Missing Women on the Streets of Delhi

19 Nov

In 1990, Amartya Sen estimated that more than 100 million women were missing in South and West Asia, and China. His NYRB article shed light on sex-discrimination in parts of Asia, highlighting, among other things, pathologies like sex-selective abortion, biases in nutrition, healthcare, and schooling.

We aim to extend that line of inquiry, and shed light on the question: “How many women are missing from a public life?” In particular, we aim to answer **how many women are missing from the streets.**

To estimate ‘missing women,’ we need a baseline. While there are some plausible ‘taste-based’ reasons for the sex ratio on the streets to differ from 50-50, for the current analysis, I assume that in a gender equal society, roughly equal number of men and women are out on the streets. And I attribute any skew to real (and perceived) threat of molestation, violence, harassment, patriarchy (allowing wives, daughters, sisters to go out), discrimination in employment, and similar such things.

Note About Data and Measurement

Of all the people out on the street over the course of a typical day in Delhi, what proportion are women? To answer that, I devised what I thought was a pretty reasonable sampling plan, and a pretty clever data collection strategy see here. Essentially, we would send people at random street locations at random times and ask them to take photos at head height, and then crowd-source counting the total number of people in the picture and the total number of women in the picture.

The data we finally collected in this round bears little resemblance to the original data collection plan. As to why the data collection went off rails, we have nothing to share publicly for now. The map of the places from which we collect data though lays bare the problems.

Data, Scripts, and Analyses are posted here.

Results

The data were collected between 2016-11-12 and 2017-01-11. And between roughly 10 am and 7 pm. In all, we collected nearly 1,958 photos from 196 locations. On average about 81.5% of the people on the street were men. The average proportion of men across various locations was 86.7% which suggests that somewhat busier places have somewhat more women.

Stereotypical Understanding

11 Jul

The paucity of women in Computer Science, Math and Engineering in the US is justly widely lamented. Sometimes, the imbalance is attributed to gender stereotypes. But only a small fraction of men study these fields. And in absolute terms, the proportion of women in these fields is not a great deal lower than the proportion of men. So in some ways, the assertion that these fields are stereotypically male is in itself a misunderstanding.

For greater clarity, a contrived example: Say that the population is split between two similar sized groups, A and B. Say only 1% of Group A members study X, while the proportion of Group B members studying X is 1.5%. This means that 60% of those to study X belong to Group B. Or in more dramatic terms: activity X is stereotypically Group B. However, 98.5% of Group B doesn’t study X. And that number is not a whole lot different from 99%, the percentage of Group A that doesn’t study X.

When people say activity X is stereotypically Group B, many interpret it as ‘activity X is quite popular among X.’ (That is one big stereotype about stereotypes.) That clearly isn’t so. In fact, the difference between the preferences for studying X between Group A and B — as inferred from choices (assuming same choices, utility) — is likely pretty small.

Obliviousness to the point is quite common. For instance, it is behind arguments linking terrorism to Muslims. And Muslims typically respond with a version of the argument laid out above—they note that an overwhelming majority of Muslims are peaceful.

One straightforward conclusion from this exercise is that we may be able to make headway in tackling disciplinary stereotypes by elucidating the point in terms of the difference between p(X|Group A) and p(X| Group B) rather than in terms of p(Group A | X).

(No) Missing daughters of Indian Politicians

29 Jun

Indian politicians get a bad rap. They are thought to be corrupt, inept, and sexist. Here we check whether there is prima facie evidence for sex-selective abortion.

According to data on the Indian Government ‘Archive’, 15th Lok Sabha members (csv) had, in all, 696 sons and 666 daughters for a sex ratio of 957 females to 1000 males. Progeny of members from states with the most skewed sex ratios (Punjab, Haryana, Jammu and Kashmir, and Haryana) had a surprisingly healthy sex ratio of 1245 females to 1000 males. Sex ratios of children of BJP and INC members were 930/1000 and 965/1000 respectively. Rajya Sabha members (csv) had 271 sons and 272 daughters for a sex ratio of 1003 females to 1000 males. Not only was there little evidence of sex-selective abortion, data also suggest that fertility rates were modest. Lok Sabha members had on average 2.5 kids while members of Rajya Sabha had on average 2.2 kids.

Does Children’s Sex Cause Partisanship?

26 May

More women identify themselves as Democrats than as Republicans. The disparity is yet greater among single women. It is possible (perhaps even likely) that this difference in partisan identification is due to (perceived) policy positions of Republicans and Democrats.

Now let’s do a thought experiment: Imagine a couple about to have a kid. Also, assume that the couple doesn’t engage in sex-selection. Two things can happen – the couple can have a son or a daughter. It is possible that having a daughter persuades the parent to change his or her policy preferences towards a direction that is perceived as more congenial to women. It is also possible that having a son has the opposite impact — persuading parents to adopt more male congenial political preferences. Overall, it is possible that gender of the child makes a difference to parents’ policy preferences. With panel data, one can identify both movements. With cross-sectional data, one can only identify the difference between those who had a son, and those who had a daughter.

Let’s test this using cross-sectional data from Jennings and Stoker’s “Study of Political Socialization: Parent-Child Pairs Based on Survey of Youth Panel and Their Offspring, 1997.”

Let’s assume that a couple’s partisan affiliation doesn’t impact the gender of their kid.

The number of kids, however, is determined by personal choice, which in turn may be impacted by ideology, income, etc. For example, it is likely that conservatives have more kids as they are less likely to believe in contraception, etc. This is also supported by the data. (Ideology is a post-treatment variable. This may not matter if the impact of having a daughter is same in magnitude as the impact of having a son, and if there are similar numbers of each across people.)

Hence, one may conceptualize “treatment” as the gender of the kids, conditional on the number of kids.

Understandably, we only study people who have one or more kids.

Conditional on number of kids, the more daughters respondent has, the less likely respondent is to identify herself as a Republican (b = -.342, p < .01) (when dependent variable is curtailed to Republican/Democrat dichotomous variable; the relationship holds—indeed becomes stronger—if the dependent variable is coded as an ordinal trichotomous variable: Republican, Independent, and Democrat, and an ordered multinomial estimated)

Future:

If what we observe is true then we should also see that as party stances evolve, the impact of gender on policy preference of a parent should vary. One should also be able to do this cross-nationally.

Some other findings:

  1. Probability of having a son (limiting to live births in the U.S.) is about .51. This natural rate varies slightly by income. Daughters are more likely to be born among people with lower incomes. However, the effect of income is extremely modest in the U.S. The live birth ratio is marginally rebalanced by the higher child mortality rate among males. As a result, among 0–21, the ratio between men and women is about equal in U.S.

    In the sample, there are significantly more daughters than sons. The female/male ratio is 1.16. This is ‘significantly’ unusual.

  2. If families are less likely to have kids after the birth of a boy, the number of kids will be negatively correlated with proportion sons. Among people with just one kid, the number of sons is indeed greater than number of daughters, though the difference is insignificant. Overall correlation between proportion sons and number of kids is also very low (corr. = -.041).

War and Sex

11 Nov

War is deadly for both sexes. A missile doesn’t differentiate between a man and a woman. Then, what is the role of gender in war?

Nearly all active militaries in the world have substantially more male soldier than female soldiers and far more men die on the battle fields than women. But the impact of wars is never limited to artificial battlefields. War enters civilian life through hunger, inadequate health care, decline in availability of potable water, rape, pillage, and many other ways, reducing life expectancy drastically for both men and women. For example, life expectancy in Afghanistan is 46 years (men), 46 years (women) according to UN figures. The figures hide an important fact that on an average women will generally live longer than men. These figure mean that more women are dying as a result of war than men. These figures still don’t take into account the large number of crimes like rape that are committed predominantly against women.