




































 
 

 

36 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 7, No. 1, 36-41, 2020 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/journal.502.2020.71.36.41 

© 2019 by the authors; licensee Asian Online Journal Publishing Group 

    

 
 
 
Socioeconomic Status Measurement: An Analysis of Incorporation of Mixed 
Variables into Principal Component Approach 

 
Md. Nayem Dewan1   

Noor Muhammad Khan2    

PK. Md. Motiur Rahman3   

  
( Corresponding Author) 

 
1Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh. 

 
2Lecturer, Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail, 
Bangladesh. 

 
3Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh. 

 

 
Abstract 

Socioeconomic status of a household in Bangladesh changes overtime for many reasons. The 
measurement of this change is a very important tool in many aspects. This paper aims to examine 
the dynamic nature of wealth status in Bangladesh. In particular, we want to capture the overall 
wealth transition in rural area of Bangladesh from year 2004 to 2015. To calculate this transition, 
we construct wealth index for each of the year 2004, 2009, and 2015 using the ‘poverty analysis 
survey data’. This survey has conducted on the same households in each three years. Nonlinear 
principal component analysis (PCA) with optimal scaling using gifi method as our PCA tool is 
used here for wealth index construction. This method is designed to use with a data set that 
contains both numerical and categorical variables jointly. Then the transition of wealth is 
calculated using these three-wealth index. Based on the transition result, we classified each of the 
households into four different social groups such as non-poor, ascending poor, descending non-
poor, and chronically poor. 

 
Keywords: Wealth Index, Socioeconomic status, Poverty analysis survey, Principal component analysis, Panel data, Mixed variable etc. 

JEL Classification: B55, C23, C33, C38, G51, N35. 
 

Citation | Md. Nayem Dewan; Noor Muhammad Khan; PK. Md. 
Motiur Rahman (2020). Socioeconomic Status Measurement: An 
Analysis of Incorporation of Mixed Variables into Principal 
Component Approach. Economy, 7(1): 36-41. 
History:  
Received: 13 February 2020 
Revised: 16 March 2020 
Accepted: 20 April 2020 
Published: 4 May 2020 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: All authors contributed to the conception and design of 
the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 37 
2. Background of the Study................................................................................................................................................................ 37 
3. Sampling Design, Data and Variables ......................................................................................................................................... 37 
4. Methodology ..................................................................................................................................................................................... 38 
5. Estimated Results and Analysis .................................................................................................................................................... 38 
6. Discussion and Conclusion ............................................................................................................................................................. 40 
References .............................................................................................................................................................................................. 40 
Appendix ................................................................................................................................................................................................ 41 
 

 
 
 
 

 

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Economy, 2020, 7(1): 36-41 

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Contribution of this paper to the literature 
This paper aims to examine the dynamic nature of wealth status in Bangladesh.  

 

1. Introduction 
As we know, wealth condition of a household is not statistic but dynamic. The dynamics of wealth indicates the 

changes in household socioeconomic condition over time. There are many reasons for which household may 
frequently move from one socioeconomic position to another. Among these seasonality, climate change, public 
policy, political change, economic crisis, natural disaster, political violence, fiscal discipline, greater employment 
opportunity etc. are important. So considering the changes in household’s wealth status over a period of time is 
critically important for effective poverty alleviation policies. 

To calculate socioeconomic position, wealth index construction is a very popular tool. Wealth index is 
constructed using proxy variables of household’s wealth instead of income or expenditure of a household. Sanitation 
facilities, construction materials, toilet facility of a household, durable assets, amount of household land, cooking 
facilities, electricity etc. are used as proxy variable for wealth index construction. In our study, we also used such 
kind of household variables. A brief description about our data set is given in Section 3. 

Principal component analysis (PCA) is the most popularly used tool for wealth index construction. Filmer. and 
Pritchett (2001) used a modified version of  PCA to construct wealth index. Due to some limitations of their 
method, later, many researchers used some other updated versions of PCA.  But, none of these methods are 
dedicated to multivariate analysis of mixed (mixture of numerical and categorical variables) data. In this paper, we 
used a modified version of PCA that can consider mixed data in PCA, since our data set contains both numeric and 
categorical variables. Section 2 contains the background of the study. Description about data and sampling process is 
well described in section 3. A brief description about the methodology is given in Section 4. Section 5 contains the 
estimated results and analyses and Section 6 concludes. 
 

2. Background of the Study 
The literature on wealth transition is now quite sizable. Contributors from different field have contributed in this 

context and contributing till now. The concept of this wealth or poverty dynamics started to get attention in early 
1980s. Some of these are Attwood et al. (1979); Duncan et al. (1993); Baulch and Hoddinott (2000) etc. Poverty  
dynamics  as well as development policy implication is provided by Barrett (2005). By using the panel data, Baulch 
and Hoddinott (2000) conducted a study on poverty dynamics in several developing  country.  Moreover, Baulch. 
and Davis (2008) conducted a study to identify Poverty  dynamics  and  life  trajectories in rural Bangladesh. Similar  
kind  of   study  was conducted  in  Kenya  by Kristjanson, Mango, Krishna, Radeny, and Johnson (2010). Addison, 
Hulme, and Kanbur (2009) provided an effective overview about poverty dynamics. More recently,  the changes in 
the poverty situation  in  Bangladesh is described by Hossain and Bayes (2009). They considered the scenario between 
1988-1989 and 2008. 

In the context of transition of wealth measurement, Rahman, Matsui, and Ikemoto (2013) described the  
poverty dynamics in rural Bangladesh between year 2004 to 2009. Their study was based on the data from 32 
villages in 8 poverty prone districts of Bangladesh. In order to examine the poverty dynamics, they classified the 
household into four different social groups based on the household’s economic condition. They include: 1) non-poor: 
where household wealth condition remains above the poverty line for 5 years. This group of households can provide 
adequate quality food and facility like health care, clothing and other necessities for all family members. 2) ascending 
poor: where household wealth condition was below the poverty line 5 years ago but is now above poverty. This group 
of households is second to the non-poor in terms of food security and other facility. Specially, children and family 
members have low level of education. 3) descending non-poor: where household wealth condition was above the 
poverty line 5 years ago but has since fallen below. This group of households is third to the non-poor in terms of 
food security. They cannot provide adequate food to all family members. This group also faces difficulty to provide 
other facilities like health care, education, clothing etc. to all family members. 4) chronically poor: where household 
wealth condition has been below the poverty line and reside poor for a long period of time. This group of households 
cannot provide food security to all family members. Moreover, they also cannot bear expenses of other facility like 
education, health care, clothing etc. Even most of them is landless. 
 

3. Sampling Design, Data and Variables 

3.1. Sampling Design 
For empirical study on wealth transition, we have used household data from a large survey. Detail descriptions 

of the survey is given by Rahman et al. (2013). Survey was conducted at random in 32 villages spread over rural 
areas of 8 poverty prone districts of Bangladesh. Details of the location are given in Appendix. Data set contains 
information on a total of 1,282 households and was collected thrice. First survey was carried out during 15 
December 2004 to 15 January 2005, second survey was carried out during 28 January to 28 February 2010 and 
third survey was in January 2016. Data set collection involved conducting both quantitative and qualitative 
surveys with the same households at three points in time. So we can define this data set as a panel data with 5 years 
interval. This panel data allows us to realize the ways in which households describe the transition that occurred 
with them among 2004, 2009, and 2015. There are about 23.53 million rural households in Bangladesh. The 
selected households for the study covered about 0.81 percent of the total rural households in the country. The 
sample households were selected at random which helps to create a representative sample size for a long term 
monitoring. 
 

3.2. Variables Description 
For our study, we used 38 household variables from the data set. 1,115 households were participated in all the 

three survey since some households were missing after first survey and second survey. Data set contains both numeric 
and categorical variables. A brief summary of the variables described in the Table 1. 



Economy, 2020, 7(1): 36-41 

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© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Table-1. Description of variables. 

Household Variable Variable type 

Structure of main dwelling room 

1=Thatched cottage 
2 = kaacha, 
3 = made of cl sheet, 
4= semi paka, 
5 = paka 

Has access to electricity 0 = no, 1 = yes 
Amount of homestead land (decimal) Numeric 
Amount of cultivable land (decimal) Numeric 
Amount of fallow land (decimal) Numeric 
Amount of garden (decimal) Numeric 
No.  of cows Numeric 
No. of goat/Pig Numeric 
No. of hen/Cock Numeric 
No.  of duck Numeric 

Main source of drinking water 
1 = pond/ river/ boiled water 
2 = Ring well, 3 = Tube-well 

No. of fruit tree (big) Numeric 
No. of fruit tree (small) Numeric 
No. of bamboo bush Numeric 
Rickshaw 0 = no, 1 = yes 
Tractor 0 = no, 1 = yes 
Power tiller 0 = no, 1 = yes 
Tube-well 0 = no, 1 = yes 
Shallow tube-well 0 = no, 1 = yes 
Shallow machine 0 = no, 1 = yes 
Fishing net 0 = no, 1 = yes 
Sewing machine 0 = no, 1 = yes 
Loom machine 0 = no, 1 = yes 
Plough 0 = no, 1 = yes 
Spade 0 = no, 1 = yes 
No. of wrist watch Numeric 
Radio/Cassette player 0 = no, 1 = yes 
Television 0 = no, 1 = yes 
Bicycle 0 = no, 1 = yes 
Motorcycle 0 = no, 1 = yes 
No. of electric fan Numeric 
No. of cot/Bed Numeric 
Almirah 0 = no, 1 = yes 
Bench 0 = no, 1 = yes 

Wardrobe 0 = no, 1 = yes 
Meatsafe 0 = no, 1 = yes 
No. of mobile phone Numeric 
No. of chair/Table Numeric 

 

4. Methodology 
In this section, we discussed about the methodology that is used to construct wealth index in our study. For 

our study, we used an updated version of PCA which is called nonlinear PCA with optimal scaling. Detail 
descriptions of the method is given by De Leeuw and Mair (2009); De Leeuw. (2011); Gifi (1990); Linting, 
Meulman, Groenen, and van der Koojj (2007); Meulman, Van Der Kooij, and Heiser (2004). An R package is 
developed by De Leeuw and Mair (2009) called ‘homals’. A brief summary of the method is given below. 

The aim of nonlinear PCA is same as the aim of linear PCA that is dimension reduction. But nonlinear PCA can 
handle the nonlinear relationships between variables. Moreover, different measurement levels of variables such as 
nominal, ordinal, numeric can be considered by this method. Persons with the same category score also obtain the 
same quantified value by nominal analysis level. The order of the original categories also considered for ordinal 
analysis level. During the performance of nonlinear PCA, each category of categorical variables assigned numeric 
values through a process called optimal quantification. Optimal quantification replaces category level in such a way 
that as much as possible of the variation in the quantified variables is accounted for. For optimal quantification, a loss 
function is developed, which is minimized as much as possible by an iterative least square algorithm. These newly 
quantified variables occupy variance as like as continuous numeric variables. This is how nonlinear PCA meets the goal 
of linear PCA by considering both the nonlinear relationships between variables and different analysis levels of 
variables. Detail mathematics can be found in the above mentioned references. For wealth transition calculation, at 
first, we calculate the wealth index for each of the year 2004, 2009, and 2015 by using the nonlinear PCA method. 
Then we divided these households into two different socioeconomic groups such as poor and non-poor in terms of their 
wealth score, where first 50 percent considered as poor and second 50 percent considered as non-poor. This cut 
point is taken arbitrarily. Then based on the different socioeconomic groups of a household, we classify each of the 
household into different social class. Classification criteria is given by Rahman et al. (2013). This is how we can see 
the transition of a household wealth condition throughout the year 2004 to 2015. 
 

5. Estimated Results and Analysis 
At first, we calculate the score for each household. Based on the household score we divided each of the 

household into poor and non-poor categories. The estimated scenarios of poor and non-poor categories in 2004, 



Economy, 2020, 7(1): 36-41 

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© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

2009, and 2015 are given in Table 2.  
 

Table-2.  Poor and non-poor scenario from 2004-2015. 

Year Poor Non-poor 

2004 
954 

(85.561%) 
161 

14.44% 

2009 
106 

(9.51%) 
1009 

(90.49%) 

2015 
193 

(17.31%) 
922 

(82.69%) 

                                            
From the result of Table 2 we can see that there was a dynamic change in the household condition between year 

2004 to 2009. We got almost opposite result in 2009 from 2004. On the other hand, the change was quite stable 
from 2009 to 2015. 
 

5.1. Transition from 2004 to 2009 
Here we calculate the directionality of changes in the wealth condition based on the wealth index result for each 

of the year. Table 3 describes the results for the period 2004 - 2009. Results are given below: 
 

Table-3. Wealth transition: year 2004-2009. 

2004 2009 No. of household Percentage Economic Class 

Non-Poor Non-poor 84 7.53 Non-Poor 
Non-Poor Poor 77 6.91 Descending Non-Poor 

Poor Non-Poor 925 82.96 Ascending Poor 
Poor Poor 29 2.6 Chronically Poor 

                            
From Table 3 we can see that a dynamic change appeared in 2009 from 2004.  Specially, the rate of ascending poor 

which is about 82.96 percent in 2009. This indicates a clear improvement in wealth condition in 2009 than 2004. 
Moreover, only 2.6 percent household remain poor who were categorized as poor in 2004 and classified as 
chronically poor in 2009.  In addition, 7.53 percent of household remain non-poor in 2009 from 2004 and is classified 
as non-poor. Besides, about 6.91 percent household became poor who were categorized as non-poor in 2004 and so 
classified as descending non-poor in 2009. 
 

5.2. Transition from 2009 to 2015 
 

Table-4. Wealth transition: year 2009-2015. 

2009 2015 No. of household Percentage Economic Class 

Non-Poor Non-Poor 891 79.91 Non-Poor 
Non-Poor Poor 118 10.58 Descending Non-Poor 

Poor Non-Poor 31 2.78 Ascending Poor 
Poor Poor 75 6.73 Chronically Poor 

                                     
From Table 4, we can see that the change from 2009 to 2015 is not so dynamic as like 2004 to 2009. In 2015, 

about 79.91 percent of total household categorized as non-poor that is 79.91 percent household who were non-poor in 
2009 remain non-poor in 2015, which   indicates a quite stable situation. Moreover, about 6.73 percent of household 
remain poor who were poor in 2009 and categorized as chronically poor.  In addition, about 2.78 percent of household 
became non-poor in 2015 who were poor in 2009 and classified as ascending poor. Besides, about 10.58 percent 
household became poor in 2015 who were categorized as non-poor in 2009 and so classified as descending non-poor 
in 2015.  
 

5.3. Transition from 2004 to 2015 
In this part, we tried to find out the direction of wealth of a household from 2004 to 2015. That is, what was the 

condition of a household in 2009 and 2015, which was categorized as poor or non-poor in 2004. So, here we consider 
the survey data of 2004, 2009, and 2015. Analysis results are given in the Table 5. 
 

Table-5. Wealth transition: year 2004-2015. 
2004 2009 2015 No. of Household Percentage 

Non-Poor Non-Poor Non-Poor 51 4.57 
Poor Non-Poor Non-Poor 840 75.33 

Non-Poor Poor Poor 56 5.02 
Poor Poor Poor 19 1.70 

Non-Poor Non-Poor Poor 33 2.96 
Non-Poor Poor Non-Poor 21 1.88 

Poor Non-Poor Poor 85 7.62 
Poor Poor Non-Poor 10 0.9 

                           
Here we can see that the number of household’s who were non-poor in 2004 and remain non-poor in 2009 and 

in 2015 is about 4.57 percent. Number of households who were categorized as poor in 2004 became non-poor in 
2009 and remain non-poor in 2015 is about 75.33 percent. Moreover, about 5.02 percent of household remain poor 
in 2009 and in 2015 who were categorized as non-poor in 2004. About 1.7 percent household remain poor from 
2004 to 2015. In addition, 2.96 percent of household became poor in 2015 who were categorized as non-poor in 
2004 and in 2009. About 1.88 percent of household who were non-poor in 2004 turned into poor in 2009 and 



Economy, 2020, 7(1): 36-41 

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became non-poor in 2015 again. About 7.62 percent of household who were poor in 2004 became   non-poor in 2009 and 
again turned into poor in 2015. About 0.90 percent of household who were categorized as poor in 2004 and in 2009 
became non-poor in 2015. 
 

5.4. Mean Wealth Score Transition 
To construct wealth index, each person in the population is given a score which represents how wealthy they 

are based on the characteristics of their household. This score is known as wealth score. A comparison of mean 
wealth score of each different class for the year between year 2009 and 2015 is presented in Figure 1. Mean wealth 
score for all four household groups increases from 2009 to 2015. This increment is small for the chronically poor 
group compared to other groups.  
 

 
Figure-1. Mean wealth score transition: 2004-2015. 

                                    

6. Discussion and Conclusion 
This paper was inspired by the current works on poverty dynamics or the changes in the directionality of wealth 

of a household. Application of nonlinear PCA approach using optimal scaling method to construct wealth index 
which is different from traditional standard PCA approach is introduced. A large panel data set is considered to 
analyses the transition of wealth in rural Bangladesh from year 2004 to 2015. The foregoing analysis gives the 
following tentative conclusions. Dynamic change from the year 2004 to 2009 was observed whereas the change was 
quite stable from the year 2009 to 2015. We have seen that in rural Bangladesh poor became non-poor on a large 
number from the year 2004 to 2015. In this part, our main focus was to obtain a comprehensive picture of the 
change in poverty groups over a period of 10 years (2004-2015). Moreover, the wealth score for different 
socioeconomic class also shows an increasing pattern. Overall, we can say that a significant change in wealth status is 
observed. This indicates a good sign for government and policy makers to achieve the vision and development goal. 
Such an understanding would also help the responsible authorities to develop appropriate policies and programs for 
poverty alleviation. For further improvement on socioeconomic condition of rural people and poverty alleviation, 
development plan specially for the poor should be strengthen. 
 

References 
Addison, T., Hulme, D., & Kanbur, R. (2009). Poverty dynamics: Interdisciplinary perspectives. Oxford: Oxford University Press. 
Attwood, D. W., Apte, M. L., Baviskar, B., Beals, A. R., Eames, E., Ferreira, J., . . . Kudryavtsev, M. (1979). Why some of the poor get richer: 

Economic change and mobility in rural western India. Current Anthropology, 20(3), 495-516.Available at: 
https://doi.org/10.1086/202321. 

Barrett, C. B. (2005). Rural poverty dynamics: Development policy implications. Agricultural Economics, 32(s1), 45–60.Available at: 
https://doi.org/10.1111/j.0169-5150.2004.00013.x. 

Baulch, B., & Hoddinott, J. (2000). Economic mobility and poverty dynamics in developing countries. The Journal of Development Studies, 
36(6), 1-24.Available at: https://doi.org/10.1080/00220380008422652. 

Baulch., B., & Davis, P. (2008). Poverty dynamics and life trajectories in rural Bangladesh. International Journal of Multiple Research 
Approaches, 2(2), 176–190.Available at: https://doi.org/10.5172/mra.455.2.2.176. 

De Leeuw, J., & Mair, P. (2009). Gifi methods for optimal scaling in R: The package homals. Journal of Statistical Software, 31(4), 1-
20.Available at: https://doi.org/10.18637/jss.v031.i04. 

De Leeuw., J. (2011). Nonlinear principal component analysis and related techniques (pp. 2–23). UCLA: Department of Statistics. 
Duncan, G. J., Gustafsson, B., Hauser, R., Schmauss, G., Messinger, H., Muffels, R., . . . Ray, J.-C. (1993). Poverty dynamics in eight 

countries. Journal of Population Economics, 6(3), 215-234. 
Filmer., D., & Pritchett, L. H. (2001). Estimating wealth effects without expenditure data, or tears: An application to educational enrollments 

in states of India. Demogra- Phy, 38(1), 115–132. 
Gifi, A. (1990). Nonlinear multivariate analysis (pp. 559). West Sussex, England: John Willey and Sons ltd. . 
Hossain, M., & Bayes, A. (2009). Rural economy and livelihoods: Insights from Bangladesh: AH Development Publishing House. 
Kristjanson, P., Mango, N., Krishna, A., Radeny, M., & Johnson, N. (2010). Understanding poverty dynamics in Kenya. Journal of 

International Development, 22(7), 978-996. 
Linting, M., Meulman, J. J., Groenen, P. J., & van der Koojj, A. J. (2007). Nonlinear principal components analysis: Introduction and 

application. Psychological Methods, 12(3), 336-358. 
Meulman, J. J., Van Der Kooij, A. J., & Heiser, W. J. (2004). Principal components analysis with nonlinear optimal scaling transformations 

for ordinal and nominal data. In The SAGE Handbook of Quantitative Methodology for the Social Sciences (pp. 50-71). SAGE 
Publications, Inc. 

Rahman, P. M. M., Matsui, N., & Ikemoto, Y. (2013). Dynamics of poverty in rural Bangladesh. London: Springer. 
 

 

 
 



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Appendix 
 

Table-1. Location of the selected villages to calculate wealth transition. 

Serial No. Division District Upazila Union Village 

01 Sylhet Sunamgonj Biswambharpur Dhanpur Halabadi puraton gaon 
02 Sylhet Sunamgonj Deri Karimpur Bangagaon 
03 Sylhet Sunamgonj Doara Bazar Narshingpur Lastobergaon 
04 Sylhet Sunamgonj Sadar Gaura rong Kamartuk 
05 Rangpur Panchagarh Atwari Dhamur Dhamur 
06 Rangpur Panchagarh Boda Kajoldighi Kaligonj Agun tola 
07 Rangpur Panchagarh Debiganj Shalbhanga Shikarpur 
08 Rangpur Panchagarh Sadar Magura Ajadpur 
09 Rangpur Kurigram Bhurungamari Bhurungamari Dkhkhin para baraitara 
10 Rangpur Kurigram Phullbari Phullbari Kabir Mamud 
11 Rangpur Kurigram Nageswari Hasnabad Beparir hat 
12 Rangpur Kurigram Rowmari Rowmari Dakhkhin notun para 
13 Khulna Satkhira Ashashuni Ashashuni Shitolpur 
14 Khulna Satkhira Kolaroa Jogi khali Paik  para 
15 Khulna Satkhira Sadar Bolle Mukundo pur 
16 Khulna Satkhira Shyamnagar Munshigonj Moukhali 
17 Dhaka Madaripur Kalkini Baligram Pashchim barigram 
18 Dhaka Madaripur Sadar Dhurail Khalashi kandi 
19 Dhaka Madaripur Shibchar Char janajat Jalal sharkar kandi 

20 Dhaka Madaripur Rajoir Bodor pasha Pathan kandi 

21 Chittagong Khagrachari Dighinala Merung Uttor rashik nogor 

22 Chittagong Khagrachari Sadar Golabari Pashchim golabari 

23 Chittagong Khagrachari Matiranga Guimara Guimara 

24 Chittagong Khagrachari Panchari Puch gang Modhu mongol para 

25 Mymensingh Sherpur Jhinaigati Jhinagati Jhinaigati 

26 Mymensingh Sherpur Nalita bari 12 koloshpar Gaglajani 

27 Mymensingh Sherpur Sadar Bhatashala Shapmari 

28 Mymensingh Sherpur Sribardi Bhelua Chokbandi 

29 Barisal Borguna Amtoli Amtoli Mohish danga 

30 Barisal Borguna Amtoli Kukua Purba kukua 

31 Barisal Borguna Sadar Dhalua Kodom tola 

32 Barisal Borguna Betagi Kajirabad Kumrakhali 

     Source: Poverty Analysis Survey Data 2004, 2009 and 2015. 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

  

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