Gender income differences among general practitioners with compulsory services in early career stage in underdeveloped areas: evidence from a prospective cohort study in China

Gender income differences among general practitioners with compulsory services in early career stage in underdeveloped areas: evidence from a prospective cohort study 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 Gender income differences among general practitioners with compulsory services in early career stage in underdeveloped areas: evidence from a prospective cohort study in China Haoqing Tang, Mingyue Li, Huixian Zheng, Xiaotian Zhang, Xiaoyun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3739819/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Gender equality and the gender income gap in medicine are long-standing global problems. Although gender-related differences have been widely studied in developed countries, they remain unclear in underdeveloped regions. In 2010, China initiated a national compulsory service program (CSP) to train qualified general practitioners in rural and remote areas. This study aimed to evaluate gender income differences for early career CSP and non-CSP (NCSP) graduates in underdeveloped areas. A cohort study was conducted with 3620 CSP and NCSP graduates from four medical universities in Central and Western China. Baseline surveys and six follow-up surveys were conducted between 2015 and 2022. Incomes, including monthly mean income and proportion of performance-based income, were measured as the key outcome variables. Multivariate linear regression models were used to identify the gender income gap. NCSP graduates had higher average monthly incomes than CSP graduates. In the seventh year after graduation, the average monthly income for NCSP graduates was 7859 CNY while was 5379 CNY for CSP graduates. After controlling for demographic characteristics, the gender monthly income gap for CSP graduates was expanded from the fourth year (3.0%) to the sixth year (5.9%) after graduation, and that for NCSP graduates was expanded from the fifth year (11.9%) to the seventh year (16.3%) after graduation. Regarding performance-based income, it was 58.9% for NCSP graduates and 45.8% for CSP graduates in the seventh year after graduation. After controlling for performance-based income proportion, the gender income gap was reduced from 5.9% to 4.0% in the sixth year after graduation for CSP graduates, and from 16.3% to 14.4% for NCSP graduates in the seventh year after graduation. An extensive and ever-increasing gender income gap exists among young doctors in the early stages of their careers in underdeveloped areas of China. The high proportion of performance-based income among men is one of the main explanations for the observed difference. A more explicit compensation system must be established to enhance support for female health workers. gender difference general practitioner income underdeveloped areas China Highlights A seven-year cohort study examined the gender income gap for practitioners in early career stage An increasing gender income gap exists for doctors in underdeveloped areas in China The income gap was smaller for those trained by China’s compulsory services program Higher performance-based income for men is the main explanation for the income gap INTRODUCTION Women account for approximately 75% of the global health workforce 1 . In China, medicine has recently undergone a strong shift toward feminization; in 2021, 73.0% of health workers were women, compared to 63.0% in 2002 2 . Gender inequality and the gender career income gap in medicine are long-standing problems globally 3–6 . Many studies across developed countries have shown that male doctors are more likely to be promoted and earn more than female doctors. A previous review showed that the total medical gender pay gap in England was 24.4% for hospital doctors, 33.5% for general practitioners, and 21.4% for clinical academics in 2020 7 . A 2012 longitudinal study in Australia found that female general practitioners (GPs) earned 24.9% less than male GPs 8 . Another cross-sectional study conducted in Iran in 2019 found that male GPs earned 35.3% more than female GPs 9 . Women often earn less than men owing to factors such as the “career effect” associated with child-rearing responsibilities 10 , labor market discrimination 11 , and because they provide different types of services 12 . Gender differences in the early stage of career development may have a more significant impact on the development of healthcare manpower and the provision of healthcare services. Low income and low financial incentive have been widely regarded as key reasons for health workforce shortage in China’s primary health care system 13,14 . In 2010, China began a national compulsory services program (CSP) for medical students. The program trains GPs in rural areas in China’s Central and Western regions, with the goal of improving the capacity of rural primary health care (PHC) services and increasing health accessibility and equity. CSP students are not required to pay for tuition or accommodations during their five-year undergraduate studies, and they may receive monthly allowances. In return, they must commit to working as GPs at appointed primary health care systems for six years after graduation. In contrast, most non-CSP (NCSP) students choose to work as specialists in county and above level hospitals. Seven years have passed since the first wave of students graduated from the CSPs. Income is a crucial factor influencing whether GPs remain in primary health care systems (B. Zhang et al. et al., 2023). Furthermore, the earnings disparity between genders has repercussions for the retention of GPs, with a notable influence on the labor supply of female GPs. This effect becomes particularly pronounced as the proportion of female GPs in the workforce increases, yet it has received insufficient attention in both policy practice and academic research. The compensation structure for doctors in China typically comprises a fixed base salary, performance-based earnings, and various allowances 16 . Moreover, an annual year-end bonus is provided, with bonuses often representing a significant proportion of the overall salary 17 . Currently, in China, there is widespread overdependence on performance incentives to motivate healthcare professionals. Zhang and Liu discovered that years of practice, educational background, technical title, management position, and specialty all influenced doctors' salaries 17 . However, there is a notable evidence gap regarding the effects of performance incentives on income disparities among GPs from a gendered perspective. To date, most studies on the CSP have focused on health workforce attraction, recruitment, and retention in rural and remote areas (M. Li et al., 2022a; R. Cheng et al., 2022; D. Hu et al., 2016). However, there has been minimal research on gender differences and the determinants of doctors’ income in underserved areas, despite financial incentive playing an important role in influencing attraction, retention, and development. As gender also has implications for the availability and acceptability of health workers in rural communities 22–24 , more research on gender differences among doctors at different levels is required. Previous studies on gender differences were mostly from developed countries and regions 25–27 . Little attention has been paid to the gender difference among doctors working in middle- and low-level economic regions, where women's social status is usually lower 28 . Therefore, we sought to evaluate gender income differences in a new population of young medical graduates in underdeveloped areas who were early in their careers. The study period ranged from the year 2015 to 2022. Our goal was to gain an up-to-date understanding of the exact level and change in the income gap between CSP and NCSP graduates and to evaluate whether the gender differences previously observed among health workers would be apparent in this younger and more recently hired cohort. In addition, we examined how performance-based income determines gender differences. This study provides evidence for other countries seeking to increase access for healthcare workers in underdeveloped areas. METHODS Study design and data collection Study design Data for this study were obtained from the Cohort Study of Medical Graduates with Compulsory Services in Rural Areas, a prospective cohort study of Chinese medical graduates. The study was established in 2015 and looks at medical education, residency training, employment, and career development to help China's rural and remote regions grow their health workforce. The Institutional Review Board (IRB) of Peking University Health Science Center provided funding for this study (IRB00001052-15027). All participants provided informed consent. Baseline data collection The four medical universities in Western and Central China that undertook the CSP were chosen to represent underdeveloped areas of China. The survey included 3620 medical graduates from Qinghai University (Qinghai Province, Northwest China), Guangxi Medical University (Guangxi Zhuang Autonomous Region, Southwest China), Jiujiang University, and Gannan Medical University (Jiangxi Province, Central China). After five years of undergraduate study, the first group of CSP-trained medical professionals graduated in 2015. This group formed the first sub-cohort, and we gathered baseline data from the four medical schools. The CSP classes were matched 1:1 with NCSP classes from the same year. Sub-cohorts were also created from the 2016, 2017, 2018, and 2019 classes. Participants completed a paper questionnaire at baseline before completing their undergraduate studies. Data on demographics, employment, postgraduate studies, residency training, and employment-related information were collected from both types of graduates. The key predictor in this study, gender, was identified as female or male (based on the dichotomous response options available in the baseline questionnaire). Follow-up data collection In the baseline survey, we established WeChat groups encompassing all participants within each school and sub-cohort. WeChat is a widely used instant messaging application in China that facilitates communication between investigators and participants. After a baseline survey, annual online follow-up surveys were conducted. By 2022, we had successfully completed six follow-up surveys for the 2015 graduates. Links to online self-administered questionnaires were disseminated annually via email, the WeChat groups, and mobile text messages. Follow-up information was collected in 2016, 2017, 2018, 2020, 2021, and 2022. Due to logistical reasons, the 2019 follow-up survey was not conducted for the 2015 to 2018 graduates. The outcome variables in this study were average monthly income (in Chinese Yuan [CNY]) and the proportion of performance-based income. Average monthly income was assessed using the question, "What is the monthly income for this job?" Performance-based income was gauged using the question, "In this context, how much of the income is based on performance?" All variables related to income were adjusted using the Consumer Price Index (CPI) in 2015. A number of labor market variables widely postulated to influence income were available in the follow-up questionnaire, including demographic characteristics (marital status and education level), workload (outpatient volume and inpatient volume), and work-related characteristics (current workplace, job performance, whether participants passed China National Medical Licensing Examinations, whether they finished standardized training for resident physicians, and whether they received a title or job promotion). Unemployed individuals and those who did not work in underdeveloped areas were excluded. The final sample comprised 2041 CSP graduates and 1059 NCSP graduates. Statistical Analysis We categorized our cohorts based on number of years since graduation. This was done to investigate income disparity patterns between males and females in the early stages of their professional development and to observe how this discrepancy evolved with each additional year since graduation. Descriptive analysis was used to identify the characteristics of the study sample and distribution of medical graduates’ income (average monthly income and proportion of performance-based income) between men and women. A t-test was used to compare the differences between men and women and CSP and NCSP graduates for each successive year after graduation. Following a log-transformation of the income variable to account for data distribution skewness, we employed multiple linear regressions to assess the gender income gap for each year since graduation, the independent associations among gender and other sample characteristics, and work-related characteristics associated with differences in occupational earnings. All statistical analyses were conducted using STATA (version 17.0; Stata Corp., College Station, TX, USA). P values less than 0.05 were considered statistically significant. RESULTS Table 1 presents the basic characteristics of the study sample. A total of 3620 medical graduates were included in the baseline survey from 2015 to 2019, including 2041 CSP graduates, accounting for 56.4%. A similar number of men and women participated (50.7% male and 49.3% female). (Insert Table 1 here) Table 2 shows the characteristics of the study sample for CSP and NCSP graduates in the latest follow-up survey in 2022. Most CSP graduates (74.1%) worked in CHC and THC, whereas most NCSP graduates (90.7%) worked in public hospitals at the county level and above. Most NCSP graduates (69.4%) had received postgraduate qualifications, while only 4.6% of CSP had postgraduate qualifications. Most medical graduates (97.4%) had passed the China National Medical Licensing Examinations, and all graduates had completed standardized training for resident physicians. The NCSP graduates had a higher workload than the CSP graduates, both in terms of the number of outpatients (25.8 vs. 18.9) and inpatients (32.1 vs. 24.8). Although the graduates had at most seven years of experience, 83.0% of CSP graduates and 68.5% of NCSP had been given title promotions. Of the CSP graduates, 16.0% had received job promotions, whereas only 2.5% NCSP graduates had been promoted. (Insert Table 2 here) Table 3 describes the differences in average monthly income between men and women for each successive year after graduation for both CSP and NCSP graduates. For both men and women, NCSP graduates had a higher average monthly income than CSP graduates in each year since graduation, and the difference between CSP and NCSP graduates widened over the seven years (from 379 CNY in year one to 2479 CNY in year seven). The average monthly income of CSP graduates in their seventh year after graduation was 5379 CNY per month, which was a significantly lower change than that of NCSP graduates (7859 CNY per month). Throughout the seven years, no significant gender gap existed in the average monthly incomes of CSP graduates. However, statistically significant gender differences were found among NCSP graduates who had been practicing for five to seven years, and the gender gap in the seventh years was 1058.7 CNY. (Insert Table 3 here) Table 4 presents the differences in the proportions of performance-based income between men and women in each year since graduation for CSP and NCSP graduates. From the second year after graduation, CSP graduates had a significantly higher proportion of performance-based income than NCSP graduates. The proportion of performance-based income for those that had graduated seven years ago was 48.7% for CSP graduates and 58.9% for NCSP graduates. Among CSP graduates, men in their fourth to seventh years after graduation received a significantly higher proportion of performance-based income than women. The gender gap increased from 2.3% (fourth year) to 4.6% (seventh year). Among NCSP graduates, men in their second to seventh years after graduation received a significantly higher proportion of performance-based income than women. The gender gap increased from 1.6% (second year) to 8.5% (seventh year). (Insert Table 4 here) The results of the multivariate regression analysis of the average monthly incomes of the CSP and NCSP graduates are presented in Table 5. After controlling for the covariates of school, years since graduation for each sub-cohort, marital status, current workplace, and whether they had received a title or job promotion, we found a statistically significant difference between genders in average monthly income (Model 1). Among CSP graduates, men had higher average monthly income than women, and the gender income gap widened between the fourth and sixth years after graduation. Women earned 3.0% less than men in the fourth year after graduation, and the gap increased to 5.9% in the sixth year after graduation. The insignificance of the gender gap in the seventh year may be related to the relatively small sample size (only 2015 graduates). For NCSP graduates, men also had higher average monthly income than women, and the gender income gap widened between the fifth and seventh years after graduation. Women earned 11.9% less than men in the fifth year after graduation, and the gap increased to 16.3% in the seventh year after graduation. The gender income gap was significantly greater among NCSP graduates (16.3%) compared to CSP graduates (3.2%) in the seventh year after graduation. When we included the proportion of performance-based income in Model 2, the gender income gap was reduced. In the seventh year after graduation, for CSP graduates, the income gap decreased by 1.2% points compared to the gap in Model 1. Meanwhile, for NCSP graduates, the income gap also decreased by 1.9% points in the seventh year after graduation compared to the gap in Model 1. This finding provides further evidence that the proportion of performance-based income may be a reason for the gender income gap. (Insert Table 5 here) DISCUSSION Gender income inequity continues to be pervasive in medicine 5,29–31 . To our knowledge, this is the first study to use a cohort study to evaluate the gender income gap in underdeveloped areas in China. We compared the level and change in the income gap between CSP and NCSP graduates and found evidence of a significant gap, with female doctors earning 3.2% (CSP graduates) less and 16.3% (NCSP graduates) less than male doctors in the seventh year after graduation, after adjusting for confounders. First, gender differences were apparent among young medical school graduates in underdeveloped areas of China, and this difference continuously widened. Labor market discrimination may be a possible explanation for the observed gender income difference. Gender discrimination still prevails in rural areas. Many elderly individuals continue to believe that male doctors have more extensive experience. This conscious or unconscious gender bias and discrimination lead to lower trust in female doctors, consequently reducing the performance-based income of female physicians. Some studies have also found that gender wage disparities may stem from differences in workloads and work patterns. Theurl and Winner found that male doctors provide more treatments (the average number of treatments per hour) than their female counterparts 11 , but our research found no significant statistical difference between male and female doctors in terms of workload. Barry also found that the significant pay disparity between male and female GPs was linked to women spending more time with their patients 32 . Another possible explanation for the gender difference is the “glass ceiling” experienced by women. The concept of a glass ceiling is recurrent in the recent literature on the career problems of women 33–37 . It is defined in Merriam-Webster's Collegiate Dictionary (10th edition) as “an intangible barrier within the hierarchy of a company that prevents women or minorities from obtaining upper-level positions” 38 . A national study of female physicians in academic medicine in the US reported that women were much less likely than men to be promoted; this gender difference persisted even after controlling for work schedule, specialty, and academic productivity, suggesting the existence of a glass ceiling for female physicians 33 . Blumenthal et al. also found that female surgeons were significantly less likely than their male counterparts to be full professors, after adjusting for several factors 39 . Owing to faster career advancement, men earn significantly higher levels of performance-based income than women, resulting in income disparities. However, because of the relatively short observation period, we could not definitively confirm the existence of a glass ceiling effect. Second, to some extent, the CSP ensures fairness and partially eliminates discrimination against women through its directed training, staffing issues, and faster career development paths. After the health system reform in 2009 in China, the salary structure for GPs in PHC primarily consists of a fixed salary (L. Hu et al., 2015). The disparities in fixed salaries are minimal, primarily determined by factors such as educational background, professional title, and years of work experience. Meanwhile, the government offers incentives in the form of policies such as hardship allowances for remote and underdeveloped areas as well as township work subsidies, with relatively minor gender disparities (X. Guo, n.d.). Additionally, owing to the scarcity of GPs in remote rural areas, there is a limited pool of doctors available for selection 42 . As a result, rural patients cannot choose their contracted GP based on their gender, but they can specify the gender of specialist doctors at the county hospital. This provides equal employment opportunities for female GPs. However, male CSP graduates earn notably less than their NCSP counterparts. This discrepancy makes them more inclined to leave PHC and seek higher-paying professions, driven by pressures from family, society, and other factors (B. Zhang et al., 2023). Therefore, male CSP graduates with higher incomes tend to leave PHC. Conversely, those who remain in PHC may experience lower incomes, potentially contributing to a reduction in income disparities between male and female GPs. Third, the proportion of performance-based income was significantly higher for men than women, making it a significant contributor to this disparity. Male doctors often experience the "breadwinner effect" as they bear the financial responsibility of supporting their families. This, in turn, stimulates stronger ambitions and determination in them, fostering a more intense drive to earn money. In contrast, female doctors, influenced by the "caregiver effect" of having children, may be more focused on caring for their children, potentially leading them to overlook performance improvement 10 . Moreover, the remuneration of doctors in China is generally composed of a basic fixed salary, performance-based income, various allowances, and an annual year-end bonus. The level of a doctor's title determines the coefficient for performance and allowances, meaning that higher titles are associated with higher performance-based incentives. Based on research from previously developed countries, it has been found that there is not a significant difference between men and women in terms of fixed income. However, the key variations lie in performance-based income that are linked to factors such as workload and work intensity 11,12,43 . Additionally, in China, hospitals primarily derive their revenue from medical services, with only a small portion coming from government financial subsidies. Therefore, to achieve higher earnings, hospitals are more likely to motivate ambitious male doctors by offering them more attractive performance-based income. This, to some extent, elucidates the income disparity observed between male and female doctors. However, this explanation only covers a fraction of the gap, as a more significant factor is likely linked to the previously mentioned gender discrimination. In the medical domain, despite female doctors demonstrating excellence and professionalism, they may still encounter gender bias, potentially hindering their opportunities for promotions, well-compensated roles, and other career advancements. Achieving genuine gender equality necessitates not only fostering ambition among male doctors but also continuous efforts to eradicate various forms of discrimination against female healthcare professionals. Such endeavors play a crucial role in dismantling gender barriers and cultivating a more fair and inclusive working environment within the medical field. This study had several limitations. First, as the income data were self-reported, we could not verify their authenticity or the presence of any additional income, which may have introduced a reporting bias. Second, as the follow-up surveys were conducted online, the questionnaire was kept relatively concise to ensure completion rates. Few variables were therefore available to decompose the gender income gap. Consequently, we focused exclusively on performance-based income, while other potentially influential factors remain unanalyzed. Third, all cohort studies inevitably encounter some participant loss in follow-up surveys. Our previous research addressed the issue of sample attrition and found that it had a minimal impact on the focal career development issues of the cohort under study 44 . In conclusion, a gender income gap was apparent in young doctors in underdeveloped areas of China, potentially hindering PHC services from attracting and retaining talent. Compared to NCSP graduates, the income gap was smaller for CSP graduates. The high proportion of performance-based income among men was a main explanation for the observed gender difference. To improve the capacity of rural PHC services and increase health accessibility and equity, a more explicit compensation system must be established to enhance support for female healthcare workers at the system level. Abbreviations CSP Compulsory services program NCSP non-CSP GPs General practitioners IRB Institutional review board CPI Consumer price index PHC Primary health care SAGER Sex and gender equity in research Declarations Ethical approval and consent to participate: The study was approved by the Institutional Review Board of Peking University Health Science Center (PUIRB) (IRB00001052-15027) and was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to questionnaire administration. Consent for publication: Not applicable. Funding ： The Cohort Study of Medical Graduates with Compulsory Services in Rural Areas Studies was funded by the China Medical Board (CMB). The funder had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. Data and materials availability: The datasets generated and/or analyzed during the current study are not publicly available due to limitations of ethical approval involving the personal data and anonymity; however, they are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no conflict of interests. Author contributions: X.L. designed the study. H.T. and X.L. conceptualized the study design. H.Z., H.T., M.L. and X.Z. collected and managed the data. H.T. participated in the data analysis. H.T. drafted the article. X.L., M.L., H.Z and X.Z. revised the paper and contributed to critical revision of the article. All authors approved the final manuscript. Acknowledgement: We gratefully acknowledge the Qinghai University, Jiujiang University, Gannan Medical University, and Guangxi Medical University for their support in the establishment of the cohorts. References Shannon G, Jansen M, Williams K, et al. 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Tables Table 1 Basic characteristics of the study sample [n(%)] Basic characteristics of the study sample Male (n=1836) Female (n=1784) Overall (n=3620) Yeas of Graduation 2015 323 (17.6%) 297 (16.6%) 620 (17.1%) 2016 383 (20.9%) 356 (20.0%) 739 (20.4%) 2017 437 (23.8%) 374 (21.0%) 811 (22.4%) 2018 355 (19.3%) 394 (22.1%) 749 (20.7%) 2019 338 (18.4%) 363 (20.3%) 701 (19.4%) School Qinghai 475 (25.9%) 669 (37.5%) 1144 (31.6%) Guangxi 446 (24.3%) 487 (27.3%) 933 (25.8%) Jiujiang 291 (15.8%) 189 (10.6%) 480 (13.3%) Gannan 624 (34.0%) 439 (24.6%) 1063 (29.4%) Type CSP 1068 (58.2%) 973 (54.5%) 2041 (56.4%) NCSP 768 (41.8%) 811 (45.5%) 1579 (43.6%) Table 2 Characteristics of the study sample in the latest follow-up survey 2022 [n(%) /mean(sd)] CSP (n=2041) NCSP (n=1579) Male Female Male Female Total 1068 973 768 811 Married 316 (58.7%) 245 (51.5%) 133 (43.5%) 125 (39.9%) Current workplace Public hospitals at county level and above 131 (26.7%) 91 (21.5%) 226 (91.9%) 222 (89.5%) CHC & THC 350 (71.4%) 325 (76.7%) 3 (1.2%) 6 (2.4%) Other 9 (1.8%) 8 (1.9%) 17 (6.9%) 20 (8.1%) Education Postgraduate qualifications 12 (2.2%) 33 (6.9%) 215 (70.2%) 215 (68.6%) Passed China National Medical Licensing Examinations 866 (96.7%) 815 (98.5%) 482 (97.0%) 511 (97.3%) Finished standardized Training for residents Physicians 720 (100.0%) 619 (100.0%) 290 (100.0%) 307 (100.0%) High job performance (≥72) 161 (29.9%) 104 (21.8%) 89 (29.1%) 79 (25.2%) Workload Outpatient numbers 18.9 (23.9) 19.0 (27.4) 24.8 (32.8) 26.7 (42.7) Inpatient numbers 27.0 (63.0) 22.6 (50.0) 36.2 (56.2) 27.9 (55.2) With title of attending doctor 267 (49.6%) 183 (38.4%) 35 (11.4%) 31 (10.2%) With job promotion 84 (15.6%) 54 (11.3%) 5 (1.6%) 8 (2.6%) Note: The total score of the job performance scale was 12 items × 7 points = 84. Table 3 Difference in average monthly income between men and women for each successive year after graduation for both CSP and NCSP graduates Graduation duration Average month income CSP (n=2039) NCSP (n=1571) Difference b (CNY) P value Male Female Difference a (CNY) P value Total mean (CNY) Male Female Difference a (CNY) P value Total mean (CNY) n mean (CNY) n mean (CNY) n mean (CNY) n mean (CNY) 1 688 2588 617 2885 -297 0.000 2728 180 3165 204 3034 131 0.339 3107 -379 <0.001 2 638 3203 602 3419 -216 0.012 3308 162 3962 160 3866 96 0.630 3914 -606 <0.001 3 659 3411 578 3583 -171 0.046 3489 216 4967 232 4827 139 0.491 4897 -1408 <0.001 4 426 3641 374 3799 -157 0.082 3717 164 6138 155 5752 385 0.160 5950 -2233 <0.001 5 381 3972 311 4121 -149 0.112 4038 162 6764 189 6178 586 0.034 6448 -2409 <0.001 6 184 4787 143 4938 -151 0.329 4857 107 7871 130 7003 867 0.020 7394 -2537 <0.001 7 84 5293 48 5531 -237 0.268 5379 71 8377 68 7318 1058 0.023 7859 -2479 <0.001 Note: (1) Difference a =Male-Female; Difference b =CSP-NCSP. (2) Average month income was adjusted using the Consumer Price Index (CPI). (3) CNY, Chinese yuan renminbi. Table 4 Differences in the proportions of performance-based income between men and women in each year since graduation for CSP and NCSP graduates Graduation duration The proportion of performance-based income CSP (n=2039) NCSP (n=1571) Difference b P value Male Female Difference a P value Total Male Female Difference a P value Total n mean n mean n mean n mean 1 —— —— —— —— —— —— —— —— —— —— —— —— —— —— —— —— 2 638 22.4% 602 22.4% 0.0% 0.888 22.4% 162 42.4% 160 40.8% 1.6% 0.003 41.6% -19.2% <0.001 3 659 28.4% 578 28.0% 0.4% 0.395 28.2% 216 50.4% 232 45.5% 4.9% 0.000 47.8% -19.6% <0.001 4 426 26.9% 374 24.6% 2.3% 0.005 25.8% 164 54.3% 155 49.2% 5.1% 0.000 51.9% -26.1% <0.001 5 381 36.0% 311 33.3% 2.7% 0.001 34.8% 162 57.7% 189 51.2% 6.5% 0.000 54.2% -19.4% <0.001 6 185 44.9% 143 41.0% 3.9% 0.002 43.2% 107 59.5% 130 53.3% 6.2% 0.006 56.1% -12.9% <0.001 7 84 50.4% 48 45.8% 4.6% 0.014 48.7% 71 63.0% 68 54.5% 8.5% 0.011 58.9% -10.2% <0.001 Note: (1) Difference a =Male-Female; Difference b =CSP-NCSP. (2) Performance-based income was adjusted using the Consumer Price Index (CPI). (3) CNY, Chinese yuan renminbi. Table 5 Multivariate regression analysis of the average monthly incomes of the CSP and NCSP graduates Graduation duration Model Sex (ref: Male) The proportion of performance-based income Marital status (ref: Not married) Workplace (ref: Public hospitals at county level and above) Title promotion (ref: Without title) Job promotion (ref: Without administrative positions) Female Married CHC & THC Other With title With administrative positions CSP (n=2039) 1 Model 1 0.019 —— 0.017 -0.091*** 0.068 —— —— Model 2 —— —— —— —— —— —— —— 2 Model 1 0.028 —— -0.034 -0.032 0.188 0 -0.072 Model 2 0.029 0.219 -0.034 -0.032 0.158 0.002 -0.069 3 Model 1 -0.01 —— -0.003 -0.115*** 0.18 -0.004 -0.003 Model 2 -0.008 0.394*** -0.004 -0.119*** 0.199 -0.002 0.002 4 Model 1 -0.030* —— 0.007 -0.155*** 0.096 0.006 0.024 Model 2 -0.021 0.494*** 0.005 -0.139*** 0.159 -0.007 0.016 5 Model 1 -0.038* —— 0.009 -0.101*** -0.067 0.023 0.068** Model 2 -0.023 0.661*** 0.018 -0.093*** -0.084 0.026 0.066** 6 Model 1 -0.059* —— -0.035 -0.142*** 0.046 0.097 0.099** Model 2 -0.04 0.584*** -0.039 -0.129*** 0.074 0.124* 0.096** 7 Model 1 -0.032 —— -0.057 -0.100** -0.02 0.114 0.027 Model 2 -0.02 0.383* -0.053 -0.089* 0.002 0.11 0.027 NCSP (n=1571) 1 Model 1 -0.011 —— 0.055 -0.023 0.164* —— —— Model 2 —— —— —— —— —— —— —— 2 Model 1 -0.231 —— 0.23 —— —— 0.315 —— Model 2 -0.317 -0.789 0.248 —— —— 0.35 —— 3 Model 1 0.063 —— 0.059 0.256 0.106 0.091 -0.145 Model 2 0.1 0.633* 0.066 0.294 0.137 0.079 -0.154 4 Model 1 -0.035 —— 0.103* 0.131 0.178 0.144** -0.154 Model 2 -0.015 0.438* 0.093 0.116 0.179* 0.137* -0.164 5 Model 1 -0.119** -0.075 -0.091 0.264** 0.162*** 0.004 Model 2 -0.100* 0.288 -0.074 -0.079 0.266** 0.168*** 0.000 6 Model 1 -0.103* —— 0.011 -0.133 -0.118 0.271*** 0.192 Model 2 -0.099* 0.082 0.011 -0.123 -0.116 0.271*** 0.19 7 Model 1 -0.163** —— 0.079 0.091 0.021 0.173 0.395** Model 2 -0.144* 0.176 0.069 0.129 0.025 0.146 0.353* Note: (1) In the first year, there is no data of title promotion, job promotion and performance-based income. (2) *** P < 0.001, ** P < 0.01, * P < 0.05; β coefficient and Confidence interval were reported. (3) The schools and years of graduation for each sub-cohort were controlled for in all regressions. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 09 May, 2024 Reviewers agreed at journal 04 Jan, 2024 Reviewers invited by journal 03 Jan, 2024 Editor assigned by journal 11 Dec, 2023 First submitted to journal 10 Dec, 2023 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. 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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-3739819","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265185003,"identity":"52852964-58bc-4512-ae48-946fcbc5c777","order_by":0,"name":"Haoqing Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYBACAxCRAGEzPgBTPCCCjbAWCSDFbADWTJQWBogWNgmitJizHz544+GO2jr+2e3XKn/+OMxgznPGgOFD2WEG/tkNWLVY9qQlWySeOS4hcedM2W2ehMMMlr09Bowzzh1mkLhzALvDDuSYSSS2HZNguJGTdpsBqMXgPI8BM28bkCGRgF3L+TcQLfJALYU/YFr+4tNyA2xLjYTBjfRjDCCHGZztMWBmxKvlGdAvbQckN97IYZbmSUvnsew5VnCw51w6j8QNXA5LPnjzZ1sdv9yN9Icff9hYy5nzJG988KPMWo5/BnYtIACMk8NAigccR2ASFFQ8ONVDtNQBKfYHEHvxKR0Fo2AUjIIRCQAwXWDfzw1VFQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5805-2116","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Haoqing","middleName":"","lastName":"Tang","suffix":""},{"id":265185004,"identity":"038c66c7-4d44-4114-b104-c5957fb0266c","order_by":1,"name":"Mingyue Li","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Mingyue","middleName":"","lastName":"Li","suffix":""},{"id":265185005,"identity":"3b316f83-f95a-4a14-acfa-a7bce22b03f8","order_by":2,"name":"Huixian Zheng","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Huixian","middleName":"","lastName":"Zheng","suffix":""},{"id":265185006,"identity":"af35e77a-2998-457b-b12b-72c0df326ebc","order_by":3,"name":"Xiaotian Zhang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaotian","middleName":"","lastName":"Zhang","suffix":""},{"id":265185007,"identity":"2ed64e24-abb0-4c79-b137-aede0cb26982","order_by":4,"name":"Xiaoyun Liu","email":"","orcid":"https://orcid.org/0000-0002-5483-0742","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2023-12-11 16:57:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3739819/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3739819/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49303658,"identity":"fcb2e6eb-fd98-4bdb-9e14-0d5880692ef1","added_by":"auto","created_at":"2024-01-08 10:42:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":564537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3739819/v1/e2c19643-dbfe-4003-8f29-937d85684555.pdf"}],"financialInterests":"","formattedTitle":"Gender income differences among general practitioners with compulsory services in early career stage in underdeveloped areas: evidence from a prospective cohort study in China","fulltext":[{"header":"Highlights","content":"\u003cp\u003eA seven-year cohort study examined the gender income gap for practitioners in early career stage\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn increasing gender income gap exists for doctors in underdeveloped areas in China\u003c/p\u003e\n\u003cp\u003eThe income gap was smaller for those trained by China’s compulsory services program\u003c/p\u003e\n\u003cp\u003eHigher performance-based income for men is the main explanation for the income gap\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eWomen account for approximately 75% of the global health workforce \u003csup\u003e1\u003c/sup\u003e. In China, medicine has recently undergone a strong shift toward feminization; in 2021, 73.0% of health workers were women, compared to 63.0% in 2002 \u003csup\u003e2\u003c/sup\u003e. Gender inequality and the gender career income gap in medicine are long-standing problems globally \u003csup\u003e3\u0026ndash;6\u003c/sup\u003e. Many studies across developed countries have shown that male doctors are more likely to be promoted and earn more than female doctors. A previous review showed that the total medical gender pay gap in England was 24.4% for hospital doctors, 33.5% for general practitioners, and 21.4% for clinical academics in 2020 \u003csup\u003e7\u003c/sup\u003e. A 2012 longitudinal study in Australia found that female general practitioners (GPs) earned 24.9% less than male GPs \u003csup\u003e8\u003c/sup\u003e. Another cross-sectional study conducted in Iran in 2019 found that male GPs earned 35.3% more than female GPs \u003csup\u003e9\u003c/sup\u003e. Women often earn less than men owing to factors such as the \u0026ldquo;career effect\u0026rdquo; associated with child-rearing responsibilities \u003csup\u003e10\u003c/sup\u003e, labor market discrimination \u003csup\u003e11\u003c/sup\u003e, and because they provide different types of services \u003csup\u003e12\u003c/sup\u003e. Gender differences in the early stage of career development may have a more significant impact on the development of healthcare manpower and the provision of healthcare services.\u003c/p\u003e \u003cp\u003eLow income and low financial incentive have been widely regarded as key reasons for health workforce shortage in China\u0026rsquo;s primary health care system \u003csup\u003e13,14\u003c/sup\u003e. In 2010, China began a national compulsory services program (CSP) for medical students. The program trains GPs in rural areas in China\u0026rsquo;s Central and Western regions, with the goal of improving the capacity of rural primary health care (PHC) services and increasing health accessibility and equity. CSP students are not required to pay for tuition or accommodations during their five-year undergraduate studies, and they may receive monthly allowances. In return, they must commit to working as GPs at appointed primary health care systems for six years after graduation. In contrast, most non-CSP (NCSP) students choose to work as specialists in county and above level hospitals. Seven years have passed since the first wave of students graduated from the CSPs. Income is a crucial factor influencing whether GPs remain in primary health care systems (B. Zhang et al. et al., 2023). Furthermore, the earnings disparity between genders has repercussions for the retention of GPs, with a notable influence on the labor supply of female GPs. This effect becomes particularly pronounced as the proportion of female GPs in the workforce increases, yet it has received insufficient attention in both policy practice and academic research.\u003c/p\u003e \u003cp\u003eThe compensation structure for doctors in China typically comprises a fixed base salary, performance-based earnings, and various allowances \u003csup\u003e16\u003c/sup\u003e. Moreover, an annual year-end bonus is provided, with bonuses often representing a significant proportion of the overall salary \u003csup\u003e17\u003c/sup\u003e. Currently, in China, there is widespread overdependence on performance incentives to motivate healthcare professionals. Zhang and Liu discovered that years of practice, educational background, technical title, management position, and specialty all influenced doctors' salaries \u003csup\u003e17\u003c/sup\u003e. However, there is a notable evidence gap regarding the effects of performance incentives on income disparities among GPs from a gendered perspective.\u003c/p\u003e \u003cp\u003eTo date, most studies on the CSP have focused on health workforce attraction, recruitment, and retention in rural and remote areas (M. Li et al., 2022a; R. Cheng et al., 2022; D. Hu et al., 2016). However, there has been minimal research on gender differences and the determinants of doctors\u0026rsquo; income in underserved areas, despite financial incentive playing an important role in influencing attraction, retention, and development. As gender also has implications for the availability and acceptability of health workers in rural communities \u003csup\u003e22\u0026ndash;24\u003c/sup\u003e, more research on gender differences among doctors at different levels is required. Previous studies on gender differences were mostly from developed countries and regions \u003csup\u003e25\u0026ndash;27\u003c/sup\u003e. Little attention has been paid to the gender difference among doctors working in middle- and low-level economic regions, where women's social status is usually lower \u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, we sought to evaluate gender income differences in a new population of young medical graduates in underdeveloped areas who were early in their careers. The study period ranged from the year 2015 to 2022. Our goal was to gain an up-to-date understanding of the exact level and change in the income gap between CSP and NCSP graduates and to evaluate whether the gender differences previously observed among health workers would be apparent in this younger and more recently hired cohort. In addition, we examined how performance-based income determines gender differences. This study provides evidence for other countries seeking to increase access for healthcare workers in underdeveloped areas.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data collection\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eData for this study were obtained from the Cohort Study of Medical Graduates with Compulsory Services in Rural Areas, a prospective cohort study of Chinese medical graduates. The study was established in 2015 and looks at medical education, residency training, employment, and career development to help China's rural and remote regions grow their health workforce. The Institutional Review Board (IRB) of Peking University Health Science Center provided funding for this study (IRB00001052-15027). All participants provided informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eBaseline data collection\u003c/h2\u003e \u003cp\u003eThe four medical universities in Western and Central China that undertook the CSP were chosen to represent underdeveloped areas of China. The survey included 3620 medical graduates from Qinghai University (Qinghai Province, Northwest China), Guangxi Medical University (Guangxi Zhuang Autonomous Region, Southwest China), Jiujiang University, and Gannan Medical University (Jiangxi Province, Central China).\u003c/p\u003e \u003cp\u003eAfter five years of undergraduate study, the first group of CSP-trained medical professionals graduated in 2015. This group formed the first sub-cohort, and we gathered baseline data from the four medical schools. The CSP classes were matched 1:1 with NCSP classes from the same year. Sub-cohorts were also created from the 2016, 2017, 2018, and 2019 classes. Participants completed a paper questionnaire at baseline before completing their undergraduate studies. Data on demographics, employment, postgraduate studies, residency training, and employment-related information were collected from both types of graduates.\u003c/p\u003e \u003cp\u003eThe key predictor in this study, gender, was identified as female or male (based on the dichotomous response options available in the baseline questionnaire).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up data collection\u003c/h2\u003e \u003cp\u003eIn the baseline survey, we established WeChat groups encompassing all participants within each school and sub-cohort. WeChat is a widely used instant messaging application in China that facilitates communication between investigators and participants. After a baseline survey, annual online follow-up surveys were conducted. By 2022, we had successfully completed six follow-up surveys for the 2015 graduates. Links to online self-administered questionnaires were disseminated annually via email, the WeChat groups, and mobile text messages. Follow-up information was collected in 2016, 2017, 2018, 2020, 2021, and 2022. Due to logistical reasons, the 2019 follow-up survey was not conducted for the 2015 to 2018 graduates.\u003c/p\u003e \u003cp\u003eThe outcome variables in this study were average monthly income (in Chinese Yuan [CNY]) and the proportion of performance-based income. Average monthly income was assessed using the question, \"What is the monthly income for this job?\" Performance-based income was gauged using the question, \"In this context, how much of the income is based on performance?\" All variables related to income were adjusted using the Consumer Price Index (CPI) in 2015.\u003c/p\u003e \u003cp\u003eA number of labor market variables widely postulated to influence income were available in the follow-up questionnaire, including demographic characteristics (marital status and education level), workload (outpatient volume and inpatient volume), and work-related characteristics (current workplace, job performance, whether participants passed China National Medical Licensing Examinations, whether they finished standardized training for resident physicians, and whether they received a title or job promotion).\u003c/p\u003e \u003cp\u003eUnemployed individuals and those who did not work in underdeveloped areas were excluded. The final sample comprised 2041 CSP graduates and 1059 NCSP graduates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe categorized our cohorts based on number of years since graduation. This was done to investigate income disparity patterns between males and females in the early stages of their professional development and to observe how this discrepancy evolved with each additional year since graduation. Descriptive analysis was used to identify the characteristics of the study sample and distribution of medical graduates\u0026rsquo; income (average monthly income and proportion of performance-based income) between men and women. A t-test was used to compare the differences between men and women and CSP and NCSP graduates for each successive year after graduation. Following a log-transformation of the income variable to account for data distribution skewness, we employed multiple linear regressions to assess the gender income gap for each year since graduation, the independent associations among gender and other sample characteristics, and work-related characteristics associated with differences in occupational earnings.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using STATA (version 17.0; Stata Corp., College Station, TX, USA). P values less than 0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable 1 presents the basic characteristics of the study sample. A total of 3620 medical graduates were included in the baseline survey from 2015 to 2019, including 2041 CSP graduates, accounting for 56.4%. A similar number of men and women participated (50.7% male and 49.3% female).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Insert Table 1 here)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows the characteristics of the study sample for CSP and NCSP graduates in the latest follow-up survey in 2022. Most CSP graduates (74.1%) worked in CHC and THC, whereas most NCSP graduates (90.7%) worked in public hospitals at the county level and above. Most NCSP graduates (69.4%) had received postgraduate qualifications, while only 4.6% of CSP had postgraduate qualifications. Most medical graduates (97.4%) had passed the China National Medical Licensing Examinations, and all graduates had completed standardized training for resident physicians. The NCSP graduates had a higher workload than the CSP graduates, both in terms of the number of outpatients (25.8 vs. 18.9) and inpatients (32.1 vs. 24.8). Although the graduates had at most seven years of experience, 83.0% of CSP graduates and 68.5% of NCSP had been given title promotions. Of the CSP graduates, 16.0% had received job promotions, whereas only 2.5% NCSP graduates had been promoted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Insert Table 2 here)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 describes the differences in average monthly income between men and women for each successive year after graduation for both CSP and NCSP graduates. For both men and women, NCSP graduates had a higher average monthly income than CSP graduates in each year since graduation, and the difference between CSP and NCSP graduates widened over the seven years (from 379 CNY in year one to 2479 CNY in year seven). The average monthly income of CSP graduates in their seventh year after graduation was 5379 CNY per month, which was a significantly lower change than that of NCSP graduates (7859 CNY per month). Throughout the seven years, no significant gender gap existed in the average monthly incomes of CSP graduates. However, statistically significant gender differences were found among NCSP graduates who had been practicing for five to seven years, and the gender gap in the seventh years was 1058.7 CNY.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Insert Table 3 here)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 presents the differences in the proportions of performance-based income between men and women in each year since graduation for CSP and NCSP graduates. From the second year after graduation, CSP graduates had a significantly higher proportion of performance-based income than NCSP graduates. The proportion of performance-based income for those that had graduated seven years ago was 48.7% for CSP graduates and 58.9% for NCSP graduates. Among CSP graduates, men in their fourth to seventh years after graduation received a significantly higher proportion of performance-based income than women. The gender gap increased from 2.3% (fourth year) to 4.6% (seventh year). Among NCSP graduates, men in their second to seventh years after graduation received a significantly higher proportion of performance-based income than women. The gender gap increased from 1.6% (second year) to 8.5% (seventh year).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Insert Table 4 here)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the multivariate regression analysis of the average monthly incomes of the CSP and NCSP graduates are presented in Table 5. After controlling for the covariates of school, years since graduation for each sub-cohort, marital status, current workplace, and whether they had received a title or job promotion, we found a statistically significant difference between genders in average monthly income (Model 1). Among CSP graduates, men had higher average monthly income than women, and the gender income gap widened between the fourth and sixth years after graduation. Women earned 3.0% less than men in the fourth year after graduation, and the gap increased to 5.9% in the sixth year after graduation. The insignificance of the gender gap in the seventh year may be related to the relatively small sample size (only 2015 graduates). For NCSP graduates, men also had higher average monthly income than women, and the gender income gap widened between the fifth and seventh years after graduation. Women earned 11.9% less than men in the fifth year after graduation, and the gap increased to 16.3% in the seventh year after graduation. The gender income gap was significantly greater among NCSP graduates (16.3%) compared to CSP graduates (3.2%) in the seventh year after graduation.\u003c/p\u003e\n\u003cp\u003eWhen we included the proportion of performance-based income in Model 2, the gender income gap was reduced. In the seventh year after graduation, for CSP graduates, the income gap decreased by 1.2% points compared to the gap in Model 1. Meanwhile, for NCSP graduates, the income gap also decreased by 1.9% points in the seventh year after graduation compared to the gap in Model 1. This finding provides further evidence that the proportion of performance-based income may be a reason for the gender income gap.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Insert Table 5 here)\u003c/em\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eGender income inequity continues to be pervasive in medicine \u003csup\u003e5,29\u0026ndash;31\u003c/sup\u003e. To our knowledge, this is the first study to use a cohort study to evaluate the gender income gap in underdeveloped areas in China. We compared the level and change in the income gap between CSP and NCSP graduates and found evidence of a significant gap, with female doctors earning 3.2% (CSP graduates) less and 16.3% (NCSP graduates) less than male doctors in the seventh year after graduation, after adjusting for confounders.\u003c/p\u003e \u003cp\u003eFirst, gender differences were apparent among young medical school graduates in underdeveloped areas of China, and this difference continuously widened.\u003c/p\u003e \u003cp\u003eLabor market discrimination may be a possible explanation for the observed gender income difference. Gender discrimination still prevails in rural areas. Many elderly individuals continue to believe that male doctors have more extensive experience. This conscious or unconscious gender bias and discrimination lead to lower trust in female doctors, consequently reducing the performance-based income of female physicians. Some studies have also found that gender wage disparities may stem from differences in workloads and work patterns. Theurl and Winner found that male doctors provide more treatments (the average number of treatments per hour) than their female counterparts \u003csup\u003e11\u003c/sup\u003e, but our research found no significant statistical difference between male and female doctors in terms of workload. Barry also found that the significant pay disparity between male and female GPs was linked to women spending more time with their patients \u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother possible explanation for the gender difference is the \u0026ldquo;glass ceiling\u0026rdquo; experienced by women. The concept of a glass ceiling is recurrent in the recent literature on the career problems of women \u003csup\u003e33\u0026ndash;37\u003c/sup\u003e. It is defined in Merriam-Webster's Collegiate Dictionary (10th edition) as \u0026ldquo;an intangible barrier within the hierarchy of a company that prevents women or minorities from obtaining upper-level positions\u0026rdquo; \u003csup\u003e38\u003c/sup\u003e. A national study of female physicians in academic medicine in the US reported that women were much less likely than men to be promoted; this gender difference persisted even after controlling for work schedule, specialty, and academic productivity, suggesting the existence of a glass ceiling for female physicians \u003csup\u003e33\u003c/sup\u003e. Blumenthal et al. also found that female surgeons were significantly less likely than their male counterparts to be full professors, after adjusting for several factors \u003csup\u003e39\u003c/sup\u003e. Owing to faster career advancement, men earn significantly higher levels of performance-based income than women, resulting in income disparities. However, because of the relatively short observation period, we could not definitively confirm the existence of a glass ceiling effect.\u003c/p\u003e \u003cp\u003eSecond, to some extent, the CSP ensures fairness and partially eliminates discrimination against women through its directed training, staffing issues, and faster career development paths. After the health system reform in 2009 in China, the salary structure for GPs in PHC primarily consists of a fixed salary (L. Hu et al., 2015). The disparities in fixed salaries are minimal, primarily determined by factors such as educational background, professional title, and years of work experience. Meanwhile, the government offers incentives in the form of policies such as hardship allowances for remote and underdeveloped areas as well as township work subsidies, with relatively minor gender disparities (X. Guo, n.d.). Additionally, owing to the scarcity of GPs in remote rural areas, there is a limited pool of doctors available for selection \u003csup\u003e42\u003c/sup\u003e. As a result, rural patients cannot choose their contracted GP based on their gender, but they can specify the gender of specialist doctors at the county hospital. This provides equal employment opportunities for female GPs. However, male CSP graduates earn notably less than their NCSP counterparts. This discrepancy makes them more inclined to leave PHC and seek higher-paying professions, driven by pressures from family, society, and other factors (B. Zhang et al., 2023). Therefore, male CSP graduates with higher incomes tend to leave PHC. Conversely, those who remain in PHC may experience lower incomes, potentially contributing to a reduction in income disparities between male and female GPs.\u003c/p\u003e \u003cp\u003eThird, the proportion of performance-based income was significantly higher for men than women, making it a significant contributor to this disparity. Male doctors often experience the \"breadwinner effect\" as they bear the financial responsibility of supporting their families. This, in turn, stimulates stronger ambitions and determination in them, fostering a more intense drive to earn money. In contrast, female doctors, influenced by the \"caregiver effect\" of having children, may be more focused on caring for their children, potentially leading them to overlook performance improvement \u003csup\u003e10\u003c/sup\u003e. Moreover, the remuneration of doctors in China is generally composed of a basic fixed salary, performance-based income, various allowances, and an annual year-end bonus. The level of a doctor's title determines the coefficient for performance and allowances, meaning that higher titles are associated with higher performance-based incentives. Based on research from previously developed countries, it has been found that there is not a significant difference between men and women in terms of fixed income. However, the key variations lie in performance-based income that are linked to factors such as workload and work intensity \u003csup\u003e11,12,43\u003c/sup\u003e. Additionally, in China, hospitals primarily derive their revenue from medical services, with only a small portion coming from government financial subsidies. Therefore, to achieve higher earnings, hospitals are more likely to motivate ambitious male doctors by offering them more attractive performance-based income. This, to some extent, elucidates the income disparity observed between male and female doctors. However, this explanation only covers a fraction of the gap, as a more significant factor is likely linked to the previously mentioned gender discrimination.\u003c/p\u003e \u003cp\u003eIn the medical domain, despite female doctors demonstrating excellence and professionalism, they may still encounter gender bias, potentially hindering their opportunities for promotions, well-compensated roles, and other career advancements. Achieving genuine gender equality necessitates not only fostering ambition among male doctors but also continuous efforts to eradicate various forms of discrimination against female healthcare professionals. Such endeavors play a crucial role in dismantling gender barriers and cultivating a more fair and inclusive working environment within the medical field.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, as the income data were self-reported, we could not verify their authenticity or the presence of any additional income, which may have introduced a reporting bias. Second, as the follow-up surveys were conducted online, the questionnaire was kept relatively concise to ensure completion rates. Few variables were therefore available to decompose the gender income gap. Consequently, we focused exclusively on performance-based income, while other potentially influential factors remain unanalyzed. Third, all cohort studies inevitably encounter some participant loss in follow-up surveys. Our previous research addressed the issue of sample attrition and found that it had a minimal impact on the focal career development issues of the cohort under study \u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, a gender income gap was apparent in young doctors in underdeveloped areas of China, potentially hindering PHC services from attracting and retaining talent. Compared to NCSP graduates, the income gap was smaller for CSP graduates. The high proportion of performance-based income among men was a main explanation for the observed gender difference. To improve the capacity of rural PHC services and increase health accessibility and equity, a more explicit compensation system must be established to enhance support for female healthcare workers at the system level.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCompulsory services program\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-CSP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral practitioners\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional review board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsumer price index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimary health care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAGER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSex and gender equity in research\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Institutional Review Board of Peking University Health Science Center (PUIRB) (IRB00001052-15027) and was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to questionnaire administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe Cohort Study of Medical Graduates with Compulsory Services in Rural Areas Studies was funded by the China Medical Board (CMB). The funder had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to limitations of ethical approval involving the personal data and anonymity; however, they are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eX.L. designed the study. H.T. and X.L. conceptualized the study design. H.Z., H.T., M.L. and X.Z. collected and managed the data. H.T. participated in the data analysis. H.T. drafted the article. X.L., M.L., H.Z and X.Z. revised the paper and contributed to critical revision of the article. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e We gratefully acknowledge the Qinghai University, Jiujiang University, Gannan Medical University, and Guangxi Medical University for their support in the establishment of the cohorts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShannon G, Jansen M, Williams K, \u003cem\u003eet al.\u003c/em\u003e Gender equality in science, medicine, and global health: where are we at and why does it matter? \u003cem\u003eThe Lancet\u003c/em\u003e 2019; \u003cstrong\u003e393\u003c/strong\u003e: 560\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eFeminization of the health workforce in China: exploring gendered composition from 2002 to 2020. 2023; published online March 16. 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Women in hospital medicine in the United Kingdom: glass ceiling, preference, prejudice or cohort effect? \u003cem\u003eJ Epidemiol Community Health\u003c/em\u003e 2000; \u003cstrong\u003e54\u003c/strong\u003e: 10\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eBlumenthal DM, Bergmark RW, Raol N, Bohnen JD, Eloy JA, Gray ST. Sex Differences in Faculty Rank Among Academic Surgeons in the United States in 2014. \u003cem\u003eAnn Surg\u003c/em\u003e 2018; \u003cstrong\u003e268\u003c/strong\u003e: 193\u0026ndash;200.\u003c/li\u003e\n\u003cli\u003eHu, L., Cao, Y., Wang, B., Liu Y., \u0026amp; Rao, K.(2015). Discussion on the reform of health service payment system in primary medical institutions in China (Chinese). Chinese Journal of Hospital Administration, ,31(2), 87\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eGuo, X. (n.d.). Notice on job placement and performance management of rural order-oriented free training medical students (Chinese). Departmental documents of The State Council. Retrieved September 10, 2023, from https://www.gov.cn/zhengce/zhengceku/2019-11/13/content_5451684.htm\u003c/li\u003e\n\u003cli\u003eLi X, Cochran C, Lu J, \u003cem\u003eet al.\u003c/em\u003e Understanding the shortage of village doctors in China and solutions under the policy of basic public health service equalization: evidence from Changzhou. \u003cem\u003eInt J Health Plann Manage\u003c/em\u003e 2015; \u003cstrong\u003e30\u003c/strong\u003e: E42-55.\u003c/li\u003e\n\u003cli\u003eDumontet M, Franc C. Gender differences in French GPs\u0026rsquo; activity: the contribution of quantile regressions. \u003cem\u003eEur J Health Econ HEPAC Health Econ Prev Care\u003c/em\u003e 2015; \u003cstrong\u003e16\u003c/strong\u003e: 421\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eLi M, Wang Z, Zhang B, Wei T, Hu D, Liu X. Sample attrition analysis in a prospective cohort study of medical graduates in China. \u003cem\u003eBMC Med Res Methodol\u003c/em\u003e 2022; \u003cstrong\u003e22\u003c/strong\u003e: 14.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBasic characteristics of the study sample\u0026nbsp;[n(%)]\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.71717171717172%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic characteristics of the study sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.985915492957748%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (n=1836)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.2112676056338%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=1784)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.80281690140845%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (n=3620)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYeas of Graduation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e323 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e297 (16.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e620 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e383 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e356 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e739 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e437 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e374 (21.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e811 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e355 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e394 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e749 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e338 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e363 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e701 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSchool\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003eQinghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e475 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e669 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e1144 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003eGuangxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e446 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e487 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e933 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003eJiujiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e291 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e189 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e480 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003eGannan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e624 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e439 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e1063 (29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003eCSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e1068 (58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e973 (54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e2041 (56.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003eNCSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e768 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e811 (45.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e1579 (43.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eCharacteristics of the study sample in the latest follow-up survey 2022 [n(%) /mean(sd)]\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCSP (n=2041)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCSP (n=1579)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e1068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarried\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e316 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e245 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e133 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e125 (39.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent workplace\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003ePublic hospitals at county level and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e131 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e91 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e226 (91.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e222 (89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eCHC \u0026amp; THC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e350 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e325 (76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e3 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e6 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e9 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e8 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e17 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e20 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003ePostgraduate qualifications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e12 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e33 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e215 (70.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e215 (68.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePassed China National Medical Licensing Examinations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e866 (96.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e815 (98.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e482 (97.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e511 (97.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinished standardized Training for residents Physicians\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e720 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e619 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e290 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e307 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh job performance (\u0026ge;72)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e161 (29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e104 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e89 (29.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e79 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorkload\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eOutpatient numbers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e18.9 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e19.0 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e24.8 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e26.7 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eInpatient numbers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e27.0 (63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e22.6 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e36.2 (56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e27.9 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith title of attending doctor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e267 (49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e183 (38.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e35 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e31 (10.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith job promotion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e\u0026nbsp;84 (15.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e54 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e5 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e8 (2.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: The total score of the job performance scale was 12 items \u0026times; 7 points = 84.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Difference in average monthly income between men and women for each successive year after graduation for both CSP and NCSP graduates\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.070707070707071%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eGraduation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eduration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"92.92929292929293%\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage month income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.47826086956522%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eCSP (n=2039)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.47826086956522%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCSP (n=1571)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CNY)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.513513513513514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.108108108108109%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CNY)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.405405405405405%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.45945945945946%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emean (CNY)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.108108108108109%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(CNY)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.405405405405405%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.45945945945946%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emean (CNY)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003emean (CNY)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003emean (CNY)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.5%\"\u003e\n 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width=\"5.376344086021505%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n 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width=\"3.225806451612903%\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e6138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e5752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e5950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-2233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e3972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e4121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e7871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e7003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e7394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-2537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e5293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e5531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e5379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e8377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.225806451612903%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e7318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e1058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.301075268817204%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e7859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e-2479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: (1)\u0026nbsp;Difference\u003csup\u003ea\u003c/sup\u003e=Male-Female; Difference\u003csup\u003eb\u003c/sup\u003e=CSP-NCSP. (2) Average month income was adjusted using the Consumer Price Index (CPI). (3) CNY, Chinese yuan renminbi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Differences in the proportions of performance-based income between men and women in each year since graduation for CSP and NCSP graduates\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.080808080808081%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eGraduation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eduration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"91.91919191919192%\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe proportion of performance-based income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.333333333333336%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eCSP (n=2039)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.333333333333336%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCSP (n=1571)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.777777777777778%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.88888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e\u003cstrong\u003emean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e\u003cstrong\u003emean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e\u003cstrong\u003emean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e\u003cstrong\u003emean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e22.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e22.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e22.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e42.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e40.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e41.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e-19.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e28.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e28.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e28.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e50.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e45.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e4.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e47.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e-19.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e26.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e24.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e25.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e54.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e49.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e51.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e-26.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e36.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e34.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e57.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e51.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e6.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e54.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e-19.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e44.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e41.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e3.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e43.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e59.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e53.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e6.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e56.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e-12.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e50.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e45.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e4.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e48.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e63.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e54.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e8.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e58.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e-10.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: (1) Difference\u003csup\u003ea\u003c/sup\u003e=Male-Female; Difference\u003csup\u003eb\u003c/sup\u003e=CSP-NCSP. (2) Performance-based income was adjusted using the Consumer Price Index (CPI). (3) CNY, Chinese yuan renminbi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Multivariate regression analysis of the average monthly incomes of the CSP and NCSP graduates\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGraduation duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ref: Male)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe proportion of performance-based income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ref: Not married)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorkplace\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ref: Public hospitals at county level and above)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTitle promotion\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ref: Without title)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob promotion\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ref: Without administrative positions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarried\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.517241379310345%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHC \u0026amp; THC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.517241379310345%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith title\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith administrative positions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.574468085106384%\" rowspan=\"14\"\u003e\n \u003cp\u003e\u003cstrong\u003eCSP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(n=2039)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\"\u003e\n \u003cp\u003e-0.091***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e-0.115***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.394***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e-0.119***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.030*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e-0.155***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.494***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e-0.139***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.038*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e-0.101***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e0.068**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.661***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e-0.093***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e0.066**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.059*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e-0.142***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e0.099**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.584***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e-0.129***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e0.124*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e0.096**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e-0.100**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.383*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e-0.089*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.574468085106384%\" rowspan=\"14\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCSP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(n=1571)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.164*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e-0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.633*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e0.103*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e0.144**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.438*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.179*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.137*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e-0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.119**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e0.264**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e0.162***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.100*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.266**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.168***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.103*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e0.271***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.099*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.271***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\"\u003e\n \u003cp\u003e-0.163**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.588235294117647%\" valign=\"top\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e0.395**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e-0.144*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e0.353*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: (1) In the first year, there is no data of title promotion, job promotion and performance-based income. (2) *** P \u0026lt; 0.001, ** P \u0026lt; 0.01, * P \u0026lt; 0.05; \u0026beta; coefficient and Confidence interval were reported. (3) The schools and years of graduation for each sub-cohort were controlled for in all regressions.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"human-resources-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hrhe","sideBox":"Learn more about [Human Resources for Health](http://human-resources-health.biomedcentral.com)","snPcode":"12960","submissionUrl":"https://submission.nature.com/new-submission/12960/3","title":"Human Resources for Health","twitterHandle":"@HRH_Journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gender difference, general practitioner, income, underdeveloped areas, China","lastPublishedDoi":"10.21203/rs.3.rs-3739819/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3739819/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGender equality and the gender income gap in medicine are long-standing global problems. Although gender-related differences have been widely studied in developed countries, they remain unclear in underdeveloped regions. In 2010, China initiated a national compulsory service program (CSP) to train qualified general practitioners in rural and remote areas. This study aimed to evaluate gender income differences for early career CSP and non-CSP (NCSP) graduates in underdeveloped areas. A cohort study was conducted with 3620 CSP and NCSP graduates from four medical universities in Central and Western China. Baseline surveys and six follow-up surveys were conducted between 2015 and 2022. Incomes, including monthly mean income and proportion of performance-based income, were measured as the key outcome variables. Multivariate linear regression models were used to identify the gender income gap. NCSP graduates had higher average monthly incomes than CSP graduates. In the seventh year after graduation, the average monthly income for NCSP graduates was 7859 CNY while was 5379 CNY for CSP graduates. After controlling for demographic characteristics, the gender monthly income gap for CSP graduates was expanded from the fourth year (3.0%) to the sixth year (5.9%) after graduation, and that for NCSP graduates was expanded from the fifth year (11.9%) to the seventh year (16.3%) after graduation. Regarding performance-based income, it was 58.9% for NCSP graduates and 45.8% for CSP graduates in the seventh year after graduation. After controlling for performance-based income proportion, the gender income gap was reduced from 5.9% to 4.0% in the sixth year after graduation for CSP graduates, and from 16.3% to 14.4% for NCSP graduates in the seventh year after graduation. An extensive and ever-increasing gender income gap exists among young doctors in the early stages of their careers in underdeveloped areas of China. The high proportion of performance-based income among men is one of the main explanations for the observed difference.\u003cstrong\u003e \u003c/strong\u003eA more explicit compensation system must be established to enhance support for female health workers.\u003c/p\u003e","manuscriptTitle":"Gender income differences among general practitioners with compulsory services in early career stage in underdeveloped areas: evidence from a prospective cohort study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 10:33:58","doi":"10.21203/rs.3.rs-3739819/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2024-05-09T10:38:11+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-01-04T11:00:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-03T16:52:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-11T15:31:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Human Resources for Health","date":"2023-12-11T01:17:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"human-resources-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hrhe","sideBox":"Learn more about [Human Resources for Health](http://human-resources-health.biomedcentral.com)","snPcode":"12960","submissionUrl":"https://submission.nature.com/new-submission/12960/3","title":"Human Resources for Health","twitterHandle":"@HRH_Journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e587b73b-0ad0-4eed-b093-f346158254fc","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-05T14:01:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 10:33:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3739819","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3739819","identity":"rs-3739819","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}