DATA COLLECTION AND CONSTRAINTS

After reviewing the literature I realized that only two methods of data collection

were appropriate for my study. These were primary data source and a very

small portion of online questionnaires focusing on 23 Executive District Officers

Education (EDOs) in the province of Sindh. A panel dataset for 23 districts of

Sindh from 2005 to 2010 was compiled for this research. All data series are

annual data.

Data for the study was collected from the Sindh Education Management

Information System (SEMIS). An annual school census (ASC) exists in

Pakistan. Such a census constitutes an important mechanism for the collection

of information on schools so that there is a sound basis for arguing for better

budgets /planning and changing policies. Given the federal structure of the

country, the ASC has primarily been implemented by provinces, with the

federal level playing a coordinating and standards setting role. Following the

devolution of powers to districts after 2001, EMIS cells were established in

districts partly in order to perform the data entry functions of the ASC, and

partly to allow for the utilization of ASC data at the district level to support the

new district functions. Sindh Education Management Information System

(SEMIS) served as the basis for understanding expenditure effectiveness

district wise. The other data source included PSLM Survey (2004-11), Federal

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2011), Government of Pakistan, giving details on household income and the

individual expenditures on education. All the relevant data on education

expenditure is taken from the Auditor General, Sindh and finance department,

government of Sindh. Total developmental expenditures in different sectors of

education were taken from the annual budget and annual development

program, planning and development department and finance department,

government of Sindh.

Our study is limited to public schools rather than both the private and public

schools. Three reasons account for excluding private schools. First, the public

sector provides an overwhelming share of education in Pakistan. Hence limiting

the study to the public sector does not mean that the study is less

representative than it would be if we included the private schools. Second and

the most practical reason was that, we only have education data on the public

sector but not for the private sector. Third, while all public sector schools may

have the objective of providing instructions in academic subjects, and therefore

teaching towards universalizing education and meeting the goals of education

for all (EFA), private schools may have additional objectives, such as religious

education, that public schools may not be concerned with. Finally, because

private schools are free from the directions of federal and provincial education

bureaucracies, they may combine school inputs in ways that are different from

those of the public sector in order to achieve their objectives. Public sector

schools do not have that luxury. This means that the production technology in

public and private schools may be different. Concentrating on public schools

allows us to assume a given underlying production technology for all schools.

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than the Gross Enrolment Rate (GER), it is still not a completely accurate

measure of the number of children who actually attend primary school. Large

enrolment rates measured at the start of the school year can mask non-

attendance and / or dropout later in the school year. Thus, regression results

for NER should be interpreted with some caution. Next, a governance indicator

has not been included in this analysis since a reliable measure of the quality of

governance across districts is difficult to obtain. Lastly, this research would also

have benefited by adding a health variable to explain differences in outcomes

across richer and poorer districts. A healthier population is more likely to invest

in education and some previous cross-country studies (Gupta et al., 2002;

Baldacci et al., 2008) use under-5 child mortality rates as a proxy for the stock

of health capital in a country. However, in Sindh, the availability of data

regarding the health and nutritional status of school students at the district level

is extremely limited, and hence, despite considerable efforts, the health

variable could not be used as an additional explanatory variable.

Serious data constraints have been faced in the course of this study. As

explained earlier, despite the institution of annual school census, significant

system improvements and third party verifications the system still suffers from

issues of inconsistency. Also, the data sets relating to budgets and

expenditures are extremely hard to obtain. Considerable time and effort have

been spent on gathering data from different appropriation accounts. The effect

of various school and teacher characteristics relating to individual districts

(which are very difficult to compare) have somewhat rendered the use of most

of the financial variables considerably weak. When combined with the

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used with caution. Data is taken mainly in whole figures (original unit of

measures) for each variable. However, for the estimation, all the main variables

have been transformed into their natural log transformations, due to which

these variables are interpreted in proportional terms.