1. Introduction
The host country’s cross-listing location in international stock exchange markets has been increasingly recognized as a strategy for firms to overcome home market segmentation. Evidence suggests that cross-listing enhances firm valuation gains, including increased market liquidity; improved information disclosure; enhanced firm visibility; and better investor protection in support of the bonding hypothesis (
Roosenboom & Van Dijk, 2009;
Hassan & Skinner, 2016;
Ghadhab & M’rad, 2018). Further studies indicated that firms whose cross-listing is located in developed exchange markets tend to have higher valuation gains than those in emerging markets (
Pagano et al., 2001;
Fernandes & Ferreira, 2008;
Temouri et al., 2016;
Ghadhab & Hellara, 2016).
Sarkissian and Schill (
2016) noted that the proximity of the home country significantly impacts the valuation gains of firms cross-listed in the host market. However, not all firms in emerging market countries have equal appeal to cross-listing in developed stock exchange markets when seeking financial market expansion. Whilst existing literature emphasizes the proximity of the home country for the host market cross-listing benefits, studies largely ignore the bias of the home country’s governance environments. This is important because the capital raised by firms in the host market is transferred to the home country for domestic investments.
Cross-listing studies indicate that the home market information environment is crucial for firms’ financial market development (
Lang et al., 2003;
H. W. Lee & Valero, 2010;
Dodd & Gilbert, 2016).
Lang et al. (
2003) suggest that a firm’s information environment contributed to enhanced firm valuation gains for cross-listed firms.
H. W. Lee and Valero (
2010) found that improvements in the information environment are more pronounced for foreign firms originating from countries characterized by greater information asymmetry, specifically those with weaker legal traditions and rules of law, and those less familiar to U.S. investors.
Khan et al. (
2020) specifically emphasize the importance of the home country’s governance quality as a determinant of firm financial market development. This aligned with
Merton’s (
1987) assertion that understanding the institutional environment can reduce information costs and explain market anomalies. Furthermore, the phenomenon of home bias, where investors exhibit a preference for domestic over foreign assets despite the diversification benefits of international financial markets, has been well-documented (
Obstfeld & Rogoff, 2000;
Belderbos et al., 2013;
Dodd & Gilbert, 2016;
Andrew Karolyi, 2016). Recent literature also highlights the importance of home market bias in a firm’s financial market development (
Andrew Karolyi, 2016;
Gaar et al., 2020). Given the importance of the home country information environment for firm financial market developments, there is a need to assess whether the political stability, government effectiveness, and voice and accountability governance quality of the home country impact cross-listing firms’ foreign market integration for better valuation gains.
The conventional choices of cross-listing locations are recognized as integral to firm valuation gains in the global financial market, yet this remains a subject of ongoing debate amongst scholars, particularly in the context of emerging market countries (
Pagano et al., 2002;
Roosenboom & Van Dijk, 2009;
Bianconi & Tan, 2010;
Del Bosco & Misani, 2016;
Ghadhab & M’rad, 2018). Therefore, this study aims to contribute to the existing corpus of literature by providing novel evidence on the influence of political stability, government effectiveness, and voice and accountability on the cross-listing destinations’ stock exchange market of firms from emerging markets countries, with a specific focus on the BRICS (Brazil, Russia, India, China and South Africa) bloc. Firms from the BRICS bloc constitute a substantial portion of cross-listed firms within emerging countries and face unique challenges ranging from geographical politics to economic development (
Wójcik & Burger, 2010;
Prakash et al., 2017). Studies indicate that political risk in BRICS countries can impede economic growth and negatively impact firm financial development (
Hoş et al., 2024).
Nzeh et al.’s (
2022) study shows that government effectiveness in BRICS nations enhances trade openness and economic growth. Moreover, the governance quality in emerging market countries, where BRICS firms often represent a substantial portion, has posed challenges to financial market development (
Ahmed et al., 2022).
The BRICS bloc is a subset of emerging market countries, including those from Latin America, Asia, Europe and Africa, which are geographically represented and have their markets segmented when cross-listed on international financial markets (
Wójcik & Burger, 2010).
Table 1 presents BRICS countries’ classification of listing destinations of cross-listed firms in developed stock exchange markets and/or emerging market countries, and agrees with the selection criteria used by the International Monetary Fund (IMF), Standard and Poor (S&P), Morgan Stanley Capital International (MSCI), Russell and Dow Jones. As at the end of 2020, 8917 firms had primarily listed on the BRICS stock exchange market. Of the total value, 304 companies (3.4%) had cross-listed in foreign financial markets, indicating that a substantial number of large firms in the BRICS markets are not listed abroad, and such deficiency in firms’ financial market expansion may hinder the financial market development and economic growth required by the BRICS cooperation. Of these 304 cross-listed firms on the foreign financial market clusters, 323 (84.8%) had cross-listing locations attributed to major exchanges (developed markets) and 58 (15.2%) non-major exchanges (emerging markets).
Wójcik and Burger (
2010) reported that the BRICS firms account for most emerging market companies with the potential for international listings, and when they do, the process remains sensitive and politically motivated. Therefore, ignoring the implication of the firm’s home country governance quality on choices of listing destination stock exchange markets for cross-listing firms may lead to lost opportunity costs for those firms seeking higher valuation gains in the international financial market.
This study differs from empirical literature on cross-listing destinations that adopted single estimation techniques, including the Ordinary Least Square (OLS) and probit (
Bianconi & Tan, 2010), Binomial logit model (
Bin-Dohry et al., 2023), tobit model (
Abdallah & Goergen, 2008) and Poisson model (
Hassan & Skinner, 2016). In contrast, the researchers employ both probit and probit regression models, as they are particularly suited for analyzing binary outcomes (developed vs. emerging market destinations) whilst accounting for the potential influence of outliers in governance indicators. This dual methodological approach provides robustness checks that address limitations in previous cross-listing studies that relied solely on OLS, probit and tobit models, amongst others. To the best of the researchers’ knowledge, this is the first study to adopt the Robit approach when analyzing the choice of listing destinations’ stock exchange market for cross-listed firms.
This study specifically examines how political stability, government effectiveness, and voice and accountability influence BRICS firms’ cross-listing destination choices between developed and emerging markets. While studies by
Roosenboom and Van Dijk (
2009) and (
Ghadhab & M’rad, 2018) have extensively examined host market benefits, they have not adequately addressed how home country governance quality moderates these benefits. Similarly,
Dodd (
2011) found that cross-listing in advanced exchange markets significantly improves the quality of a firm’s information environment and stock price efficiency in the home market but does not address how the firm’s home country information environment impacts the cross-listing adoption. Furthermore,
Hope et al. (
2007) explored cross-listing motivations but overlook how home governance quality drives destination choices. Based on theoretical foundations and existing literature, this study hypothesizes that BRICS firms originating from countries with stronger political stability, greater government effectiveness, and higher voice and accountability are more likely to cross-list in developed markets to maximize valuation gains. This approach aligns with the bonding hypothesis, signaling theory and information environment literature, whilst extending one’s understanding of how home country governance shapes international listing strategies. Collectively, these theories suggest that firms strategically select cross-listing destinations to overcome governance limitations in their home environments whilst signaling their commitment to higher standards of transparency and accountability. The theoretical framework posits that the quality of home country governance creates varying incentives and opportunities for firms seeking access to international capital.
The empirical findings of this study reveal a positive and statistically significant relationship between political stability, government effectiveness, and voice and accountability in the home country, as well as the adoption of listing destinations in foreign financial markets, particularly in developed exchange markets offering higher valuation gains. This holds true for both probit and robit model estimations. The positive and significant impact of political stability suggests that firms domiciled in BRICS countries characterized by stable political climates and lower levels of violence are better positioned to access developed capital markets, achieve greater integration, enhanced liquidity, increased investor confidence, and valuation premiums. Similarly, the positive and significant relationship between government effectiveness and cross-listing location indicates that the effective implementation of policies in the home country is a crucial determinant of where cross-listed firms choose to list internationally. Furthermore, the positive and significant association between voice and accountability and cross-listing location suggests that the freedom of expression, press independence and public participation in governance in the home country can be leveraged to access premium markets on international exchanges for enhanced valuations.
Theoretically, BRICS firms from countries with stronger political stability, government effectiveness, and voice and accountability are effectively bonding themselves to the higher governance standards prevalent in advanced markets, thereby signaling their commitment to transparency. This aligns with the Bonding Theory proposed by
Coffee (
1999), which posits that firms from weaker institutional environments seek to internationalize through stronger governance mechanisms. A strong home country governance quality serves as a credible signal that reduces information asymmetry between BRICS firms and potential foreign investors, supporting the Signaling Theory articulated by
Spence (
2002). This aligns with
Fernandes and Ferreira’s (
2008) and
Bris et al.’s (
2012) argument that cross-listing benefits are enhanced by better information environments. This also supports
Roosenboom and Van Dijk (
2009) and
Gu and Reed (
2013), who show that overcoming market segmentation is a primary reason for firm cross-listing motivation.
The remainder of this paper is structured as follows.
Section 2 provides a review of the relevant literature and develops the research hypotheses.
Section 3 details the data, variable analysis and research methodology employed.
Section 4 presents the empirical findings and an analysis of the results. Finally,
Section 5 offers concluding remarks and policy recommendations.
3. Data Variable Analysis and Research Method
3.1. Data Variable Analysis
The study data population comprises cross-listed firms from the BRICS countries’ stock exchange markets, which has seven exchanges, including the São Paulo Stock Exchange (BOVESPA) in Brazil; Moscow Exchange (MOEX) in Russia; Bombay Stock Exchange (BSE) and National Stock Exchange (NSE) in India; Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE) in China; and Johannesburg Stock Exchange (JSE) in South Africa. This study employs non-probability purposive sampling with panel data characteristics (
Rahman, 2023).
Table 1 presents 304 firms that cross- listed from the BRICS bloc spanning from 2000 to 2020, which were sourced from the S&P capital IQ database. Political stability, government effectiveness, and voice and accountability were sourced from the World Bank’s Worldwide Governance Indicators (WGI). The sample size of the cross-listed firms from the BRICS countries consisted of 293 companies with one or more listings in different countries’ stock exchanges around the world. The sample is restricted to cross-listed firms from BRICS countries and aligns with positivist research, where samples are chosen based on predefined criteria to test hypotheses. The sample was selected from the initial 304 companies’ population of the study due to the unavailability of data, which represents 96% of the total population of cross-listed firms from the BRICS countries. The data of BRICS firms has been the subject of several studies in corporate finance literature (
Syriopoulos et al., 2015;
Umar & Sun, 2016;
Osaseri & Osamwonyi, 2019;
Rehman, 2021). The BRICS countries are a subset of emerging market countries, which include Latin America, Asia, Europe and Africa, and are geographically represented and segmented when cross-listed on the international financial market. The classification of BRICS countries as emerging economies agrees with the selection of the International Monetary Fund (IMF), Standard and Poor (S&P), Morgan Stanley Capital International (MSCI), Russell and Dow Jones, amongst others.
The cross-listing destination is the dependent binary variable, political stability, government effectiveness, and voice and accountability are the independent variables, and the firm size, firm performance, and firm leverage are the firm-specific control variables.
3.2. Cross-Listing Destination Measures
To test the empirical effect of political stability, government effectiveness, and voice and accountability on the cross-listing destination of firms from BRICS emerging market countries, the study first analyzed the cross-listing destination in developed and/or emerging markets using the following equation:
where
is the dependent binary variable of firms from emerging countries that cross-listed in developed country stock exchange markets (DCSEM) and/or emerging countries’ stock exchange markets (ECMES) on the international financial market. This predicts the probability that the cross-listed firm’s listing destination is either characteristic of a developed or advanced stock exchange market, which is associated with higher valuation gains on the international financial market or an emerging stock exchange market. The dummy variable equals one if a firm from an emerging market country cross-lists in a developed stock exchange market. The dummy variable equals zero if firms from an emerging market country cross-list in another emerging country stock exchange market on the foreign financial market cluster. The classification of listing destinations for cross-listed firms in both developed and emerging stock exchange markets aligns with selection criteria established by the International Monetary Fund (IMF), Standard and Poor’s (S&P), Morgan Stanley Capital International (MSCI), Russell, and Dow Jones.
3.3. Political Stability and Absence of Violence Measure
The political stability and absence of violence measure within the WGI framework evaluates the effectiveness of governance structures, policies and institutions in maintaining stability and reducing violence or terrorism within a country (
Al-Mulali & Ozturk, 2015;
Berg & Ostry, 2017;
Uddin et al., 2017). According to data sourced from WGI indicators, the political stability and absence of violence measurement ranges from −2.5 to 2.5, with a negative or lower score value indicating higher levels of instability, and a positive or higher score value indicating greater political stability and a lower level of violence or terrorism. A higher score indicates greater political stability and a lower level of violence or terrorism, while a lower score suggests political uncertainty and high levels of violence (
Uddin et al., 2017).
3.4. Government Effectiveness Measure
Government effectiveness is a vital aspect of governance that assesses the competency and efficiency of government institutions in formulating and implementing policies and delivering public services (
Rodríguez-Pose & Di Cataldo, 2015;
Sabir et al., 2019). Government effectiveness is one of the key dimensions measured within the WGI framework and it evaluates the capacity of governments to efficiently and transparently manage public resources, provide essential services, enforce laws, and respond to the needs and demands of citizens (
De Villiers & Marques, 2016). The government effectiveness indicator provides insights into the overall quality of governance and the ability of governments to deliver results and meet the expectations of citizens.
According to data sourced from the WGI indicator, the government effectiveness measurement ranges from −2.5 to 2.5, with a negative or lower score value indicating weaknesses in government capacity and a positive or higher score value indicating greater government efficiency, transparency and responsiveness to the needs of society. A higher score on this indicator suggests greater government efficiency, transparency and responsiveness to the needs of society (
De Villiers & Marques, 2016). Conversely, a lower score indicates weaknesses in government capacity, bureaucratic inefficiencies, and a lack of trust in government institutions (
Rodríguez-Pose & Di Cataldo, 2015). Countries with better score indicators are generally associated with stronger institutions, better public services, and higher levels of citizen satisfaction.
3.5. Voice and Accountability Measure
Voice and accountability is another important dimension of governance measured within the WGI framework and it assesses the extent to which a country’s citizens are able to participate in the selection of their government, as well as freedom of expression, association and the media (
Torgler et al., 2011;
Ng et al., 2016;
Ojeka et al., 2019). The voice and accountability indicator captures perceptions of the ability of citizens to participate in the political process and hold their government accountable, and reflects the degree to which individuals are free to express their opinions, assemble peacefully and engage in political activities without fear of repression or retaliation (
Ojeka et al., 2019). According to data sourced from the WGI indicator, the voice and accountability measurement ranges from −2.5 to 2.5, with a positive or higher score value indicating better freedom of expression and a negative or lower value indicating limitations of rights and freedom of expression. A higher score in the voice and accountability indicator suggests greater freedom of expression, participation and accountability within a country, whilst a lower score indicates limitations on these rights and freedoms.
3.6. Firm-Specific Control Variables
3.6.1. Firm Size
A few literature studies emphasized the importance of firm size when analyzing stock market liquidity (
Sensoy, 2017;
Dang et al., 2018;
Reschiwati et al., 2020;
Sitorus et al., 2021). Firm size discusses the scale of a company measured by factors such as total assets (
Arilyn, 2020). The natural logarithms of total assets is commonly used to measure firm size (
Dang et al., 2018;
Hart, 2021).
Sensoy (
2017) noted that a small firm size is affected by the liquidity commonality of firms in emerging market countries’ financial markets. Firms with larger sizes tend to have more resources, established market presence and a wider range of operations, which thus improves the firm’s stock market liquidity (
Reschiwati et al., 2020). This can lead to increased investor confidence and stock market participation as larger firms are often perceived as being more stable and less risky (
Sitorus et al., 2021). This study employed the natural logarithms of total assets as a measure of firm size, following previous work by
Dang et al. (
2018),
Reschiwati et al. (
2020) and
Hart (
2021), amongst others, on corporate finance literature. Following the importance of firm size when analyzing stock market liquidity (
Sensoy, 2017;
Dang et al., 2018;
Reschiwati et al., 2020;
Sitorus et al., 2021), this study observed firm size as a control variable whilst analyzing the effect of home market political stability, government effectiveness, and voice and accountability on cross-listing destinations.
3.6.2. Firm Performance
Firm performance assesses the effectiveness of a firm in meeting strategic goals and delivering value to stakeholders (
Batchimeg, 2017;
Arilyn, 2020;
Sukesti et al., 2021).
Singh et al. (
2015) show that firm performance using both Tobin’s Q and ROA enhances stock market development. Higher performance reflects favourable prospects of a firm to investors and market participants, which increases the stock market liquidity of firms (
Husna & Satria, 2019;
Sukesti et al., 2021). Improved performance also conveys a stronger value to investor sentiment and has the potential to result in stock price increases. To address the implications of firm performance on investor recognition of cross-listed firms, this study incorporates both Tobin Q and ROA as accounting-based and market-based measures of firm performance.
Tobin’s Q
Tobin’s Q ratio is described as the market value of the enterprise’s equity plus the book value of interest-bearing debt to the replacement cost of its total assets (
Bartlett & Partnoy, 2020;
Butt et al., 2023). Tobin’s Q ratio has been widely used in corporate finance studies and stock market development (
Bennett et al., 2020). It is described as the sum of the market value of equity and book value of debt over the book value of total assets. This indicates the actual market value of the firm over the replacement cost of its assets. It evaluates the overall market value of the firm from the perception of investors and corporate financial analysts to gauge firm performance. The market value of the firm includes the market value of debt and the market value of equity, while the value of debt can be measured by the book value (
Bennett et al., 2020). These indicate that a firm’s leverage preference on investments depends on the market value of debt compared with the book-value. Therefore, this study controls Tobin’s Q market-based measure of firm performance whilst analyzing the effects of the home country political stability, government effectiveness, and voice and accountability on cross-listing destinations.
Return on Assets
As an accounting-based measure of the financial performance, ROA is described as operating earnings after interest and tax divided by the book value of total assets (
Batchimeg, 2017;
Husna & Satria, 2019;
Sukesti et al., 2021).
Batchimeg (
2017) states that ROA is a financial ratio that shows the percentage of profit that a firm earns in relation to its overall resources, which is constrained by the accounting standards set by professional bodies. ROA is described as operating profit after tax divided by total assets. This reflects management’s ability to use financial and real resources to generate revenue for the firm.
Following the importance of firm performance when analyzing stock market liquidity, this study observed firm performance as a control variable whilst analyzing the effects of the home country political stability, government effectiveness, and voice and accountability on cross-listing destinations.
3.6.3. Firm Leverage
Firm leverage explained the degree to which a company uses debt to finance its operations or growth, and is a critical factor that influences investor sentiment and recognition (
Bhagat et al., 2015;
DeAngelo & Roll, 2015;
Nenu et al., 2018). The ratio of total debt to total assets is often used as measure of firms, and a high level of leverage indicates that a company is using a significant amount of debt to fund its activities (
Nenu et al., 2018;
Yustrianthe & Mahmudah, 2021). This can amplify returns on investment when the company is performing well, but it also increases the risk of financial distress if the company’s performance falters. The optimal level of firm leverage depends on various factors, including industry norms, business risk and financial market conditions (
Nenu et al., 2018).
Bhagat et al. (
2015) show that high leverage signals posed greater risk to higher returns and can influence investor sentiment. Equally, lower leverage suggest that a firm relies more on equity financing and can provide stability, but may limit the potential returns (
DeAngelo & Roll, 2015). While studies have observed the importance of firm leverage for stock market development, little attention has been paid to firm leverage variables and investor recognition of cross-listed firms. Therefore, this study seeks to control the firm leverage of cross-listed firms.
3.7. Research Model Estimation and Methods
In this study, the dependent variable is dichotomous, including two responses, i.e., emerging market firms that cross-listed in the developed country stock exchange market or emerging country stock market. Given this dichotomous nature of the outcome variable, a binary regression framework is appropriate. Several studies on cross-listing destinations had used the binary regression model (
Bianconi & Tan, 2010;
Bin-Dohry et al., 2023). The binary response model is implemented within the framework of generalized linear models (GLMs) using a Bernoulli distribution for the dependent variable.
This study employs probit model estimates: the effects of political stability, government effectiveness, and voice and accountability on cross-listing destination adoption of firms. A probit model is a statistical model used to analyze the relationship between a binary dependent variable and one or more independent variables. In a binomial probit model, the dependent variable is binary, meaning that it can take on two values, typically coded as 0 (emerging market firms that cross-listed in the emerging stock exchange market) and 1 (emerging market firms that cross-listed in the developed stock exchange market). The independent variables, denoted as , … , can be either continuous or categorical.
The probit model equation can be expressed as:
where
is the probability of the binary outcome of dependent variables being 1 given the values of independent variables. The
is the standard normal cumulative distribution function (CDF).
β0 is the intercept of the slope and
β1,
β2, …,
βk are the coefficients of the independent variables, which represent the effect of each independent variable on the log-odds of the dependent variable being 1. The
β coefficients are typically estimated using maximum likelihood estimation (MLE) methods. These coefficients indicate the direction and strength of the relationship between the independent variables and the log-odds of the dependent variable being 1.
Alternatively, this study employs the robit regression model for a robustness check of the effects of political stability, government effectiveness, and voice and accountability on the cross-listing destination of BRICS firms. Robit is defined as generalized linear models (GLMs) with a binomial family (usually Bernoulli) variance function and a robit link function with ν (nu) degrees of freedom (
Newson & Falcaro, 2023). The Robit model is designed to be more robust to outliers and heavy-tailed distributions (
Liu, 2004). While the probit model assumes a normal distribution of errors, which can be sensitive to outliers, the Robit model assumes a t-distribution, which has heavier tails, making it more resilient to outliers and deviations from normality. Specifically, robit regression corresponds to a GLM with a binomial (usually Bernoulli) variance function and a robit link function (
Newson & Falcaro, 2022). The maximum likelihood estimates for the robit model can be efficiently computed using Expectation-Maximization (EM)-type algorithms (
Newson & Falcaro, 2022;
Liu, 2004). These algorithms not only enhance computational efficiency but also yield valuable diagnostic information (
Liu, 2004). In addition to frequentist estimation, the structure of EM-type algorithms lends itself naturally to Bayesian inference freedom (
Newson & Falcaro, 2023). These algorithms can be easily adapted into Data Augmentation (DA) algorithms, facilitating the implementation of Bayesian robit regression. The DA algorithms for the robit model are notably more straightforward to implement than the Gibbs sampling methods typically used for Bayesian logistic regression. This simplicity, combined with robustness and flexibility, makes the robit model a compelling choice for both classical and Bayesian binary response modelling.
The robit model equation can be expressed as:
where
P (
Y = 1|
X) is the probability of the binary outcome being 1. The
Ft (·) is the cumulative distribution function (CDF) of the robit link, which is designed to be robust to outliers.
β0 is the intercept and
β1 is the coefficient associated with the predictor variable
X1.
The combined application of both probit and robit regression models strengthens the robustness of the empirical analysis by ensuring that the findings on the effects of political stability, government effectiveness, and voice and accountability institutional quality on cross-listing destination choices are not unduly affected by potential data irregularities, outliers or model misspecifications. This dual-model approach allows for the cross-validation of results under different distributional assumptions of normality in the probit model and heavier-tailed robustness in the robit model.
Prior to model estimation, a pre-estimation diagnostic test will be conducted using a correlation matrix to assess the potential presence of multicollinearity amongst the explanatory variables. Multicollinearity, which arises when two or more independent variables are highly correlated, can inflate standard errors and compromise the reliability of coefficient estimates. The post-estimation will consider the Likelihood Ratio, Likelihood Sensitive (Lsens) graph, Pseudo R-square, Wald Chi2 test Marginal Effects, Receiver Operating Characteristic (ROC) graph, Akaike information criterion and Bayesian information criterion. The Likelihood Ratio test will be employed to compare the goodness-of-fit between nested models, helping to determine whether the inclusion of additional variables significantly improves model fit, and is used to compare the fit of nested models. Likelihood Sensitive (Lsens) graphs will be used to understand the sensitivity of the model likelihood function to changes in parameters or covariates. Pseudo R-square statistics will be reported to assess the explanatory power of the binary models and provide a general indication of model adequacy to assess the model fit. The Marginal Effects will be used to understand the impact of each predictor on the outcome. The Receiver Operating Characteristic (ROC) graph will be used to examine the degree of other institutional factors or firm-specific factors that influence the overall performance.
3.8. Research Method Gap
The cross-listing of firms on international stock exchanges has been extensively studied, with researchers examining various factors influencing cross-listing decisions and outcomes.
Table 2 below highlights a selection of studies investigating cross-listing premiums, market reactions, destination choices and other related topics using diverse research methods. Despite the wealth of existing studies, there remains a gap in understanding the specific impact of home country political stability, government effectiveness, and voice and accountability on the listing destination of firms from emerging markets, particularly the BRICS countries, choosing cross-listing destinations for higher valuation gain. This study aims to fill this gap by employing probit and robit binary regression models to examine how home country political stability, government effectiveness, and voice and accountability influence cross-listing destinations.
Existing studies largely focus on the effects of cross-listing on firm performance, market reactions and choice of listing destinations, but they do not adequately address how political stability, government effectiveness, and voice and accountability of the firm’s home country influence the likelihood of cross-listing destination. In the context of emerging markets, there is a lack of comprehensive analysis specific to BRICS countries, which represent a significant portion of emerging markets. These countries have unique institutional environments that can significantly impact cross-listing destinations.
While previous studies have used models like descriptive statistics, OLS, probit, Logit, Tobit and event studies combined, this study employs the probit model and the robust Robit model to analyze the binary outcomes of the home country’s political stability, government effectiveness, and voice and accountability on cross-listing destinations.
3.9. Empirical Model Specification
An empirical model is employed to validate the three hypotheses on political stability, government effectiveness, and voice and accountability relating to the listing destinations of cross-listed firms from BRICS emerging market countries. Based on the dataset nature of the binary model, the generalized linear model (GLM) estimation is employed using the probit and robit regression models for each empirical model for the hypotheses as follows:
Hypothesis 1—Political stability and absence of violence in the firm’s home country is more likely to improve listing adoption in developed and/or emerging markets when cross-listing on the foreign financial markets cluster. The model equation is explained as follows:
where
is the lag of political stability and the absence of violence perception in the firm’s home country a year before the cross-listing year. The presence of political stability and the absence of violent conditions in a country have significant implications for its development and financial growth (
Hosny, 2017;
Lohwasser & Hoch, 2019;
Watabaji & Shumetie, 2022;
Zhang et al., 2022). Political stability not only contributes to a favourable institutional environment but also ensures the continuity of economic policies and reduces future uncertainty. As political stability and the absence of violence remain some of the measures of a firm’s home market governance quality indicators, the study explicitly analyzed whether political stability and the absence of violence in the firm’s home country is more likely to improve listing adoption in developed and/or emerging markets when cross-listing on the foreign financial market cluster.
Hypothesis 2—The effectiveness of government implementation of policies in the firm’s home country is more likely to improve listing adoption in developed and/or emerging markets when cross-listing on the foreign financial market cluster. The model equation is explained as follows:
where
is the lag of government effectiveness perception in the firm’s home country a year before the cross-listing year. Government effectiveness measures the ability of a government to formulate and implement policies, regulations and programs that align with the needs and expectations of its stakeholders (
S.-Y. Lee & Whitford, 2009;
Alam et al., 2017). The effectiveness of government policies and its implementation may affect financial development because its fiscal policies shape and attract external interest (
S.-Y. Lee & Whitford, 2009;
Alam et al., 2017;
Saliya, 2022). As government effectiveness is one of the dimensions of the governance quality indicator, this study explicitly analyzed whether the effectiveness of government policy implementation in the firm’s home country is more likely to improve listing adoption in developed and/or emerging markets when cross-listing on the foreign financial market cluster.
Hypothesis 3—Voice and accountability in the firm’s home country is more likely to improve listing adoption in developed or emerging markets when cross-listing on the foreign financial market cluster. The model equation is explained as follows:
where
is the lag of voice and accountability perception in the firm home country a year before the cross-listing year. Voice and accountability are key factors in assessing citizens’ perceptions of their ability to participate in the democratic process, exercise their rights to freedom of expression and association, and access an independent press (
Torgler et al., 2011;
Ojeka et al., 2019). As one of the measures of the governance quality indicator, the study explicitly analyzed whether political stability and the absence of violence in the firm’s home country is more likely to improve listing adoption in developed and/or emerging markets when cross-listing on the foreign financial market cluster.
4. Empirical Findings and Analysis
4.1. Descriptive Analysis
The detailed descriptive summary analysis of the mean, median, standard deviation, minimum and maximum of the firm’s home country political stability, government effectiveness, and voice and accountability and listing location of cross-listed firms from BRICS countries are presented in
Table 3. The cross-listing location dummy variable created equals one if firms from emerging market countries cross-listed in developed markets and zero if firms from emerging market countries cross-listed in another emerging market country.
The listing location adoption of cross-listed firms reported a mean of 84.51%, indicating that the BRICS cross-listed firms’ listing preferences were in the developed market countries. This suggests that firms from emerging market countries frequently seek financial market expansion to developed markets more than emerging market countries.
The listing location adoption of cross-listed firms from the reported 84.51% mean indicates that the BRICS cross-listed firms’ listing preferences were in the developed market countries. This suggests that firms from emerging market countries frequently seek financial market expansion to developed markets more than emerging market countries.
The lagged average lower value for government effectiveness (−0.4062) is found amongst BRICS countries, which indicated a severe problem of governance quality before the cross-listing year. The study also found the lagged average higher value for voice and accountability (0.0574), regulatory quality (0.1268), and the political stability and absences of violence (0.0761) amongst BRICS countries, which indicated stronger governance before the cross-listing year from 2000 to 2020.
Moreover, the firm size natural logarithm of the total assets is 8.5426. The firm leverage measured with total debt divided by total assets shows an average of 2.8618. The firm’s performance proxies with Tobin Q and ROA exhibited an average of 0.4794 and 1.5744, respectively. The firm performance’s positive implication is consistent with firm-specific information that can be observed on the listing location benefits of cross-listed firms, which improves firm valuation (
Bianconi & Tan, 2010;
Boubakri et al., 2016).
4.2. Correlation Matrix
Table 4 reports the pre-estimation results of the Pearson correlation test amongst the variables of the firm’s home country political stability, government effectiveness, voice and accountability, and the listing destination of cross-listed firms from the BRICS bloc. It shows that the cross-listing location has a significant association with almost all the explanatory variables, except for firm size and leverage.
Government effectiveness shows a strong positive and highly significant correlation (0.6966), and suggests that BRICS firms from countries with higher past government effectiveness are significantly more likely to choose developed markets as their cross-listing destination. The BRICS firms from countries with a proper implementation of government policies and programmes tend to choose certain cross-listing destinations associated with higher valuation gains.
The political stability and absence of violence show moderate correlations with cross-listing destinations (0.6862), indicating that these factors influence destination choices. BRICS firms from countries with strong political stability are more likely to cross-list in advanced financial markets for better valuation gains.
Voice and accountability’s strong positive and highly significant correlation (0.8909) indicates an even stronger relationship between a BRICS country’s voice and accountability levels and the likelihood of its firms cross-listing on developed markets. This supports the notion that greater transparency, freedom of expression and public participation in governance in the home country enhances a firm’s attractiveness to investors in advanced markets.
The strong positive correlations of home country government effectiveness, voice and accountability, and political stability with the cross-listing destination strongly support the idea that a sound domestic governance quality is a significant enabler for international market integration of BRICS firms.
The firm-specific control variables of firm size (−0.2589) and leverage (−0.2746) show a negative and significant correlation with the cross-listing destination and suggest that company size alone does not determine where BRICS firms choose to cross-list. The firm performance measured by Tobin’s Q (0.1525) and Return on Assets (0.1458) both show statistically significant positive correlations with cross-listing destinations, suggesting that firms with better market valuations and profitability tend to select certain cross-listing destinations.
The Pearson correlation test amongst variables ranges between −0.40 and 0.89. Therefore, this study tests for multicollinearity amongst the variables and generates an unreported variance inflation factor (VIF) mean of 4.30, and all were all less than 10, which represents the most accepted threshold (
Daoud, 2017;
Shrestha, 2020). This confirms that multicollinearity does not pose a concern in the dataset of the firm’s home country political stability, government effectiveness, voice and accountability and listing destination of cross listed firms from the BRICS bloc.
4.3. Analysis of Findings
4.3.1. Analysis of Finding on Political Stability and the Absence of Violence and Listing Destinations of Cross-Listed Firms from BRICS Countries
Hypothesis 1 is that the political stability and absence of violence perception in the firm’s home country is more likely to improve the cross-listing adoption of cross-listing in an exchange market with a stronger valuation gain using the probit and robit model. This study first provided an outlook on political stability and the absence of violence perception index in the home country of BRICS countries in
Figure 1 from 2000 to 2020. The political stability and absence of violence perception index in the plot graph shows that South Africa and Brazil have hovered between low and high scores in the last two decades. This indicates a mixed perception of political stability and absence of violence in these countries, suggesting a degree of political uncertainty and potential presence of violence during certain periods, which can have implications for cross-listed firms operating in these markets.
On the other hand, Russia, India and China consistently maintain low scores on the political stability and absence of violence perception index. This suggests a higher level of political uncertainty and the presence of violence in the domestic markets of cross-listed firms from these countries. These factors can potentially impact the liquidity benefits experienced by these firms during their integration into foreign financial markets.
Using probit regression model Equation (4) in
Table 5, the findings show that political stability and the absence of violence in the home country of cross-listed firms are economically statistically significant and positively related to the listing location adoption of firms from the BRICS countries, with a coefficient of 1.2636 (t = 1.89). This indicates that political stability and the absence of violence in emerging market countries improved firms’ cross-listing destination adoption in advanced stock exchange markets with higher valuation gains. The finding aligned with
Watabaji and Shumetie (
2022) who find that political stability and environment are determinants for firms’ financial market development and liberalization.
Moreover, the marginal effect in model Equation (4) of
Table 6 showed that an increase in political stability in the BRICS bloc home countries increases the prospects of destinations in developed exchange markets by 25%. The marginal effect suggests that political stability in a firm’s home country significantly enhances its prospects of cross-listing in exchanges offering higher valuation gains. Politically stable environments in the home country of BRICS strongly predict successful listing locations for cross-listing firms.
In the related robit in model Equation (4) in
Table 7, alternative results show that political stability and the absence of violence in the firm’s home country is positive and significantly related to cross-listing destinations in advanced stock exchange markets, with a coefficient of 1.3278 (z = 3.97). This affirmed the overall results that political stability and the absence of violence in emerging market countries are appealing for firm financial market expansion in an exchange market with higher valuation gains better than the home market. This finding is similar to the BRICS study of
Wójcik and Burger (
2010), which found that political stability is appealing to listing destinations on international stock exchange markets. In addition, the findings align with
Lohwasser and Hoch (
2019) and
Watabaji and Shumetie (
2022), amongst others, who emphasize the importance of political stability and the absence of violence as determinants for firm financial market development and liberalization.
According to the bonding hypothesis by
Coffee (
1999), firms from emerging markets seek to bond themselves to stronger institutional contexts to reform their corporate governance structures. The findings regarding political stability align with this theory, as companies from politically stable BRICS countries are better positioned to commit to the higher scrutiny and regulatory standards of advanced markets. The positive relationship between political stability and cross-listing in advanced markets strongly aligns with the signaling theory by
Spence (
2002). Political stability serves as a credible signal to international investors about the quality of the firm’s home environment. When firms from politically stable environments cross-list, this action signals confidence and reliability, reducing information asymmetry between the firm and potential investors. The 25% increased probability of listing in developed markets when political stability improves demonstrates the signaling value of a stable political environment. The results support the Information Environment Hypothesis by
Fernandes and Ferreira (
2008), which suggests that cross-listing leads to improved information environments. Political stability contributes to a more transparent and predictable domestic information environment, which enhances the benefits of cross-listing. The results are consistent with the Market Segmentation Hypothesis by
Roosenboom and Van Dijk (
2009), which posits that cross-listing helps overcome market segmentation by accessing diverse investor bases. Political stability appears to be a crucial factor in bridging the gap between emerging and developed markets, allowing BRICS firms to access the distinct investor segments in advanced markets that value stability and predictability. The conclusion shows that improving political stability in emerging markets can significantly enhance firms’ prospects of successfully accessing advanced capital markets with higher valuation potential.
Probit regression model:
Table 5.
Empirical effect of political stability, government effectiveness, voice and accountability on the listing destinations of cross-listed firms from BRICS countries.
Table 5.
Empirical effect of political stability, government effectiveness, voice and accountability on the listing destinations of cross-listed firms from BRICS countries.
| Model 1 | Model 2 | Model 3 |
---|
Political Stability | 1.2636 *** | | |
| (1.89) | | |
Government Effectiveness | | 1.4563 *** | |
| | (6.00) | |
Voice and Accountability | | | 1.6659 *** |
| | | (0.46) |
Firm Sizes | 0.0619 | 0.0377 | 0.1053 * |
| (1.10) | (0.67) | (−0.52) |
Firm Leverage | −0.0562 | 0.0316 | 0.1204 |
| (−0.63) | (0.34) | (1.52) |
Tobin Q | 0.5821 * | 0.4511 * | 0.5856 ** |
| (1.70) | (2.31) | (1.37) |
Return on asset | 0.0674 | 0.0802 | 0.0368 |
| (0.73) | (0.81) | (−0.57) |
Constants | 0.4508 | 1.0882 * | −0.3862 |
| (0.74) | (1.68) | (1.20) |
Observation | 305 | 305 | 305 |
Log Likelihood | −121.05 | −102.25 | −106.03 |
Prob | 0.0223 | 0.0000 | 0.0000 |
Pseudo (R2) | 0.0514 | 0.1987 | 0.1692 |
4.3.2. Analysis of Findings on Government Effectiveness and the Listing Destinations of Cross-Listed Firms from BRICS Countries
Hypothesis 2 that government effectiveness perceptions in the firm’s home country are more likely to improve cross-listing adoption in an exchange market with a stronger valuation gain is analyzed using the Probit and Robit models. The outlook of the government effectiveness perception index in BRICS countries from 2000 to 2020 is presented in
Figure 2. The plot graph shows that Brazil, Russia, India and China exhibit a fluctuating range of scores between low and high values throughout the observed period. This indicates that government effectiveness in these countries has shown variations in policy enforcement and implementation.
On the other hand, South Africa stands out with consistently stronger high score values for government policy enforcement perceptions. This suggests a relatively higher level of government effectiveness in terms of policy implementation and enforcement, as compared to the other BRICS countries.
Using the probit model in model Equation (5),
Table 5 with a coefficient of 1.4563 (t = 6.00), government effectiveness in the BRICS bloc countries is positively statistically significantly correlated to the listing destinations’ acceptance of cross-listed firms. The results demonstrate that government transparency in its decision-making processes and policies in the BRICS emerging market countries is likened to cross-listing firm adoption in advanced exchange markets with a strong valuation gain on the international financial market. The strong effect of government effectiveness suggests that the ability of governments in the BRICS bloc to design, implement and enforce policies creates a market-friendly environment that enhances firm credibility in international markets. This finding corroborates (
Asongu, 2012), who highlighted a positive correlation between government quality and stock market performance. It also aligns with
Boadi and Amegbe (
2017), who demonstrated that government effectiveness improves the stock market performance of cross-listed firms.
In addition, the marginal effect in model Equation (5),
Table 6 showed that an increase in effective government policies and programme implementation in the BRICS bloc home countries increases the probabilities of destinations in developed exchange markets by 26%. This finding extends
Law and Azman-Saini’s (
2012) work by demonstrating that government effectiveness not only promotes domestic financial development, but also facilitates international market integration through cross-listing channels.
In the related robit regression model in model Equation (5),
Table 7, the alternative results showed that government effectiveness in the home country is positive and significantly related to cross-listing destinations in advanced stock exchange markets with a coefficient of 1.4563 (z = 6.00). This suggests that administrative capability, policy implementation quality and public service efficiency in BRICS nations significantly influence host markets’ receptivity to cross-listed firms. This finding is consistent with previous studies of
Asongu (
2012),
Montes and Paschoal (
2016) and
Hoai et al. (
2017), amongst others, whose findings show that the effectiveness of government policy implementation improves firm stock market development. The overall results indicate that the effective implementation of government fiscal policies and programs in emerging market countries provides incentives for firm stock market expansion in advanced exchange markets with stronger valuation gains.
These findings regarding government effectiveness in BRICS countries and its positive correlation with cross-listing acceptance in advanced markets are consistent with the Bonding Hypothesis (
Coffee, 1999,
2002). This theory posits that firms from emerging markets with weaker institutional environments seek to “bond” themselves to markets with stronger institutional frameworks. Effective government policies in BRICS countries can serve as a positive signal to international financial markets, thereby reducing information asymmetry between domestic firms and foreign investors, as suggested by Signaling Theory (
Bergh et al., 2014). When BRICS governments implement transparent and effective policies, they create an environment that narrows the information gap between local firms and international investors, addressing the fundamental challenge identified by Asymmetric Information Theory (
Bebczuk, 2003). The results also align with the Market Segmentation Hypothesis (
Roosenboom & Van Dijk, 2009), which suggests that cross-listing helps firms overcome market segmentation limitations. Government effectiveness in BRICS countries facilitates this process by creating conditions that make firms more attractive to international investors, helping them bridge the gap between segmented markets. The observed 26% increase in the probability of acceptance in developed markets demonstrates how effective government policies reduce barriers to market integration, allowing firms to access more liquid and developed financial markets (
Geranio, 2012).
Post-estimation test: Marginal effect
Table 6.
Marginal effect of political stability, government effectiveness, voice and accountability on listing destinations of cross-listed firms from BRICS countries.
Table 6.
Marginal effect of political stability, government effectiveness, voice and accountability on listing destinations of cross-listed firms from BRICS countries.
| Model 1 | Model 2 | Model 3 |
---|
Political Stability | 0.2463 *** | | |
| (3.69) | | |
Government Effectiveness | | 0.2565 *** | |
| | (5.52) | |
Voice and Accountability | | | 0.2833 *** |
| | | (0.46) |
Firm Sizes | 0.0127 | 0.0067 | 0.0179 * |
| (1.11) | (0.67) | (−0.52) |
Firm Leverage | −0.0118 | 0.0055 | 0.0204 |
| (−0.63) | (0.34) | (1.52) |
Tobin Q | 0.1225 | 0.4511 * | 0.0996 ** |
| (2.47) | (1.70) | (1.37) |
Return on asset | 0.0142 | 0.0795 | 0.0063 |
| (0.72) | (0.81) | (0.37) |
Pr(cld) | 0.871 | 0.0141 | 0.1692 |
4.3.3. Analysis of Findings on Voice and Accountability and Listing Destinations of Cross-Listed Firms from BRICS Countries
The results of Hypothesis 3 that the voice and accountability perceptions in the firm’s home country are likely to facilitate listing destination acceptance of cross-listing firm in an exchange market with a higher valuation gain using the probit and robit models. This study provides the outlook of the voice and accountability perception index in the BRICS countries in
Figure 3, from 2000 to 2020. The voice and accountability perception index plot on the graph shows that Brazil, India and South Africa maintain stronger high scores. Brazil, India and South Africa consistently exhibit stronger scores, indicating a relatively higher level of voice and accountability perception in the firm’s home market throughout the observed period. This suggests that individuals and groups in these countries perceive a greater ability to choose their government, freedom of expression, association and access to a free press.
On the other hand, Russia and China consistently maintain low scores on the voice and accountability perception index. This suggests a lower level of voice and accountability in the domestic markets of cross-listed firms from these countries. This implies limited citizen participation in governance, restrictions on freedom of expression, and limited access to independent media.
The results demonstrate a positive and statistically significant relationship between voice and accountability in BRICS countries and the likelihood of their firms cross-listing on advanced stock exchanges (coefficient = 1.6659, t = 0.46) in model Equation (6),
Table 5. This suggests that a greater degree of freedom of expression, association and access to a free press in these emerging economies increase the propensity of their firms to seek listings in more developed international financial markets. This finding is consistent with
Asongu (
2012), which links voice and accountability to positive stock market performance.
The marginal effect in model Equation (6),
Table 6 indicates that a 1% increase in perceived voice and accountability in the home country is associated with a 28% increase in a firm’s probability of listing on an advanced exchange. The marginal effect strongly supports Hypothesis 3, confirming that voice and accountability facilitates listing destinations in exchanges with higher valuation gains. This aligns with
Ojeka et al. (
2019), who found that voice and accountability significantly contribute to firm financial performance.
Additionally, robustness checks using a robit analysis in model Equation (6),
Table 7 corroborate this positive and significant association (coefficient = 1.9356, z = 0.46). This reinforces the notion that freedom of expression, association and a free press promote transparency and a reliable information flow that improve firm credibility in international markets, facilitating access to prestigious exchange listings, consistent with
Asongu (
2012) and
Ojeka et al. (
2019).
Theoretically, this positive relationship suggests that BRICS firms originating from countries with stronger voice and accountability mechanisms are better equipped to meet the stringent disclosure and governance requirements of major international exchanges (
Coffee, 1999,
2002). From a Signaling Theory perspective (
Spence, 2002), higher voice and accountability in the home country enhances the credibility of the signals conveyed by cross-listing firms regarding their quality. The resulting transparent information environment reduces information asymmetry (
Fernandes & Ferreira, 2008), complementing enhanced monitoring and disclosure in host markets and improving information flow about cross-listed firms. Consequently, greater voice and accountability may enable BRICS firms to overcome market segmentation by increasing their attractiveness to investors in advanced markets who prioritize transparency and good governance (
Roosenboom & Van Dijk, 2009). The conclusion shows that stronger freedom of expression, association and media access not only foster transparency but also enhance firm credibility, facilitating access to prestigious listing exchange markets for an improved valuation outcome.
These overall findings indicate that voice and accountability in the home country contribute to firm financial expansion in advanced stock exchange markets. This aligns with previous studies by (
Torgler et al., 2011) and
Ojeka et al. (
2019), which underscore the significance of voice and accountability for financial market development. In support of the Market Segmentation Hypothesis (MSH), this showed that freedom of expression, association and access to a free press in the home country help emerging market cross-listing firms to overcome market segmentation.
The firm-specific control variable results demonstrate that the firm performance proxies with Tobin Q are positive and significantly associated with the listing destinations’ adoption of cross-listed firms from the BRICS bloc using the probit and robit regression models for all the models estimated in
Table 5,
Table 6 and
Table 7 respectively. This suggests that the market-based performance of cross-listing firms from the emerging market countries improves the firm listing destination acceptance on the foreign financial exchange market when seeking expansion. This supports the Receiver Operating Characteristic (ROC) graph in
Figure 4 that shows political stability (AUC = 0.7285), government effectiveness (AUC = 0.8022) and voice and accountability (AUC = 0.7998) as having moderate predictive power in distinguishing cross-listing destination choices, suggesting that it meaningfully influences firms’ decisions to seek stable markets. However, the sub-optimal AUC highlights that additional institutional variables or firm-level factors (e.g., firm performance market, firm size) likely complement predictor variables in explaining cross-listing destinations for higher valuation. This result is similar to
Roosenboom and Van Dijk (
2009), who stated that firm-specific information is important for the stock market development of cross-listed firms.
Robit regression model (Robustness)
Table 7.
Empirical effect of political stability, government effectiveness, voice and accountability on listing destinations of cross-listed firms from BRICS countries.
Table 7.
Empirical effect of political stability, government effectiveness, voice and accountability on listing destinations of cross-listed firms from BRICS countries.
| Model 1 | Model 2 | Model 3 |
---|
Political Stability | 1.3278 *** | | |
| (3.97) | | |
Government Effectiveness | | 1.6055 *** | |
| | (5.38) | |
Voice and Accountability | | | 1.9356 *** |
| | | (0.46) |
Firm Sizes | 0.0085 | 0.0087 | 0.0811 |
| (0.15) | (0.17) | (−0.52) |
Firm Leverage | −0.0093 | −0.0029 | 0.0004 |
| (−1.70) | (−0.46) | (1.52) |
Tobin Q | 0.4170 * | 0.3789 ** | 0.3928 *** |
| (2.93) | (2.78) | (1.37) |
Return on asset | −0.0041 | −0.0039 * | −0.0028 |
| (0.56) | (−0.534) | (−0.57) |
Constants | 0.7127 | 1.4589 ** | 0.0342 |
| (2.46) | (1.18) | (1.20) |
Observation | 305 | 305 | 305 |
Log Likelihood | −137.13 | −117.35 | −121.55 |
Prob | 0.0348 | 0.0000 | 0.0000 |
Wald chi2(5) | 11.996 | 40.839 | 25.116 |
The probit estimations with Log Likelihood (LL) values across the models range from −121.05 to −102.25. Log Likelihood is a measure of model fit; higher (less negative) values indicate better model performance. This suggests that political stability, government effectiveness and voice and accountability variables in the better-performing model explain the cross-listing location adoption more effectively. The likelihood sensitivity (Lsens) in
Figure 4 shows political stability, government effectiveness and voice and accountability variables and the cross-listing location adoption. The Lsens graph reveals that government effectiveness significantly predicts cross-listing decisions. At a 0.5 cut-off, the model achieves 75% sensitivity and 80% specificity, indicating reliable classification. The BRICS policymakers in emerging markets should prioritize governance reforms to improve cross-listings, while foreign exchange markets can use lower cut-offs (0.25) to identify cross-listing firms. The Lsens graph comparing political stability’s effect on cross-listing destinations reveals a trade-off; higher probability cut-offs (0.75) prioritize specificity, while lower cut-offs (0.25) favor sensitivity (detecting cross-listings to developed markets). This implies that political stability is a stronger predictor of firms remaining in emerging markets, whilst its role in enabling cross-listings requires balancing sensitivity with increased false positives. Additionally, the Lsens graph indicates that higher probability cut-offs (e.g., 0.75) maximize sensitivity (1.00), prioritizing the accurate identification of cross-listing decisions linked to strong voice and accountability, albeit with reduced specificity. This implies that a robust voice and accountability variable strongly predicts cross-listing location behavior.
The pseudo-R2 values used to gauge the explanatory power of probit models range from 0.0514 and 0.1987. Although pseudo-R2 values are generally lower than traditional R2 values in OLS regressions, they still provide useful comparative information on model fit. Model 2 (Government Effectiveness) again stands out with the highest Pseudo-R2 value of 0.1987, meaning that approximately 19.87% of the variation in listing destination choices can be explained by this model. This further supports the significant role that perceived government effectiveness plays in attracting cross-listings to developed or emerging markets. The second-highest pseudo-R2 appears in Model 3 (Voice and Accountability) with 0.1692, while the lowest value is seen in Model 1 (Political stability and the absence of violence) at 0.0514, implying minimal explanatory power. The relatively high log likelihood and Rseudo-R2 values of models 2 and 3 highlight Government Effectiveness and Voice and Accountability as the most influential governance dimensions affecting cross-listing destination decisions.
The Wald Chi-squared (χ2) statistic tests the joint significance of all explanatory variables in the Robit model robustness check. A higher χ2 value with a statistically significant p-value (typically less than 0.05) indicates that the model as a whole provides a good fit and that at least one predictor variable has a statistically significant impact on the dependent variable. The substantially higher chi-square values for Models 2 and 3 compared to Model 1 suggest that government effectiveness and voice and accountability have stronger associations with cross-listing destinations than political stability, although all three governance indicators show statistically significant relationships.
The probit and robit model post-estimation model test of Akaike’s Information Criterion (AIC) and Bayesian information criterion in
Table 8 suggest that political stability, government effectiveness and voice and accountability are significant predictors in the respective models, as indicated by the substantial improvement in log-likelihood from the null model and the relatively lower AIC and BIC values for the full models compared to the null.
5. Conclusions and Recommendations
This study examined the role of political stability, government effectiveness, and voice and accountability in cross-listing destination premiums using data from BRICS cross-listed firms between 2000 and 2020. The study employs generalized linear models (GLMs), including probit and robit specifications to analyze this relationship.
The results highlight that political stability, government effectiveness, and voice and accountability in the home country significantly influence the adoption of cross-listing in foreign financial markets, particularly in developed exchange markets with higher valuation gains. Countries with stable political climates, effective implementation of government policies, and freedom of expression are more favorably positioned to enable their firms to access capital market valuation premiums through cross-listing. The findings suggest that improvements in political stability, government effectiveness, and voice and accountability in BRICS countries can create positive externalities for domestic firms seeking international capital market expansion. A strong institutional environment can generate substantial economic benefits for domestic firms, with implications for aggregate capital flows and economic development in BRICS countries.
Firms domiciled in countries with stronger political stability, government effectiveness, and voice and accountability mechanisms are better positioned to access developed capital markets and capture substantial valuation premiums. This implies that managers should consider home country governance quality as a strategic factor when timing cross-listing decisions and selecting target exchanges. This study contributes to the growing literature on emerging market finance by providing systematic evidence on how home country political stability, government effectiveness, and voice and accountability heterogeneity within the BRICS bloc influences international capital market access in host markets.
While the study focuses solely on cross-listed firms from BRICS countries, this potentially limits the applicability of the findings to firms from other emerging market countries and BRICS + countries. Firstly, due to the focus on BRICS countries, the findings may not be applicable to cross-listed firms from other emerging market countries. Future studies could broaden the scope to include cross-listed firms from a wider range of emerging market economies to enhance the generalizability of the results. Secondly, while firm control variables such as firm size, firm leverage and firm performance are included in the study, other uncaptured firm-level and industrial factors may yield different results. Future studies could employ other control variables that may influence cross-listing destination premiums. Thirdly, this study employed various econometric methods such as probit and probit models to achieve the study objectives. The choice of econometric methods (probit and robit) provides robustness but also limits the scope of the analysis. Different methods or model specifications could potentially yield varying results, and the study’s conclusions are contingent on the chosen methodologies. Future research could explore alternative methodologies to validate or complement the findings. Finally, while the study empirically observed the role of political stability, government effectiveness, and voice and accountability on cross-listing destination premium, future studies could consider other governance indicators such as the rule of law and regulatory quality, amongst others.