Lower Poverty Rates Research Articles

Save A Leg – Exploring Disparities in Community Characteristics Between Regions of High and Low Leg Amputation Rates

ObjectiveTo explore the disparities in community characteristics between the regions with the highest and lowest leg amputation rates. ApproachThis study used an exploratory method to describe the disparities between community characteristics of these two regions. The study team used several data sources: 1) We used the Dartmouth Atlas Healthcare Map to identify the highest and lowest leg amputation areas. This data was based on leg amputations per 1,000 Medicare enrollees by Hospital referral regions (HRRs) in 2015. 2) We used population estimates and income data from The Census Bureau. 3) We gathered climate, crime index, walk score, bike lanes, and park data from numerous websites. ResultsFlorence, SC has the highest leg amputation rate and Bridgeport, CT has the lowest across the nation. Florence also shows higher cardiovascular disease and diabetes discharge rates. Florence’s population has a slightly higher percentage of persons 65 years and over, a higher percentage of bachelor’s degrees, a higher per capita income, and a lower poverty rate. Florence also has fewer parks, is less walkable or bikeable, and has worse crime. It is worth noting that Bridgeport is close to Yale New Haven Hospital, which has a Limb Preservation Program that could contribute to the region’s extremely low leg amputation rate. ConclusionsThere are disparities in community characteristics between the regions with the highest and lowest leg amputation rates. ImplicationsFuture work needs to investigate the association between community characteristics and leg amputation rates and assess the impact of the Limb Preservation Program.

The influence of selective mortality on poverty rates in later life: evidence from a Swedish cohort born in 1926.

Mortality influences the composition of the surviving population. Higher mortality among low-income individuals than high-income individuals may result in lower poverty rates in the surviving population. The objective of this study was to describe poverty rates for both survivors and deceased individuals in a cohort born in 1926. We used Swedish total population data on the 1926 birth cohort (n = 83,382), calculating annual poverty rates from 1991 to 2016. We compared poverty rates for the entire cohort, those who died the next year, and those who survived for 5, 10, or 20+ years, measuring the impact of selective mortality as the differences in poverty rates between the cohort and these subgroups. Individuals who died the following year had higher poverty rates than the cohort at ages 65-90. Conversely, individuals who survived 5, 10, or 20+ years had lower poverty rates, with relatively small differences (1.1% to 6.9% lower) for survivors of 5 years or longer, and larger differences (26.4% to 32.8% lower) for those who survived 20+ years. Despite differences in mortality rates by income, selective mortality had only a modest impact on poverty rates. If life expectancy rises for all, and a more diverse population reaches old age, our findings indicate a potential slight increase in poverty rates due to reduced mortality as a selective factor. These findings emphasise the need to consider mortality selection when addressing future poverty rates in older adults.

Pakistan’s path to universal health coverage: national and regional insights

BackgroundUniversal Health Coverage (UHC) is a common health policy objective outlined in the Sustainable Development Goals. With provincial governments taking the initiative, Pakistan has implemented and extended UHC program amid a complex public health landscape. In this context, we assess Pakistan’s progress toward achieving UHC at the national and subnational level.MethodsWe use data from the Demographic and Health Surveys and the Household Integrated Economic Survey to construct a UHC index at the national and subnational level for 2007, 2013, and 2018. Furthermore, we use Concentration Index (CI) and CI decomposition methodologies to assess the primary drivers of inequality in accessing medical services. Logistic regression and Sartori’s two-step model are applied to examine the key determinants of catastrophic health expenditure (CHE).ResultsOur analysis underscores Pakistan’s steady progress toward UHC, while revealing significant provincial disparities in UHC progress. Provinces with lower poverty rate achieve higher UHC index, which highlights the synergy of poverty alleviation and UHC expansion. Among the examined indicators, child immunization remains a key weakness that one third of the children are not fully vaccinated and one sixth of these not-fully-vaccinated children have never received any vaccination. Socioeconomic status emerges as a main contributor to disparities in accessing medical services, albeit with a declining trend over time. Household socioeconomic status is negatively correlated with CHE incidence, indicating that wealthier households are less susceptible to CHE. For individuals experiencing CHE, medicine expenditure takes the highest share of their health spending, registering a staggering 70% in 2018.ConclusionPakistan’s progress toward UHC aligns closely with its economic development trajectory and policy efforts in expanding UHC program. However, economic underdevelopment and provincial disparities persist as significant hurdles on Pakistan’s journey toward UHC. We suggest continued efforts in UHC program expansion with a focus on policy consistency and fiscal support, combined with targeted interventions to alleviate poverty in the underdeveloped provinces.

Assessing economic landscapes: city rankings (2010–2022)

Urban assessment is crucial for understanding the dynamics of city development, identifying areas for improvement, and formulating targeted policies to enhance the quality of life for residents. This study focuses on evaluating Kazakhstan’s megacities across nine indicators grouped into financial and economic condition, business development, and living standards. Using comparative analysis and ranking methods, the study assesses budgetary allocations, population dynamics, industrial growth, and the impact of strategic investments on urban development. Drawing on current economic and demographic trends, the study examines the role of cities as drivers of economic growth and highlights the importance of addressing social and economic inequalities. Through a literature review, the study contextualizes the findings within the broader discourse on urbanization and economic development, emphasizing the need for nuanced strategies to address regional disparities. Methodologically, a rating system based on quantitative indicators from 2010 to 2022 is employed, with a heatmap visualization to illustrate city rankings across various indicators. Using aggregated data on key economic indicators, the cities were classified into three categories: high-ranking, mid-ranking and low-ranking. The study contributes to a deeper understanding of urban development in Kazakhstan and provides insights for policy formulation aimed at promoting sustainable and inclusive growth across its megacities. The city ranking analysis depicts a varied economic landscape in Kazakhstani cities. High-ranked cities exhibit robust economic performance with low poverty rates, strong average salaries, and active retail trade, hinting at promising business prospects. Mid-ranked cities show mixed performances, while low-ranked cities face considerable economic challenges, including higher poverty, lower salaries, and potential declines in trade.

Unveiling the influence of reproductive health on poverty: a cross-sectional study in Magwi County, South Sudan

Objective: To understand the relationship between household poverty and reproductive health characteristics in Magwi County, South Sudan. Background: Household poverty is a significant issue in many countries, including South Sudan. Poverty has been identified as a major obstacle to international development, affecting various aspects of development goals such as food security and health. Despite interventions by organizations like the World Bank, poverty rates in beneficiary countries like South Sudan have not seen significant reductions, suggesting the presence of predictors of poverty from behavioral, structural, and political origins. Methods: The study was conducted using an analytical cross-sectional survey design. The study targeted female and male spouses in households and employed a multistage sampling approach to select participants. Result: The results of the study revealed that the level of household poverty in Magwi County was 42%. The prevalence of household poverty was found to be influenced by reproductive health characteristics. The use of short-acting contraception by women was associated with a higher prevalence of poverty, whereas women who had ever aborted a pregnancy had a lower prevalence of poverty. Unintended pregnancy and inconsistent condom use by men were also associated with higher poverty rates, whereas men with only one sexual partner had lower poverty rates. Conclusion: This study provides valuable insights into the relationship between household reproductive health characteristics and poverty in Magwi County, South Sudan. It emphasizes the need for comprehensive approaches that address both behavioral and structural factors to effectively reduce poverty and improve development outcomes.

INTER-REGIONAL POVERTY DISPARITIES IN JAVA, INDONESIA: AN ANALYSIS OF KEY INFLUENCING FACTORS (2010-2020)

This study seeks to investigate the evolution of poverty in Java from 2010 to 2020, as well as the regional factors that contributing to disparities in poverty between regions. The results of the analysis indicate a downward trend in the number and proportion of poor people on the island of Java but a rise during the COVID-19 pandemic. There are regional distribution groups with high and low poverty rates. The results of the ANOVA reveal significant differences in the occurrence of urban poverty on the island of Java based on province and regional status but not by zone. Positively and negatively, the majority of the predictors have a very significant relationship with poverty. Six regional variables (level of consumption (expenditure), level of health (life expectancy), income per capita, level of education (number of years of schooling), population density (people/km2), and economic potential (GRDP at current prices)) account for 68.10% of the variation in the number of poor people in Java, while the remaining 31.9% is determined by other variables. The model of variation in Java's poverty is determined by three variables: per capita income, GRDP, and public consumption level.

STUDY ON EXCHANGE RATES OF INDONESIAN FARMERS FROM THE PERSPECTIVE OF SUSTAINABLE DEVELOPMENT GOALS

The agriculture sector is critical to the achievement of the first and second Sustainable Development Goals (SDGs). Studying the Farmer Exchange Rate provides insight into the impact of this exchange rate and its influencing elements from the standpoint of the SDGs. One of the issues is the necessity for farmers to gain greater expertise. The purpose of this research is to examine the relationship between the Indonesian Farmer Exchange Rate and the SDGs. A literature review was used in this study, using secondary data gathered from papers and journals containing talks on Farmer Exchange Rates and SDGs that were curated and identified as many as 40 selected research titles. Data analysis was carried out in stages that included data gathering, data reduction, presentation, and conclusion drafting. Data was gathered from the Published or Perish application utilizing Google Scholar literature sources. According to the study's findings, the Farmer Exchange Rate corresponds with poverty, a key target of the SDGs. Because the Farmer Exchange Rate is a measure of farmer welfare, which is tied to poverty, the factors that influence it also affect the SDGs. Lower poverty rates are connected with higher farmer welfare.

Peran Program Dumisake Terhadap Peningkatan Pendapatan UMKM dan Perekonomian Masyarakat Provinsi Jambi Masa Covid-19

The purpose of this study is to determine how the Dumisake Programme affected the MSME income and the general economy of the people living in the Province of Jambi in COVID-19. This study uses a qualitative method and is descriptive in nature. The Cooperatives Service, UMK, Jambi Province served as the research site. The Dumisake Programme, a priority programme for the Jambi Provincial Government, was used to determine the research location to examine the capital assistance programme for MSMEs. The program's goals are to enhance the regional economy, lower rates of poverty and unemployment, and ultimately improve community welfare. Through the distribution of questionnaires and in-person interviews with MSME capital assistance grantees under the Jambi Provincial Government's Dumisake Programme, primary data was gathered. Legal and regulatory records, the 2021–2026 Jambi Province RPJMD, the Strategic Plan and Work Plan of the Cooperative Service, and the Jambi Province UMK serve as research supporting data for this study. Researchers conducted direct interviews with MSME players in Jambi Province who benefited from capital support under the Dumisake Programme. Data Reduction, Data Display (Data Presentation), and Conclusion Drawing/Verification (Conclusion) were the methods used for data analysis in this study. The study's findings demonstrate that the Covid-19 Pandemic had an impact on Micro, Small, and Medium-Sized Enterprises (MSMEs) that were given support by the Jambi Provincial Government. These MSMEs considered the assistance to be highly beneficial for business development, which boosted their revenue. When it comes to helping MSME players in Jambi Province with their business growth, the Jambi Provincial Government's Economic Recovery Programme has shown to be quite beneficial in providing them with products and equipment. The assistance programme has three (three) primary objectives: achieving economic recovery; enhancing MSMEs' capacity to become resilient and independent; and putting in place efficient, transparent, effective, and targeted help channels. The Cooperatives & MSMEs Service (Government) and MSME actors are the next two players engaging in the support programme for MSME actors.

Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia

Extensive research has been conducted on poverty in developing countries using conventional regression analysis, which has limited prediction capability. This study aims to address this gap by applying advanced machine learning (ML) methods to predict poverty in Somalia. Utilizing data from the first-ever 2020 Somalia Demographic and Health Survey (SDHS), a cross-sectional study design is considered. ML methods, including random forest (RF), decision tree (DT), support vector machine (SVM), and logistic regression, are tested and applied using R software version 4.1.2, while conventional methods are analyzed using STATA version 17. Evaluation metrics, such as confusion matrix, accuracy, precision, sensitivity, specificity, recall, F1 score, and area under the receiver operating characteristic (AUROC), are employed to assess the performance of predictive models. The prevalence of poverty in Somalia is notable, with approximately seven out of ten Somalis living in poverty, making it one of the highest rates in the region. Among nomadic pastoralists, agro-pastoralists, and internally displaced persons (IDPs), the poverty average stands at 69%, while urban areas have a lower poverty rate of 60%. The accuracy of prediction ranged between 67.21% and 98.36% for the advanced ML methods, with the RF model demonstrating the best performance. The results reveal geographical region, household size, respondent age group, husband employment status, age of household head, and place of residence as the top six predictors of poverty in Somalia. The findings highlight the potential of ML methods to predict poverty and uncover hidden information that traditional statistical methods cannot detect, with the RF model identified as the best classifier for predicting poverty in Somalia.

How much does immigration contribute to national poverty rates? A decomposition analysis for 17 European welfare states

Abstract Over the past decades, many European welfare states failed to reduce poverty. We examine two coinciding trends: the inability to lower poverty rates, and the growth of immigrant populations. Immigrants have become the main contributor to population growth in Europe and have higher poverty risks than natives. This contribution quantifies to what extent national poverty rates were driven by population change. We present a Kitagawa–Blinder–Oaxaca decomposition of national poverty rates in 17 western European countries between 2005 and 2019 using EU-SILC data. The effect of shifting EU and non-EU immigrant populations on poverty rates is heterogeneous: while poverty in some countries increased substantially due to compositional changes, for the majority the effect was small or negligible, including in countries with above-average growth of foreign-born populations. Overall, in two-thirds of country–years changes in the population composition were not the main driver of national poverty rates.

Single-family rental (SFR) investor types, property conditions, and implications for urban neighbourhoods: evidence from Memphis, Tennessee

Over the last decade the American single-family rental (SFR) market experienced consequential financialisation. The American SFR industry was once dominated by more local, smaller ‘mom-and-pop’ landlords. Despite this, little is known regarding the different types of SFR investors, what types of properties different investor types buy, and what types of neighbourhoods are targeted by different investor types. This article examines a majority-Black American city, Memphis, Tennessee. A city impacted significantly by the subprime mortgage crisis and this subsequent rise in SFR investors in the years that followed. Using multivariate statistics, we find institutional investors are less likely to buy a property in relatively poorer condition than categorically smaller investors. Moreover, institutional actors are more likely to purchase an SFR if it is newer, lower-valued, and located in a neighbourhood with relatively large Black or Hispanic populations and a lower-poverty rate. These findings challenge some of the existing SFR investor literature and have implications for urban planning, research, and policy.

Women farmers and community well‐being under modeling uncertainty

AbstractWe examine the association between woman farmers and community well‐being using U.S. county‐level data. We address modeling uncertainty around three measures of community well‐being by using a spatial Bayesian model averaging approach and find that a higher share of farms operated or owned by women in a county is associated with higher rates of new business formation, longer life expectancies, and lower poverty rates. The results are consistent with a growing literature that finds women business owners approach their businesses more holistically, with positive community spillovers.

Interregional Labor Migration and Its Role in Shaping Economic Growth: A Case Study of Casual Worker Migrants in Indonesia

Purpose: This study analyzed the determinants and impacts of casual migrant workers that was conducted in East Java, which is known as one of highest economic growth area in Indonesia and experiencing a higher influx of migrants compared to migrants leaving the region. Design/Methodology/Approach: This study employs the two-step heckman method. The sample consists of workers who engaged in interregional migration within East Java on 2015. The sample was selected based on characteristics that align with the research requirements, extracted from secondary data derived from the 2015 inter-census population survey. The data type used is cross-sectional data. Findings: The research findings indicate that significant determinants of casual migrant workers are migrants of non-productive age, male migrants, migrants with non-bachelor's/diploma, unmarried migrants, migrants without family under 5 years old, migrants originating from predominantly wooden/board/earthen floors, migrants from higher minimum wage regions than the destination, non-metropolitan city migrants, and migrants from areas with a lower poverty rate than destination region, which are more likely to move and become migrants. The results for the significant impact of casual migrant workers on the economic sector are influenced by casual migrant workers originating from non-metropolitan city regions, which contribute the most to the destination region's GRDP. Research, practical & social implications: This study formulates policy strategies that can be employed by government to enhance the quality of human resources and expand employment opportunities, particularly for casual migrant workers, as an endeavor to boost economic growth in the destination regions of migration.

Credit Cooperatives and Income Growth: Analyzing the Role of Financial Sustainability

Credit cooperatives not only promote the rural economy but also play an important role in providing effective and green financing. This paper aims to study the role of financial sustainability in the relationship between credit cooperation and income growth. The scale of the credit cooperatives and the financial sustainability were selected as two instrumental variables to mitigate the endogeneity of credit cooperative loans. Based on the data from 74 village‐level surveys collected from Inner Mongolia, the credit effect and income increase impact of the credit cooperative scale and financial sustainability were analyzed by two‐stage least‐squares (2SLS) analysis. The study found that the scale of the credit cooperatives can positively affect credit cooperative loans to increase farmer income, with financial sustainability playing as a moderator; credit cooperatives will have a more significant income‐increasing effect on villages with a better economic foundation or lower poverty rate but will have no significant effect on villages with a poor economic foundation or high poverty rate.

A regional stocktake of maize yield vulnerability to droughts in the Horn of Africa.

Climate projections in sub-Saharan Africa predict increased frequency of droughts with parallel impacts on crop yield. The Horn of Africa is among the most vulnerable regions in Africa to these changes because agriculture in general andmaize productionin particularly is highly climate driven, and rain-fed. Current research approaches have mostly focused on the climatic and biophysical drivers of crop yield without including the socio-economic drivers of crop yield. This study fills this gap by investigating the vulnerability of maize yield in the Horn of Africa to climate and socio-economic indicators. The hypothesis is that there is an inverse relationship between vulnerability and adaptive capacity. The vulnerability index is a composite index that integrates sensitivity, exposure, and adaptive capacity sub-indices. Maize yield data to compute the sensitivity index were collected from FAOSTAT, precipitation data to compute the exposure index were collected from the Climate Research Unit (CRU), and the data for the proxies of adaptive capacity were collected from the readiness index database on figshare. From the results, Somalia records the highest vulnerability index of 1.15, followed by Ethiopia with a vulnerability index of 0.61. Kenya records the lowest vulnerability index of 0.33. Also, there is a positive relationship between the vulnerability, sensitivity, and the exposure indices and an inverse relationship between the vulnerability index and the adaptive capacity index. The high vulnerability index recorded in Somalia is accentuated by a low adaptive capacity index of 0.44 that is anchored on low literacy and high poverty rates. As Somalia records the lowest adaptive capacity index of 0.44, Ethiopia and Kenya record 0.91 and 0.99 respectively. This study has shown that to better understand vulnerability, a shift from the old paradigm that focuses on the climatic variables to integrating socio-economic variables or proxies of adaptive capacity whichenhances ourunderstanding ofvulnerability. Though leveraging the benefits of climatic and non-climatic variables is important, the challenge so far has been on how to integrate these in the same model; a challenge this work has succinctly overcome by integrating adaptive capacity in the vulnerability equation.

Determining the Optimum Number of Clusters in Hierarchical Clustering Using Pseudo-F

Poverty refers to the condition where a person cannot meet the basic necessities based on the minimum living standards. Statistics Indonesia proxied an increase in the poverty rate in North Sumatra Province in 2021 from 8.75% to 9.01%. However, this increase is exclusive to North Sumatra Province, which has Indonesia's 3rd largest number of districts/cities. This study discussed mapping the North Sumatra Province region based on 10 poverty factor variables. The 10 variables are life expectancy, health complaints, poverty line, Gross Regional Domestic Product (GRDP), population growth rate, Expected Years of Schooling (EYS), Human Development Index (HDI), labor force participation rate, open unemployment rate, and district/city minimum wage. The Hierarchical Clustering analysis was employed to compare single, complete, and average linkage methods. The best method was determined based on the pseudo-F statistic value. 4 clusters had complete linkage methods, each of which possessed varied characteristics. Cluster 1 contains cities with the lowest poverty rate, including Medan City and Pematang Siantar City. Cluster 2 consists of cities with low poverty rates, while Cluster 3 consists of cities with high poverty rates. Cities that are included in Cluster 4 have very high poverty rates, including South Nias District and Pakpak Bharat District. The clusters present significant poverty rate gaps among North Sumatra Province regions.

Poverty Level Grouping in West Java Province with the K-Means Clustering Method

Poverty in a region will have an impact on hampering national development. Poverty is an economic disease that is often faced by every country, including Indonesia. According to information obtained from the Central Bureau of Statistics, we can gather data on the poverty rate in all provinces of Indonesia, with a particular focus on the province of West Java. West Java province is one of the provinces with the highest population density on the island of Java, which is ranked 2nd after the province of DKI Jakarta and ranks 4th for the province with a high percentage of poor people after DI. Yogyakarta, Central Java, and East Java. Consequently, it is crucial for the regional government to identify areas with high, moderate, or low poverty rates. This information will enable the local government to formulate appropriate policies and prioritize interventions to address poverty effectively. In this study, the K-Means clustering method was used to classify poverty rates based on two variables, namely the community development index and the open unemployment rate using the help of RStudio software. The findings indicated that the application of the elbow method in West Java province resulted in the identification of three distinct clusters of districts/cities that stood out as the most prominent. Cluster 1 (districts/cities with relatively high poverty rates), cluster 2 (districts/cities with low poverty rates), cluster 3 (districts/cities with high poverty rates). Regencies/cities that fall into the category with a high poverty rate are Sukabumi, Cianjur, Garut, Tasikmalaya, Ciamis, Kuningan, Cirebon, Majalengka, Indramayu, Subang, West Bandung, and Pangandaran.

Regional rural transformation and its association with household income and poverty incidence in Indonesia in the last two decades

Increasing rural household income and reducing poverty rank among Indonesia’s top development priorities. Promoting rural transformation is one strategic policy framework to achieve these goals. In the last three decades, agricultural production has shifted from low-value food crops to high-value commodities, such as horticulture, estate crops, and livestock. Previous studies have analyzed rural transformation in Indonesia at the national level, but information on the magnitudes of impact across regions remains scarce. This study aims to analyze the changes in rural transformation at a regional level in the past two decades. The research utilizes secondary data from Statistics Indonesia (BPS), covering 34 provinces from 2000 to 2020, analyzed using descriptive and panel data regression analyses. The results show an increasing trend in the share of high-value agriculture (RT1) and rural non-farm employment (RT2). Both RT1 and RT2 are positively associated with the growth of rural household income and a lower poverty rate. However, the speed of structural transformation (ST), RT1, RT2, rural income growth, and poverty reduction vary across regions. This research implies that improving rural income and reducing poverty should be done by integrating policies, i.e., promoting high-value agriculture and expanding rural non-farm employment. Particular attention should also be given to provinces with slow growth in ST, RT1, RT2, and rural household income.

Transplantation, Waitlist Status, and County Indices of Economic Health in West Virginia.

Kidney transplantation is the optimal treatment for patients with end stage kidney disease. Given lower rates of kidney transplant in West Virginia (WV), we searched for potential predictors, focusing on indices of economic health. Data on initiation of kidney replacement therapy, being waitlisted for and receiving a kidney transplant between 1965 and 2020 were collected from United States Renal Data System (USRDS). Data on county poverty rate (PR) and economic distress were collected from the Appalachian Regional Commission. Of the 23,055 WV patients identified, 2,999 (13%) were transplanted compared to 514,050 (15.3%) for the rest of the US (p< 0.001). Patients who never received a kidney transplant were from counties with higher PRs (17.95%) compared to transplanted patients (17.44%); p<0.001. Waitlisted patients (2,375) came from counties with lower PRs than those who were never waitlisted (17.48 vs 17.94%; p<0.001). Waitlisted patients were less likely to be from distressed or at-risk counties (32 vs 34.3%; p=0.05) than patients who were never waitlisted. In multivariable logistic regression, county PR remained an independent predictor of being transplanted (OR 0.91, 95% CI: 0.85-0.97; p=0.008) or waitlisted (OR 0.9, 95% CI: 0.84-0.96; p=0.001) per 5% increase in poverty rate after adjusting for age, sex, BMI, alcohol use, functional status and comorbid medical conditions. Group/employer health insurance appeared to modify the relationship between PR and waitlist, where PR remained an independent predictor of waitlisting among patients with insurance. Waitlisted patients and transplant recipients from WV were more likely to hail from counties with lower PRs and those in better economic health.

A Best-Bet System of Rice Intensification and Poverty Alleviation in Zamfara State, Nigeria: A Mixed Method Analysis

CONTEXTZamfara State is impoverished due to low agricultural productivity, inadequate infrastructure, and insecurity. Research has explored System of Rice Intensification (SRI)’s agronomic benefits but not its monetary and non-monetary advantages for rural households in the State. OBJECTIVEThe study investigates the effectiveness of a best-bet SRI (BB-SRI) as a poverty alleviation strategy in Zamfara State by utilizing Bakolori Irrigation Scheme as a case study. The study's anticipated outcome is to provide insights that inform policies and practices in the agricultural sector. METHODA mixed-method approach, including survey and field experiment, was used to gather quantitative and qualitative data from 300 rice farming households, consisting of both users and non-users of BB-SRI in the BIS, Zamfara State. Foster-Greer-Thorbecke method with two poverty lines and a three-dimensional poverty index with three poverty cutoffs were used to analyze monetary and non-monetary poverty, respectively, while three impact parameters were estimated using two selection models. RESULTS AND CONCLUSIONAgriculture is a critical sector in the BIS area of Zamfara State, Nigeria, but limited electricity and large household sizes pose challenges for productivity and food security. The BB-SRI farming method, which is costlier but yields more rice, has been found to increase revenue and profitability for farmers. Poverty incidence varies significantly depending on the production method used, with BB-SRI users experiencing lower poverty rates than traditional farming methods. BB-SRI reduces non-monetary poverty by 52% among its users in BIS and has the potential to decrease poverty by 16% if adopted by all rice farming households in the region. However, the monetary benefits mainly go to those near the upper monetary poverty line, with the poorest sector benefiting the least. While BB-SRI reduces monetary poverty incidence by 83% among its users, there is a 10% increase in extreme monetary poverty. The poverty alleviation impact of BB-SRI varies across different sectors, with some showing larger reductions than others. Overall, if all farmers used BB-SRI, monetary poverty incidence would decrease by 25%, with an actual reduction of 52% among its users. SIGNIFICANCETargeted policies can ensure disadvantaged communities benefit from SRI adoption, balancing trade-offs between increased yields and reduced off-farm work earnings. Poverty alleviation policies are needed to support vulnerable populations, as SRI adoption can improve food security and livelihoods. This research on SRI's impact on poverty reduction in Zamfara State and northern Nigeria contributes to sustainable agriculture literature and policy development.