Does Female Leadership Lead to Better Economic and Social Development? Evidence from Local Chinese Governments

Does Female Leadership Lead to Better Economic and Social Development? 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Evidence from Local Chinese Governments Qian Guo, Yanchuan Teng, Zhixing Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4316230/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract What are the consequences of having a female leader for economic and political outcomes? This study investigates the impact of female leadership on policy outcomes at the local government level in China. Based on institutional theory and role theory, we investigate the impacts of women’s service as Chinese Communist Party (CCP) secretaries or government mayors on local economic growth and social policies. Based on municipal economic performance, government financial expenditures, and demographic data from the period between 1995 and 2015 in China, the results suggest that the influence of gender roles is eliminated when women are involved in leading local economic development, which receives strong institutional pressure. In contrast, female leadership is more conducive to social development, which is more likely to be subject to a leader’s discretion. Female leaders may not ensure better economic growth than male leaders, as indexed by GDP growth rates, but they usually produce more balanced and welfare-oriented development, as measured by higher expenditureson social security and employment. Leadership and Ethics Female Leadership Institutional Approach Role Theory Chinese Government Economic Growth Social Policy. Figures Figure 1 Figure 2 Introduction After decades of considerable legislative, social, and policy efforts, there are undoubtedly more female leaders at present than at any other time in world history. According to the IPU-UN Women’s Map of Women in Politics (International Parliamentary Union, 2019), female representation in political leadership positions has followed an upward trend in recent decades. Records indicate that at the beginning of 2020 worldwide, 20 women held the highest national office as presidents or prime ministers, and the proportion of female ministers was at an all-time high of 20.7 percent (812 out of 3,922). The growing proportion of women in politics has attracted attention from multidisciplinary scholars who want to know the implications of female leadership for public policy outcomes (Coscieme et al., 2020 ; Lemoine, & Blum, 2021 ). Although the representation of females in public administration is increasing, the inescapable reality is that female leaders in public office remain scarce and conspicuous by their absence (Mendelberg and Karpowitz, 2016 ). Research from economics and political science investigates whether electing female leaders indicates different policy and political outcomes; however, the results are quite mixed and controversial. According to the classic work of Downs ( 1957 ), the preferences of male and female leaders should not influence policy outcomes, since politicians would converge their policy to cater to the preferences of median voter. This perspective is supported by some empirical studies. For example, Ferreira and Gyourko (2014) found that gender does not matter to political leadership. After analyzing a novel dataset of U.S. mayoral elections from 1950 to 2005, they found no effect of gender on policy outcomes, such as the size of local government, the composition of municipal spending and employment, or crime rates. Another line of research from developmental economics, however, shows that female politicians affect policy outcomes. Chattopadhyay and Duflo’s (2004) research found that female participation in rural governance in India resulted in increased expenditures in areas such as public investments in providing clean water. Their study is echoed by similar studies that found that female politicians affect policy outcomes regarding education (Beaman et al., 2012 ; Clots-Figueras, 2012 ), healthcare (Besley and Case, 2003), forest conservation (Leone, 2019 ), and child-related spending (Clots-Figueras, 2012 ). Recently, some scholars found that states with women leaders had fewer deaths during the COVID-19 crisis than states with male leaders, and this female leadership impact was not only found in the U.S. (Sergent and Stajkovic, 2020 ) but also confirmed worldwide (Garikipati and Kambhampati, 2020). These inconsistent results regarding female leadership in the public sector indicate that more perspectives are welcome in this field of study. More importantly, the existing studies mainly investigate whether female leadership matters but lack of explaining the underline mechanism how female leadership influence the political outcomes. The role of female leadership in public administration still lacks theoretical and empirical frameworks through which to fully understand the impact of female leadership in public policy making. In the field of management, although we can learn from the literature on female directors on corporate boards, the impact of female leadership beyond the scope of private organizations has only recently attracted limited attention (Abdullah, Ismail, and Nachum, 2016 ; Guy and Meier, 2016; Leone, 2019 ). How female leadership in government influence the policy outcomes remain a black box. In this article, we aim to answer this question by combining the two competing approaches from management and organizational studies. The first is institutional approach that emphasizes that organizations can constrain decision makers (March and Olsen, 1989 ; Peters, 2000). Institutional features such as routines, procedures, and conventions mould individuals’ values and working styles. To a certain extent, intuitions can direct or indirect shape and guide individual behaviors, so that gender on its own is an insufficient explanation for possible different political outcomes. The impact of formal organizational role in intuitions eclipse the possible influence of gender role. Another perspective from social roles theory argues that leaders’ behaviors are defined by their position in an organization as well as function under the constraints of their gender role (Eagly, 1997 ; Eagly, Karau, and Makhijani, 1995; Chrobot-Mason, Hoobler, and Burno, 2019). Although some gender-stereotypic differences vanish under the influence of institutional constrains, others do not vanish. Research on social roles theory (e.g., Eagly and Karau, 2002 ) suggests that people attribute more communal characteristics, such as helpfulness, concern for others, kindness and gentleness, to females and agentic characteristics, like assertive, competitive, and decisive to males. People respond to a leader with gender stereotypic expectation, and leaders may internalize gender roles to some extent due to social norm conformity (Lafferty, Phillipson, and Jacobs, 2019; Wood et al., 1997). As a result, male and female leaders may form different social identities and different role perceptions in an organizational setting. When the intuitional constrains are not strong and leaders have discretion, organizational behavior that may vary according to gender. To test these hypotheses, we focus on female mayors and secretaries in China and answers the major question of whether or not having women as mayors or secretaries at the prefecture level have different impact on public policy-making and local development. We used the publicly available data of prefecture-level cities’ economic performance, government financial expenditures, and demographic data from the period between 1995 and 2015 in China. China is a suitable research context to study how an organization contains and shapes the role of female leadership effectiveness since the state bureaucracy has established different performance and reward systems for government leaders who are held accountable for implementing the state’s economic development and social development goals (Zhou, 2010 : 19; Wu et al., 2013). Leading economic development is the vital factor that determines Chinese government leaders’ career development within the bureaucracy, and promoting social development is difficult to quantify and usually receives less attention from government leaders. China’s dramatic economic and social development in recent decades makes it an ideal empirical context to test our framework. The heterogeneity and high-speed development in China allow us to examine the impact of female leadership on economic growth and social development. In addition, the Chinese Communist Party (CCP) and the government have tremendous involvement in the country’s economic and societal development (Zhou, 2007 ), which grants politicians great discretion and a high impact within their jurisdiction. Furthermore, the Chinese culture has rigid gender role norms and emphasizes separate roles for men and women (Hofstede, 1980 ); this culture provides an ideal context to test the influence of gender roles. Our results suggest that the impact of gender disappears when leading economic growth but exists in promoting social development. Female leaders may not ensure better economic growth than male leaders, as indexed by GDP growth rates, but they usually produce more balanced and welfare-oriented development, as measured by higher expenditures on social security and employment. Our article contributes to the female leadership literature in several key ways. First, the present study contributes to the unsolved institutional-individual debate, that is, which is more important in shaping organizational behavior (Christensen and Lægreid, 2018)? Second, this study joins in the increasing number of studies that bridge the micro-macro divide by relating a micro predictor (leader’s gender) to macro outcomes (economic performance and expenditures on social security and employment). This cross-level approach answers the call for conducting more studies that serve to integrate different levels of analysis in gender and leadership studies (Hoobler et al., 2018; Sergent and Stajkovic, 2020 ). Third, we extend the literature on female leadership with empirical evidence from an important nondemocratic society, and this is an important strength of this study. The existing research on female leadership mainly focuses on Western countries. However, we need to know much more about the impact of women in leadership roles outside of the Western, educated, industrialized, rich, and democratic (WEIRD) countries. In addition, we complement the theoretical knowledge and empirical evidence regarding role theory by extending its context to public administration. Our study examines the impact of female leadership on public organizations and demonstrates that gender role effect is also important at the political level. The rest of the paper is organized as follows. Section 2 presents the theoretical framework and the formulated hypotheses. Section 3 describes the data and the sample used for the analysis and provides some descriptive statistics of the relevant variables. Section 4 reports the results and provides robustness checks. Finally, Section 5 contains a discussion and future research directions. Literature Review Before establishing the relationships between female leadership and political outcomes, we discuss the findings of the literature on women in politics and the Chinese context, and then, we provide the theoretical framework of this study. Women in Politics Although women are still underrepresented at senior levels in both the public and economic sectors, there are currently more female leaders globally than at any other time in history. This dramatic change has drawn attention from economists, managers, policy analysts, and other social scientists who aim to understand the implications of women in public appointments. Classic political science theory suggests that there will be no changes in policy once a woman takes office, since all politicians care only about winning elections (e.g., Downs, 1957 ). Democracy and median voters force women and men to the center of policy spaces and offer similar platforms. In this case, the impact of gender should not matter since women and men need to cater to the preferences of median voters. This argument is supported by some empirical studies. For example, Ferreira and Gyourko (2014) used a dataset of U.S. mayoral elections from 1950 to 2005 and found no effect of mayors’ gender on policy outcomes related to the size of the local government, the composition of municipal spending and employment, or crime rates. However, other research evidence suggests that gender does matter in political leadership. Chattopadhyay and Duflo (2004) used political reservations for women in India to study the impact of women’s leadership on policy and decision making; they found that an increase in female participation resulted in increased expenditures in areas such as public investments in providing clean water. Chattopadhyay and Duflo’s research is echoed by similar studies that found that female politicians affect policy outcomes regarding education (Beaman et al., 2012 ; Clots-Figueras, 2012 ), healthcare (Besley and Case, 2003), forest conservation (Leone, 2019 ), and child-related spending (Clots-Figueras, 2012 ). These results suggest that the contexts in which women function in terms of political leadership influence the impact of gender on policy and political outcomes. In India, for example, clean drinking water is important to the needs of both adults and their children. Female leaders are often more knowledgeable about areas related to childcare and thus devote themselves to providing clean water to gain votes from other women. For the most part, however, this existing literature only examines the performance of women in democratic societies and is limited to electoral politics. We know little about the impact of women in leadership roles in nondemocratic societies, such as China. In the next sections, we first introduce the Chinese context and then propose a theory of female leadership that connects role congruity and female leaders’ impact on political outcomes. The State’s Different Goals and Female Leaders in China Since its reform and opening up in 1978, China’ development strategy has focused on economic development. As part of the market reform, the central government has built an effective state bureaucracy to bolster economic growth (Li, 1998 ; Bo, 2004 ). In this system, the central government makes short- and long-term development plans and allows local governments to compete with one another for resources and prosperity. This strategy has enabled China to evolve over the past four decades from a marginal economic player to the second-largest economy in the world. Economic development is an imperative goal for the government (Choi, 2012 ; Zhou, 2007 ). In fact, as such an important government objective, economic growth even comes at the cost of rising social inequality, environmental pollution, and corruption (Nie, Jiang, and Wang, 2013 ). The campaign for “Building a Harmonious Socialist Society” of former President Hu Jingtao in 2004 demonstrates that the state has come to consider maintaining social stability as important as sustaining economic growth (Wang, 2015 ). The state bureaucracy has established development goals and evaluation and promotion criteria for government leaders, who are held accountable for implementing the state’s dual goals. Government leaders’ performance is evaluated through the following three categories of indicators: hard targets, which refer to economic indicators, such as the GDP growth rate; soft targets, which include social welfare indicators, such as improving education, providing health care, and alleviating environmental damage; and targets with veto power, which are political tasks, including maintaining social stability (Edin, 2003 ). Hard targets are the most important factors that determine Chinese government leaders’ career development within the bureaucracy. In contrast, soft targets are difficult to quantify and are not immediately related to career advancement; therefore, they usually receive less attention from government leaders (Zhou, 2010 : 19; Wu et al., 2013). The Chinese government's emphasis on social issues has largely coincided with the rise in female political participation. Figure 1 represents the number of female secretaries and mayors. In the 1990s, there were few female leaders in the Chinese government, and the female leadership ratio was less than 1 percent in the early 1990s. The Program for the Development of Chinese Women (1995–2000) set out well-defined goals relating to women’s participation in government and politics; among these goals were, by the end of the twentieth century, a minimum of one female member in the core leadership teams of party and government organizations at the municipal level. The Chinese central government also declared from 2001 to 2005 that the leading groups of prefectures should contain at least one female member, and the deployment of female cadres should be prioritized in the departments of education, science and technology, culture, health, sports, civil affairs, labor and social security, which are related to the state’s soft targets (Organization Department of the Central Committee of CPC, 2001 ). In 2006, the Chinese government held a national forum on training and selecting female cadres, which gives preference to selecting and appointing female cadres when they are equally qualified in other aspects. The number of female leaders increased annually after these policies were established. Figure 2 shows the increasing number of female secretaries and mayors at the prefectural level; twice as many women have been elected to leadership positions within prefectures since 2006. This pattern conforms to the data from the Organization Department of the CCP Central Committee. In 2009, at the prefecture (director-general) level, female cadres accounted for 13.7 percent of the total number of cadres at the same level (United Nations, 2012 ) . In fact, at all levels of government, there is typically a female leader in charge of science, education, culture and health, which are sectors that are relevant to the state’s soft targets. For example, Vice Premier Sun Cunlan is currently responsible for handling the COVID-19 pandemic in China. Although the state has a history of assigning female cadres to fulfil its social development goals, it is still not certain whether female leaders are effective in implementing this state goal and whether they pay sufficient attention to soft targets. We do not know whether the extant findings regarding female leaders in other countries and their concerns about “women’s issues”, such as clean drinking water, education, and forest conservation, are also relevant in China (e.g., Besley and Case 2003; Chattopadhyay and Duflo 2004; Thomas, 1991 ; Thomas and Welch, 2001). ------------------------ Insert Fig. 1 about here ----------------------- ------------------------ Insert Fig. 2 about here ----------------------- The Institutional Approach and Role Theory Prior studies have pointed out that government leaders may balance the state’s multiple goals differently based on their political incentives at different career stages (Wang and Luo, 2019 ); however, we do not know whether there is a gender effect on how government leaders implement the state’s goals. The institutional approach emphasizes that organizations can constrain decision makers. Guy Peters (2000) defines an institution as “a formal or informal, structural, societal or political phenomenon that transcends the individual level, that is based on more or less common values, has a certain degree of stability and influences behavior” (18). The routines, procedures, conventions, organizational forms and technologies in institutions all offer and constrain behavior alternatives and to a certain extent, shape and guide individual behaviors (March and Olsen, 1989 ). This means that males and females who occupy the same leadership roles defined by their specific position in a hierarchy would behave very similarly when then there are strong organizational constraints (Peters, 2000). Leaders not only occupy roles defined by their position in an organization but also simultaneously function under the constraints of their gender role. Research in a natural setting found that although some gender-stereotypic differences vanish under the influence of an organizational role, others do not vanish (Moskowitz, Suh, and Desaulniers, 1994 ). Despite pressures to conform to institutional routines and norms, gender roles often exert some influence, with the result that males and females with the same organizational role may behave somewhat differently. Research associated with female leadership emphasizes the “feminine” qualities of cooperation, listening, caring, communication, and knowledge sharing (e.g., Book, 2000 ; Eagly and Carli, 2003 ; Ali and Kulik, 2014). Such female leadership styles are increasingly important for building, opening, and sharing internal environments that are conducive to high-quality decision making and the recognition of strategic opportunities in contemporary organizations (e.g., Adams and Ferreira, 2009; Cohen and Levinthal, 1990 ; Helfat and Martin, 2015 ; Huang and Kisgen, 2013 ). According to role theory (Eagly and Karau, 2002 ), females have a communal gender role but not a very agentic role, and males have an agentic role but not a communal role. Communal characteristics primarily relate to a concern for the welfare of other people, while agentic characteristics primarily describe an assertive, confident and dominating tendency. Organizational behaviors include many actions. The selective and discretionary aspects of organizational behaviors are most likely subject to a gender effect. Female leadership and economic growth. China’s GDP growth rate fluctuated but remained high from 1995 to 2015. This favorable macroeconomic environment provides a good context to test local governors’ effectiveness. For local Chinese governments, economic growth represents the main metric to assess officials (Choi, 2012 ; Zhou, 2007 ). Because of the fiscal decentralization and political centralization since 1978, the GDP competition among local Chinese governments has induced local officials to focus mainly on economic development (Xu, 2011 ). The government plays an important role in economic growth through activities such as providing fiscal incentives, introducing industrial policies, and creating favorable business environments (e.g., Jensen, 2008 ; Dollery and Wallis, 2001). Leaders’ roles in leading economic development should be of primary importance in the government, because these roles endow them with legitimate authority and are regulated by clear rules and norms. It is likely that leadership roles in leading economic development provide many norms about how tasks should be performed. Institutional constraints should be strong here since achieving economic development goals represents the hard targets in leaders’ performance evaluation and regard system within the bureaucracy (Zhou, 2010 : 19; Wu et al., 2013). Having a leading role in local economic development means that both male and female leaders need to play an organizational role that involves activities such as striving for support from upper-level government (Zhou, 2006 ), attracting foreign direct investment (Tung and Cho, 2000 ; Wang, Zhang, and Qin, 2007), and mobilizing local companies to invest (Liu et al., 2019). In line with our argument that the influence of gender roles can be diminished or even eliminated with strong institutional pressures, the gender-stereotypic differences should disappear when males and females play their organizational role in leading economic development. Therefore, from an institutional perspective, we have our first hypothesis: Hypothesis 1 There is no direct relationship between female leadership and local economic growth. Female Leadership and Social Policy . Economic development is of primary importance to the Chinese government, therefore, it is unsurprising that the institutional constraints are sufficiently strong to transcend the impact of individual factors such as gender when leaders play their organizational role in leading economic development. The bureaucracy has a clear performance and reward system related to economic development. In contrast, social development is difficult to quantify and is not immediately related to government officials’ career advancement; thus, social development usually receives less attention from government leaders. Despite increasing pressure to emphasize social development from the central government, local Chinese governors generally have some leeway to vary the extent to which they carry out the required activities. For example, Nie, Jiang, and Wang, ( 2013 ) found evidence that local government officials took risks of failing to maintain social stability when coping with coalmine accidents. In addition, unlike their counterparts in democratic countries, Chinese governors do not need to respond to voters, which means that they have greater discretion in regard to social policy. This discretionary part of organizational behavior is likely subject to the gender effect. The influence of gender roles occurs not only because people respond to a leader with gender stereotypic expectation but also because most people have internalized gender roles to some extent (Lafferty, Phillipson, and Jacobs, 2019; Conry-Murray, 2015 ; Wood, et al., 1997). As a result, males and females may form different social identities and different role perceptions in an organizational setting. According to role theory, people attribute more communal characteristics, such as helpfulness, concern for others, kindness and gentleness, to females (Eagly and Karau, 2002 ). Females’ preferences are more related to promoting social development. Empirical results have also found that women desire more social services to substitute for their reduced production within the home (Cavalcanti and Tavares, 2011). Studies about female state legislators have found a positive relationship between gender ratios and the propensity of introducing or passing bills concerning “women-related issues” (e.g., Thomas, 1991 ; Berkman and O’Connor, 1993; Thomas and Welch, 2001). In addition, having more women in decision-making positions increases female policy makers’ responses to female residents’ complaints, such as complaints that concern clean drinking water (Chattopadhyay and Duflo, 2004), education (Clots-Figueras, 2012 ), health spending (Rehavi, 2007 ), and forest conservation (Leone, 2019 ). Although it is understood that female leadership has some impact on social policy, none of these studies were conducted with data on local governments in China; therefore, it is not clear how these findings can be extrapolated from democratic countries to a nondemocratic setting with a leader’s high discretionary power and no pressure from voters. Drawing on role theory and the empirical studies conducted in other countries, we expect that female leadership will influence the government to make more effort to develop social policy. Therefore, we formulate our second hypothesis: Hypothesis 2 There is a positive relationship between female leadership and social development. Method Data and Sample Previous studies usually use financial indicators as the primary meter of whether females are effective in their leader role (Hoobler et al., 2018; Walsh, Weber, and Margolis, 2003), and relatively few studies consider female leaders’ impact on welfare outcomes (Sergent and Stajkovic, 2020 ). In the present study, we evaluate female leader effectiveness with more balanced meters that combine economic effectiveness and social welfare. We merged two different types of datasets. The first contains data on prefectures’ economic performance and government financial expenditures. The second dataset consists of detailed data on the leadership composition and basic prefecture information. We collected data from the period of 1995 to 2015, in which the Chinese economy showed a high growth rate . The main reason for choosing this period is that local Chinese statistics were established in the late 1990s . The data from the prefectural level of the state were the focus for two major reasons. First, we cannot evaluate the performance of female leaders at the provincial level because there have been only 4 female leaders at the provincial level since 1982. Second, prefectural governments have greater financial autonomy than the county level government, and leaders can arrange local expenditure patterns according to their own preferences. We collected prefectures’ economic performance and government financial expenditures by using several datasets. The first dataset is the National Collection of Financial Statistics of Prefectures and Counties . It was collated by the Budget Department of the Ministry of Finance and published annually by the China Financial & Economic Publishing House from 1993 to 2009. This dataset contains basic information regarding the economic performance, budget balance sheet and characteristics of all prefectures in China. It includes variables that we are interested in, such as GDP, social security, employment and education expenditures at the prefecture level; it also includes the numbers of residents so that we can calculate each variable in per capita terms. We obtained the data for GDP and expenditures on education and science from the China City Statistical Yearbook for recent years and replenished the missing data . We obtained the social security, employment and education expenditure variables from the China Statistical Yearbook for the Regional Economy for recent years. The final yearbook datasets that we used in this paper are the statistical yearbooks for each province in China , which we used to fill in the missing data. We combined three datasets to complete the leadership composition details and basic information of prefectures. The first dataset was the Data on Prefectural Party Secretaries and Mayors of the Peoples’ Republic of China from 2000 to 2010. This database was hand-collected by Fudan WTF SOSC Lab (Chen, 2016 ). The second dataset was the Chinese Local Government Officials Database (CGOD), which was collected by Chinese Research Data Services. This dataset collects the demographic information of most Chinese government officials at the prefecture level. The CGOD information is publicly available. We merged these two datasets to obtain prefecture leaders and basic information from 1990 to 2015 and then filled in the missing data according to the China Political Elite Database (CPED) . These three databases had similar collection processes with different variables. For example, the first dataset collected the basic demographic characteristics of all secretaries and mayors of the prefectures and municipal party committees. The names of the leaders were obtained from the provincial yearbooks. Their curricula vitae were searched on the government website and official media, such as the Xinhua News Agency and People’s Daily Online. We also used some prefectural yearbooks to filled some missing data. We double-checked and extracted the information among these three datasets about leaders’ demographic characteristics, educational background and work experience, such as their gender, age, education level, and time of joining the CCP. We collected only the name of the most recent secretary or mayor in a certain year in each city if there was more than one occupant . These databases have been widely used in Chinese political and economic studies (e.g., Jiang, 2018 ; Chen, 2016 ; Que, Zhang, and Schulze, 2019 ; Jin, Shen, and Li, 2020 ). The Chinese government has a convention that precludes any occupant of a position from serving more than two terms, which usually lasts ten years. This means that all the cities included in our data changed secretaries and mayors during the period of this study. Variables Dependent variable The most common way to measure economic achievement is through GDP and GDP growth rate. The extant literature suggests that the likelihood of the promotion of provincial leaders increases with the enhancement of their jurisdiction’s economic performance (Li and Zhou, 2005 ). We use GDP per capita and GDP growth rate to measure the influence of the gender effect on the prefecture’s economic performance. We assume that if female leaders are more concerned about social development, then they will spend more on public works. Thus, a female secretary or mayor will increase expenditures on social security and employment. We also notice that prefecture-level cities’ scale, such as the population, affects government expenditure. We used Expenditures on Social Security and Employment per capita and Expenditures on Education per capita to test our hypothesis in this study . The level of economic development and fiscal revenue also shows different expenditure levels. Thus, we used Expenditure ratio on Social Security and Employment and Expenditure ratio on Education to address this issue. It is also a concern that the economy fluctuates in a single year due to unexpected strikes. This will mislead us when calculating gender effects. We use Average GDP Growth Rate , Expenditure ratio on Social Security and Employment and Expenditure ratio on Education during leaders’ tenure to overcome this time effect. Independent variables The key variable of concern is a female leader at the prefectural level. We find in our data that few prefecture-level cities had female secretaries and mayors simultaneously . Thus, we define Female Leadership as the presence of a female secretary or mayor at the prefecture level. Control variables Since Mincer’s ( 1974 ) study, economists have used individuals’ education attainment and work experience to measure their human capital. They found that these variables are strongly related to one’s ability and earnings. Leaders’ abilities and experiences definitely affect regional economic performance (Wang and Xu, 2008 ). In this sense, we included age, party standing and educational background as control variables. Age is strongly related to several aspects of leadership. First, age is highly correlated with work experience. Thus, age may negatively or non-significantly affect a prefecture’s economic outcomes because there is no possibility of promotion for older officials. Finally, older secretaries may have a more conservative policy because they want to reach retirement uneventfully (Wang and Luo, 2019 ). Party membership is another important factor to measure a leader’s work experience and ability. First, the process of applying for Chinese Communist Party membership is lengthy and involves rigorous screening. Individuals must pass at least five stages, including (1) self-selection, (2) political participation, (3) daily monitoring, (4) a closed-door evaluation, and (5) a probationary examination, which are called "loyalty filters" (Walder, 1995 ), to achieve membership. The process of joining the party may take several years. This means that a talented person can become a party member earlier than a normal person. Second, party membership has a significant effect on mobility into elite positions with political and managerial authority (Bian, Shu and Logan, 2001 ). Third, a party member can take the opportunity to join party activities that promote human capital and social network formation. Moreover, party membership is positively correlated with income (Knight and Yueh, 2008 ). These aspects indicate that a long party standing can be a proxy variable for one’s remarkable ability. The educational background of a leader may be an important factor for leadership, behavior, and promotion . We used years of education to capture this influence. Because education is a key factor in human capital development, well-educated leaders can be far-sighted and sagacious. A highly educated background also brings circles of well-educated friends. Importantly, many officials attend party schools or other universities for advanced degrees after taking office. Our calculation of a leader's years of education includes the degrees obtained on the job. Endogeneity issues Endogeneity is a key concern in the analysis. In the following, we offer some strategies that help reduce endogeneity to make our conclusion more robust. First, endogeneity can be caused by omitted variables and unobserved heterogeneity. Due to the limitations of our data, we cannot control more information about prefectures and leaders. To address this concern, we used a fixed effects model at the city level to absorb the unobserved heterogeneity at the prefecture level. In addition, we used a leader’s party membership to control unobserved ability. The CCP admission process has merit selection characteristics (Walder, 1995 ). A longer party membership indicates that a leader exhibited strong ability when he or she was young. Second, measurement error can also result in endogeneity. The statistical indicators for any single year are unstable and vulnerable to manipulation (Lyu, et al., 2018). This may bias our estimation of a leader’s effectiveness. One way to avoid this concern is using average indicators during the leader’s tenure as independent variables to reduce the endogeneity. Third, the gender differences inside cities are almost exogenous in this study. Chinese government officials are appointed rather than elected. Within the state bureaucracy, the upper-level government makes decisions regarding the promotion of secretaries and mayors within the prefecture. The incumbents of a prefecture cannot determine who will be their successors or partners. Another possible endogeneity concern is reverse causality because it is very likely that women are more likely to be promoted to a high-profile position in more developed prefectures characterized by higher GDP and more social expenditures. We sorted the prefectures geographically and confirmed that there were no significant differences in the distribution of female leaders among cities with different development levels (see Appendix Figs. 1). Estimation From 1995 to 2015, the number of female leaders changed according to cities and time. Due to the uncertainty of leadership changes, our data structure is one special type of staggered treatment adoption (Athey and Imbens, 2018). The staggered treatment adoption refers to prefectures adopt treatment at a particular point in time, and then remain exposed to this treatment at all times afterwards. The treatment here refers to the prefecture led by female leader. In addition, our data also face a uncertainty of female leader’s tenure, which increases difficulties of identification. This complex data structure requires us to find a sophisticated way to evaluate the treatment effects of female leadership. Fortunately, Athey and Imbens (2018) noticed that under a random assignment of the adoption date, the standard difference-in-differences estimator is an unbiased estimator of a particular weighted average causal effect. In this study, the selection and appointment of prefectural leaders was decided by upper government, and tenure of local officials was not fixed, which implies the adoption date was approximate under a random assignment. We chose the prefectures that have a female leader in one year as the treatment group, and the prefectures led by males as the control group. Then, we use a flexible version of a panel data difference-in-differences (DID) model to calculate female leadership effects. This flexible DID looks like prefectural two-way fixed effects models. $${\text{Y}}_{it}={\beta }_{0}+{\beta }_{1}{treat}_{it}+ {\beta }_{2}{\text{X}}_{it}+{\gamma }_{i}+{\delta }_{t}+{ϵ}_{it}$$ 1 where y is the dependent variables, \({treat}_{it}\) is our core independent variable which refers to female’s leadership in prefectural i and year t , and X is a set of control variables. \(\gamma \text{a}\text{n}\text{d} \delta\) refer to the fixed effect of the prefecture and year, which are equal to the control dummies of the control group and time of treatment. \({\epsilon }\) is an error term. Subscript i refers to the prefecture, and t refers to the CCP General Secretary transition which implies different governance patterns of central government. We control year effects as robust check. All variables are measured at time t, while the average performance is measured based on the leader’s tenure. In addition, we apply the flexible conditional difference-in-differences approach (flex panel DID) as a robustness check and to achieve unbiased policy effect, which is useful for a causal analysis of the treatments with varying start dates and varying treatment durations (Dettmann, Alexander, and Antje, 2020). In detail, this method modifies the conditional DID approach of Heckman et al. ( 1998 ) and gains more flexibility. The basic idea of this method is to combine matching and DID to find adequate controls for the treated units. The flex-panel-DID estimator can be regarded as a special case of the group-time average treatment effects. We apply this flexible panel DID approach in three steps. First, we rearrange the data to incorporate the observation date of all matching variables and outcomes. Second, we use an exact matching process or a matching process based on a combined statistical distance function to match the control and treatment groups. Third, we obtain the average treatment effect for the treatment group using the DID model. We use a standard difference-in-difference model to estimate the following equation. $${Y}_{it}={\beta }_{0}+{\beta }_{1}{treat}_{it}+ {\beta }_{2}{treat}_{it}*{post\_treat}_{it}+ {\beta }_{3}{post\_treat}_{it}+{\gamma }_{i}+{\delta }_{t}+{ϵ}_{it}$$ 2 where Y is the dependent variables, we use leader’s average performance during his or her tenure. \(treat\) is a dummy which refers to female’s leadership in prefectural i and year t ; \({post\_treat}_{}\) is a dummy which equal 1 since prefectural led by female leader. The interaction term is our main concerned, which means that prefectural i was led by female leader at time t . This term is also called as DID term. \(\gamma \text{a}\text{n}\text{d} \delta\) refer to the fixed effect of the prefecture and year. Results Data Summary Table 1 presents the summary statistics for all leaders’ and prefectures’ features. After excluding the prefectures with missing information on the key variables, such as leaders’ gender, we obtained 4,009 prefecture-year observations that pertain to 321 unique prefectures from 1995–2015. In total, 161 female party secretaries and 250 female mayors accounted for 9 percent of the sample during this time period. Table 1 Summary of Statistics and Pairwise Correlations N Mean S.E. Min Max 1 2 3 4 5 6 1 Female leadership 4009 0.087 0.281 0.000 1.000 2 Mayor's age 4009 49.868 4.045 36.000 60.000 -0.044*** 3 Mayor's Years of Education 4009 17.985 2.733 12.000 22.000 -0.016 -0.239*** 4 Mayor's party 4009 24.382 8.174 0.000 40.000 -0.002 0.325*** -0.068*** 5 Secretary’s age 4009 51.825 3.845 38.000 61.000 -0.029* 0.085*** 0.042** 0.118*** 6 Secretary’s Years of Education 4009 17.787 2.803 12.000 22.000 -0.007 -0.010 0.102*** -0.051*** -0.288*** 7 Secretary’s party 4009 26.989 7.637 0.000 41.000 0.051*** 0.039** 0.055*** 0.133*** 0.327*** -0.058*** 8 Per capita GDP (yuan) 4009 11576.492 19100.257 389.709 67764.022 0.093*** 0.128*** -0.175*** 0.118*** 0.136*** -0.196*** 9 GDP growth rate 3952 0.152 0.072 0.046 0.279 -0.005 -0.022 0.080*** 0.024 -0.008 0.065*** 10 Per capita Expenditure on Social Security and Employment (yuan) 3753 3.656 4.562 0.013 89.385 0.068*** 0.115*** -0.080*** 0.133*** 0.156*** -0.075*** 11 Proportion in budget expenditure on Social Security and Employment 3740 0.094 0.061 0.002 0.682 0.032* 0.019 0.025 0.086*** 0.108*** -0.009 12 Per capita expenditure on education (yuan) 3913 6.165 7.486 0.358 122.283 0.056*** 0.140*** -0.061*** 0.160*** 0.147*** -0.076*** 13 Proportion in budget expenditure on education 3913 0.186 0.050 0.018 0.494 -0.001 -0.060*** 0.034** 0.015 -0.057*** -0.015 14 Mayor's tenure 4009 3.932 1.657 1.000 12.000 -0.065*** 0.156*** -0.003 0.096*** -0.055*** 0.125*** 15 Secretary’s tenure 4009 4.364 1.839 1.000 10.000 -0.033** 0.091*** -0.004 0.026 0.008 0.027 16 Average GDP growth rate 3672 12.544 2.572 6.440 18.000 -0.066*** -0.013 0.124*** 0.028* -0.042** 0.116*** 17 Average rate of government expenditures on social security and employment 3902 0.093 0.055 0.004 0.340 0.037** -0.006 0.018 0.084*** 0.106*** -0.022 18 Average rate of expenditures on education 3979 0.186 0.047 0.023 0.378 0.004 -0.058*** 0.043** 0.018 -0.053*** -0.013 7 8 9 10 11 12 13 14 15 16 17 8 Per capita GDP (yuan) 0.126*** 9 GDP growth rate 0.006 -0.179*** 10 Per capita Expenditure on Social Security and Employment (yuan) 0.111*** 0.667*** -0.105*** 11 Proportion in budget expenditure on Social Security and Employment 0.044*** 0.168*** 0.050*** 0.519*** 12 Per capita expenditure on education (yuan) 0.138*** 0.716*** -0.095*** 0.722*** 0.125*** 13 Proportion in budget expenditure on education 0.015 -0.118*** -0.052*** -0.307*** -0.154*** -0.098*** 14 Mayor's tenure 0.039** -0.136*** 0.107*** -0.065*** -0.046*** -0.021 -0.097*** 15 Secertary's tenure 0.042** -0.166*** 0.130*** -0.119*** -0.033** -0.101*** -0.002 0.296*** 16 Average GDP growth rate 0.000 -0.395*** 0.538*** -0.234*** -0.012 -0.247*** -0.162*** 0.183*** 0.161*** 17 Average rate of government expenditures on social security and employment 0.038** 0.198*** 0.053*** 0.516*** 0.895*** 0.131*** -0.212*** -0.054*** -0.041** -0.011 18 Average rate of expenditures on education 0.022 -0.131*** -0.065*** -0.320*** -0.205*** -0.114*** 0.927*** -0.102*** -0.003 -0.170*** -0.229*** Note: *** p < 0.01, ** p < 0.05, * p < 0.1. Sample is based on model 1 of Table 2 . ------------------------ Insert Table 1 about here ----------------------- Overall, the average secretary of the CCP municipal committee was 2 years older and had been a party member for two years longer than the average mayor ( M = 51.83, SD = 3.85 vs M = 49.86, SD = 4.05, p < 0.01). Although the younger mayors had a better educational background, the average education level was between an undergraduate and postgraduate education, and the average education time of both younger and older mayors was nearly 18 years. We found that most of the mayors and secretaries hold a part-time graduate degree. The GDP, social security, employment and education expenditure variables, due to their differences at various levels and across different years, were processed logarithmically in a subsequent analysis. Main findings Table 2 reports our basic results. Models 1 to 6 are the regression results of per capita GDP , GDP growth rate, Expenditure on Social Security and Employment per capita, Expenditure on Education per capita, Proportion of Expenditures on Social Security and Employment , and Proportion of Expenditures on Education , separately. We add time effect dummies to each models to control the effects of CPC general secretaries transition. Table 2 Predicting a Prefecture-Level City’s (at T + 0) Per Capita GDP, GDP Growth, Expenditures on Social Security and Employment per capita and Expenditure on Education per capita by Female Leadership Model (1) (2) (3) (4) (5) (6) Female leadership 0.240*** 0.007 0.229*** 0.125*** 0.008** -0.001 (0.066) (0.004) (0.077) (0.039) (0.003) (0.002) Mayor’s age 0.013*** -0.000 0.015*** 0.012*** -0.000 0.000 (0.005) (0.000) (0.006) (0.003) (0.000) (0.000) Secretary’s age 0.018*** 0.001** 0.044*** 0.022*** 0.001*** -0.000*** (0.005) (0.000) (0.006) (0.003) (0.000) (0.000) Secretary’s education -0.067*** -0.001 0.001 -0.006 0.001*** -0.000 (0.007) (0.000) (0.008) (0.004) (0.000) (0.000) Mayor’s education -0.050*** -0.000 0.007 -0.001 0.001*** 0.000 (0.007) (0.000) (0.008) (0.004) (0.000) (0.000) Secretary’s party standing 0.019*** 0.000* 0.019*** 0.012*** 0.000*** 0.000 (0.003) (0.000) (0.003) (0.002) (0.000) (0.000) Mayor’s party standing 0.011*** 0.001*** 0.021*** 0.010*** 0.001*** -0.000* (0.002) (0.000) (0.003) (0.001) (0.000) (0.000) CCP General Secretary Y Y Y Y Y Y Observations 4,009 3,956 3,753 3,914 3,779 3,956 Adjusted R -squared 0.592 0.286 0.449 0.577 0.211 0.062 Number of cities 285 285 285 285 297 297 1. Models 1 to 6 are the regression results of per capita GDP , GDP growth rate , Expenditure on Social Security and Employment per capita , Expenditure on Education per capita , Proportion of Expenditures on Social Security and Employment , and Proportion of Expenditures on Education separately. 2. We use a fixed effects model to control the unobservable prefecture-level characteristics that are constant over time. We report standard errors in parentheses. 3. *** p < 0.01, ** p < 0.05, * p < 0.1 ------------------------ Insert Table 2 about here ----------------------- The result of model 1 show that female leadership had a significant positive impact on per capita GDP ( b GDP per capita =0.24, SE = 0.07, p < 0.01), which indicates that female leaders usually worked at prefectures with a stronger economy. It is evident that the age and party standing of the secretaries and mayors were positively correlated with GDP per capita. However, after we changed our independent variable to the GDP growth rate in model 2, the impact of female leadership became non-significant. This result indicates that there was no gender effect when male and female leaders played their organizational role in leading economic development. A basic conclusion here is that prefectures with female leaders have a higher economic scale, but we do not observe that female leaders bring a significantly higher GDP growth rate. Hypothesis 1 is supported by the empirical results. Our findings indicate that female leaders can achieve the same GDP growth rate as their male counterparts. Model 3 and 4 show the influence of female leadership on expenditures for social security and employment and expenditures for education. They show the same pattern of the impact of female leadership on social development. It is evident that female leadership can significantly increase expenditures on social security, employment and education per capita ( b social security and employment =0.23, SE = 0.08, p < 0.01; b education = 0.13, SE = 0.04, p < 0.01) at the prefecture level, which indicates that female leadership can increase people’s livelihood within a city. We still find that the age and years of party membership of the secretaries and mayors helped increase expenditures related to social security and employment. Therefore, Hypothesis 2 is strongly supported by our results. Hypotheses 1 and 2 indicate that female leaders make higher expenditures on social issues, such as social security and employment, without undermining local economic growth. However, prefecture-level characteristics, such as fiscal conditions, may affect our findings. It is common sense that governments with higher fiscal revenue spend more on social development and give more attention to residents’ welfare. Given this concern regarding our finding, we use the ratio of expenditures on social security, employment and education to the general budget expenditures as an alternative dependent variable to avoid the city effect. The last two columns in Table 2 illustrates the regression results by using these variables. We found that female leadership still has a positive effect on the proportion of expenditures on social security and employment ( b exp_sse = 0.01, SE = 0.00, p 0.1 ). The lack of significance for the effects of female leadership on the proportion of expenditures on education suggests the complexity of expenditures on education, which needs further study. The independent variables in the previous analysis are indicated by the statistics for the officials who take office (T + 0). This data collection strategy may suffer from some measurement errors. First, government leaders may balance the state’s different goals and their related targets differently in different years (Wang and Luo, 2019 ), and they may not commit to economic and social development targets in the same way every year. Second, local Chinese governments may intentionally manipulate regional GDP figures to meet or beat growth targets under different conditions (Ma et al., 2014 ; Lyu et al., 2018). To overcome these measurement errors and achieve a reliable evaluation of the officials’ effectiveness, we average their performance in their term as an alternative measurement of the independent variables. Model 1 in Table 3 shows that female leadership has a negative effect on the average regional GDP growth rate ( b = -0.26, SE = 0.19, p > 0.1 ). Models 2 and 3 indicate that female leadership still has a significantly positive effect on the average expenditure rate on social security and employment ( b = 0.003, SE = 0.002, p 0.1 ). Table 3 Effects of Female Leadership on Average Local Economic Growth and Social Policy Model (1) (2) (3) Female leadership -0.255 0.003** 0.000 (0.190) (0.002) (0.001) Mayor’s tenure 0.204*** 0.000 -0.001*** (0.035) (0.000) (0.000) Secretary’s tenure 0.012 -0.000 0.000 (0.031) (0.000) (0.000) Secretary’s education -0.064*** 0.000 0.000* (0.019) (0.000) (0.000) Mayor’s education -0.058*** 0.000*** 0.000 (0.019) (0.000) (0.000) Year Fixed Effect Y Y Y Observations 3,693 3,992 4,072 R -squared 0.326 0.664 0.097 Number of cities 284 299 299 1. Models 1 to 3 are regressions of the average GDP growth rate, average rate of government expenditures on social security and employment and average rate of expenditures on education. 2. *** p < 0.01, ** p < 0.05, * p < 0.1. ------------------------ Insert Table 3 about here ----------------------- It is found that a female leader (secretary or mayor) increases the prefectural city’s expenditures on social security and employment in terms of both the total amount and the proportion in the general budget. To eliminate a sample selection bias and achieve unbiased estimators, we apply the flex panel DID approach. We reorganize the dataset and use matching based on a combined statistical distance function. Specifically, every female leader is matched with a male leader with similar human capital characteristics, and only the gender difference is analyzed to obtain the average treatment effect. This means that only gender influences our independent variables in prefecture-level cities. We use leaders’ characteristic variables, including education and tenure, as covariates to calculate our statistical distance. Table 4 reports the DID estimator for the average treatment effects after matching. Model 1 in the table shows that female leaders do not have a significant impact on the regional average economic growth rate ( b = 0. 09, SE = 0.46, p > 0.1). Therefore, Hypothesis 1 is supported. Model 2 indicates that female leadership still has a significantly positive effect on the average expenditures on social security and employment ( b = 0. 01, SE = 0. 01, p < 0.05). Therefore, Hypothesis 2 is supported. This result is not only statistical significance but also economically important. For example, our data imply that regions with female political leader increased by 390-million-yuan social security and employment expenditures at 2015. This increase means additional 100-yuan expenditures per capita to local residents. Table 4 Using a Flex-panel-DID estimator to Evaluate the Effects of Female Leadership on the Average Local Economic Growth and Social Policy Model (1) (2) (3) DID estimator 0.092 0.011** -0.005 (0.459) (0.005) (0.004) Observations 2796 3276 3353 R -squared 0.249 0.641 0.139 Number of cities 249 269 269 1. These models used the statistical distance function and radius matching procedures. We applied standard DID estimation procedure after matching. We reported the interaction terms, which is also called a DID estimator. The independent variable in all regressions is the leader’s average performance during his/her term. Models 1 to 3 are the regressions of the GDP growth rate and government expenditures on social security, employment and education, separately. 2. We report the robust standard errors in parentheses. 3. *** p < 0.01, ** p < 0.05, * p < 0.1. ------------------------ Insert Table 4 about here ----------------------- Further Analyses We conducted a set of additional analyses to check the robustness of our findings. Given that an incoming secretary or mayor might not participate in all types of policy making and that he or she might have less impact on the economic development and budget of the local government in the first year in office, we checked our model by examining the effect of female leadership on economic growth and social development in the city in the next year (T + 1) rather than the year (T + 0), which is what is shown in our baseline models in Tables 1 to 4 . We supposed that after one year, the incoming secretary or mayor would participate in all decision-making processes and have a full impact on the development of the city. The results are consistent with our previous findings. Female leadership still has significant positive effects on social security and employment expenditures in the next year, but not significant on GDP growth. After time dummy variables are added, the coefficient is relatively stable. Since we used unbalanced panel data with a fixed effects model in this study, this data analysis specification loses some observations. To address this limitation, we still estimated the random effects models and OLS regression models for each dependent variable and controlled a set of prefecture dummies to use more observations. These estimators show the same pattern as the fixed effects model, and all our results remain substantively robust. Finally, we also address some possible moderating factors that may impact our results. First, although both party and government leaders are subject to the Chinese state’s bureaucracy, which stipulates officials’ responsibilities, they have different preferences and priorities (Wang and Luo, 2019 ). The CCP secretary is mainly responsible for ideology and social stability, while the mayor’s responsibility lies in formulating and executing specific policies, such as economic development. As a result, it seems necessary to differentiate female CCP secretaries and mayors in the study. However, the results from the separation analysis show a similar pattern to our main models, except that female secretaries spend significantly less on education. This suggests that the effect of female leadership may be closely tied to gender rather than specific duties. Second, the heterogeneity and complexity of Chinese regional development may distort our results. It is very likely that women are more likely to be promoted to be top political leaders in more developed prefectures. To address this concern, we further split the sample and present the results for East, Central, West and Northeast China. All the above patterns remain robust except that female leaders devote significantly more to education in Northeast China. Third, female leaders’ age may be a confounding factor since a previous study showed that Chinese officials have political incentives to prioritize social stability in order to retire in peace (Wang and Luo, 2019 ). In our study, we find that female leaders of different ages show almost the same patterns of economic performance and expenditures on social security and education. Accordingly, these three possible moderating factors are not significant in our analysis, which may be due to the limited number of female leaders in our sample. Discussion and Conclusion This study investigates the impact of female leadership on policy outcomes at the local level of government in China. Specifically, we investigate the impact of female CCP secretaries and government mayors on local economic and social development. Similar findings in other countries reveal no impact of female mayors on the size of the local government, the composition of its expenditures, or local crime rates (Ferreira and Gyourko, 2014). Our results, however, provide evidence that gender matters to political leadership. By matching municipal economic performance and government financial expenditures with demographic data from the period between 1995 and 2015 in China, our findings suggest that female leaders tend to work in economically developed cities, as measured by high GDP and per capita GDP. Because of institutional constraints, the gender effect does not exist when male and female leaders play their organizational role in leading economic development. However, in regard to promoting social development, over which leaders have more discretionary power, female leaders, on average, ensure more balanced and welfare-oriented developmental preferences, as measured by higher expenditures on social security and employment. We find that female leadership in a high-profile political position results in more inclusive development in a nondemocratic government. These results indicate that having more women in politics is the right move to make in terms of equity in society. Although the growing proportion of women in politics is encouraging, there is still a steep road ahead. To gain the unique contributions of women as decision makers, there is still ample room to dramatically increase women’s representation in political decision making. Our study has practical implications in China, especially as Beijing’s priorities slowly shift toward more balanced and people-oriented growth. We can achieve sustainable economic and social development by increasing women’s participation in politics and political affairs. First, by examining the gender effects of officials who implement state policies in China, our study contributes to the long-lasting debating about whether institutions or individuals are more important in shaping organizational behavior (Christensen and Lægreid, 2018; Evans and Rauch, 1999 ; Rauch and Evans, 2000). From an institutional perspective, the normative rules and standardized procedures in the bureaucracy recruit similar officials and prevent gender from influencing policy outcomes, and they prevent female leaders from implementing divergent policies in the United States (Ferreira and Gyourko, 2014). Our study reveals that institutional constraints do not exert homogeneous pressures on shaping organizational behaviors. For some aspects such as leading economic development, which is of primary importance to an organization, the strong institutional constraints can directly or indirectly guide organizational behaviors. Individual characteristics such as gender, however, may have influence when leaders have discretionary power and the institutional pressures on leaders are relatively weak. In our study, female leaders achieve a better balance of the different goals of the state. Without sacrificing economic growth, women facilitate social development more effectively than their male counterparts. To the extent that male officials do not give as much attention as female governors give to social development, the central government fails to ensure that soft targets be implemented with coordinated efforts within the bureaucracy. Our view of the gender effect thus helps us to gain a better understanding of the "Weberian state hypothesis” about how state goals can be fulfilled (Evans and Rauch, 1999 ; Rauch and Evans, 2000). Second, our article contributes to the limited empirical studies that have investigated the impact of women in politics (Chattopadhyay and Duflo, 2004; Beaman et al., 2012 ; Clots-Figueras, 2012 ; Leone, 2019 ). Our results demonstrate that gender matters at the political level. Increasing women’s participation in political decision making is not only politically correct but also has social benefits. Women’s preferences, values, and perspectives usually differ from those of men with regard to making decisions that are more inclusive and that lead to more efforts being expended on social issues. Our results show that having a local female political leader leads to improved public welfare in a nondemocratic country. All else being equal, prefecture-level cities with female party secretaries or mayors have approximately 6 percent higher expenditures on social security and employment. This spending bonus amounts to approximately 2,510,000 Yuan per city for prefectures where a woman served as leader in 2015. Greater policy intervention and more stringent enforcement of antidiscrimination laws are needed to provide targeted support for women and promote the entry of more women into leadership positions in the state. At the end of the day, having more women in politics would benefit not only Chinese women but also the entire society. Third, our study enriches the research on female leadership. Most research characterizes female leaders as possessing the stereotypically “feminine” qualities of cooperation, listening, caring, communication, and knowledge sharing (e.g., Book, 2000 ; Eagly and Carli, 2003 ; Wang et al., 2016 ). Such qualities are increasingly important in contemporary organizations (e.g., Chen, Kang, and Butler, 2018 ; Chin, Hambrick, and Treviño, 2013; Helfat and Martin, 2015 ). Previous studies have focused mainly on the proportion of women on executive teams as explanatory variables. However, the assertion that women are capable of being effective leaders and that “men could become losers in a global economy that values mental power more than might” is too arbitrary (Rose and Rudolph, 2006). The Chinese state’s different goals enable us to examine the effectiveness of female leadership in different contexts. Prejudice and discrimination against women offset any advantages that female leaders have in traditionally more male-dominated arenas, such as economic development. However, in fields such as social development, which is typically defined as less masculine, female leaders encounter less severe prejudice and discrimination; therefore, their performance is usually equal to or even exceeds that of men. Our study contributes to role theory by extending the context to public administration. Despite these interesting results, there is still a need for future investigations to assess the impact of female leaders on political outcomes. First, the context in which a woman leads is important; one cannot simply extrapolate our findings in China to different institutions and market settings. The different institutional contexts in many countries may distort the possible positive value that female leaders can otherwise create. Second, without a direct way to measure female leadership, we cannot definitively claim that female leadership explains these political outcomes. Future studies could consider the differences between female and male leaders and study the psychological and social processes that serve to transform gender differences into strategic political decision making. For example, it would be helpful to carry out in-depth interviews to increase the level of reliability. Mixed research methods would increase the quality of the research and strengthen its impact. Finally, our results indicate that women bring different leadership styles and preferences when they take office. Without sacrificing economic growth, they make more inclusive decisions to improve public welfare than male leaders in a nondemocratic setting. This view emphasizes the interactions among political team members during decision-making processes; however, another plausible explanation is that compared to their male counterparts, female political leaders have superior political skills (Anzia and Berry, 2011 ). In a society that is biased against female leaders, only the most talented and most qualified women can emerge as candidates and seize leadership positions. It is possible that since these female secretaries and mayors are highly qualified and politically ambitious, they perform better, on average, than their male counterparts. 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Psychol Bull, 132(1): 98 Sergent K, Stajkovic AD (2020) Women’s leadership is associated with fewer deaths during the COVID-19 crisis: Quantitative and qualitative analyses of United States governors. J Appl Psychol 105:771–783 Thomas S (1991) The impact of women on state legislative policies. J Politics 53(4):958–976 Thomas S, and S. Welch (2001) The impact of women in state legislatures. Impact Women Public Office 1:166–181 Tung S, Cho S (2000) The impact of tax incentives on foreign direct investment in China. J Int Acc Auditing Taxation 9(2):105–135 United Nations (2012) The People’s Republic of China Implementation of the Convention on the Elimination of All Forms of Discrimination against Women. Combined Seventh and Eighth Reports. Walder AG (1995) Career mobility and the communist political order. Am Sociol Rev, 309–328 Walsh JP, Weber K and J. D. Margolis 2003 Social issues and management: Our lost cause found. J Manag, 29, 859–881 Wang D, Luo XR (2019) Retire in peace: Officials’ political incentives and corporate diversification in China. Adm Sci Q 64(4):773–809 Wang G, Holmes R Jr, Oh IS, Zhu W (2016) Do CEOs matter to firm strategic actions and firm performance? A meta-analytic investigation based on upper echelons theory. Pers Psychol 69(4):775–862 Wang J (2015) Managing social stability: the perspective of a local government in china. J East Asian Stud 15(01):1–25 Wang W, Zhang J, and C. Qin (2007) Fiscal Decentralization, Competition between Local Governments, and the Economic Growth of FDI. Manage World, (3):13–22. (In Chinese). Wang X, Xu X (2008) The source of Local Officials, their Ways to Go, and their Term; and Economic Growth. Manage World, (3):16–26. (In Chinese). Wood W, Christensen PN, Hebl MR and H. Rothgerber 1997 Conformity to sex-typed norms, affect, and the self-concept. J Personal Soc Psychol, 73(3), 523 Wu J, Deng Y, Huang J, Morck R and B. Yeung 2013 Incentives and outcomes: China's environmental policy. National Bureau of Economic Research Working Paper Xu C (2011) The fundamental institutions of China's reforms and development. J Econ Lit 49(4):1076–1151 Xu X (2002) China’s GDP Accounting. China Economic Q 2(1):23–36 (In Chinese) Zhou F (2006) A Decade of Tax-Sharing: The System and its Evolution. Social Sci China, (6): 100–115. (In Chinese). Zhou L (2007) Governing China’s local officials: An analysis of promotion tournament model. Econ Res J 7:36–50 (In Chinese) Zhou L (2010) Incentives and Governance: China’s Local Governments. Cengage Learning Asia, Singapore Footnotes These leading groups contain the prefectural committees of the CCP, the People’s Congress, the government, and the Chinese People’s Political Consultative Conference (CPPCC). This ratio is higher than ours because it includes female cadres at the director-general level, such as directors of the provincial education department, and at the head of public institutions, such as public universities or hospitals. During this period, China’s supreme leadership changed several times. We add several time effect dummies to control the effects of different leaders. For example, the expenditure method was used to account for GDP since 1993. The local GDP data before this year were calculated later (Xu, 2002 ). The Chinese regional code is adjusted dynamically. In this time series data, we use the regional code in 2009 as the baseline. Because the statistical range of expenditures on education changed several times, we attempt our best to harmonize across sample years. Because of data limitations, the expenditures on social security, employment and education were calculated separately since 1999 and 1998. We do not use provinces’ statistical yearbooks as our main datasets. The main reason is that the statistical yearbook of each province provides different variables in each year, and several provinces do not provide financial data at the prefecture level. We still lost some basic demographic information for some leaders, especially before 2000. The Chinese governments, including the central and local governments, usually change leaders after two sessions (People's Congress and the Political Consultative Conference). Most prefectures hold two sessions before. This means that there will be two groups of leaders in the re-election year, which is normally every five years. This treatment guarantees that each secretary and mayor holds the post for more than one year. Because of data limitations, we measure expenditures on social security and employment and expenditures on education after 2000. We found this phenomenon in only 5 observations. Former Chinese leader Deng Xiaoping suggested in 1980 that “cadres should become more revolutionary, better-educated, more professional, more competent and younger” (Deng, 1993 ). Civil servants need to obtain at least a Master’s degree to achieve rapid promotion. For example, President Xi graduated from Tsinghua University with a doctorate in law and ideology in 2002 when he was Governor of Fujian. The treat term was omitted at regression because of collinearity with prefectural and year fixed effect. For the sake of completeness of the DID equation, we put this term in the equation. China started a reform in 1983 of the administrative divisions at the prefecture level. Therefore, the number of prefectures changed. In recent years, the overall number of prefectures in China has been 333. The negative effect of education may result from obtaining a Master’s or doctorate degree through on-the-job education, which overestimates leaders’ years of education. For details about the matching methods specification, please refer to Dettmann, Alexander, and Antje. (2020). We do not report these results because of space limitations (the results can be provided upon request). Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4316230","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294866862,"identity":"bc4b365d-e4dd-4c6b-ad9b-3610539353ca","order_by":0,"name":"Qian Guo","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Guo","suffix":""},{"id":294866863,"identity":"7bb2e5d7-5bff-4988-9c6e-0c230b42e486","order_by":1,"name":"Yanchuan Teng","email":"","orcid":"","institution":"Chinese Academy of Fiscal Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yanchuan","middleName":"","lastName":"Teng","suffix":""},{"id":294866864,"identity":"f186cd0d-8e84-40b2-b4d2-4d8f68f3d17b","order_by":2,"name":"Zhixing Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYHACNoYEBgkGBvYeMI+xgXgtPGdI0QIGEjlEajG4kf7swcM2izz5yLfHHvMw2MhuOMD87AE+LZIzEtINEs5IFBvezks35mFIM95wgM3cAJ8WfomEYxIJFRKJG2fnmEnzMBxO3HCAh00Cr0ckEtskEgyAWmaeAWn5T1gLv0QyG9iW+RI8IC0HCGuR7HkG1HJGInEDT16a5ByDZOOZh9nM8GoxOJ7+TPJnW13i/PazxyTeVNjJ9h1vfoZXC0LvATAJxMxEqQcC+QZiVY6CUTAKRsGIAwDyjEM0PgCUfQAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Normal University","correspondingAuthor":true,"prefix":"","firstName":"Zhixing","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-04-24 07:28:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4316230/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4316230/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55273779,"identity":"77c58f30-3e1b-40ca-852a-e1a545ea5abe","added_by":"auto","created_at":"2024-04-25 04:06:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32488,"visible":true,"origin":"","legend":"\u003cp\u003eNumbers and percentages of female leaders\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4316230/v1/f1ea2d6c622e6a48d9f067e9.png"},{"id":55273778,"identity":"01db8ac9-eb76-4b33-aa24-2ade765d59d6","added_by":"auto","created_at":"2024-04-25 04:06:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33006,"visible":true,"origin":"","legend":"\u003cp\u003eNumbers of female secretaries and mayors at the prefectural level\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4316230/v1/337d756c2e2fa22013c74f63.png"},{"id":55274698,"identity":"976c980a-549b-4201-930a-5f25f26712d2","added_by":"auto","created_at":"2024-04-25 04:22:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":445730,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4316230/v1/7b045b99-9886-401b-8275-df4e9f844e67.pdf"},{"id":55273780,"identity":"d6cdeb39-f68f-4b99-9e11-9c45aacf3b44","added_by":"auto","created_at":"2024-04-25 04:06:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19397,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4316230/v1/3e4478afc367ebb3fe811e36.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDoes Female Leadership Lead to Better Economic and Social Development? Evidence from Local Chinese Governments\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAfter decades of considerable legislative, social, and policy efforts, there are undoubtedly more female leaders at present than at any other time in world history. According to the IPU-UN Women\u0026rsquo;s Map of Women in Politics (International Parliamentary Union, 2019), female representation in political leadership positions has followed an upward trend in recent decades. Records indicate that at the beginning of 2020 worldwide, 20 women held the highest national office as presidents or prime ministers, and the proportion of female ministers was at an all-time high of 20.7 percent (812 out of 3,922). The growing proportion of women in politics has attracted attention from multidisciplinary scholars who want to know the implications of female leadership for public policy outcomes (Coscieme et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lemoine, \u0026amp; Blum, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although the representation of females in public administration is increasing, the inescapable reality is that female leaders in public office remain scarce and conspicuous by their absence (Mendelberg and Karpowitz, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch from economics and political science investigates whether electing female leaders indicates different policy and political outcomes; however, the results are quite mixed and controversial. According to the classic work of Downs (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1957\u003c/span\u003e), the preferences of male and female leaders should not influence policy outcomes, since politicians would converge their policy to cater to the preferences of median voter. This perspective is supported by some empirical studies. For example, Ferreira and Gyourko (2014) found that gender does not matter to political leadership. After analyzing a novel dataset of U.S. mayoral elections from 1950 to 2005, they found no effect of gender on policy outcomes, such as the size of local government, the composition of municipal spending and employment, or crime rates. Another line of research from developmental economics, however, shows that female politicians affect policy outcomes. Chattopadhyay and Duflo\u0026rsquo;s (2004) research found that female participation in rural governance in India resulted in increased expenditures in areas such as public investments in providing clean water. Their study is echoed by similar studies that found that female politicians affect policy outcomes regarding education (Beaman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Clots-Figueras, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), healthcare (Besley and Case, 2003), forest conservation (Leone, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and child-related spending (Clots-Figueras, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Recently, some scholars found that states with women leaders had fewer deaths during the COVID-19 crisis than states with male leaders, and this female leadership impact was not only found in the U.S. (Sergent and Stajkovic, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but also confirmed worldwide (Garikipati and Kambhampati, 2020). These inconsistent results regarding female leadership in the public sector indicate that more perspectives are welcome in this field of study. More importantly, the existing studies mainly investigate whether female leadership matters but lack of explaining the underline mechanism how female leadership influence the political outcomes. The role of female leadership in public administration still lacks theoretical and empirical frameworks through which to fully understand the impact of female leadership in public policy making. In the field of management, although we can learn from the literature on female directors on corporate boards, the impact of female leadership beyond the scope of private organizations has only recently attracted limited attention (Abdullah, Ismail, and Nachum, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Guy and Meier, 2016; Leone, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). How female leadership in government influence the policy outcomes remain a black box.\u003c/p\u003e \u003cp\u003eIn this article, we aim to answer this question by combining the two competing approaches from management and organizational studies. The first is institutional approach that emphasizes that organizations can constrain decision makers (March and Olsen, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Peters, 2000). Institutional features such as routines, procedures, and conventions mould individuals\u0026rsquo; values and working styles. To a certain extent, intuitions can direct or indirect shape and guide individual behaviors, so that gender on its own is an insufficient explanation for possible different political outcomes. The impact of formal organizational role in intuitions eclipse the possible influence of gender role. Another perspective from social roles theory argues that leaders\u0026rsquo; behaviors are defined by their position in an organization as well as function under the constraints of their gender role (Eagly, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Eagly, Karau, and Makhijani, 1995; Chrobot-Mason, Hoobler, and Burno, 2019). Although some gender-stereotypic differences vanish under the influence of institutional constrains, others do not vanish. Research on social roles theory (e.g., Eagly and Karau, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) suggests that people attribute more communal characteristics, such as helpfulness, concern for others, kindness and gentleness, to females and agentic characteristics, like assertive, competitive, and decisive to males. People respond to a leader with gender stereotypic expectation, and leaders may internalize gender roles to some extent due to social norm conformity (Lafferty, Phillipson, and Jacobs, 2019; Wood et al., 1997). As a result, male and female leaders may form different social identities and different role perceptions in an organizational setting. When the intuitional constrains are not strong and leaders have discretion, organizational behavior that may vary according to gender.\u003c/p\u003e \u003cp\u003eTo test these hypotheses, we focus on female mayors and secretaries in China and answers the major question of whether or not having women as mayors or secretaries at the prefecture level have different impact on public policy-making and local development. We used the publicly available data of prefecture-level cities\u0026rsquo; economic performance, government financial expenditures, and demographic data from the period between 1995 and 2015 in China. China is a suitable research context to study how an organization contains and shapes the role of female leadership effectiveness since the state bureaucracy has established different performance and reward systems for government leaders who are held accountable for implementing the state\u0026rsquo;s economic development and social development goals (Zhou, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2010\u003c/span\u003e: 19; Wu et al., 2013). Leading economic development is the vital factor that determines Chinese government leaders\u0026rsquo; career development within the bureaucracy, and promoting social development is difficult to quantify and usually receives less attention from government leaders. China\u0026rsquo;s dramatic economic and social development in recent decades makes it an ideal empirical context to test our framework. The heterogeneity and high-speed development in China allow us to examine the impact of female leadership on economic growth and social development. In addition, the Chinese Communist Party (CCP) and the government have tremendous involvement in the country\u0026rsquo;s economic and societal development (Zhou, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which grants politicians great discretion and a high impact within their jurisdiction. Furthermore, the Chinese culture has rigid gender role norms and emphasizes separate roles for men and women (Hofstede, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1980\u003c/span\u003e); this culture provides an ideal context to test the influence of gender roles. Our results suggest that the impact of gender disappears when leading economic growth but exists in promoting social development. Female leaders may not ensure better economic growth than male leaders, as indexed by GDP growth rates, but they usually produce more balanced and welfare-oriented development, as measured by higher expenditures on social security and employment.\u003c/p\u003e \u003cp\u003eOur article contributes to the female leadership literature in several key ways. First, the present study contributes to the unsolved institutional-individual debate, that is, which is more important in shaping organizational behavior (Christensen and L\u0026aelig;greid, 2018)? Second, this study joins in the increasing number of studies that bridge the micro-macro divide by relating a micro predictor (leader\u0026rsquo;s gender) to macro outcomes (economic performance and expenditures on social security and employment). This cross-level approach answers the call for conducting more studies that serve to integrate different levels of analysis in gender and leadership studies (Hoobler et al., 2018; Sergent and Stajkovic, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Third, we extend the literature on female leadership with empirical evidence from an important nondemocratic society, and this is an important strength of this study. The existing research on female leadership mainly focuses on Western countries. However, we need to know much more about the impact of women in leadership roles outside of the Western, educated, industrialized, rich, and democratic (WEIRD) countries. In addition, we complement the theoretical knowledge and empirical evidence regarding role theory by extending its context to public administration. Our study examines the impact of female leadership on public organizations and demonstrates that gender role effect is also important at the political level.\u003c/p\u003e \u003cp\u003eThe rest of the paper is organized as follows. Section 2 presents the theoretical framework and the formulated hypotheses. Section 3 describes the data and the sample used for the analysis and provides some descriptive statistics of the relevant variables. Section 4 reports the results and provides robustness checks. Finally, Section 5 contains a discussion and future research directions.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eBefore establishing the relationships between female leadership and political outcomes, we discuss the findings of the literature on women in politics and the Chinese context, and then, we provide the theoretical framework of this study.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eWomen in Politics\u003c/h2\u003e\n \u003cp\u003eAlthough women are still underrepresented at senior levels in both the public and economic sectors, there are currently more female leaders globally than at any other time in history. This dramatic change has drawn attention from economists, managers, policy analysts, and other social scientists who aim to understand the implications of women in public appointments. Classic political science theory suggests that there will be no changes in policy once a woman takes office, since all politicians care only about winning elections (e.g., Downs, \u003cspan class=\"CitationRef\"\u003e1957\u003c/span\u003e). Democracy and median voters force women and men to the center of policy spaces and offer similar platforms. In this case, the impact of gender should not matter since women and men need to cater to the preferences of median voters. This argument is supported by some empirical studies. For example, Ferreira and Gyourko (2014) used a dataset of U.S. mayoral elections from 1950 to 2005 and found no effect of mayors\u0026rsquo; gender on policy outcomes related to the size of the local government, the composition of municipal spending and employment, or crime rates. However, other research evidence suggests that gender does matter in political leadership.\u003c/p\u003e\n \u003cp\u003eChattopadhyay and Duflo (2004) used political reservations for women in India to study the impact of women\u0026rsquo;s leadership on policy and decision making; they found that an increase in female participation resulted in increased expenditures in areas such as public investments in providing clean water. Chattopadhyay and Duflo\u0026rsquo;s research is echoed by similar studies that found that female politicians affect policy outcomes regarding education (Beaman et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Clots-Figueras, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), healthcare (Besley and Case, 2003), forest conservation (Leone, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), and child-related spending (Clots-Figueras, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). These results suggest that the contexts in which women function in terms of political leadership influence the impact of gender on policy and political outcomes. In India, for example, clean drinking water is important to the needs of both adults and their children. Female leaders are often more knowledgeable about areas related to childcare and thus devote themselves to providing clean water to gain votes from other women.\u003c/p\u003e\n \u003cp\u003eFor the most part, however, this existing literature only examines the performance of women in democratic societies and is limited to electoral politics. We know little about the impact of women in leadership roles in nondemocratic societies, such as China. In the next sections, we first introduce the Chinese context and then propose a theory of female leadership that connects role congruity and female leaders\u0026rsquo; impact on political outcomes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eThe State\u0026rsquo;s Different Goals and Female Leaders in China\u003c/h2\u003e\n \u003cp\u003eSince its reform and opening up in 1978, China\u0026rsquo; development strategy has focused on economic development. As part of the market reform, the central government has built an effective state bureaucracy to bolster economic growth (Li, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e; Bo, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). In this system, the central government makes short- and long-term development plans and allows local governments to compete with one another for resources and prosperity. This strategy has enabled China to evolve over the past four decades from a marginal economic player to the second-largest economy in the world. Economic development is an imperative goal for the government (Choi, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhou, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). In fact, as such an important government objective, economic growth even comes at the cost of rising social inequality, environmental pollution, and corruption (Nie, Jiang, and Wang, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The campaign for \u0026ldquo;Building a Harmonious Socialist Society\u0026rdquo; of former President Hu Jingtao in 2004 demonstrates that the state has come to consider maintaining social stability as important as sustaining economic growth (Wang, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The state bureaucracy has established development goals and evaluation and promotion criteria for government leaders, who are held accountable for implementing the state\u0026rsquo;s dual goals. Government leaders\u0026rsquo; performance is evaluated through the following three categories of indicators: hard targets, which refer to economic indicators, such as the GDP growth rate; soft targets, which include social welfare indicators, such as improving education, providing health care, and alleviating environmental damage; and targets with veto power, which are political tasks, including maintaining social stability (Edin, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). Hard targets are the most important factors that determine Chinese government leaders\u0026rsquo; career development within the bureaucracy. In contrast, soft targets are difficult to quantify and are not immediately related to career advancement; therefore, they usually receive less attention from government leaders (Zhou, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e: 19; Wu et al., 2013).\u003c/p\u003e\n \u003cp\u003eThe Chinese government\u0026apos;s emphasis on social issues has largely coincided with the rise in female political participation. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e represents the number of female secretaries and mayors. In the 1990s, there were few female leaders in the Chinese government, and the female leadership ratio was less than 1 percent in the early 1990s. The Program for the Development of Chinese Women (1995\u0026ndash;2000) set out well-defined goals relating to women\u0026rsquo;s participation in government and politics; among these goals were, by the end of the twentieth century, a minimum of one female member in the core leadership teams of party and government organizations at the municipal level. The Chinese central government also declared from 2001 to 2005 that the leading groups\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e of prefectures should contain at least one female member, and the deployment of female cadres should be prioritized in the departments of education, science and technology, culture, health, sports, civil affairs, labor and social security, which are related to the state\u0026rsquo;s soft targets (Organization Department of the Central Committee of CPC, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). In 2006, the Chinese government held a national forum on training and selecting female cadres, which gives preference to selecting and appointing female cadres when they are equally qualified in other aspects. The number of female leaders increased annually after these policies were established. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the increasing number of female secretaries and mayors at the prefectural level; twice as many women have been elected to leadership positions within prefectures since 2006. This pattern conforms to the data from the Organization Department of the CCP Central Committee. In 2009, at the prefecture (director-general) level, female cadres accounted for 13.7 percent of the total number of cadres at the same level (United Nations, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e. In fact, at all levels of government, there is typically a female leader in charge of science, education, culture and health, which are sectors that are relevant to the state\u0026rsquo;s soft targets. For example, Vice Premier Sun Cunlan is currently responsible for handling the COVID-19 pandemic in China.\u003c/p\u003e\n \u003cp\u003eAlthough the state has a history of assigning female cadres to fulfil its social development goals, it is still not certain whether female leaders are effective in implementing this state goal and whether they pay sufficient attention to soft targets. We do not know whether the extant findings regarding female leaders in other countries and their concerns about \u0026ldquo;women\u0026rsquo;s issues\u0026rdquo;, such as clean drinking water, education, and forest conservation, are also relevant in China (e.g., Besley and Case 2003; Chattopadhyay and Duflo 2004; Thomas, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e; Thomas and Welch, 2001).\u003c/p\u003e\n \u003cp\u003e------------------------\u003c/p\u003e\n \u003cp\u003eInsert Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e-----------------------\u003c/p\u003e\n \u003cp\u003e------------------------\u003c/p\u003e\n \u003cp\u003eInsert Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e-----------------------\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eThe Institutional Approach and Role Theory\u003c/h2\u003e\n \u003cp\u003ePrior studies have pointed out that government leaders may balance the state\u0026rsquo;s multiple goals differently based on their political incentives at different career stages (Wang and Luo, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); however, we do not know whether there is a gender effect on how government leaders implement the state\u0026rsquo;s goals. The institutional approach emphasizes that organizations can constrain decision makers. Guy Peters (2000) defines an institution as \u0026ldquo;a formal or informal, structural, societal or political phenomenon that transcends the individual level, that is based on more or less common values, has a certain degree of stability and influences behavior\u0026rdquo; (18). The routines, procedures, conventions, organizational forms and technologies in institutions all offer and constrain behavior alternatives and to a certain extent, shape and guide individual behaviors (March and Olsen, \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e). This means that males and females who occupy the same leadership roles defined by their specific position in a hierarchy would behave very similarly when then there are strong organizational constraints (Peters, 2000).\u003c/p\u003e\n \u003cp\u003eLeaders not only occupy roles defined by their position in an organization but also simultaneously function under the constraints of their gender role. Research in a natural setting found that although some gender-stereotypic differences vanish under the influence of an organizational role, others do not vanish (Moskowitz, Suh, and Desaulniers, \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e). Despite pressures to conform to institutional routines and norms, gender roles often exert some influence, with the result that males and females with the same organizational role may behave somewhat differently. Research associated with female leadership emphasizes the \u0026ldquo;feminine\u0026rdquo; qualities of cooperation, listening, caring, communication, and knowledge sharing (e.g., Book, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Eagly and Carli, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ali and Kulik, 2014). Such female leadership styles are increasingly important for building, opening, and sharing internal environments that are conducive to high-quality decision making and the recognition of strategic opportunities in contemporary organizations (e.g., Adams and Ferreira, 2009; Cohen and Levinthal, \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e; Helfat and Martin, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Huang and Kisgen, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to role theory (Eagly and Karau, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), females have a communal gender role but not a very agentic role, and males have an agentic role but not a communal role. Communal characteristics primarily relate to a concern for the welfare of other people, while agentic characteristics primarily describe an assertive, confident and dominating tendency. Organizational behaviors include many actions. The selective and discretionary aspects of organizational behaviors are most likely subject to a gender effect.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFemale leadership and economic growth.\u003c/strong\u003e China\u0026rsquo;s GDP growth rate fluctuated but remained high from 1995 to 2015. This favorable macroeconomic environment provides a good context to test local governors\u0026rsquo; effectiveness. For local Chinese governments, economic growth represents the main metric to assess officials (Choi, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhou, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Because of the fiscal decentralization and political centralization since 1978, the GDP competition among local Chinese governments has induced local officials to focus mainly on economic development (Xu, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). The government plays an important role in economic growth through activities such as providing fiscal incentives, introducing industrial policies, and creating favorable business environments (e.g., Jensen, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dollery and Wallis, 2001). Leaders\u0026rsquo; roles in leading economic development should be of primary importance in the government, because these roles endow them with legitimate authority and are regulated by clear rules and norms. It is likely that leadership roles in leading economic development provide many norms about how tasks should be performed. Institutional constraints should be strong here since achieving economic development goals represents the hard targets in leaders\u0026rsquo; performance evaluation and regard system within the bureaucracy (Zhou, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e: 19; Wu et al., 2013). Having a leading role in local economic development means that both male and female leaders need to play an organizational role that involves activities such as striving for support from upper-level government (Zhou, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), attracting foreign direct investment (Tung and Cho, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wang, Zhang, and Qin, 2007), and mobilizing local companies to invest (Liu et al., 2019). In line with our argument that the influence of gender roles can be diminished or even eliminated with strong institutional pressures, the gender-stereotypic differences should disappear when males and females play their organizational role in leading economic development. Therefore, from an institutional perspective, we have our first hypothesis:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eThere is no direct relationship between female leadership and local economic growth.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Leadership and Social Policy\u003c/strong\u003e. Economic development is of primary importance to the Chinese government, therefore, it is unsurprising that the institutional constraints are sufficiently strong to transcend the impact of individual factors such as gender when leaders play their organizational role in leading economic development. The bureaucracy has a clear performance and reward system related to economic development. In contrast, social development is difficult to quantify and is not immediately related to government officials\u0026rsquo; career advancement; thus, social development usually receives less attention from government leaders. Despite increasing pressure to emphasize social development from the central government, local Chinese governors generally have some leeway to vary the extent to which they carry out the required activities. For example, Nie, Jiang, and Wang, (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) found evidence that local government officials took risks of failing to maintain social stability when coping with coalmine accidents. In addition, unlike their counterparts in democratic countries, Chinese governors do not need to respond to voters, which means that they have greater discretion in regard to social policy. This discretionary part of organizational behavior is likely subject to the gender effect. The influence of gender roles occurs not only because people respond to a leader with gender stereotypic expectation but also because most people have internalized gender roles to some extent (Lafferty, Phillipson, and Jacobs, 2019; Conry-Murray, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wood, et al., 1997). As a result, males and females may form different social identities and different role perceptions in an organizational setting.\u003c/p\u003e\n \u003cp\u003eAccording to role theory, people attribute more communal characteristics, such as helpfulness, concern for others, kindness and gentleness, to females (Eagly and Karau, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). Females\u0026rsquo; preferences are more related to promoting social development. Empirical results have also found that women desire more social services to substitute for their reduced production within the home (Cavalcanti and Tavares, 2011). Studies about female state legislators have found a positive relationship between gender ratios and the propensity of introducing or passing bills concerning \u0026ldquo;women-related issues\u0026rdquo; (e.g., Thomas, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e; Berkman and O\u0026rsquo;Connor, 1993; Thomas and Welch, 2001). In addition, having more women in decision-making positions increases female policy makers\u0026rsquo; responses to female residents\u0026rsquo; complaints, such as complaints that concern clean drinking water (Chattopadhyay and Duflo, 2004), education (Clots-Figueras, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), health spending (Rehavi, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), and forest conservation (Leone, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although it is understood that female leadership has some impact on social policy, none of these studies were conducted with data on local governments in China; therefore, it is not clear how these findings can be extrapolated from democratic countries to a nondemocratic setting with a leader\u0026rsquo;s high discretionary power and no pressure from voters. Drawing on role theory and the empirical studies conducted in other countries, we expect that female leadership will influence the government to make more effort to develop social policy.\u003c/p\u003e\n \u003cp\u003eTherefore, we formulate our second hypothesis:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eThere is a positive relationship between female leadership and social development.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData and Sample\u003c/h2\u003e \u003cp\u003ePrevious studies usually use financial indicators as the primary meter of whether females are effective in their leader role (Hoobler et al., 2018; Walsh, Weber, and Margolis, 2003), and relatively few studies consider female leaders\u0026rsquo; impact on welfare outcomes (Sergent and Stajkovic, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the present study, we evaluate female leader effectiveness with more balanced meters that combine economic effectiveness and social welfare.\u003c/p\u003e \u003cp\u003eWe merged two different types of datasets. The first contains data on prefectures\u0026rsquo; economic performance and government financial expenditures. The second dataset consists of detailed data on the leadership composition and basic prefecture information. We collected data from the period of 1995 to 2015, in which the Chinese economy showed a high growth rate\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e. The main reason for choosing this period is that local Chinese statistics were established in the late 1990s\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003cp\u003eThe data from the prefectural level of the state were the focus for two major reasons. First, we cannot evaluate the performance of female leaders at the provincial level because there have been only 4 female leaders at the provincial level since 1982. Second, prefectural governments have greater financial autonomy than the county level government, and leaders can arrange local expenditure patterns according to their own preferences.\u003c/p\u003e \u003cp\u003eWe collected prefectures\u0026rsquo; economic performance and government financial expenditures by using several datasets. The first dataset is the \u003cem\u003eNational Collection of Financial Statistics of Prefectures and Counties\u003c/em\u003e. It was collated by the Budget Department of the Ministry of Finance and published annually by the China Financial \u0026amp; Economic Publishing House from 1993 to 2009. This dataset contains basic information regarding the economic performance, budget balance sheet and characteristics of all prefectures in China. It includes variables that we are interested in, such as GDP, social security, employment and education expenditures\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e at the prefecture level; it also includes the numbers of residents so that we can calculate each variable in per capita terms. We obtained the data for GDP and expenditures on education and science from the \u003cem\u003eChina City Statistical Yearbook\u003c/em\u003e for recent years and replenished the missing data\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e. We obtained the social security, employment and education expenditure variables from the \u003cem\u003eChina Statistical Yearbook for the Regional Economy\u003c/em\u003e for recent years. The final yearbook datasets that we used in this paper are the statistical yearbooks for each province in China\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e, which we used to fill in the missing data.\u003c/p\u003e \u003cp\u003eWe combined three datasets to complete the leadership composition details and basic information of prefectures. The first dataset was the Data on Prefectural Party Secretaries and Mayors of the Peoples\u0026rsquo; Republic of China from 2000 to 2010. This database was hand-collected by Fudan WTF SOSC Lab (Chen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The second dataset was the Chinese Local Government Officials Database (CGOD), which was collected by Chinese Research Data Services. This dataset collects the demographic information of most Chinese government officials at the prefecture level. The CGOD information is publicly available. We merged these two datasets to obtain prefecture leaders and basic information from 1990 to 2015 and then filled in the missing data according to the China Political Elite Database (CPED)\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e. These three databases had similar collection processes with different variables. For example, the first dataset collected the basic demographic characteristics of all secretaries and mayors of the prefectures and municipal party committees. The names of the leaders were obtained from the provincial yearbooks. Their curricula vitae were searched on the government website and official media, such as the Xinhua News Agency and People\u0026rsquo;s Daily Online. We also used some prefectural yearbooks to filled some missing data. We double-checked and extracted the information among these three datasets about leaders\u0026rsquo; demographic characteristics, educational background and work experience, such as their gender, age, education level, and time of joining the CCP. We collected only the name of the most recent secretary or mayor in a certain year in each city if there was more than one occupant\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e. These databases have been widely used in Chinese political and economic studies (e.g., Jiang, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Que, Zhang, and Schulze, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jin, Shen, and Li, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Chinese government has a convention that precludes any occupant of a position from serving more than two terms, which usually lasts ten years. This means that all the cities included in our data changed secretaries and mayors during the period of this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDependent variable\u003c/h2\u003e \u003cp\u003eThe most common way to measure economic achievement is through GDP and GDP growth rate. The extant literature suggests that the likelihood of the promotion of provincial leaders increases with the enhancement of their jurisdiction\u0026rsquo;s economic performance (Li and Zhou, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). We use \u003cem\u003eGDP per capita\u003c/em\u003e and \u003cem\u003eGDP growth rate\u003c/em\u003e to measure the influence of the gender effect on the prefecture\u0026rsquo;s economic performance.\u003c/p\u003e \u003cp\u003eWe assume that if female leaders are more concerned about social development, then they will spend more on public works. Thus, a female secretary or mayor will increase expenditures on social security and employment. We also notice that prefecture-level cities\u0026rsquo; scale, such as the population, affects government expenditure. We used \u003cem\u003eExpenditures on Social Security and Employment per capita\u003c/em\u003e and \u003cem\u003eExpenditures on Education per capita\u003c/em\u003e to test our hypothesis in this study\u003ca class=\"FNLink\" href=\"#Fn10\" id=\"#FNLinkFn10\"\u003e\u003c/a\u003e. The level of economic development and fiscal revenue also shows different expenditure levels. Thus, we used \u003cem\u003eExpenditure ratio on Social Security and Employment\u003c/em\u003e and \u003cem\u003eExpenditure ratio on Education\u003c/em\u003e to address this issue.\u003c/p\u003e \u003cp\u003eIt is also a concern that the economy fluctuates in a single year due to unexpected strikes. This will mislead us when calculating gender effects. We use \u003cem\u003eAverage GDP Growth Rate\u003c/em\u003e, \u003cem\u003eExpenditure ratio on Social Security and Employment\u003c/em\u003e and \u003cem\u003eExpenditure ratio on Education\u003c/em\u003e during leaders\u0026rsquo; tenure to overcome this time effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eIndependent variables\u003c/h2\u003e \u003cp\u003eThe key variable of concern is a female leader at the prefectural level. We find in our data that few prefecture-level cities had female secretaries and mayors simultaneously\u003ca class=\"FNLink\" href=\"#Fn11\" id=\"#FNLinkFn11\"\u003e\u003c/a\u003e. Thus, we define \u003cem\u003eFemale Leadership\u003c/em\u003e as the presence of a female secretary or mayor at the prefecture level.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eControl variables\u003c/h2\u003e \u003cp\u003eSince Mincer\u0026rsquo;s (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) study, economists have used individuals\u0026rsquo; education attainment and work experience to measure their human capital. They found that these variables are strongly related to one\u0026rsquo;s ability and earnings. Leaders\u0026rsquo; abilities and experiences definitely affect regional economic performance (Wang and Xu, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In this sense, we included age, party standing and educational background as control variables.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAge\u003c/em\u003e is strongly related to several aspects of leadership. First, age is highly correlated with work experience. Thus, age may negatively or non-significantly affect a prefecture\u0026rsquo;s economic outcomes because there is no possibility of promotion for older officials. Finally, older secretaries may have a more conservative policy because they want to reach retirement uneventfully (Wang and Luo, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParty membership is another important factor to measure a leader\u0026rsquo;s work experience and ability. First, the process of applying for Chinese Communist Party membership is lengthy and involves rigorous screening. Individuals must pass at least five stages, including (1) self-selection, (2) political participation, (3) daily monitoring, (4) a closed-door evaluation, and (5) a probationary examination, which are called \"loyalty filters\" (Walder, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), to achieve membership. The process of joining the party may take several years. This means that a talented person can become a party member earlier than a normal person. Second, party membership has a significant effect on mobility into elite positions with political and managerial authority (Bian, Shu and Logan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Third, a party member can take the opportunity to join party activities that promote human capital and social network formation. Moreover, party membership is positively correlated with income (Knight and Yueh, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These aspects indicate that a long party standing can be a proxy variable for one\u0026rsquo;s remarkable ability.\u003c/p\u003e \u003cp\u003eThe educational background of a leader may be an important factor for leadership, behavior, and promotion\u003ca class=\"FNLink\" href=\"#Fn12\" id=\"#FNLinkFn12\"\u003e\u003c/a\u003e. We used \u003cem\u003eyears of education\u003c/em\u003e to capture this influence. Because education is a key factor in human capital development, well-educated leaders can be far-sighted and sagacious. A highly educated background also brings circles of well-educated friends. Importantly, many officials attend party schools or other universities for advanced degrees after taking office. Our calculation of a leader's years of education includes the degrees obtained on the job.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEndogeneity issues\u003c/h2\u003e \u003cp\u003eEndogeneity is a key concern in the analysis. In the following, we offer some strategies that help reduce endogeneity to make our conclusion more robust.\u003c/p\u003e \u003cp\u003eFirst, endogeneity can be caused by omitted variables and unobserved heterogeneity. Due to the limitations of our data, we cannot control more information about prefectures and leaders. To address this concern, we used a fixed effects model at the city level to absorb the unobserved heterogeneity at the prefecture level. In addition, we used a leader\u0026rsquo;s party membership to control unobserved ability. The CCP admission process has merit selection characteristics (Walder, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). A longer party membership indicates that a leader exhibited strong ability when he or she was young.\u003c/p\u003e \u003cp\u003eSecond, measurement error can also result in endogeneity. The statistical indicators for any single year are unstable and vulnerable to manipulation (Lyu, et al., 2018). This may bias our estimation of a leader\u0026rsquo;s effectiveness. One way to avoid this concern is using average indicators during the leader\u0026rsquo;s tenure as independent variables to reduce the endogeneity.\u003c/p\u003e \u003cp\u003eThird, the gender differences inside cities are almost exogenous in this study. Chinese government officials are appointed rather than elected. Within the state bureaucracy, the upper-level government makes decisions regarding the promotion of secretaries and mayors within the prefecture. The incumbents of a prefecture cannot determine who will be their successors or partners.\u003c/p\u003e \u003cp\u003eAnother possible endogeneity concern is reverse causality because it is very likely that women are more likely to be promoted to a high-profile position in more developed prefectures characterized by higher GDP and more social expenditures. We sorted the prefectures geographically and confirmed that there were no significant differences in the distribution of female leaders among cities with different development levels (see \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Figs.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEstimation\u003c/h2\u003e \u003cp\u003eFrom 1995 to 2015, the number of female leaders changed according to cities and time. Due to the uncertainty of leadership changes, our data structure is one special type of staggered treatment adoption (Athey and Imbens, 2018). The staggered treatment adoption refers to prefectures adopt treatment at a particular point in time, and then remain exposed to this treatment at all times afterwards. The treatment here refers to the prefecture led by female leader. In addition, our data also face a uncertainty of female leader\u0026rsquo;s tenure, which increases difficulties of identification.\u003c/p\u003e \u003cp\u003eThis complex data structure requires us to find a sophisticated way to evaluate the treatment effects of female leadership. Fortunately, Athey and Imbens (2018) noticed that under a random assignment of the adoption date, the standard difference-in-differences estimator is an unbiased estimator of a particular weighted average causal effect. In this study, the selection and appointment of prefectural leaders was decided by upper government, and tenure of local officials was not fixed, which implies the adoption date was approximate under a random assignment. We chose the prefectures that have a female leader in one year as the treatment group, and the prefectures led by males as the control group. Then, we use a flexible version of a panel data difference-in-differences (DID) model to calculate female leadership effects. This flexible DID looks like prefectural two-way fixed effects models.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\text{Y}}_{it}={\\beta }_{0}+{\\beta }_{1}{treat}_{it}+ {\\beta }_{2}{\\text{X}}_{it}+{\\gamma }_{i}+{\\delta }_{t}+{ϵ}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere y is the dependent variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({treat}_{it}\\)\u003c/span\u003e\u003c/span\u003eis our core independent variable which refers to female\u0026rsquo;s leadership in prefectural \u003cem\u003ei\u003c/em\u003e and year \u003cem\u003et\u003c/em\u003e, and X is a set of control variables. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma \\text{a}\\text{n}\\text{d} \\delta\\)\u003c/span\u003e\u003c/span\u003erefer to the fixed effect of the prefecture and year, which are equal to the control dummies of the control group and time of treatment. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }\\)\u003c/span\u003e\u003c/span\u003eis an error term. Subscript \u003cem\u003ei\u003c/em\u003e refers to the prefecture, and \u003cem\u003et\u003c/em\u003e refers to the CCP General Secretary transition which implies different governance patterns of central government. We control year effects as robust check. All variables are measured at time t, while the average performance is measured based on the leader\u0026rsquo;s tenure.\u003c/p\u003e \u003cp\u003eIn addition, we apply the flexible conditional difference-in-differences approach (flex panel DID) as a robustness check and to achieve unbiased policy effect, which is useful for a causal analysis of the treatments with varying start dates and varying treatment durations (Dettmann, Alexander, and Antje, 2020). In detail, this method modifies the conditional DID approach of Heckman et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and gains more flexibility. The basic idea of this method is to combine matching and DID to find adequate controls for the treated units. The flex-panel-DID estimator can be regarded as a special case of the group-time average treatment effects. We apply this flexible panel DID approach in three steps. First, we rearrange the data to incorporate the observation date of all matching variables and outcomes. Second, we use an exact matching process or a matching process based on a combined statistical distance function to match the control and treatment groups. Third, we obtain the average treatment effect for the treatment group using the DID model. We use a standard difference-in-difference model to estimate the following equation.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${Y}_{it}={\\beta }_{0}+{\\beta }_{1}{treat}_{it}+ {\\beta }_{2}{treat}_{it}*{post\\_treat}_{it}+ {\\beta }_{3}{post\\_treat}_{it}+{\\gamma }_{i}+{\\delta }_{t}+{ϵ}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eY\u003c/em\u003e is the dependent variables, we use leader\u0026rsquo;s average performance during his or her tenure. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(treat\\)\u003c/span\u003e\u003c/span\u003e is a dummy which refers to female\u0026rsquo;s leadership in prefectural \u003cem\u003ei\u003c/em\u003e and year \u003cem\u003et\u003c/em\u003e\u003ca class=\"FNLink\" href=\"#Fn13\" id=\"#FNLinkFn13\"\u003e\u003c/a\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({post\\_treat}_{}\\)\u003c/span\u003e\u003c/span\u003e is a dummy which equal 1 since prefectural led by female leader. The interaction term is our main concerned, which means that prefectural \u003cem\u003ei\u003c/em\u003e was led by female leader at time \u003cem\u003et\u003c/em\u003e. This term is also called as DID term. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma \\text{a}\\text{n}\\text{d} \\delta\\)\u003c/span\u003e\u003c/span\u003erefer to the fixed effect of the prefecture and year.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eData Summary\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the summary statistics for all leaders\u0026rsquo; and prefectures\u0026rsquo; features. After excluding the prefectures with missing information on the key variables, such as leaders\u0026rsquo; gender, we obtained 4,009 prefecture-year observations that pertain to 321 unique prefectures from 1995\u0026ndash;2015. In total, 161 female party secretaries and 250 female mayors accounted for 9 percent of the sample during this time period.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Statistics and Pairwise Correlations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale leadership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMayor\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.044***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMayor\u0026apos;s Years\u003c/p\u003e\n \u003cp\u003eof Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.239***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMayor\u0026apos;s party\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.068***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecretary\u0026rsquo;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.029*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecretary\u0026rsquo;s Years\u003c/p\u003e\n \u003cp\u003eof Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.051***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.288***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecretary\u0026rsquo;s party\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.327***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.058***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer capita GDP (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11576.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19100.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e389.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67764.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.175***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.196***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP growth rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer capita Expenditure\u003c/p\u003e\n \u003cp\u003eon Social Security\u003c/p\u003e\n \u003cp\u003eand Employment (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.080***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.075***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion in budget\u003c/p\u003e\n \u003cp\u003eexpenditure on Social\u003c/p\u003e\n \u003cp\u003eSecurity and Employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer capita expenditure\u003c/p\u003e\n \u003cp\u003eon education (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.061***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.147***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.076***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion in budget\u003c/p\u003e\n \u003cp\u003eexpenditure on education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.057***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMayor\u0026apos;s tenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.065***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.055***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecretary\u0026rsquo;s tenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.033**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage GDP growth rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.066***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.042**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage rate of government expenditures\u003c/p\u003e\n \u003cp\u003eon social security and employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage rate of expenditures\u003c/p\u003e\n \u003cp\u003eon education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.058***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer capita GDP (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP growth rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.179***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer capita Expenditure\u003c/p\u003e\n \u003cp\u003eon Social Security\u003c/p\u003e\n \u003cp\u003eand Employment (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.667***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.105***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion in budget\u003c/p\u003e\n \u003cp\u003eexpenditure on Social\u003c/p\u003e\n \u003cp\u003eSecurity and Employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.168***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.519***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer capita expenditure\u003c/p\u003e\n \u003cp\u003eon education (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.716***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.095***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.722***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion in budget\u003c/p\u003e\n \u003cp\u003eexpenditure on education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.118***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.052***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.307***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.154***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.098***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMayor\u0026apos;s tenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.136***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.065***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.046***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.097***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecertary\u0026apos;s tenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.166***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.119***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.033**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.101***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.296***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage GDP growth rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.395***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.234***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.247***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.162***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.161***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage rate of government\u003c/p\u003e\n \u003cp\u003eexpenditures on social security\u003c/p\u003e\n \u003cp\u003eand employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.516***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.895***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.212***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.041**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage rate of expenditures\u003c/p\u003e\n \u003cp\u003eon education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.131***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.065***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.320***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.205***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.114***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.927***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.102***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.170***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.229***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1. Sample is based on model 1 of Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e-----------------------\u003c/p\u003e\n \u003cp\u003eOverall, the average secretary of the CCP municipal committee was 2 years older and had been a party member for two years longer than the average mayor (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;51.83, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.85 vs \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;49.86, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Although the younger mayors had a better educational background, the average education level was between an undergraduate and postgraduate education, and the average education time of both younger and older mayors was nearly 18 years. We found that most of the mayors and secretaries hold a part-time graduate degree. The GDP, social security, employment and education expenditure variables, due to their differences at various levels and across different years, were processed logarithmically in a subsequent analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eMain findings\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reports our basic results. Models 1 to 6 are the regression results of \u003cem\u003eper capita GDP\u003c/em\u003e, \u003cem\u003eGDP growth rate, Expenditure on Social Security and Employment per capita, Expenditure on Education per capita, Proportion of Expenditures on Social Security and Employment\u003c/em\u003e, and\u0026nbsp;\u003cem\u003eProportion of Expenditures on Education\u003c/em\u003e, separately. We add time effect dummies to each models to control the effects of CPC general secretaries transition.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePredicting a Prefecture-Level City\u0026rsquo;s (at T\u0026thinsp;+\u0026thinsp;0) Per Capita GDP, GDP Growth, Expenditures on Social Security and Employment per capita and Expenditure on Education per capita by Female Leadership\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFemale leadership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.240***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMayor\u0026rsquo;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSecretary\u0026rsquo;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSecretary\u0026rsquo;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.067***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMayor\u0026rsquo;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.050***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSecretary\u0026rsquo;s party standing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMayor\u0026rsquo;s party standing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCP General Secretary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e1. Models 1 to 6 are the regression results of \u003cem\u003eper capita GDP\u003c/em\u003e, \u003cem\u003eGDP growth rate\u003c/em\u003e, \u003cem\u003eExpenditure on Social Security and Employment per capita\u003c/em\u003e, \u003cem\u003eExpenditure on Education per capita\u003c/em\u003e, \u003cem\u003eProportion of Expenditures on Social Security and Employment\u003c/em\u003e, and \u003cem\u003eProportion of Expenditures on Education\u003c/em\u003e separately.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e2. We use a fixed effects model to control the unobservable prefecture-level characteristics that are constant over time. We report standard errors in parentheses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e3. *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e-----------------------\u003c/p\u003e\n \u003cp\u003eThe result of model 1 show that female leadership had a significant positive impact on per capita GDP (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003eGDP per capita\u003c/em\u003e\u003c/sub\u003e =0.24, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), which indicates that female leaders usually worked at prefectures with a stronger economy. It is evident that the age and party standing of the secretaries and mayors were positively correlated with GDP per capita. However, after we changed our independent variable to the GDP growth rate in model 2, the impact of female leadership became non-significant. This result indicates that there was no gender effect when male and female leaders played their organizational role in leading economic development.\u003c/p\u003e\n \u003cp\u003eA basic conclusion here is that prefectures with female leaders have a higher economic scale, but we do not observe that female leaders bring a significantly higher GDP growth rate. Hypothesis \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e is supported by the empirical results. Our findings indicate that female leaders can achieve the same GDP growth rate as their male counterparts.\u003c/p\u003e\n \u003cp\u003eModel 3 and 4 show the influence of female leadership on expenditures for social security and employment and expenditures for education. They show the same pattern of the impact of female leadership on social development. It is evident that female leadership can significantly increase expenditures on social security, employment and education per capita (\u003cem\u003eb\u003c/em\u003e\u003csub\u003esocial security and employment\u003c/sub\u003e =0.23, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003cem\u003eb\u003c/em\u003e\u003csub\u003eeducation\u003c/sub\u003e = 0.13, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) at the prefecture level, which indicates that female leadership can increase people\u0026rsquo;s livelihood within a city. We still find that the age and years of party membership of the secretaries and mayors helped increase expenditures related to social security and employment. Therefore, Hypothesis \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e is strongly supported by our results.\u003c/p\u003e\n \u003cp\u003eHypotheses 1 and 2 indicate that female leaders make higher expenditures on social issues, such as social security and employment, without undermining local economic growth. However, prefecture-level characteristics, such as fiscal conditions, may affect our findings. It is common sense that governments with higher fiscal revenue spend more on social development and give more attention to residents\u0026rsquo; welfare.\u003c/p\u003e\n \u003cp\u003eGiven this concern regarding our finding, we use the ratio of expenditures on social security, employment and education to the general budget expenditures as an alternative dependent variable to avoid the city effect. The last two columns in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the regression results by using these variables. We found that female leadership still has a positive effect on the proportion of expenditures on social security and employment (\u003cem\u003eb\u003c/em\u003e\u003csub\u003eexp_sse\u003c/sub\u003e = 0.01, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eb\u003c/em\u003e\u003csub\u003eexp_sse_\u003cem\u003eper\u003c/em\u003e\u003c/sub\u003e = -0.00, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, \u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.1\u003c/em\u003e). The lack of significance for the effects of female leadership on the proportion of expenditures on education suggests the complexity of expenditures on education, which needs further study.\u003c/p\u003e\n \u003cp\u003eThe independent variables in the previous analysis are indicated by the statistics for the officials who take office (T\u0026thinsp;+\u0026thinsp;0). This data collection strategy may suffer from some measurement errors. First, government leaders may balance the state\u0026rsquo;s different goals and their related targets differently in different years (Wang and Luo, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), and they may not commit to economic and social development targets in the same way every year. Second, local Chinese governments may intentionally manipulate regional GDP figures to meet or beat growth targets under different conditions (Ma et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lyu et al., 2018). To overcome these measurement errors and achieve a reliable evaluation of the officials\u0026rsquo; effectiveness, we average their performance in their term as an alternative measurement of the independent variables. Model 1 in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that female leadership has a negative effect on the average regional GDP growth rate (\u003cem\u003eb\u003c/em\u003e = -0.26, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.1\u003c/em\u003e). Models 2 and 3 indicate that female leadership still has a significantly positive effect on the average expenditure rate on social security and employment (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but not on the average expenditure rate on education (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00,\u0026nbsp;\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.1\u003c/em\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffects of Female Leadership on Average Local Economic Growth and Social Policy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFemale leadership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMayor\u0026rsquo;s tenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSecretary\u0026rsquo;s tenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSecretary\u0026rsquo;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.064***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMayor\u0026rsquo;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.058***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear Fixed Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e1. Models 1 to 3 are regressions of the average GDP growth rate, average rate of government expenditures on social security and employment and average rate of expenditures on education.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e2. *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e-----------------------\u003c/p\u003e\n \u003cp\u003eIt is found that a female leader (secretary or mayor) increases the prefectural city\u0026rsquo;s expenditures on social security and employment in terms of both the total amount and the proportion in the general budget. To eliminate a sample selection bias and achieve unbiased estimators, we apply the flex panel DID approach. We reorganize the dataset and use matching based on a combined statistical distance function. Specifically, every female leader is matched with a male leader with similar human capital characteristics, and only the gender difference is analyzed to obtain the average treatment effect. This means that only gender influences our independent variables in prefecture-level cities. We use leaders\u0026rsquo; characteristic variables, including education and tenure, as covariates to calculate our statistical distance.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e reports the DID estimator for the average treatment effects after matching. Model 1 in the table shows that female leaders do not have a significant impact on the regional average economic growth rate (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0. 09, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1). Therefore, Hypothesis \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e is supported. Model 2 indicates that female leadership still has a significantly positive effect on the average expenditures on social security and employment (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0. 01, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0. 01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Therefore, Hypothesis\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e is supported. This result is not only statistical significance but also economically important. For example, our data imply that regions with female political leader increased by 390-million-yuan social security and employment expenditures at 2015. This increase means additional 100-yuan expenditures per capita to local residents.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUsing a Flex-panel-DID estimator to Evaluate the Effects of Female Leadership on the Average Local Economic Growth and Social Policy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDID estimator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.459)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e1. These models used the statistical distance function and radius matching procedures. We applied standard DID estimation procedure after matching. We reported the interaction terms, which is also called a DID estimator. The independent variable in all regressions is the leader\u0026rsquo;s average performance during his/her term. Models 1 to 3 are the regressions of the GDP growth rate and government expenditures on social security, employment and education, separately.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e2. We report the robust standard errors in parentheses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e3. *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e-----------------------\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eFurther Analyses\u003c/h2\u003e\n \u003cp\u003eWe conducted a set of additional analyses to check the robustness of our findings. Given that an incoming secretary or mayor might not participate in all types of policy making and that he or she might have less impact on the economic development and budget of the local government in the first year in office, we checked our model by examining the effect of female leadership on economic growth and social development in the city in the next year (T\u0026thinsp;+\u0026thinsp;1) rather than the year (T\u0026thinsp;+\u0026thinsp;0), which is what is shown in our baseline models in Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e to \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. We supposed that after one year, the incoming secretary or mayor would participate in all decision-making processes and have a full impact on the development of the city.\u003c/p\u003e\n \u003cp\u003eThe results are consistent with our previous findings. Female leadership still has significant positive effects on social security and employment expenditures in the next year, but not significant on GDP growth. After time dummy variables are added, the coefficient is relatively stable.\u003c/p\u003e\n \u003cp\u003eSince we used unbalanced panel data with a fixed effects model in this study, this data analysis specification loses some observations. To address this limitation, we still estimated the random effects models and OLS regression models for each dependent variable and controlled a set of prefecture dummies to use more observations. These estimators show the same pattern as the fixed effects model, and all our results remain substantively robust.\u003c/p\u003e\n \u003cp\u003eFinally, we also address some possible moderating factors that may impact our results. First, although both party and government leaders are subject to the Chinese state\u0026rsquo;s bureaucracy, which stipulates officials\u0026rsquo; responsibilities, they have different preferences and priorities (Wang and Luo, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The CCP secretary is mainly responsible for ideology and social stability, while the mayor\u0026rsquo;s responsibility lies in formulating and executing specific policies, such as economic development. As a result, it seems necessary to differentiate female CCP secretaries and mayors in the study. However, the results from the separation analysis show a similar pattern to our main models, except that female secretaries spend significantly less on education. This suggests that the effect of female leadership may be closely tied to gender rather than specific duties. Second, the heterogeneity and complexity of Chinese regional development may distort our results. It is very likely that women are more likely to be promoted to be top political leaders in more developed prefectures. To address this concern, we further split the sample and present the results for East, Central, West and Northeast China. All the above patterns remain robust except that female leaders devote significantly more to education in Northeast China. Third, female leaders\u0026rsquo; age may be a confounding factor since a previous study showed that Chinese officials have political incentives to prioritize social stability in order to retire in peace (Wang and Luo, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In our study, we find that female leaders of different ages show almost the same patterns of economic performance and expenditures on social security and education. Accordingly, these three possible moderating factors are not significant in our analysis, which may be due to the limited number of female leaders in our sample.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eThis study investigates the impact of female leadership on policy outcomes at the local level of government in China. Specifically, we investigate the impact of female CCP secretaries and government mayors on local economic and social development. Similar findings in other countries reveal no impact of female mayors on the size of the local government, the composition of its expenditures, or local crime rates (Ferreira and Gyourko, 2014). Our results, however, provide evidence that gender matters to political leadership. By matching municipal economic performance and government financial expenditures with demographic data from the period between 1995 and 2015 in China, our findings suggest that female leaders tend to work in economically developed cities, as measured by high GDP and per capita GDP. Because of institutional constraints, the gender effect does not exist when male and female leaders play their organizational role in leading economic development. However, in regard to promoting social development, over which leaders have more discretionary power, female leaders, on average, ensure more balanced and welfare-oriented developmental preferences, as measured by higher expenditures on social security and employment. We find that female leadership in a high-profile political position results in more inclusive development in a nondemocratic government. These results indicate that having more women in politics is the right move to make in terms of equity in society. Although the growing proportion of women in politics is encouraging, there is still a steep road ahead. To gain the unique contributions of women as decision makers, there is still ample room to dramatically increase women’s representation in political decision making. Our study has practical implications in China, especially as Beijing’s priorities slowly shift toward more balanced and people-oriented growth. We can achieve sustainable economic and social development by increasing women’s participation in politics and political affairs.\u003c/p\u003e\u003cp\u003eFirst, by examining the gender effects of officials who implement state policies in China, our study contributes to the long-lasting debating about whether institutions or individuals are more important in shaping organizational behavior (Christensen and Lægreid, 2018; Evans and Rauch, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Rauch and Evans, 2000). From an institutional perspective, the normative rules and standardized procedures in the bureaucracy recruit similar officials and prevent gender from influencing policy outcomes, and they prevent female leaders from implementing divergent policies in the United States (Ferreira and Gyourko, 2014). Our study reveals that institutional constraints do not exert homogeneous pressures on shaping organizational behaviors. For some aspects such as leading economic development, which is of primary importance to an organization, the strong institutional constraints can directly or indirectly guide organizational behaviors. Individual characteristics such as gender, however, may have influence when leaders have discretionary power and the institutional pressures on leaders are relatively weak. In our study, female leaders achieve a better balance of the different goals of the state. Without sacrificing economic growth, women facilitate social development more effectively than their male counterparts. To the extent that male officials do not give as much attention as female governors give to social development, the central government fails to ensure that soft targets be implemented with coordinated efforts within the bureaucracy. Our view of the gender effect thus helps us to gain a better understanding of the \"Weberian state hypothesis” about how state goals can be fulfilled (Evans and Rauch, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Rauch and Evans, 2000).\u003c/p\u003e\u003cp\u003eSecond, our article contributes to the limited empirical studies that have investigated the impact of women in politics (Chattopadhyay and Duflo, 2004; Beaman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Clots-Figueras, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Leone, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our results demonstrate that gender matters at the political level. Increasing women’s participation in political decision making is not only politically correct but also has social benefits. Women’s preferences, values, and perspectives usually differ from those of men with regard to making decisions that are more inclusive and that lead to more efforts being expended on social issues. Our results show that having a local female political leader leads to improved public welfare in a nondemocratic country. All else being equal, prefecture-level cities with female party secretaries or mayors have approximately 6 percent higher expenditures on social security and employment. This spending bonus amounts to approximately 2,510,000 Yuan per city for prefectures where a woman served as leader in 2015. Greater policy intervention and more stringent enforcement of antidiscrimination laws are needed to provide targeted support for women and promote the entry of more women into leadership positions in the state. At the end of the day, having more women in politics would benefit not only Chinese women but also the entire society.\u003c/p\u003e\u003cp\u003eThird, our study enriches the research on female leadership. Most research characterizes female leaders as possessing the stereotypically “feminine” qualities of cooperation, listening, caring, communication, and knowledge sharing (e.g., Book, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Eagly and Carli, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such qualities are increasingly important in contemporary organizations (e.g., Chen, Kang, and Butler, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chin, Hambrick, and Treviño, 2013; Helfat and Martin, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Previous studies have focused mainly on the proportion of women on executive teams as explanatory variables. However, the assertion that women are capable of being effective leaders and that “men could become losers in a global economy that values mental power more than might” is too arbitrary (Rose and Rudolph, 2006). The Chinese state’s different goals enable us to examine the effectiveness of female leadership in different contexts. Prejudice and discrimination against women offset any advantages that female leaders have in traditionally more male-dominated arenas, such as economic development. However, in fields such as social development, which is typically defined as less masculine, female leaders encounter less severe prejudice and discrimination; therefore, their performance is usually equal to or even exceeds that of men. Our study contributes to role theory by extending the context to public administration.\u003c/p\u003e\u003cp\u003eDespite these interesting results, there is still a need for future investigations to assess the impact of female leaders on political outcomes. First, the context in which a woman leads is important; one cannot simply extrapolate our findings in China to different institutions and market settings. The different institutional contexts in many countries may distort the possible positive value that female leaders can otherwise create. Second, without a direct way to measure female leadership, we cannot definitively claim that female leadership explains these political outcomes. Future studies could consider the differences between female and male leaders and study the psychological and social processes that serve to transform gender differences into strategic political decision making. For example, it would be helpful to carry out in-depth interviews to increase the level of reliability. Mixed research methods would increase the quality of the research and strengthen its impact. Finally, our results indicate that women bring different leadership styles and preferences when they take office. Without sacrificing economic growth, they make more inclusive decisions to improve public welfare than male leaders in a nondemocratic setting. This view emphasizes the interactions among political team members during decision-making processes; however, another plausible explanation is that compared to their male counterparts, female political leaders have superior political skills (Anzia and Berry, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In a society that is biased against female leaders, only the most talented and most qualified women can emerge as candidates and seize leadership positions. It is possible that since these female secretaries and mayors are highly qualified and politically ambitious, they perform better, on average, than their male counterparts. Further research should analyze the performance of female politicians at an individual level and observe their contributions during decision-making processes. Although the precise causal mechanisms behind the positive impact of female leadership remain an open question, our findings indicate that the exclusion of women from politics may indeed cause failure in terms of equity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eThe authors declare no interests conflict.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullah SN, Ismail KNIK, Nachum L (2016) Does having women on boards create value? The impact of societal perceptions and corporate governance in emerging markets. 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(In Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Xu X (2008) The source of Local Officials, their Ways to Go, and their Term; and Economic Growth. Manage World, (3):16\u0026ndash;26. (In Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood W, Christensen PN, Hebl MR and H. Rothgerber 1997 Conformity to sex-typed norms, affect, and the self-concept. J Personal Soc Psychol, 73(3), 523\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Deng Y, Huang J, Morck R and B. Yeung 2013 Incentives and outcomes: China's environmental policy. National Bureau of Economic Research Working Paper\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu C (2011) The fundamental institutions of China's reforms and development. J Econ Lit 49(4):1076\u0026ndash;1151\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu X (2002) China\u0026rsquo;s GDP Accounting. China Economic Q 2(1):23\u0026ndash;36 (In Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou F (2006) A Decade of Tax-Sharing: The System and its Evolution. Social Sci China, (6): 100\u0026ndash;115. (In Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou L (2007) Governing China\u0026rsquo;s local officials: An analysis of promotion tournament model. Econ Res J 7:36\u0026ndash;50 (In Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou L (2010) Incentives and Governance: China\u0026rsquo;s Local Governments. Cengage Learning Asia, Singapore\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e These leading groups contain the prefectural committees of the CCP, the People\u0026rsquo;s Congress, the government, and the Chinese People\u0026rsquo;s Political Consultative Conference (CPPCC).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e This ratio is higher than ours because it includes female cadres at the director-general level, such as directors of the provincial education department, and at the head of public institutions, such as public universities or hospitals.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e During this period, China\u0026rsquo;s supreme leadership changed several times. We add several time effect dummies to control the effects of different leaders.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e For example, the expenditure method was used to account for GDP since 1993. The local GDP data before this year were calculated later (Xu, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The Chinese regional code is adjusted dynamically. In this time series data, we use the regional code in 2009 as the baseline.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Because the statistical range of expenditures on education changed several times, we attempt our best to harmonize across sample years. Because of data limitations, the expenditures on social security, employment and education were calculated separately since 1999 and 1998.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We do not use provinces\u0026rsquo; statistical yearbooks as our main datasets. The main reason is that the statistical yearbook of each province provides different variables in each year, and several provinces do not provide financial data at the prefecture level.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We still lost some basic demographic information for some leaders, especially before 2000.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The Chinese governments, including the central and local governments, usually change leaders after two sessions (People's Congress and the Political Consultative Conference). Most prefectures hold two sessions before. This means that there will be two groups of leaders in the re-election year, which is normally every five years. This treatment guarantees that each secretary and mayor holds the post for more than one year.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Because of data limitations, we measure expenditures on social security and employment and expenditures on education after 2000.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We found this phenomenon in only 5 observations.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Former Chinese leader Deng Xiaoping suggested in 1980 that \u0026ldquo;cadres should become more revolutionary, better-educated, more professional, more competent and younger\u0026rdquo; (Deng, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Civil servants need to obtain at least a Master\u0026rsquo;s degree to achieve rapid promotion. For example, President Xi graduated from Tsinghua University with a doctorate in law and ideology in 2002 when he was Governor of Fujian.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The \u003cem\u003etreat\u003c/em\u003e term was omitted at regression because of collinearity with prefectural and year fixed effect. For the sake of completeness of the DID equation, we put this term in the equation.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e China started a reform in 1983 of the administrative divisions at the prefecture level. Therefore, the number of prefectures changed. In recent years, the overall number of prefectures in China has been 333.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The negative effect of education may result from obtaining a Master\u0026rsquo;s or doctorate degree through on-the-job education, which overestimates leaders\u0026rsquo; years of education.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e For details about the matching methods specification, please refer to Dettmann, Alexander, and Antje. (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We do not report these results because of space limitations (the results can be provided upon request).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Beijing Normal University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Female Leadership, Institutional Approach, Role Theory, Chinese Government, Economic Growth, Social Policy.","lastPublishedDoi":"10.21203/rs.3.rs-4316230/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4316230/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhat are the consequences of having a female leader for economic and political outcomes? This study investigates the impact of female leadership on policy outcomes at the local government level in China. Based on institutional theory and role theory, we investigate the impacts of women’s service as Chinese Communist Party (CCP) secretaries or government mayors on local economic growth and social policies. Based on municipal economic performance, government financial expenditures, and demographic data from the period between 1995 and 2015 in China, the results suggest that the influence of gender roles is eliminated when women are involved in leading local economic development, which receives strong institutional pressure. In contrast, female leadership is more conducive to social development, which is more likely to be subject to a leader’s discretion. Female leaders may not ensure better economic growth than male leaders, as indexed by GDP growth rates, but they usually produce more balanced and welfare-oriented development, as measured by higher expenditureson social security and employment.\u003c/p\u003e","manuscriptTitle":"Does Female Leadership Lead to Better Economic and Social Development? Evidence from Local Chinese Governments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 04:06:50","doi":"10.21203/rs.3.rs-4316230/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d00dc95b-8f26-4939-9dc6-cc243364480e","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31086777,"name":"Leadership and Ethics"}],"tags":[],"updatedAt":"2024-04-25T04:06:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 04:06:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4316230","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4316230","identity":"rs-4316230","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}