahakyan and Stiegert (2012) and Phan and Archer (2020). Also, the positive CIND and CBREQ influence agrees with Li and Liu's (2015) and Imran et al.’s (2019) evidence.

Table 4 indicates that the EEAS loadings on performance were negative in 2010 and 2013 but positive in 2016 and 2019. Also, Table 4 shows that IQCT improves firms' performance; the effect is insignificant only in the 2019 era. Table 4 shows further that performance decreases with firms' age. The negative external auditor service effect may result from firms' inability or unwillingness to implement auditors' recommendations or little public confidence in auditors' opinions. Firms may engage external auditors to satisfy regulatory requirements or outside investors but not necessarily to improve their internal structures and performance. The negative EEAS effect appears inconsistent with Imran et al. (2019) and Jusoh and Ahmad's (2013) evidence. The IQCT finding suggests that investors or consumers may reward firms that adhere to internationally recognized quality standards. This agrees with Liu et al. (2021) and Jang and Lin's (2008) findings. The negative relationship between performance and firm age corroborates the findings of Martins et al. (2020) but contradicts Hatem's (2014) findings.

4.1 Ownership and sector effects on the drivers of firm performance in middle-income economies

The preceding results show sector and ownership influence on firm performance. We explore the issues further by examining whether or not there exist ownership and sector effects in the drivers of firm performance. We adopt Equation (2) in exploring these issues and present the results in Table 5.

(2)PERFij=α0+β1PINSTi,j+β2CINDi,j+β3CBREQi,j+β4ETSi,j+β5EEASi,j+β6IQCTi,j+β7AGEFLi,j+Di,j(β8PINSTi,j+β9CINDi,j+β10CBREQi,j+β11ETSi,j+β12EEASi,j+β13IQCTi,j+β14AGEFLi,j)+ϵi,j

D=indicator variable,which takes the value of 1 for manufacturing firms and 0 for

service firms or takes the value of 1 for domestically owned firms and 0 for foreign

firms,

ϵ=errorterm.

Table 5 (Panel A) suggests a neutral effect of firm ownership on the impact of PINST on firm performance. Also, ETS impairs the performance of domestic firms more than foreign ones in an economically meaningful manner (excluding 2013). The 2013 findings suggest that ETS enhances the performance of both domestic and foreign firms. The findings show that the performance of domestic firms is generally constrained by ETS. This PINST evidence supports that of Lupton et al. (2021) and Krammer and Kafouros (2022) but contradicts Zafar et al. (2016) and Kapri (2019). The ETS evidence may be linked to variations in efficiency between domestic and foreign firms. This appears consistent with Boamah et al. (2023).

Table 5 (Panel A) shows a statistically significant variation in the effects of CIND on domestic and foreign firms' performance in 2010. However, the difference in the CIND influence is economically meaningful for all years. Also, CIND improved the performance of foreign firms more than domestic ones in 2010 and 2019, benefited domestic firms more than foreign firms in 2013 and impaired the performance of foreign companies more than domestic companies in 2016. Additionally, the influence of CBREQ on foreign firms' performance is positive across all years. However, its impact on domestic firms was positive in 2010 and 2016 and negative in 2013 and 2019. The difference in the influence of CBREQ in foreign and domestic firms' performance is statistically significant only in 2013 but appears economically meaningful for all years. The benefits of CBREQ to the performance of foreign firms exceed those of domestic firms across all years. The findings indicate that the negative 2013 and 2019 CBREQ coefficients, recorded in Table 4, were driven mainly by domestic firms. This evidence infers that corruption profits foreign firms more than domestic ones. This finding corroborates that of Park and Khanoi (2017), Sharma and Mitra (2015) and Li and Liu (2015) but contradicts that of Ashyrov and Masso (2020) and Martins et al. (2020).

Table 5 (Panel B) records the results of sector effects in MIE firms' performance drivers. Panel B shows that PINST constrains firm performance across all sectors. The effect of PINST was severe for manufacturing firms over the 2013–2019 period and relatively more severe for the service firms in 2010. The PINST evidence is consistent with Lupton et al. (2021) and Matta et al. (2018). Additionally, Panel B shows that rising ETS impaired both the manufacturing and service firms' performance in 2010 and 2019, although it enhanced their performance in 2013. The ETS effect was positive and negative for manufacturing and service firms, respectively, in 2016. The results show that ETS negatively influences service firms' performance (aside from 2013). That is, ETS generally constrains the performance of service firms. In addition, the ETS influence is negative for manufacturing in 2 out of the 4 cases, including the most recent period. Thus, in the recent period, ETS has impaired the performance of both service and manufacturing firms. ETS appears to undermine service firms' performance more than manufacturing firms. The ETS evidence contradicts Roca-Puig et al. (2015) and Ortega and Marchante (2010). Also, the effect of AGEFL on performance is negative and consistent across sectors.

Panel B (Table 5) shows that firm performance increased (in 2010) and decreased (in 2016) with CIND. Also, the performance of the service and manufacturing firms, respectively, increased and decreased with CIND in 2019—however, CIND impaired service firms' performance but improved manufacturing firms' performance in 2013. The CIND effects were stronger among service firms than manufacturing firms in 2010 and 2019. Additionally, Panel B provides no evidence of significant sector variations in the ability of CBREQ to describe MIE firms' performance over the 2010–2019 periods. The evidence shows that in the most recent period, corruption improved the performance of service firms but undermined that of manufacturing firms. This evidence shows that corruption profits service firms but harms manufacturing entities. The sector effects in the impact of corruption on firm performance corroborate the findings of Maruichi and Abe (2019) and Bahri et al. (2021).

5. Discussion

5.1 Theoretical implications

Our evidence supports the appropriateness of using the MBV to explain the political uncertainty–performance nexus in the MIE context. Prior research, such as Matta et al. (2018), Alexandre et al. (2022) and Montes and Nogueira (2022), supports the notion that political uncertainty impairs firm performance, while others, such as El-Bassiouny and Letmathe (2020), indicate a positive effect of political instability on firm performance. This study supports the idea that firm performance is constrained by political uncertainty. It thus aligns with the studies that find adverse outcomes of political instability for firm performance. The study thus provides evidence supporting the MBV (see Porter, 1979; Tallman, 1991) that external factors such as political uncertainty impact firm performance.

In addition, our evidence raises questions on the appropriateness of the RBV and SET in explaining the ETS and firm performance nexus in the MIE context. Studies such as Lim and Mali (2023), Lisi and Malo (2017) and Roca-Puig et al. (2015) observe that employee tenure certainty improves performance. Others, such as Duan et al. (2021), Koen et al. (2020) and Kimya (2019), argue that ETS constrains firm performance. The evidence of this study concurs with the notion that ETS hurts firms' performance. Therefore, it does not support the predictions by the RBV and the SET (see Homans, 1958; Blau, 1964; Aryee et al., 2002) that ETS improves firm performance.

Nevertheless, it concurs with these theories that internal factors affect firm performance. The study provides evidence of performance variations between the manufacturing and service sectors and among domestic and foreign firms, suggesting the need to control for sector and ownership influence by studies exploring firm performance drivers. Finally, the evidence of this study supports the proposition that both external and internal factors have implications for the performance of firms.

5.2 Policy/managerial implications

The findings of the study have implications for managers and policy makers in MIEs. First, managers must rely more on temporary staff to enhance performance. Second, policy makers should champion policies required to improve the political environment of MIEs. Such policies may minimize uncertainties and risk-taking to improve firm performance. Also, firms operating in politically unstable MIEs may not enhance performance by changing the ownership structure or sector.

Additionally, MIE firms must acquire international quality certification and engage external auditors to enhance transparency and disclosure and boost their performance. Such external quality assurance may enable firms to access a relatively cheaper cost of capital. Also, regulators are encouraged to channel more incentives to manufacturing firms during periods of political instability. These incentives are likely to reduce costs and minimize the performance decline of manufacturing firms around periods of political uncertainty. Finally, minimizing corruption will go a long way to create a level playing field for all firms and improve MIEs' firm performance.

5.3 Limitations and future research agenda

Due to limited available time series data, the study relied mainly on yearly cross-sectional analysis. However, firm performance may have both cross-sectional and time-series variabilities; future research may adopt models that enable an analysis of both the cross-sectional and time-series dimensions of firm performance as sufficient time-series data become available. Future research may also examine the relevance of ETS and corruption to firms' innovation.

6. Conclusion

The study examines the effects of political instability and ETS on the performance of firms in MIEs. We control the impact of firm age, corruption, internationally recognized quality certification and the engagement of external auditors on firm performance. We also examine sector and ownership effects in firm performance and the performance drivers.

The findings show that rising political instability, ETS and firm age undermine firm performance. The political instability finding is consistent with Hypothesis 1a, whereas those of ETS are inconsistent with Hypothesis 2a. There is a mixed effect of external auditor engagement on firm performance. The findings show that recognized international quality certification enhances firm performance in MIEs. Also, foreign-owned firms perform well than domestic-owned firms, while the sector influence on performance is mixed. The evidence shows that gifts to public officers mostly improved firm performance. Also, the bribe incidence effect is mixed. Additionally, gift inducements and bribe incidence generally tend to have opposite effects on performance. The findings imply that the corruption effect is complex.

The study findings suggest firm-ownership effects on firm performance drivers. Specifically, ETS generally impairs the performance of domestic firms more than their foreign counterparts. Thus, we fail to provide evidence in support of Hypothesis 2b. Additionally, there is no significant difference in the effect of political instability on the performance of foreign- and domestic-owned firms, implying Hypothesis 1b is supported. Also, gifts to induce public officials affect the performance of foreign-owned companies more than domestic ones. The gains from bribe incidence for foreign firms exceed those of domestic firms. Our evidence shows that corruption profits foreign firms more than domestic firms. International quality certification improves the performance of firms, but the gain appears higher for foreign-owned firms. The study shows ownership effect in the impact of external auditor influence on performance which appears stronger for domestic firms.

The evidence shows that political instability undermines manufacturing firms' performance more than service firms, which disagrees with Hypothesis 1c. Also, ETS undermines service firms' performance more than manufacturing firms. Thus, Hypothesis 2c is not supported. Additionally, we observe sector effects in the influence of external auditor engagement on firm performance. Also, international quality certification impacts the performance of manufacturing entities more than service firms in an economically meaningful way. The gift effect was more substantial for the service firms than manufacturing firms in 2010 and 2019. There is no significant sector influence on the bribe incidence effects.

Table 1

Descriptive statistics

	Mean	Standard deviations	Skewness	Kurtosis	Mean	Standard deviations	Skewness	Kurtosis
	Panel A: 2010				Panel B: 2013
RASG	0.033	0.261	1.349	7.969	−0.025	0.228	0.396	7.927
PINSTO	0.084	0.278	2.998	9.985	0.202	0.402	1.488	3.214
GIFPO	0.142	0.349	2.055	5.224	0.185	0.389	1.620	3.623
BRIBE	0.138	0.345	2.103	5.424	0.169	0.375	1.765	4.115
QUAC	0.219	0.414	1.356	2.840	0.216	0.412	1.378	2.899
EAUD	0.598	0.490	−0.401	1.161	0.496	0.500	0.015	1.000
PMWP	0.908	0.227	−3.242	12.657	0.846	0.319	−2.062	5.613
AGEF	22.637	18.253	1.974	9.490	16.646	13.319	2.079	10.552
	Number of observations: 11,703				Number of observations: 23,363
	Panel C: 2016				Panel D: 2019
RASG	0.001	0.223	0.310	7.248	0.009	0.213	0.670	9.366
PINSTO	0.163	0.370	1.822	4.318	0.112	0.315	2.466	7.083
GIFPO	0.197	0.398	1.522	3.315	0.111	0.315	2.471	7.108
BRIBE	0.135	0.341	2.142	5.589	0.082	0.274	3.050	10.302
QUAC	0.155	0.362	1.909	4.645	0.200	0.400	1.503	3.259
EAUD	0.574	0.494	−0.301	1.091	0.364	0.481	0.565	1.320
PMWP	0.908	0.251	−3.078	11.077	0.893	0.274	−2.699	8.785
AGEF	20.679	15.854	1.936	8.949	17.107	13.185	2.235	12.099
	Number of observations: 8,162				Number of observations: 16,267

Source(s): Authors' own construction

Table 2

Correlation matrix

	RASG	PINSTO	GIFPO	BRIBE	QUAC	EAUD	PMWP
RASG	1
PINSTO	−0.032	1
GIFPO	0.003	−0.020	1
BRIBE	−0.002	−0.009	0.373	1
QUAC	0.020	0.001	−0.029	−0.004	1
EAUD	0.008	0.058	−0.036	0.016	0.215	1
PMWP	−0.004	0.001	−0.029	−0.032	−0.062	−0.032	1
AGEFL	−0.045	0.041	−0.034	−0.033	0.134	0.153	0.0050

Note(s): The correlations are significant at the 5% level of significance

Source(s): Authors' own construction

Table 3

Details of the study's variables

Variable	Code	Scale	Definition of variables
Firm performance	PERF	Ratio	Real annual sales growth (RASG)
Political instability	PINST	Ratio	Percentage of firms choosing political instability as an obstacle (PINSTO)
Gift to induce public officers	CIND	Ratio	Percentage of firms expected to give gifts to public officials to get things done (GIFPO)
Bribery request by public officers	CBREQ	Ratio	Percentage of firms experiencing at least one bribe payment request (BRIBE)
International quality certification	IQCT	Ratio	Percentage of firms with internationally recognized certification (QUAC)
Engagement of external auditor services	EEAS	Ratio	Percentage of firms with annual financial statements reviewed by an external auditor (EAUD)
Employee tenure security	ETS	Ratio	Proportion of permanent workers out of all workers (PMWP)
Firm age	AGEFL	Log	Age of the firm in years
Firms' ownership	DUMO	Dummy	It takes 1 for domestically owned firms and 0 for foreign firms
Firms' sector	DUMA	Dummy	It takes 1 for manufacturing firms and 0 for service firms

Source(s): Authors' own construction

Table 4

Drivers of firm performance

	a	CIND	CBREQ	PINST	ETS	AGEFL	IQCT	EEAS	DUMO	DUMA
	Panel A: 2010
1	17.461***	3.734***	4.068***	−0.022**	−4.969***	−3.238***	2.046***	−3.220***
2	19.817***	3.520***	4.158***	−0.021**	−4.622***	−3.165***	1.475**	−3.456***	−3.059***
3	17.843***	3.694***	4.070***	−0.023**	−4.902***	−3.144***	2.136***	−3.317***		−1.176**
4	20.129***	3.479***	4.165***	−0.022**	−4.561**	−3.079**	1.573***	−3.544***	−3.006***	−1.100**
	Panel B: 2013
1	0.460	0.317	−1.112**	−0.012***	2.105***	−1.769***	2.860***	−1.030***
2	1.680**	0.342	−1.162**	−0.012***	2.153***	−1.745***	2.686***	−1.114***	−1.389**
3	0.291	0.303	−1.103**	−0.013***	2.138***	−1.790***	2.812***	−1.034***		0.384
4	1.507**	0.332	−1.155**	−0.012***	2.184***	−1.765***	2.642***	−1.118***	−1.381**	0.373
	Panel C: 2016
1	2.888**	−2.247***	2.538***	−0.009	−0.987	−1.139***	1.350*	2.260***
2	2.370*	−2.271***	2.626***	−0.009	−1.027	−1.197***	1.452**	2.399***	0.709
3	2.436**	−2.159***	2.510***	−0.009	−0.978	−1.196***	1.010	2.297***		1.213**
4	1.937	−2.186***	2.596***	−0.009	−1.012	−1.250***	1.118	2.431***	0.700	1.171**
	Panel D: 2019
1	7.231***	1.495**	−0.960	−0.025***	−2.797***	−1.585***	0.528	0.769**
2	9.684***	1.356**	−1.035	−0.023***	−2.734***	−1.675***	0.363	0.724**	−2.480***
3	7.569***	1.498**	−0.968	−0.025***	−2.760***	−1.571***	0.720*	0.706**		−0.782**
4	10.105***	1.360**	−1.034	−0.024***	−2.696***	−1.660***	0.579	0.652*	−2.510***	−0.895**

Note(s): This table presents the results of estimating Equation (1). ***, ** and * are, respectively, the 1%, 5% and 10% significance levels

Source(s): Authors' own construction

Table 5

Ownership and sector effects on MIE firms' performance

Model 1	Panel A: Ownership effects					Panel B: Sector effects
	2010	2013		2016	2019	2010	2013	2016	2019
a	17.492***	0.419		3.070**	7.442***	17.155***	0.597	3.322***	7.168***
CIND	10.142***	0.115		−3.733*	4.061**	5.053***	−0.434	−4.421***	0.612
CBREQ	7.150**	2.143		4.381*	0.748	4.967***	−1.043*	2.183**	−0.510
PINST	−0.035*	−0.004		0.029	−0.016	−0.035**	−0.012**	−0.008	−0.024***
ETS	−1.447	0.950		−0.066	−1.547	−4.959***	1.746***	−2.748**	−2.422***
AGEFL	−2.485***	−0.995**		−2.208***	−1.398***	−2.825***	−1.586	−0.779**	−1.489***
IQCT	2.015	2.253**		1.984	1.146	3.134***	1.354**	−0.216	0.183
EEAS	−8.235***	−1.601		3.226*	−0.021	−4.068***	−1.399***	2.791***	0.703
CIND *D	−7.342**	0.244		1.675	−3.023	−2.539	1.292	5.112***	1.698
CBREQ *D	−3.537	−3.676**		−2.066	−1.988	−1.699	−0.089	0.581	−0.899
PINST *D	0.017	−0.009		−0.043*	−0.007	0.023	−0.001	−0.002	−0.002
ETS *D	−3.784	1.348		−1.237	−1.313	0.141	0.630	3.351**	−0.633
AGEFL*D	−0.779	−0.831*		1.161*	−0.311	−0.533	−0.402*	−0.911**	−0.170
IQCT *D	−0.496	0.495		−0.590	−0.879	−1.747	2.326***	1.991	0.818
EEAS *D	5.249**	0.550	−0.944		0.841	1.412	0.618	−1.207	0.011

Note(s): This table presents the results of estimating Equation (2). ***, ** and * are, respectively, the 1%, 5% and 10% significance levels

Source(s): Authors' own construction

Table A1

Sampled middle-income economies

Argentina	Cote d’Ivoire	Guinea	Mexico	Rwanda
Armenia	Costa Rica	Guyana	Moldova	Serbia
Azerbaijan	Djibouti	Honduras	Mongolia	St Lucia
Bangladesh	Democratic Republic of Congo	Jamaica	Montenegro	El Salvador
Benin	Dominica	Jordan	Morocco	Suriname
Belarus	Dominican Republic	Kazakhstan	Myanmar	Tajikistan
Belize	Ecuador	Kenya	Nepal	Tanzania
Bolivia	Egypt	Kosovo	Nicaragua	Thailand
Paraguay	St Vincent and Grenadines	Kyrgyz Republic	North Macedonia	Togo
Botswana	Eswatini	Lao PDR	Pakistan	Tunisia
Bulgaria	Georgia	Lesotho	Venezuela	Turkey
Cambodia	Ghana	Lebanon	Peru	Uganda
Ukraine	Bosnia and Herzegovina	Yemen	Zimbabwe	Zambia
Uzbekistan	West Bank and Gaza

Note(s): The table presents the list of countries where data were collected from

Source(s): Authors' own construction

Note

1. Details of the 77 sampled countries are presented in Table A1 in the Appendix.