Human Development

Teacher-student gender matching and learning outcomes

  • Blog Post Date 22 March, 2021
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Studies have found positive effects of teacher-student gender matching on students’ learning outcomes in certain countries. Based on a theoretical model – tested empirically with survey data from Andhra Pradesh – this article shows that the positive effect in the Indian context seems to be a consequence of higher quality female teachers and more competent female students ‘self-selecting’ into urban, private schools on account of gender norms and institutional structures of teacher hiring.

 

The effect of teacher-student identity matching – in terms of gender or race – on the learning outcomes of students, has received a great deal of attention in economics research. While studies based on countries such as India, Chile, and South Korea find positive effects of gender matching (Lim and Meer 2017, Muralidharan and Sheth 2016, Paredes 2014, Rawal and Kingdon 2010), there is no strong evidence in this regard in studies based on countries like United States, Britain and Australia (Cho 2012 Francis et al. 2008, Marsh et al. 2008, Price 2010). In general, there are two potential explanations for the existence of identity matching effect: (i) role model effect, which refers to the argument that a student of a certain gender or race idolises a teacher belonging to the same social identity and is inspired to perform better; and (ii) Pygmalion effect, which conjectures that if a teacher expects high performance from a student, the student reciprocates by putting up a good performance.1

In our research (Bhattacharya et al. 2017), we propose a novel mechanism that may explain the positive effect of teacher-student gender matching on the learning outcomes of students. Our theoretical model argues that the positive effect of female gender matching is an urban, private-school phenomenon and it happens because the best female students and female teachers ‘self-select’ into urban private schools. There are two aspects central to our hypothesis – the institutions of teacher-hiring in the Indian school system, and certain social norms that guide gender-specific investments in children and mobility in the labour market. Together, these drive the selection mechanisms that yield the hypothesis, which we examine empirically using 2016-2017 Young Lives School Survey (YLSS)2 data from the state of Andhra Pradesh.

Teacher hiring: Institutional structure and gender norms

There are two ‘selection’ mechanisms in our model – one pertaining to teachers and the other to students. In India, the government-school systems – at least in some states – hire teachers centrally and then assign them to schools in different locations. Upon recruitment, candidates are often posted in locations far from their homes. On the other hand, private schools by and large engage in school-specific hiring. Gender norms in India endorse household-level specialisation, which sees household chores as the primary duty of women. Hence, workplaces near their residence allow women extra time for household chores. This, in turn, makes the actual travelling/relocation cost for women much higher than their male counterparts. Given this norm, we argue that women do not prefer government schools even when government schools pay more than private ones.

Further, the quality of public amenities is much higher in urban areas relative to rural areas, making urban areas the preferred choice of residence. Given this preference, high cost of travelling for women, and decentralised hiring practice of private schools – urban private schools are the most preferred workplaces for women. Private schools in urban areas with decentralised hiring practices are therefore, better placed to attract higher quality female teachers.

Student enrolment: Parental discrimination against girls

On the students' side, a different type of selection mechanism is at work. Parents are likely to make the decision to send their child to a private school or a government school depending on (a) the child's ability and (b) their gender. There is a natural inclination for a parent to enrol a more talented child in a higher-quality school (private school) even if it costs more. At the same time, norms in Indian society are such that parents tend to favour boys over girls when it comes to spending during their formative years (Rammohan and Vu 2018). The fact that the parents can depend more on their sons' earnings rather than daughters', may be partly responsible for this disparity. This can explain why the bar for qualifying for private schools is higher for girls in Table 1, where we present the Peabody Picture Vocabulary Test (PPVT) conducted among students before they started going to school. This can be thought of as a measure of the innate abilities of students. The differences between private and government school students are statistically significant.

Table 1. Ability distribution of students across gender and type of school, before starting school

Number of government schools in study sample

Number of private schools in study sample

Average score in government schools

Average score in private schools

Difference between government and private

Normalised PPVT score for female students

285

139

0.164

0.277

-0.113

Normalised PPVT score for male students

284

204

0.172

0.238

-0.067

Positive gender-matching effects in urban, private schools

With these two selection mechanisms for teachers and students in place, urban private schools receive high-quality female teachers along with high-ability female students resulting in a strong, positive gender-matching effect, which is not visible for male teachers/students. Hence, in contrast to the standard position of most related studies, the positive gender-matching effect that we find is not generated because female students are being taught by female teachers. The model we construct, however, is context specific; it depends on gender norms and the institutional structure of school hiring prevailing in a country. Our results, therefore, may only be generalised for societies where the discussed norms and institutions prevail. But on the upside, unlike behavioural explanations such as Pygmalion effect and role model effect, we provide a rational choice-based understanding of the gender-matching phenomenon.

We test our theoretical hypothesis using data from the Young Lives School Survey conducted in state of Andhra Pradesh in 2016-2017 as part of the YLS programme, to assess the effectiveness of secondary schools in the state. Our results hold for expensive schools with decentralised hiring in the urban setting. Even though most urban private schools3 fit this description, the match is not exact. We expect our results to hold most strongly for unaided private schools with decentralised hiring. We examine how students’ mathematics scores vary by gender-matching effects and other individual- and household-level characteristics. We find that the gender-interaction effect is consistently positive and significant for private, unaided schools in urban locations.

Policy implications

We have discussed how our findings critically depend on the norms and institutions prevailing in a society and therefore, may not be widely generalisable. However, this may explain why gender-matching effects have been reported consistently in studies on India but not so robustly in certain developed societies where gender norms are more equitable. Our theoretical model also predicts inequality in average learning outcomes between urban and rural areas, which is primarily driven by concentration of female teachers of higher quality in urban private schools.

One possible intervention to reduce this inequality is to provide incentives to female teachers for joining government schools. But, since our results are mostly driven by existing gender norms in society, such incentives may not work and it is difficult to come up with direct policy suggestions based on our research. Nevertheless, we detail a selection mechanism prevailing in the Indian context and therefore, any policy that seeks to restore the gender imbalance in the Indian school education system must consider this mechanism.

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Notes:

  1. Pioneered by Rosenthal and Jacobson (1968).
  2. Descriptive statistics have also been used from previous rounds of the Young Lives Survey (YLS).
  3. Schools in India mainly include i) government schools which fully funded by the government and provide free education to children up to the age of 14, ii) government aided schools which are private schools which receive partial funding from the government, and iii) unaided private schools which receive no government funding and therefore charge considerably higher fees.

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