I am back in Lucknow since a week or so, and this time concentrate on economic questions - both those relating to real estate (see here and here) and to the scandal of poverty. Both issues are frequently linked, of course, since many people claim that Muslims could not find housing in new Lucknow because they are by and large poor (see my earlier discussion of residential segregation). As a quantifiable basis to discuss this proposition, I had analysed data from the Public Distribution System (PDS) before my departure. The key finding: Muslims in Lucknow are poor - but not poorer than their non-Muslim counterparts.

In last week's post, I began to introduce Lucknow's real estate market in an attempt to unearth the story behind the city's residential segregation along religious lines. I have shown where Muslims achieve higher and where lower prices than non-Muslims for comparable property (importantly, as one commentator pointed out by email, adopting a seller's perspective -- for sellers, higher prices are good, while for buyers, lower ones would be. I am currently wrapping my head around how this impacts my findings). Today, I want to volunteer one explanation for why this price difference (still firmly from a sellers perspective) might be as it is: it has to do with social networks and proximity - and with the uneven opportunities in colluding with the "actually existing state".

Pretty much a year ago, I blogged about residential segregation of Lucknow's Muslims. There were three prominent explanations for why Muslims tend not to live in newer parts of the city, and if so, then more segregated: a) they do not want to because they prefer old-city conviviality, b) they cannot afford to because they are poor, and c) they are not allowed to, i.e. discriminated against in the housing market. All three explanations have important implications for my overall interest in Muslim belonging. But which is the most likely?

As one comment back then pointed out, data on real estate would be key to sort this out. I now have that data, and will attempt to solve the riddle in two posts. Today, I will give an overview of Lucknow's real estate market, while taking a closer look at the local state's involvement next week. This is all quite experimental still, and I would be very interested in your comments (if you are interested in a more extensive analysis, please drop me a line)!

The basis for my analysis is data from Lucknow's Property Index Register, which records all registered property sales since 2006, more than 250.000 transactions. As a first step, have a look at the following map (larger version), which shows the average sales prices per square meter over this period:

A few weeks ago, I wrote about additional estimates for the accuracy of my namematching algorithm, and also commented once more on the test corpus of names used to establish these. In the meantime, Gilles let me access his dataset of the social profiles of all MLAs in Uttar Pradesh since independence in full, i.e. including a manually coded variable for religion (thanks again!). Using his data, I was able to alleviate some potential biases in my original test corpus (particularly in terms of non-Muslim names), making my accuracy estimates more robust still.

The new test corpus consists of three raw name lists: the Haj Qurrah for 2012 (which by law only includes Muslim names, and should be fairly representative of those, as argued earlier), the undergraduate admissions list of Lucknow University under SC quota (which by law excludes Muslims, but has a bias towards lower economic strate of non-Muslims as well towards the young), and finally the names of all MLAs since independence (both Muslim and non-Muslim, and arguably with a bias towards higher economic strata as well as older people). The former two lists provide names and father names, the latter has name and gender. In the overall corpus, the ratio of Muslims to non Muslims is roughly 50:50 (since the Qurrah is fairly extensive); the following figures weighed the corpus to reflect the religious demographic of UP (which does not affect sensitivity and specificity, but renders predictive values more meaningful).

Today, I follow up on my initial post on names ("What's in a name?"), which later inspired the map of Muslim Lucknow and my ongoing election analyses. The key idea back then was: if micro-level datasets on religion are unavailable, can we not create our own by making informed guesses about the religion of registered voters - lists of which are readily available? This methodology and its surprisingly high accuracy created quite some excitement over the last months, and a "research note" on it is on the way to publication (here). It thus seems to be about time to clarify the limits of this strategy: what is not in a name?

One thing that is not - or at least not clearly enough - is sectarian affiliation. Quite some people who got excited about my earlier posts asked whether the same strategy would also work to separate Shia and Sunni based on their names. This would open interesting analyses in the case of Lucknow in particular (see here), but I honestly did not think it would fly. People insisted, so I gave it a shot - which by and large confirmed my hesitation: inferring sectarian belonging from names is frought with difficulties. That much is clearly not in a name.