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Intersectionality-Informed Quantitative Research: An Introduction

U S Mishra

Centre for Development Studies (CDS)

Trivandrum

Outline

The presentation would emphasise on four points Improve survey design and data collection Study intersectional inequalities using indices Test perceptions and associations with

descriptives, regressions and decompositions Build theoretical insights for advanced

econometric analysis

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Point 1: Improve data collection and surveys to allow quantitative research on intersectionality

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Introduction

Quantitative analysis at two levels Descriptive level (tests association) Causal analysis (needs theory)

Generalization of results is sought because Understanding the magnitude and patterns critical Estimates help in resource allocations

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Introduction

Generalization is based on statistical tests of significance

Basic element that lends statistical strength is the number of cases in the sample

The principle of generalization, by design, overlooks cases which are particular or specific (small N of cases)

It will never work in the absence of data

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Introduction

Why quantitative analysis on intersectionality has limited scope in India?

Mainly, data collection in India is governed by policy needs which is largely about the average picture

More importantly, little or no emphasis to integrate research issues in data collection exercise

Consequently, we do not get sufficient sample to use quantitative methods to study intersectionality in detail

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Point 1

What can be done? Most of the surveys are nationwide surveys and they are (uniformly) designed to provide only national and state level averages

This puts the onus on designing state level surveys that can incorporate specific contextual issues and to provide adequate sample for statistical tests

For example, with existing data we cannot study within group differences across OBCs, SCs or STs in a particular state

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Point 2: Use axiomatic properties of inequality indices to document the magnitude, trends and rate of change of intersectional inequalities

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Inequality examination: The purpose

Amartya Sen (1992) Inequality Re-examined, OUP

The idea of equality is confronted by two different types of diversities

The basic heterogeneity of human beings

The multiplicity of variables in terms of which equality can be judged

The heterogeneity of people leads to divergences in the assessment of equality in terms of different variables

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Inequality examination: The purpose Once we have identified the question of

“equality of what?”, then the purpose of inequality examination is to examine the distribution of the concerned variable

Distribution, as such an statistical construct, can have many facets and varied dimensions

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Inequality examination: The purpose Measurement of inequalities is all about defining an “ideal”

distribution and then examining (weighted) departures or deviations from the ideal pattern

Conventional approach to distributional inequalities

Vertical inequality

Horizontal inequality

What do they mean and are these sufficient for understanding of inequality?

How can it be nuanced; both from ‘equity’ as well as ‘policy’ perspective

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Conventional approach

Vertical inequality

Involves ranking of attributes (for eg. income)

Can be expressed as within group + between group inequality

Horizontal inequality

It is concerned with between group inequality

Intrinsic value: unfairness or injustice because these are plainly associated with groups

Instrumental value: can cause undesirable social outcome; conflicts

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Horizontal inequality

Principles to view these inequalities

Representative (for eg. income shares should be matching population share of the social groups)

Subjective ideals (all the social groups should have equal, say monthly income per person)

Philosophical ideals (Rawlsian leximin principle)

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Measuring inequalities

Vertical inequalities

Inter-personal comparisons (Gini-type approach) Individual mean difference (variance-based approach)

Horizontal inequalities

Inter-group comparisons (Group analogue of Gini) Group-mean difference (for eg. Comparing state averages

with the country average)

Desirable axiomatic properties Normalization Monotonocity Continuity Decomposability Transfer sensitivity Replication and Scale Invariance Ease of interpretation

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Beyond conventional approach Why intersectionality?

A person can have multiple identities

Each identity has its own (dis)advantage

There can be people with all plus (+++) and then people with all minus (---) and then combinations of plus and minus (+-+ or -+-)

Each of these categories and individuals have own story

Why plus became plus (but that is not focused)? What is happening to those with a plus-minus combination? Why some groups are the most vulnerable (all minus)?

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Why intersectionality

A prominent example:

Place of residence Social group Gender

Who are more disadvantaged at a descriptive level? (between group aspects)

Rural or Urban? SCST or Others? Men and Women?

What about more complicated within-group aspects?

Within rural areas or urban areas? Are all rural areas equally deprived? Within SCST group or Others group? Do all caste or tribe groups have same issues? Within gender constructs? Do all women face similar disadvantages?

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Why intersectionality

Are we certain our answers are clear (or there is more to it?)

Can a rural man have better education then urban woman?

Do some tribal or caste groups have better education then other groups or even better than non-SCST woman?

Of course, there are intersections and dynamics of inequality cannot be adequately captured from vertical and horizontal perspectives

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Intersections: A simple example R-Rural, U-Urban, M-Male, F-Female, SCST and O-Others

Horizontal group 1 - (R,U) Horizontal group 2 - (M,F) Horizontal group 3 - (SCST,O)

Cartesian product: 2x2x2 = 8 groups RMSCST, RMO, RFSCST, RFO UMSCST, UMO, UFSCST, UFO

Does it make sense to have such grouping? Why?

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Intersections: A simple example R-U represents developmental disparities; M-F represents

gender biases; SCST-O represents societal disadvantages

Most advantaged group: UMO and UFO Most disadvantaged group: RMSCST and RFSCST

What about the remaining intersections?

The middle groups: UMSCST, UFSCST, RFO, RMO

Their status varies and this is captured by what is referred as ‘leveraging’ and ‘leveraging power’

Intersectional inequalities: An illustration

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Child undernutrition: intersectional groups

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Intersectional view: Range of disparities

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Point 2

Why inequality indices? Because of axiomatic properties it is the most easily comprehensible way to summarize the distribution and keep track of changes in inequalities over time

How we define inequality normative is critical

For example, should groups with smaller population share be given: more weight, less weight or equal weight

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Point 3: Apart from simple descriptive and logistic regression; a simple decomposition analysis can be used to understand group differences

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Use the CSDH framework

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Identify the variables from CSDH Health behaviours:

Smoking Alcohol Physical inactivity Diet and nutrition

Physical and social environment Water, sanitation and air quality Housing conditions Infrastructure, transport and urban design Social capital

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Identify the variables from CSDH Gender

Norms and values Economic participation Sexual and reproductive health

Material working conditions, stress Health care coverage, healthcare

infrastructure Social protection coverage and generosity

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Identify the variables from CSDH Social inequities

Social exclusion Income and wealth distribution Education

Sociopolitical contexts Civil rights Employment conditions Governance and public spending priorities Macroeconomic conditions

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Multivariate analysis

One obtains a finer description by standardizing for age, gender and other related variables

It helps to understand the major causes of health inequality

Attempts and identifies causality between health and its various determinants

Simple OLS is descriptive but not an exercise that establishes causality; issues such as ommited variable bias, selection bias and endogeneity has to be controlled for

Sample design, weights, survey area effects all needs attention

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Blinder – Oaxaca decomposition The basic idea is to understand the distribution of health

outcome by a set of factors that vary systematically

Even if we have eliminated a few inequalities it is likely that the determinants are among other factors which are unequally distributed across population subgroups

We can analyse how much of inequality is because of variation within groups and how much is contributed because of differences between groups

Also one can analyse the overall contribution of different factors in total health inequality

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Blinder – Oaxaca decomposition This technique decomposes the gap between

average health outcome of two groups into two major components Endowment effect Coefficient effect (Interaction effect)

For example, poor children may be less healthy not only because they have less access to piped water but also because their parents are less knowledgeable about how to obtain the maximum health benefits from piped water

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Blinder – Oaxaca decomposition

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Blinder – Oaxaca decomposition The results for undernutrition shows strong

endowment disadvantages for rural children Maternal education is important but in rural areas

maternal education is low Income is important but rural areas have low

education Sanitation is important but urban areas have

better sanitation Birth order is important and again rural areas have

more births of higher birth order

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Point 3: A simple decomposition can easily inform you whether endowments matter more or discrimination or differences in effects from same level of endowment

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Point 4: Towards a causal analysis

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Leveraging?

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Gender leverage? Three issues Whether there is a distinct advantage of gender identity

irrespective of group composition?

After controlling for prominent confounding factors, rural males certainly display much better chances of health care than any other intersectional groups.

Is the leverage greater among non-SCST groups than SCST community?

Are males from SCST groups better-off than females from non-SCST group?

Theory: Leveraging (Sen & Iyer 2012) An individual’s position depends both on

The social and economic characteristics of her/his household (wealth, income, caste, ethnicity)

And on her/his own characteristics (gender, age, marital/lifecycle status, assets, income earning) and therefore position within the household

Leveraging occurs as groups use their advantages along some dimensions to compensate for disadvantages along others.

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% non-treatment across groups

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Odds of borrowing/sale of asset for treatment

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Key Findings: Sen & Iyer (2012) Striking similarities between middle groups

(poor men and non-poor women) in terms of rates of treatment, non-treatment, discontinuation and sources of financing

Interesting example of leveraging of gender and economic status that reiterate the dynamics within groups and role of multiple identities

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Point 4: A causal analysis is possible if it is guided by sound theoretical insights or framework. Regression analysis without theoretical basis will remain at a descriptive level only.

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Conclusion

View through the intersectionality lens opens up the entire discourse on equity and calls for nuanced understanding the causal pathways

The examples shown here using an intersectional lens has perhaps emerged with several questions which are often obscured from the conventional view

Why gender has a major influence among poor and why females continue to be deprived in high income settings are some concerns that have emerged only because of an intersectional perspective

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Conclusion

One clear motivation to apply intersectionality is that it can help identify questions and concerns that were relatively unknown or unexpected

But the challenge is to connect the intersectional groups from an instrumental and policy perspective

It has also opened up the entire field of analyzing the middle groups which hitherto has remained a neglected aspect in research on health inequalities