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. Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40 37 REFERENCES Abdi Ali, D., Mohamed, N. A., Ismail, A. I., Moahmed, J., & Sahabuddin, M. (2024). Modelling the determinants of rural household poverty: Empirical evidence from Somalia. Cogent Food & Agriculture, 11(1), 2445139. https://doi.org/10.1080/23311932.2024.2445139 Abdiwahab, B., Menza, M., & Mohamed, A. A. (2024). Multidimensional poverty and its determinants in Somalia: A household level analysis. International Journal of Developing Country Studies, 6(1), 36-57. Retrieved from https://www.carijournals.org/journals/index.php/IJDCS/article/view/2108/2502 Agyeman-Boaten, S. Y. (2024). Determinants of poverty in rural cocoa farming communities in Ghana: Unidimensional and multidimensional analysis. Cogent Economics & Finance, 12(1), 2397808. https://doi.org/10.1080/23322039.2024.2397808 Alkire, S., Haq, R. U., & Alim, A. (2019). The state of multidimensional child poverty in South Asia: A contextual and gendered view (OPHI Working Paper No. 127). University of Oxford. Retrieved from https://ophi.org.uk/publications/WP-127 Almquist, Y. B. (n.d.). Link test – A guide to applied statistics with STATA. Retrieved from https://statsapplied.com/part-ii- regression-analysis/ordinal-regression/model-diagnostics/link-test-2/ Ambaye, T. K., Tsehay, A. S., & Hailu, A. G. (2021). Application of ordered logit model to analyze determinants of rural households multidimensional poverty in western Ethiopia. International Journal of Development and Economic Sustainability, 9(1), 18–62. Retrieved from https://www.eajournals.org/wp-content/uploads/Application-of-Ordered- Logit-Model-to-Analyze-Determinants-of-Rural-Households-Multidimensional-Poverty-in-Western-Ethiopia.pdf Anyanwu, J. C. (2009). Marital status, household size and poverty in Nigeria: Evidence from the 2009-2010 survey data (Working Paper Series No. 180). African Development Bank. Retrieved from https://www.afdb.org/sites/default/files/documents/publications/working_paper_180_-_marital_status- _household_size_and_poverty_in_nigeria-_evidence_from_the_2009-2010_survey_data.pdf Asian Development Bank. (2024). Basic statistics 2024. https://doi.org/10.22617/ARM240241-2 Balasubramanian, P., Burchi, F., & Malerba, D. (2023). Does economic growth reduce multidimensional poverty? Evidence from low- and middle-income countries. World Development, 161, 106119. https://doi.org/10.1016/j.worlddev.2022.106119 Bhatta, S. D., & Sharma, S. K. (2006). The determinants and consequences of chronic and transient poverty in Nepal (Chronic Poverty Research Centre Working Paper No. 66). http://dx.doi.org/10.2139/ssrn.1753615 Bourguignon, F., & Chakravarty, S. R. (2003). The measurement of multidimensional poverty. Journal of Economic Inequality, 1, 25–49. https://doi.org/10.1023/A:1023913831342 Brady, D. (2019). Theories of the causes of poverty. Annual Review of Sociology, 45, 155-175. https://doi.org/10.1146/annurev- soc-073018-022550 Bray, R., de Laat, M., Godinot, X., Ugarteg, A., & Walker, R. (2020). Realising poverty in all its dimensions: A six-country participatory study. World Development, 134, 105025. https://doi.org/10.1016/j.worlddev.2020.105025 Brenčič, V., & Young, D. (2009). Time-saving innovations, time allocation, and energy use: Evidence from Canadian households. Ecological Economics, 68(11), 2859–2867. https://doi.org/10.1016/j.ecolecon.2009.06.005 Chambers, R. (2006). What is poverty? Who asks? Who answers? UNDP International Poverty Centre (IPC). Retrieved from https://opendocs.ids.ac.uk/articles/journal_contribution/What_is_poverty_Who_asks_Who_answers_/26478025/1/file s/48233443.pdf Chambers, R. (2007). Poverty research: Methodologies, mindsets and multidimensionality (IDS Working Paper No. 29). The Institute of Development Studies and Partner Organisations. Retrieved from https://opendocs.ids.ac.uk/articles/report/Poverty_research_methodologies_mindsets_and_multidimensionality/26445 418?file=48093856 Chan, S. M., & Wong, H. (2024). Measurement and determinants of multidimensional poverty: The case of Hong Kong. Journal of Asian Public Policy, 1-21. https://doi.org/10.1080/17516234.2024.2325857 Chen, K. M., & Wang, T. M. (2015). Determinants of poverty status in Taiwan: A multilevel approach. Social Indicators Research, 123(2), 371–389. https://doi.org/10.1007/s11205-014-0741-4 Chhetry, D. (2002). Understanding rural poverty in Nepal. In C. Edmonds & S. Christopher (Eds.). Defining an agenda for poverty reduction: Proceedings of the first Asia and Pacific forum on poverty (pp. 293–314). Asian Development Bank. Cho, S., & Kim, T. (2017). Determinants of poverty status in Rwanda. African Development Review, 29(2), 337–349. https://doi.org/10.1111/1467-8268.12260 Cramer, J. S. (2003). Logit models from economics and other fields. Cambridge University Press. Curtain, R. (2004). Youth in extreme poverty: Dimensions and policy implications with particular focus on South East Asia. Retrieved from https://www.un.org/esa/socdev/unyin/workshops/curtain.pdf Dartanto, T., & Nurkholis. (2013). The determinants of poverty dynamics in Indonesia: Evidence from panel data. Bulletin of Indonesian Economic Studies, 49(1), 61–84. https://doi.org/10.1080/00074918.2013.772939 De Silva, I. (2008). Micro‐level determinants of poverty reduction in Sri Lanka: A multivariate approach. International Journal of Social Economics, 35(3), 140-158. https://doi.org/10.1108/03068290810847833 Delamónica, E., Ndiaye, M., & Malgioglio, S. (2021). Measuring poverty’s multiple dimensions: New guidance for countries developing multidimensional poverty measures. World Bank. Retrieved from https://blogs.worldbank.org/en/opendata/measuring-povertys-multiple-dimensions-new-guidance-countries- developing-multidimensional Deutsch, J., Silber, J., Wan, G., & Zhao, M. (2020). Asset indexes and the measurement of poverty, inequality and welfare in Southeast Asia. Journal of Asian Economics, 70, 101220. https://doi.org/10.1016/J.ASIECO.2020.101220 Diallo, A., & Moussa, R. K. (2020). Does access to electricity affect poverty? Evidence from Côte d’Ivoire. Economics Bulletin, hal-02956563. Retrieved from https://hal.science/hal-02956563 Ding, S. (2022). A comparative analysis of vulnerability to poverty between urban and rural households in China. Economies, 10(10), 243. https://doi.org/10.3390/economies10100243 Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40 38 Epo, B. N. (2010). Determinants of poverty in Cameroon: A binomial and polychotomous logit analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1424672 Eyasu, A. M. (2020). Determinants of poverty in rural households: Evidence from North-Western Ethiopia. Cogent Food and Agriculture, 6(1), 1823652. https://doi.org/10.1080/23311932.2020.1823652 Faharuddin, F., & Endrawati, D. (2022). Determinants of working poverty in Indonesia. Journal of Economics and Development, 24(3), 230-246. https://doi.org/10.1108/JED-09-2021-0151 Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3), 761. https://doi.org/10.2307/1913475 Gewali, G. P. (1994). Poverty alleviation measures in Nepal: Commitments and review. NRB Economic Review, 7(3), 42–55. Retrieved from https://www.nrb.org.np/contents/uploads/2021/09/vol7_art3.pdf Goli, S., Maurya, N. K., Moradhvaj, & Bhandari, P. (2019). Regional differentials in multidimensional poverty in Nepal: Rethinking dimensions and method of computation. Sage Open, 9(1). https://doi.org/10.1177/2158244019837458 Gujarati, D. (2015). Econometrics by example (2nd ed.). Palgrave. Haque, S., Salman, M., Hira, F. T. Z., & Hossain, M. E. (2024). Multidimensional poverty status in rural Bangladesh and the pathways of sustainable poverty alleviation. Forum for Social Economics, 1–22. https://doi.org/10.1080/07360932.2024.2395914 Hassan, A. A., Muse, A. H., & Chesneau, C. (2024). Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia. Scientific Reports, 14(1), 5956. https://doi.org/10.1038/s41598-024-56466-8 Hilbe, J. M. (2015). Practical guide to logistic regression. CRC Press. Hulme, D., Moore, K., & Shepherd, A. (2001). Chronic poverty: Meanings and analytical frameworks (Chronic Poverty Research Centre Working Paper No. 2). Chronic Poverty Research Centre. http://dx.doi.org/10.2139/ssrn.1754546 Huluka, A. T. (2024). How is the multidimensional poverty changing in Ethiopia? An empirical examination using demographic and health survey data. Cogent Economics & Finance, 12(1), 2364359. https://doi.org/10.1080/23322039.2024.2364359 Huyser, K. R., Takei, I., & Sakamoto, A. (2014). Demographic factors associated with poverty among American Indians and Alaska natives. Race and Social Problems, 6(2), 120–134. https://doi.org/10.1007/s12552-013-9110-1 International Monetary Fund. (2024). World economic outlook (October 2024). Retrieved from https://www.imf.org/external/datamapper/NGDPDPC@WEO/OEMDC/ADVEC/WEOWORLD?year=2023 Islam, D., Sayeed, J., & Hossain, N. (2016). On determinants of poverty and inequality in Bangladesh. Journal of Poverty, 21(4), 352–371. https://doi.org/10.1080/10875549.2016.1204646 Islam, T., Newhouse, D., & Yanez-Pagans, M. (2018). International comparisons of poverty in South Asia (Policy Research Working Paper No. 8683). World Bank. Retrieved from https://openknowledge.worldbank.org/entities/publication/3655b36a-2c87-5ca9-b07f-8e0985a41ea2 Joshi, N. P., Maharjan, K. L., & Piya, L. (2010). Poverty and food insecurity in Nepal a review (MPRA Paper No. 35387). Retrieved from https://mpra.ub.uni-muenchen.de/35387/ Jula, K. M., & Beriso, B. S. (2023). Determinants of household poverty in Ethiopia. Journal of Poverty, 27(5), 391–403. https://doi.org/10.1080/10875549.2022.2113589 Kassie, G. T., Abate, T., Langyintuo, A., & Maleni, D. (2014). Poverty in maize growing rural communities of Southern Africa. Development Studies Research, 1(1), 311–323. https://doi.org/10.1080/21665095.2014.969844 Khatiwada, S. P., Deng, W., Paudel, B., Khatiwada, J. R., Zhang, J., & Su, Y. (2017). Household livelihood strategies and implication for poverty reduction in rural areas of central Nepal. Sustainability, 9(4), 612. https://doi.org/10.3390/su9040612 King, M. F., Renó, V. F., & Novo, E. M. L. M. (2014). The concept, dimensions and methods of assessment of human well-being within a socioecological context: A literature review. Social Indicators Research, 116(3), 681–698. https://doi.org/10.1007/s11205-013-0320-0 Klasen, S. (2008). Economic growth and poverty reduction: Measurement issues using income and non-income indicators. World Development, 36(3), 420–445. https://doi.org/10.1016/j.worlddev.2007.03.008 Lanjouw, P., & Ravallion, M. (1995). Poverty and household size. The Economic Journal, 105(433), 1415–1434. https://doi.org/10.2307/2235108 Lewis, W. A. (1954). Economic development with unlimited supplies of labour. The Manchester School, 22(2), 139–191. https://doi.org/10.1111/j.1467-9957.1954.tb00021.x Li, Y., Jin, Q., & Li, A. (2022). Understanding the multidimensional poverty in South Asia. Journal of Geographical Sciences, 32(10), 2053–2068. https://doi.org/10.1007/s11442-022-2036-z Lokshin, M. M., Glinskaya, E., & Garcia, M. (2004). The effect of early childhood development programmes on women’s labour force participation and older children’s schooling in Kenya. Journal of African Economies, 13(2), 240–276. https://doi.org/10.1093/jae/ejh009 Lyu, Y., Xu, Q., & Liu, J. (2024). Exploring the medical decision-making patterns and influencing factors among the general Chinese public: A binary logistic regression analysis. BMC Public Health, 24(1), 887. https://doi.org/10.1186/s12889- 024-18338-8 Maltsoglou, I., & Taniguchi, K. (2004). Poverty, livestock and household typologies in Nepal (ESA Working Paper No. 04–15). The Food and Agriculture Organization. Retrieved from https://www.fao.org/4/ae125e/ae125e00.htm Maru, S. (2010). Magnitude and determinants of rural poverty in Zeghe Peninsula, Ethiopia. Journal of Poverty, 14(3), 308–328. https://doi.org/10.1080/10875549.2010.494953 Mdluli, P., & Dunga, S. (2022). Determinants of poverty in South Africa using the 2018 general household survey data. Journal of Poverty, 26(3), 197-213. https://doi.org/10.1080/10875549.2021.1910100 Ministry of Finance. (2024). Economic survey 2022/23. Retrieved from https://mof.gov.np/site/publication-detail/3344 Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40 39 Mora‐Rivera, J., Fierros‐González, I., & García‐Mora, F. (2024). Determinants of poverty among Indigenous people in Mexico’s Guerrero Mountain Region. Development Policy Review, 42(1), e12733. https://doi.org/10.1111/dpr.12733 National Planning Commission. (2016). Nepal and the millennium development goals: Final status report 2000-2015. Retrieved from https://www.npc.gov.np/images/category/mdg-status-report-2016.pdf National Planning Commission. (2024a). 16th plan (2081/82-2085/86). Retrieved from https://npc.gov.np/en/category/periodic_plans National Planning Commission. (2024b). LDC graduation: Smooth transition strategy. Retrieved from https://www.undp.org/nepal/publications/ldc-graduation-smooth-transition-strategy National Statistics Office. (2024). Nepal living standards survey IV 2022-23. https://nsonepal.gov.np/content/9707/9707- summary-results-of-nepal-livin/ Neway, M. M., & Massresha, S. E. (2022). The determinants of household poverty: The case of Berehet Woreda, Amhara regional state, Ethiopia. Cogent Economics & Finance, 10(1), 2156090. https://doi.org/10.1080/23322039.2022.2156090 Olarinde, L. O., Abass, A. B., Abdoulaye, T., Adepoju, A. A., Fanifosi, E. G., Adio, M. O., Adeniyi, O. A., & Wasiu, A. (2020). Estimating multidimensional poverty among Cassava producers in Nigeria: Patterns and socioeconomic determinants. Sustainability, 12(13), 5366. https://doi.org/10.3390/su12135366 Olsson, L., Opondo, M., Tschakert, P., Agrawal, A., Eriksen, S., Ma, S., ... & Zakieldeen, S. (2014). Livelihoods and poverty. In Climate Change 2014 Impacts, Adaptation and Vulnerability: Part A: Global and Sectoral Aspects (pp. 793-832). Cambridge University Press. Retrieved from http://www.ipcc.ch/pdf/assessment-report/ar5/wg2/WGIIAR5- Chap13_FINAL.pdf Özpinar, Ş., & Akdede, S. H. (2022). Determinants of the attribution of poverty in Turkey: An empirical analysis. Social Indicators Research, 164(2), 949-967. https://doi.org/10.1007/s11205-022-02988-5 Paulos Borko, Z. (2017). Determinants of poverty in rural households (The Case of Damot Gale district in Wolaita Zone): A household level analysis. International Journal of African and Asian Studies, 29, 68-75. Retrieved from https://core.ac.uk/download/pdf/234690246.pdf Peters, D. H., Garg, A., Bloom, G., Walker, D. G., Brieger, W. R., & Hafizur Rahman, M. (2008). Poverty and access to health care in developing countries. Annals of the New York Academy of Sciences, 1136(1), 161–171. https://doi.org/10.1196/annals.1425.011 Pokharel, T. (2015). Poverty in Nepal: Characteristics and challenges. Journal of Poverty, Investment and Development, 11, 44– 55. Retrieved from https://dms.nasc.org.np/sites/default/files/documents/Poverty%20in%20Nepal.pdf Poston, D. L., Conde, E., & Field, L. M. (2024). Applied regression models in the social sciences. Cambridge University Press. https://doi.org/10.1017/9781108923071 Quispe-Mamani, J. C., Aguilar-Pinto, S. L., Calcina-Álvarez, D. A., Ulloa-Gallardo, N. J., Madueño-Portilla, R., Vargas- Espinoza, J. L., Quispe-Mamani, F., Cutipa-Quilca, B. E., Tairo-Huamán, R. N., & Coacalla-Vargas, E. (2022). Social factors associated with poverty in households in Peru. Social Sciences, 11(12), 581. https://doi.org/10.3390/socsci11120581 Rahman, S. (2009). Microdeterminants of poverty among the farming population in Bangladesh. Outlook on Agriculture, 38(4), 349-355. https://doi.org/10.5367/000000009790422188 Ravallion, M., Chen, S., & Sangraula, P. (2009). Dollar a day revisited. The World Bank Economic Review, 23(2), 163–184. https://doi.org/10.1093/wber/lhp007 Rigg, J. (2018). Rethinking Asian poverty in a time of Asian prosperity. Asia Pacific Viewpoint, 59(2), 159–172. https://doi.org/10.1111/apv.12189 Sahoo, P., Mondal, S., & Paltasingh, K. R. (2024). Unveiling poverty dynamics in India: Examining convergence and determinants at sub-national level. Journal of Poverty, 1-24. https://doi.org/10.1080/10875549.2024.2393126 Saleem, S., Aslam, M., Sherwani, R. A. K., Jadoon, A. K., Sarwar, A., & Butt, I. (2023). Determinants of rural household poverty in Pakistan with multilevel approach. Cogent Economics & Finance, 11(1), 2202048. https://doi.org/10.1080/23322039.2023.2202048 Salike, N., Wang, J., & Regis, P. (2022). Remittance and its effect on poverty and inequality: A case of Nepal. NRB Economic Review, 34(2), 1–19. Retrieved from https://www.nrb.org.np/er-article/remittance-and-its-effect-on-poverty-and- inequality-a-case-of-nepal Sen, A. (1976). Poverty: An ordinal approach to measurement. Econometrica, 44(2), 219. https://doi.org/10.2307/1912718 Sen, A. (1992). The political economy of targeting. World Bank. Retrieved from https://socialprotection.gov.bd/wp- content/uploads/2017/07/Amartya-Sen-on-Targeting.pdf Sen, A. (1999). Development as freedom. Oxford University Press. 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 Sewell, S. J., Desai, S. A., Mutsaa, E., & Lottering, R. T. (2019). A comparative study of community perceptions regarding the role of roads as a poverty alleviation strategy in rural areas. Journal of Rural Studies, 71, 73–84. https://doi.org/10.1016/j.jrurstud.2019.09.001 Shah, S., & Debnath, N. (2022). Determinants of multidimensional poverty in rural Tripura, India. Journal of Quantitative Economics, 20(1), 69-95. https://doi.org/10.1007/s40953-021-00256-w Studenmund, A. H. (2021). Using econometrics: A practical guide. Pearson Education Limited. Sumner, A. (2007). Meaning versus measurement: Why do ‘economic’ indicators of poverty still predominate? Development in Practice, 17(1), 4–13. https://doi.org/10.1080/09614520601092485 Sun, H., Li, X., Li, W., & Feng, J. (2022). Differences and influencing factors of relative poverty of urban and rural residents in China based on the survey of 31 provinces and cities. International Journal of Environmental Research and Public Health, 19(15), 9015. https://doi.org/10.3390/ijerph19159015 Gajurel et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 29-40 40 Thapa, S. B. (2013). Relationship between education and poverty in Nepal. Economic Journal of Development Issues, 15(2), 148– 161. https://doi.org/10.3126/ejdi.v15i1-2.11873. Townsend, P. (1954). Measuring poverty. The British Journal of Sociology, 5(2), 130. https://doi.org/10.2307/587651 Townsend, P. (1971). Concept of poverty. Heinemann Educ. Townsend, P. (1979). Poverty in the United Kingdom. Allen Lane and Penguin Books. Uematsu, H., Shidiq, A. R., & Tiwari, S. (2016). Trends and drivers of poverty reduction in Nepal: A historical perspective (Policy Research Working Paper No. WPS 7830). World Bank Group. Retrieved from http://documents.worldbank.org/curated/en/285041474464529392/Trends-and-drivers-of-poverty-reduction-in-Nepal- a-historical-perspective United Nations Development Programme. (2024). Human development report 2023/2024. Retrieved from https://www.undp.org/sites/g/files/zskgke326/files/2024-03/hdr_report_2024.pdf United Nations. (2009). Rethinking poverty: Report on the world social situation 2010. Retrieved from https://www.un.org/esa/socdev/rwss/docs/2010/fullreport.pdf United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. Retrieved from https://sdgs.un.org/2030agenda United Nations. (2022). Ending poverty. Retrieved from https://www.un.org/en/global-issues/ending-poverty Ventura, L. (2024). Poorest countries in the world 2024. Global Finance Magazine. Retrieved from https://gfmag.com/data/economic-data/poorest-country-in-the-world/ Vera-Toscano, E., Shucksmith, M., Brown, D. L., & Brown, H. (2024). The rural–urban poverty gap in England after the 2008 financial crisis: Exploring the effects of budgetary cuts and welfare reforms. Regional Studies, 58(6), 1264–1281. https://doi.org/10.1080/00343404.2023.2235374 Wagle, U. R. (2008). Economic inequality in the ‘democratic’ Nepal: Dimensions and political implications [Himalayan Policy Research Conference]. Retrieved from https://microdata.worldbank.org/index.php/citations/2390 Wang, C., Zeng, B., Luo, D., Wang, Y., Tian, Y., Chen, S., & He, X. (2021). Measurements and determinants of multidimensional poverty: Evidence from mountainous areas of southeast China. Journal of Social Service Research, 47(5), 743-761. https://doi.org/10.1080/01488376.2021.1914283 Worku, Y., & Muchie, M. (2012). An attempt at quantifying factors that affect efficiency in the management of solid waste produced by commercial businesses in the city of Tshwane, South Africa. Journal of Environmental and Public Health, 2012(1), 165353. https://doi.org/10.1155/2012/165353 World Bank. (2001). World Bank report 2000/2001: Attacking poverty. Oxford University Press. Retrieved from https://openknowledge.worldbank.org/entities/publication/e32e2552-b75e-56de-9675-107cb047d078 World Bank. (2022). Fact sheet: An adjustment to global poverty lines. World Bank. Retrieved from https://www.worldbank.org/en/news/factsheet/2022/05/02/fact-sheet-an-adjustment-to-global-poverty-lines World Bank. (2023). Accelerating the productive use of electricity: Enabling energy access to power rural economic growth. Retrieved from http://documents.worldbank.org/curated/en/099092023192023389/P1751521d3f58f6f1307c1499619e141b8baef6de8d d World Bank. (2024). Multidimensional poverty measure. Retrieved from https://www.worldbank.org/en/topic/poverty/brief/multidimensional-poverty-measure World Health Organization. (2003). DAC guidelines and reference series poverty and health. OECD Publishing. Retrieved from https://www.oecd.org/content/dam/oecd/en/publications/reports/2003/04/poverty-and- health_g1gh3188/9789264100206-en.pdf Publisher’s Note: CRIBFB stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. © 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). Bangladesh Journal of Multidisciplinary Scientific Research (P-ISSN 2687-850X E-ISSN 2687-8518) by CRIBFB is licensed under a Creative Commons Attribution 4.0 International License.` http://creativecommons.org/licenses/by/4.0). http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/