Economic survey of pakistan 2017-18 pdf free download

On the one hand, Lipton [29] argued that internal remittances exacerbate rural inequalities in India, because they are mainly from high-income villagers. Evidence from Mali and Senegal also suggested that remittances cause rural households to reduce their work effort, thereby reducing the effectiveness of migration as an instrument for poverty reduction [30]. On the other hand, evidence from Mexico indicated that remittances from internal and international migrants have an egalitarian impact on rural income distribution [23].

Recent studies from Thailand and Vietnam also showed that migration reduced the rural income inequality through a balanced distribution of productive assets [31], and had a positive impact on poverty status [7]. Studies that analyzed the impact of migration on the well-being of the migrant households in the destination places concluded that the degree of success of migrant workers depend on human and social capital [32,33], the length of the migration period, the quality of working conditions, and the existence of social networks [34].

Sustainability , 10, 4 of 14 In theory, if migration is successful after several years of migration, then it should have a positive impact on the rural—urban income gap. However, as the World Development Report shows, this is not the case, and Pakistan is still one of the countries with a high rural—urban divide [15].

In Pakistan, most of the analysis on migration used the primary data from population censuses, labor force surveys, and special surveys such as migration and labor force surveys. Several studies have analyzed the patterns and trends of internal and external migration, covering a wide range of issues, including interprovincial and intraprovincial migration [35], determinants of internal migration [36—39], the gender dimensions of internal migration [40], and the impact of migration on development [41].

In addition, studies using official datasets have the disadvantage of not being able to link migrants to their native households, and therefore cannot accurately estimate the impact on household welfare.

In addition, previous studies have encountered problems due to unreliable datasets. This study uses a unique dataset of migrant and non-migrant families in rural areas, and a follow-up survey linking migrants to households in their places of origin.

It aims to provide new insights into the success of migration as a livelihood support strategy for rural households in Punjab, Pakistan. The rest of the paper addresses three main research questions. First, are migrant workers in urban areas better off regarding living and working conditions? Second, what are the determinants of the employment quality of migrant workers?

Materials and Methods 2. Description of the Study Area and Data For data collection, we used a multi-stage random sampling technique. In the second phase, we purposely selected four districts of Punjab, namely Faisalabad, Lahore, Sialkot, and Gujranwala. We chose these districts because of the agricultural bases in the rural areas of these districts and the agriculture-related industries in the neighboring cities. In the third phase, 10 villages in each district were randomly selected.

In the final phase, eight migrant and non-migrant families were randomly selected from each village. These households were randomly selected from a list of migrant and non-migrant families of a village, which was prepared with the help of village leaders called lambardars.

We tried to reduce the potential for interviewer misconduct by ensuring that different interviewers selected the households and conducted interviews. However, we note that according to Bauer [43], a random sampling of households in this way still violates the assumption of equal selection probabilities, which may result in biased expected values of multiple variables.

We defined a migrant household as those from which a former household member had migrated to other places in the past five years, was gone for at least three months, and was living elsewhere at the time of survey. Prior to data collection, the purpose of the research was described to respondents, and a verbal consent was obtained from all of the respondents. Participation in the survey was entirely voluntary. We ensured participants of the anonymity and confidentiality of their responses.

Data was collected by trained enumerators who were graduate students in agricultural economics. A structured survey instrument was used for data collection. Initially, migrant and non-migrant households were surveyed from 40 villages in the four districts. The equal proportion of migrants and non-migrants in the sample represents the existing situation in the sample districts.

However, in this study, we used the household as the unit of analysis, and only interviewed one migrant member during the follow-up survey. The choice of migrant members was based on their close ties with rural families. In Pakistan, families tend to be larger, and older parents are less likely to migrate with their migrating children. In addition, it is not uncommon for a migrating adult to leave his or her children and spouse with their grandparents and siblings.

However, some migrant members permanently shifted to the urban area, their immediate family members were with them, and they did not send money to parents who live with other brothers and sisters.

We excluded these migrant members from the follow-up survey and interviewed only those who had close relationships with their native households in rural areas. Therefore, the survey, by design, compares rural households according to the existence of migrant members, and does not represent urban migrants.

For rural households, we included only those households in the final sample that could also be interviewed in the tracking survey. Therefore, we used data from migrant households in rural areas. Due to the comparative nature of the study, the number of non-migrant households in the final sample was the same. This is done by randomly selecting the number of non-migrant families in a village, which is equal to the number of migrant families with a migrating member available for the follow-up survey.

However, the results of the study remain largely unaffected after inclusion of data on all non-migrant households in the sample. A full set of results is available from the authors upon request. The final sample used for analysis included three categories of respondents: non-migrant and migrant households from rural areas, and migrant members from the tracking survey.

Our data represent only Punjab because of the different rural-to-urban migration rates in the other provinces in the country. Empirical Model 2. Quality of Employment and Its Determinants This study attempts to provide a deeper understanding of how the internal migration, especially rural—urban migration, provides new, better, and more productive employment opportunities for migrants.

More importantly, we investigated all of the important factors that may affect the likelihood of finding better-quality employment opportunities in urban areas, conditional on migration decisions. In doing so, the primary concern is endogeneity, as the decision to migrate and better-quality employment in urban areas may be affected by the correlation of unobservable heterogeneities.

Therefore, using a simple logit or probit model, in this case, may result in inconsistent and biased estimates [32,44]. If one of the independent variables is endogenous, the maximum likelihood estimator through the probit model may also lead to inconsistent estimates.

To overcome these problems, we use an instrumental variable IV probit for a subjective indicator of employment quality, and two-step sequential estimates for the employment quality index, which is an objective measure of the employment quality. In both models, a residual is defined for the equation of the employment quality model, and the IV estimator is used based on the originality of the instrument and this residual. Following Cameron and Trivedi [44] and Amare et al.

We run Equation 2 employment quality indicator on Equation 1 migration decision to assess the impact of migration on the quality of employment. We use two indicators to measure the quality of employment: employment quality proxy and the employment quality index. Employment quality proxy is simply measured by a binary variable. We asked the respondents if their work and living conditions improved after immigration.

If the working and living conditions are improved, the value of the variable is 1; otherwise, it was 0. The employment quality index is created based on various indicators of quality employment. All of these indicators have a binary nature, with a value of 1 indicating a positive response; otherwise, it was 0.

These indicators are as follows: I. Overall better working conditions for migrants compared to the last job II. Improved livelihood conditions since the migration decision III. As compared to previous year, migrants feel better off IV. Written and permanent working contracts V. Migrant reports a stable income VI. Migrant has accumulated savings VIII. Migrant has one or more insurance contracts 2.

The comparison between migrant and non-immigrant households is biased, because the characteristics of the two groups are different. One way to solve this problem is to use a difference-in-difference propensity score matching PSM estimator [7,32]. The PSM estimator constructs a reasonable comparison group by using a list of control variables to match migrant households to non-migrant households.

Our main interest is to assess the average treatment effect migration on the treated rural households with migrants. We use the PSM method, because we cannot simultaneously observe two outcome variables. The primary assumption underlying the matching estimator is the conditional independence assumption, which states that non-migrant households have the same mean outcome as migrant households under a given set of observable variables [32,45]. In our case, the outcome variable is total income of migrant and non-migrant households.

Results This section presents the descriptive and empirical results of this study. In Section 3. Section 3. Descriptive Results The selected descriptive statistics in this section illustrate the important characteristics of migrants and non-migrants that describe the migration process. These results support the underlying hypotheses of this study, and highlight the important variables that are later used for modeling.

Table 1 shows a comparison of migrant and non-migrant characteristics. We noticed a statistically significant difference in the mean of some variables. For instance, the family size of migrant households is smaller than that of non-migrant households, suggesting that smaller families are more likely to have migrant members. The mean age of a migrant household is lower than that of non-migrants, indicating that younger households are more likely to have migrant members.

The mean difference between migrant and non-migrant education is statistically significant, and migrants are more educated, indicating that apparently households with a more educated head are more likely to have migrants.

Table 1 also shows that the average difference in the farm size of migrants and non-migrants is statistically significant, and households with less agricultural land prefer to migrate due to lower agricultural income to meet their livelihood needs. Another important reason behind migration is agriculture and health-related shocks. The results show that migrant households face more agriculture and health-related shocks than non-migrant households.

The access to education and market facilities in the rural areas is another important distinction between migrant and non-migrant households, as it is more difficult for a migrant household to access such facilities. This is why people leave their native places.

Finally, the distance from the city center shows a statistically significant difference between migrant and non-migrant households. Since the average distance of migrant households is smaller than that of non-migrant households, households close to cities tend to move more.

Table 1. Summary statistics of the households by migration status. Family size No. Source: Survey results. Sustainability , 10, 8 of 14 Table 2 lists the percentage distribution of the reasons behind the migration. It is worth noting that most people migrate to urban centers in order to receive higher education because of the lack of educational facilities in rural areas. The second most important reason for migration is the lack of funds and food. People migrate to cities to generate income to meet their basic needs.

The third important reason for the migration is employment, which can also be seen in other categories of responses. People migrate to find better jobs in the city. Other reasons for rural—urban migration include family reunification in urban areas, as well as conflicts and natural disasters in rural areas.

Table 2. Reasons behind migration. Reasons Percent Education The descriptive results on the employment quality proxy and employment quality index are presented in Table 3.

According to the employment quality index, migrants seem to have improved their income and living conditions since they left their village. Data Appraisal Estimates of Sampling Error. The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors.

Although numerous efforts were made during the implementation of the Pakistan Demographic and Health Survey PDHS to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the PDHS is only one of many samples that could have been selected from the same population, using the same design and expected size.

Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. Sampling error is usually measured in terms of the standard error for a particular statistic mean, percentage, etc. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the PDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas.

These programmes use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report. Data Appraisal. Access policy Access authority. Access conditions. To request dataset access, you must first be a registered user of the website. You must then create a new research project request. The request must include a project title and a description of the analysis you propose to perform with the data.

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