The impact of teacher training on teacher and student outcomes ...

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The impact of teacher training on teacher and student outcomes: evidence from a randomised experiment in Beijing migrant schools a

Linxiu Zhang , Fang Lai Scott Rozelle

a c

b

a

, Xiaopeng Pang , Hongmei Yi &

d

a

Center for Chinese Agricultural Policy , Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences , Beijing , China b

School of Agricultural and Rural Development , Renmin University of China , Beijing , China c

Department of Economics, LICOS (Centre for Institutions and Economic Performance) , Katholic University of Leuven , Leuven , Belgium d

Freeman Spogli Institute for International Studies , Stanford University , Stanford , CA , USA Published online: 02 Jul 2013.

To cite this article: Journal of Development Effectiveness (2013): The impact of teacher training on teacher and student outcomes: evidence from a randomised experiment in Beijing migrant schools, Journal of Development Effectiveness, DOI: 10.1080/19439342.2013.807862 To link to this article: http://dx.doi.org/10.1080/19439342.2013.807862

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Journal of Development Effectiveness, 2013 http://dx.doi.org/10.1080/19439342.2013.807862

The impact of teacher training on teacher and student outcomes: evidence from a randomised experiment in Beijing migrant schools


Linxiu Zhanga, Fang Laia,c*, Xiaopeng Pangb, Hongmei Yia and Scott Rozelled a Center for Chinese Agricultural Policy, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; b School of Agricultural and Rural Development, Renmin University of China, Beijing, China; c Department of Economics, LICOS (Centre for Institutions and Economic Performance), Katholic University of Leuven, Leuven, Belgium; d Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA, USA

This article exploits a randomised controlled trial to evaluate the impact of an intensive, short-term inservice teacher training programme on the performance of English teachers in Beijing migrant schools and their students. The results show no significant impacts of teacher training on either teacher or student English test scores and thus imply the ineffectiveness of short-term teacher training programmes on teacher and student performance. Keywords: education; development; inservice teacher training; random assignment; test scores; China; migration

1.

Introduction

Educational policy-makers and practitioners have had a longstanding interest in improving teacher quality. Officials in many education systems around the world have championed inservice teacher training programmes as a cost-effective way to augment the subject knowledge and pedagogical skills of teachers, thus helping them better teach their students. For example, in the United States, teacher training has become one of the most prominent components of Title II of No Child Left Behind (United States Department of Education 2002). Most teachers (72%) report having engaged in training in the subject they teach. Inservice teacher training, which can be used to improve teacher quality and student educational performance in developing countries, has also been included as an important component in social development programmes. In developing countries, preservice teacher preparation is not always required. As a consequence, inservice training is often the only preparation teachers receive (Villegas-Reimers 2003). Moreover, in moving towards the Millennium Goal of Universal Primary Education, countries have invested heavily in the construction of new schools and school facilities (Duflo 2004). In order to adequately meet the rapidly expanding demand for qualified teachers, governments and other development organisations have often invested heavily in inservice teacher training programmes (for example, AIR 2007; World Bank 2010). *Corresponding author. Email: [email protected] © 2013 Taylor & Francis


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Despite the widespread implementation of inservice teacher training, there have been few rigorous empirical studies on their impact on teacher quality. Among studies employing nonexperimental or natural experiment approaches, no unanimous conclusion has been reached so far. For example, using a matching method, Angrist and Lavy (2001) find that teacher training programme improves student test scores in Israel. Using a difference-indifference with matching approach, Machin and McNally (2008) also find significant effect of the Literacy Hour Program (of which the teacher training was an important component) on pupil attainment. However, using a natural experiment introduced by a school reform in Chicago, Jacob and Lefgren (2004) find no significant effects of teacher training on student outcomes in Chicago public schools. Following a natural experiment approach, Lai, Sadoulet and de Janvry (2009) also examine the effects of teacher inservice training on student outcomes in public schools in Beijing. They find no significant impact, possibly due to the unsystematic nature of the training programmes. Harris and Sass (2011) have generated models that include detailed measures of preservice and inservice training, developing a rich set of time-varying covariates that account for student and teacher characteristics. They find positive effects of content-focused teacher training on middle school and high school students, but not on elementary students. They also find that pedagogical training does not enhance teacher productivity. Two key limitations exist in the given literature. First, reliable evidence identifying the causal relationship between teacher training and educational outcomes is difficult to collect. Rather than randomly assigning teachers to inservice teacher training, teachers’ training opportunities are usually determined by the teachers themselves, colleagues, principals or school administrators. This may mean, of course, that those that are assigned to a training programme and those that are not assigned differ with regard to some combination of observable and unobservable teacher and/or school characteristics. As a result, it is difficult to disentangle the genuine effect of teacher training from the influences of the unobservable teacher or school characteristics that might affect both teacher effectiveness and access to teacher training. Second, most of the above recent rigorous impact evaluations of teacher training programmes are conducted in the context of developed countries. Whereas inservice teacher training is expanding in developing countries, rigorous empirical evidence of the effectiveness of teacher training in the context of school systems that are serving underserved populations is still lacking. There are notable exceptions (for example, AIR 2007; Duflo et al. 2006). Little consistent evidence of the effectiveness of these programmes, however, has emerged in the literature thus far. The overall goal of this article is to examine the impact of a short-term, intensive inservice teacher training programme on teacher and student performance for an underserved population in a developing country – China. To meet this goal, we examine the impact of inservice teacher training on the teachers’ skills and knowledge in the subject they teach. Improvement in this outcome is one of the main mechanisms through which teacher training may affect student performance. We also examine the effect of teacher training on student academic performance, measured by student test scores. To our knowledge, this is the only evaluation of teacher training programmes among migrant school teachers in China. Moreover, this is among the very few studies that explore the mechanism through which teacher training influences student performance. To achieve these objectives, in this article, we report on the results of a randomised field experiment involving 123 English teachers from 70 migrant schools in Beijing. The intervention that we evaluate is a 3-month, inservice intensive English training programme for teachers implemented during the summer of 2009. The programme was designed to

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improve teacher effectiveness in English teaching and consisted of training sessions in both English subject content and pedagogy.


2. Context: Migrant schools in urban China As China’s economy has grown over the past 30 years, the number of rural-to-urban migrants has also increased dramatically – from almost 0 to 150 million (ACWF 2008; Sa 2004). Significantly, among these, 20 million are children of school age. The number of children who migrate with their parents is still increasing. Chief among the challenges that migrant families face is limited access to educational resources (Han 2004). Urban public schools can only accommodate a fraction of migrant children due to limited space. Steep out-of-district tuition fees, unaffordable to most migrant families, deter migrant children from attending more popular public schools. The large numbers of migrant children thus attend privately run, for-profit schools, specifically serving children from poor migrant families (migrant schools). While no one really knows for sure, in Beijing, it is estimated that 70 per cent of migrant children attend migrant schools (Ma et al. 2008). Migrant schools tend to be of poor quality. Due to their private nature, migrant schools rarely receive support from the government (Kwong 2004). These schools are often transient, with sudden closings occurring due to anything from having their leases pulled because of rebuilding projects to local regulation violations. Transience discourages investment. As a result, migrant schools are typically characterised by poor facilities and under-qualified teachers with high turnover rates (CCAP 2009; Ding 2004; Han 2004; Kwong 2004; Liu 2002; Ma et al. 2008). In addition, teachers in migrant schools are poor, often lacking adequate content knowledge of the subjects they teach. In a share of the cases, teachers are often migrants themselves (Ma et al. 2008). In some cases, they have relatively low levels of formal education and little teaching experience. According to Zhang (2011), only 66 per cent of migrant school teachers have formal teacher certificates. Only 14 per cent of them have university degree. Indeed, the teachers in migrant schools in Beijing have even poorer qualifications than those of teachers in poor rural public schools (Lai et al. 2013). Studies have shown that these under-qualified teachers and poor-quality facilities have suppressed the academic performance of migrant children (for example, Lai et al. 2013; Song, Prashant, and Wei 2010). Lai et al. (2013) found that after controlling for individual and family backgrounds, students in public schools still outperformed those in migrant schools. The longer migrant children stayed in migrant schools, the worse was their performance. While there are no formal studies on the quality of English language instruction in China’s migrant schools, observation and anecdotes suggest it is not high. Grade 3 and grade 4 students in most migrant schools have English classes. However, in a vast majority of the cases, the level of English proficiency of the English teacher is poor. The fact that migrant schools have poor English teachers is a matter of particular concern because about 15 to 20 per cent of China’s high school entrance examination measures English skills (Beijing Zhongkao net 2012). As a result, migrant education, including the teaching of English, has become a core challenge for China’s educational system. Improving educational quality and student performance in migrant schools has important implications for China’s 150 million migrant workers and their children. As such, in addition to contributing broadly to the literature on inservice teacher training, this study also aims to evaluate a solution to the migrant education challenge in China’s cities.

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3. Sampling, data and methods 3.1. Sampling and the process of randomisation We conducted an randomised controlled trial of teacher training among English teachers in Beijing migrant schools in the summer of 2009. A total of 123 English teachers in 70 migrant schools and the students they taught (8387 students in total) were enrolled in our study. Among them, for reasons we will describe in detail later, 87 teachers and their students constitute the main sample for the impact evaluation part of the study (Figure 1). The first step in the sampling process was to collect a comprehensive list of schools. Unlike the case of public schools, no official list of Beijing migrant schools is available. To collect a comprehensive list of migrant schools in Beijing, we contacted all educational and research institutes and nonprofit organisations in the greater Beijing area that might have contact information for Beijing migrant schools. We then called each school to confirm that the school was still open. During each phone call, we also asked the principal of each school if there were any other migrant schools in their area. By proceeding in this way, we believe that we were able to establish as complete a list of Beijing migrant schools as possible – certainly more complete than any other existing list. A total of 230 schools are on our list. Using this list, the second step was to choose our sample schools. To do so, we first divided the list by district and excluded all districts with fewer than five migrant schools. This focused our study on districts where migrant schools are more concentrated and facilitated the management of the inservice training and evaluation activities. There are a total Description of programme and experimental design All migrant schools in Beijing

Enrollment (June 2009)

70 migrant schools randomly selected; all fourth and fifth grade English teachers in these schools are included in the sample (123 teachers)

Baseline (June 2009)

123 teachers randomised across two treatments + control

Allocation (June 2009)

37 teachers allocated to control group

Follow-up (Sept. and Dec. 2009)

55 teachers allocated to training group

11 failed to follow-up or unwilling/unable to take the test in Sept/Dec. 2009

24 rejected to participate, among whom 14 failed to follow-up or unwilling/unable to take the Sept/Dec. test; among the rest teachers, 9 failed to follow-up or unwilling/unable to take the Sept/Dec. test

Analysis

26 teachers analysed

Figure 1. Trial profile.

31 teachers allocated to incentive group

2 failed to follow-up or unwilling/unable to take the Sept/Dec.test




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of eight districts in our sample. We randomly chose 70 schools. The initial selection was done in June 2009, while the schools were still in session and before summer recess. The next step was to identify the participants in the schools. Among the 70 schools, we included all grade 4 and 5 English teachers and their students. We focused on grade 4 and 5 English teachers because some schools did not start English class until grade 4. Moreover, we wanted to follow up on students taught by these teachers the following academic year (in December 2009). In total, 123 grade 4 and 5 English teachers and 8387 grade 4 and 5 students were included in our sample. 3.1.1. Randomisation. After choosing our sample, the next step of our study was to randomly select teachers to receive inservice teacher training. To do so, we first conducted a baseline survey to collect teacher information (described below). Following the baseline survey, our research team randomly selected 55 teachers to receive the teacher training (main treatment group). Among the rest of the teachers, we randomly chose 31 teachers and provided them with a performance-based monetary incentive (without any form of training) to encourage self-paced English learning, leaving the remaining 37 teachers as the pure control group. 3.1.2. Issues of Attrition. Although the sample at the baseline survey included a total of 123 teachers, there was attrition by the end of the study primarily due to absence during the final evaluation survey (for some reason – for example, illness or conflicts in schedule). This means that by the time of the evaluation survey, we were only able to follow up with 32 out of the 55 the teachers in the main treatment group (58%); 26 out of the 37 teachers in the control group (70%) and 29 out of the 31 teachers who received no training but were offered a performance-based monetary incentive (94%). In total, 87 teachers out of the initial 123 teachers were included in our evaluation survey and the subsequent statistical analysis. These assignments and rates of attrition are spelled out in Figure 1. We also examine the nature of this attrition below. In order to understand the nature of the attrition and assess if it will affect the interpretation of the findings of the study, we undertake a two-step process to examine the nature of the attrition. In Step 1, we seek to understand if the characteristics of attriting teachers were any different than the characteristics of nonattriting teachers. The analysis in Table 1 (column 1) shows that attriting teachers and nonattriting teachers differ with respect to two observed characteristics. Specifically, compared with the nonattriting teachers, attriting teachers were younger (Table 1, column 1, row 2). In addition, teachers who had had other types of jobs before teaching at the migrant schools were also more likely to attrite from the sample (compared to those teachers who had only taught in the past – column 1, row 8). In Step 2, we seek to understand whether the differences in the characteristics of the attriting teachers are related to the randomised assignment (to the treatment or control arm of the study). To test this, we create a sample of the attriting teachers only. We then create a new dummy variable, making the variable equal to one if the attriting teacher was originally assigned to the treatment group and making it equal to zero if the attriting teacher was originally assigned to the control group. Using this variable as the dependent variable, we show in the analysis in Table 1 (column 2) that all of coefficients are insignificant. This means that there is no systematic difference between those attriting from the treatment group and those attriting from the control group. Hence, although we have relatively high rates of attrition, the attrition is idiosyncratic relative to the treatment/control group assignment and this means that our programme evaluation is not biased by the attrition.

0.08 (0.21) −0.09 (0.08) 0.41 (0.64) 0.80 (0.49) 0.01 (0.38) −0.13 (0.31) −0.02 (0.16) −0.40 (0.37) 34

−0.01 (0.04) −0.02 (0.01) 0.03 (0.11) 0.10 (0.13) 0.01 (0.08) −0.00 (0.09) 0.00 (0.02) 0.17 (0.08) 123

−0.09 (0.09) 0.03 (0.03) 0.02 (0.18) −0.19 (0.17) 0.04 (0.16) −0.07 (0.18) −0.04 (0.05) −0.12 (0.19) 55

Notes: Robust standard errors clustered at the school level in brackets. a The baseline English score is the score on the standardised English test that was given to all teachers in the sample before the Summer Fresh programme. b Other jobs before indicates whether the teacher had taken other jobs before becoming a teacher. c The sample includes the nonattrition teachers who were included in the main analytic sample and teachers who attrited the sample. d The differences between the attrited and nonattrition teachers are calculated by the regression of the attrition dummy variable (1 = yes; 0 = no) on the row variables. e The sample only includes teachers who attrited the main sample and belonged to either the training or the control group. f The differences between the the training and control groups are demonstrated by regressing the treatment dummy (0 = control; 1 = teacher training) on the row variables. g The sample includes only the teacher who were initially assigned to the training group because only this group had noncompliance (that is, some teachers were assigned to the training group yet did not participate). h The differences between the compliance and noncompliance teachers are calculated by the regression of the compliance dummy variable (1 = yes; 0 = no) on the row variables.

(9) Observations

(8) Other jobs before (1 = yes; 0 = no)b

(7) Years of teaching

(6) Monthly wage more than 1000 yuan

(5) Has teacher credential

(4) Has a university degree

(3) Male (1 = yes; 0 = no)

(2) Age (number of years)

(1) Baseline English score (units of standard deviation)a

Variables

Sample: teachers who were initially assigned to the training groupg Difference (compliance–noncompliance)h (3)

Sample: attrited observations onlye Difference between the training and control groupsf (2)

Sample: nonattrition teachers + attrited observationsc Difference (attrition–nonattrition)d (1)

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Table 1. Comparison of the teacher characteristics: (1) between the attrited teachers and those remaining in the sample; (2) among attriting teachers who were assigned to the training, incentive and control groups; and (3) among teachers who were initially assigned to the training group and complied to the assignment and those who were initially assigned to the training group but did not comply.


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3.1.3. Issues of noncompliance. In our sample, we also faced the issue of noncompliance. With noncompliance, we can still estimate the intention to treat effect (ITT) without bias by examining the differences in outcomes between teachers who were randomly assigned to the treatment group and those assigned to the control group. When the noncompliance rate is high, the ITT estimate might not be equal to the effect of the treatment. This might happen since the actual assignment of treatment differs significantly from the initial assignment. Therefore, we must go extra step to examine the effect of the treatment itself. In the programme evaluation literature, many studies use an IV approach to estimate the Local Average Treatment Effect (LATE), or, in other words, the treatment effect on compliers. The endogenous variable is the actual treatment status, and the instrumental variable is the initial random assignment of the treatment status. The estimated effects can only be interpreted as the treatment effects on compliers (that is, those individuals that would take the treatment if and only if they were assigned to the treatment group). Whether the LATE results could be generalised to noncompliers (in our study, as there were no teachers in the control group participating in the training, the noncompliers only consist of teachers who were assigned to the treatment group but did not participate in the training) depends in part on whether compliers and noncompliers in the treatment group differ in terms of their characteristics. To explore this, we use a sample of those in the treatment group only. We then create a dummy variable, assigning those that complied (the compliers) with a value of one and assigning those that did not comply with a value of zero. We then use this new variable as a dependent variable and examine if noncompliance is systematically related to any of the observed teacher characteristics. The analysis in Table 1 (column 3) suggests that noncompliance will not seriously affect the interpretation of our results. The coefficients on all of the variables are insignificant from zero. This means that our compliers and noncompliers are drawn from an identical pool of sample individuals. In other words, there were no observable characteristics that could be identified as leading to noncompliance. This also means that the noncompliance did not create any systematic deviation from the initial randomisation design and that noncompliance does not affect our findings. Moreover, since there are no statistically significant differences in characteristics between compliers and noncompliers, the IV estimate (or the LATE estimator) for compliers can be generalised to noncompliers. Most importantly, the analytical sample is well balanced. In Table 2, we compare the means and standard deviations of the treatment and control groups across a number of control variables and baseline English scores. Within the sample consisting of the remaining 87 teachers, differences in these characteristics between the teacher training and control groups were statistically insignificant (columns 7 and 8). This suggests that the attrition and noncompliance do not seriously affect the validity of the initial randomisation design. Nonetheless, in addition to these preliminary checks, in the analysis part of the article (below), we conduct various empirical analyses to check the robustness of our results to departures from the initial random assignment. 3.2. Experiment arms/interventions Our study focused on one treatment group (the teacher training group) and one control group that did not receive any form of intervention. In our main analytic sample, there is another experiment arm that consisted of 29 teachers who received no teacher training but rather a performance-based monetary incentive. However, as the purpose of this article is to explore the impacts of teacher training on teacher and student outcomes, we limit our discussion about the impact of performance-based monetary incentive group. However,

1.07 3.21 0.43 0.40 0.45 0.45 2.00 0.51

0.05 25.58 0.23 0.19 0.73 0.27 2.48 0.54

27.00 0.19 0.09 0.66 0.19 3.41 0.47

0.02 6.52 0.40 0.30 0.48 0.40 3.66 0.51

1.01 5.96 0.35 0.44 0.38 0.48 3.83 0.48

0.96

−0.06 27.24 0.14 0.24 0.83 0.34 3.02 0.34

SD (6)

Mean (5)

Incentive group (29 observations)

0.95 0.03 −0.15 −0.01 −0.07 0.67 −0.07

0.05 −0.50 0.30 0.45 0.28 −0.04 −0.49

−0.04

Differencef (training–control) (8)

Differenced (training–control) (7) 0.01

Control and incentive (55 observations)e

Control and training (58 observations)c

Notes: Robust standard errors in brackets, clustered at the school level. a The baseline English score is the score on the standardised English test that was given to all teachers in the sample before the Summer Fresh programme. b Other jobs before indicates whether the teacher had taken other jobs before becoming a teacher. c The sample includes the control and teacher training groups in the main sample only. d The differences between the training and control groups are demonstrated by the regression of the teacher training dummy variable (1 = yes; 0 = control group) on the row variables. e The sample only includes the control and incentive groups in the main sample. f The differences between the incentive and control groups are demonstrated by the regression of the incentive group dummy variable (1 = yes; 0 = control group) on the row variables.

(1) Baseline English score (units of standard deviation)a (2) Age (number of years) (3) Male (1 = yes; 0 = no) (4) Has a university degree (5) Has teacher credential (6) Monthly wage more than 1000 yuan (7) Years of teaching (8) Other jobs before (l = yes; 0 = no)b

Variable

SD (4)

Mean (3)

Mean (1)

SD (2)

Training group (32 observations)

Control group (26 observations)

Table 2. Comparison of characteristics among teachers in the training, incentive and control groups for the 87 teachers who were followed up through the evaluation in December 2009 (the main sample).


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in order to increase the power of our analysis, we still include the 29 teachers who were assigned to the monetary incentive group in our analytic sample.

3.2.1. Teacher training group (the treatment group). The main intervention for the teacher training group involved teacher training sessions intended to improve the teacher’s English knowledge and pedagogy. To achieve this goal, the research team recruited three experts in English language and teaching to help design and deliver the training curriculum. One trainer was an expert in English teaching, who herself was a rank IV English teacher (the highest rank in China’s teacher ranking system). Another was a professor in Capital Normal University, one of the best schools of education in Beijing. The main responsibilities of these two trainers were to enhance teacher knowledge about English vocabulary, grammar and reading comprehension. Moreover, they were responsible for teaching pedagogy to the trainees. A third expert was a PhD student at the School of Education at Stanford University, who is a native English speaker. Her responsibilities focused on improving the competency in spoken English (pronunciation and conversation) for the trainees. In addition to designing the curriculum, these experts also led training sessions with the help of five volunteer teaching-assistants selected on the basis of their English proficiency. Together, the teaching team worked intensively with the research team to design a curriculum (textbooks, exercise brochure and teaching plan) appropriate to the needs of the English teachers in migrant schools. All teachers were exposed to the training in a highly standardised manner. Shortly after the baseline survey, the migrant English teachers in the training group received an invitation letter to join the training programme. Those who accepted the invitation were asked to gather at a hotel conference room for the 3-week training in English knowledge, skills and teaching techniques. The full-day teacher training sessions were run daily from July 20 to August 9. In the morning, the three trainers led lectures introducing English language knowledge, skills and pedagogical methods in English teaching. In the afternoon, the assistant trainers led the teachers to review and practice materials and skills learnt during the morning through drills, games, conversation exercises and mock classroom activities. Monetary incentives were also used to increase compliance during the training. First, all expenditures in room and board, transportation and course materials were covered for the training. Second, a compensation plan was implemented to encourage teacher attendance at all training sessions. Conditional on full attendance in all training sessions, each trainee would receive 600 yuan as compensation for his/her time at the end of the training programme. This amount is equivalent to 75 per cent of the typical monthly wage of a migrant school English teacher.

3.2.2. Control group. Teachers that were assigned to the control group (37 teachers, 26 of whom were followed up with by the final evaluation survey) did not receive any kind of intervention. To minimise their awareness of being part of our study, for teachers in this group, we only conducted the standardised English test and collected data on their characteristics during the baseline, midterm and final evaluation survey (in June, September and December, as we did with the other two groups). We did not contact these teachers other than conducting the three rounds of survey. Nor did we inform teachers in the control group of the teacher training programme to avoid any form of spillover of the teacher training programme. Since the invitation for the teacher training for teachers in the training group was not extended until after school was out for the summer, and since the midterm

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evaluation was done at the beginning of the school year, the possibility for spillovers was minimised (because there was little time for interaction between teachers in the treatment and control groups – even if in a small number of schools, some teachers were assigned to the treatment group and others from the same school to the control group). 3.3. Data collection 3.3.1. Teacher survey. The 123 teachers in this study were surveyed three times. The first-round survey was a baseline survey conducted in early June 2009, before the implementation of the teacher training programme. The second-round survey was a midterm evaluation survey conducted in September at the beginning of the fall semester in 2009. The third-round survey was a final evaluation survey conducted in late December, a time that coincided with the end of fall semester. In each round of survey, the enumeration team conducted a two-block survey. In the first block, teachers were given a standardised English test. The test questions were selected from the official Preliminary English Test, Level Two (PET2). The English test included 50 questions. Teachers were required to finish the test in 40 minutes. Our enumeration team monitored the test and strictly enforced time limits, ensuring also that there was no cheating. We used the scores of the teachers on the English test as one of our main measures of their proficiency in the subject they teach. In the second block, enumerators collected data on the characteristics of the teachers. The data set includes measures of each teacher’s age (measured in years), gender, education level (has a university degree), teacher qualification (whether the teacher has teacher credential), years of teaching and monthly wage (whether the teacher has monthly wage more than 1000 yuan). There was also one question asking about the teacher’s previous working experience (whether the teacher had taken other jobs before becoming a teacher). 3.3.2. Student survey. To examine the impact of teacher training on student performance, we also conducted two rounds of survey on the fourth and fifth grade students who were taught by the teachers in the sample. The first-round survey was a two-block survey conducted at the end of June 2009, and 8387 students were surveyed. The first block of the survey consisted of an English subject test appropriate for the level of students in each of the fourth and fifth grades. The English teachers monitored the test and strictly enforced time limits. We use the scores of the students on this test as our measure of student English performance. In the second block, enumerators collected data on the characteristics of students and their families. From this part of the survey we are able to create demographic and socioeconomic variables that include each student’s age (measured in years), gender, number of siblings in Beijing, father’s and mother’s education level (junior high school, senior high school, professional college or above). There was also one question asking about each student’s years of stay in Beijing as a migrant. To create indicators of parental care, during the survey the students were also asked whether they lived with both of their parents for most of the time during the semester (living with parents in Beijing). The second-round survey had the same structure and content as the first-round survey and it was conducted at the end of December 2009 (the end of fall semester). As some teachers and students transferred between schools during the period from June to December, the students included in the two rounds of survey were not necessarily the same students. Yet all of them were fourth or fifth grade students in Spring 2009, taught by the teachers in our sample either in spring 2009 or in fall 2009.



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3.4. Statistical methods We used both unadjusted and adjusted ordinary least squares (OLS) regression analysis to estimate how teacher and student outcomes changed in the treatment group relative to the control group. Our unadjusted analysis regressed changes in the teacher or student outcome variables (that is, post-programme outcome value minus pre-programme outcome value) on a categorical variable of the treatment status (teacher training, incentive programme and control). We used adjusted analyses as well to control for some accidental differences after randomisation between the treatment and control groups and improved precision. In all regressions, we accounted for the clustered nature of our sample by constructing Huber– White standard errors corrected for school-level clustering (relaxing the assumption that disturbance terms are independent and identically distributed within schools). We also used unbalanced panel data analysis to examine how teacher training affected the student performance in English. Each wave in the panel includes students in each round of the survey. By doing so, we can accommodate the fact that teachers might have taught different students in June and December due to the high mobility of the migrant student population. The models for the effect of teacher training on teacher and student outcomes are presented in order of increasing comprehensiveness. First, the unadjusted model for teacher outcome is yisc = α +

βc · treatmentc + εisc

(1)

c=1,2

where yisc is the change in the outcome variable during the programme period for teacher i in school s and group c, and εisc is a random disturbance term (clustered at the school level). The primary teacher outcome variable of our analysis is the teacher English performance, measured by the teacher standardised English test score. The variable treatmentc is a dummy variable for a teacher’s group assignment, which is equal to one if the teacher is in group c, and zero otherwise. Without loss of generosity, c is equal to one for teachers in the training group, two for teachers in the monetary incentive group. The control group serves as the base category. By construction, the coefficient of the dummy variable treatmentc , βc , is equal to the unconditional difference in the change in the outcome (yisc ) between the treatment and control groups over the programme period. Given random assignment of the treatments (and the balanced nature of the compliers/noncompliers, as discussed above), βc is an unbiased estimate of the effect of treatmentc on teacher standardised English test scores (taking account of imperfect compliance, and thus the Intention to Treat Effect). Specifically, β1 measures the impact of the inservice teacher training programme on teacher standardised English test scores under imperfect compliance. To improve the efficiency of the estimation, we build on the model in Equation (1) by including a set of control variables: yisc = α +

βc · treatmentc + θ · y0isc + Xisc γ + εisc

(2)

c=1,2

The variables and parameters are the same as those in Equation (1), except we have added additional control variables. Specifically, we control for y0isc , the baseline English test score for teacher i in school s and treatment group c and Xisc , a vector of control variables. The variables in Xisc are teacher characteristics (age, male, has a university degree, has teacher

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credential, monthly wage more than 1000 yuan, years of teaching and other jobs before). By including y0isc and Xisc as control variables, β1 in Equation (2) is also an unbiased estimate of the treatment effects (ITT, as is βc in Equation (1)), except that β1 has smaller standard errors and improved efficiency. Besides the effect of teacher training on teacher outcomes, we also examine the effect of teacher training on student performance. In order to accommodate the high mobility of teachers and students in migrant schools from June to December 2009, we employed a model of unbalanced panel data analysis. The model was set up as follows: Tisct = α +

c=1,2

μc · treatmentc +

c=1,2

δc · t · treatmentc + λt + Zisct σ + νisct , (3)


t = 0, 1 where Tisct is each student’s standardised English test score in the baseline survey in June (t = 0) or final evaluation in December (t = 1), λt denotes the time-fixed effects capturing influences from time-fixed factors (that is, systematic changes in student English test scores μc · treatmentc captures systematic differences among the treatment and over time); c=1,2

control groups and Zisct includes a vector of time-varying student characteristics (student age, gender, years of stay in Beijing, whether the student stays with both parents in Beijing, and the grade of the student). By construction, the coefficient δ1 measures the difference between the treatment and control groups in terms of gains in student standardised English test scores over the study period. In other words, δ1 indicates the effect of the inservice teacher training programme on student performance.

4. Results 4.1. The impact of teacher training on teacher English performance The data show no statistically significant improvement in the teacher standardised English test scores. The unadjusted analysis, based on Equation (1) suggests that there were no statistically significant differences in test scores across the treatment and control groups (Table 3, columns 1 and 3, row 1). In fact, the point estimates of the impact of the inservice training programme in both the midterm evaluation (−0.08) and the final evaluation (−0.18) are not even positive. The results of the adjusted models from Equation (2) are similar (Table 3). The point estimate of the impact of inservice teacher training (taking account of imperfect implementation) at the time of the midterm evaluation is not significant (column 2, row 1). The estimate of the impact at the time of the final evaluation is not statistically different from zero, either (column 4, row 1). Both point estimates are actually negative (as in the case of the unadjusted models discussed in the previous paragraph). In order to account for the effects on our estimated impacts due to noncompliance, we report in Table 4 the coefficients from the IV analysis (that is, the estimates of the treatment effect on compliers). The results for both the unadjusted IV model (columns 1 and 3, row 1) and the adjusted IV model (columns 2 and 4, row 1) are similar to the results based on regression run using the OLS specifications. All four coefficients are not positive and none of them are statistically significant. The fact that the results based on the IV model are similar to those based on the OLS model should not be surprising given our discussion of the nature of noncompliers (compared to compliers). If compliers and noncompliers do


13

Table 3. OLS estimates of the short-term and long-term impacts of teacher training on teacher English test scores for the 87 teachers in the main sample. Dependent variable: standardised teacher English test scores Midterm English score – baseline English score in Junec


(1) Training group (yes = 1; 0 = control group) Control variables (2) Incentive group (yes = 1; 0 = control group)

OLS (1)

OLS (2)

OLS (3)

OLS (4)

−0.08 (0.19)

−0.09 (0.19)

−0.18 (0.20)

−0.23 (0.19)

0.08 (0.22)

0.06 (0.21) −0.30 (0.08) 0.01 (0.03) 0.12 (0.19) 0.17 (0.30) −0.21 (0.20) 0.14 (0.18) 0.00 (0.04) 0.13 (0.17) 87 0.22

0.09 (0.20)

0.07 (0.20) −0.24 (0.08) 0.01 (0.03) 0.09 (0.18) −0.37 (0.20) 0.12 (0.20) 0.07 (0.19) 0.04 (0.04) 0.18 (0.15) 87 0.25

(3) Baseline English score (units of standard deviation)a (4) Age (number of years) (5) Male (1 = yes; 0 = no) (6) Has a university degree (7) Has teacher credential (8) Monthly wage more than 1000 yuan (9) Years of teaching (10) Other jobs before (1 = yes; 0 = no)b (11) Observations (12) R-squared

December English score – baseline English score in Juned

87 0.01

87 0.02

Notes: Robust standard errors in brackets, clustered at the school level. a The baseline English score is the score on the standardised English test that was given to all teachers in the sample in June before the Summer Fresh programme. b Other jobs before indicates whether the teacher had taken other jobs before becoming a teacher. c The baseline and midterm English tests were conducted in June and early September (1 month after the end of the training programme), respectively. Columns 1 and 2 measure the short-term effect of teacher training on teacher English test score. d The December English test was conducted in late December, 4 months after the end of the teacher raining programme. Columns 3 and 4 measure the long-term effect of teacher training on teacher English test score.

not differ in their characteristics, the noncompliance should not be expected to affect the estimated results. 4.2. The impact of teacher training on student English performance In some cases, even though teacher training might not improve the teacher’s own performance on the subject content, it might indirectly benefit the students by improved pedagogical skills from the training. In fact, most studies on teacher training or teacher development focused on the impacts of the teacher training on student performance because improved student performance is the ultimate goal of teacher training. Some studies

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L. Zhang et al.

Table 4. Instrumental Variable estimators (using generalised method of moments) of the impact of teacher training on teacher English test scores. Dependent variable: standardised teacher English test scores Midterm English score – baseline English score in Junec

(1) Training group (yes = 1; 0 = control group)


Control variables (2) Incentive group (yes = 1; 0 = control group)

(1)

(2)

(3)

(4)

−0.12 (0.28)

−0.13 (0.28)

−0.26 (0.28)

−0.34 (0.27)

0.08 (0.22)

0.06 (0.21) −0.31 (0.08) 0.01 (0.03) 0.13 (0.19) 0.16 (0.31) −0.21 (0.20) 0.14 (0.18) 0.00 (0.04) 0.12 (0.17) 87 0.22

0.09 (0.20)

0.07 (0.20) −0.25 (0.08) 0.01 (0.03) 0.12 (0.18) −0.39 (0.21) 0.12 (0.20) 0.07 (0.18) 0.03 (0.04) 0.17 (0.15) 87 0.25

(3) Baseline English score (units of standard deviation)a (4) Age (number of years) (5) Male (1 = yes; 0 = no) (6) Has a university degree (7) Has teacher credential (8) Monthly wage more than 1000 yuan (9) Years of teaching (10) Other jobs before (1 = yes; 0 = no)b (11) Observations (12) R-squared

December English score – baseline English score in Juned

87 0.00

87 0.01

Notes: Robust standard errors in brackets, clustered at the school level. a The baseline English score is the score on the standardised English test that was given to all teachers in the sample in June before the Summer Fresh programme. b Other jobs before indicates whether the teacher had taken other jobs before becoming a teacher. c The baseline and midterm English tests were conducted in June and early September (1 month after the end of the training programme), respectively. Columns 1 and 2 measure the short-term effect of teacher training on teacher English test score. d The December English test was conducted in late December, 4 months after the end of the teacher training programme. Columns 3 and 4 measure the long-term effect of teacher training on teacher English test score

in the literature did find that teacher training increased student academic performance (for example, Angrist and Lavy 2001; Bressoux 1996; Dildy 1982). However, using panel data analysis (from Equation (3)), we find that the teacher training programme did not have any significant effect on student performance (Table 5, row 1). None of the coefficients are significantly different than zero. The standard errors are all large relative to the size of the coefficients. Our results are also robust to the inclusion of teacher and student individual characteristics into the sample. We also find similar results when we account for noncompliance by using an IV approach (tables not included for brevity). We can find no significant impact of inservice teacher training on their students.

Training group∗ timea

Teacher age (number of years)

Male teacher (1 = yes; 0 = no)

Teacher has a university degree

Teacher has teacher credential

Monthly wage more than 1000 yuan

Years of teaching

Other jobs before (1 = yes; 0 = no)c

(7)

(8)

(9)

(10)

(11)

(12)

(13)

Incentive group ∗ time

(5)

Teacher’s baseline English score (units of standard deviation)b

Time (evaluation survey = 1; 0 = baseline survey)

(4)

(6)

Incentive group (yes = 1; 0 = control group)

(3)

Control variables (2) Training group (yes = 1; 0 = control group)

(1) 0.13 (0.16) −0.12 (0.17) −0.00 (0.22) 0.08 (0.26)

−0.03 (0.23)

Basic model (1)

(0.07) 0.01 (0.01) −0.48∗∗∗ (0.12) −0.04 (0.18) 0.10 (0.12) −0.23∗∗ (0.11) 0.05∗∗ (0.02) 0.15 (0.11)

(0.08) 0.01 (0.01) −0.45∗∗∗ (0.13) −0.08 (0.19) 0.12 (0.13) −0.24∗∗ (0.12) 0.04 (0.02) 0.13 (0.12)

(Continued)

0.03 (0.14) −0.16 (0.17) −0.17 (0.20) 0.17 (0.23) 0.03

0.12 (0.21)

Controlling for teacher and student characteristics (3)

0.06 (0.15) −0.12 (0.18) −0.05 (0.21) 0.16 (0.24) 0.01

0.09 (0.22)

Controlling for teacher characteristics (2)

Dependent variable: standardised student English test scores

Table 5. (Unbalanced) Panel data analysis of the impact of teacher training on student English performance for students who were taught by the 87 teachers in the main sample.


Journal of Development Effectiveness 15

Male student (1 = yes; 0 = no)

Years of stay in Beijing

Stay with both parents in Beijing (1 = yes; 0 = no)

Student grade fixed effects and interactions of grade and time Observations R-squared

(20)

(21)

(22)

(23)

(24) 11798 0.01

Y

11634 0.05

Y

Controlling for teacher characteristics (2)

8989 0.11

0.07∗∗∗ (0.02) −0.03 (0.03) 0.01 (0.05) 0.12∗∗∗ (0.03) 0.12∗∗∗ (0.03) 0.01 (0.06) −0.09∗∗∗ (0.02) −0.36∗∗∗ (0.03) 0.02∗∗∗ (0.00) 0.02 (0.04) Y

Controlling for teacher and student characteristics (3)

Notes: Robust standard errors in brackets, clustered at teacher and school level. ∗∗ significant at 5%; ∗∗∗ significant at 1%. a We pooled student observations from both the baseline and the evaluation survey together and form an unbalanced panel. Time = 1 if for evaluation survey, and time = 0 for baseline survey. b The baseline English score is the score on the standardised English test that was given to all teachers in the sample before the Summer Fresh programme. c Other jobs before indicates whether the teacher had taken other jobs before becoming a teacher.

(25) (26)

(19)

(18)

(17)

(16)

(15)

Basic model (1)

Dependent variable: standardised student English test scores

Mother finished junior high school (1 = yes; 0 = no) Mother finished senior high school (1 = yes; 0 = no) Mother finished professional college and above (1 = yes; 0 = no) Father finished junior high school (1 = yes; 0 = no) Father finished senior high school (1 = yes; 0 = no) Father finished professional college and above (1 = yes; 0 = no) Student age (number of years)

(14)

Table 5. (Continued)


16 L. Zhang et al.


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Thus, the benefits of the teacher training programme, if there were any, did not translate into higher student performance through improved English pedagogical skills of their teachers. In fact, these results are consistent with the findings in Jacob and Lefgren (2004), who also find no significant effects of teacher training on student outcomes in low-performing schools. This finding also echoes those in Harris and Sass (2011), who find no significant effects of teacher training on student performance at the elementary school level. 4.3. Robustness check: combining the samples from the midterm and final evaluation In order to examine the robustness of our results, in Appendix 1, we report the findings of the analysis when we combine the teacher-based samples from the midterm and final evaluations. This effectively doubles our sample size. Effectively, we are examining whether either the midterm or final evaluations can detect an impact of inservice training on the English test scores of our sample teachers. The idea of combining the samples means that there is more explanatory power, given the larger number of observations. We run both the OLS and IV models. The results of both modelling approaches are similar to the findings in the previous sections of the article (Appendix 1, row 1; Tables 3 and 4, row 1). The coefficients are statistically insignificant from zero. The point estimates are all negative. There is no evidence that the inservice training programme increased the teacher English test scores at all. 5. Conclusion We have presented the results from a randomised field experiment of a teacher training programme involving 87 fourth- and fifth-grade English teachers in migrant schools in Beijing and 8387 of their students. The intervention evaluated was a teacher training programme held during summer break. Of the 87 teachers, 32 teachers were assigned to teacher training, 26 to the control group and the remaining 29 were assigned to monetary incentives. The teacher training programme lasted for 3 weeks during summer break, when teachers received intensive training in both English subject content and pedagogical skills 6 days a week. Our results suggest that in our sample, short-term inservice teacher training programmes in English subject content and English pedagogy do not improve either teacher or student English test scores. The estimates of teacher training effects on teacher English test scores are insignificant at the 10 per cent level. The point estimates of teacher training effects on student English test scores are insignificant as well. How do we explain these findings? As the training only ran for a short period of time (in our case, the training went on during the summer break), teachers might not have had enough time to digest the knowledge and skills learned from the training sessions and apply them to their teaching routines. Over time, teachers might even forget the content and skills learned during the ‘one-shot’ training programme. Another explanation might be that teachers were too stressed or busy to integrate their new knowledge into teaching. So if our findings are at all representative of the types of short-term inservice teacher training programme that run in low-performing school districts, the article calls for caution in thinking that this is a solution to the poor performance of schools in these districts. For this reason, the findings of this article also have important policy implications. If short-term inservice teacher training programmes do not significantly improve English teacher effectiveness or student learning outcomes for teachers with low qualifications,

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policy-makers shall consider other forms of teacher training or direct resources to more productive use in order to effectively improve student and teacher performance. So in general, this article contributes to the understanding of the effect of teacher training on both teacher and student learning outcomes for underserved populations in developing countries in two respects. First, the randomisation design provides unbiased estimates of the impact of teacher training on teacher and student performance, which is rare in the existing literature. Second, this article explores the impacts of inservice teacher training on both teacher and student outcomes, providing a broad perspective of the impacts of teacher training and insights into the mechanism through which teacher training affects student performance. Most existing studies only directly link teachers’ training to student outcomes, neglecting the mechanism through which teacher training influences student performance.

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Ma, X., Y. Bai, C. Liu, X. Pang, and S. Rozelle. 2008. “Migrant Schools in China’s Capital City: A Descriptive Review of a Representative Migrant Survey.” Working Paper, Center for Chinese Agricultural Policy, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing. Machin, S., and S. McNally. 2008. “The Literacy Hour.” Journal of Public Economics 92 (5–6): 1441–1462. Sa, Z. (2004). “A Case Study of Rural-urban Migrant Children’s Education Barriers in China.” American Sociological Association annual meeting, San Francisco, CA. Song, Y., L. Prashant, and J. Wei. 2010. “Does Going to Public Schools Matter for Migrant Children’s Academic Achievement in China?” Presentation at the Center for Chinese Agricultural Policy workshop on Education for Migrant Children, China Institute for Educational Finance Research and Peking University, Bejing, April 8. The American Institutes for Research. 2007. “Malawi Teacher Training Activity.” January– December 2007 Annual Report. Malawi: Zomba. United States Department of Education. 2002. “No Child Left Behind Act.” Public Law 107–110. Accessed June 12, 2013. http://www2.ed.gov/policy/elsec/leg/esea02/107-110.pdf Villegas-Reimers, E. 2003. “Teacher Professional Development: A International Review of the Literature.” UNESCO, International Institute for Educational Planning. Accessed June 13, 2013. http://www.iiep.unesco.org/fileadmin/user_upload/Research_Challenges_and_Trends/ 133010e.pdf World Bank. 2010. “Transforming Indonesia’s Teaching Force.” Report No. 53732, vol. 2. Jakarta: World Bank. Zhang, Z. 2011. “2010 Nian Beijing Dagongzidi Xian Zhuang Fen Xi Bao Gao.” Beijing Dagongzidi Xue Xiao Gong Yi Wang Zhan. Accessed June 12, 2013. http://dgzd.org.cn/info. asp?newsid=1721

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Appendix 1. Ordinary least square and Instrumental Variable estimates of the impacts of teacher training on teacher English test scores for the 87 teachers in the main sample – pooling baseline, midline and final evaluation data together. Dependent variable: change in standardised teacher English test scores OLS estimator (1) (1) Training group (yes = 1; 0 = control group) Control variables (2) Incentive group (yes = 1; 0 = control group)


(3)

−0.09 − 0.09 −0.13 −0.13 (0.10) (0.13) (0.14) 0.04 (0.10)

(3) Baseline English score (units of standard deviation) (4) Age (number of years) (5) Male (1 = yes; 0 = no) (6) Has a university degree (7) Has teacher credential (8) Monthly wage more than 1000 yuan (9) Years of teaching (10) Other jobs before (1 = yes; 0 = no) (11) Time fixed effects (12) Observation (13) R-squared

(2)

IV estimator

Y 174 0.00

(4) −0.18 (0.18)

0.02 0.04 0.03 (0.12) (0.10) (0.12) −0.33 −0.34 (0.07) (0.07) 0.00 0.00 (0.02) (0.02) 0.09 0.10 (0.11) (0.10) −0.10 −0.11 (0.10) (0.11) 0.02 0.02 (0.11) (0.12) 0.12 0.12 (0.11) (0.11) 0.02 0.02 (0.02) (0.02) 0.08 0.07 (0.09) (0.09) Y Y Y 174 174 174 0.19 0.00 0.19

Notes: a The baseline English score is the score on the standardised English test that was given to all teachers in the sample in June before the Summer fresh programme. b Other jobs before indicates whether the teacher had taken other jobs before becoming a teacher. c Columns 1 and 2 measure the effect of teacher training on teacher English test score pooling the data of three rounds of survey using OLS. d Columns 3 and 4 measure the effect of teacher training on teacher English test score pooling the data of three founds of survey using Instrumental Variable Approach.