Monthly Archives: November 2016

Fatima Bi, who are you voting for?

The majority of pollsters got it wrong about Trump, about Brexit and the Modi victory in India in 2014. What gives? How can experienced, smart pollsters get it wrong in three different democratic settings? Besides the obvious need for introspection for liberal politics, there is also a need to understand why polls get it so wrong. I had written earlier of the difficulty of conducting surveys in India, and this is a follow-up to that – here, I reflect a bit more on surveying itself.

I think two aspects are important in thinking about the usefulness of polls in predicting elections. First, social cleavages are far more complex than anyone can imagine. Prediction polls tend to paint people in broad strokes but people are complex beings with multiple identities. And belonging in one group (e.g. white women) does not mean that you subscribe to all views that members of that group are expected to hold (eg. support for Hillary Clinton).

A related dimension is that polls look for outcomes and rarely engage with the why that underlies decision-making.  At different points in time, individuals may exhibit similar behaviour, but they are for entirely different reasons. In the absence of surveys capturing this information (many do not), the we are left with information that captures apparent choices, but not causes. This is significant – during fieldwork, I it puzzling as political relations in two parts of the field site were vastly different, though the party ID of voters was the same. It was only when I was able to move myself away from inquiring about party preference towards understanding why political relations took a particular form that I was able to understand shifts and changes better. I realised that particular material conditions brought about peculiar forms of political relations which allowed voters to switch more easily. Capturing the presence of these conditions helped me predict outcomes better.*

Second, the the existence of social desirability bias and our ability to capture it also needs re-thinking. The existence of the bias itself is unquestionable – we all want to seem better than we are. For example, during fieldwork here in India, voters of the BJP in a Congress dominated area who chose to share their choice with me did so in secret.* Similarly, in the US, in the face of demeaning characterisations for Trump supporters (not just his politics), it is quite likely voters shied away from committing support to seem like somebody else. Surveyors identify shy voters by looking at other behavioural traits – for instance, an inclination to oppose migrants and outsourcing may be seen as tacit support for Trump. But social desirability extends to these as well.

A related issue arises from confirmation bias – it is a tendency for us all to confirm initial hypotheses, beliefs and attitudes. In the US electoral context, Trump started out as a non-serious candidate and later on, he received little support from the GOP. On the other hand, the Democratic campaign was serious, Hillary Clinton had contested in the primaries earlier and it was widely believed she would win. This possibly fuelled pollsters assumptions and combined with people’s desire to appear better and compromised predictive ability.

In sum, surveys are an instrument whose efficacy is complicated both by the surveyor as well as t he surveyed. Lack of knowledge about the why and social desirability bias come together to refract the results.

*This desire for secrecy may be linked to the flows (or perceived flows) of benefits locally – these are often linked to party ID

Address illa -2 : De-monetisation, identity and the migrant poor

In a move to curb black money, counterfeiting, and fight corruption, India de-monetised two currency notes today – the INR500 and INR1000 ones. I rushed about to find these notes to keep (for memories’ sake, you know), debated the silliness of my friend owing me money for what are essentially pieces of paper as I gave her INR 1000, and contemplated how to live cash free in urban India. At some point I even tried to sell the notes I have at higher values as memorabilia. Droll indeed.

The new changes mandate that anyone with these currency notes seeking to have the equivalent value must exchange this in a bank or a post office. The idea is that this will bring dishonesty surrounding these notes out – counterfeiters will not be able to get notes exchanged, and hoarders will be left with piles of useless paper if they don’t whitewash their holdings. Presently hoarded money will possibly come out as a result of this move, but unfortunately, this is a bit like easing constipation –  it is unlikely to prevent being stopped up in the future.

So far, there doesn’t seem to be much that is terribly wrong. Except this – in order to exchange money, you need to show a valid ID to the bank/post office. In the past, I have written about the difficulties the urban poor, particularly migrants, face in getting access to identity documentation. Because so many of them live in shanty towns and slums with no documentation to show for their occupation, they lack ‘address proof’, a crucial step to securing identity documentation. Even Aadhaar, which is reliant on biometric information, requires proof of address.

The absence of ID documentation creates big difficulties for the poorest in accessing the formal banking system for liquidity. This is particularly hard for women, who more often don’t have IDs. Never has the digital divide been so wide. This policy consequently creates the space for intermediaries to emerge. Local leaders who have IDs offer to covert money for the poor in exchange for a fee, which I see already happening in some of the communities I worked in. Brokerage and intermediation are not always bad, but when brokers are able to position themselves in an all or nothing scenario (no cash without ID), they are able to extract greater value for themselves, at the expense of the poor.

A spot of sunshine in this bleak story for poor migrants – many migrant workers in the construction industry, do not receive salaries, but are paid lump sums. This means that the employer (usually the broker/maistry) keeps monthly payments, giving workers a weekly “stipend”, and pays over the balance every few months or so, usually prior to a trip home. In these cases, the money held by migrants is notional, and they can withdraw from their mastery when they desire, hopefully with new INR2000 notes.

Overall, however, the numbers of poor affected by this policy is high as many industries in urban areas are reliant on cheap, undocumented migrant labour from the same or neighbouring states. For a poor migrant, whose savings are usually in the form of high-denomination banknotes stuffed with immense care into a wallet, this move will thus be adverse. Migrants are just one category of individuals who transact (and often save up) in cash – there are plenty of others.

The wide reach of the Aadhaar and the Jan Dhan programmes may minimise some of the difficulties with this move, and people may move to a cashless economy. Until that is established, offer change to your fellow citizens.