Social Identity

What can Question Hour tell us about representation in the Indian Parliament?

  • Blog Post Date 13 January, 2020
  • Print Page

Is a numeric representation of Indian Members of Parliament (MPs) from particular sociological backgrounds important to achieve a substantive representation of their group interests? This article answers this using the content of issues raised by MPs in the Parliament in the last 20 years. It finds that male MPs and non-Muslim MPs are less likely to raise questions concerning women and Indian Muslims, respectively. However, India’s diversity makes proportional representation for all groups a mathematical impossibility.

In a country of over a billion citizens, the Indian Member of Parliament (MP) on an average, represents 2.5 million people (Vaishnav and Hintson 2019). In comparison, other countries barely have a citizen to representative ratio of above 1 million. To emphasise the scale being spoken about, the number of pollution masks distributed in November 2019 at schools in New Delhi, the country’s capital city, was almost equal to the entire population of Norway. Coupled with these large numbers is India’s unmatched regional and cultural diversity, immensely complicating the idea of political representation, that is, who should constitute the elected bodies in the country so that various groups can be adequately represented.

The Indian Parliament also grapples with this question, particularly in the case of representation of women and minorities in the law-making bodies. The low share of female and Muslim MPs in the institution has been a growing cause for concern, so much so that certain political parties have self-mandated the nomination of a minimum share of female candidates in the 2019 Lok Sabha elections to reduce the gender gap in legislative representation. This is despite the fact that neither gender nor religious identities form homogenous groups in India as these identities are largely disparate across states and geographies. The issue of under-representation matters as theory suggests that the representation of a community’s interest in a democracy is linked to the presence of that community in the Parliament in the first place, implying that the interests of a community is better represented by its own members. This may be because experiences of belonging to an identity make these politicians better placed to express the concerns and interests of their group, as effective representation requires a deep understanding of those being represented. On the other hand, some scholars have argued that the identity of the representative matters less than the content of their interventions, and that groups can be represented through actions in the absence of descriptive, or mirror-like, representation.

Hannah Pitkin explores these ideas in her book, ‘The Concept of Representation’, which lays down the approaches to study political representation. She explains that descriptive representation is an aspiration towards an ideal numerical composition of an institution. Here, the attributes of elected representatives would mirror that of the State’s electorate ─ be it along the axes of religion, income, caste, gender, and so on, in a way that would result in a composition similar to a random sample of the population. While this approach can be useful to point out cases of under-representation, it is insufficient to understand how representation is manifested through the actions of elected representatives. That is, politicians can represent communities or issues through their performance and actions that may not solely depend on their characteristic backgrounds and identities.

The question then is, how can we test whether these identities are actually affecting the behaviour and performance of elected representatives in India? And what may be the consequences of representative acts for these communities?

Use of parliamentary questions as data

It is possible to answer these questions by looking at the content of questions raised by MPs during the Question Hour of the Parliament. A unique dataset of questions and tools from computer science help to determine the quantum of issues that find salience in the House. Questions are categorised by using natural language processing algorithms, which extract the questions representing issues pertaining to women and Indian Muslims, raised by MPs in the last 20 years, out of over 300,000 questions asked in the same period. These algorithms automate a laborious task with an accuracy of about 95%, when their performance is compared to the same task carried out by human coders.

The algorithm is tasked with finding the questions about issues raised in the Parliament. Intuitively, this can be done by finding words that may be indicating specific concerns about topics. This is performed using word embeddings, which are based on an artificial neural network algorithm1, which help in discovering the parliamentary questions in which issues pertaining to women and Muslims are being raised.

Testing the link between identity of an MP and raising meaningful issues about a community

In recent research (Bhogale 2018), I explore the link between raising meaningful issues about a community and the identity of the MP by employing a simple regression test using the shortlisted dataset of questions. The results show a significant effect of gender and religion on the content of parliamentary questions that concern the two groups respectively. That is, overall, male MPs are less likely to raise questions concerning women, and non-Muslim MPs are less likely to raise questions about Indian Muslims. These observations are further confirmed by using statistical models2. Interpreting the results, male MPs raise 25% fewer questions than female MPs about women’s issues in the parliament. There is a stronger effect of religion on the content of parliamentary questions, wherein non-Muslim MPs raise 74% fewer questions than Muslim MPs about issues concerning Indian Muslims.

This implies that there are significant and substantial benefits gained from a community’s presence in institutions. The positive consequence of representation has been supported by earlier work on the same subject. Simon Chauchard’s (2014) work on the impact of reservation in village-level institutions shows that such representation affects the psychology of dominant castes ─ that is they affect the norms of interaction and decrease discriminatory behaviour. Similarly, Francesca Jensenius finds that quotas for Scheduled Castes (SCs) improve social justice for members of the SCs by reducing caste-based discrimination. These findings indicate the importance of inclusivity in public institutions.

Notwithstanding these positive outcomes, it is important to address that proportional representation to all the diverse communities in India still remains a mathematical impossibility. There are thousands of threads making up the diverse social fabric of India, and only 543 seats to be filled at the end of every term through direct elections to the national legislature. With each MP representing the interests and views of millions of constituents, it becomes important that MPs focus on providing avenues for their constituents to access them in a way that groups in their constituency can have their interests represented irrespective of the parliamentarian’s identity. It thus becomes incumbent on parliamentarians, once in power, to express concerns and act substantively to benefit their large community in constituencies rather than catering to group interests based on their own identities.

Note:

  1. Simply put, a neural network is any system (biological or artificial) that is able to mimic learning. In the particular case of word embeddings, the idea is to employ artificial neural network algorithms to numerically represent words as vectors, to measure syntactic and semantic word similarities, as proposed by Mikolov et al. (2013).
  2. A simple linear regression and a negative binomial regression is employed to conduct the analysis. The IRR (incidence rate ratios) are computed to interpret the results.

Further Reading

Tags:
gender 
No comments yet
Join the conversation
Captcha Captcha Reload

Comments will be held for moderation. Your contact information will not be made public.

Related content

Sign up to our newsletter