The importance of qualitative research

In a previous blog post, I discussed the content of Tariq Thachil’s book “Elite Parties, poor voters. How social services win votes in India?” In this book, he tries to understand how the Hindu nationalist Bharatiya Janata Party (BJP), identified as an elite party, massively gained votes from marginal and poor citizens in the national elections of 2004 compared to the national elections of 1996. This party has been so successful among poor voters that the party’s national leader is the current prime minister of India since 2014.

In this blog post, I want to zoom on a particular chapter (chapter 5), which is a nice illustration of how qualitative and quantitative tools can be used as complements to each other to answer a question.

Chapter 5 is entitled “How services win votes”. The goal of Tariq Thachil in this chapter is to pin down the mechanisms underlying the observed correlation between votes for BJP and services to the poor. To do so, he focuses on a specific State, Chhattisgarh, where there was a surge in services targeted to the poor as well as a surge in the vote share for the BJP. Chhattisgarh has a very large tribal marginalized community (31.8 % of the total population), which has historically largely voted for the Congress, the party of Independence.

To understand if the observed increase in votes for the BJP is a consequence of the increase of services to the poor, Tariq Thachil divides the big question “how services win votes” into several logical subquestions.

1) He first tries to understand how the services to the poor by an organization that is related to a political party got accepted by the tribal villagers.

2) He then explores how the tribal villagers moved from acceptance to use of the services.

3) Then he underlines the strategy used by the organization to translate the use of services into votes.

4) He finally shows that tribal people that benefited from the services offered by the organization tend to vote more for the BJP, as well as other people in the villages where the organization was providing services.

While point 4 is a fact that can be “easily” observed using survey data, points 1 to 3 are mechanisms or “channels” that are hard to identify without fieldwork. To understand point 1, Tariq Thachil interviewed several activists from the organization, who revealed that they were introducing themselves as “ideologically neutral service providers”, so that villagers would accept their presence in the village. To answer question 2, he asked villagers what was different in the services provided by the organization compared to public services. For schooling services they answered that the infrastructures were not as good as the public schools, that the cost was higher, but that the quality of education was much better. In particular, the teachers were present all the time, and they were focused on transmitting knowledge to the kids. Finally, to pin down point 3, he again interviewed activists, who detailed their strategy to convince people to vote for the BJP. They explained that thanks to their provision of services they had acquired a high status among villagers, and villagers would listen to their advice. So they would organize meetings where they would voice their opinion about politics, not as represents of the BJP, but as “non-political individual citizens”.

This book is not only interesting because it provides convincing answers to a puzzle, namely “why poor people vote for an elite party”, but also because it illustrates how several research tools can be complementary to each other. In this context, Tariq Thachil would not have been able to properly answer the question without a mix of fieldwork and econometric analysis.  Mixing fieldwork and econometric analysis enables him to go beyond mere correlations between service use and votes. In fact, the observed fact that service users vote more for the BJP does not mean that services have an impact on votes. The correlation could be driven by unobserved characteristics – BJP voters have different unobserved characteristics and these characteristics also impact the probability to use services- or by reversal causality: BJP voters could enroll more into services provided by organization that are close to the BJP. This mix usage of methodologies is particularly important because the observed fact (poor people voting for the BJP) is counter-intuitive. As economists, we do not always have the opportunity or the institutional incentives to gather first-hand qualitative observations. It is why, for some questions, collaboration with researchers from other fields, such as political scientists, sociologists, historians or anthropologists can help figuring out the mechanisms underlying observed facts.

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