




































American Interdisciplinary Journal of Business 

and Economics 
ISSN: 2837-1909| Impact Factor : 4.6 

Volume. 9, Number 1; January-March, 2022; 

Published By: Scientific and Academic Development Institute (SADI) 

8933 Willis Ave Los Angeles, California 

https://sadipub.com/Journals/index.php/aijbe 

 

 

1 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

UNDERSTANDING THE ROOT CAUSES OF POVERTY IN PAKISTAN: 

DEMOGRAPHIC, HOUSEHOLD AND LOCATIONAL FACTORS 

 

 

McCulloch N. and Cheema Farooq 

Department of Economics, Balochistan University of Information Technology, Engineering and 

Management Sciences, Pakistan 

 

Abstract: This study examines the factors contributing to poverty in Pakistan, with a focus on demographic, 

household, and locational characteristics. Using the PSLM survey (2014-2015), the study employs a logistic 

regression model to investigate the impact of different factors on poverty. The results indicate that household 

head education, size, age, marital status, health status, remoteness, region, and gender are significant 

determinants of poverty. The study recommends that the government should improve basic facilities, quality 

of education, employment opportunities, and health facilities in remote areas of Pakistan to reduce poverty 

rates. 

Keywords: poverty, Pakistan, demographic factors, household characteristics, locational factors, logistic 

regression model, household head education, household size, household age, health status, remoteness, region, 

gender, basic facilities, education quality, employment opportunities, health facilities. 

 

Introduction: 

Poverty remains a severe problem in Pakistan, and its eradication is essential for the country's socio-economic 

development. Different studies have examined the relationship between poverty and various economic and 

social factors, such as demographics, household characteristics, and locational aspects. However, the specific 

factors contributing to poverty in Pakistan may be different from those in other countries, making it necessary 

to investigate the issue within the country's context. 

In this study, we aim to investigate the impact of demographic, household, and locational factors on poverty 

in Pakistan by utilizing the PSLM survey (2014-2015) data. We consider variables such as household head 

education, size, age, marital status, health status, remoteness, region, and gender as determinants of poverty. 

We employ a logistic regression model to identify the significant factors contributing to poverty. The study's 

findings reveal that household head education, size, age, marital status, health status, remoteness, region, and 

gender have a significant impact on the level of poverty in Pakistan. We find that the differences in region, 

gender, and provinces cause a rise in poverty rates due to low health facilities, poor educational systems, low 

infrastructure, low employment opportunities, and low economic growth. 

The study's recommendations include improving basic facilities, quality of education, employment 

opportunities, and health facilities in remote areas of Pakistan to reduce poverty rates. The study's findings 

contribute to the existing literature on poverty and provide a basis for policymakers to formulate effective 

poverty reduction strategies in Pakistan.   



McCulloch N. and Cheema Farooq (2022) 

2 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

Objectives of the Study  

The major purpose of the research is to analyse the impact of demographic factors, household characteristics 

and locational factors on poverty in Pakistan.   

Literature Review  

Phenomenon of poverty is prevalent in developing world and literature is increasing on the subject as well. 

Various research studies have found different economic and social factors that cause the phenomenon of 

poverty.  

Major social and economic variables influencing poverty include location of the household, household 

characteristics and Household head characteristics.  

The section comprises literature on the impacts of location of the household, household characteristics and 

Household head characteristics on poverty. Location of the household mainly includes the urban or rural 

locality of household. The mostly used household characteristics are household dependency ratio and 

household size. Household head’s characteristics include education of the household head, gender of the 

household head, age of the household head and marital status.  

The literature illustrates that there are mixed evidences regarding the impacts of location of the household on 

poverty. Ravallian et al. (2007) explored that increase in the cost of living standards would rise poverty rate 

in urban areas. Gertler and Glewwe (1990) analysed that rural and urban areas have different determinants of 

poverty, meaning that policies for poverty reduction should vary between the two localities. Likewise, in 

Woolard & Klasen (2000) found that there exist strong geographic elements to the occurrence of poverty. For 

Pakistan, Baulch and McCulloch (2002) found that district of the residence have significant impact on poverty 

status. Poverty in rural areas is higher than urban areas in Africa (The World Bank, 2001). This is mostly due 

of lack of infrastructure, employment opportunities, and better services in rural regions.  

The literature showed that household characteristics such as higher ratio of dependency, large size of the 

household and marital status of household members have significant impacts on poverty. Most of the studies 

found positive relationship between poverty and household size (Sekhampu, 2013). Some studies concluded 

that growing household size reduce the household welfare (Litchfield & McGregor, 2008; Fagernas & 

Wallace, 2007; Mukherjee & Benson, 2003).  

The literature also demonstrates evidences regarding the negative impacts of household dependency ratios on 

poverty. Baulch and McCulloch (1999) constituted that, higher dependency ratios of household have higher 

probability to be poor as compare to those having lower dependency ratios in Pakistan. Likewise, Akerele and 

Adewuyi (2011) revealed that a rise in the dependency ratio has exercised a harmful impact on the welfare of 

household in Nigeria and Tanzania. A number of studies have found impacts of marital status of household 

members on poverty. Such as Anyanwu (2013) found that married people enhance economic prosperity of a 

country, as marriage provides a bunch of economic benefits for households because it would normally add an 

additional earner to the household.   

Household head characteristics also influence poverty significantly as depicted by literature. Household head 

characteristics have included education, gender, age, and marital status of the household head. The literature 

suggests that education of the household head has significant negative impacts on household welfare and 

poverty. These studies include Grootaert (1997) for Cote d’Ivoire, Serumaga and Naude (2002) for South 

Africa and Cheema and Sial (2012) for Pakistan, explored that household heads having lower levels of 

education practice higher poverty levels and household heads with higher level of education lead to lower 

poverty level. For instance, an increase in the level of education would reduce the probability of being poor 

in the above mentioned countries. Likewise, higher levels of schooling are connected with higher levels of 

household wellbeing in Malawi. Litchfield and Sekhampu (2013) established that the level of employment of 

the household head was inversly related with the likelihood of being poor in the South Africa. 



McCulloch N. and Cheema Farooq (2022) 

3 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

Correspondingly, Benson and Mukherjee (2003) established that formal wage employment led to significantly 

increased in level of household’s wellbeing in Malawi.  

Several studies have found mixed evidences regarding the impacts of gender of the household head on 

household welfare and poverty and concluded that Female headed households are more probable to be poor 

than male headed households. These studies include Geda (2005) for Kenya, Anyanwu (2013) for Nigeria. 

Similarly, Female headed households in Nigeria and Tanzania had poorer living conditions compared to male 

headed households (Litchfield & McGregor, 2008; Akerele & Adewuyi, 2011). For Pakistan, Baulch and 

McCulloch (2002) concluded that gender of household head and basic education has insignificant impact on 

poverty.   

Age of the household head may result in more work experience, which lead to higher level of income and 

asset ownership, both of which improve household wellbeing. Several studies have found that age is negatively 

related with the chance of being poor (Grootaert, 1997; Sekhampu, 2013). Similarly, some other studies found 

that age is directly related with wellbeing (Datt & Jolliffe, 2005; Litchfield & McGregor, 2008; Cheema & 

Sial, 2012). Thus, an increase in age of the Household head may enhance household wellbeing.  

Methodology and Data  

This  section  discusses  theoretical  framework,  empirical  model, methodology and 

data source.  

Theoretical Framework Table 1  

Channels for Poverty  

Empirical Model  

The researchers used different methods and techniques for the estimation of the models, Neff (2007) employed 

the multiple correspondence analyses contrary to probit regression. Baulch and McCulloch (2002) used 

proportional hazards model of poverty transitions and logit model of poverty status. Azam and Imai (2009) 

Used feasible generalized least square estimation technique.   

In this study we have incorporated the Logistic regression technique and binary logistic regression model for 

the estimation of poverty model. The model is as followed.   

P = β1 + β2HHS + β3AHH + β4EDU + β5HS + β6MS + β7R + β8PR 

 + β9HH + β10RE + μ                                                         (1)    

 Poverty level = β1 + β2Household size + β3Age of household 

Variables  Channel  Channel  Poverty  

Remoteness  

( Non remote )  

↑Infrastructure and services  ↑ Employment opportunities  
↓ poverty  

↑ Household size  
↑ Economies of scale  

↑ Income  ↓ Poverty  

↑Age of HH head  ↑ Work experience  ↑ Living standard  ↓ Poverty  

↑ Education of HH    head  ↑Employment 

opportunities  

↑ Income per capita  
↓ Poverty  

↑ Health status of  

HH head  
↓Medical expenditure  

↑Selfemployment  

opportunities  ↓ Poverty  

↑ Marital status of  

HH head  
↑Earning hands  

↓Collective spending  
↓ Poverty  

Region (Urban)  ↑Employment  
↑ income per capita  

↓ poverty  

Gender (Male HH )  
↑Employment 

opportunities  
↑ income  ↓ poverty  



McCulloch N. and Cheema Farooq (2022) 

4 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

+ β4Education of household + β5Health status 

+ β6Marital status + β7Region + β8Province 

+ β9 ender of household + β10Remoteness + μ          (2)  

Principal Component Analysis  

To lessen the dimensionality of the original data set, the idea of principal component analysis (hereafter PCA) 

was given by Pearson (1901) originally and developed by Hotelling (1933). PCA is a statistical method that 

linearly converts an original set of variables into a significantly smaller set of uncorrelated variables that 

corresponds to mainly information in the original set of variables.   

The index of remoteness is combination of basic health unit, school, bank, road, drinking water, bus, railway 

and post office. The index is calculated by aggregating variables through PCA.   

RE = (0.0726)BHU + (−0.0014)SC + (−0.0058)BN + (−0.0128)RD 

+ (−0.0444)DW + (−0.3581)BS + (−0.2708)RA 

+ (0.6609)PO                                                                          (3)  

Remoteness = β1Basic health unit + β2school + β3bank + β4road  

+ β5drinking water + β6bus + β7railway 

+ β8post office                                                                        (4)  

The above equation shows that, remoteness is the combination of basic health unit, school, bank, road, 

drinking water, bus, railway and post office. Results of PCA are given below.   

Table 2  

Principal Component Analysis  

Variables  Component   

Basic health unit  0.0726  

School  -0.0014  

Variables  Component   

Bank  -0.0058  

Road  -0.0128  

Drinking water  -0.0444  

Bus  -0.3581  

Railway  -0.2708  

Post office  0.6609  

Variable Description  

This study analyzes the impact of demographic factors, household characteristics and locational factors on 

poverty in Pakistan. This study focus on variable such as age of household head, education of household head, 

, marital status, size of household, health status, region, gender, province and remoteness. These variables are 

selected according to availability of data and the nature of topic. We have 

introduced unique poverty line for estimation of poverty rate in Pakistan which is $1.25 determined by the 

World Bank. Individuals living below $1.25 per day are considered to be poor while individuals living on this 

line or above this line are considered to be non-poor.   

Data Source   

This study incorporates the data from the Pakistan social and living standards measurement survey (hereafter 

PSLM) covering period 2004-15. PSLM data deals with income approach. The study use income approach 

for determining poverty as per capita income has a direct relationship with poverty, i.e. a rise in per capita 

income lead to decline in poverty rate and vice versa. For determining poverty, Income approach is also 

employed by several studies (Arif, 2011; Malik, 1988).   



McCulloch N. and Cheema Farooq (2022) 

5 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

Empirical Findings and Discussion Regression Analysis for Poverty  

In this part, binary logistic regression model is estimated for $1.25 a day poverty line for Pakistan and the 

following results are obtained.  

Logistic Regression Analysis at $1.25 per day Poverty Line  

For analysis of poverty model, we have used binary logistic regression model using poverty at $1.25a day 

poverty line (The World Bank, 2008), the following regression equation is obtained.    

P = β1 + β2HHS + β3AHH + β4EDU + β5HS + β6MS + β7R + β8PR  

 + β9HH + β10RE + μ                                                        (5)    

Poverty level = β1 + β2Household size + β3Age of household 

+ β4Education of household + β5Health status 

+ β6Marital status + β7Region + β8Province 

+ β9ender of household + β10Remoteness + μ        (6)    

Where P is our dependent variable showing Poverty level, and  

𝛽�𝛽�′𝑠�𝑠� expresses coefficients of following independent variables.  

Table 3  

Logistic Regression Model for Poverty at $1.25 per Day  

Note.*, ** and *** correspondingly represent level of significance at 10%, 5% and 1%.   

The coefficient of remoteness is negative and highly statistically significant 

which shows that overall individuals utilize more than three above-mentioned facilities which results non-

poor. Similarly, the coefficient of HH head Education is negative and highly statistically significant. So, 

household heads with higher level of education experience lower poverty rate, while household heads with 

lower education experience higher rate of poverty. The coefficient of household size is negative and highly 

statistically significant. It means that as the size of household increases, potential income earner increases 

which are positively associated with well-being of household, proposing economies of scale in household 

consumption, as a result it would decrease in poverty derived from increasing household size. The coefficients 

of household age are negative and highly statistically significant. It implies that household age reflects increase 

in work experience, which is connected to increase income as a result living standard and welfare increases as 

a result poverty rate will decline.   

Variables  Coefficients  Standard Errors  

Remoteness  -0.0827545***  0.0041111  

HH head Education  -0.0697279***  0.0133992  

Household size  -0.1642378***  0.002127  

Young HHA  -1.172442***  0.097135  

Middle HHA  -1.660221***  0.0884349  

Old HHA  -0.9131145***  0.0817799  

Marital status  -0.0748548***  0.0132289  

Health status  -0.1363285***  0.0224191  

Region  -0.3715258***  0.0176567  

Gender  -2.887227***  0.0142295  

KPK  1.485564***  0.0245341  

Punjab  0.3491576***  0.0239847  

Sindh  0.522812**  0.0273447  

Constant  1.901461***  0.1204552  



McCulloch N. and Cheema Farooq (2022) 

6 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

Gender of household head also determines the level of poverty. The coefficient of household gender variable 

is negative and highly statistically significant which represents that male headed households are less likely to 

be poor in terms of employment opportunities which leads to increase in income and decrease in poverty 

status. While it is generally believed the families with female household heads are more likely to be poor due 

to of lack of proper planning and employment opportunities for female and low labour force participation in 

rural areas. The coefficients of all provinces are positive and highly statistically significant, which shows 

different poverty rates in all provinces.  

The coefficient of marital status is negative and highly statistically significant. It means that, married people 

may attain the similar level of utility with less collective spending rather than individual’s sum of consumption 

if they were living separately which improves standard of living and decrease in the rate of poverty. The 

coefficient of health status is negative and highly statistically significant. Thus individuals with better health 

can seek employment opportunities which lead to increase in income and reduce poverty.   

Region variable (urban area or rural area) also depict the nature of poverty. Households living in urban areas 

are less poor as compare to rural areas in term of facilities, employment opportunities, infrastructure and 

services. Here the coefficient of region variable is negative and highly statistically significant which shows 

that as households moves from rural areas to urban areas they are less discriminated in terms of facilities and 

which leads to lower the rate of poverty. While in rural areas basic facilities, employment opportunities, 

infrastructure and services are less advanced as compare to urban counterpart, this is mainly because of lack 

of infrastructure, employment opportunities, and better services in rural localities which leads to poverty in 

that region.   

Conclusion  

This study analyzes the impact of demographic factors, household characteristics and locational factors on 

poverty in Pakistan. Logistic regression model has been used to achieve this objective. The data for the study 

is obtained from PSLM survey covering period 2014 to 2015. The index of remoteness is combination of, 

basic health unit, school, bank, road, drinking water, bus, railway and post office. Remoteness determines the 

nature of poverty, individuals who utilize more than three above-mentioned facilities are considered as non-

remote and non-poor, if three or less than facilities are utilized are considered remote and poor. The study has 

introduced unique poverty line for estimation of poverty rate in Pakistan which is $1.25 determined by the 

World Bank. Individuals living below $1.25 per day are considered to be poor while individuals living on this 

line or above this line are considered to be non-poor. Findings of the study revealed that household head 

education, household size, household head age, marital status, health status, remoteness, region and gender 

have significant impacts on poverty level. The study further conclude that the differences in region, gender, 

and provinces cause rise in poverty rate due to low health facilities, poor educational system, low 

infrastructure, low employment opportunities and low economic growth.   

Policy Recommendations  

The findings of this study show that region, gender, and provinces causes increase in poverty and income 

inequality. It is revealed that that higher rate of poverty is related to greater income inequality and lower 

poverty rate is related to lesser income inequality between gender, region and among provinces of Pakistan. 

These differences come into existence due to lack of proper planning from the government side. Due to these 

differences in gender, region and among provinces causes low health facilities, poor educational system, low 

infrastructure, low employment opportunities and low economic growth. So, government should take some 

serious steps to improve basic facilities, quality education, in both the regions and provide equal employment 

opportunities for males and females, as well as health facilities in remote areas of Pakistan.  



McCulloch N. and Cheema Farooq (2022) 

7 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

References  

Akerele, D., & Adewuyi, S. A. (2011). Analysis of poverty profiles and socioeconomic determinants of welfare 

among urban households of Ekiti State, Nigeria. Current Research Journal of Social Sciences, 3(1), 1–

7.  

Anyanwu, J. C. (2013). Characteristics and macroeconomic determinants of youth employment in Africa. 

African Development Review, 25(2), 107– 129. https://doi.org/10.1111/j.1467-8268.2013.12019.x   

Arif G. M., Iqbal, N., & Farooq, S. (2011). The persistence and transition of rural poverty in Pakistan, 1998–

2004 (PIDE Working Papers No. 74). https://www.pide.org.pk/pdf/Working%20Paper/WorkingPaper- 

74.pdf    

Azam, M. S., & Imai, K. S. (2009). Vulnerability and poverty in Bangladesh (Chronic Poverty Research Centre 

Working Paper No. 141). http://dx.doi.org/10.2139/ssrn.1531577     

Baulch, B., & McCulloch, N. (2002). Being poor and becoming poor: Poverty status and poverty transitions 

in rural Pakistan. Journal of Asian and African Studies, 37(2), 168–185. 

https://doi.org/10.1177/002190960203700208      

Cheema, A. R., & Sial, M. H. (2012). Poverty, income inequality, and growth in Pakistan: A pooled regression 

analysis. The Lahore Journal of Economics, 17(2), 137–157. 

https://doi.org/10.35536/lje.2012.v17.i2.a6  

Datt, G., & Jolliffe, D. (2005). Poverty in Egypt: Modeling and policy simulations. Economic Development 

and Cultural Change, 53(2), 327– 346. https://doi.org/10.1086/425224  

Fagernas, S., & Wallace, L. (2007). Determinants of poverty in Sierra Leone, 2003 (Economic and Statistics 

Analysis Unit Working Paper, 19). https://www.files.ethz.ch/isn/32691/esau_wp19.pdf    

Geda, A. (2005). Export development strategy, export success stories and lessons for Africa. The challenge 

for Afreximbank (Paper presented).  

Conference of African Export Import Bank Board of Governers Meeting. Harare, Zimbabwe.   

Gertler, P., & Glewwe, P. (1990). The willingness to pay for education in developing countries: Evidence from 

rural Peru. Journal of Public Economics, 42(3),  251–275. 

https://doi.org/10.1016/00472727(90)90017- C   

Grootaert, C., Kanbur, R., & Oh, G. T. (1997). The dynamics of welfare gains and losses: An African case 

study. The Journal of Development Studies, 33(5), 635–657. 

https://doi.org/10.1080/00220389708422487   

Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of 

Educational Psychology, 24(6), 417– 441. https://doi.org/10.1037/h0071325   

Klasen, S., & Woolard, I. (2009). Surviving unemployment without state support: Unemployment and 

household formation in South Africa. Journal of African Economies, 18(1), 1–51. 

https://doi.org/10.1093/jae/ejn007   

https://doi.org/10.1111/j.1467-8268.2013.12019.x
https://doi.org/10.1111/j.1467-8268.2013.12019.x
https://www.pide.org.pk/pdf/Working%20Paper/WorkingPaper-
https://www.pide.org.pk/pdf/Working%20Paper/WorkingPaper-74.pdf
https://www.pide.org.pk/pdf/Working%20Paper/WorkingPaper-74.pdf
http://dx.doi.org/10.2139/ssrn.1531577
http://dx.doi.org/10.2139/ssrn.1531577
https://doi.org/10.1177/002190960203700208
https://doi.org/10.1177/002190960203700208
https://doi.org/10.35536/lje.2012.v17.i2.a6
https://doi.org/10.35536/lje.2012.v17.i2.a6
https://doi.org/10.1086/425224
https://doi.org/10.1086/425224
https://www.files.ethz.ch/isn/32691/esau_wp19.pdf
https://www.files.ethz.ch/isn/32691/esau_wp19.pdf
https://doi.org/10.1016/0047-2727(90)90017-C
https://doi.org/10.1016/0047-2727(90)90017-C
https://doi.org/10.1016/0047-2727(90)90017-C
https://doi.org/10.1016/0047-2727(90)90017-C
https://doi.org/10.1080/00220389708422487
https://doi.org/10.1080/00220389708422487
https://doi.org/10.1037/h0071325
https://doi.org/10.1037/h0071325
https://doi.org/10.1093/jae/ejn007
https://doi.org/10.1093/jae/ejn007


McCulloch N. and Cheema Farooq (2022) 

8 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

Litchfield, J., & McGregor, T. (2008). Poverty in Kagera, Tanzania: Characteristics, causes and constraints 

(Poverty Research Unit at Sussex Working Paper No. 42).  

Malik, M. H. (1988) Some new evidence of poverty in Pakistan. The Pakistan  Development  Review, 

27(4),  509–515. https://www.jstor.org/stable/41239040   

McCulloch, N., & Baulch, B. (1999). Distinguishing the chronically from the transitorily poor: evidence from 

Pakistan (IDS Working Paper No. 97). 

https://opendocs.ids.ac.uk/opendocs/bitstream/handle/20.500.12413/34 20/Wp97.pdf?sequence=1    

Mukherjee, S., & Benson, T. (2003). The determinants of poverty in Malawi, 1998. World Development, 31(2), 

339–358. https://doi.org/10.1016/S0305-750X(02)00191-2    

Neff, D. F. (2007). Subjective well-being, poverty and ethnicity in South  

Africa: Insights from an exploratory analysis. Social Indicators Research, 80(2), 313–341.  

https://doi.org/10.1007/s11205-005-5920-x Pearson, K. (1901). Principal components analysis. The 

London,  

Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 6(2), 559–572. 

https://doi.org/10.1080/14786440109462720   

Ravallion, M., Chen, S., & Sangraula, P. (2007). New evidence on the urbanization of global poverty. 

Population and Development Review, 33(4),  667–701. 

https://doi.org/10.1111/j.17284457.2007.00193.x  

Sekhampu, T. J. (2013). Determinants of poverty in a South African township. Journal of Social Sciences, 

34(2), 145–153. https://doi.org/10.1080/09718923.2013.11893126  

Sen, A. (1987). Gender and cooperative conflicts (United Nations University  Working  Paper  18). 

https://www.wider.unu.edu/sites/default/files/WP18.pdf    

Serumaga-Zake, P., & Naudé, W. (2002). The determinants of rural and urban household poverty in the North 

West province of South Africa. Development Southern Africa, 19(4), 561–572. 

https://doi.org/10.1080/0376835022000019392  

The World Bank.  (2001). World development report 2000/2001: Attacking poverty. Oxford University Press. 

https://openknowledge.worldbank.org/handle/10986/11856   

The World Bank. (2008, August 26). World bank updates poverty estimates for the developing world. 

http://rb.gy/lvyzoq    

https://www.jstor.org/stable/41239040
https://www.jstor.org/stable/41239040
https://opendocs.ids.ac.uk/opendocs/bitstream/handle/20.500.12413/3420/Wp97.pdf?sequence=1
https://opendocs.ids.ac.uk/opendocs/bitstream/handle/20.500.12413/3420/Wp97.pdf?sequence=1
https://opendocs.ids.ac.uk/opendocs/bitstream/handle/20.500.12413/3420/Wp97.pdf?sequence=1
https://doi.org/10.1016/S0305-750X(02)00191-2
https://doi.org/10.1016/S0305-750X(02)00191-2
https://doi.org/10.1007/s11205-005-5920-x
https://doi.org/10.1007/s11205-005-5920-x
https://doi.org/10.1080/14786440109462720
https://doi.org/10.1080/14786440109462720
https://doi.org/10.1080/14786440109462720
https://doi.org/10.1080/14786440109462720
https://doi.org/10.1111/j.1728-4457.2007.00193.x
https://doi.org/10.1111/j.1728-4457.2007.00193.x
https://doi.org/10.1111/j.1728-4457.2007.00193.x
https://doi.org/10.1080/09718923.2013.11893126
https://doi.org/10.1080/09718923.2013.11893126
https://www.wider.unu.edu/sites/default/files/WP18.pdf
https://www.wider.unu.edu/sites/default/files/WP18.pdf
https://doi.org/10.1080/0376835022000019392
https://doi.org/10.1080/0376835022000019392
https://openknowledge.worldbank.org/handle/10986/11856
https://openknowledge.worldbank.org/handle/10986/11856
http://rb.gy/lvyzoq
http://rb.gy/lvyzoq
http://rb.gy/lvyzoq
http://rb.gy/lvyzoq

