The Multiple Meanings of Prosperity and Poverty in Tanzania

This book focuses heavily on changing patterns in ownership of assets. In other analyses where assets are used to investigate change then one of the standard means of investigating wealth and poverty is to construct asset indices to examine patterns across space and over time. Assets are important to local definitions of poverty and wealth in rural Africa. Yet their use in asset indices can miss locally valued change. The chapter presents data from seventeen villages across Tanzania to explore differences in the meaning of wealth and poverty across the country. Despite limitations in our site selection we found considerable diversity that makes a single asset index difficult to compile. Current abbreviated asset indices risk counting assets that do not matter locally.

Development of a temporally harmonized asset index: evidence from across 50 years of follow up of a birth cohort in Guatemala

Background Asset-based indices are widely-used proxy measures of wealth in low and middle-income countries (LMIC). The stability of these indices within households over time is not known. Methods We develop a harmonized household asset index using Principal Component Analysis for the participants (n = 2392) of INCAP Longitudinal Study, Guatemala using data from six waves of follow-up over the period of 1965–2018. We estimate its cross-sectional association with parental schooling (in 1967–75) and attained schooling (in 2015–18) of cohort members. We study how patterns of cross-sectional loadings change over time and between urban-rural settings. We assess its robustness to omission of assets or study waves and alternate specifications of factor extraction procedure (exploratory factor analysis, multiple correspondence analysis). Results The harmonized index constructed using 8 assets and 11 housing characteristics explained 32.4% of the variance. Most households increased in absolute wealth over time with median wealth (25th percentile, 75th percentile; households) increasing from − 3.74 (− 4.42, − 3.07; 547) in 1967 to 2.08 (1.41, 2.67; 1145) in 2017–18. Ownership of television, electricity, quality of flooring and sanitary installation explained the largest proportion of variance. The index is positively associated with measures of schooling (maternal: r = 0.16; paternal: r = 0.10; attained: r = 0.35, all p < 0.001). In 2015–18, house ownership versus housing characteristics and ownership of electronic goods differentiate households in urban and rural areas respectively. The index is robust for omission of assets or study waves, indicator categorization and factor extraction method. Conclusion A temporally harmonized asset index constructed from consistently administered surveys in a cohort setting over time may allow study of associations of life-course social mobility with human capital outcomes in LMIC contexts. The approach permits exploration of trends in household wealth of the sample over a follow-up period against repeated cross-sectional surveys which permit the estimation of only the mean trajectory.

Development of a temporally harmonized asset index: Evidence from across 50 years of follow up of a birth cohort in Guatemala

Background Asset-based indices are widely-used proxy measures of wealth in low and middle-income countries (LMIC). The stability of these indices within households over time is not known. Methods We develop a harmonized household asset index for the participants (n = 2392) of INCAP Longitudinal Study, Guatemala using data from six waves of follow-up over the period of 1965–2018. We estimate its cross-sectional association with parental schooling (in 1967-75) and attained schooling (in 2015-18) of cohort members. We study how patterns of cross-sectional loadings change over time and between urban-rural settings. We assess its robustness to omission of assets or study waves and alternate specifications of factor extraction procedure. Results The harmonized index created using 8 assets and 11 housing characteristics explained 32.4% of the variance. Most households increased in absolute wealth over time with median wealth (25th percentile, 75th percentile; households) increasing from − 3.74 (-4.42, -3.07; 547) in 1967 to 2.08 (1.41, 2.67; 1145) in 2017-18. Ownership of television, electricity, quality of flooring and sanitary installation explained the largest proportion of variance. The index is positively associated with measures of schooling (maternal: r = 0.16; paternal: r = 0.10; attained: r = 0.35, all p < 0.001). In 2015-18, house ownership versus housing characteristics and ownership of electronic goods differentiate households in urban and rural areas respectively. The index is robust for omission of assets or study waves, indicator categorization and factor extraction method. Conclusion A temporally harmonized asset index administered consistently over time may allow study of associations of life-course social mobility with human capital outcomes in LMIC contexts. Our approach permits exploration of trends in household wealth of the sample over a follow-up period against repeated cross-sectional surveys which permit the estimation of only the mean trajectory.

ÁP DỤNG PHƯƠNG PHÁP NỘI SUY NGHỊCH ĐẢO KHOẢNG CÁCH CÓ TRỌNG SỐ ĐÁNH GIÁ PHÂN BỐ KHÔNG GIAN TÀI SẢN SINH KẾ TẠI TỈNH ĐỒNG THÁP

Việc tính toán các chỉ số sinh kế góp phần nắm bắt sự khác biệt về sinh kế của các hộ nông dân trên một khu vực nghiên cứu nhất định. Tuy nhiên, công tác điều tra sinh kế sẽ bị giới hạn bởi nhiều yếu tố như chi phí, nhân công, khoảng cách khiến cho các điểm điều tra không thể bao trọn cả vùng nghiên cứu. Các phương pháp thống kê không gian mà cụ thể là phương pháp nội suy cho phép tính toán giá trị tại một vị trí thông qua các giá trị tại những vị trí đã biết bao quanh nó. Nghiên cứu áp dụng phương pháp IDW (Inverse Distance Weighting) để tính toán chỉ số tài sản sinh kế LAI (Livelihood Asset Index) cho toàn bộ khu vực gồm 3 huyện Tam Nông, Tháp Mười và Tân Hồng. Kết quả cho thấy, có sự phân bố không đồng đều về các nguồn vốn và chỉ số tài sản sinh kế giữa các xã cũng như các huyện trong khu vực nghiên cứul; đồng thời, còn chứng minh rằng, phương pháp IDW là một công cụ hữu hiệu trong thống kê không gian với độ chính xác cao. Hơn nữa, kết quả của nghiên cứu có thể được dùng để đánh giá hiện trạng sinh kế, góp phần tạo sự liên kết giữa các vùng trong khu vực nghiên cứu và hướng đến phát triển bền vững.

Using asset index to reassess poverty in the Northern States of Malaysia: do the poor stay poor?

PurposeCurrently, Malaysia uses the Poverty Line Income (PLI) to measure poverty. This is because income measurement is the easiest way to collect data, but in its simplicity, it fails to capture the broader meaning and implications of poverty. Asset index is one of the non-monetary poverty measurements which have been established by researchers but not used in measuring poverty in Malaysia. A household might be poor in income, but assets may prevent them from being trapped in poverty.Design/methodology/approachThis study will reassess the poverty of 302 households in the Northern States of Malaysia using the asset index and also the current state of poverty incidence with change under asset index.FindingsThe results show that households in the Northern States of Malaysia are interpreted as being ‘poorer’ when poverty is measured using assets as opposed to income alone. Besides that, poverty incidence of Malay households, households living in urban area and households with middle-aged heads have high poverty incidence, while households with a head of households that is single and highly educated have low poverty incidence. The logistic regression analysis shows that the determinants of poverty incidence based on the asset index are Indian, Penang and Perak State, the age of the head of household, distance to the education centre from home.Originality/valueThis study shows the asset index measurement which have not been done in Malaysia. This will contribute to the improvement of poverty measurement of the country.

Keputusan Anak Sekolah dan Bekerja: Pengaruh Aset Produktif Rumah Tangga di Indonesia

This study aims to look at the effect of productive assets and nonproductive household assets on school children's decisions and work in Indonesia by using ordered probit estimation techniques. The data used in this study are IFLS 2007. There are four school decisions, and work children sorted from the worst conditions to the best, namely children not in school and not working, children working, school and working children, and school children. The Principal Component Analysis (PCA) method calculates the Nonproductive asset index. This study found that productive assets and nonproductive assets of the household allow children to go to school and reduce the likelihood of children to work.