




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 10(2) (2025), 29-40 

29 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 10 NO 2 (2025) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                     

REVISITING THE SOCIOECONOMIC DIMENSIONS OF POVERTY IN 

NEPAL: A LOGISTIC REGRESSION ANALYSIS      
 

 Ram Prasad Gajurel (a)     Anil Niroula (b)    Dil Nath Dangal (c)1      Tanka Mani Poudel (d)     Kul Prasad 

Lamichhane (e)     
 

(a)Lecturer, Ratna Rajyalaxmi Campus, Tribhuvan University, Kathmandu, Nepal; E-mail: gajurelrp@gmail.com 
(b)PhD Scholar, Steven J. Green School of International and Public Affairs, Florida International University, USA; E-mail: aniro001@fiu.edu 
(c)Lecturer, Ratna Rajyalaxmi Campus, Tribhuvan University, Kathmandu, Nepal; E-mail: dangaldilnath@gmail.com 
(d)Lecturer, Padma Kanya Multiple Campus, Tribhuvan University, Kathmandu, Nepal; E-mail: tpoudel059@gmail.com 
(e)Associate Professor, Ratna Rajyalaxmi Campus, Tribhuvan University, Kathmandu, Nepal; E-mail: kul.lamchhane@rrlc.tu.edu.np 
                    

 
A R T I C L E I N F O 

 
 

Article History: 

 

Received: 4th January 2025 

Reviewed & Revised: 4th January 2025 

to 29th April 2025 
Accepted: 30th April 2025 

Published: 8th May 2025 

 
Keywords: 

 
Household Poverty, Logit, Determinants  

of Poverty, Infrastructure, Nonfarm  

Business   

 

 
JEL Classification Codes: 

 

C55, I32, R20, R28 

 
       

      Peer-Review Model:  

 

      External peer review was done through  

      double-blind method.        

 
A B S T R A C T      

 
Poverty, a multifaceted concept influenced by several socioeconomic factors, is not only an outcome of 

individual destiny but also socioeconomically created by severe deprivation of basic needs. Individuals 

could escape poverty and enhance the quality of life through the government’s ample policy 

interventions. This study examines socioeconomic determinants of poverty in Nepal from 

multidimensional perspectives. Using household survey data—9600 households—from Nepal Living 

Standards Survey IV 2022/23, this study applied binary logistic regression analysis. Considering 

approximately 18% of poor and 82% of nonpoor households, the results revealed the poverty status of 

households could not be significantly influenced by demographic factors, such as age, gender, and 

marital status. Rather, poverty status might be influenced by family size, residential status, remittance, 

nonfarm or side business, agricultural landholdings and livestock, access to electricity, better health and 

road infrastructure, dwelling status, and preference for cooking fuel. Thus, it is observed that the 

households may fall into poverty due to higher family dependency, urban residency, agro-landholdings 

and livestock, firewood as cooking food, availability of the dwelling, and larger family size with the 

remittance—and that they would walk away from poverty thanks to remittance, family business, 

electricity, adequate road, and health facilities. This study's findings suggest that nonfarm businesses, 

less agricultural dependence strategies and programs, and the promotion of physical and social 

overheads are crucial for achieving Nepal's sustainable development goals by reducing 

multidimensional poverty.   

 
 

© 2025 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

 

INTRODUCTION 

Policymakers, academicians, and scholars have invested considerable effort in measuring and examining poverty. 

Nonetheless, there still exists debate around the issue of measuring and taking into account the various dimensions of poverty 

for socioeconomic transformation to increase human well-being and welfare (Bourguignon & Chakravarty, 2003; King et 

al., 2014; Klasen, 2008; Sumner, 2007). The general measure of poverty is mainly based on consumption or income, 

depriving one of well-being (World Bank, 2001). The modern literature recognizes poverty as a multidimensional 

phenomenon that interacts and reinforces each other, and its measurement accounts for diverse characteristics adversely 

affecting human life (Chambers, 2007; Hulme et al., 2001; Olsson et al., 2014).  

The early literature measured poverty regarding income or consumption expenditure (Sen, 1976; Townsend, 1954, 

1971, 1979). Similarly, the conventional measure of poverty based on income or consumption, such as a dollar-a-day and 

headcount ratio by the World Bank, is still prominent worldwide (Ravallion et al., 2009). Amartya Sen conceptualizes 

poverty not just as an insufficiency of income; it is the deprivation of fundamental human capabilities (Sen, 1992). Since 

1976, poverty has been recognized as a multidimensional phenomenon (Foster et al., 1984; Townsend, 1979), such as lack 

of opportunities to change the situation, health and education, access to credit and productive resources, justice, and low 

voice in institutions (Sen, 1976). Further, poverty also contributes to unemployment, fear for the future, minimal 

representation in the community, lack of shelter, and illness due to unclean water (United Nations, 2009). Hence, 

                                                      
1Corresponding author: ORCID ID: 0000-0003-1381-6239 
© 2025 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  

https://doi.org/10.46281/bjmsr.v10i2.2362 

 
To cite this article: Gajurel, R. P., Niroula, A., Dangal, D. N., Poudel, T. M., & Lamichhane, K. P. (2025). REVISITING THE SOCIOECONOMIC 

DIMENSIONS OF POVERTY IN NEPAL: A LOGISTIC REGRESSION ANALYSIS. Bangladesh Journal of Multidisciplinary Scientific Research, 

10(2), 29-40. https://doi.org/10.46281/bjmsr.v10i2.2362 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/bjmsr.v10i2.2362
https://orcid.org/0000-0001-5693-1953
https://orcid.org/0009-0004-8201-7806
https://orcid.org/0000-0003-1381-6239
https://orcid.org/0000-0002-5510-7505
https://orcid.org/0009-0006-9737-7083


Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40

 

30 

understanding the various socioeconomic dimensions of poverty and its nature is indispensable to formulating appropriate 

policies, one of the significant challenges for developing countries (Epo, 2010).  Recent studies (Hassan et al., 2024; Mdluli 

& Dunga, 2022) found that several socioeconomic factors—including income, household size, and social and demographic 

attributes of the household (age, gender, marital status, population groups, etc.)—significantly influenced poverty. 

Considering seven dimensions of multidimensional poverty, an empirical study found that education, employment, gender, 

income, and age were treated as crucial factors of poverty (Chan & Wong, 2024). Moreover, some recent empirical findings 

also revealed that socioeconomic factors—such as location, residential status, demographic attributes, crop farming, income 

activities, and livestock—determined multidimensional poverty (Haque et al., 2024; Huluka, 2024). These findings provide 

crucial insights into the importance of socioeconomic dimensions of poverty.    

Similarly, two key international standards have been established for measuring poverty: The first is the World 

Bank’s income-based poverty line set at $1.25 till 2008 and currently at $2.15 per day (PPP) (World Bank, 2022), and the 

second is the multidimensional poverty index (MPI), introduced by the United Nations Development Program and Oxford 

Poverty and Human Development Initiative (OPHI) (World Bank, 2024). The multidimensional poverty measures have 

been introduced to focus on human well-being through a broader lens (Delamónica et al., 2021). However, despite 

significant progress in addressing various aspects of poverty—and advancing efforts to eliminate extreme poverty—its 

persistence remains a critical issue in the least-developed countries (United Nations, 2022). The initial multidimensional 

study of poverty can be traced back to Townsend (1979). In 2010, the global-level MPI utilized various indicators to assess 

poverty beyond traditional income-based measures (World Bank, 2024).  

Nepal, one of the least developed countries (LDC), is now graduating to a developing country status by 2026 

(United Nations Development Programme [UNDP], 2024). Achieving this goal requires substantially reducing both absolute 

and multidimensional forms of poverty. Nepal still ranks 41st among the poorest countries globally (Ventura, 2024), and as 

of 2022, 20.3% of the people live below the national poverty line (Asian Development Bank, 2024). Likewise, as per HDR 

(2023/2024), Nepal is ranked 146 among 193 nations in the Human Development Index (HDI) for the year 2022 (UNDP, 

2024). In addition, the GDP per capita of Nepal ($1348) is far below the average GDP of advanced economies ($59000) 

(International Monetary Fund, 2024). Thus, a national-level figure often blurs the within-country inequality in poverty 

(Uematsu et al., 2016), leading us to understand determinants that act as primary instruments in alleviating poverty and 

progressing toward socioeconomic transformation in Nepal.  

Over the past several decades, poverty alleviation has been one of the primary goals of the Nepalese government, 

including the aim to spur socioeconomic transformation. Nepal has completed fifteen development plans, and sixteen are 

underway. Moreover, the programs and policies—like the Integrated Rural Development Program (IRDP) in the 1970s, land 

reform policy, and rural credit policy—have already been implemented (Gewali, 1994); similarly, following the United 

Nations’ Millennium Development Goals (MDGs), Nepal was able to reduce poverty from 42% to 21.6% (National Planning 

Commission, 2016) and it is also graduating to a developing country status by 2026 (National Planning Commission, 

2024b)—inferring that Nepal has made remarkable progress in reducing income-related poverty. Thus, it is essential to 

establish a clear definition of poverty that facilitates meaningful comparisons and integrates the concept of multidimensional 

poverty into the policy development measure (Bray et al., 2020). Therefore, this study attempts to answer the following 

questions: Which socioeconomic dimensions should a country consider for the effectiveness of a poverty alleviation policy? 

What variables are to be considered in the country-specific MPI system? What are the underlying causes of multidimensional 

poverty?  

This paper aims to explore these questions in the context of Nepal. This paper employed binary logistic analysis. 

The strength of this model lies in capturing a comprehensive picture of poverty by estimating the likelihood of a household 

being categorized as poor or nonpoor when several indicators—such as income, healthcare, residence, and household size—

are employed. This paper thus attempts to highlight those socioeconomic dimensions that have a tremendous impact on 

devising and alleviating poverty in Nepal. Using the logit regression model, we analyze the data obtained from Nepal Living 

Standards Survey IV: 2022/23 (NLSS-IV), a survey representing all provinces of Nepal, which contains information on 

sixteen socioeconomic variables. This study is relevant to poverty reduction in Nepal through a socioeconomic lens. Because 

public welfare is vital to address in Nepal for graduating to a developing nation, this paper offers some insight into poverty 

status, its determinants, and policy gridlock to address it. Along with the introduction, this study is organized as follows: a 

literature review, methods and materials, results and discussion, and conclusion, respectively.   

 

LITERATURE REVIEW 

Theoretically, there are several perspectives on poverty. The behavioral, structural, and political theories observe poverty 

from multiple perspectives. Structure theories view poverty as demographic and labor market factors; political theories 

emphasize institutional aspects; and behavioral theories focus on individual behavioral factors (Brady, 2019). Moreover, 

poverty is a multidimensional concept, referring to lack of income that fails to meet basic needs, material lack, capability 

deprivation, illbeing (material poverty, physical illness, powerlessness, insecurity, and bad social relations), and multiplicity 

of deprivation (Chambers, 2006). Poverty can be explored as the deprivation of capability (Sen, 1999). Historically, poverty 

emerged as an economic consequence, but now it is multidimensional, covering several aspects of deterioration of quality 

of life. Thus, multidimensional measures—monetary factors, education, and infrastructure services—have been considered 

for poverty estimation in recent times (World Bank, 2024). Relative poverty is not eliminated or isolated as compared with 

human capabilities. Socioeconomic context, therefore, determines how poverty affects the individual's livelihood. 

Employing a multivariate logit model with socioeconomic characteristics of Somalian households, Abdi Ali et al. (2024) 

found that remittance, energy access, household size and dependency, house and agricultural land ownership, and the 

household's gender and age were the crucial determinants of poverty. Poverty can be reduced through channeling 



Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40

 

31 

remittances, utilizing modern energy, providing better opportunities for arable land and housing, and addressing gender 

issues. Similarly, Sahoo et al. (2024) explored that income inequality, educational arrangement, size of household, infant 

mortality, and income significantly influenced poverty in India. These studies explore the several socioeconomic 

determinants of multidimensional poverty. Although substantial progress has been made in understanding the factors leading 

to multifaceted poverty, a debate remains in the literature around the importance of commonly studied variables while 

assessing poverty (Balasubramanian et al., 2023).  

In South Asia, several authors have studied the multidimensional poverty index to capture significant poverty 

variables (Alkire et al., 2019; Curtain, 2004; Deutsch et al., 2020; Li et al., 2022; Rigg, 2018). Likewise, some studies have 

been conducted in Nepal regarding the measurement and determinants of poverty with multiple variables. Chhetry (2002) 

found that over 95% of the income-and-education-deprived population resides in rural areas. Bhatta and Sharma (2006) 

centered on asset accumulation and human capital and found transient and chronic poverty in Nepalese households. Wagle 

(2008) examined multiple dimensions of poverty in Kathmandu and concluded that the human capability dimension is the 

most important determinant of poverty. Pokharel (2015) recommended empowering disadvantaged people and improving 

their financial assets, including health, education, and employment. Goli et al. (2019) found economic progress and relative 

reduction in education and health poverty in Nepal; however, wealth poverty and inequality existed across the regions.   

Similarly, several other authors have studied poverty in a single variable context. Joshi et al. (2010) examined the 

relationship between poverty and food insecurity and recommended food security for the targeted population to reduce 

poverty in Nepal. Thapa (2013) examined the relationship between education and poverty and found a proportional 

relationship between these variables. Lokshin et al. (2004) found that poverty reduction is attributed to remittances and 

work-related migration.  

Many studies explore various socioeconomic variables that influence poverty in different contexts. Generally, 

significant sources of poverty involve demographic and household status, sociocultural factors, monetary factors, 

agricultural farming and livestock, and facilities and service availability. Agyeman-Boaten (2024) explored that healthcare, 

infant mortality, children's schooling, farm inputs, education and age of household head, marital status of household head, 

migration, external labor, family size, credit availability, cooperative membership, occupational diversity, and irrigation 

were significant determinants of poverty. Faharuddin and Endrawati (2022) found that household, individual, and 

employment-related variables significantly influenced poverty. Furthermore, Özpinar and Akdede (2022) identified that 

respondents' demographics (age, gender, marital status), income, class, destiny, education, and political belief were the 

major determinants of poverty.    

In summary, poverty, many studies reveal, is determined by many socioeconomic variables. Many demographic 

variables (age, gender, marital status, household size, and dependency ratio), income, remittance, agricultural land, 

irrigation, energy, basic facilities, and financial services are crucial to determining poverty in this changing context. The 

debate of multidimensional poverty, rather than absolute poverty, concludes that poverty is social and multifactorial rather 

than merely economic. This study, thus, investigates the socioeconomic determinants of poverty in Nepal: following recent 

literature (Abdiwahab et al., 2024; De Silva, 2008; Olarinde et al., 2020; Rahman, 2009; Saleem et al., 2023; Shah & 

Debnath, 2022; Wang et al., 2021), which explores socioeconomic dimensions of poverty, this study presumes the following 

hypothesis.   

H1: Socioeconomic factors influence the poverty status of households significantly.     

 

MATERIALS AND METHODS  

Study Area and Data Sources 

Nepal has experienced severe poverty—a crucial socioeconomic stigma—and still has 20.3% of people under the national 

poverty line (National Planning Commission, 2024a). Having a poor state—and a moderate level of an average of 2010 to 

2022 Gini coefficient (32.8), multidimensional poverty index based on the 2017 survey (0.074) (UNDP, 2024), human 

development index of 2022 (0.601), and unemployment rate of 2022/23 (12.6) (National Statistics Office [NSO], 2024)—

Nepal's policymakers always aim to alleviate poverty for achieving economic prosperity.  

 

Table 1. Variable Description 

 
Variables  Description Values and coding/recoding  

Poor  Poverty status of household  1 = poor, 0 = non-poor  

Gender  Gender of household head  1 = male, 0 = female  

Marital status  Present marital status of household head 1 = married, 0 = single  

Age  Age of the household head  continuous variable  

Household size  Household size (family members)  continuous variable  

Urban Residential area  1 = municipality, 0 = rural municipality 

Remittance Money sent by family members  1 = yes, 0 = no  

Family business Family non-agricultural business  1 = yes, 0 = no 

Agricultural land Ownership of agricultural land  1 = yes, 0 = no 

Livestock  Ownership of livestock 1 = yes, 0 = no 

Electricity access Electricity meters of household  1 = yes, 0 = no 

Dwelling status  Dwelling ownership of household  1 = yes, 0 = no 

Cooking fuel  Types of fuel used by households  1 = firewood, 0 = otherwise  

Health care Perception of healthcare facilities 1 = adequate, 0 = not adequate  

Children education Perception of children’s schooling 1 = adequate, 0 = not adequate 

Road facility  Perception of road facility  1 = satisfied, 0 = otherwise (not satisfied)  



Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40

 

32 

Employing binary logistic regression, this paper has revisited the socioeconomic dimensions of poverty in Nepal. 

The data were obtained from Nepal Living Standards Survey IV: 2022/23 (NLSS-IV), which was nationally surveyed from 

July 2022 to July 2023. This survey collected data from 9,600 nationally and provincially representative households on 

various aspects of welfare—such as consumption, housing, access to facilities, education, health, labor, agriculture, income, 

migration, wage and employment, household loans, remittance, adequacy of government and private facilities, and 

security—to inform the government of poverty reduction programs and Sustainable Development Goals (SDGs) in Nepal 

(NSO, 2024). This study used survey data and examined the socioeconomic dynamics of poverty in Nepal. The data were 

collected through a reliable, comprehensive, and representative survey, covering a wide range of household living standard 

dimensions, conducted nationally by the government institution NSO. The data were used after institutional permission, and 

thus, the results of this study would be valid, with greater generalizability. Following the previous studies (Ambaye et al., 

2021; Islam et al., 2018). 

 

Model Specification  

This study aims to review the socioeconomic dimensions of poverty in Nepal. To meet the objective, this study used 

descriptive and inferential studies. The descriptive analysis covered the respondents' profiles and attributes, and logistic 

regression was estimated based on the variable descriptions in Table 1. The logit model used the cumulative logistic function 

to avoid the unbounded prediction problem found in the linear probability model for equations with binary dummy 

dependent variables (Gujarati, 2015; Studenmund, 2021).  

Pi = 
1

1 + e-Zi
 = 

1

1 + e-(βX + ui)
 

Where Pi = true probability that the dummy Di = 1, and the likelihood of Di = 0—no chance of occurrence of the event—

given by, 1 - Pi = 
1

1 + eZi
 . Further, the linear transformation is the ratio of true probability to no chance. It takes a log as the 

odds ratio to determine the parameters (βs) of the explanatory variables (Xs). 

 

Pi

1 - Pi
 = 

1 + eZi

1 + e-Zi
 = eZi   ln 



Pi

1 - Pi
 = Zi = βX + ui 

 

Thus, the estimated model of this paper can be specified as follows: 

 

ln 






Ppoor

i

1 - Pnonpoor
i

 = β0 + β1Residencei + β2 Family businessi + β3Agricultural landi + β4Livestocki + β5Electricity accessi +  

 

β6 Cooking fueli + β7Gnderi + β8Health carei + β9Children educationi + β10Road facilityi + β11Marital statusi +  

β12Dwelling statusi + β13Agei + β14Household sizei + β15Household size*Remittancei + ϵi 

 

RESULTS AND DISCUSSIONS 

Socioeconomic Attributes and Their Association with Poverty Status  

With this study based on large-scale survey data by the government of Nepal, Table 2 describes socioeconomic dimensions 

and their association with poverty status in Nepal. According to Table 2, nonpoor households residing in the municipality 

(7857) exceeded poor ones (1743), and they were highly significantly associated with their poverty status (χ2 = 31.29, p < 

0.01).  However, remittance-recipient households were more than half of the households in both poor and nonpoor 

categories, and they were no significant association between remittance and the poverty status of the household. Table 2 

also reveals only a few members of the households engaged in the family business other than agriculture (nonpoor = 22.51%, 

poor = 16.29%) and also offered an insight into the significant relationship between the family business and poverty status 

(χ2 = 32.84, p < 0.01). The data also showed that most of the households had their agricultural land and livestock; therefore, 

they were significantly associated with household poverty status at 1% level of significance.  

Table 2 further infers that most households enjoyed their electricity meter facility, but most depended on firewood 

as cooking fuel. Moreover, both electricity access and cooking fuel were statistically significant, with the poverty status of 

households being at a 1% level of significance. Furthermore, the data also showed no significant association between female-

dominated gender and the poverty status of households. In addition, Table 2 shows that a majority of households perceived 

adequate health care, children's education, and adequate road facilities as associated with the household's poverty status; 

this finding was statistically significant at a 1% level. However, the marital status associated with the household's poverty 

status was statistically nonsignificant, and the results also reported having the married-household domination in the present 

marital status. The data also noted that the dwelling status and poverty status of households were statistically associated (χ2 

= 71.48, p < 0.01), and most of them owned dwelling facilities. As Table 2 showed, the age and household size were 

numerical variables, and the mean age of the household was 45.43 years, approximately an average of 4 members in the 

family.  

 

 

 

 



Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40

 

33 

Table 2. Socioeconomic Attributes & Their Associations  

 
Variables Poverty Status Association:  

χ2 (p-value) Non-poor (%) [N = 7857] Poor (%) [N = 1,743]  

Residence  

 Rural municipality  
 Municipality  

 

3225 (41.05%) 
4632 (58.59%) 

 

843 (48.36%) 
900 (51.64%) 

31.2917 (0.000) 

Remittance 

 No 

 Yes 

 

3666 (46.66%) 

4191 (53.34%) 

 

839 (48.14%) 

904 (51.86%) 

1.2485 (0.264) 

Family business 

 No 

 Yes 

 

6088 (77.49%) 

1769 (22.51%) 

 

1459 (83.17%) 

284 (16.29%) 

32.8406 (0.000) 

Agricultural land 

 No 

 Yes 

 
2478 (31.54%) 

5379 (68.46%) 

 
380 (21.80%) 

1363 (78.20%) 

64.6917 (0.000) 

Livestock  

 No 

 Yes 

 
2827 (35.98%) 

5030 (64.02%) 

 
395 (22.66%) 

1348 (77.34%) 

113.4830 (0.000) 

Electricity access 

 No 
 Yes 

 

950 (12.09%) 
6907 (87.91%) 

 

446 (25.59%) 
1297 (74.41%) 

209.1148 (0.000) 

Cooking fuel  

 Otherwise 
 Firewood  

 

3745 (47.66%) 
4112 (52.34%) 

 

436 (25.01%) 
1307 (74.99%) 

297.6930 (0.000) 

Gender 

 Female  
 Male 

 

2974 (37.85%) 
4883 (62.15%) 

 

675 (38.73%) 
1068 (61.27%) 

0.4633 (0.496) 

Health care 

 Not adequate 

 Adequate  

 

1644 (20.92%) 

6213 (79.08%) 

 

522 (29.95%) 

1221 (70.05%) 

66.4930 (0.000) 

Children education 

 Not adequate 

 Adequate 

 

1136 (14.46%) 

6721 (85.54%) 

 

362 (20.77%) 

1381 (79.23%) 

43.1348 (0.000) 

Road facility  

 Otherwise 

 Satisfied  

 
1955 (24.88%) 

5902 (75.12%) 

 
617 (35.40%) 

1126 (64.60%) 

80.4382  (0.000) 

Marital status  

 Single  

 Married  

 
1241 (15.79%) 

6616 (84.21%) 

 
251 (14.40%) 

1492 (85.60%) 

2.1130 (0.146) 

Dwelling status  

 No 
 Yes 

 

1330 (16.93%) 
6527 (83.07%) 

 

154 (8.84%) 
1589 (91.16%) 

71.4804 (0.000) 

Age (mean years) 45.43 years  

Household size (mean)  3.97 ~ 4 members  

 

Socioeconomic Dimensions of Poverty in Nepal  

Employing binary logistic regression, this paper estimated the socioeconomic dimensions of poverty in Nepal. The logit 

model examined the marginal effects of dummy and continuous variables on dichotomous dependent variables (Cramer, 

2003). This study considered poverty status (poor = 1, nonpoor = 0) as outcome variables and assessed the impact of different 

socioeconomic variables.  

Poverty is a multifaceted concept. Economic misery is not merely a fundamental of poverty; socioeconomic status 

also ruins humans' livelihoods. Table 3 identifies some socioeconomic dimensions of poverty in Nepal. The results revealed 

that gender, age, marital status, and children's education might not play a significant role in poverty, the results that partly 

contradicted some previous (Anyanwu, 2009; Chen & Wang, 2015; Huyser et al., 2014; Sun et al., 2022); however, these 

factors could be vital for the socioeconomic dimensions of poverty in Nepal. A mire of poverty seemed to be a grinding 

problem in the underprivileged section of Nepal. Nowadays, foreign employment and reliance on remittance appear to be 

the better choices for individuals; therefore, individual attributes did not turn out to be significant for poverty status. 

Likewise, with informal economies, poverty may not significantly arise in Nepal owing to alternative economic 

opportunities, family support growing in the economy, and personal attributes.  

 However, healthcare (β = -0.176, p < 0.05, OR = 0.839) and road facilities (β = -0.240, p < 0.01, OR = 0.787) 

significantly influenced the poverty status in Nepal, highlighting the fact that people were less likely to experience poverty 

if there were adequate healthcare and satisfying road facilities. This result is similar to many prior studies (Khatiwada et al., 

2017; Peters et al., 2008; Sewell et al., 2019; World Health Organization [WHO], 2003). Generally, better health care may 

promote physically and mentally capable human beings and overall human development, enhancing economically gainful 

activities to break a vicious circle of poverty. On the other hand, adequate road facilities will connect geographically isolated 

populations and provide access to basic facilities, thereby enhancing productive activities, employment opportunities, and 

socioeconomic conditions, thereby reducing poverty in the household.   

Additionally, dwelling status (β = 0.168, p < 0.10, OR =1.183) and household size (β = 0.226, p < 0.01, OR =1.254) 

were positively associated with poverty status, signifying that dwelling ownership and bigger family size might raise the 

likelihood of poverty. As evidenced by many studies (Chen & Wang, 2015; Lanjouw & Ravallion, 1995; Mora‐Rivera et 



Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40

 

34 

al., 2024; Quispe-Mamani et al., 2022), larger household size could lead to dependence on household resources, resulting 

in poverty. On the other hand, households with dwelling facilities are likely to increase the cost of maintenance, ratchet 

effect, and neighbor demonstration, causing more poverty too. Likewise, the interacting effect of household size and 

remittance was significantly associated with poverty. As indicated by the odds ratio (β = 0.050, p < 0.10, OR = 1.051), 

remittance recipients with larger household sizes may increase in poverty. Generally, the remittance would promote a better 

livelihood for the households and thus could reduce poverty and inequality (Salike et al., 2022). On the flip side, the 

remittance-recipient household with a large family size might become poorer.   

 

Table 3. Odd Ratios and Estimates of Logit of Poverty Status of Household  

 
 

Variables 

β S.E. Wald p OR 95% CI for OR 

LL UL 

Gender: Male  

(Reference group: Female) 
-0.069 0.062 1.260 0.262 0.933 0.826 1.053 

Marital status: Married  

(Reference group: Single) 

0.054 0.085 0.403 0.525 1.056 0.893 1.248 

Age  -0.002 0.002 0.605 0.437 0.998 0.994 1.002 

Household size  0.226 0.020 131.096 0.000 1.254 1.206 1.303 

Residence: Urban  

(Reference group: Rural) 

0.213 0.061 12.327 0.000 1.238 1.099 1.394 

Remittance: Yes  

(Reference group: No) 
-0.251 0.134 3.525 0.060 0.778 0.599 1.011 

Family business: Yes  

(Reference group: No) 

-0.278 0.074 14.169 0.000 0.757 0.655 0.875 

Agricultural land: Yes  

(Reference group: No) 
0.136 0.074 3.365 0.067 1.145 0.991 1.324 

Livestock: Yes  

(Reference group: No) 

0.211 0.074 8.049 0.005 1.235 1.067 1.429 

Electricity access: Yes  

(Reference group: No) 

-0.545 0.072 57.123 0.000 0.580 0.503 0.668 

Cooking fuel: Firewood  

(Reference group: Otherwise) 

0.704 0.071 96.957 0.000 2.021 1.757 2.325 

Healthcare: Adequate  

(Reference group: Not adequate) 

-0.176 0.073 5.844 0.016 0.839 0.727 0.967 

Children education: Adequate  

(Reference group: Not adequate) 

-0.037 0.082 0.202 0.653 0.964 0.820 1.132 

Road facility: Satisfied  

(Reference group: Otherwise) 

-0.240 0.062 14.851 0.000 0.787 0.696 0.889 

Dwelling status: Yes  

(Reference group: No) 

0.168 0.101 2.744 0.098 1.183 0.970 1.442 

Household size*Remittance   0.050 0.027 3.419 0.064 1.051 0.997 1.108 

Constant -2.519 0.198 161.795 0.000 0.081     

Note. p = probability value; OR = odds ratio; CI = confidence interval; LL = lower limit; UL = upper limit. 

 

The results (Table 3) highlighted that residential status (β = 0.213, p < 0.01) significantly influenced poverty status. 

The odds ratio (OR = 1.238) indicated that urban households were more likely to experience poverty, compared to those 

with a rural residence, ceteris paribus. This result is consistent with other empirical findings (Jula & Beriso, 2023; Serumaga-

Zake & Naudé, 2002) and contrasts with (Ding, 2022; Neway & Massresha, 2022; Vera-Toscano et al., 2024). Because of 

the green rural economy of Nepal, rural households might secure their basic needs compared to urban ones. On the other 

hand, many constraints—including the cost of living, spillover effect on living standards, lack of housing facilities, 

inadequate job opportunities, and inequality—could increase the likelihood of urban residents falling into poverty compared 

to rural residents.      

Moreover, remittance inversely influenced the poverty status (β = -0.251, p < 0.10, OR = 0.778), indicating that 

remittance recipients could alleviate poverty to some extent, making references to nonrecipients. Following the threads of 

studies (Islam et al., 2016; Paulos Borko, 2017; Salike et al., 2022), this study experienced similar results. Remittance-

recipient households were likely to have greater opportunities for regular income, investing their resources for human 

capital, maintaining quality of life, and enhancing overall wealth than non-recipients, resulting in fewer chances of being 

poor.    

Furthermore, the family business was significantly associated with poverty status (β = -0.278, p < 0.01, OR = 

0.757), suggesting that households with family business were less likely to be poor, and thus nonfarm income provided a 

chance of household well-being (Eyasu, 2020; Jula & Beriso, 2023; Kassie et al., 2014). Because of surplus labor in 

agriculture, less chance of manufacturing jobs, and inactive labor market participation (Lewis, 1954; National Statistics 

Office, 2024), family businesses may be side jobs for gainful activities that help them become less poor.  Additionally, 

agricultural landholding (β = 0.136, p < 0.10, OR = 1.145) and livestock (β = 0.211, p < 0.01, OR = 1.235) were also 

positively associated with poverty status, implying that agricultural dependency might raise the chance of households falling 

into poverty. The result of livestock is consistent with the previous study (Cho & Kim, 2017). Conversely, Maru (2010) 

found that landholding may reduce poverty. Moreover, it is observed that the modernization and commercialization of 

agricultural landholding and livestock, significant sources of livelihood sustainability in Nepal (Maltsoglou & Taniguchi, 

2004; Ministry of Finance, 2024), would help to curb poverty in Nepal.  



Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40

 

35 

Similarly, according to results of Table 3, the coefficient of electricity access (β = -0.545, p < 0.01, OR = 0.580) 

was negative and was also supported by time series analysis (Dartanto & Nurkholis, 2013); cooking fuel (β = 0.704, p < 

0.01, OR = 2.021), positively associated with poverty status, indicated that electricity might reduce poverty. Still, the 

dependency on firewood for cooking might exacerbate poverty. The reason is apparent: Households having access to 

electricity were most likely to save time on energy-intensive activities, promoting health and educational outcomes, offering 

a chance to raise per capita income (Brenčič & Young, 2009; Diallo & Moussa, 2020; World Bank, 2023), and thereby 

reducing the poverty level. In contrast, more time and effort for firewood collection, deteriorating health and educational 

outcomes, and climate change are thus making them even poorer, as corroborated by counter findings of electricity access 

in Nepal. 

Finally, the overall findings revealed that the hypothesis of the study H1—the socioeconomic factors influence the 

poverty status of households significantly—was confirmed. Excluding the non-significant effect of the household's 

demographic attributes, the social status, healthcare, wealth, income, agricultural activities, remittance, and facilities were 

significantly associated with poverty status, as evidenced by the χ2-test for association and t-test for logit coefficients. Thus, 

this study offers substantial evidence regarding the multidimensional socioeconomic characteristics of poverty that 

policymakers have aimed to rejuvenate the relative quality of life, which has become a tailspin in developing nations.  

 

Model Robustness  

Initially, this study employed a linktest to address the issues of model specification. The p-value of the linear predicted value 

(_hat) and its square value (_hatsq) were less than 1 percent, indicating that at least one criterion was violated, thereby 

raising the issue of the model being misspecified. Although the linktest may be helpful, it should not suppress theory and 

common sense, especially when the goal is to investigate associations rather than optimize predictions about outcomes 

(Almquist, n.d.). Moreover, count R2 (0.818) was more than 0.7, implying the model was well-fitted. Likewise, regarding 

the multicollinearity diagnostic test, the VIF of regressors was not more than 10, and the average VIF was 1.98, implying 

the model remained free from multicollinearity, indicating no correlation among predictors.  

The estimated logit regression was statistically robust and well-fitted. The McFadden R² ranged between 0 to 1, 

revealing the model's fitness to the data, not explained by variance in R2 of OLS (Poston et al., 2024). The McFadden R² 

(0.092) indicated that the estimated model was more fitted than the null model in social science research, where the perfectly 

fitted model may be rarely observed (Lyu et al., 2024). The significant LR statistic (χ2 = 835.02, p<0.01), statistically 

significant omnibus tests of model coefficients (χ2 = 835.022, p< 0.01), Pearson chi-square [χ2
 = 8784.47, p>0.05), Hosmer-

Lemeshow statistic (χ2 = 17.05, p>0.05)—with adjusted degrees of freedom for samples outside the estimation sample—

confirmed that the estimated model was statistically fitted.   

Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive power of a model, particularly 

for classification analysis, by illustrating the trade-off between sensitivity (true positive rate) and 1 - specificity (false 

positive rate) (Hilbe, 2015).  As shown in Figure 1, the area under the ROC curve (0.7177) indicated that the model had a 

moderate predictive value and was well-fitted, thus demonstrating acceptable discrimination in the estimated model. 

 

 
 

Figure 1. Receiver Operator Characteristic (ROC) Curve 

 

In addition, an S–S plot displays sensitivity and specificity across cut-points from 0 to 1, with their intersection 

showing where the two values were closest (Hilbe, 2015). As evidenced by Worku and Muchie (2012), Figure 2 below 

illustrates a sensitivity and specificity plot against probability cut-off points, where the two lines intersected near the vertical 

axis, indicating that the fitted model demonstrated sufficient sensitivity and specificity, thereby resulting in 81.84% correctly 

classified in the cases.  

 



Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40

 

36 

 
 

Figure 2. Sensitivity–Specificity (S–S) Plot 

 

CONCLUSIONS 

Relative poverty and its multidimensional aspects are ongoing debates that should motivate academia and policymakers 

about how it can be minimized. Thus, this study examines the socioeconomic dimensions of poverty in Nepal that 

policymakers should prioritize to improve the quality of life and people's welfare. Apart from demographics—age, gender, 

marital status, and children's education adequacy—this study found that crucial determinants of the poverty in Nepal were 

family size, residential status, remittance, nonfarm or side business, agricultural landholdings and livestock, access to 

electricity, better health and road infrastructure, dwelling status, and preference of cooking fuel. Moreover, the poverty 

status of households in Nepal remained unaffected no matter whether the household head was either male or female, married 

or single, younger or older, and adequate or inadequate children's education; therefore, poverty had no own gender, age, or 

marital status. Moreover, this study revealed that higher family dependency, urban residency, agro-landholdings and 

livestock, firewood as cooking food, availability of dwell, and larger family size with remittance were the main drivers for 

making households poorer—and however that remittance, family business, electricity, and adequate road and health 

facilities were crucial to alleviating poverty in Nepal. This study's findings provided evidence for policy stalemates to 

alleviate poverty in Nepal. The policymakers should focus on entrepreneurship and reduce the families' dependency on 

agriculture by providing startup loans, offering small business subsidies, promoting agro-entrepreneurship, advancing 

agrotech, providing facilities for agromarket, enhancing agricultural and nonfarm skills and literacy, and financing 

remittance to entrepreneurial development. Furthermore, the government should prioritize financing the basic and 

sustainable physical and human infrastructure (electricity, road, cooking fuel, health). It should take appropriate measures, 

as shown by the findings of this study, to achieve sustainable development goals (United Nations, 2015)—good health, 

quality education, affordable and renewable energy, decent work, infrastructure, innovation, industry, and sustainable 

cities—and to secure a better life, resulting in lower poverty in Nepal.    

 Given the limited socioeconomic variables, this study relied only on binary regression. Because the survey had 

already been completed on overall living standards, this study was based merely on limited socioeconomic and demographic 

determinants, excluding other psychological, personal, and institutional dimensions of poverty.  Thus, future researchers 

could apply dynamic causal modeling and machine learning techniques with multidimensional factors and spatial division 

that should predict the poverty dimensions of Nepal more accurately. Additionally, future researchers should compare 

previous living standard survey datasets for comprehensive generalizability and the best policy implications.   

 
Author Contributions: Conceptualization, R.P.G., A.N., D.N.D., T.M.P. and K.P.L.; Methodology, R.P.G.; Software, R.P.G.; Validation, R.P.G.; Formal 

Analysis, R.P.G., A.N., D.N.D., T.M.P. and K.P.L.; Investigation, R.P.G., A.N., D.N.D., T.M.P. and K.P.L.; Resources, R.P.G..; Data Curation, R.P.G.; 

Writing –Original Draft Preparation, R.P.G., A.N., D.N.D., T.M.P. and K.P.L.; Writing –Review & Editing, R.P.G., A.N., D.N.D., T.M.P. and K.P.L.; 

Visualization, R.P.G., Supervision, R.P.G.; Funding Acquisition, R.P.G., A.N., D.N.D., T.M.P. and K.P.L. Authors have read and agreed to the published 
version of the manuscript.  

Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups 

or sensitive issues. 
Funding: Authors received no funding for this research. 

Acknowledgments: Not applicable.  

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions.  

Conflicts of Interest: The authors declare no conflict of interest.     

 



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37 

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