Table A1 Variables definitions Data...

Table A1

Variables definitions

		Data source
Dependent variable in the main model
ESG	Natural logarithm of Huazheng Environmental, Social, and Governance (ESG) composite scores The Huazheng rating system uses a hierarchical aggregation system to compute the composite scores using over 80 fourth-tier performance indicators with over 300 underlying data points that supports 44 third-tier key issues and 16 second-tier themes across the E, S, and G dimensions (also known as the first-tier pillars). Each indicator is assigned a score of 0 (lowest) to 100 (highest) and multiplied by an industry-specific weight before being hierarchically aggregated into a score for each dimension. The final composite score is the weighted average score from the three dimensions	Huazheng (WIND database)
Test variable in the main model
PPI	The average of PPI_INV assigned to each director based on their nationality. PPI_INV is the raw positive peace overall score of a country each year multiplied by minus one. A higher PPI indicates higher average positive peace level on board. The raw positive peace overall score comprises eight pillars: free flow of information, sound business environment, equitable distribution of resources, high levels of human capital, good relations with neighbors, acceptance of the rights of others, well-functioning government, and low levels of corruptions (IEP, 2019)	Directors’ nationality was manually collected from corporate annual reports and website PPI was obtained from the IEP.
Control variables in the main model
Duality	Dichotomous variable equals 1 if the CEO serves as the Chairperson of the board of directors, and 0 otherwise	CSMAR
B_Size	Natural logarithm of the number of directors on board	CSMAR
B_Female	Proportion of female directors on board	CSMAR
B_Ind	Proportion of the number of independent directors on board	CSMAR
Firm_Size	Natural logarithm of total assets	CSMAR
Firm_Age	Natural logarithm of the number of years since firm was listed	CSMAR
ROA	Net income divided by total assets	CSMAR
LEV	Total debts divided by total assets	CSMAR
Slack	Free cash flows scaled by total assets	CSMAR
Exchange	Dichotomous variable equals 1 if the firm is listed on the Shenzhen Stock Exchange, and 0 if it is listed on the Shanghai stock exchange	CSMAR
Foreign	Foreign sales as a percentage of total sales	CSMAR
SOE	Dichotomous variable equals 1 if firm is a state-owned entity, and 0 otherwise	CSMAR
Top1	Shareholding ratio of the largest shareholder	CSMAR
Media	Natural logarithm of the number of news articles about a focal firm in a given year plus 1	CNRDS
Other variables used in tabulated additional and robustness analyses
ProdQuality	Total strengths minus total concerns in the product quality category	CNRDS
CommunityDev	Total strengths minus total concerns in the community development category	CNRDS
DivPrac	Total strengths minus total concerns in the diversity practices category	CNRDS
CorpGov	Total strengths minus total concerns in the corporate governance category	CNRDS
EmpRelations	Total strengths minus total concerns in the employee relations category	CNRDS
EnvProtection	Total strengths minus total concerns in the environmental protection category	CNRDS
GI	The annual Government and Market Relationship (GMR) Index is computed using three factors: (1) the ratio of provincial government revenue to its GDP; (2) the ratio of average time spent by a firm’s manager in dealing with government to their weekly average working hours; and (3) the ratio of government employees to provincial population (Wang et al., 2018a). GMR index is constructed in the way that a higher value captures a lower degree of government intervention in the local economy, hence we take the inverse value of GMR to proxy local government intervention (GI). Firms operating in a province with GI above (below) yearly median are assumed to subject to high (low) local government intervention	Wang et al. (2018a)
CEO_Power	A CEO Power Index estimated based on the average value of the following eight variables that capture four CEO power dimensions (Wu et al., 2011)	CSMAR, the Wind Economic (WIND) database
	(i) CEO structural power proxied by two dichotomous variables equal 1, if the CEO also serves as the Chairperson or the CEO is an inside director, and 0 otherwise
	(ii) CEO expert power proxied by two dichotomous variables equal 1, namely if the CEO has a professional certificate or the CEO tenure is longer than the median tenure of industry, and 0 otherwise
	(iii) CEO ownership power proxied by two dichotomous variables equal 1, namely if the CEO has shareholdings of the firm or if the institutional shareholding of the focal firm is the below the industry median, and 0 otherwise
	(iv) CEO prestige power proxied by two dichotomous variables equal 1, if the CEO holds at least a master’s degree or if the CEO has outside job opportunities (e.g. sitting on multiple boards)
	Firms with above (below) industry median of CEO_Power are deemed to exhibit high (low) CEO power
B_Meet	Higher versus lower board meeting frequency is determined by its industry-median	CSMAR
Policy	The number of years since the foreign talent policy was adopted in the province in which the Chinese firms are headquartered	Manual collection of the policy adoption years for each province from the official provincial government website
Predicted_PPI	Predicted value of PPI obtained from the first stage of two-stage least squares regression	See PPI
PPI_Alt	The sum of the “relative PPI_INV differences” between foreign directors’ home countries and China, scaled by the number of directors. The “relative PPI_INV difference” is computed by subtracting the PPI_INV of foreign directors’ home countries from the PPI_INV of China, then divided by the PPI_INV of foreign directors’ home countries. A higher PPI_Alt indicates higher average positive peace level on board	See PPI
ESG_Alt1	Natural logarithm of the LSEG ESG rating score	LSEG
ESG_Alt2	Natural logarithm of the CNRDS ESG rating score	CNRDS
PDI	The average of PDI assigned to each director based on their nationality. Power distance (PDI) reflects the degree of society’s acceptance about unequal power distribution	Hofstede’s official website
IDV	The average of IDV assigned to each director based on their nationality. IDV reflects the degree to which individuals prioritize own interests (i.e. individualism) as opposed to group interests (i.e. collectivism)	Hofstede’s official website
MAS	The average of MAS assigned to each director based on their nationality. MAS captures the degree to which a society is characterized with masculine values such as aggressiveness and competition for material outcomes, as opposed to feminine values such as modesty, equality and cooperation	Hofstede’s official website
UAI	The average of UAI assigned to each director based on their nationality. Uncertainty avoidance (UAI) reflects the extent to which a society resists uncertainty and ambiguity	Hofstede’s official website
B_Cultural_Diversity	The average of Hofstede cultural distance scores in all pairs of board directors for firm i calculated based on Frijns et al. (2016) and Luo et al. (2021). To calculate the cultural distance scores, each director is assigned with the four Hofstede’s cultural dimensions’ scores based on their nationality: (1) power distance; (2) individualism versus collectivism; (3) masculinity versus femininity; and (4) uncertainty avoidance	Hofstede’s official website, Frijns et al. (2016), Luo et al. (2021)
	The following formula is used to estimate the distance for each cultural dimension between directors
	CDT_ij = $\sqrt{\sum_{k = 1}^{6} {[(I_{k i} - I_{k j})}^{2} / V_{k}]} \forall i \neq j$
	where I_ki is the score on cultural dimension k for a director i, and I_kj is the score on cultural dimension k for a director j. V_k is the in-sample variance of the specific cultural dimension score. CDT_ij captures the distance of specific cultural dimension between each two directors (i, j)
	Firm-level board cultural diversity (B_Cultural_Diversity) is then measured with the average of cultural distances in all pairs of board directors using the following formula
	B_Cultural_Diversity_nt = $\frac{\sum_{i, j} {C D T}_{i j, n t}}{m (m - 1) / 2} \forall i < j$
	Where B_Cultural_Diversity_nt reflects the board cultural diversity of firm n in year t, and m denotes the number of board members. CDT is scaled by the number of pairs of board members to normalize for the board size

		Data source
Dependent variable in the main model
ESG	Natural logarithm of Huazheng Environmental, Social, and Governance (ESG) composite scoresThe Huazheng rating system uses a hierarchical aggregation system to compute the composite scores using over 80 fourth-tier performance indicators with over 300 underlying data points that supports 44 third-tier key issues and 16 second-tier themes across the E, S, and G dimensions (also known as the first-tier pillars). Each indicator is assigned a score of 0 (lowest) to 100 (highest) and multiplied by an industry-specific weight before being hierarchically aggregated into a score for each dimension. The final composite score is the weighted average score from the three dimensions	Huazheng (WIND database)
Test variable in the main model
PPI	The average of PPI_INV assigned to each director based on their nationality. PPI_INV is the raw positive peace overall score of a country each year multiplied by minus one. A higher PPI indicates higher average positive peace level on board. The raw positive peace overall score comprises eight pillars: free flow of information, sound business environment, equitable distribution of resources, high levels of human capital, good relations with neighbors, acceptance of the rights of others, well-functioning government, and low levels of corruptions (IEP, 2019)	Directors’ nationality was manually collected from corporate annual reports and websitePPI was obtained from the IEP.
Control variables in the main model
Duality	Dichotomous variable equals 1 if the CEO serves as the Chairperson of the board of directors, and 0 otherwise	CSMAR
B_Size	Natural logarithm of the number of directors on board	CSMAR
B_Female	Proportion of female directors on board	CSMAR
B_Ind	Proportion of the number of independent directors on board	CSMAR
Firm_Size	Natural logarithm of total assets	CSMAR
Firm_Age	Natural logarithm of the number of years since firm was listed	CSMAR
ROA	Net income divided by total assets	CSMAR
LEV	Total debts divided by total assets	CSMAR
Slack	Free cash flows scaled by total assets	CSMAR
Exchange	Dichotomous variable equals 1 if the firm is listed on the Shenzhen Stock Exchange, and 0 if it is listed on the Shanghai stock exchange	CSMAR
Foreign	Foreign sales as a percentage of total sales	CSMAR
SOE	Dichotomous variable equals 1 if firm is a state-owned entity, and 0 otherwise	CSMAR
Top1	Shareholding ratio of the largest shareholder	CSMAR
Media	Natural logarithm of the number of news articles about a focal firm in a given year plus 1	CNRDS
Other variables used in tabulated additional and robustness analyses
ProdQuality	Total strengths minus total concerns in the product quality category	CNRDS
CommunityDev	Total strengths minus total concerns in the community development category	CNRDS
DivPrac	Total strengths minus total concerns in the diversity practices category	CNRDS
CorpGov	Total strengths minus total concerns in the corporate governance category	CNRDS
EmpRelations	Total strengths minus total concerns in the employee relations category	CNRDS
EnvProtection	Total strengths minus total concerns in the environmental protection category	CNRDS
GI	The annual Government and Market Relationship (GMR) Index is computed using three factors: (1) the ratio of provincial government revenue to its GDP; (2) the ratio of average time spent by a firm’s manager in dealing with government to their weekly average working hours; and (3) the ratio of government employees to provincial population (Wang et al., 2018a). GMR index is constructed in the way that a higher value captures a lower degree of government intervention in the local economy, hence we take the inverse value of GMR to proxy local government intervention (GI). Firms operating in a province with GI above (below) yearly median are assumed to subject to high (low) local government intervention	Wang et al. (2018a)
CEO_Power	A CEO Power Index estimated based on the average value of the following eight variables that capture four CEO power dimensions (Wu et al., 2011)	CSMAR, the Wind Economic (WIND) database
	(i) CEO structural power proxied by two dichotomous variables equal 1, if the CEO also serves as the Chairperson or the CEO is an inside director, and 0 otherwise
	(ii) CEO expert power proxied by two dichotomous variables equal 1, namely if the CEO has a professional certificate or the CEO tenure is longer than the median tenure of industry, and 0 otherwise
	(iii) CEO ownership power proxied by two dichotomous variables equal 1, namely if the CEO has shareholdings of the firm or if the institutional shareholding of the focal firm is the below the industry median, and 0 otherwise
	(iv) CEO prestige power proxied by two dichotomous variables equal 1, if the CEO holds at least a master’s degree or if the CEO has outside job opportunities (e.g. sitting on multiple boards)
	Firms with above (below) industry median of CEO_Power are deemed to exhibit high (low) CEO power
B_Meet	Higher versus lower board meeting frequency is determined by its industry-median	CSMAR
Policy	The number of years since the foreign talent policy was adopted in the province in which the Chinese firms are headquartered	Manual collection of the policy adoption years for each province from the official provincial government website
Predicted_PPI	Predicted value of PPI obtained from the first stage of two-stage least squares regression	See PPI
PPI_Alt	The sum of the “relative PPI_INV differences” between foreign directors’ home countries and China, scaled by the number of directors. The “relative PPI_INV difference” is computed by subtracting the PPI_INV of foreign directors’ home countries from the PPI_INV of China, then divided by the PPI_INV of foreign directors’ home countries. A higher PPI_Alt indicates higher average positive peace level on board	See PPI
ESG_Alt1	Natural logarithm of the LSEG ESG rating score	LSEG
ESG_Alt2	Natural logarithm of the CNRDS ESG rating score	CNRDS
PDI	The average of PDI assigned to each director based on their nationality. Power distance (PDI) reflects the degree of society’s acceptance about unequal power distribution	Hofstede’s official website
IDV	The average of IDV assigned to each director based on their nationality. IDV reflects the degree to which individuals prioritize own interests (i.e. individualism) as opposed to group interests (i.e. collectivism)	Hofstede’s official website
MAS	The average of MAS assigned to each director based on their nationality. MAS captures the degree to which a society is characterized with masculine values such as aggressiveness and competition for material outcomes, as opposed to feminine values such as modesty, equality and cooperation	Hofstede’s official website
UAI	The average of UAI assigned to each director based on their nationality. Uncertainty avoidance (UAI) reflects the extent to which a society resists uncertainty and ambiguity	Hofstede’s official website
B_Cultural_Diversity	The average of Hofstede cultural distance scores in all pairs of board directors for firm i calculated based on Frijns et al. (2016) and Luo et al. (2021). To calculate the cultural distance scores, each director is assigned with the four Hofstede’s cultural dimensions’ scores based on their nationality: (1) power distance; (2) individualism versus collectivism; (3) masculinity versus femininity; and (4) uncertainty avoidance	Hofstede’s official website, Frijns et al. (2016), Luo et al. (2021)
	The following formula is used to estimate the distance for each cultural dimension between directors
	CDT_ij = $\sqrt{\sum_{k = 1}^{6} {[(I_{k i} - I_{k j})}^{2} / V_{k}]} \forall i \neq j$
	where I_ki is the score on cultural dimension k for a director i, and I_kj is the score on cultural dimension k for a director j. V_k is the in-sample variance of the specific cultural dimension score. CDT_ij captures the distance of specific cultural dimension between each two directors (i, j)
	Firm-level board cultural diversity (B_Cultural_Diversity) is then measured with the average of cultural distances in all pairs of board directors using the following formula
	B_Cultural_Diversity_nt = $\frac{\sum_{i, j} {C D T}_{i j, n t}}{m (m - 1) / 2} \forall i < j$
	Where B_Cultural_Diversity_nt reflects the board cultural diversity of firm n in year t, and m denotes the number of board members. CDT is scaled by the number of pairs of board members to normalize for the board size

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