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«SUMMARY | APRIL 2 WORKING PAPER No. 030 | May 2015 Religion, Land and Politics: Shrines and Literacy in Punjab, Pakistan Adeel Malik and Rinchan Ali ...»

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Appendix 3: Historical Mapping A key challenge in using historical information to explain variation in current development outcomes is the mapping of geographical boundaries. Over time, tehsil boundaries have undergone periodic changes due to several SUMMARY | APR rounds of administrative restructuring. Our historical data on religious diversity, education, population, rainfall and other development dimensions is available at the level of colonial tehsils. In contrast, data on current development outcomes is available for contemporary tehsil boundaries. Using historical data to predict variation in current development outcomes requires mapping current tehsils onto historical boundaries.

While such mapping has been previously conducted at the district level by Bharadjwaj et al. (forthcoming), this is the first time a mapping has been undertaken at the lowest available level of geographic unit (i.e. tehsil). In order to accomplish this we used a mapping procedure that allowed us to collapse data based on the current boundaries of 115 tehsils into data based on boundaries of the 69 historical (colonial) tehsils. In this section, we will describe the details of our mapping procedure. Our computations in this mapping exercise involve two steps.

The first step is to select a base year—in our case, the 1931 census year—that will serve as the reference point for consistently calculating variables across the same tehsil boundaries. The 1931 census year is our preferred reference point, since it was the first census year prior to the 1947 partition of India when reliable data was consistently made available for all historical tehsils of Punjab. Our second step is to identify all the administrative restructurings that took place in each tehsil from 1931 to 1998, which is the most recent census year for Pakistan. In fact, the MICS data on current development outcomes corresponds to 2008, but tehsil boundaries in 2008 are, for the most part, similar to those in 1998 census.

Essentially, there were two main types of administrative restructuring. Both of these are explained below with the aid of relevant examples.

TYPE 1 The first kind of restructuring corresponds to cases when a tehsil in 1931, or any of the later census years, was split into two or more smaller tehsils during the period leading up to 1998. In such cases, we first identified all instances of administrative restructuring from 1931 to the latest census year. Then, starting from 1931, as the number of tehsils increased, we collapsed them to their previous census round. For example, if, in between two census rounds, tehsil Z was split into tehsils X and Y we used the following formula to make tehsil data comparable

over different rounds:

[(No. of literates for tehsil X) + (No. of literates for tehsil Y)] in the latter round = [No. of literates for tehsil Z] in the earlier round Example We select one district, Muzaffargarh, to illustrate this type of mapping exercise. We need to consider four tehsils of Muzaffargarh in 1931 (Muzaffargarh, Alipur, Kot Addu) and then map the administrative restructuring through subsequent census rounds: 1951, 1961, 1972, 1981, and 1998. Tehsil boundaries for Muzaffargarh did not change until 1981. However, between 1981 and 2008, a new tehsil of Jatoi was carved out of the pre-existing Alipur tehsil. Similarly, the old Leiah tehsil was divided up into three tehsils that included a smaller Leiah tehsil and two new tehsils, Kahror and Chaubara.

We use this information on administrative restructuring to compute variables that are geographically comparable over time. For example, for all the tehsils of Muzaffargarh, we compute the current number of literates at their

historical tehsil boundaries in the following manner:

Muzaffargarh: 2008 No. of Literates at the 1931 tehsil level = 2008 No. of Literates Alipur: 2008 No. of Literates at the 1931 tehsil level = 2008 No. of Literates + 2008 No. of Literates for Jatoi Kot Addu: 2008 No. of Literates at the 1931 tehsil level = 2008 No. of Literates Leiah: 2008 No. of Literates at the 1931 tehsil level for Leiah = 2008 No. of Literates + 2008 No. of Literates for Kahror + 2008 No. of Literates for Chaubara TYPE 2 The second type of restructuring is relatively more complicated. It involved instances where parts of two or more tehsils in 1931—or any of the later census years—were combined to form a new tehsil between any two census

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[(Area given to X from tehsil 2/total area of X)*(data for X) + (data for tehsil 2)] in the later round = [data for tehsil 2] in the earlier round...

[(Area given to X from tehsil n/total area of X)*(data for X) + (data for tehsil n)] in the later round = [data for tehsil n] in the earlier round where N=1,2,....n are the pre-existing tehsils from which areas were taken to form new tehsil X.


We use an illustration from Rawalpindi to describe this. In 1931 the Rawalpindi district consisted of four tehsils:

Rawalpindi, Gujar Khan, Murree and Kahuta. Tehsil boundaries did not change until 1981. Between 1981 and 2008, administrative boundaries of Rawalpindi tehsils were altered in the following way. A new tehsil of Kotli Satian was formed by combining portions of the previous Murree and Kahuta tehsils. Another tehsil of Taxila was formed by taking out a portion of the pre-existing Rawalpindi tehsil.

Again, we utilize this information on administrative restructuring to compute the current number of literates at

their historical tehsil boundaries. The following example will illustrate the mapping procedure set out above:

Rawalpindi: 2008 No. of literates at the 1931 tehsil level = 2008 No. of literates + 2008 No. of literates for Taxila Gujar Khan: 2008 No. of Literates at the 1931 tehsil level = 2008 No. of Literates Kahuta: 2008 No. of Literates at the 1931 tehsil level = 2008 No. of Literates + (Area given to Kotli Sattian from Kahuta/Total area of Kotli Sattian) x 2008 No. of literates in Kotli Sattian Murree: 2008 No. of Literates at the 1931 tehsil level = 2008 No. of Literates + (Area given to Kotli Sattian from Murree/Total area of Kotli Sattian) x 2008 No. of literates in Kotli Sattian We repeat this procedure for administrative restructuring between any two census years. While this appeared to us as an eminently feasible mapping strategy it is not without limitations. One pitfall of this mapping strategy is that it assumes a direct correspondence between the proportion of area and the proportion of literates. It assumes that development outcomes are uniformly distributed across different areas of a tehsil. If, for example, development outcomes are more unevenly distributed in urban areas then the bias induced by our mapping procedure will be stronger. Unfortunately, we don’t have data capturing such within tehsil variation. In the absence of other feasible means of aggregation, this is an imperfect but the best available option.

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Shrines and Physical Access to Schools We rely on three categories of physical access provided by the MICS database, all based on distance from the nearest school. Physical access is closer if the nearest school is situated less than two kilometres away from the surveyed household. Schools that are located between 2 and 5 kilometres away are relatively more distant. Finally, schools that are more than five kilometres away are the farthest. We explore variation in these categories across two metrics: gender (boy versus girl) and provider (public versus private). Main results for these specifications are presented above. Apart from the main variables, each specification contains the natural log of population and the full range of geographic controls.94 The population variable is included to account for the fact that more populous regions are likely to have higher levels of school provision.

Firstly, we consider models measuring physical access to government schools. Results are presented only for categories for which our main explanatory variable (shrines per capita) is statistically significant. As documented in column (1), a greater concentration of riverine shrines is associated with provision of government schools that are SUMMARY | APRIL 2 relatively more distant (2-5 km). A similar result is obtained for public provision of schools for girls. Prima facie, when shrines are concentrated in tehsils more proximate to river, physical access to girls’ schools is relatively more difficult (see column 2). The shrine variable lacks explanatory power, however, for the two extreme categories: boys less than two and boys and girls more than five kilometers away (results not reported in Table A4). However, tehsils with riverine shrines tend to have a lower provision of more accessible government schools for girls (i.e. those less than 2 km away).

We next turn to models for access to private schools. Results for these are reported in columns (4) and (5).

Evaluated at the mean distance from river, the shrines per capita indicator is positively associated with measures of physical access to private schools relatively more distant. Specifically, greater concentration of shrines in riverine tehsils is thus associated with a relatively defective provision of private schools, both for boys and girls. Although this is consistent with the pattern observed for access to government schools, private school provision can encapsulate a variety of other effects, such as income and demand for schooling. The results are robust to the inclusion of a variety of geographic controls and the population variable. 95 Interestingly, our land inequality measure is a not a significant correlate of physical access of schools in any of the reported models. It is, however, more strongly correlated with access measures denoting more distant provision, i.e., schools situated more than 5 km away (results not reported).


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ifprihomeoffice | ifpri@cgiar.org This publication has been prepared as an output for the Pakistan Strategy Support Program, funded by USAID. It has not been peer reviewed. Any opinions stated herein are those of the author(s) and are not necessarily representative of or endorsed by the International Food Policy Research Institute.

Copyright © 2015, International Food Policy Research Institute. All rights reserved. To obtain permission to republish, contact ifpri-copyright@cgiar.org.

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