Correlating the Sustainable Development Goals 5 and 8: Impact of industrial robot expansion on women’s marriage rate in China

Correlating the Sustainable Development Goals 5 and 8: Impact of industrial robot expansion on women’s marriage rate in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlating the Sustainable Development Goals 5 and 8: Impact of industrial robot expansion on women’s marriage rate in China Yanhua Xu, Huiyuan Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7174450/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 The rapid industrial robot expansion has increased concerns over its impact on marital choices as well as scrutiny over the interactions between SDG 5 (Gender Equality) and SDG 8 (Decent Work and Economic Growth). This study measures the impact on women’s marriage rate in China, with the results showing that a 10 percent increase in robot exposure leads to a 5 percent decrease in women’s marriage rate. Specifically, higher robot exposure predicts relatively higher wages and increased job opportunities for women, which improves women’s overall economic status. This, however, reduces women’s incentive to a household life as well as to getting married. Moreover, the impact is stronger for younger medium-skilled women in the manufacturing industry. These findings support the neoclassical theory that change of women’s labor market position can influence their marital decision-making. Policy recommendations are also provided for synergizing the two SDGs. Sustainable Development Goals (SDGs) industrial robot employment wage marriage China Figures Figure 1 Introduction Industrial Robot Expansion and Two SDGs The industrial robot expansion has emerged as a crucial factor in the discourse surrounding the United Nations Sustainable Development Goals (SDGs), particularly SDG 5 (Gender Equality) and SDG 8 (Decent Work and Economic Growth). However, the relationship between robotic automation and these goals is complex and multifaceted, which reveals significant research gaps that require further exploration. This complexity arises from the dual nature of industrial robots, which can both enhance productivity and exacerbate existing inequalities, particularly in the context of gender and labor dynamics. Regarding gender equality (SDG 5), the impact of industrial robot expansion is not straightforward. Research shows that the displacement effect, while affecting both genders, is more likely to fall upon male than female works, such as in the manufacturing industry (Gao et al., 2023). On the other hand, the use of industrial robots increased the wages of both genders as well as the wage gap in favor of medium- and high-skilled male workers (Aksoy et al., 2021). Moreover, a McKinsey report (2019) shows that females would pay far greater transitional costs of displacement by automation due to gendered barriers. Conversely, the industrial robot expansion can also create new job opportunities in fields such as robotics maintenance, programming, and management, which may be more accessible to women if targeted training programs are implemented (Chung & Lee, 2023; Liang et al., 2023). Therefore, the net effect of industrial robot expansion on gender equality remains an open question, which calls for empirical research that examines these dynamics across various sectors and regions. Regarding decent work and economic growth (SDG 8), the intersection with the industrial robot expansion presents another layer of complexity. While robots can significantly enhance productivity and economic growth by reducing costs and increasing efficiency (Giordano et al., 2023), this does not automatically translate into decent work conditions for all workers. The rise of automation has been linked to the phenomenon of precarious work , where job security, benefits, and working conditions deteriorate as companies seek to minimize labor costs (Damiani et al., 2020; Hu et al. 2022). This trend raises critical questions about the quality of jobs created in an increasingly automated economy and whether they meet the criteria for decent work as outlined in SDG 8. Research is needed to explore how industrial robot expansion influences job quality, wage levels, and worker rights, particularly for marginalized groups, including women. Moreover, the global context of industrial robot expansion, particularly in developing countries, presents unique challenges and opportunities for achieving SDG 5 and SDG 8. In many developing nations, the rapid adoption of robotics may outpace the establishment of regulatory frameworks that protect workers’ rights and promote gender equality (Ossiannilsson, 2023). This lack of regulation can exacerbate inequalities and lead to exploitative labor practices, undermining the potential benefits of automation. Nevertheless, country-based studies of how the industrial robot expansion intersects with SDGs 5 and 8 are still relatively rare. Industrial Robot Expansion in China The global expansion of industrial robots has increased significantly since the 1990s, leading to numerous research into its impact on the labor market. For example, Bergholt et al. (2022) found that industrial robot expansion may lower labor share. Faber et al. (2023) examined geographic mobility of labor force and found that the industrial robot expansion caused a decrease in local population size by reducing in-migration. Several studies examined the impact on employment and wages and found varying degrees across different age groups, skill levels, industries, and countries (Acemoglu & Restrepo, 2020; Autor & Dorn, 2013; Autor, 2015; Dauth et al., 2021; Giuntella & Wang, 2019; Graetz & Michaels, 2018; Maloney & Molina, 2019). Ge & Zhou (2020) also found that the impact affects men and women differently, in the sense that it replaces brawn jobs with brain jobs, resulting in relatively higher wages and increased job opportunities for women. This change in the labor market may help improve women’s overall economic status relative to men, which adds new insights to the studies of marriage rate (Becker, 1973; Blau et al., 2000; Galor & Weil, 1996; Jensen, 2012; Schaller, 2016; Stevenson & Wolfers, 2007). Nevertheless, the literature on this relationship is limited, especially regarding women’s marital decision-making. This study aims at filling the gap by examining how industrial robot expansion affects women’s marital decision-making. To do that, the study chooses the national context of China and examines information about women’s marital decision-making using the China Labor-force Dynamics Survey data. To measure robot exposure, the study adopts Acemoglu & Restrepo (2020)’s methodology and draws upon data from the International Federation of Robotics (2017) as well as China’s Second National Economic Census. Furthermore, this study uses industrial robot data from countries at the forefront of industrial robot applications as a variable for robot exposure. This approach aims at capturing the exogenous trends in certain sectors that are brought about by advancements in the technological frontier, which are likely to be independent of the demographic trends in China. The results are two-fold. Firstly, the industrial robot expansion causes a decrease in women’s marriage rate, with a 10 percent increase in robot exposure leading to a 5 percent decrease in women’s marriage rate. Secondly, industrial robot expansion has a significant impact on women’s labor market position, which also affects marriage rate. With the shift from braw n to brain jobs, women’s overall economic status improves with better opportunities and higher wages. Consequently, they are less incentivized to lead a traditional household life or to get married. The results are consistent with Becker (1973) which theorizes that change of women’s labor market position can influence their marital decision-making. Thirdly, the impact is stronger for younger medium-skilled women in the manufacturing industry. A deeper dive The reviewed literature covers two strands of which the first discusses the impact of women’s rising labor market position and economic status on marital decision-making. In economics, research on marriage theory can be traced back to Becker (1973), whose framework predicts that improvement in women’s economic status will reduce their marriage rate because of decreased benefits of intra-household specialization. Empirically testing this hypothesis has proven to be a challenging task due to lack of exogenous variation in the relevant explanatory variables. Nevertheless, several studies have tried to reveal the causal effects through exogenous shocks. Jensen (2012) conducted field experiments in rural Indian households and found that increase in women’s labor force opportunities resulted in a significant decrease in the likelihood of young women getting married. Braga (2018) identified the causal effects through trade shocks by discovering that trade shock narrows gender income gap, causing marriage rate in Brazil to decline. Similar methods were adopted by Keller & Utar (2018), Sengupta (2019), Autor et al. (2019), and Ouyang et al. (2022) who respectively examined trade shock effects on marriage rates in India, United States, and China. Natural disasters, such as COVID-19 and hurricanes, provide another lens through studies such as Komura & Ogawa (2022) and Manning & Payne (2021). Comparatively, very little is known about industrial robot expansion in terms of the impact of structural economic changes they may bring about on important life choices such as marriage. This study contributes to this strand of literature. The second strand of literature focuses on the impact of industrial robot expansion on the labor market. Studies of this strand increased significantly in recent years. Acemoglu & Restrepo (2018) found that industrial robot expansion lowers employment-to-population ratio. Carbonero et al. (2018) found that industrial robot expansion leads to a decline in employment in all countries of different development stages. Nevertheless, Acemoglu and Restrepo (2020) indicated that the net effect of the expansion is uncertain since it may be negative because of a displacement effect or positive because of a productivity effect. Using panel data for 17 major developed countries during 1993-2007, Graetz & Michaels (2018) found that the expansion did not significantly reduce total employment, because the displacement effect is offset by new labor demand in the service sector, i.e., re-allocation effects. Dauth et al. (2021) confirmed this finding using the data for Germany. Some studies found polarization of employment and wages in the low-skill service sector due to industrial robot expansion (Autor & Dorn, 2013; Autor, 2015; Autor et al., 2019; Michaels et al., 2014). Other studies found industrial robot expansion also has different effects in terms of age, education, and country. Dauth et al. (2021) found that young workers, who constitute most of the employment, tend to face lower-labor industries upon entering the labor force and adjust by taking over jobs in the expanding service sector. Autor et al. (2019) found that employment losses are particularly large among workers with non-college vis-vis college degrees. Maloney & Molina (2019) found that polarization also exists between developed and developing countries, as the offshore location of FDI (Foreign Direct Investment) may require more operators to operate the machines. Ge & Zhou (2020) offers special empirical guidance to this study. Their research found polarization in the levels of decreased wages between men and women during 1990-2015 in the U.S. due to industrial robot expansion. The larger decrease in wages for men helped reduce the gender wage gap. The current study adds to the literature by being the first to provide empirical evidence for industrial robot expansion as a leverage to the gender gap in terms of employment and wage in the Chinese labor market. Background and Data of Industrial Robot Expansion in China Industrial robots came into use relatively late in China. Table 1 shows sales of only 380 industrial robot units in 2000, which accounts for only 0.4 percent of the whole world. Since 2005, rising labor costs and policy support together contributed to rapid sales increase. By 2010, the sales volume had reached 15,000 units, which accounts for 12.4 percent of the whole world. By 2016, it had reached 87,000 units, which accounts for 29.6 percent of the whole world. This made China the world’s largest industrial robot market, surpassing other major industrial robot markets (see Figure 1). Table 1 Annual Robot Sales in China and the World Year World (1,000 units) China (1,000 units) China’s Share in the World (%) 1995 69.3 0.0 0.0 2000 98.7 0.4 0.4 2005 120.1 4.5 3.7 2010 120.6 15 12.4 2011 166 22.6 13.6 2012 159.3 23 14.4 2013 178.1 36.6 20.5 2014 220.6 57.1 25.9 2015 253.7 68.6 27 2016 294.3 87 29.6 Note. Table from Cheng et al. (2019). The significant industrial robot expansion in China was mainly due to the rising labor costs. According to Giffi et al. (2016), real wages in China had an average annual growth rate of 10 percent from 2005 to 2016, and the manufacturing industry’s annual growth rate was 9.7 percent. The labor cost in China was $3.3 per hour, which was higher than countries such as India, Thailand, and Indonesia. As a result, companies turned to industrial robots to reduce labor costs. It was also a result of the strong support of government policies. In 2013, for example, the Ministry of Industry and Information Technology (MIIT) released a guidance report aimed at increasing China’s global market share of high-end robot products to over 45 percent. The report also promotes the use of robots in factories, aiming for a density of 100 robots per 10,000 workers (Cheng et al., 2019). Materials & Methods Data Source This study utilizes data on industrial robot expansion in China from the International Federation of Robotics (IFR), a professional organization of robot suppliers established in 1987 to promote the robotics industry around the world. IFR conducts annual surveys among 70 member countries during 1993–2019 to monitor industrial robot sales, covering more than 90 percent of the world market. As such, the IFR database has been utilized by related studies such as Acemoglu & Restrepo (2020) and Giuntella & Wang (2019). To construct China’s robot penetration index at the province level, following Acemoglu & Restrepo (2020), this study utilizes pre-existing distribution of employment across provinces and industries to redistribute robot numbers by sectors across provinces. China’s Second National Economic Census was conducted by the National Bureau of Statistics in 2008 on all legal entities, industrial activity units, and individual businesses within China. The Census covers information such as enterprise location, industry, and number of employees. This study draws initial industry employment data for provinces from the Census. The China Labor-force Dynamics Survey (CLDS) is a series of surveys conducted by the Social Science Survey Center of Sun Yat-sen University. This biannual national survey began in 2012, with sample sizes ranging from 17,000 to 25,800 individuals. CLDS covers 29 out of 34 provinces and collects individual information such as gender, age, residence, religious belief, employment, marital status, etc. Because CLDS adopts a rotation method, which removes some samples from the previous survey, following Sayrs (1989), this study combines those data into mixed cross-sectional to explore our question. The sample restriction criteria are applied as follows. This study includes CLDS survey years 2012, 2014, and 2016 and focuses on female individuals aged 18–39. It then organizes demographic, economic, and robot exposure data by province and survey year, which generates a longitudinal sample containing 7,404 observations from 29 provinces. The demographic and economic data by province come from the China Urban Statistical Yearbooks which report on industry share, natural population growth rate, minimum wage, GDP, unemployment rate, and other data concerning this study. Table 2 presents descriptive statistics for individual variables in Panel A as well as province variables in Panel B. Table 2 Statistical Description of Main Variables Variables (1) (2) (3) (4) (5) Obs Mean Std. Dev. Min Max Panel A: Individual Variables Married or not 7404 0.790 0.407 0 1 Age 7404 29.94 5.824 18 39 Communist Party member or not 7404 0.0783 0.269 0 1 Urban household registration or not 7404 0.327 0.469 0 1 Education level 7404 3.405 2.165 1 9 Income (yuan) 7404 25,532 51,082 0 3.000e+06 Have religious belief or not 7404 0.159 0.366 0 1 Self-rated health 7404 1.298 0.529 1 3 Self-evaluation of social class 7404 4.473 1.732 0 10 First marriage age 3560 23.15 3.038 18 38 Fertility intension 4750 1.939 0.619 0 10 Panel B: Province Variables 7404 2.020 0.795 0.380 3.622 Average education level 7404 9.087 0.673 7.609 12.389 Urbanization level 7404 5.808 0.725 3.489 7.735 Log Per capita GDP 7404 10.789 0.382 9.889 11.680 Sex ratio 7404 106.555 5.013 98.230 118.620 Unemployment rate 7404 3.148 0.614 1.300 4.500 Natural population growth rate 7404 5.883 2.538 -0.490 11.470 Industrial upgrading index 7404 6.649 0.266 6.207 7.600 Number of colleges and universities per 10,000 people 7404 0.018 0.006 0.012 0.043 Internet use share 7404 0.502 0.118 0.276 0.770 First Industry share 7404 0.091 0.045 0.004 0.231 Note. Wage and per capita GDP are adjusted based on the CPI of 2012. Empirical Strategy We estimate the causal effects of Industrial robot application shocks on marriage rate of female aged 18–39 by the following linear regression model: (1) Where is the dependent variable - the marital status of individual i in province c of period t (married = 1, unmarried = 0). is the logarithm of industrial robot exposure in province c of period t. is the coefficient of our interest, which captures the effect of the exposure of industrial robots on marriage rate. Vector and are control variables, includes a set of individual’s demographic characteristics, such as age, age square, education level, political outlook, religious beliefs, wages, self-evaluation of health, self-evaluation of social state, etc. includes a set of province demographic and economic characteristics, such as the natural population growth rate, the Sex ratio of gender, per capita GDP, urbanization level, the number of colleges and universities per 10,000 people, the share of the Primary sector of the economy, etc. (Autor et al., 2019; Ge & Zhou, 2020; Ouyang et al., 2022). is time fixed effect, and is province fixed effect. represents an idiosyncratic error term. Following Acemoglu & Restrepo (2020) and Goldsmith-Pinkham et al. (2020), this study defines a province’s exposure to robots as a Bartik-style measure based on each industry’s robot penetration in China and baseline industry employment shares in province c . Because industrial robots emerged in the China in 2006, the study chooses 2008 as the baseline year. In practice, the study computes the ratio of robots to employed workers in industry at the national level and multiplies it by the province’s baseline employment share in industry and then sums separately for each province, over all sectors. Formally, the study constructs as follows: (2) where and refer to the numbers of robots and employed people in China industry at time , is the density, captures robots adopted replacing its initial employment in industry , is the employment share of industry in province c in baseline year , disaggregates the replacement of initial employment onto regions. Although exposure to industrial robots is exogenous for individuals, as ’s construction depends on the baseline year employment distribution across the industry and region, concerns about endogeneity still exist. For example, there may be uncontrolled factors that simultaneously affect robot adoption, marriage, and childbirth behavior, such as policy shocks, cultural and geographical factors, etc. Therefore, this study employs the Instrumental Variable (IV) to address the potential concerns. Following Acemoglu & Restrepo (2020), the study utilizes the industry-level robot stock in other economies as a proxy to replace China’s with those in five other countries. In practice, the study constructs as follows: (3) where n indicates the five leading countries on robot technology, i.e., the United States, Japan, South Korea, Germany, and Sweden. The IV should be valid, because there is no reason to expect that robot adoption in exporting countries has a direct effect on female marriage rates in China. To test the instruments’ relevance and validity of the underidentification restrictions, the study computes Kleibergen-Paap rk Wald F statistic and Kleibergen-Paap rk LM statistic in the first stage of 2SLS (Kleibergen & Paap, 2006; Wooldridge, 2010) and reports the results in Tables 2, 3, 5, and 6. Results Table 3 presents the impact of robot penetration on women’s marriage rate, through OLS and two-stage least squares (2SLS) results. Table 3 Basic Results Variables Marriage Marriage OLS 2SLS (1) (2) \(\:ln{\text{e}\text{x}\text{p}\text{o}\text{s}\text{u}\text{r}\text{e}\:\text{t}\text{o}\:\text{r}\text{o}\text{b}\text{o}\text{t}}^{\text{C}\text{N}}\) -0.270*** -0.511*** (-2.784) (-3.646) First-stage Result \(\:ln{\text{e}\text{x}\text{p}\text{o}\text{s}\text{u}\text{r}\text{e}\:\text{t}\text{o}\:\text{r}\text{o}\text{b}\text{o}\text{t}}^{\text{I}\text{V}}\) 2.571*** (60.07) Individual Var YES YES Province Var YES YES Year FE YES YES Province FE YES YES K-P F-stat 3608.39 K-P LM-stat 1046.81 Observations 7,404 7,404 \(\:{R}^{2}\) 0.457 0.456 Note. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. t value or z value in Parentheses. Column (1) and Column (2) respectively show the regression results of OLS and 2SLS. Column (2) shows a significant negative correlation between the exposure to robots and the rate at which women get married. The coefficient indicates that a 10 percent increase in robot exposure leads to a 5 percent decrease in the rate of women getting married. This effect is noteworthy because industrial robots are anticipated to spread rapidly in the next several decades. Overall, the result is consistent with Anelli et al. (2021) which finds that robot penetration caused a decline in the number of marriages in the United States. Column (2) also shows the first-stage regression results of the instrumental variable (IV). The results indicate that industrial robot expansion in China has a significant positive correlation with leading international markets, with K-P F statistics values of 3608.39, which far exceeds the recommended threshold (K-P F ≥ 10), and K-P LM statistics values of 1046.81, which far exceeds the recommended threshold (K-P LM > 0) according to the literature (Stock et al., 2002 ). This demonstrates that the IV is strong with sufficient identification. In other words, the IV is deemed valid. Robustness Testing The study then checks the robustness of the empirical specifications by running regressions on various sub-samples and including different controls that are likely to be important determinants of young women’s marriage. Braga ( 2018 ) explored the impact of trade liberalization on the marriage of young women by focusing on the age group of 20–35. This study is similarly revealing when focusing on the sub-sample aged 18–35 (see Column 1 in Table 4 ). Some variables may affect women’s marriage rate and need to be controlled. Firstly, cross-regional labor mobility may affect matching efficiency of marriage and to some extent inhibit marriage (Xiong, 2023 ). Therefore, this study chooses to remove samples with different household registrations and residence locations, with no substantial impact on the results (see Column 2 in Table 4 ). Secondly, changes to the minimum wage standard may affect women’s employment status (Nguyen, 2021 ) and in turn their marriage rate. Therefore, this study chooses to control the minimum wage standard, with no substantial impact on the results (see Column 3 in Table 4 ). Thirdly, structural changes due to China’s market-oriented development strategy may affect women’s marriage rate. China’s transition from a planned to a market-oriented economy began in the late 1970s, which inspired a flurry of reforms leading to rapid urbanization (Fan et al., 2021 ) as well as evolving values and behavioral patterns concerning marriage. Following Wang et al. ( 2019 ), this study utilizes the Marketization Index of China [1] as a proxy variable for assessing the degree of market-oriented structural changes, which returns similar results in Column 4 of Table 4 . Finally, housing prices may also affect marriage rate (Atalay, 2021; Nie, 2020 ; Zhao et al., 2023 ), Following Autor et al. ( 2019 ), this study utilizes average price of urban commercial housing as a proxy variable, which returns similar results in Column 6 of Table 4 . Table 4 Robustness Test – 2SLS Estimation Results Variables Marriage Marriage Marriage Marriage Marriage (1) (2) (3) (4) (5) \(\:ln{\text{e}\text{x}\text{p}\text{o}\text{s}\text{u}\text{r}\text{e}\:\text{t}\text{o}\:\text{r}\text{o}\text{b}\text{o}\text{t}}^{\text{C}\text{N}}\) -0.551*** -0.494*** -0.476*** -0.475*** -0.473** (-3.201) (-2.871) (-2.580) (-3.359) (-2.325) First Stage Result \(\:ln{\text{e}\text{x}\text{p}\text{o}\text{s}\text{u}\text{r}\text{e}\:\text{t}\text{o}\:\text{r}\text{o}\text{b}\text{o}\text{t}}^{\text{I}\text{V}}\) 2.706*** 2.543*** 2.680*** 2.678*** 2.903*** (62.52) (64.35) (66.90) (73.22) (67.18) K-P F-stat 3908.796 4140.555 4475.974 5361.031 4513.213 K-P LM-stat 1156.928 1166.192 1702.103 1497.357 735.932 Observations 5,737 6,681 7,404 7,404 7,404 \(\:{R}^{2}\) 0.445 0.464 0.457 0.456 0.456 Control Var YES YES YES YES YES Year FE YES YES YES YES YES Provinces FE YES YES YES YES YES Note. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. z value in Parentheses. Mechanisms and Heterogeneity Analyses This section discusses how industrial robot expansion may influence marital decision-making of young women. For them, marriage offers household specialization. However, as women’s economic position rises relative to men, their marriage rate falls (Autor et al., 2019 ; Braga, 2018 ; Jensen, 2012 ; Keller & Utar, 2022 ; Sengupta, 2019 ). If the industrial robot expansion, which causes rise in women’s economic position relative to men (e.g., increased employment and wage), leads to reduced gains from household specialization as well as reduced marriage rate for women, then does it show from the impact of the industrial robot expansion on employment and wage for men and women aged 18–39? To answer this question, following Giuntella & Wang ( 2019 ), this study constructs an econometric model as follows: $$\:{z}_{ict}={\pi\:}_{0}+{\pi\:}_{1}ln\:{R\_Robot}_{ct}+{\pi\:}_{3}{V}_{ict}+{\pi\:}_{4}{W}_{ct}+{\delta\:}_{t}+{\rho\:}_{c}+{\phi\:}_{ict}$$ 4 The dependent variables \(\:{\:\text{z}}_{\text{i}\text{c}\text{t}}\) in formula (4) are employment status (employed = 1, unemployed = 0) and wage. \(\:{V}_{ict}\) and \(\:{W}_{ct}\) capture individual and urban characteristics. \(\:{{\delta\:}}_{\text{t}},\:{{\rho\:}}_{\text{c}}\:\) and \(\:{{\phi\:}}_{\text{i}\text{c}\text{t}}\) represent the time fixed effect, city fixed effect, and idiosyncratic error term. The regression is conducted separately for men and women aged 18–39. Table 5 displays the specific results. Table 5 Mechanism Analysis – Differential Effects of Robots on Labor Market Opportunities and Wages of Women and Men Variables Employment \(\:\text{l}\text{n}\text{w}\text{a}\text{g}\text{e}\) Women Man Women Man OLS OLS OLS OLS (1) (2) (3) (4) \(\:ln{\text{e}\text{x}\text{p}\text{o}\text{s}\text{u}\text{r}\text{e}\:\text{t}\text{o}\:\text{r}\text{o}\text{b}\text{o}\text{t}}^{\text{C}\text{N}}\) -0.130*** -0.202*** 0.146*** 0.0692** (-3.511) (-5.427) (4.292) (2.390) Constant 1.617** 1.708* 2.750*** 2.845*** (2.033) (1.941) (3.297) (3.814) Observations 4,441 5,169 3,875 4,155 \(\:{R}^{2}\) 0.148 0.153 0.323 0.275 Control Var YES YES YES YES Year FE YES YES YES YES Provinces FE YES YES YES YES Note. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. z value in Parentheses. Columns 1 and 2 in Table 5 show that a 10 percent increase in robot exposure leads to a 1.3 and 2 percent decrease in employment respectively for women and men. The decrease in employment for men is significantly larger than for women, which amounts to a gap of 0.7 percent. Columns 3 and 4 show that a 10 percent increase in robot exposure leads to a 1.4 and 0.6 percent increase in wage respectively for women and men. Women’s wage increase more than doubles that for men, which amounts to a gap of 0.8 percent. Overall, the industrial robot expansion seems to have improved women’s economic stature relative to men (Ge & Zhou, 2020 ; Giuntella & Wang, 2019 ; Welch, 2000 ), which may account for why their expected gains from household specialization as well as marriage rate are reduced. The study then conducts some heterogeneity analyses along several dimensions. Firstly, it explores the heterogeneity of the impact by age groups (see Columns 1–3 in Table 6 ). The analysis shows that the impact of robot exposure on women’s marriage rate is mainly driven by younger age groups (e.g., the age group of 18–24). This result is consistent with Dauth et al. ( 2021 ) which analyzed the impact of industrial robot expansion on different age groups and found that younger people’s jobs are more readily transformed by industrial robots. Therefore, marriage rate of younger women is likely to be influenced more. Table 6 Heterogeneity Analysis – 2SLS Estimation Results Variables Age Skill Industry 18 ~ 24 25 ~ 32 33 ~ 39 Low Medium High Manufacturing Others (1) (2) (3) (4) (5) (6) (7) (8) \(\:ln{\text{e}\text{x}\text{p}\text{o}\text{s}\text{u}\text{r}\text{e}\:\text{t}\text{o}\:\text{r}\text{o}\text{b}\text{o}\text{t}}^{\text{C}\text{N}}\) -1.048*** -0.552** -0.0794 -0.460*** -0.617** -0.283 -0.698** -0.538*** (-2.789) (-2.328) (-0.706) (-2.749) (-2.522) (-0.552) (-2.518) (-3.183) First-stage regression results \(\:ln{\text{e}\text{x}\text{p}\text{o}\text{s}\text{u}\text{r}\text{e}\:\text{t}\text{o}\:\text{r}\text{o}\text{b}\text{o}\text{t}}^{\text{I}\text{V}}\) 2.892*** 2.652*** 2.570*** 2.496*** 2.920*** 2.387*** 3.109*** 2.571*** (29.34) (46.18) (42.41) (48.88). (43.48) (19.57) (37.29) (60.07) K-P F-stat 860.865 2132.236 1798.533 2389.070 1890.278 382.956 243.649 3608.389 K-P LM-stat 255.432 651.375 512.930 618.171 540.784 165.952 1390.290 1046.813 Observations 1,544 3,057 2,803 3,935 2,450 1,019 1,455 5,949 \(\:{R}^{2}\) 0.398 0.250 0.046 0.414 0.488 0.484 0.428 0.470 Control Var YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES Provinces FE YES YES YES YES YES YES YES YES Note. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. z value in Parentheses. Secondly, the study explores the heterogeneity of the impact by skill level (see Columns 4–6 in Table 6 ). It shows that the impact of robot exposure on women’s marriage rate is mainly driven by medium skill level. This is consistent with the noticeable change in job polarization, in which wage gains go disproportionately to those at the top and the bottom of the skill distribution, not to those in the middle, brought about by the industrial robot expansion (Autor & Dorn, 2013 ). Therefore, the relative economic gains of medium-skilled women are larger than low- and high-skilled women, the effect also being the strongest. Finally, the study explores the heterogeneity of the impact by industry (see Columns 7–8 in Table 6 ), which shows a slightly larger impact on women’s marriage rate in the manufacturing industry. The reason may be that the industrial robot expansion concentrates more on the manufacturing industry (Dauth et al., 2021 ), therefore more frequently affecting women’s marriage rate in this industry. Discussion and Conclusion The impact of industrial robot expansion on our daily lives is rapidly growing, with numerous studies choosing the lens of labor market (Acemoglu & Restrepo, 2020 ; Autor & Dorn, 2013 ; Dauth et al., 2021 ; Graetz & Michaels, 2018 ; Maloney & Molina, 2019 ). Nevertheless, few studies examined the implications on gender and life choices (e.g., marriage) in developing countries, to which this study responded by exploring the implications on young women’s marriage rate in China. The study found that the industrial robot expansion in China may cause a decreased marriage rate of Chinese women. Specifically, a 10 percent increase in robot exposure leads to a 5 percent decrease in women’s marriage rate. Younger, medium-skilled women in the manufacturing industry are more sensitive to this impact. In terms of potential mechanisms, the study shows that the industrial robot expansion steadily replaces brawn jobs with brain jobs, which improves women’s economic stature relative to men, as measured by employment and wage, and thereby reduces their expected gains from household specialization as well as their marriage rate. This result is consistent with the neoclassical theory which posits that improved labor market positions for women will cause decreased marriage rates (Becker, 1973 ). The policy implications of this study are substantial. Firstly, a family of three is currently the most common family size in China (Yu & Xie, 2021 ). With the industrial robot expansion likely to change the family size due to decreased marriage rate, policymakers should more closely monitor the changes in family size for the purpose of redistributing public goods. Secondly, since the premarital fertility rate in China is relatively low (Frejka, 2010), a decreased marriage rate may lead to a decreased fertility rate. With population ageing, policymakers should more promptly develop comprehensive policy frameworks for guiding family preparation. For example, there can be policies to improve the income level of women in marriage through redistributive measures, such as taxation, to control marriage rate as well as to guard against potential social problems arising due to a decreased marriage rate. It is recommended that future research continue to explore women’s marriage rate by linking the industrial robot expansion with fertility rate, first marriage age, divorce, etc. There are two implications related to SDG 5 and 8 at the global level. Firstly, the role of education and training in mitigating the negative impacts of automation on gender equality (SDG 5) and decent work (SDG 8) is an area ripe for investigation. As industries adopt more advanced robotic technologies, the demand for skilled labor increases, creating a skills gap that can disproportionately affect women (Nadeem et al., 2020 ). Studies have shown that targeted educational initiatives can help bridge this gap, enabling women to gain the necessary skills to thrive in a more automated workforce. However, the effectiveness of such initiatives often depends on the broader socio-economic context, including access to education and training resources, societal attitudes towards women in technology, and the availability of supportive policies. Therefore, future research should focus on identifying the best practices for educational programs that promote gender equality in the context of industrial automation. Secondly, the environmental implications of industrial robot expansion also intersect with the goals of SDG 5 and SDG 8. The push for sustainable manufacturing practices, driven by the need to reduce carbon footprints and enhance resource efficiency, can create new opportunities for women in green technology sectors (Johansson & Ringblom, 2017 ). However, if the transition to automation prioritizes efficiency over sustainability, it could lead to adverse environmental outcomes that disproportionately affect vulnerable populations, including women (Xu & Ye, 2021 ). It is therefore recommended that future research explore how the integration of sustainable practices in robotic manufacturing can align with gender equality and decent work objectives, ensuring that the benefits of automation are equitably distributed. Declarations Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. 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Zhao, C., Chen, B., & Li, X. (2023). Rising housing prices and marriage delays in China: Evidence from the urban land transaction policy. Cities, 135 , 104214. Footnote 1 . It was established by Wang et al. (2019) which assessed the process of marketization reform in China. Additional Declarations No competing interests reported. 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-7174450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505378805,"identity":"2c0b92c6-b7cb-4be8-bafe-92d07be8af59","order_by":0,"name":"Yanhua Xu","email":"","orcid":"","institution":"Shanghai Jian Qiao University","correspondingAuthor":false,"prefix":"","firstName":"Yanhua","middleName":"","lastName":"Xu","suffix":""},{"id":505378806,"identity":"93c3f694-675e-4a1d-b789-e704a5d33531","order_by":1,"name":"Huiyuan Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACAzjJ3tj44APRWg6ASJ7DzYYziNcCIiTS26Q5iNFizt778POHApvEfsmHDdIMDHZyug0EtFj2HDeWOGCQljhzdmKDcQFDsrHZAUIOu5HGANRyOHHD7cSG5BkMBxK3EaGF+QdIy/6bBxsO8xCphQ1iiwRjYzNxWs4cY7M4Y5BmPONMYjPjDANi/HK8jflGxR8b2f72489/fKiwkyOoBQYcGyAmEKkcBOxJUDsKRsEoGAUjDQAABhNIiWpW6agAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Jian Qiao University","correspondingAuthor":true,"prefix":"","firstName":"Huiyuan","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2025-07-21 07:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7174450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7174450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90079540,"identity":"da6764b5-e04e-4bee-8450-2d1e7f1016a1","added_by":"auto","created_at":"2025-08-28 08:40:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStock of Operational Robots in Major Countries 2016\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Figure from Cheng et al. (2019).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7174450/v1/f98f36a6a4731ab4b07d0d1f.png"},{"id":95312849,"identity":"7db74d42-937b-4699-9e3f-d431cee62fca","added_by":"auto","created_at":"2025-11-06 15:50:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1090423,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7174450/v1/c68b1f57-72dc-440a-a29e-e5ed8c23b1f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlating the Sustainable Development Goals 5 and 8: Impact of industrial robot expansion on women’s marriage rate in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndustrial Robot Expansion and Two SDGs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe industrial robot expansion has emerged as a crucial factor in the discourse surrounding the United Nations Sustainable Development Goals (SDGs), particularly SDG 5 (Gender Equality) and SDG 8 (Decent Work and Economic Growth). However, the relationship between robotic automation and these goals is complex and multifaceted, which reveals significant research gaps that require further exploration. This complexity arises from the dual nature of industrial robots, which can both enhance productivity and exacerbate existing inequalities, particularly in the context of gender and labor dynamics.\u003c/p\u003e\n\u003cp\u003eRegarding gender equality (SDG 5), the impact of industrial robot expansion is not straightforward. Research shows that the displacement effect, while affecting both genders, is more likely to fall upon male than female works, such as in the manufacturing industry (Gao et al., 2023). On the other hand, the use of industrial robots increased the wages of both genders as well as the wage gap in favor of medium- and high-skilled male workers (Aksoy et al., 2021). Moreover, a McKinsey report (2019) shows that females would pay far greater transitional costs of displacement by automation due to gendered barriers. Conversely, the industrial robot expansion can also create new job opportunities in fields such as robotics maintenance, programming, and management, which may be more accessible to women if targeted training programs are implemented (Chung \u0026amp; Lee, 2023; Liang et al., 2023). Therefore, the net effect of industrial robot expansion on gender equality remains an open question, which calls for empirical research that examines these dynamics across various sectors and regions.\u003c/p\u003e\n\u003cp\u003eRegarding decent work and economic growth (SDG 8), the intersection with the industrial robot expansion presents another layer of complexity. While robots can significantly enhance productivity and economic growth by reducing costs and increasing efficiency (Giordano et al., 2023), this does not automatically translate into decent work conditions for all workers. The rise of automation has been linked to the phenomenon of \u003cem\u003eprecarious work\u003c/em\u003e, where job security, benefits, and working conditions deteriorate as companies seek to minimize labor costs (Damiani et al., 2020; Hu et al. 2022). This trend raises critical questions about the quality of jobs created in an increasingly automated economy and whether they meet the criteria for decent work as outlined in SDG 8. Research is needed to explore how industrial robot expansion influences job quality, wage levels, and worker rights, particularly for marginalized groups, including women.\u003c/p\u003e\n\u003cp\u003eMoreover, the global context of industrial robot expansion, particularly in developing countries, presents unique challenges and opportunities for achieving SDG 5 and SDG 8. In many developing nations, the rapid adoption of robotics may outpace the establishment of regulatory frameworks that protect workers\u0026rsquo; rights and promote gender equality (Ossiannilsson, 2023). This lack of regulation can exacerbate inequalities and lead to exploitative labor practices, undermining the potential benefits of automation. Nevertheless, country-based studies of how the industrial robot expansion intersects with SDGs 5 and 8 are still relatively rare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndustrial Robot Expansion in China\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe global expansion of industrial robots has increased significantly since the 1990s, leading to numerous research into its impact on the labor market. For example, Bergholt et al. (2022) found that industrial robot expansion may lower labor share. Faber et al. (2023) examined geographic mobility of labor force and found that the industrial robot expansion caused a decrease in local population size by reducing in-migration. Several studies examined the impact on employment and wages and found varying degrees across different age groups, skill levels, industries, and countries (Acemoglu \u0026amp; Restrepo, 2020; Autor \u0026amp; Dorn, 2013; Autor, 2015; Dauth et al., 2021; Giuntella \u0026amp; Wang, 2019; Graetz \u0026amp; Michaels, 2018; Maloney \u0026amp; Molina, 2019). Ge \u0026amp; Zhou (2020) also found that the impact affects men and women differently, in the sense that it replaces \u003cem\u003ebrawn\u003c/em\u003e jobs with \u003cem\u003ebrain\u003c/em\u003e jobs, resulting in relatively higher wages and increased job opportunities for women. This change in the labor market may help improve women\u0026rsquo;s overall economic status relative to men, which adds new insights to the studies of marriage rate (Becker, 1973; Blau et al., 2000; Galor \u0026amp; Weil, 1996; Jensen, 2012; Schaller, 2016; Stevenson \u0026amp; Wolfers, 2007). Nevertheless, the literature on this relationship is limited, especially regarding \u003cem\u003ewomen\u0026rsquo;s\u003c/em\u003e marital decision-making. This study aims at filling the gap by examining how industrial robot expansion affects women\u0026rsquo;s marital decision-making.\u003c/p\u003e\n\u003cp\u003eTo do that, the study chooses the national context of China and examines information about women\u0026rsquo;s marital decision-making using the China Labor-force Dynamics Survey data. To measure robot exposure, the study adopts Acemoglu \u0026amp; Restrepo (2020)\u0026rsquo;s methodology and draws upon data from the International Federation of Robotics (2017) as well as China\u0026rsquo;s Second National Economic Census. Furthermore, this study uses industrial robot data from countries at the forefront of industrial robot applications as a variable for robot exposure. This approach aims at capturing the exogenous trends in certain sectors that are brought about by advancements in the technological frontier, which are likely to be independent of the demographic trends in China.\u003c/p\u003e\n\u003cp\u003eThe results are two-fold. Firstly, the industrial robot expansion causes a decrease in women\u0026rsquo;s marriage rate, with a 10 percent increase in robot exposure leading to a 5 percent decrease in women\u0026rsquo;s marriage rate. Secondly, industrial robot expansion has a significant impact on women\u0026rsquo;s labor market position, which also affects marriage rate. With the shift from \u003cem\u003ebraw\u003c/em\u003en to \u003cem\u003ebrain\u003c/em\u003e jobs, women\u0026rsquo;s overall economic status improves with better opportunities and higher wages. Consequently, they are less incentivized to lead a traditional household life or to get married. The results are consistent with Becker (1973) which theorizes that change of women\u0026rsquo;s labor market position can influence their marital decision-making. Thirdly, the impact is stronger for younger medium-skilled women in the manufacturing industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eA deeper dive\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reviewed literature covers two strands of which the first discusses the impact of women\u0026rsquo;s rising labor market position and economic status on marital decision-making. In economics, research on marriage theory can be traced back to Becker (1973), whose framework predicts that improvement in women\u0026rsquo;s economic status will reduce their marriage rate because of decreased benefits of intra-household specialization. Empirically testing this hypothesis has proven to be a challenging task due to lack of exogenous variation in the relevant explanatory variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNevertheless, several studies have tried to reveal the causal effects through exogenous shocks. Jensen (2012) conducted field experiments in rural Indian households and found that increase in women\u0026rsquo;s labor force opportunities resulted in a significant decrease in the likelihood of young women getting married. Braga (2018) identified the causal effects through trade shocks by discovering that trade shock narrows gender income gap, causing marriage rate in Brazil to decline. Similar methods were adopted by Keller \u0026amp; Utar (2018), Sengupta (2019), Autor et al. (2019), and Ouyang et al. (2022) who respectively examined trade shock effects on marriage rates in India, United States, and China. Natural disasters, such as COVID-19 and hurricanes, provide another lens through studies such as Komura \u0026amp; Ogawa (2022) and Manning \u0026amp; Payne (2021). Comparatively, very little is known about industrial robot expansion in terms of the impact of structural economic changes they may bring about on important life choices such as marriage. This study contributes to this strand of literature.\u003c/p\u003e\n\u003cp\u003eThe second strand of literature focuses on the impact of industrial robot expansion on the labor market. Studies of this strand increased significantly in recent years. Acemoglu\u0026ensp;\u0026amp; Restrepo (2018) found that industrial robot expansion lowers employment-to-population ratio. Carbonero\u0026ensp;et\u0026ensp;al. (2018) found that industrial robot expansion leads to a decline in employment in all countries of different development stages. Nevertheless, Acemoglu and Restrepo (2020) indicated that the net effect of the expansion is uncertain since it may be negative because of a displacement effect or positive because of a productivity effect. Using panel data for 17 major developed countries during 1993-2007, Graetz \u0026amp; Michaels (2018) found that the expansion did not significantly reduce total employment, because the displacement effect is offset by new labor demand in the service sector, i.e., re-allocation effects. Dauth et al. (2021) confirmed this finding using the data for Germany. Some studies found polarization of employment and wages in the low-skill service sector due to industrial robot expansion (Autor \u0026amp; Dorn, 2013; Autor, 2015; Autor et al., 2019; Michaels et al., 2014). Other studies found industrial robot expansion also has different effects in terms of age, education, and country. Dauth et al. (2021) found that young workers, who constitute most of the employment, tend to face lower-labor industries upon entering the labor force and adjust by taking over jobs in the expanding service sector. Autor et al. (2019) found that employment losses are particularly large among workers with non-college vis-vis college degrees. Maloney \u0026amp; Molina (2019) found that polarization also exists between developed and developing countries, as the offshore location of FDI (Foreign Direct Investment) may require more operators to operate the machines. Ge \u0026amp; Zhou (2020) offers special empirical guidance to this study. Their research found polarization in the levels of decreased wages between men and women during 1990-2015 in the U.S. due to industrial robot expansion. The larger decrease in wages for men helped reduce the gender wage gap. The current study adds to the literature by being the first to provide empirical evidence for industrial robot expansion as a leverage to the gender gap in terms of employment and wage in the Chinese labor market.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBackground and Data of Industrial Robot Expansion in China\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndustrial robots came into use relatively late in China. Table 1 shows sales of only 380 industrial robot units in 2000, which accounts for only 0.4 percent of the whole world. Since 2005, rising labor costs and policy support together contributed to rapid sales increase. By 2010, the sales volume had reached 15,000 units, which accounts for 12.4 percent of the whole world. By 2016, it had reached 87,000 units, which accounts for 29.6 percent of the whole world. This made China the world\u0026rsquo;s largest industrial robot market, surpassing other major industrial robot markets (see Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eAnnual Robot Sales in China and the World\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0745%;\"\u003e\n \u003cp\u003eWorld\u003c/p\u003e\n \u003cp\u003e(1,000 units)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.6476%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003cp\u003e(1,000 units)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5186%;\"\u003e\n \u003cp\u003eChina\u0026rsquo;s Share\u003c/p\u003e\n \u003cp\u003ein the World (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e69.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e120.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e120.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e159.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e178.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e220.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e253.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e68.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7593%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.0745%;\"\u003e\n \u003cp\u003e294.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6476%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.5186%;\"\u003e\n \u003cp\u003e29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Table from Cheng et al. (2019).\u003c/p\u003e\n\u003cp\u003eThe significant industrial robot expansion in China was mainly due to the rising labor costs. According to Giffi et al. (2016), real wages in China had an average annual growth rate of 10 percent from 2005 to 2016, and the manufacturing industry\u0026rsquo;s annual growth rate was 9.7 percent. The labor cost in China was $3.3 per hour, which was higher than countries such as India, Thailand, and Indonesia. As a result, companies turned to industrial robots to reduce labor costs.\u003c/p\u003e\n\u003cp\u003eIt was also a result of the strong support of government policies. In 2013, for example, the Ministry of Industry and Information Technology (MIIT) released a guidance report aimed at increasing China\u0026rsquo;s global market share of high-end robot products to over 45 percent. The report also promotes the use of robots in factories, aiming for a density of 100 robots per 10,000 workers (Cheng et al., 2019).\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Source\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilizes data on industrial robot expansion in China from the International Federation of Robotics (IFR), a professional organization of robot suppliers established in 1987 to promote the robotics industry around the world. IFR conducts annual surveys among 70 member countries during 1993\u0026ndash;2019 to monitor industrial robot sales, covering more than 90 percent of the world market. As such, the IFR database has been utilized by related studies such as Acemoglu \u0026amp; Restrepo (2020) and Giuntella \u0026amp; Wang (2019). To construct China\u0026rsquo;s robot penetration index at the province level, following Acemoglu \u0026amp; Restrepo (2020), this study utilizes pre-existing distribution of employment across provinces and industries to redistribute robot numbers by sectors across provinces.\u003c/p\u003e\n\u003cp\u003eChina\u0026rsquo;s Second National Economic Census was conducted by the National Bureau of Statistics in 2008 on all legal entities, industrial activity units, and individual businesses within China. The Census covers information such as enterprise location, industry, and number of employees. This study draws initial industry employment data for provinces from the Census.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe China Labor-force Dynamics Survey (CLDS) is a series of surveys conducted by the Social Science Survey Center of Sun Yat-sen University. This biannual national survey began in 2012, with sample sizes ranging from 17,000 to 25,800 individuals. CLDS covers 29 out of 34 provinces and collects individual information such as gender, age, residence, religious belief, employment, marital status, etc. Because CLDS adopts a rotation method, which removes some samples from the previous survey, following Sayrs (1989), this study combines those data into mixed cross-sectional to explore our question. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sample restriction criteria are applied as follows. This study includes CLDS survey years 2012, 2014, and 2016 and focuses on female individuals aged 18\u0026ndash;39. It then organizes demographic, economic, and robot exposure data by province and survey year, which generates a longitudinal sample containing 7,404 observations from 29 provinces. The demographic and economic data by province come from the China Urban Statistical Yearbooks which report on industry share, natural population growth rate, minimum wage, GDP, unemployment rate, and other data concerning this study. Table 2 presents descriptive statistics for individual variables in Panel A as well as province variables in Panel B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eStatistical Description of Main Variables\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eObs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003ePanel A: Individual Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eMarried or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e29.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eCommunist Party member or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eUrban household registration or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eEducation level\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e3.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eIncome (yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e25,532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e51,082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.000e+06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eHave religious belief or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eSelf-rated health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eSelf-evaluation of social class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eFirst marriage age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e3560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e23.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eFertility intension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e4750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePanel B: Province Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cimg width=\"127\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eAverage education level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e9.087\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.673\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7.609\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e12.389\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eUrbanization level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e5.808\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.725\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.489\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.735\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eLog Per capita GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e10.789\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.382\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e9.889\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.680\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eSex ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e106.555\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.013\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e98.230\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e118.620\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eUnemployment rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e3.148\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.614\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.300\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eNatural population growth rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e5.883\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.538\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.490\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.470\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eIndustrial upgrading index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e6.649\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.266\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e6.207\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eNumber of colleges and universities per 10,000 people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.018\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.043\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eInternet\u0026nbsp;use share\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.502\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.118\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.276\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.770\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eFirst Industry share\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.091\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.231\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Wage and per capita GDP are adjusted based on the CPI of 2012.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEmpirical Strategy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe estimate the causal effects of Industrial robot application shocks on marriage rate of female aged 18\u0026ndash;39 by the following linear regression model:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"401\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003eWhere \u003cimg width=\"20\" height=\"15\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;is the dependent variable - the marital status of individual i in province c of period t (married = 1, unmarried = 0). \u003cimg width=\"142\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;is the logarithm of industrial robot exposure in province c of period t. \u003cimg width=\"13\" height=\"15\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;is the coefficient of our interest, which captures the effect of the exposure of industrial robots on marriage rate. Vector \u003cimg width=\"20\" height=\"15\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;and \u003cimg width=\"16\" height=\"15\" src=\"data:image/png;base64,R0lGODlhGAAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAYABEAhAAAAAAAAAAAOgAAZgA6ZgA6kABmtjoAADoAZjpmkDqQ22YAAGYAOmY6AGaQ22a2/5A6AJDb/7ZmALZmOrbb/7b//9uQOtuQZtu2Ztvb/9v///+2Zv/bkP/b2///tv//2wV4oLcEZBkUGqCu7CqiqxQ8be0y0codhu23H4gg9ytuAopiUTSoKH+W2dO3g01rkuF0Y1UdeyoOwmmzdIPN1Sb9mZAED4yJFk2q0E4JgRKkW0UmJU0iditmKU87NCyHUyKLhl1FQTAXORYDER0ZTx4NAQIOKp4BCRohADs=\" alt=\"image\"\u003e\u0026nbsp;are control variables, \u003cimg width=\"20\" height=\"15\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;includes a set of individual\u0026rsquo;s demographic characteristics, such as age, age square, education level, political outlook, religious beliefs, wages, self-evaluation of health, self-evaluation of social state, etc. \u003cimg width=\"16\" height=\"15\" src=\"data:image/png;base64,R0lGODlhGAAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAYABEAhAAAAAAAAAAAOgAAZgA6ZgA6kABmtjoAADoAZjpmkDqQ22YAAGYAOmY6AGaQ22a2/5A6AJDb/7ZmALZmOrbb/7b//9uQOtuQZtu2Ztvb/9v///+2Zv/bkP/b2///tv//2wV4oLcEZBkUGqCu7CqiqxQ8be0y0codhu23H4gg9ytuAopiUTSoKH+W2dO3g01rkuF0Y1UdeyoOwmmzdIPN1Sb9mZAED4yJFk2q0E4JgRKkW0UmJU0iditmKU87NCyHUyKLhl1FQTAXORYDER0ZTx4NAQIOKp4BCRohADs=\" alt=\"image\"\u003e\u0026nbsp;includes a set of province demographic and economic characteristics, such as the natural population growth rate, the Sex ratio of gender, per capita GDP, urbanization level, the number of colleges and universities per 10,000 people, the share of the Primary sector of the economy, etc. (Autor et al., 2019; Ge \u0026amp; Zhou, 2020; Ouyang et al., 2022). \u003cimg width=\"11\" height=\"15\" src=\"data:image/png;base64,R0lGODlhEQAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAEACgAQAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kDoAADo6ADpmkDqQ22Y6AGa2/5A6AJC2/5Db/7ZmZraQkLbb/7b//9uQOtu2Ztvb/9v///+2Zv/b2///tv//2wECAwECAwECAwECAwECAwVT4BUkAKAxg1SupUiaqMq2Y3mmM/3e8uzasVbBogPiAJNhEYajBJ6BhWioeRxSEEuSCLgIDgFC5GIQI5VdgSNX2rbU7DP3x54MHJiKtxHPKAIIFiEAOw==\" alt=\"image\"\u003e\u0026nbsp;is time fixed effect, and \u003cimg width=\"15\" height=\"15\" src=\"data:image/png;base64,R0lGODlhFwAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACgAWAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kABmtjoAADo6ADo6OjpmtjqQ22YAAGY6AGaQ22a222a2/5A6AJDb/7ZmALZmOraQOrbb/7b//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wVrICBqimhijqmuWPCs3LGscDDTLUSbkyCJnIAO4GEUNrviESgUfSKDS9KIBASHz+guK7XaRErkhxIICHRBw+aZKFg+lS9gQnhHhhpEWbHJHMwvRAw3O4UiHQdDhoVFios0T0sZP48qHg1mKSEAOw==\" alt=\"image\"\u003e\u0026nbsp;is province fixed effect. \u003cimg width=\"18\" height=\"15\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;represents an idiosyncratic error term.\u003c/p\u003e\n\u003cp\u003eFollowing Acemoglu \u0026amp; Restrepo (2020) and Goldsmith-Pinkham et al. (2020),\u0026nbsp;this study defines a province\u0026rsquo;s\u003cem\u003e\u0026nbsp;exposure to robots\u0026nbsp;\u003c/em\u003eas a Bartik-style measure based on each industry\u0026rsquo;s robot penetration in China and baseline industry employment shares in province \u003cem\u003ec\u003c/em\u003e. Because industrial robots emerged in the China in 2006, the study chooses 2008 as the baseline year. In practice, the study computes the ratio of robots to employed workers in industry \u003cimg width=\"5\" height=\"15\" src=\"data:image/png;base64,R0lGODlhCAAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAHABEAhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGaQ22a2/5A6AJDb/7ZmALb//9uQOtu2kNv///+2Zv//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwUwICACUqCMwCMwaOuSQXCgj0kPzkgtRDROiAKqdBqpWKJd7xccylCN1QhimI0EiVYIADs=\" alt=\"image\"\u003e\u0026nbsp;at the national level and multiplies it by the province\u0026rsquo;s baseline employment share in industry \u003cimg width=\"5\" height=\"15\" src=\"data:image/png;base64,R0lGODlhCAAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAHABEAhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGaQ22a2/5A6AJDb/7ZmALb//9uQOtu2kNv///+2Zv//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwUwICACUqCMwCMwaOuSQXCgj0kPzkgtRDROiAKqdBqpWKJd7xccylCN1QhimI0EiVYIADs=\" alt=\"image\"\u003e\u0026nbsp;and then sums separately for each province, over all sectors. Formally, the study constructs \u003cimg width=\"127\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"247\" height=\"32\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;(2)\u003c/p\u003e\n\u003cp\u003ewhere \u003cimg width=\"55\" height=\"18\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;and \u003cimg width=\"46\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;refer to the numbers of robots and employed people in China industry \u003cimg width=\"5\" height=\"15\" src=\"data:image/png;base64,R0lGODlhCAAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAHABEAhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGaQ22a2/5A6AJDb/7ZmALb//9uQOtu2kNv///+2Zv//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwUwICACUqCMwCMwaOuSQXCgj0kPzkgtRDROiAKqdBqpWKJd7xccylCN1QhimI0EiVYIADs=\" alt=\"image\"\u003e\u0026nbsp;at time \u003cimg width=\"5\" height=\"15\" src=\"data:image/png;base64,R0lGODlhCAAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAAIAAsAhAAAAAAAAAAAOgAAZgA6kDpmtjqQ22YAAGY6AGa2/5A6AJDb/7aQZrb//9uQOtv///+2Zv/btv//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwUsICBCQyOK0UE84qQEMGyMQXKKTnm76y0dsxukdgM4BIuiozZhBCWIgKDACgEAOw==\" alt=\"image\"\u003e, \u003cimg width=\"44\" height=\"32\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;is the density, captures robots adopted replacing its initial employment in industry \u003cimg width=\"5\" height=\"15\" src=\"data:image/png;base64,R0lGODlhCAAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAHABEAhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGaQ22a2/5A6AJDb/7ZmALb//9uQOtu2kNv///+2Zv//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwUwICACUqCMwCMwaOuSQXCgj0kPzkgtRDROiAKqdBqpWKJd7xccylCN1QhimI0EiVYIADs=\" alt=\"image\"\u003e, \u003cimg width=\"42\" height=\"28\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;is the employment share of industry \u003cimg width=\"5\" height=\"15\" src=\"data:image/png;base64,R0lGODlhCAAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAHABEAhAAAAAAAAAAAOgAAZgA6kABmtjoAADqQ22YAAGaQ22a2/5A6AJDb/7ZmALb//9uQOtu2kNv///+2Zv//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwUwICACUqCMwCMwaOuSQXCgj0kPzkgtRDROiAKqdBqpWKJd7xccylCN1QhimI0EiVYIADs=\" alt=\"image\"\u003e in province \u003cem\u003ec\u0026nbsp;\u003c/em\u003ein baseline year \u003cimg width=\"11\" height=\"15\" src=\"data:image/png;base64,R0lGODlhEQAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAAQAA8AhAAAAAAAAAAAOgAAZgA6kABmtjoAADpmkDpmtjqQ22YAAGY6AGa2/5A6AJDb/7ZmOraQZrbb/7b//9uQOtuQZtv///+2Zv/btv//tv//2wECAwECAwECAwECAwECAwECAwVZICBagySe6HkpRJWmWRPMc/KiVsDc71TyqFgLeMIobCmKIXBwAXI73CCyKogmAkewgfSZJroMxGZEWrIAzCIgQLiM0bP2Vh79bjErYDLk+SIxSDwZDzNuACEAOw==\" alt=\"image\"\u003e,\u0026nbsp;disaggregates the replacement of initial employment onto regions.\u003c/p\u003e\n\u003cp\u003eAlthough exposure to industrial robots is exogenous for individuals, as \u003cimg width=\"23\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026rsquo;s construction depends on the baseline year employment distribution across the industry and region, concerns about endogeneity still exist. For example, there may be uncontrolled factors that simultaneously affect robot adoption, marriage, and childbirth behavior, such as policy shocks, cultural and geographical factors, etc. Therefore, this study employs the Instrumental Variable (IV) to address the potential concerns. Following Acemoglu \u0026amp; Restrepo (2020), the study utilizes the industry-level robot stock in other economies as a proxy to replace China\u0026rsquo;s with those in five other countries. In practice, the study constructs \u003cimg width=\"117\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"269\" height=\"28\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(3)\u003c/p\u003e\n\u003cp\u003ewhere n indicates the five leading countries on robot technology, i.e., the United States, Japan, South Korea, Germany, and Sweden. The IV should be valid, because there is no reason to expect that robot adoption in exporting countries has a direct effect on female marriage rates in China. To test the instruments\u0026rsquo; relevance and validity of the underidentification restrictions, the study computes Kleibergen-Paap rk Wald F statistic and Kleibergen-Paap rk LM statistic in the first stage of 2SLS (Kleibergen \u0026amp; Paap, 2006; Wooldridge, 2010) and reports the results in Tables 2, 3, 5, and 6.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the impact of robot penetration on women’s marriage rate, through OLS and two-stage least squares (2SLS) results.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eBasic Results\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln{\\text{e}\\text{x}\\text{p}\\text{o}\\text{s}\\text{u}\\text{r}\\text{e}\\:\\text{t}\\text{o}\\:\\text{r}\\text{o}\\text{b}\\text{o}\\text{t}}^{\\text{C}\\text{N}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.270***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.511***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.784)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.646)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst-stage Result\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln{\\text{e}\\text{x}\\text{p}\\text{o}\\text{s}\\text{u}\\text{r}\\text{e}\\:\\text{t}\\text{o}\\:\\text{r}\\text{o}\\text{b}\\text{o}\\text{t}}^{\\text{I}\\text{V}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.571***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(60.07)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual Var\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eProvince Var\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYear FE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eProvince FE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eK-P F-stat\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3608.39\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eK-P LM-stat\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1046.81\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,404\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,404\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote.\u003c/em\u003e *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. \u003cem\u003et\u003c/em\u003e value or \u003cem\u003ez\u003c/em\u003e value in Parentheses.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eColumn (1) and Column (2) respectively show the regression results of OLS and 2SLS. Column (2) shows a significant negative correlation between the exposure to robots and the rate at which women get married. The coefficient indicates that a 10 percent increase in robot exposure leads to a 5 percent decrease in the rate of women getting married. This effect is noteworthy because industrial robots are anticipated to spread rapidly in the next several decades. Overall, the result is consistent with Anelli et al. (2021) which finds that robot penetration caused a decline in the number of marriages in the United States.\u003c/p\u003e\n\u003cp\u003eColumn (2) also shows the first-stage regression results of the instrumental variable (IV). The results indicate that industrial robot expansion in China has a significant positive correlation with leading international markets, with K-P F statistics values of 3608.39, which far exceeds the recommended threshold (K-P F ≥ 10), and K-P LM statistics values of 1046.81, which far exceeds the recommended threshold (K-P LM \u0026gt; 0) according to the literature (Stock et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). This demonstrates that the IV is strong with sufficient identification. In other words, the IV is deemed valid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRobustness Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study then checks the robustness of the empirical specifications by running regressions on various sub-samples and including different controls that are likely to be important determinants of young women’s marriage.\u003c/p\u003e\n\u003cp\u003eBraga (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) explored the impact of trade liberalization on the marriage of young women by focusing on the age group of 20–35. This study is similarly revealing when focusing on the sub-sample aged 18–35 (see Column 1 in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSome variables may affect women’s marriage rate and need to be controlled. Firstly, cross-regional labor mobility may affect matching efficiency of marriage and to some extent inhibit marriage (Xiong, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, this study chooses to remove samples with different household registrations and residence locations, with no substantial impact on the results (see Column 2 in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSecondly, changes to the minimum wage standard may affect women’s employment status (Nguyen, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and in turn their marriage rate. Therefore, this study chooses to control the minimum wage standard, with no substantial impact on the results (see Column 3 in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThirdly, structural changes due to China’s market-oriented development strategy may affect women’s marriage rate. China’s transition from a planned to a market-oriented economy began in the late 1970s, which inspired a flurry of reforms leading to rapid urbanization (Fan et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) as well as evolving values and behavioral patterns concerning marriage. Following Wang et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), this study utilizes the Marketization Index of China \u003csup\u003e[1]\u003c/sup\u003e as a proxy variable for assessing the degree of market-oriented structural changes, which returns similar results in Column 4 of Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, housing prices may also affect marriage rate (Atalay, 2021; Nie, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Following Autor et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), this study utilizes average price of urban commercial housing as a proxy variable, which returns similar results in Column 6 of Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eRobustness Test\u003c/em\u003e – \u003cem\u003e2SLS Estimation Results\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln{\\text{e}\\text{x}\\text{p}\\text{o}\\text{s}\\text{u}\\text{r}\\text{e}\\:\\text{t}\\text{o}\\:\\text{r}\\text{o}\\text{b}\\text{o}\\text{t}}^{\\text{C}\\text{N}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.551***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.494***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.476***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.475***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.473**\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.201)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.871)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.580)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.359)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.325)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eFirst Stage Result\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln{\\text{e}\\text{x}\\text{p}\\text{o}\\text{s}\\text{u}\\text{r}\\text{e}\\:\\text{t}\\text{o}\\:\\text{r}\\text{o}\\text{b}\\text{o}\\text{t}}^{\\text{I}\\text{V}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.706***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.543***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.680***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.678***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.903***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(62.52)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(64.35)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(66.90)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(73.22)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(67.18)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eK-P F-stat\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3908.796\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4140.555\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4475.974\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5361.031\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4513.213\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eK-P LM-stat\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1156.928\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1166.192\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1702.103\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1497.357\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e735.932\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,737\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,681\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,404\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,404\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,404\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eControl Var\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYear FE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eProvinces FE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. z value in Parentheses.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cp\u003e\u003cstrong\u003eMechanisms and Heterogeneity Analyses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis section discusses how industrial robot expansion may influence marital decision-making of young women. For them, marriage offers household specialization. However, as women’s economic position rises relative to men, their marriage rate falls (Autor et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Braga, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jensen, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Keller \u0026amp; Utar, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sengupta, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). If the industrial robot expansion, which causes rise in women’s economic position relative to men (e.g., increased employment and wage), leads to reduced gains from household specialization as well as reduced marriage rate for women, then does it show from the impact of the industrial robot expansion on employment and wage for men and women aged 18–39? To answer this question, following Giuntella \u0026amp; Wang (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), this study constructs an econometric model as follows:\u003c/p\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:{z}_{ict}={\\pi\\:}_{0}+{\\pi\\:}_{1}ln\\:{R\\_Robot}_{ct}+{\\pi\\:}_{3}{V}_{ict}+{\\pi\\:}_{4}{W}_{ct}+{\\delta\\:}_{t}+{\\rho\\:}_{c}+{\\phi\\:}_{ict}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe dependent variables\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\text{z}}_{\\text{i}\\text{c}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e in formula (4) are employment status (employed = 1, unemployed = 0) and wage. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{ict}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{ct}\\)\u003c/span\u003e\u003c/span\u003e capture individual and urban characteristics. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\delta\\:}}_{\\text{t}},\\:{{\\rho\\:}}_{\\text{c}}\\:\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\phi\\:}}_{\\text{i}\\text{c}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e represent the time fixed effect, city fixed effect, and idiosyncratic error term. The regression is conducted separately for men and women aged 18–39. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e displays the specific results.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eMechanism Analysis – Differential Effects of Robots on Labor Market Opportunities and Wages of Women and Men\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{l}\\text{n}\\text{w}\\text{a}\\text{g}\\text{e}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMan\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMan\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln{\\text{e}\\text{x}\\text{p}\\text{o}\\text{s}\\text{u}\\text{r}\\text{e}\\:\\text{t}\\text{o}\\:\\text{r}\\text{o}\\text{b}\\text{o}\\text{t}}^{\\text{C}\\text{N}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.130***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.202***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0692**\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.511)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-5.427)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.292)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.390)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.617**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.708*\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.750***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.845***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.033)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.941)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.297)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.814)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,441\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,169\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,875\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,155\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eControl Var\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYear FE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eProvinces FE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. z value in Parentheses.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eColumns 1 and 2 in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e show that a 10 percent increase in robot exposure leads to a 1.3 and 2 percent decrease in employment respectively for women and men. The decrease in employment for men is significantly larger than for women, which amounts to a gap of 0.7 percent. Columns 3 and 4 show that a 10 percent increase in robot exposure leads to a 1.4 and 0.6 percent increase in wage respectively for women and men. Women’s wage increase more than doubles that for men, which amounts to a gap of 0.8 percent. Overall, the industrial robot expansion seems to have improved women’s economic stature relative to men (Ge \u0026amp; Zhou, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Giuntella \u0026amp; Wang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Welch, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), which may account for why their expected gains from household specialization as well as marriage rate are reduced.\u003c/p\u003e\n\u003cp\u003eThe study then conducts some heterogeneity analyses along several dimensions. Firstly, it explores the heterogeneity of the impact by age groups (see Columns 1–3 in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The analysis shows that the impact of robot exposure on women’s marriage rate is mainly driven by younger age groups (e.g., the age group of 18–24). This result is consistent with Dauth et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) which analyzed the impact of industrial robot expansion on different age groups and found that younger people’s jobs are more readily transformed by industrial robots. Therefore, marriage rate of younger women is likely to be influenced more.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eHeterogeneity Analysis – 2SLS Estimation Results\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSkill\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e18 ~ 24\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e25 ~ 32\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e33 ~ 39\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eManufacturing\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln{\\text{e}\\text{x}\\text{p}\\text{o}\\text{s}\\text{u}\\text{r}\\text{e}\\:\\text{t}\\text{o}\\:\\text{r}\\text{o}\\text{b}\\text{o}\\text{t}}^{\\text{C}\\text{N}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.048***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.552**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0794\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.460***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.617**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.283\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.698**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.538***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.789)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.328)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.706)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.749)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.522)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.552)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.518)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.183)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eFirst-stage regression results\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln{\\text{e}\\text{x}\\text{p}\\text{o}\\text{s}\\text{u}\\text{r}\\text{e}\\:\\text{t}\\text{o}\\:\\text{r}\\text{o}\\text{b}\\text{o}\\text{t}}^{\\text{I}\\text{V}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.892***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.652***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.570***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.496***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.920***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.387***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3.109***\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.571***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(29.34)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(46.18)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(42.41)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(48.88).\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(43.48)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(19.57)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(37.29)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e(60.07)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eK-P F-stat\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e860.865\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2132.236\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1798.533\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2389.070\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1890.278\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e382.956\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e243.649\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3608.389\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eK-P LM-stat\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e255.432\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e651.375\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e512.930\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e618.171\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e540.784\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e165.952\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1390.290\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1046.813\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,544\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,057\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,803\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,935\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,450\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,019\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,455\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,949\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eControl Var\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYear FE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eProvinces FE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote.\u003c/em\u003e *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. z value in Parentheses.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSecondly, the study explores the heterogeneity of the impact by skill level (see Columns 4–6 in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). It shows that the impact of robot exposure on women’s marriage rate is mainly driven by medium skill level. This is consistent with the noticeable change in job polarization, in which wage gains go disproportionately to those at the top and the bottom of the skill distribution, not to those in the middle, brought about by the industrial robot expansion (Autor \u0026amp; Dorn, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, the relative economic gains of medium-skilled women are larger than low- and high-skilled women, the effect also being the strongest.\u003c/p\u003e\n\u003cp\u003eFinally, the study explores the heterogeneity of the impact by industry (see Columns 7–8 in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), which shows a slightly larger impact on women’s marriage rate in the manufacturing industry. The reason may be that the industrial robot expansion concentrates more on the manufacturing industry (Dauth et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), therefore more frequently affecting women’s marriage rate in this industry.\u003c/p\u003e\n\n\n\n\n\n"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eThe impact of industrial robot expansion on our daily lives is rapidly growing, with numerous studies choosing the lens of labor market (Acemoglu \u0026amp; Restrepo, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Autor \u0026amp; Dorn, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dauth et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Graetz \u0026amp; Michaels, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Maloney \u0026amp; Molina, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nevertheless, few studies examined the implications on gender and life choices (e.g., marriage) in developing countries, to which this study responded by exploring the implications on young women’s marriage rate in China.\u003c/p\u003e\u003cp\u003eThe study found that the industrial robot expansion in China may cause a decreased marriage rate of Chinese women. Specifically, a 10 percent increase in robot exposure leads to a 5 percent decrease in women’s marriage rate. Younger, medium-skilled women in the manufacturing industry are more sensitive to this impact. In terms of potential mechanisms, the study shows that the industrial robot expansion steadily replaces \u003cem\u003ebrawn\u003c/em\u003e jobs with \u003cem\u003ebrain\u003c/em\u003e jobs, which improves women’s economic stature relative to men, as measured by employment and wage, and thereby reduces their expected gains from household specialization as well as their marriage rate. This result is consistent with the neoclassical theory which posits that improved labor market positions for women will cause decreased marriage rates (Becker, \u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe policy implications of this study are substantial. Firstly, a family of three is currently the most common family size in China (Yu \u0026amp; Xie, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the industrial robot expansion likely to change the family size due to decreased marriage rate, policymakers should more closely monitor the changes in family size for the purpose of redistributing public goods. Secondly, since the premarital fertility rate in China is relatively low (Frejka, 2010), a decreased marriage rate may lead to a decreased fertility rate. With population ageing, policymakers should more promptly develop comprehensive policy frameworks for guiding family preparation. For example, there can be policies to improve the income level of women in marriage through redistributive measures, such as taxation, to control marriage rate as well as to guard against potential social problems arising due to a decreased marriage rate. It is recommended that future research continue to explore women’s marriage rate by linking the industrial robot expansion with fertility rate, first marriage age, divorce, etc.\u003c/p\u003e\u003cp\u003eThere are two implications related to SDG 5 and 8 at the global level. Firstly, the role of education and training in mitigating the negative impacts of automation on gender equality (SDG 5) and decent work (SDG 8) is an area ripe for investigation. As industries adopt more advanced robotic technologies, the demand for skilled labor increases, creating a skills gap that can disproportionately affect women (Nadeem et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies have shown that targeted educational initiatives can help bridge this gap, enabling women to gain the necessary skills to thrive in a more automated workforce. However, the effectiveness of such initiatives often depends on the broader socio-economic context, including access to education and training resources, societal attitudes towards women in technology, and the availability of supportive policies. Therefore, future research should focus on identifying the best practices for educational programs that promote gender equality in the context of industrial automation.\u003c/p\u003e\u003cp\u003eSecondly, the environmental implications of industrial robot expansion also intersect with the goals of SDG 5 and SDG 8. The push for sustainable manufacturing practices, driven by the need to reduce carbon footprints and enhance resource efficiency, can create new opportunities for women in green technology sectors (Johansson \u0026amp; Ringblom, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, if the transition to automation prioritizes efficiency over sustainability, it could lead to adverse environmental outcomes that disproportionately affect vulnerable populations, including women (Xu \u0026amp; Ye, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is therefore recommended that future research explore how the integration of sustainable practices in robotic manufacturing can align with gender equality and decent work objectives, ensuring that the benefits of automation are equitably distributed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Xu, Yanhua] and [Ye, Huiyuan]. The first draft of the manuscript was written by [Xu, Yanhua] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcemoglu, D., \u0026amp; Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. \u003cem\u003eAmerican Economic Review, 108\u003c/em\u003e(6), 1488\u0026ndash;1542.\u003c/li\u003e\n\u003cli\u003eAcemoglu, D., \u0026amp; Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. \u003cem\u003eJournal of Economic Perspectives, 33\u003c/em\u003e(2), 3\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eAcemoglu, D., \u0026amp; Restrepo, P. (2020). Robots and jobs: Evidence from U.S. labor markets. \u003cem\u003eJournal of Political Economy, 128\u003c/em\u003e(6), 2188\u0026ndash;2244.\u003c/li\u003e\n\u003cli\u003eAksoy, C. 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(2019) which assessed the process of marketization reform in China.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Sustainable Development Goals (SDGs), industrial robot, employment, wage, marriage, China","lastPublishedDoi":"10.21203/rs.3.rs-7174450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7174450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid industrial robot expansion has increased concerns over its impact on marital choices as well as scrutiny over the interactions between SDG 5 (Gender Equality) and SDG 8 (Decent Work and Economic Growth). This study measures the impact on women\u0026rsquo;s marriage rate in China, with the results showing that a 10 percent increase in robot exposure leads to a 5 percent decrease in women\u0026rsquo;s marriage rate. Specifically, higher robot exposure predicts relatively higher wages and increased job opportunities for women, which improves women\u0026rsquo;s overall economic status. This, however, reduces women\u0026rsquo;s incentive to a household life as well as to getting married. Moreover, the impact is stronger for younger medium-skilled women in the manufacturing industry. These findings support the neoclassical theory that change of women\u0026rsquo;s labor market position can influence their marital decision-making. Policy recommendations are also provided for synergizing the two SDGs.\u003c/p\u003e","manuscriptTitle":"Correlating the Sustainable Development Goals 5 and 8: Impact of industrial robot expansion on women’s marriage rate in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-28 08:39:33","doi":"10.21203/rs.3.rs-7174450/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":"3581b4b6-7c9f-45d3-9361-2a4093a5607d","owner":[],"postedDate":"August 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-05T20:23:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-28 08:39:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7174450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7174450","identity":"rs-7174450","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}