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Research Detail

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Muhammad Tareq
Department of Statistics, Jagannath University, Dhaka, Bangladesh

Assim Ibrahim Abdel-Razzaq
Accounting & Finance, Prince Mohammad Bin Fahd University, Saudi Arabia

Md Arafat Rahman
Macquarie Business School, Macquarie University, New South Wales, Australia

Tonmoy Choudhury
School of Business and Law, Edith Cowan University, Australia

Due to some of the limitations of monetary measures, various non-monetary approaches for assessing household wealth have been developed as alternative tools for classifying household socio-economic status. Among them, wealth indices based on household durable assets are being used. The literature revealed that two basic methods of constructing wealth indices are employed: an unweighted method, where assets are weighted equally; and a weighted method, where specific weights are assigned to assets. In the case of using the weighted method, weighting can be assigned using various techniques. The overall objective of the study is to compare the wealth indices constructed by using weighted and unweighted methods for assessing the socio-economic status of households in rural Bangladesh. Firstly, the study attempts to construct wealth indices based on durable assets using the unweighted method and two techniques of the weighted method: weighted index using the inverse of proportion, and weighted index using principal component analysis (PCA). Following this, the study compares some distributional characteristics of these indices as well as monetary indicators. At the same time, the study evaluates and examines some attractive properties of these indices such as the extent of clumping and truncation, consistency with traditional monetary measures. Comparative analysis revealed that the unweighted asset index, as well as weighted asset index using PCA, can be treated as an efficient alternative to the monetary measures to evaluate the living standard of the households in the present study. However, due to some advantage's asset index using PCA can be considered to be somewhat better than the unweighted index. But, as the unweighted asset index is not very different from the weighted asset index using PCA, it can also be used as an alternative to the monetary measures without the need to use weighting.

  Principal component analysis, PCA, Asset index, Socio-economic position
  In Bangladesh
  
  
  Socio-economic and Policy
  Socio-economic status

The specific objectives are: * To construct wealth indices based on household durable assets by using the unweighted method and two techniques of weighted method * To compare some distributional characteristics of these indices as well as of traditional monetary indicators * To assess the extent of clumping and truncation of these indices using graphical presentations * To measure the consistency of these indices with monetary indicators numerically and graphically.

This section describes the materials and methods used in the study. It discusses the methods of constructing the wealth indices, the sampling procedure, the selection of durable assets, and the analytical techniques. Methods of constructing wealth indices In the present study, three methods of constructing asset-based indices have been used. These are the unweighted asset index, the weighted asset index using the inverse of proportion, and the weighted asset index using Principal Component Analysis (PCA). For convenience, the indices are named index 1, index 2, and index 3 respectively. Among others, Filmer and Pritchett (2001), McKenzie (2003), Prakongsai (2006), Vyas and Kumaranayake (2006), Howe et al. (2008), Zerin (2009), Rahman et al. (2013) have developed and applied several methods for constructing asset-based indices to measure the level of asset ownership of households and to classify their socioeconomic positions. In light of those studies, the aforementioned methods of constructing asset indices are described. Unweighted asset index: This is the easiest method, which involves simply summing up the number of assets possessed by a household. This method is often called the index using equal weight. The unweighted asset index is widely used in the literature. Suppose there are n households selected as the sample for the study. Let there be k types of assets considered in the study that a household may possess. Sampling procedure: In this study, a two-stage sampling design has been used, where the Primary Sampling Units (PSUs) are designated as villages, while the Secondary Sampling Units (SSUs) comprise households within the village. In the first stage, 20 PSUs (villages) have been selected from the list of all villages in Bangladesh following the standard systematic probability proportional to size (PPS) sampling method. A village wise household list was used from the Population and Housing Census 2011 (BBS, 2012) to apply the PPS method. While conducting PPS, all villages of the whole of Bangladesh were arranged geographically from the starting of the north to the end of the south, irrespective of the division, district, or other administrative units or any other criteria. It is noted that this study intends to provide the results, in general, in the overall areas of rural Bangladesh. The study was conducted for three months, started on 1st September 2013 and ending on 30th November 2013. The data were collected using face-to-face interviews. To collect primary data, a well-designed questionnaire was prepared for this study. Taking in mind the difficulties in collecting monetary indicators, data on household income and expenditure were collected technically and carefully. Data on household income were collected in the following ways: * Income comes out from different occupations of main earning members of the households; Income from different occupations of secondary earning members in the households if any; * Income from many other sources excluding the above two ones. It may include agriculture (crops/vegetables/trees/fruits), poultry, livestock, fishing, fishery, irregular business or handicraft, rent of house/shop, labor, scholarship, donation, allowance, private tuition, Vulnerable Group Development (VGD)/Vulnerable Group Feeding (VGF) card, etc. Analytical techniques: For the analytical purpose of the wealth indices as proxy measures, different analytical techniques are cited in the literature, such as Pearson's product-moment correlation, scatter plots, Spearman's rank correlation, use of Kappa statistic, classification in different quantiles (e. g. tercile, quintile), graphical representation, etc. For example, Ucar (2015) and Prakongsai (2006) conducted Pearson's correlations between the asset index and household income and household expenditure, and Howe et al. (2008) used classification in quintiles and a graphical representation, and Kappa statistic, while Filmer and Pritchett (2001) performed rank correlation and classification in terciles. Zerin (2009) and Bhuiya et al. (2007) used the scatter plot along with other methods.

  Heliyon 7 (2021) e06163
  
Funding Source:
1.   Budget:  
  

As evident from correlation analysis and graphical presentation as well as distributional characteristics presented above, unweighted as well as weighted asset indices using PCA are better alternatives to the monetary measures to assess the household living standard in the study while weighted asset index using the inverse of proportion does not work well as an alternative to income or expenditure. Findings reveal that unweighted and weighted asset indices using PCA have almost similar efficiency as proxy measures. However, in respect of attractive theoretical features, the weighted index by using PCA can be treated as a somewhat better option than the unweighted index. Nevertheless, since the unweighted index has almost similar characteristics as the weighted index by using PCA does and it has the advantage of simplicity, it can also be used. That is, by using an unweighted asset index one can assess the socio-economic status of households with enough accuracy without bothering with weights. It is noted that a common limitation of all three methods of wealth indices used in this study is that they capture only the ownership of assets and ignore their quality and quantity. So, there is a great scope for further research to develop some methods that can take into account of quality and quantity of the assets and can examine their contribution while constructing wealth indices as proxy measures.

  Journal
  


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