Social Identity

Gender-caste intersectionality in discrimination: Do patients care about doctor’s social identity?

  • Blog Post Date 12 January, 2022
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Debayan Pakrashi

Indian Institute of Technology, Kanpur

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Soubhagya Sahoo

Indian Institute of Technology Kanpur

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Yves Zenou

Monash University

Due to widespread prevalence of discrimination based on social identity, India provides a unique setting for studying caste-gender intersectionality in discrimination. Based on a field experiment in Uttar Pradesh, this article shows that when patients prefer male doctors over female doctors, caste-related prejudices can worsen this gender discrimination. Given the increasing share of low-caste professionals in India, this gender-caste intersectionality can exacerbate gender disparities among professionals.

On the 2018 Gender Inequality Index, India ranked 122 out of 162 nations (United Nations Development Program (UNDP), 2019). India has both low rates of female labour force participation (FLFP) and large pay disparities between women and men in India. The FLFP is about 25% in rural regions, and less than 20% in urban areas (Lahoti and Swaminathan 2016), with the average wage of female employees being about 65% of average male wages in 2018-2019 (Chakraborty 2020). Aside from the steps taken to improve women’s political representation, no constitutional mandate or law ensures seats for women in public-sector employment or educational institutions. Only a few states – like Bihar, Gujarat, Madhya Pradesh, and Punjab – have introduced reservations for women in government jobs during the last decade. In terms of educational institutions, the Indian Institutes of Technology (IITs) introduced a reservation of 20% seats for women in 2018 to correct the low levels of female participation in STEM (science, technology, engineering, and mathematics) disciplines. This measure has been quite successful in increasing the share of women, from about 14% of total seats in 2018-19 to 20% in 2020-21.

There exists evidence that lower caste communities continue to be stigmatised and discriminated against (Madheswaran and Attewell 2007, Thorat and Attewell 2007, Siddique 2011, Banerjee et al. 2009, Banerjee et al. 2013, Islam et al. 2018, Islam et al. 2021). In India, caste-based reservation was introduced in 1950 to provide equal opportunities to historically disadvantaged classes such as SCs (Scheduled Castes) and STs (Scheduled Tribes) in public-sector jobs, education, and politics (Deshpande 2013). This was eventually expanded to include OBCs (Other Backward Classes) in public-sector job quotas in the early 1990s, following the Mandal Commission's recommendations. The 93rd Constitutional Amendment also introduced educational quotas for OBCs in 2006. As is evident from Figure 1 below, the caste-based quotas of 7.5% for STs, 15% for SCs, and 27% for OBCs in government-funded educational institutions (Deshpande 2013) have resulted in an increasing share of lower caste students enrolled in various medical science programmes – especially OBCs. This has been possible due to the qualifying scores for admissions being set differently across caste groups (Bertrand et al. 2010).

Figure 1. Share of students enrolled in medical sciences, by caste and year

Notes: (i) Authors’ calculation is based on data from the All-India Survey of Higher Education (AISHE). (ii) This data include students enrolled in all medical science programmes (for example, MBBS (Bachelor of Medicine and Bachelor of Surgery), pharmacy and pharmacology, nursing, etc.).

Our study

In a recent study (Islam et al. 2021), we test whether the coexistence of gender and caste discrimination exacerbates gender inequality. We conducted a randomised field experiment among 3,128 participants across 40 different locations in Kanpur Nagar district of Uttar Pradesh (UP) state, in 2017. As part of the experiment, each participant was invited to register for a health check-up at a mobile health clinic organised by the research team.

The experiment was conducted in multiple stages: first, each participant was randomly assigned to either the female- or male-doctor ‘treatment group’. At the time of registration, each participant was asked to rank four doctors of a particular gender (based on treatment group assigned; that is, each participant was asked to rank either female or male doctors but not a combination of both) but with varying caste-experience combination, from most preferred (rank 1) to least preferred (rank 4). Each doctor had a different caste-experience combination: (1) Upper caste surname and a high level of experience; (2) Lower caste surname and a high level of experience; (3) Upper caste surname and a low level of experience; and (4) Lower caste surname and a low level of experience. The two upper caste surnames belong to the general caste category (GC) while the two lower caste surnames belong to SC, ST, or OBC groups. The two doctors with high level of experience have either 12 years or eight years of experience. In contrast, low level of experience is always four years. Illustration 1 gives an example of four doctors a participant ranked.

Table 1. An example of four doctors a participant was asked to rank

Rank the following four female doctors from the most preferred (1) to the least preferred (4):

Doctor with an upper caste surname (Four years of experience)

Doctor with a lower caste surname (Four years of experience)

Doctor with an upper caste surname (Eight years of experience)

Doctor with a lower caste surname (Eight years of experience)

Note: The four doctors are either all females or all males.

The rankings were incentive compatible as the participants were told that they were more likely to be assigned to the more preferred doctor for the check-up rather than the less preferred doctor. For ease of understanding, we have, however, used the reverse rank to present the results, which is five minus the rank received, that is, higher reverse rank means stronger preference.

Second, the participants completed a brief survey questionnaire, which collected information on their basic demographic, social, and economic characteristics. Participants were then assigned physicians and appointments. Actual health services were delivered at the final stage.

Gender- and caste-based discrimination

When patients statistically discriminate against doctors based on gender, theory predicts that the returns to experience for female doctors are lower than that for male doctors. Figure 2 below shows that female doctors with a low experience level receive a reverse rank of 1.84 on average. In comparison, male doctors with a low experience level receive a reverse rank of 1.79 on average. But, with a high level of experience, male doctors are preferred over female doctors (reverse rank of 3.21 versus 3.16). Thus, the results suggest that female doctors suffer from greater labour market disadvantage as they accumulate more work experience.

Figure 2. Evidence of gender-based discrimination

Notes: (i) Reverse rank is five minus the rank that a doctor receives from a participant. A doctor who receives a higher reverse rank is more preferred. (ii) Error bars represent mean/average ±SEM (standard error of the mean).

On the other hand, there is also evidence of caste-based discrimination. Figure 3 shows that average reverse rank is higher for upper caste doctors compared to lower caste doctors, both at a high level as well as at a low level of experience. Thus, it indicates that lower caste doctors are always discriminated against irrespective of experience.

Figure 3. Evidence of caste-based discrimination

Notes: (i) Reverse rank is five minus the rank of a doctor receives from a participant. A doctor who receives a higher reverse rank is more preferred. (ii) Error bars represent mean/average ±SEM.

Gender-caste Intersectionality

Finally, we examine how the intersectionality between caste and gender affects gender inequality by separately reporting the reverse ranks of doctors by caste, gender, and experience. The left-hand panel of Figure 4 shows that among lower caste doctors, while female doctors and male doctors are similarly preferred at a low level of experience, female doctors are significantly less preferred than male doctors at a high level of experience. On the other hand, the right-hand panel of Figure 4 shows that among upper caste doctors, while female doctors are significantly more preferred than male doctors at a low level of experience, both male and female doctors are equally preferred at a high level of experience. Thus, gender inequality is worse among lower caste doctors than among upper caste doctors due to a lower rate of return to experience, between genders, for lower caste doctors.

Figure 4. Statistical discrimination of doctors based on gender, by caste of doctor

Notes: (i) Reverse rank is five minus the rank of a doctor receives from a participant. A doctor who receives a higher reverse rank is more preferred. (ii) Error bars represent mean/average ±SEM.

Policy implications

Our findings call for greater policy attention to individuals at the intersection of different discriminated groups. The introduction of gender- and caste-based reservations in India has been quite successful in remedying the underrepresentation of women and lower caste individuals in politics and governments. Caste-based reservations in higher education have also successfully improved the representation of those from the backward classes in a range of high-skilled jobs, which have in turn increased their incomes (Bertrand et al. 2010). As the share of low-caste individuals in the medical profession has risen from about 12% in 1999 to above 50% in 2009, younger generations of doctors are more likely to come from a lower caste background. Our findings imply that gender inequality among high-skilled professionals is expected to grow. However, despite caste-based reservations, caste-based discrimination persists, raising the question of whether alternative approaches in the implementation of affirmative action – other than reservations – should be considered (Islam et al. 2018).

An alternative affirmative action strategy to reservations may be to devote more educational resources to better prepare students from underrepresented groups for higher education, to improve their enrolment in medical schools without having to change their admission criteria. This approach may enhance representation while reducing negative stereotypes that women and lower caste groups have lower productivity or provide lower quality services. Enhanced representation and reduced discrimination against women and lower caste groups in high-skilled occupations can encourage competition and improve the overall quality of services.

Further Reading

  • Banerjee, Abhijit, Marianne Bertrand, Saugato Datta and Sendhil Mullainathan (2009), “Labor market discrimination in Delhi: Evidence from a field experiment”, Journal of Comparative Economics, 37(1): 14-27.
  • Banerjee, Abhijit, Esther Duflo, Maitreesh Ghatak and Jeanne Lafortune (2013), “Marry for what? Caste and mate selection in modern India”, American Economic Journal: Microeconomics, 5(2): 33-72. Available here.
  • Bertrand, Marianne, Rema Hanna and Sendhil Mullainathan (2010), “Affirmative action in education: Evidence from engineering college admissions in India”, Journal of Public Economics, 94(1-2): 16-29.
  • Chakraborty, Shiney (2020), “Gender Wage Differential in Public and Private Sectors in India”, The Indian Journal of Labour Economics, 63(3): 765-780. Available here.
  • Deshpande, A (2013), ‘Social justice through affirmative action in India: An assessment”, in J Wicks-Lim and R Pollin (eds.), Capitalism on Trial.
  • Islam, Asad, Debayan Pakrashi, Soubhagya Sahoo, Liang Choon Wang and Yves Zenou (2021), “Gender inequality and caste: Field experimental evidence from India”, Journal of Economic Behavior and Organization, 190: 111-124.
  • Islam, A, D Pakrashi, LC Wang and Y Zenou (2018), ‘Determining the Extent of Statistical Discrimination: Evidence from a field experiment in India’, CEPR Discussion Paper No. 12955. Available here.
  • Islam, Asad, Debayan Pakrashi, Michael Vlassopoulos, Liang Choon Wang (2021), “Stigma and misconceptions in the time of the COVID-19 pandemic: A field experiment in India”, Social Science & Medicine, 278: 113966.
  • Lahoti, Rahul and Hema Swaminathan (2016), “Economic development and women's labor force participation in India”, Feminist Economics, 22(2): 168-195.
  • Madheswaran, S and Paul Attewell (2007), “Caste discrimination in the Indian urban labour market: Evidence from the National Sample Survey”, Economic and Political Weekly, 42(41): 4146-4153.
  • Siddique, Zahra (2011), “Evidence on caste-based discrimination”, Labour Economics, 18: S146-S159.
  • Thorat, Sukhadeo and Paul Attewell (2007), “Legacy of social exclusion: A correspondence study of job discrimination in India”, Economic and Political Weekly, 42(41): 4141-4145.
  • United Nations Development Programme (2019), ‘Human Development Report 2019: Beyond income, beyond averages, beyond today: Inequalities in human development in the 21st century’, Report.
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