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A number of multidimensional poverty measures that respect the ordinal nature of dimensions have recently been proposed within the counting approach framework. Besides ensuring a reduction in poverty, however, it is important to monitor distributional changes to ensure that poverty reduction has been inclusive in reaching the poorest. Distributional issues are typically captured by adjusting a poverty measure to be sensitive to inequality among the poor. This approach, however, has certain practical and conceptual limitations. It conflicts, for example, with some policy-relevant measurement features, such as the ability to decompose a measure into dimensions post-identification and does not create an appropriate framework for assessing disparity in poverty across population subgroups. In this chapter, we propose and justify the use of a separate decomposable inequality measure – a positive multiple of “variance” – to capture the distribution of deprivations among the poor and to assess disparity in poverty across population subgroups. We demonstrate the applicability of our approach through two contrasting inter-temporal illustrations using Demographic Health Survey data sets for Haiti and India.

Post reform India has generated high economic growth, yet progress in income poverty and many other key development outcomes has been modest. This chapter primarily examines how inclusive economic growth has been in India between 2005–2006 and 2015–2016 in reducing multidimensional poverty captured by the global multidimensional poverty index (MPI). The authors employ a constellation of elasticity and semi-elasticity measures to examine vertical, horizontal as well as dimensional inclusiveness of economic growth. Nationally, the authors estimate that a 1% annual economic growth in India during their study period is associated with an annual reduction in MPI of 1.34%. The association of the national growth to state poverty reduction (horizontal inclusiveness) is, however, not uniform. Some states have been successful in reducing poverty faster than the national average despite slower economic growth between 2005–2005 and 2015–2016; whereas, other states have been less successful to do so despite faster economic growth during the same period. The authors’ analyses and findings show how these tools may be used in practical applications to measure inclusive growth and inform policy.

Most poverty research has explored monetary poverty. This chapter presents and analyzes the global multidimensional poverty index (MPI) estimations for China. Using China Family Panel Studies (CFPS), we find China’s global MPI was 0.035 in 2010 and decreased significantly to 0.017 in 2014. The dimensional composition of MPI suggests that nutrition, education, safe drinking water, and cooking fuel contribute most to overall non-monetary poverty in China. Such analysis is also applied to subgroups, including geographic areas (rural/urban, east/central/west, provinces), as well as social characteristics such as gender of the household heads, age, education level, marital status, household size, migration status, ethnicity, and religion. We find the level and composition of poverty differs significantly across certain subgroups. We also find high levels of mismatch between monetary and multidimensional poverty at the household level, which highlights the importance of using both complementary measures to track progress in eradicating poverty.

There has been a rapid expansion in the literature on the measurement of multidimensional poverty in recent years. This paper focuses on the longitudinal aspects of multidimensional poverty and its link to dynamic income poverty measurement. Using panel household survey data in Vietnam from 2007, 2008, and 2010, the paper analyses the prevalence and dynamics of both multidimensional and monetary poverty from the same dataset. The results show that the monetary poor (or non-poor) are not always multidimensionally poor (or non-poor) – indeed the overlap between the two measures is much less than 50 percent. Additionally, monetary poverty shows faster progress as well as a higher level of fluctuation than multidimensional poverty. We suggest that rapid economic growth as experienced by Vietnam has had a larger and more immediate impact on monetary than on multidimensional poverty.

The human development and capability approach (HDCA) and its associated participatory method is receiving growing attention as a useful conceptual development for comparative international education. HDCA challenges the economism so prevalent in world development thinking and, instead, looks at development as a process of enhancing persons’ incrementally achieved substantive freedoms from deprivations. The centrality of the person replaces the centrality of income growth.

The application of HDCA to the study of the role of education that promotes social justice change is illustrated by using an empowerment-capability framework to the long-term study of the benefits of village schooling for rural girls in western China.

Using HDCA to identify influences on social change, we derive a much more nuanced and valuable multi-dimensional view of human development, which enables us to draw broad implications for more effective policy. National policies should use a multi-dimensional informational base including equality, sustainability, and non-market dimensions of well-being as well as market production.

The distinction between discussing human capital (HC) and its actual measurement is the presence of indices and equations to substantiate the belief of measuring intangibles. The chapter makes a concise mention of research precedents, deriving leads for the foundation of HC. The chapter aims to provide clarity on the concept of HC measurement and bring to light the tools that can confer tangibility to intangibles. It argues that the measurement of HC is an achievable idea; furthering that a systematic review into the inter-disciplinary studies can offer viable solutions to the challenge of measuring intangibles. The chapter while discussing the contention makes a vivid mention of Bhutan’s gross national happiness (GNH), Happiness Seismograph, Cobb–Douglas model and others to make an impression on the minds of the reader.