La logística en la distribución comercial: una actividad

The Pakistan Social and Living Standards Measurement Survey (PSLM), published by the Federal Bureau of Statistics – a division of the Ministry of Finance – is a population-

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based survey of socioeconomic and living conditions. The survey is used to monitor the implementation of the Poverty Reduction Strategy Papers (PRSP). The PRSP is a poverty reduction policy blueprint most developing countries are committed to developing, implementing and monitoring in order to remain eligible for foreign aid from

international financial institutions (IFIs), such as the World Bank and the International Monetary Fund (IMF).

According to Pakistan’s PRSP, the country is committed to implementing 16 poverty- reduction targets and monitoring 37 socioeconomic indicators, out of which the PSLM tracks 14 (Ministry of Finance, 2010). These targets are based on the Millennium Development Goals (MDGs), ratified at the United Nations’ (UN) Millennium Summit by all 189 UN member countries, as a means to eradicating poverty across the global South.

PSLM data is collected and published at the district and the provincial level. The

provincial-level is conducted every year while the district-level is conducted every other year. So far, four district-level PSLM’s have been completed and published, including the PSLM 2004-05, PSLM 2006-07, PSLM 2008-09 and PSLM 2010-11.

Since the PMN data is available quarterly, there is a period mismatch between the two surveys. This is dealt with using statistical imputation described later in this section. B. (i) Sampling and survey methodology

The PSLM survey universe includes all urban and rural areas of the four provinces and the country capital, Islamabad, excluding military restricted areas. There are separate sampling frames for urban and rural areas. The urban area frame divides each city and

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town into “enumeration blocks”, each consisting of 200-250 households. Each

enumeration block is further divided into three categories: low, high and middle income (Ministry of Finance, 2010).

The rural frame is based on the last population census conducted in 1998. In the

provinces of Sindh, Punjab and Khyber Pakhtunkhwa (KPK), each district is defined as a strategic unit or stratum to develop the rural sampling frame. For the province of

Balochistan, a larger geographical space, referred to as a “division”, is used to define each stratum, since it is the most sparsely populated province.

The primary sampling unit (PSU) is the village in the case of rural areas and the

enumeration block in the case of urban areas. The secondary sampling unit (SSU) is the household within each primary sampling unit. A total of 16 SSUs from each rural PSU and 12 SSUs from each urban PSU are included in the district-level survey. The following table provides the total number of rural and urban PSUs and SSUs:

Table 3 Breakdown of PSLM Sampling Units

Level Primary Sampling Units Secondary Sampling Units Urban Rural Total Urban Rural Total District 2333 3230 5563 27996 51680 79676 *Source: Federal Bureau of Statistics, Pakistan (2013)

Indicator selection for the loan supply functions is informed by the capability approach, which was reviewed in Section I. The three dimensions of wellbeing used in the model are education, health and living standards.

Since data on life expectancy, child and maternal mortality, the most commonly used indicators of health, is not available through the PSLM, I use the following indicators: the percentage of fully immunized children - based on parent recall and immunization

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records, the percentage of pregnant women that received tetanus shots, and the

percentage of births that took place in a government or private clinic. These indicators are meant to proxy infant and maternal health status.

For measuring educational attainment, the UNDP’s HDI uses mean years of schooling and expected years of schooling, while the other measure of multi-dimensional

deprivation, the MPI, uses average years of schooling and school attendance. Again this data is not available through the PSLM and the following indicators are used instead: the literacy rate for ages ten and older, primary enrollment rates for children between the ages of five and nine, and the percentage of the female population that has ever attended school. While literacy and enrollment rates are no longer considered ideal indicators for measuring actual educational attainment, the issue is data availability. The last indicator serves as an additional measure of gender-based disparities in education – an important dimension of the MDGs.

Finally, for living standards the HDI uses income per capita, while the MPI includes measures of housing conditions and assets. The district-level PSLM does not collect data on income, but uses a subjective measure of economic wellbeing. It also includes data on housing facilities.

The subjective measure is an economic perceptions indicator that asks respondents to measure their economic wellbeing relative to the year before. Responses are based on a five-point scale, with choices including much better, slightly better, the same, slightly worse and much worse. For this paper I add the “slightly worse” and “much worse” categories to create an indicator of subjective deprivation. In addition, I also include four

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housing facilities indicators, including availability of tap water, availability of toilet facilities, availability of oil and gas for cooking purposes and the percentage of electrified households.

Ratio of Urban Population

Using Schreiner’s (2001) suggestion to include rural-urban differences, I also incorporate an urban population ratio into the model. Data for this comes from the 1998 census. Rural poverty in Pakistan is much more severe than urban poverty, which makes this ratio an important one to include in this analysis. Specifically, one-third of all rural households are extremely poor, but only eight percent of all urban households can be categorized as extremely poor (Naveed and Ali, 2012).

The following table provides descriptive statistics for the main variables: Table 4 Descriptive Statistics

Variable Mean Std. Dev. Min. Max.

Average Loan Size - MFBs (in Rupees)

13,119.73 6,494.36 0 53,502 Average Loan Size – MFIs

(in Rupees)

10,836.25 4,634.93 0 37,168 Economic perceptions – worse

+ much worse

0.3554 0.2045 0 1 Percentage of urban population

by district (1998 census) 0.2213 0.1664 0 0.9475

Indicators for Education:

Literacy rate (10 and up) 0.5013 0.1495 0.0625 1 Net primary enrollment (ages:

5-9)

0.5297 0.1522 0 1 Percentage of females ever

attended school

0.3382 0.1826 0 0.81

Indicators for Health:

Percentage children fully immunized – based on record and recall

0.7097 0.2183 0 1

Percentage pregnant women received tetanus shot

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Percentage women delivering baby at private or government clinic

0.3087 0.2017 0 1

Indicators for Housing Utilities:

Percentage homes electrified 0.84245 0.1905 0 1 Percentage homes with tap

water

0.3136 0.2377 0 1 Percentage homes with oil and

gas avail. for cooking

0.22 0.2444 0 1 Percentage homes with toilet 0.7773 0.1819 0.13 1

These indicators are used to develop loan supply functions for MFBs and MFIs, after performing a series of data manipulations, which are described below, but first I review the main limitations of the study related to issues of data availability and quality. C. Data Limitations

Objections can be raised regarding the PMN dataset’s quality. The dataset constitutes self-reported data from each institution and despite the fact that the PMN runs regular quality checks, errors occur often, especially with smaller institutions that do not

maintain the same level of quality control as the larger institutions. Therefore, in order to reduce the level of error, I have spent a considerable amount of time running trends on each institution’s data-series. The staff at the PMN has been extremely helpful and has gone over every outlier that I have pointed out to them and even gone back to check with the institutions in question to make sure the data was as error-free as possible.

Data availability is a secondary, but nevertheless noteworthy issue with the microfinance outreach survey. As pointed out before the best measures of depth of outreach incorporate the borrower’s poverty status but this data is unavailable in the survey.

Another important measure of outreach is interest, but again data on effective interest rates charged by each institution for the districts and time periods covered in this study

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are unavailable for this study. All we know is that the average effective interest rates charged on microcredit are 35.9 percent (Shorebank International, 2011).

As far as the PSLM is concerned, the only issue is data availability. For instance, income per capita is unavailable in the district-level PSLM and a subjective measure of economic wellbeing has to be used in its place.

In addition, data on what are currently considered the ideal measures of health and educational status, such as actual time spent in school and infant and maternal mortality, is unavailable through the district-level PSLM. Instead less than ideal measures have to be used to proxy district-level socioeconomic development.

Finally, I would have liked to incorporate conflict data into the model, since conflict has been a strong determinant of the extent to which microfinance has been able to reach the poor in Pakistan, as detailed in the Political Economy Chapter, but reliable district-level conflict data is hard to find. Currently, the most reliable source is the Armed Conflict and Event Dataset (ACLED), but they only have conflict data on Pakistan for the year 2008- 09, while data for the other years is currently in the process of being verified.

Finally, there are other factors that should have been included in the model had the data on them been available. For instance, distance from the institutional headquarter is an important consideration for selecting the location of a branch, as are cultural

considerations such as the segregation of the sexes and the stigma attached to women’s earnings in the remote districts of Pakistan, particularly Balochistan and KPK. It is a limitation of the model that it does not incorporate these factors.

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of the supply-side dynamics of microfinance outreach. The section below reviews the steps involved in preparing this data for analysis.