Informal Social Protection and Poverty: A Case Study In Pakistan
Date
2022
Authors
Mumtaz, Zahid
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Abstract
Many countries in the global south face financial constraints restricting their capacity to provide welfare to their populations. As a result, informal networks such as immediate and extended family and friends, NGOs, and religious organizations meet the welfare needs of a large segment of poor and vulnerable people in such countries through informal social protection. Madrassas are religious schools that have been prevalent in many Muslim countries for centuries. My research in Pakistan shows the importance of these institutions in satisfying the welfare needs of a large segment of the country's poor and vulnerable population. In this research, I compared the informal social protection provided by madrassas with formal social protection offered by the government to households to determine the usefulness of the former.
Owing to the lack of data on informal welfare in Pakistan, I adopted an original multi-stage sampling methodology for data collection for the comparison mentioned above. To collect the data for the study, 570 households were surveyed, and 90 were interviewed in-depth across 14 randomly selected cities of Pakistan based on the multidimensional poverty index. I found that most of the surveyed households were impoverished and facing serious insecurities and vulnerabilities. Unemployment, precarious informal sector jobs such as street vendors, coal mining, agricultural tenants, child labour, the prevalence of infectious diseases, absence of adequate insurance, loss of lives and migration because of conflict and natural disasters were common among the surveyed families. The insecurities made them eligible to receive the benefits of formal welfare by the state. However, a sizeable majority were not receiving these benefits but were aware of such programmes. I found that madrassas are a significant source of welfare for the surveyed families apart from education providers. The benefits received from madrassas included cash assistance to the families in times of need, health treatments, helping in marriage and burial services, and most importantly, madrassas education makes their children employable.The big, collected data was also explored by using an efficient unsupervised machine learning K-means clustering algorithm triangulated with semi-structured interviews to form clusters representing multiple welfare regimes in Pakistan at one point in time. Each cluster exhibit the features of a distinct regime coexisting simultaneously at one point in time. A fifth regime was also found by using secondary data.
I conclude that in a lower-income country such as Pakistan, a large section of the overlooked marginalized groups relies on informal welfare administered by madrassas because the coverage of formal welfare is low- and the-income benefits are inadequate. Meeting the eligibility criteria to receive welfare by the government appears to limit access to formal welfare programmes. The accurate identification of the poor for remains an unaddressed issue. In contrast, beneficiary families consider informal welfare more valuable because it is timely provided, no bureaucratic eligibility criteria are expected to be achieved by beneficiaries, and sufficient support is received to manage their requirements.
By doing so, this study makes the following critical theoretical and empirical contributions: a) the study used existing literature to define and conceptualise informal social protection. The conceptualisation provided a framework that was used to compare informal social protection with formal social protection; b) a unique data collection and analysis methodology is presented for identifying poor and vulnerable households for social policy interventions in a low-income country; c) this methodology also helps identify various welfare regimes present within a country at one point in time; and d) the study highlights the importance of integrating informal with formal actors in social policy-making in low income countries.
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