Which Teacher Qualifications Matter in China?

Transcription

1 Which Teacher Qualifications Matter in China? Jessica Chu a (jessica.chu1127@gmail.com) Prashant Loyalka a (loyalka@stanford.edu) James Chu a (jchu1225@gmail.com) Yaojiang Shi b (syj @yahoo.com.cn) Scott Rozelle a (rozelle@stanford.edu) a Center for Food Security and the Environment, Freeman Spogli Institute, Stanford University b CEEE, Shaanxi Normal University February 2014 Corresponding Author: Prashant Loyalka 5 th Floor Encina Hall Stanford University Stanford, CA

2 Which Teacher Qualifications Matter in China? A key finding in the economics literature is that education is important for economic growth (Hanushek and Woessmann, 2010; Schultz, 1961). Studies have found a positive and consistent relationship between educational attainment (as measured by average number of years of schooling attained by a population) and economic growth (Aghion and Howitt, 1998; Romer, 1990; Lucas, 1998). Studies have also documented a larger, positive relationship between student achievement (reflecting cognitive achievement) and economic growth (Hanushek and Woessmann, 2008; Hanushek and Kimko, 2000). Indeed, researchers have argued that failing to raise levels of student achievement in a country can ultimately cause that economy to stagnate (Hanushek and Woessman, 2012; Hanushek and Woessman, 2010). An important educational input that consistently has been shown to raise student achievement is teacher quality (Hanushek and Rivkin, 2010; Rivkin et al., 2005; Rockoff, 2004). In the United States, being taught by a teacher at the twenty-fifth percentile compared to the seventy-fifth percentile of the teacher quality distribution can raise a student from the fiftieth to the fifty-eighth percentile of the achievement distribution (Hanushek and Rivkin, 2010). Although fewer in number, studies from developing countries have also documented how substantial variation in teacher quality within school systems leads to substantial differences in student achievement outcomes (e.g. Kingdon and Teal, 2011). Recognizing the potential of teacher quality for raising student achievement and seeking to better identify which teachers are of high quality, policymakers and researchers in developed countries have identified specific, observable teacher qualifications that have positive impacts on student achievement. For example, in primary schools, teacher qualifications such as educational background (Harris and Sass, 2011; Kukla-Acevedo, 2009), professional certifications (for 2

3 example, fulfilling professional teaching requirements set by a national agency Clotfelter, Ladd, and Vigdor, 2007; Harris and Sass, 2009; Boyd et al, 2006), and experience (Rockoff, 2004; Ferguson & Ladd, 1996) have all been found to have positive impacts on student achievement. Identifying which teacher qualifications raise student achievement is important because policymakers and educational administrators frequently use such qualifications to make decisions about teacher hiring, assignment, compensation and promotion. In contrast to research from developed countries, however, there is much less research from developing countries on the impacts of teacher qualifications on student achievement. Although studies from developing countries have documented substantial variation in teacher qualifications within a school system (e.g. Kingdon and Teal, 2011), there is little evidence to suggest what kinds of qualifications matter for student achievement. Notable exceptions include studies that estimate the positive impacts of teacher s union membership in India (Kingdon and Teal, 2010) and teacher s subject-specific knowledge in Peru (Metzler and Woessman, 2012). These studies, while noteworthy, do not explicitly focus on those teacher characteristics that are more often used by policymakers or school administrators in teacher hiring, assignment, compensation and promotion decisions (such as educational background or professional certification). Furthermore, with the exception of one unpublished study from over a decade ago (Hannum and Park, 2002), we know of no study from China that systematically examines whether teacher qualifications raise student achievement and to what degree. Lacking empirical evidence on which teacher qualifications matter for student achievement, policymakers and school administrators in developing countries such as China may make inefficient decisions about teacher hiring, assignment, compensation, and promotion. 3

4 Perhaps the most important indicator of teacher quality, used by policymakers in China, is teacher rank ( ). Because the number of high rank teachers in a school is frequently used by policymakers as an indicator of school quality, teacher rank plays an important role in teacher hiring and assignment decisions. Because teacher rank is directly linked to opportunities for compensation and promotion, teachers are also highly motivated to improve their rank. Despite the importance of teacher rank to school and teacher behaviors in China, to our knowledge there has been no evaluation of whether teacher rank (and which dimensions of teacher rank) actually leads to improvements in student outcomes. The overall goal of this paper is to examine whether (and how) the main teacher qualification used in China teacher rank positively impacts student achievement. As part of this goal, we have three specific objectives. First, we examine the degree to which teacher rank impacts the achievement of the average student. Second, we consider whether there are heterogeneous impacts of teacher ranking on the achievement of economically poor and lowperforming students as well as by gender. Third, we explore whether extra teacher effort (the total amount of non-class time that teachers spend on their teaching activities, both inside and outside of school) is a potential mechanism through which teacher rank impacts student achievement. To estimate the impact of teacher rank, we analyze data on approximately 4,500 students from 70 rural primary schools in 3 provinces of Northwest China. We choose to focus on rural schools because improving student achievement in rural China is relevant for the country s future economic development. Rural China will provide a large share of the labor force in the coming years and there is a danger that China could fail to make the transition from developing to developed country if the average rural child is not being educated to support a modern, 4

5 industrial society (State Council Research Office, 2006). Unfortunately, a number of studies already show that rural students are far behind their urban counterparts in terms of educational outcomes (Li et al., 2014; Yi et al., 2012; Mo et al., 2012; Liu et al., 2009). Understanding which teacher qualifications matter for improving student achievement is a potentially important policy lever for improving the state of education in rural China. Similar to a number of recent studies, our identification strategy relies on a cross-subject student-fixed effects model (Kingdon and Teal, 2011; Van Klaveren, 2011; Clotfelter et al., 2010; Dee, 2007; Dee, 2005). The model removes potential sources of bias that can result from the non-random sorting of students to teachers and schools. In attempting to remove remaining sources of within-student, cross-subject bias, the model also controls for a rich set of crosssubject student and cross-subject teacher covariates. Research Design 2.1 The Teacher Ranking System in China China has one of the largest education systems, and hence one of the largest teacher labor forces in the world. For example, there were approximately 5.6 million teachers for nearly 100 million primary school students in China in About 42% of these teachers work in rural areas (not including urban-rural transitional area MOE, 2014) 1. The number of teachers in rural China alone is larger than that of developed countries such as the United States (which had approximately 2 million primary school teachers in NCES, 2012). To facilitate the hiring, assignment, promotion, and compensation of such a vast number of teachers, in 1986 the Chinese government established a system of professional titles (commonly called ranks ) to categorize teachers. 2 There are a total of four ranks at the elementary school level (in descending order of prestige: senior primary, level one, level two, 1 Minster of Education of the People s republic of China [In Chinese, last checked: Feb, 2014] 2 [1986]27 5

6 level three). As of 2011, 54% of elementary school teachers had senior primary rank. The remaining 46% of teachers either did not have a rank or were ranked at level one, two, or three. 3 Teachers are evaluated once every year on whether they should be promoted in rank. This evaluation is a routine part of teacher management practices in China. To move up in rank, teachers must fulfill two sets of requirements. First, they must possess certain characteristics that are easily measured (characteristics that are routinely recorded in administrative databases or common measures, see Hanushek, 2002). Such characteristics include years of teaching experience, highest degree earned, honorary awards,, and experience as a homeroom teacher. Second, teachers are evaluated based on their performance and behavior. Ranks reflect the degree to which a teacher is equipped with content knowledge and pedagogical skills. From the point of the view of the government, higher rank teachers are better teachers. This view is reflected in the higher salaries received by high rank teachers. 2.2 Sampling The data for our study is based on a survey in a northwestern province of China. The survey includes 4050 students and the 254 math and Chinese teachers who taught these students during the school year. To select our sample, we created a sampling frame of all elementary schools from Ankang Prefecture in Shaanxi Province, a landlocked province in Northwest China. We chose Ankang Prefecture primarily because we are interested in understanding what teacher qualifications benefit students especially in poor, rural areas. Ankang Prefecture is predominantly poor, with a per capita GDP of 2,300 dollars and ranking 9 th out of the 10 prefectures in Shaanxi (National Bureau of Statistics 2013). Its neighbor (the other southern 3 6

7 Shaanxi province) Shangluo is ranked last. As such, the prefecture represents one of the poorest regions of inland China. From this sampling frame, we first excluded 54 (out of a total of 147) schools with fewer than 30 students from our sampling frame. We excluded small schools because policymakers informed us that these schools were at high risk of being closed or merged during the school year. We then excluded all schools located in county seats, as these tend to be richer schools that enroll non-rural students. The number of excluded schools enrolled less than 10 percent of the students in Ankang Prefecture. We then enrolled the remaining 70 schools to our sample. In each of the 70 sample schools, we enrolled all students from grades 2 through 5 and administered a baseline survey to all students in these classes in June 2011 (the end of the school year). At this time, we administered a 30-minute standardized examination in both mathematics and Chinese. The exam was based on questions from the XXX. One year later (June 2012), we conducted an endline survey with the same set of students in the 72 sample schools. At this time, we also identified and surveyed all of the mathematics and Chinese teachers of the sample students. Altogether, we surveyed and administered standardized examinations to 4050 students (clustered across 127 classes) and surveyed 254 of their teachers. 2.3 Data At both the baseline (June 2011) and endline (June 2012) surveys, we administered student-level questionnaires through which we collected information on student assets. We did so because we planned to examine heterogeneous impacts of poor versus nonpoor students polychoric PCA. We also asked students to report the mathematics and Chinese courses they completed and the number of hours per week they spent learning each type of subject. 7

8 As part of both baseline and endline data collections, we obtained measures on student achievement in math and Chinese subjects by administering subject-specific standardized tests. Students were given 30 minutes to finish each examination. The math examination contained 25 items, and the Chinese examination contained 35 items. Before analysis, the mathematics and Chinese exam scores were normalized into z-scores by subtracting the mean and dividing by the standard deviation (SD) of the exam score distribution (for mathematics and Chinese separately). In June 2012, we surveyed all of the mathematics and Chinese teachers who had ever taught our sample students during the school year. We first asked teachers to indicate whether they had achieved the highest possible rank in their school (our main treatment variable). As mentioned above, primary school teachers in China can receive four possible ranks: senior primary (the most prestigious), level one, level two, and level three (the least prestigious). To control for other qualifications, we asked if teachers had ever received a teaching award at the township, county, prefecture, province, or national level. In contrast to teacher rank (which are largely determined by principals), teaching awards are offered by higher levels of government (often based on yearly teaching competitions CITE). We also asked teachers to report on whether they graduated from college. To control for additional variation between teachers, we then collected information on each teacher s individual characteristics. We asked teachers to report on their age (in years) and gender (whether female or not). We also asked if the teacher attended elementary school in the same county that he or she was currently teaching4 and whether the teacher was affiliated with the Communist Party (to account for possible implications regarding his or her qualifications). We then asked how many years the teacher had worked as a teacher (years of teaching 4 Adams finds that whether a teacher attended elementary school in the same area he or she teachers has strong implications for their teaching quality and motivation. 8

9 experience). Finally, we asked if the teacher had ever served as a homeroom teacher for a specific class. Finally, as a measure of teacher effort: which means hours worked on weekends (class- related) and hours worked during weekdays beyond designated class hours (class- related again). 2.4 Analytic Strategy One of the main challenges in estimating the causal impact of teacher qualifications on student achievement is the selection bias that can arise due to the non-random sorting of students and teachers into classrooms. For example, higher-achieving students can be placed with teachers with higher qualifications, resulting in an upward bias when estimating the effect of teacher qualifications on student achievement. Alternatively, lower-achieving students can be matched with teachers with higher qualifications, perhaps as the result of an intentional policy to compensate for the weakness of lower-achieving students. This type of sorting can result in downward bias estimates of the effect of teacher qualifications on student achievement. In an attempt to estimate unbiased causal effects of teacher qualifications on student achievement, researchers have used student fixed effects models. In particular, researchers have relied on a cross-subject student fixed effects model that removes the potentially confounding influence of unobservable, subject-invariant characteristics (e.g. Clotfelter et al., 2010; Dee 2007; Dee, 2005). The concern is that such subject-invariant characteristics could be simultaneously correlated with teacher qualifications and student outcomes and therefore bias estimates of the effects of teacher qualifications on student achievement. We use this type of model to estimate the impact of teacher qualifications on student achievement. Specifically, we use within-student variation across language and mathematics subjects to identify the causal impacts. To illustrate how the cross-subject student fixed effects model removes the potentially confounding effects of unobservable, subject-invariant 9

10 characteristics, we first examine the relationship between student achievement and teacher qualifications using a standard linear regression model:!!",! = α + π!!",!!! + β!!" + δ!!" +!! +!!" (1) where A is,t is the achievement of student i in subject s at time t, A is,t-1 is the achievement of student i in subject s at time t-1. T is is a vector of teacher qualifications for student i in subject s: indicator variables for whether the teacher was of the highest teaching rank (1=yes), whether the teacher had a teaching award (1=yes), whether the teacher had a college degree (1=yes), and whether the teacher s college degree was relevant to his or her current subject (1=yes). C is is a vector of student, teacher and classroom characteristics that vary across students i and subject s and that serve as control variables. 5 In our study, C is includes several pre-treatment, cross-subject teacher and classroom characteristics, including students baseline scores and class hours assigned in a particular subject. We also controlled for teacher age (in years), gender (1=female) and indicator variables for whether a teacher attended elementary school in the same county as his or her students (1=yes) or was a Communist Party member (1=yes). Other control variables include teacher experience (a series of indicator variables). To account for other potential confounders, we include the term λ i, a student fixed effect. The student fixed effect effectively controls all student characteristics that do not vary across subjects. In fact, the student characteristics also include family, school, and broader contextual characteristics (associated with the student) that do not differ across subjects. Finally, the symbol, ε is, represents an error term that varies across both students and subjects. 5 Note that the concept of a class in China is a group of students (of a particular grade) that take the same subjects/courses together. We thus do not control for class characteristics (such as class size), since students of the same class take all of their (e.g. hardware and software) courses as a class. 10

11 The other terms (π, α, β, and δ) in equation (1) are coefficients (or vectors of coefficients) to be estimated. The coefficients reflect the relationship between the variables on the right hand side and student achievement on the left hand side. We are most interested in β, which identifies the relationship between teacher qualifications and student achievement. Because the student fixed effects (λ i ) are equivalent across both subjects, differencing equation (1) for the two subjects yields an equivalent equation (2) as follows: (!!!,!!!!,! ) =!(!!!,!!!!!!,!!! ) + β!!!!!! + δ (!!!!!! ) + (!!!!!! ) (2) Unobserved student, teacher, and classroom characteristics that vary across subjects are captured in the differenced error term (!!!!!! ). To obtain unbiased estimates of β, this model relies on the assumption that the error term!!!!!! in equation (2) must be uncorrelated with treatment across the two subjects!!!!!! and achievement(!!!!!! ). We include (!!!,!!!!!!,!!! ) and the vector (!!!!!! ) to account for this possibility. Table 1 details the mean characteristics of math and Chinese teachers that are controlled for in our model. With the exception of the gender variable, there are no statistically significant differences (across the other characteristics) between the mean of language teachers and the mean of math teachers. This set of results in Table 1 suggests there is no systematic sorting of teachers across language and math subjects. Although the nature of differences between the teachers of the math and Chinese subjects is small, we control for all of these observable teacher characteristics in our analysis since it is possible that one or more of the variables may be systematically correlated with both teacher qualifications and student achievement. 11

12 Results The Main Impacts of Teacher Qualifications on Student Achievement According to our results, having the highest teacher rank has a positive and significant impact on student achievement (Table 2). Specifically, the unadjusted results show that teacher rank increases student achievement by 0.23 standard deviations (Table 2, Column 1). The result is statistically significant at the 1 percent level. The adjusted results also show that teacher rank increases student achievement by 0.20 standard deviations (Table 2, Column 5). The adjusted result is significant at the 5 percent level. Taken together, the results suggest that teachers with highest rank are able to increase student learning more than teachers who are not of the highest rank. In contrast, our results show that having a teacher award or a college degree do not have significant impacts on student learning (Table 2). According to the unadjusted results, a teacher award or a college degree has an impact of 0.07 standard deviations (Table 2, Column 2) and 0.06 standard deviations on student achievement (Table 2, Column 3). However, none of these results are statistically significant, even at the 10 percent level. Taken together, the results suggest that teacher award and college degree do not (in and of themselves) improve student learning. Heterogeneous Impacts of Teacher Qualifications on Student Achievement According to the unadjusted results of heterogeneity analysis show that teachers with highest rank appear to have a positive and statically significant impact on poor students. To further decompose the impact of teacher qualifications on different types of students, we 12

13 examined the heterogeneity in treatment effects by interacting the treatment variables with binary variables representing students were poor versus non-poor students achievement (Table 3). As stated in the unadjusted results of teacher rank has an impact of 0.12 standard deviations at the 5 percent level (Table 3, Column1). Similarly, the adjusted results indicate that teacher rank also improves student test score by 0.12 standard deviations, significant at the 10 percent level (Table 3, Column 5). All in all, the results suggest that teachers with highest rank have a significant impact on poor student achievement. While the effect of unadjusted results of teachers who received award increase 0.02 standard deviations in poor student achievement (Table 3, Column 2), yet according to the adjusted results show that teachers who received teacher award decrease poor student achievement by 0.02 standard deviations (Table 3, Column 5). According to the adjusted results, teachers attended college appears to reduce poor student achievement by negative 0.05 standard deviations (Table 3, Column 5), and subject-training teachers also have a small negative impact of 0.09 standard deviations on student achievement. However, none of these results are statistically significant, even at the 10 percent level. As reported to our results, teacher rank has a positive heterogeneous impact of male student achievement. Conforming to the heterogeneity analysis, teacher with highest rank is associated with a negative 0.08 standard deviations to decrease in female student achievement (Table 4, Column 1). The adjusted results also show that teacher rank has negative heterogeneous impact of 0.09 standard deviations on female student achievement at the 10 percent level (Table 4, Column 5). The unadjusted results indicate that teacher award, college degree and subject-trained teachers have a heterogeneous impact of 0.03 standard deviations (Table 4, Column 2),

14 standard deviations (Table 4, Column 3) and 0.05 standard deviations on female student achievement (Table 4, Column 4), but as reported by the results show that teacher award, college degree, subject-trained teachers do not have a significant heterogeneous impact of female student achievement, even at the 10 percent level. According to the results, teacher rank has positive impacts on both low and highachieving students. The unadjusted results indicate that teacher rank improves low and highachieving students by 0.23 standard deviations at the 5 percent level (Table 5, Column 1). The adjusted results also indicate that teacher rank improves low and high-achieving students by 0.19 standard deviations at the 10 percent level (Table 5, Column 5). However, according to heterogeneity analysis of the unadjusted results show that teacher rank decreases 0.02 standard deviations on low-achieving students (Table 5, column 1), and the adjusted results also show that teacher rank decreases 0.03 standard deviations on low-achieving students (Table 5, Column 5), but none of these results are statistically significant, even at the 10 percent level. Correlation of teacher qualification on extra teacher effort To understand how teacher rank matters for student achievement but not the other teacher qualifications, we examine how these qualifications are correlated to extra teacher effort (Table 6). First, teacher rank has significant and positive correlation by 1.97 standard deviations on extra teacher effort at the 5 percent level. According to the results, we can interpret that teachers with highest rank spent more time on their work. Discussion and Conclusion Principals could place the estimates in context and appear to be able to judge differences in effectiveness at least at the tails of the distribution (Jacob and Lefgren 2008). 14

15 The within-school focus, taken because of the difficulty accounting for differences among schools, raises concerns for performance evaluation, since some schools may have much better teachers on average than others, and it would be important to recognize such differences. 15

16 References Aghion, P. and Howitt, P. (1998). Endogenous Growth Theory. Cambridge, MA: MIT Press. Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2010). Teacher credentials and student achievement in high school a cross-subject analysis with student fixed effects. Journal of Human Resources, 45(3), Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2007). Teacher credentials and student achievement: longitudinal analysis with student fixed effects. Economics of Education Review 26(6), Hanushek, E. A. and Kimko, D. D. (2000). Schooling, labor force quality, and the growth of nations. American Economic Review 90(5), Hanushek, E.A. & Rivkin, S.G. (2010). Generalizations about using value-added measures of teacher quality. The American Economic Review 100(2), Hanushek, E. A., and Woessmann, L Schooling, educational achievement, and the Latin American growth puzzle. Journal of Development Economics, 99(2), Hanushek E A and Wößmann L (2010), Education and Economic Growth. In: Penelope Peterson, Eva Baker, Barry McGaw, (Editors), International Encyclopedia of Education. volume 2, pp Oxford: Elsevier. Hanushek, E. A. and Woessmann, L. (2008). The role of cognitive skills in economic development, Journal of Economic Literature 46(3), Harris, D. N., & Sass, T. R. (2009). The effects of NBPTS- certified teachers on student achievement. Journal of Policy Analysis and Management, 28(1), Harris, D. N., & Sass, T. R. (2011). Teacher training, teacher quality and student achievement. Journal of public economics, 95(7), Kingdon, G., & Teal, F. (2010). Teacher unions, teacher pay and student performance in India: A pupil fixed effects approach. Journal of Development Economics, 91(2), Metzler, J., & Woessmann, L. (2012). The impact of teacher subject knowledge on student achievement: Evidence from within-teacher within-student variation. Journal of Development Economics, 99(2), Rivkin, S.G., Hanushek, E.A., & Kain, J.F. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2), Rockoff, J.E. (2004). The impact of individual teachers on student achievement: evidence from panel data. The American Economic Review, 94 (2),

17 Schultz, T. W. (1961). Investment in human capital. The American economic review, 51(1), Van Klaveren, C. (2011). Lecturing style teaching and student performance. Economics of Education Review, 30(4),

18 Table 1: Average Math and Chinese Language Teacher Characteristics Math Teachers Chinese Teachers Highest rank (y/n) Received teacher award (y/n) Attended college (y/n) Class hours Teacher Age Female Home county (y/n) Communist Party Member (y/n) Experience (2-5 years) Experience (5-10 years) Experience (10-15 years) Experience (15-20 years) Experience (20+ years) Home room teacher (years) Number of teachers

19 Table 2: The Impact of Teacher Qualifications on Student Achievement (1) (2) (3) (4) Highest rank (y/n) 0.23*** 0.20** (0.09) (0.09) Received teacher award (y/n) (0.06) (0.06) Attended college (y/n) (0.09) (0.09) Baseline score 0.21*** 0.21*** 0.21*** 0.21*** (0.02) (0.02) (0.02) (0.02) Class hours 0.02* 0.02* 0.02** 0.02* Age * 0.01* 0.00 Female (y/n) 0.16*** 0.17*** 0.16*** 0.15*** (0.06) (0.06) (0.06) (0.06) Home county (y/n) (0.18) (0.17) (0.17) (0.18) Communist party affiliation (y/n) (0.07) (0.08) (0.08) (0.08) Experience 2 to < 5 years (0.20) (0.20) (0.21) (0.20) Experience 5 to < 10 years (0.19) (0.19) (0.22) (0.20) Experience 10 to < 15 years (0.20) (0.20) (0.22) (0.21) Experience 15 to < 20 years (0.23) (0.23) (0.27) (0.26) Experience 20+ years (0.28) (0.27) (0.29) (0.28) Home room teacher (years) Constant -0.62* -0.83** -0.89** -0.67* (0.33) (0.34) (0.34) (0.35) Observations 7,894 7,894 7,816 7,816 R-squared Number of students 4,575 4,575 4,524 4,524 Cluster-robust SEs in parentheses *** p<0.01, ** p<0.05, * p<0.1 19

20 Table 3: Impact of Teacher Qualifications on Poor versus Non-Poor Student Achievement (1) (2) (3) (4) Highest rank (y/n) 0.19** 0.16 (0.09) (0.10) Highest rank * poor student (y/n) 0.12** 0.12* (0.06) (0.07) Received teacher award (y/n) (0.06) (0.06) Teacher award * poor student (y/n) (0.05) (0.06) Attended college (y/n) (0.09) (0.09) Attended college * poor student (y/n) (0.06) (0.06) Baseline score 0.21*** 0.21*** 0.21*** 0.21*** (0.02) (0.02) (0.02) (0.02) Class hours taught 0.02* 0.02* 0.02** 0.02* Age * 0.01* 0.00 Female (y/n) 0.16*** 0.17*** 0.16*** 0.16*** (0.06) (0.06) (0.06) (0.06) Home county (y/n) (0.17) (0.17) (0.17) (0.18) Communist party affiliation (y/n) (0.07) (0.08) (0.08) (0.08) Experience 2 to < 5 years (0.20) (0.20) (0.21) (0.20) Experience 5 to < 10 years (0.19) (0.19) (0.21) (0.20) Experience 10 to < 15 years (0.20) (0.20) (0.22) (0.21) Experience 15 to < 20 years (0.23) (0.23) (0.27) (0.26) Experience 20+ years (0.27) (0.27) (0.29) (0.28) Home room teacher (years) Constant -0.61* -0.82** -0.90** -0.67* (0.33) (0.34) (0.34) (0.35) Observations 7,894 7,894 7,816 7,816 R-squared Number of students 4,575 4,575 4,524 4,524 Cluster-robust SEs in parentheses *** p<0.01, ** p<0.05, * p<0.1 20

21 Table 4: Impact of Teacher Qualifications on Female versus Male Student Achievement (1) (2) (3) (4) Highest rank (y/n) 0.26*** 0.24** (0.09) (0.10) Highest rank * female student (y/n) (0.05) (0.06) Received teacher award (y/n) (0.06) (0.06) Teacher award * female student (y/n) (0.06) (0.07) Attended college (y/n) (0.09) (0.09) Attended college * female student (y/n) (0.07) (0.07) Baseline score 0.21*** 0.21*** 0.21*** 0.21*** (0.02) (0.02) (0.02) (0.02) Class hours taught 0.02* 0.02* 0.02** 0.02* Age * 0.01* 0.01 Female (y/n) 0.16*** 0.17*** 0.16*** 0.16*** (0.06) (0.06) (0.06) (0.06) Home county (y/n) (0.18) (0.17) (0.17) (0.18) Communist party affiliation (y/n) (0.07) (0.08) (0.08) (0.08) Experience 2 to < 5 years (0.20) (0.20) (0.20) (0.20) Experience 5 to < 10 years (0.19) (0.19) (0.21) (0.20) Experience 10 to < 15 years (0.20) (0.20) (0.22) (0.21) Experience 15 to < 20 years (0.23) (0.23) (0.27) (0.26) Experience 20+ years (0.28) (0.27) (0.29) (0.28) Home room teacher (years) Constant -0.62* -0.83** -0.89** -0.68* (0.33) (0.34) (0.34) (0.35) Observations 7,894 7,894 7,816 7,816 R-squared Number of students 4,575 4,575 4,524 4,524 Cluster-robust SEs in parentheses *** p<0.01, ** p<0.05, * p<0.1 21

22 Table 5: Impact of Teacher Qualifications on Low Achieving Students (1) (2) (3) (4) Highest rank (y/n) 0.23** 0.21** (0.09) (0.10) Highest rank * low achieving student (y/n) (0.08) (0.09) Received teacher award (y/n) (0.06) (0.06) Teacher award * low achieving student (y/n) (0.08) (0.08) Attended college (y/n) (0.08) (0.08) Attended college * low achieving student (y/n) (0.10) (0.09) Baseline score 0.21*** 0.21*** 0.21*** 0.21*** (0.02) (0.02) (0.02) (0.02) Class hours taught 0.02* 0.02* 0.02** 0.02* Age * 0.01* 0.01 Female (y/n) 0.16*** 0.17*** 0.17*** 0.16*** (0.06) (0.06) (0.06) (0.06) Home county (y/n) (0.18) (0.17) (0.17) (0.18) Communist party affiliation (y/n) (0.07) (0.08) (0.08) (0.08) Experience 2 to < 5 years (0.20) (0.20) (0.20) (0.20) Experience 5 to < 10 years (0.19) (0.19) (0.21) (0.20) Experience 10 to < 15 years (0.20) (0.20) (0.22) (0.21) Experience 15 to < 20 years (0.23) (0.23) (0.26) (0.26) Experience 20+ years (0.28) (0.26) (0.29) (0.28) Home room teacher (years) Constant -0.62* -0.83** -0.92*** -0.72** (0.33) (0.34) (0.35) (0.35) Observations 7,894 7,894 7,816 7,816 R-squared Number of students 4,575 4,575 4,524 4,524 Cluster-robust SEs in parentheses *** p<0.01, ** p<0.05, * p<0.1 22

23 Table 6: The Impact of Teacher Qualifications on (Extra) Teacher Effort Highest rank (y/n) 1.97** (0.93) Received teacher award (y/n) (0.56) Attended college (y/n) (0.87) Degree Major matches Subject Taught (y/n) 3.76*** (1.03) Baseline score (0.15) Class hours taught -0.18* (0.10) Age 0.04 (0.08) Female (y/n) (0.55) Home county (y/n) 3.06* (1.63) Communist party affiliation (y/n) 0.80 (0.96) Experience 2 to < 5 years 1.27 (1.52) Experience 5 to < 10 years 0.38 (1.45) Experience 10 to < 15 years 0.83 (1.36) Experience 15 to < 20 years 1.47 (1.79) Experience 20+ years (2.03) Home room teacher (years) 0.10* (0.05) Subject is math (y/n) -1.64*** (0.61) Constant 3.33 (2.97) Observations 8,498 6 R-squared Cluster robust SEs in parentheses *** p<0.01, ** p<0.05, * p<0.1 6 Note that the observations noted here refer to a teacher*student combination 23