The Politics of “Data”

"Whatever you can rightly say about India, the opposite is also true" — Joan Robinson, economist

This is true of data in India too. Because data has to be interpreted. Especially in India. The act of interpretation is politics. It can be partisan, deployed to serve one's own side. Or it can try to be "apolitical" in the public interest. Or it can be the politics of public interest, parties and representatives competing on governance and on better lives for our people. The latter requires using data as a starting point for conversations and action.

A low unemployment rate looks like a healthy job market. A district full of small businesses looks enterprising. More young people at work looks like our country reaping its demographic dividend. Read this way, the numbers are promising. But they could also mean the opposite of what they first suggest. Consider five such confounding statistics.

The unemployment rate. A low rate looks like a working economy. But where people cannot afford to sit idle, it can mean the reverse: everyone is absorbed into subsistence and family work, and the rate is low only because no one has the luxury to be unemployed. Whether it reads as health or as hidden distress depends on what those counted as working actually do, the nature of their job (regular, casual), their wages and so on.

Young people not in work, education or training. A high share of young people out of work, education or training is a generation disengaged from productive pathways. A low share looks like a generation being busy and productive. But in the poorest district, this falls precisely because everyone, young women included, is pulled into unpaid or distress work, while in more affluent districts it can rise as young people opt out of work which does not meet their aspirations. The same low NEET statistic can mark the most deprived district or a thriving one.

The number of enterprises. A district thick with small businesses looks entrepreneurial. But most may be one-person stalls and household units too small to employ anyone beyond the owner, surviving rather than growing. Whether this is a living economy or the absence of any better option is a question that requires a closer look and local knowledge.

The number of schools. Two schools count the same in any tally. But one may be a full school and the other a single teacher holding several grades together in one room. This may perhaps be the right administrative answer for a remote hamlet and the wrong one where a proper school should stand. The count tells you little; the place tells you everything.

Women in the workforce. More women working looks like progress, and often it is. But a rise can equally mean women drawn into unpaid family labour and farm work because the household needs the income, not because new opportunities opened. Which one it is turns on where the women are going, and that is apparent only with a deeper look at other corresponding factors to get the complete picture.

Each of these examples illustrates the same point. A number rarely stands on its own. Its meaning comes from what lies behind it, and what lies behind it changes from one place to the next. The data can tell you where to look. It cannot, by itself, tell you what you are looking at.

Where a number can point two ways at once, we have kept it out of the score and shown it only as evidence. The data carries real information. The knowledge to read, interpret and act on it is primarily local.

That, in fact, is the whole premise of representation in a democracy: that the representative carries the texture of a place, its particular constraints and its particular possibilities, into the decisions made about it.

So this is how the work is meant to be used. The numbers, and the questions they raise, belong in the hands of those who hold the local context, to be read and acted upon together with them. Not because the data is weak, but because reading it well is a local act, and a democratic one.