4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Understanding multidimensional poverty in pakistan: implications for regional and demographic-specific policies

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This study enriches the limited literature on multidimensional poverty by focusing on household demographic characteristics as determinants of household-specific living arrangements in Pakistan. The study employs the Alkire and Foster methodology to measure the multidimensional poverty index (MPI) using data drawn from the latest available nationally representative Household Integrated Economic Survey (HIES 2018-19). The analysis investigates multidimensional poverty levels among households in Pakistan according to various criteria (such as access to education and healthcare, basic living standards, and monetary status) and how they differ across Pakistan’s regions and provinces. The results indicate that 22% of Pakistanis are multidimensionally poor in terms of health, education, basic living standards, and monetary status; and that multidimensional poverty is more common in rural areas and Balochistan. Furthermore, the logistic regression results show that households with more working-age people, employed women, and employed young people are less likely to be poor, whereas households with more dependents and children are more likely to be poor. This study recommends policies for addressing poverty that consider the needs of multidimensionally poor Pakistani households in various regions and with various demographic characteristics.

          Related collections

          Most cited references66

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Understanding logistic regression analysis

          Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            A Class of Decomposable Poverty Measures

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Measuring Acute Poverty in the Developing World: Robustness and Scope of the Multidimensional Poverty Index

                Bookmark

                Author and article information

                Journal
                Environmental Science and Pollution Research
                Environ Sci Pollut Res
                Springer Science and Business Media LLC
                1614-7499
                June 06 2023
                Article
                10.1007/s11356-023-28026-6
                f0e2c3e2-3aaa-417f-b545-3abff6a2a323
                © 2023

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

                History

                Comments

                Comment on this article