Rent‐Imputation for Welfare Measurement: A Review of Methodologies and Empirical Findings

Balcázar, Carlos Felipe; Ceriani, Lidia; Olivieri, Sergio; Ranzani, Marco

doi:10.1111/roiw.12312

Cited by 20 publications

(12 citation statements)

References 67 publications

(78 reference statements)

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“…Ideally, a consumption aggregate should capture consumption across a broad range of categories, such as that proposed by the Classification of Individual Consumption according to Purpose (COICOP), published by the UN Statistics Division, including the use value of durables and the value of the flow of services that the household receives from occupying its dwelling. 22,23 We did not attempt to reconstruct a consumption aggregate for our datapoints, which would be a massive undertaking, but rather we relied on datasets for which an aggregate already exists.…”

Section: Ability To Pay Defined As Total Consumption or Incomementioning

confidence: 99%

Progress on catastrophic health spending in 133 countries: a retrospective observational study

Wagstaff

Flores

Hsu

et al. 2018

The Lancet Global Health

426

470

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Section: Ability To Pay Defined As Total Consumption or Incomementioning

confidence: 99%

Progress on catastrophic health spending in 133 countries: a retrospective observational study

Wagstaff

Flores

Hsu

et al. 2018

The Lancet Global Health

426

470

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show abstract

“…The second part of imputations concerns imputed rents, which are included in SILC but are not covered by the HFCS. To do so, we follow the capital market approach (Balcazar, Ceriani, Olivieri, & Ranzani, 2014). The HFCS provides information on the current value of an owned dwelling 5 and of outstanding mortgages, from which the net value of the residence is derived.…”

Section: Imputation Of Additional Variablesmentioning

confidence: 99%

Distributional National Accounts (DINA) with Household Survey Data: Methodology and Results for European Countries

Ederer

Humer

Jestl

et al. 2021

Review of Income and Wealth

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The paper builds Distributional National Accounts (DINA) using household survey data. We present a transparent and reproducible methodology to construct DINA whenever administrative tax data are not available for research and apply it to various European countries. By doing so, we build synthetic microdata files which cover the entire distribution, include all income components individually aligned to national accounts, and preserve the detailed socioeconomic information available in the surveys. The methodology uses harmonised and publicly available data sources (SILC, HFCS) and provides highly comparable results. We discuss the methodological steps and their impact on the income distribution. In particular, we highlight the effects of imputations and the adjustment of the variables to national accounts totals. Furthermore, we compare different income concepts of both the DINA and EG-DNA approach of the OECD in a consistent way. Our results confirm that constructing DINA is crucial to get a better picture of the income distribution. Our methodology is well suited to build synthetic microdata files which can be used for policy evaluation like social impact analysis and microsimulation.

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“…Implicit rental values (i.e. values obtained by asking how much households would have paid if they were renting the house) and hedonic pricing (using econometric model to predict house values) are common approaches used to estimate the value for self-owned housing service in the World Banks's Living Standards Measurement Studies [4,6,7].…”

Section: Plos Onementioning

confidence: 99%

Predicting the rental value of houses in household surveys in Tanzania, Uganda and Malawi: Evaluations of hedonic pricing and machine learning approaches

2021

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Housing value is a major component of the aggregate expenditure used in the analyses of welfare status of households in the development economics literature. Therefore, an accurate estimation of housing services is important to obtain the value of housing in household surveys. Data show that a significant proportion of households in a typical Living Standard Measurement Survey (LSMS), adopted by the Word Bank and others, are self-owned. The standard approach to predict the housing value for such surveys is based on the rental cost of the house. A hedonic pricing applying an Ordinary Least Squares (OLS) method is normally used to predict rental values. The literature shows that Machine Learning (ML) methods, shown to uncover generalizable patterns based on a given data, have better predictive power over OLS applied in other valuation exercises. We examined whether or not a class of ML methods (e.g. Ridge, LASSO, Tree, Bagging, Random Forest, and Boosting) provided superior prediction of rental value of housing over OLS methods accounting for spatial autocorrelations using household level survey data from Uganda, Tanzania, and Malawi, across multiple years. Our results showed that the Machine Learning methods (Boosting, Bagging, Forest, Ridge and LASSO) are the best models in predicting house values using out-of-sample data set for all the countries and all the years. On the other hand, Tree regression underperformed relative to the various OLS models, over the same data sets. With the availability of abundant data and better computing power, ML methods provide viable alternative to predicting housing values in household surveys.

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Rent‐Imputation for Welfare Measurement: A Review of Methodologies and Empirical Findings

Cited by 20 publications

References 67 publications

Progress on catastrophic health spending in 133 countries: a retrospective observational study

Progress on catastrophic health spending in 133 countries: a retrospective observational study

Distributional National Accounts (DINA) with Household Survey Data: Methodology and Results for European Countries

Predicting the rental value of houses in household surveys in Tanzania, Uganda and Malawi: Evaluations of hedonic pricing and machine learning approaches

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