Human Development Index-like Small Area Estimates for Africa Computed from IPUMS-International Integrated Census Microdata

Permanyer, Iñaki; Esteve, Albert; Garcı́a, Joan; McCaa, Robert

doi:10.1080/19452829.2014.956300

Cited by 12 publications

(12 citation statements)

References 29 publications

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“…In temporal comparisons, FOD metrics penalise any backsliding in the distribution of outcomes through time. These features of the FOD approach are appealing and reflect the calls for such features by Permanyer et al (2014) and the broader discussions on the 2030 Sustainable Development Goals approved by the UN General Assembly in September 2015.…”

Section: Discussionmentioning

confidence: 76%

Poverty Mapping Based on First‐Order Dominance with an Example from Mozambique

Arndt

Hussain

Salvucci

et al. 2015

J of Intl Development

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We explore a novel first-order dominance (FOD) approach to poverty mapping and compare its properties to small-area estimation. The FOD approach uses census data directly, is straightforward to implement, is multidimensional allowing for a broad conception of welfare and accounts rigorously for welfare distributions in both levels and trends. An application to Mozambique highlights the value of the approach, including its advantages in the monitoring and evaluation of public expenditures. We conclude that the FOD approach to poverty mapping constitutes a useful addition to the toolkit of policy analysts.

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Section: Discussionmentioning

confidence: 76%

Poverty Mapping Based on First‐Order Dominance with an Example from Mozambique

Arndt

Hussain

Salvucci

et al. 2015

J of Intl Development

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“…There have been some earlier attempts to uncover within-country variation in human development along different lines, like income groups 2 , 3 , migrants and nonmigrants 4 , and municipalities 5 , 6 . However, these efforts only produced subnational HDI data for a handful of countries.…”

Section: Background and Summarymentioning

confidence: 99%

The Subnational Human Development Database

2019

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In this paper we describe the Subnational Human Development Database. This database contains for the period 1990-2017 for 1625 regions within 161 countries the national and subnational values of the Subnational Human Development Index (SHDI), for the three dimension indices on the basis of which the SHDI is constructededucation, health and standard of living-, and for the four indicators needed to create the dimension indices-expected years of schooling, mean years of schooling, life expectancy and gross national income per capita. The subnational values of the four indicators were computed using data from statistical offices and from the Area Database of the Global Data Lab, which contains indicators aggregated from household surveys and census datasets. Values for missing years were estimated by interpolation and extrapolation from real data. By normalizing the population-weighted averages of the indicators to their national levels in the UNDP-HDI database, values of the SHDI and its dimension indices were obtained that at national level equal their official versions of the UNDP.

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“…2005; Grimm et al 2008, 2010; Harttgen and Klasen 2011a, 2011b; Permanyer 2013; Permanyer et al. 2015), until now it was not possible to study these differences for more than a handful of countries. Here, we document, for the first time, the levels and trends in within and between‐country variation in human development across the globe.…”

Section: Introductionmentioning

confidence: 99%

Inequality in Human Development across the Globe

Permanyer¹,

Smits²

2020

Population & Development Rev

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The Human Development Index is the world's most famous indicator of the level of development of societies. A disadvantage of this index is however that only national values are available, whereas within many countries huge subnational variation in development exists. We therefore have developed the Subnational Human Development Index (SHDI), which shows within-country variation in human development across the globe. Covering more than 1,600 regions within 161 countries, the SHDI and its underlying dimension indices provide a 10 times higher resolution picture of human development than previously available. The newly observed within-country variation is particularly strong in low-and middle-developed countries. Education disparities explain most SHDI inequality within low-developed countries, and standard of living differences are most important within the more highly developed ones. Strong convergence forces operating both across and within countries have compensated the inequality enhancing force of population growth. These changes will shape the twenty-first century agenda of scientists and policy-makers concerned with global distributive justice. Iñaki Permanyer, Centre d'Estudis Demogràfics (a member of the CERCA Programme /

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Human Development Index-like Small Area Estimates for Africa Computed from IPUMS-International Integrated Census Microdata

Cited by 12 publications

References 29 publications

Poverty Mapping Based on First‐Order Dominance with an Example from Mozambique

Poverty Mapping Based on First‐Order Dominance with an Example from Mozambique

The Subnational Human Development Database

Inequality in Human Development across the Globe

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