AGRARIS: Journal of Agribusiness and Rural Development Research Vol.
11 No.
2 July Ae December 2025.
Pages: 255-275
RESEARCH ARTICLE
Article history:
Submitted : December 27th, 2024 Revised : June 28th, 2025 Accepted : November 27th, 2025 Lukman Hakim 1,2*.
Lala M.
Kolopaking 1.
Sofyan Sjaf 1, and Rilus A.
Kinseng1 Department of Communication and Community Development Sciences.
Faculty of Human Ecology.
IPB University.
Bogor.
Indonesia Rural Sociology Study Program.
Faculty of Human Ecology.
IPB University.
Bogor.
Indonesia *) Correspondence email: lukmanhakim@apps.
Understanding Poverty and Social Assistance Distribution: A Multidimensional Approach to Rural Poverty in Indonesia DOI: https://doi.
org/10.
18196/agraris.
ABSTRACT
Poverty is a crucial issue to address in sustainable development, particularly in rural areas.
Despite the governmentAos attempts to intervene through social assistance programs for people experiencing poverty, poverty remains unabated.
This study investigated rural poverty in Indonesia using three major analytical frameworks: the World BankAos poverty line, the BPS-Statistics Indonesia poverty line, and IndonesiaAos Law on Poverty Alleviation.
Employing a quantitative approach with 289 respondents across three villages in West Java, the study integrated income-based and access-based indicators to construct six categories of poverty: .
extreme poverty, .
vulnerable to extreme poverty, .
non-monetary poverty, .
regional poverty, .
vulnerable to regional poverty, and .
non-poor.
The findings revealed that 35.
99% of social assistance distribution was misallocated, with 12.
46% of poor people excluded and 23.
53% of non-poor beneficiaries incorrectly included.
By introducing a refined, empirically grounded classification system, this research enhanced multidimensional poverty measurement and advocated for systemic reforms in data collection to improve the accuracy and targeting of rural social assistance programs.
Keywords: Poverty categories.
Rural poverty.
Social assistance.
Social protection INTRODUCTION Poverty remains a persistent and multifaceted challenge in global development, particularly in developing countries.
In this context, the concept of social protection has emerged as an essential policy framework for poverty alleviation.
According to Barrientos .
, social protection encompasses various public policies and programs designed to safeguard individuals and households against risks, vulnerabilities, and poverty.
In developing countries, social protection has a primary focus on reducing extreme poverty and empowering the most vulnerable populations.
Social protection has three key roles: ensuring the fulfillment of basic needs, facilitating investment in productive assets to improve long-term welfare, and strengthening the participation of people experiencing poverty in economic However, challenges such as a weak governance system, corruption, and limited state capacity remain significant obstacles to its implementation (Banerjee.
Hanna.
Olken, & Lisker, 2.
ISSN: 2407-814X .
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AGRARIS: Journal of Agribusiness and Rural Development Research Both structural and cultural factors cause poverty in Indonesia.
Structural drivers include economic inequality, limited access to resources, inadequate education, deficient basic services, and a bureaucracy often lacking transparency and accountability (Asian Development Bank, 2023.
World Bank Group, 2.
At the cultural level, a fatalistic worldview, dependency norms, and entrenched social constructs that marginalize vulnerable groups, particularly women and youth, serve to perpetuate poverty (Handoyo.
Hidayatina, & Purwanto, 2021.
Sriwahyuni et al.
, 2025.
Tetep.
Suherman.
Susanti, & Nisa, 2.
These structural and cultural dimensions are mutually reinforcing, generating a persistent cycle of poverty that is hard to overcome, especially in rural areas (Ambya.
Nairobi, & Rizqiandri.
Hanandita & Tampubolon, 2016.
Sjaf, 2.
Countries have adopted social assistance programs since the 1980s to address poverty.
However, their effectiveness is still widely debated.
Social protection, primarily through income support and cash transfers, plays a significant role in stabilizing household incomes during major crises such as the COVID-19 pandemic (Brik, 2024.
Brollo.
Ibarra, & Vale.
Gasior.
Jara, & Makovec, 2024.
Roelen et al.
, 2.
In the early stages, social assistance can provide real benefits to ensure peopleAos purchasing power (Agustanta.
Dewi Anggalini.
Septiningrum, & Dewanti, 2024.
Unnikrishnan, 2.
, but in the later stages, these programs often create dependency that hinders the economic independence of recipients (Gassmann.
Martorano, & Waidler, 2022.
Jara & Ludeya, 2024.
Reyes, 2.
This challenge is widely felt by developing countries encountering structural poverty issues.
Although social assistance provides economic benefits in the initial stages, the emergence of economic dependency indicates challenges to the long-term effectiveness of these social protection programs.
The politicization of social protection programs exacerbates this further.
Politically, social protection programs are often employed as political instruments.
According to Tekgyy .
, social assistance serves not only as an instrument to overcome poverty but also as a means for political actors to raise their popularity and garner support from the electorate in general elections.
Social protection programs, such as direct cash assistance, have different impacts on the level of political support for the government between recipients and non-recipients.
The politicization of social assistance harms accurate distribution targets, exacerbating social inequality and contributing to political instability (Kosec & Mo, 2.
The deviation in the utilization of social assistance instruments from the government in addressing peopleAos economic problems also impacts ecological degradation (Gupta et al.
, 2.
Poverty is a significant development issue in Indonesia.
The national poverty rate declined from 17.
8% in 2006 to 9.
4% in 2019.
However, the COVID-19 pandemic has led to a temporary rise in poverty rates, particularly among women and young workers, although the government has responded by implementing social assistance programs (Adam & Negara.
Several studies have demonstrated that IndonesiaAos social assistance programs remain It has been depicted that social assistance spending has no discernible impact on poverty rates in several locations (Putri.
Abral, & Amri, 2.
In the context of managing COVID-19, social assistance programs frequently encounter issues with disparate and ISSN: 2407-814X .
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overlapping budget allocations in each ministry, unintegrated recipient data, non-inclusive data collection, and an aid distribution system that fails to reach its targets (Kari.
Satar, & Tedong, 2024.
Ncube & Murray, 2024.
Saddam & Saribulan, 2.
Unsynchronized budget allocations, unintegrated data recipients, improper distribution methods, and politicization of social assistance are among the general issues with the operation of social assistance programs (Gromadzki.
SaCach, & BrzeziEski, 2024.
Hidayat, 2.
The top-down method of selecting aid recipients is frequently exclusive, which means it does not fully address the needs of people experiencing poverty.
It implies that the accuracy of the data is critical to the effectiveness of social assistance programs.
Ineffective policies, exclusive innovations, and program budgeting that could lead to corruption are all consequences of insufficient data (Gaduh.
Hanna, & Olken, 2024.
Pitaloka.
Hendriyani.
Eriyanto, & Haryatmoko, 2022.
Saffanah.
Hapsari, & Iskanda, 2024.
Sjaf et al.
, 2.
As a result, social assistance programs, which frequently serve as instruments to alleviate poverty, do not, in actuality, significantly reduce poverty.
Since 2010, the Indonesian government has implemented various initiatives to alleviate poverty, one of which is the establishment of the National Team for Accelerating Poverty Reduction (TNP2K).
This strategy involves reducing the financial burden on low-income groups through subsidies and social assistance, while also empowering them to enhance their economic productivity (Amin, 2.
In 2022.
President Joko Widodo abolished the elimination of extreme poverty through Presidential Instruction No.
4 of 2022.
These efforts include reducing the expenditure of the extreme poor, empowering communities, and building basic service infrastructure.
However, the challenge of poverty is increasingly acute in rural areas, which are often the epicenter of extreme poverty.
Village governments, through the Direction of the Ministry of Villages.
Development of Disadvantaged Regions, and Transmigration (Kemendes PDTT), have implemented programs such as Village Cash for Work (PKTD).
Village Fund Direct Cash Assistance .
ereafter referred to as BLT in Indonesi.
, and strengthening Village-Owned Enterprises (BUMDe.
However, the implementation of programs at the village level also faces various obstacles, including poor data collection, minimal community participation, and dependence on external assistance.
Poverty in rural Indonesia is not only about low income but also the inability to meet basic needs.
There is a consensus among poverty researchers that poverty is multidimensional (Hick, 2016.
Iglesias.
Suter.
Beycan, & Vani, 2017.
Mideros M.
, 2012.
Peichl & Pestel, 2013.
Rao & Min, 2.
Poverty conditions are characterized by a lack of income, limited access to basic services, restricted social networks, and limited economic opportunities (Barrientos.
Therefore, this study attempts to identify the level of poverty in rural areas based on three main measurement approaches: the World Bank (WB) Poverty Line.
Statistics Indonesia Poverty Line, and the Poverty Line as defined in IndonesiaAos Law on Poverty Alleviation (Law No.
13/2011, hereafter UUFM), and integrate the three measurements.
The WB and Statistics Indonesia poverty measurements emphasize income parameters, whereas the UUFM focuses on the accessibility of basic needs.
By integrating monetary and non-monetary approaches, this study evaluated the relationship between income and fulfillment of basic ISSN: 2407-814X .
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AGRARIS: Journal of Agribusiness and Rural Development Research This multidimensional poverty measurement helps see the relationship between income and the fulfillment of basic needs (Alkire & Foster, 2011.
DAoAttoma & Matteucci.
Evans.
Nogales, & Robson, 2024.
You.
Kontoleon, & Wang, 2.
Additionally, this study examined the accessibility of social assistance programs and the effectiveness of their distribution in rural areas.
The urgency of this research lies in addressing the knowledge gap regarding the effectiveness of poverty alleviation policies, particularly in rural areas.
Through multidimensional poverty measurement, this research aims to provide data-driven recommendations that enhance program implementation, support sustainable development, and significantly reduce poverty in Indonesia.
This study offers two main implications: theoretically, it advances the multidimensional measurement of poverty by introducing six empirically derived categories that integrate both monetary and non-monetary indicators, thus providing a more comprehensive understanding of rural poverty.
and practically, it provides evidence-based recommendations to improve the accuracy of social assistance targeting through the strengthening of data systems that position villages as key subjects within a bottom-up framework and by formulating social assistance program interventions specifically designed to meet the needs of each poverty category in rural
RESEARCH METHOD
This study was conducted through sequential stages: .
site selection and justification, .
sampling design and respondent criteria, .
primary data collection, .
poverty measurement using three established frameworks (WB.
BPS, and UUFM), and .
analysis using the Poverty Severity Index (PSI) and Poverty Gap Index (PGI).
These stages were designed to ensure systematic methodology and procedural transparency throughout the research process.
The research was carried out in three villages in West Java Province.
Indonesia: .
Gelaranyar Village.
Cianjur Regency.
Margahayu Village.
Subang Regency.
Pantai Bahagia Village.
Bekasi Regency.
These locations were purposively selected based on four First.
West Java Province had one of the highest poverty rates in Indonesia, with 4,070,980 people living in povertyAi3,010,360 in urban areas and 1,060,630 in rural areas (BPS-Statistics Indonesia, 2023.
Second, the province had the highest inequality rate nationally, with a Gini Index consistently above 0.
4 from 2014 to 2022, peaking at 0.
(BPS-Statistics Indonesia of Jawa Barat Province, 2.
Third.
Cianjur and Subang regencies possessed poverty rates of 11.
18% and 10.
03%, respectivelyAiboth higher than the provincial average of 8.
40%Aiwhile Bekasi regency depicted a contrasting case with a relatively low rate 21% (BPS-Statistics Indonesia, 2023.
Fourth, the selected villages could represent distinct ecological typologies: a highland plantation (Gelaranya.
, a rice field (Margahay.
, and a coastal area (Pantai Bahagi.
Using a proportionate cluster random sampling technique, a total of 289 individuals aged 17 years and above were selected from different community units (RW), across the three ISSN: 2407-814X .
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The sampling frame was based on secondary data drawn from the Precision Village Data (PVD), a village-level census-based data system developed in Indonesia, covering a total population of 9,054 eligible individuals for the period 2021Ae2022.
This study analyzed poverty using three measurement approaches: WB.
BPS, and UUFM.
It also calculated the Poverty Severity Index (PSI) and Poverty Gap Index (PGI).
The WB framework defined poverty as a loss of welfare, determined through income or Under this standard, individuals earning less than USD 2.
15 per day or IDR 973,634 per capita per month .
ased on an exchange rate of IDR 15,095.
10 /USD) were classified as poor.
The BPS framework defined poverty as the inability to meet minimum basic needs, as measured by monthly per capita expenditure (BPS-Statistics Indonesia, 2.
The poverty line varied by region.
in this study, the thresholds were IDR 466,509 in Gelaranyar.
IDR 674,924 in Pantai Bahagia, and IDR 434,161 in Margahayu (BPS-Statistics Indonesia, 2.
Individual income was estimated by aggregating each respondentAos monthly expenditure, including food, non-food essentials, housing, utilities, education, debt repayments, savings, social costs .
akat, infaq, and sedeka.
, and transportation.
These expenditure values were then compared against both WB and BPS poverty thresholds to determine classification.
According to UUFM, individuals were classified as poor if they were unemployed or unable to meet basic needs despite having income.
Basic needs included food, clothing, housing, health, education, employment, and access to social assistance.
This study assessed deprivation based on a range of indicators, including occupation, education level, source of drinking water, type of housing structure .
loor, wall, and roo.
, toilet ownership, type of cooking fuel, completeness of daily meals, eating frequency, frequency of clothing purchases, electricity access, and house occupancy status.
All data were processed using Microsoft Excel for initial data handling and Household expenditures were aggregated and converted into monthly per capita values to assess income-based poverty according to the WB and BPS thresholds.
For the UUFM approach, poverty was measured using a weighted scoring system based on previously defined indicators of basic needs.
Individuals whose scores were below the established adequacy threshold were classified as poor.
Subsequently, these three frameworks were integrated to generate six intersecting categories of poverty.
The final analysis examined mismatches in social assistance distribution by comparing classification outcomes with actual recipient data.
This study also measured PSI and PGI.
PSI is proper for providing an overview of the distribution of expenditure among people experiencing poverty.
The higher the PSI value, the greater the inequality of expenditure among people with low incomes.
Furthermore.
PGI is useful for providing an overview of the gap between the average expenditure of the poor and the poverty line.
The higher the PGI value, the further the average expenditure of the poor is from the poverty line.
The following is the calculation formula for PSI and PGI.
z- y PSI= n Oci=1 UO Z iUU ISSN: 2407-814X .
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AGRARIS: Journal of Agribusiness and Rural Development Research was 2.
Z indicates the poverty line.
yi denotes the average monthly per capita expenditure of the population living below the poverty line .
=1,2,3,A,.
, yi < Z.
q signifies the number of people living below the poverty line.
and n represents the number of people.
z- y PGI= n Oci=1 UO Z iUU z depicts the poverty line.
yi exhibits the average monthly per capita expenditure of the population living below the poverty line .
=1,2,3,A,.
, yi < Z.
q implies the number of people living below the poverty line.
and n refers to the number of people.
RESULTS AND DISCUSSION
Applying the WB Poverty Measurement In three study areas, nearly two-thirds of the sample were classified as poor based on the WB poverty measurement.
The percentage of people living in poverty was 68.
04% in Gelaranyar Village, 66.
32% in Margahayu Village, and 68,04% in Pantai Bahagia Village.
According to this, 68.
04% of people in Gelaranyar Village and 66.
32% of people in Margahayu Village made less than IDR 973,634 per month.
Furthermore, 68,04% of the people in Pantai Bahagia Village made less than IDR 973,634 a month.
TABLE 1.
NUMBER AND PERCENTAGE OF POOR POPULATION BASED ON WB POVERTY MEASUREMENT
Village Poor
Non-poor
Total
Individual Percentage (%) Individual Percentage (%) Individual Percentage (%) Gelaranyar
RW 1
RW 2
RW 3
RW 4
RW 5
Margahayu
RW 1
RW 2
RW 3
RW 4
RW 5
RW 6
RW 7
RW 8
Pantai Bahagia
RW 1
RW 2
RW 3
RW 4
RW 5
RW 6
Total Note: RW or called as Rukun Warga in Indonesian is the smallest neighborhood-level administrative unit in Indonesia ISSN: 2407-814X .
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Moreover, an unequal distribution was observed in each village when examining the distribution at the RW level (Table .
Nearly all RW in Gelaranyar Village contributed significantly to the poverty rate of over 14%.
Areas with high poverty rates were RW1.
RW3.
RW4, and RW5.
The only area with a low contribution to Gelaranyar VillageAos 4.
12% poverty rate was RW2.
The majority of RWs in Margahayu Village possessed poverty rates around 10%, except for RW1 .
68%) and RW5 .
58%), accounting for a larger portion of the villageAos poverty rate.
Three RWs in Pantai Bahagia VillageAiRW1.
RW3.
RW5, and RW6Ai significantly raised the villageAos poverty rate.
The poverty percentage in the area was more than In Pantai Bahagia Village.
RW2 and RW4 had minimal effect on the poverty rate.
These data indicated that poverty in the three villages was concentrated in microadministrative areas.
These findings support the structural poverty theory of Hanandita & Tampubolon .
, which posits that poor distribution is linked to the allocation of resources, infrastructure, and community institutions.
The concentration of poverty in numerous RWs illustrated that village leadership, public service quality, and social networks affected welfare.
This finding exhibited that income-based measurements like the WB Poverty Line ignored spatial and institutional factors, unlike Sjaf .
and Widyastuti.
Hartono.
Sidig, & Rusmawati .
, who emphasized multidimensional poverty based on access to services and social resilience.
This study has expanded the understanding of aggregate poverty and revealed micro-level clustering patterns in RW areas, which have been rarely discussed.
Applying the Statistics Indonesia Poverty Measurement According to Statistics Indonesia poverty measures conducted in three research areas, the most significant proportion of poverty was identified in Gelaranyar Village .
08%),
followed by Margahayu Village .
58%) and Pantai Bahagia Village .
74%).
It signified 08% of the people in Gelaranyar Village earned less than IDR 466,509 per month, 58% of people in Margahayu Village earned less than IDR 434,161 per month.
Additionally, 24.
74% of people in Pantai Bahagia Village earned less than IDR 674,924 RWs 1 and 4 in Gelaranyar Village emerged as the main contributors to the villageAos poverty rate.
The poverty rate in these RWs exceeded 10%.
Areas RW2.
RW3, and RW5 had minimal contributions to Gelaranyar VillageAos poverty rate, standing at 1.
03%, 2.
06%, and 22%, respectively.
In Margahayu Village, less than 10% of RWs lived in poverty, except for RW1, which accounted for the most considerable portion of Margahayu VillageAos poverty rate .
58%).
Additionally, five RWs in Pantai Bahagia VillageAiRW1.
RW2.
RW3.
RW5, and RW6Aisignificantly raised the villageAos poverty rate.
The poverty rate in this region exceeded The only area not classified as having a poor population was RW4.
According to BPS poverty measures, the number and percentage of persons living in poverty are distributed as follows (Table .
According to structural poverty theory, poverty develops from systematic disparity in resource availability between regions (Najitama.
Maski, & Manzilati, 2.
, and this studyAos ISSN: 2407-814X .
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AGRARIS: Journal of Agribusiness and Rural Development Research findings disclosed that poverty was concentrated at the RW level.
These findings suggested that income-based measures, such as the BPS version, remained relevant but did not fully capture the complexity of rural poverty.
In contrast.
DAoAttoma & Matteucci .
and Sjaf .
emphasized non-monetary factors, including social resilience and access to basic These findings have contributed to a local approach to poverty identification, a novelty not previously observed in earlier studies.
TABLE 2.
NUMBER AND PERCENTAGE OF THE POOR BASED ON THE BPS POVERTY MEASUREMENT
Village Gelaranyar
RW 1
RW 2
RW 3
RW 4
RW 5
Margahayu
RW 1
RW 2
RW 3
RW 4
RW 5
RW 6
RW 7
RW 8
Pantai Bahagia
RW 1
RW 2
RW 3
RW 4
RW 5
RW 6
Total Poor Individual Percentage (%) Non-poor Individual Percentage (%) Total Individual Percentage (%) Poverty Severity Index (PSI) and Poverty Gap Index (PGI) This study also calculated PSI and PGI using the WB poverty line, which was IDR 973,634 per capita per month, and the BPS poverty line, which was IDR 466,509 per capita per month in Gelaranyar Village.
IDR 434,161 per capita per month in Margahayu Village, and IDR 674,924 per capita per month in Pantai Bahagia Village.
Based on the BPS and WB poverty lines in the three villages.
PSI and PGI were calculated as follows (Table .
Based on the calculations of PSI and PGI.
Gelaranyar Village generated the highest PSI and PGI compared to the other two villages.
High PSI indicated high income inequality among people experiencing poverty in Gelaranyar Village.
In Gelaranyar Village, a high PGI signified the extreme severity of poverty.
It implied that the gap between the average income ISSN: 2407-814X .
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and the WB and BPS poverty lines in Gelaranyar Village was significantly lower.
Meanwhile.
Margahayu Village was depicted as having a lower level of poverty severity compared to Gelaranyar Village, which fell into the medium category.
However.
Margahayu VillageAos PGI belonged to the high category.
TABLE 3.
PSI AND PGI BASED ON THE WB AND BPS POVERTY LINES
Village PSI Gelaranyar Margahayu Pantai Bahagia Low (< 0.
Medium .
high (>0.
PGI
BPS
BPS
Meanwhile.
Pantai Bahagia village had a relatively lower PSI and PGI compared to other Pantai Bahagia VillageAos low PSI indicated a relatively even distribution of income among the poor population.
However, the classification of Pantai Bahagia VillageAos PGI as high indicated that the average income of the poor remained significantly lower than the WB and BPS poverty lines.
TABLE 4.
NUMBER AND PERCENTAGE OF POOR POPULATION BASED ON UUFM POVERTY MEASUREMENT
Village Gelaranyar
RW 1
RW 2
RW 3
RW 4
RW 5
Margahayu
RW 1
RW 2
RW 3
RW 4
RW 5
RW 6
RW 7
RW 8
Pantai Bahagia
RW 1
RW 2
RW 3
RW 4
RW 5
RW 6
Total Individual Poor Percentage (%) ISSN: 2407-814X .
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Individual Non-poor Percentage (%) Individual Total Percentage (%) AGRARIS: Journal of Agribusiness and Rural Development Research Applying the IndonesiaAos Law on Poverty Alleviation (UUFM) Measurement UUFM was the third measure of poverty in this study.
UUFM has placed a strong emphasis on non-income factors, including the availability of peopleAos basic needs, such as food, clothing, housing, health, occupation, and education.
According to the measurement results, 25 people from the three villages lived in poverty.
The analysis unveiled that Margahayu Village had no poor people, while Pantai Bahagia Village possessed 5.
15% and Gelaranyar Village had 20.
The UUFM poverty measurement revealed the following numbers and percentages of individuals living in poverty (Table .
According to the UUFM criteria, the majority of people in the three villages were able to fulfill their basic needs.
They also had adequate access to clothing, food, shelter, health, occupation, and education.
UUFM classified a small percentage of the population as poor in Gelaranyar Village and Pantai Bahagia Village.
Low incomes could also contribute to their limited ability to fulfill basic needs.
They were impoverished people lacking both financial resources and having low accessibility to fulfilling basic needs.
Intersection of poverty: World Bank (WB).
Statistics Indonesia (BPS), and IndonesiaAos Law on Poverty Alleviation (UUFM) The intersection between the poverty measurements of WB.
BPS, and UUFM has become an intriguing observation.
The WB poverty measurement successfully identified 195 people categorized as poor, accounting for 67.
47% of the population in the three villages.
The BPS poverty measurement identified 89 people categorized as poor, amounting to 30.
79% of the population in the three villages.
Finally, the UUFM poverty measurement acknowledged 25 individuals as poor, totaling 8.
65% of the population in the three villages.
The following represents the total number of poor people as determined by the WB.
BPS, and UUFM (Table TABLE 5.
NUMBER OF POOR PEOPLE ACCORDING TO WORLD BANK (WB).
STATISTICS INDONESIA (BPS).
AND INDONESIAAoS LAW
ON POVERTY ALLEVIATION (UUFM)
Village Gelaranyar Margahayu Pantai Bahagia Total Number of Poor (Individual.
BPS
UUFM
As the previous subchapter indicated, it is intriguing to observe the connections between these three measures of poverty.
Are all three measures valid for classifying populations as poor? A Venn diagram was utilized to illustrate the intersection of WB.
BPS, and UUFM.
The Venn diagram revealed several patterns in the distribution of the number of poor people based on WB.
BPS, and UUFM standards (Figure .
WB identified a total of 93 individuals living in poverty.
UUFM alone distinguished eight poor people.
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individuals classified as poor solely based on BPS, without any overlap with either WB or UUFM.
It implied that both WB and UUFM standards entirely covered the BPS poverty line.
WB utilized a poverty line of IDR 973,634 per capita per month, which was higher than the poverty line set by the BPS.
It explained why, according to BPS, people experiencing poverty were also included in the WB category.
Most people experiencing poverty, according to the WB, were not considered poor by BPS or UUFM.
It indicated that WB has focused more on global economic vulnerability and poverty, without considering access to basic services.
Thus, these 93 individuals required attention because they may be on the verge of local poverty but have not received adequate social intervention.
FIGURE 1.
VENN DIAGRAM OF POVERTY ACCORDING TO WORLD BANK.
STATISTICS INDONESIA.
AND INDONESIAAoS LAW ON POVERTY ALLEVIATION BPS, which utilized the regional poverty line, failed to include individuals not covered by the WB.
It indicated that the WB standard was more comprehensive in identifying impoverished individuals within the regional context.
Even though their income exceeded the WB poverty line, individuals classified as poor by the UUFM and not covered by the WB or BPS .
p to eight peopl.
struggled to access necessities such as food, clothing, housing, healthcare, employment, and education.
It highlights the significance of incorporating nonmonetary dimensions into the analysis of rural poverty, as some individuals, despite not being economically impoverished, still faced multidimensional vulnerabilities.
Therefore, programs prioritizing access to education, health, and basic needs were necessary for the eight individuals identified as poor by the UUFM.
According to BPS and WB, there were 85 poor according to WB and UUFM, there were 13 poor people.
and according to BPS and UUFM, there were no poor people.
Finally, four poor people were identified according to WB.
BPS, and UUFM.
It demonstrated the distinct focuses of the WB.
BPS, and UUFM approaches: First.
WB highlights economically vulnerable individuals in a global context.
Second.
BPS stresses absolute regional poverty.
Third.
UUFM focuses on the dimensions of basic needs and access to services.
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and Rural Development Research These findings have reinforced Alkire & FosterAos .
multidimensional poverty model, which links low income to limited access to fundamental services, including education, health, and housing.
According to WB.
BPS, and UUFM, these metrics failed to capture the majority of impoverished people, highlighting the limitations of income metrics in assessing local vulnerability aspects.
Those classified as poor by UUFM but not WB or BPS .
ight peopl.
expressed significant non-monetary poverty.
Institutional determinants and availability to essential services have become crucial to rural poverty dynamics, as Najitama et .
DAoAttoma & Matteucci .
emphasized the need to quantify poverty using income and non-income factors.
According to WB.
BPS, and UUFM, the intersection of poverty formed six categories of poverty: extreme poverty, vulnerable to extreme poverty, non-monetary poverty, regional poverty, vulnerable to regional poverty, and non-poor (Table .
Extreme poverty refers to individuals who have incomes below the BPS and WB poverty lines, and have minimal accessibility to basic needs for food, housing, clothing, health, occupation and education.
Vulnerable to extreme poverty denotes individuals who have minimal fulfillment of basic needs for food, housing, clothing, health, occupation and education, have incomes above the BPS poverty line and are below the WB poverty line.
Non-monetary poverty exhibits individuals who have minimal fulfillment of basic needs for food, housing, clothing, health, occupation and education.
Regional poverty demonstrates individuals with incomes below the BPS and WB poverty lines but whose basic needs for food, housing, clothing, health, occupation and education are met.
Vulnerable to regional poverty indicates individuals who have incomes below the WB poverty line but above the BPS poverty line and whose basic needs for food, housing, clothing, health, occupation and education are met.
Non-poor depicts individuals with incomes above the BPS and WB poverty lines and whose basic needs for food, housing, clothing, health, occupation, and education are met.
TABLE 6.
POVERTY CATEGORIZATION BASED ON THE INTERSECTION OF WORLD BANK.
STATISTICS INDONESIA.
AND INDONESIAAoS
LAW ON POVERTY ALLEVIATION STANDARDS
No.
Category Extreme poverty Vulnerable to extreme poverty Non-monetary poverty Regional poverty Vulnerable to regional poverty Non-poor Income Below the poverty lines of WB and BPS Above the BPS poverty line and below the WB poverty line Above the WB poverty line Below the BPS poverty line and below the WB poverty line Above the BPS poverty line and below the WB poverty line Above the WB poverty line Access to Basic Needs Low Low Low Sufficient Sufficient Sufficient These data confirmed the significance of integrating income (WB.
BPS) and nonincome (UUFM) parameters to understand the complexity of poverty.
UUFM has complemented the measurement deficiencies of WB and BPS by capturing dimensions of basic needs not quantified through income.
Considering the fundamental differences between the approaches, this analysis could strengthen the argument for a more holistic poverty policy oriented toward the multidimensionality of basic needs, especially in rural ISSN: 2407-814X .
2527-9238 .
Understanding Poverty and Social AssistanceA.
(Hakim et al.
These findings proved that poverty in rural Indonesia could not be fully captured by income-based measures alone.
The distinct results across WB.
BPS, and UUFM have reflected what Alkire and Foster .
defined as multidimensional poverty, where deprivation stems not only from low income but also from limited access to basic needs such as health, education, and shelter.
It supports the structural poverty perspective, which views poverty as the result of systemic failures in data governance and resource distribution (Ambya et al.
Hanandita & Tampubolon, 2016.
Sjaf, 2.
Distribution of Social Assistance The main novelty of this study lies in its development of six categories of poverty, derived from the intersection of WB.
BPS, and UUFM poverty measurements.
This approach not only provided a more accurate identification of poverty but also challenged conventional classification models by integrating income and access-based metrics.
Based on these categories, this study further examined the distribution of social assistance in rural areas to assess the accuracy and equity of current targeting practices.
This study investigated the implementation of social assistance programs in rural areas.
Five categories, encompassing extreme poverty, vulnerable to extreme poverty, non-monetary poverty, regional poverty, and vulnerable to regional poverty, have been deemed eligible for social assistance, based on the intersection of poverty measurements by WB.
BPS, and UUFM.
Meanwhile, the entitlement to social assistance should not extend to non-poor individuals (Table .
In the extreme poverty category, out of 1.
38% of individuals, 0.
69% received the Family Hope Program (IndonesiaAos conditional cash transfer program or PKH in Indonesia.
attained two to three social assistance programs, and 0.
35% did not obtain any social assistance at all.
It indicated that the extreme poverty group had limited access to social assistance programs.
In fact, this group was the one most in need of social assistance programs to improve and ease their standard of living.
Second, of the 4.
50% of individuals in the vulnerable to extreme poverty category, 2.
08% received the Non-Cash Food Assistance .
n Indonesia called as Bantuan Pangan Non-Tunai (BPNT)), 0.
69% acquired two to three social assistance programs, 0.
69% obtained rice food assistance, and 1.
04% did not receive any social Thirdly, of the 2.
77% of the population living in non-monetary poverty, only a small portion received assistance, such as BPNT .
69%) and rice food assistance .
69%), while 04% did not receive any social assistance at all.
Fourth, out of the 29.
41% who fell under the regional poverty category, 4.
48% received direct cash assistance from the Village Fund (Village Fund BLT), 9.
69% obtained BPNT, 1.
04% attained PKH, 8.
65% acquired two to three social assistance programs, 0.
35% accepted more than three social assistance programs, 38% received rice food assistance, and 3.
46% had no social assistance whatsoever.
Fifth, of 18% vulnerable to regional poverty, 2.
77% received Village Fund BLT, 4.
15% attained BPNT, 0.
69% acquired PeopleAos Business Credit (KUR), 1.
38% obtained PKH, 12.
11% had two to three social assistance programs, 0.
35% got more than three social assistance programs.
ISSN: 2407-814X .
2527-9238 .
Journal of Agribusiness 12 AGRARIS:
and Rural Development Research 15% received rice food assistance, and 6.
23% acquired no social assistance at all.
Meanwhile, out of the 29.
76% of non-poor individuals, 4.
obtained Village Fund BLT, 3.
46% acquired BPNT, 0.
35% attained KUR, 4.
15% received PKH, 7.
61% acknowledged two to three social assistance programs, 0.
69% accepted more than three social assistance programs, 2.
42% received rice food assistance, and 6.
23% did not receive any social TABLE 7.
NUMBER AND PERCENTAGE OF INDIVIDUALS BASED ON POVERTY CATEGORIZATION AND ACCESS TO SOCIAL ASSISTANCE
Category Extreme poverty Non-monetary poverty Vulnerable to extreme Regional poverty Vulnerable to regional Non-poor Grand Total Direct cash assistance (Village Fund BLT) Non-cash Food Assistance (BPNT) PeopleAos Business Credit (KUR) Family Hope Program (PKH) 2-3 Social Assistance Programs > 3 Social Assistance Programs Rice Food Assistance Not received social assistance Grand Total The results of this study uncovered that 12.
46% of the eligible community groups for social assistance did not receive any.
On the other hand, 53% of the community groups that should not be entitled to social assistance actually received social assistance programs.
In fact, 7.
61% of the nonpoor community groups attained two to three social assistance programs, and 0.
69% acquired more than three social assistance programs.
Thus, the level of inaccuracy in the distribution of social assistance was 35.
The main programs, such as Village Fund BLT.
PKH.
Rice Food Assistance.
BPNT, and KUR, were primarily targeted at the extremely poverty, non-monetary poverty, regional poverty, and those vulnerable to extreme poverty.
It suggested an issue with the distribution of social assistance.
On the other hand, the non-poor category still obtained a significant proportion of assistance, indicating the need for an evaluation of the distribution of social assistance.
This discovery corroborates previous studies, which mentioned that the implementation of social assistance often fails and is ineffective due to inappropriate program targeting (Noerkaisar, 2021.
Walker.
Li, & Yang, 2.
These findings align with those of Pitaloka et .
and Sjaf .
, who noted that data exclusion and centralized targeting systems often overlook local realities.
However, unlike previous studies that focused on one-dimensional classifications, this study offers an empirically grounded, multidimensional categorization, enabling more accurate policy responses.
ISSN: 2407-814X .
2527-9238 .
Understanding Poverty and Social AssistanceA.
(Hakim et al.
Theoretical and Practical Implications Theoretically, this study has contributed to the literature on multidimensional poverty by proposing six categories of poverty, thereby offering a more comprehensive depiction of rural poverty.
Unlike traditional unidimensional models that focus solely on income (Jadoon.
Tanveer.
Faiq Javed, & Sarwar, 2.
, this approach incorporates both economic capacity and access to basic needs.
While the dual cutoff method of Alkire & Foster .
identifies poverty based on complex deprivation aggregation and produces categories like poor, vulnerable non-poor, and non-poor (Ervin.
Gayoso de Ervin.
Molinas Vega, & Sacco, 2.
, this study has introduced a simpler, context-driven classification based on intersecting WB.
BPS, and UUFM criteria, resulting in six categories of poverty that are more distinct and policy-relevant for rural interventions.
This study introduced six poverty categories: extreme poverty, vulnerable to extreme poverty, non-monetary poverty, regional poverty, vulnerable to regional poverty, and nonpoor.
It has addressed criticisms of IndonesiaAos fragmented and overly technical poverty indices (Hanandita & Tampubolon, 2.
while responding to the global call for locally grounded measurements (DAoAttoma & Matteucci, 2.
By building upon and enriching existing global typologiesAisuch as monetary poverty (Jadoon.
Tanveer.
Faiq Javed, & Sarwar, 2.
, human capital poverty (Burchi.
Espinoza-Delgado.
Montenegro, & Rippin, 2021.
Mitra.
Posarac, & Vick, 2.
, social inclusion poverty (Sindzingre, 2.
, living standards poverty, and disability-related poverty (Park & Nam, 2.
Aithis model could strengthen both the analytical relevance and practical applicability of multidimensional poverty frameworks in rural contexts.
Practically, this study reported that 35.
99% of social assistance was misallocatedAi 46% of people experiencing poverty received no aid, while 23.
53% of the non-poor did.
has confirmed earlier findings that social assistance programs often fail due to inaccurate targeting (Noerkaisar, 2021.
Walker et al.
, 2.
Three core issues underpin this failure:
policymakersAo inability to accurately identify people experiencing poverty.
measurement that overlooks non-monetary deprivations.
and the lack of systemic reform to ensure inclusive and responsive social protection.
Improving rural poverty alleviation requires a multidimensional data system that accurately captures income and basic needs, built on a bottom-up approach that empowers villages as data agents.
This system should guide targeted social protection aligned with Sustainable Development Goals (SDG.
3 on inclusive CONCLUSION This study has confirmed that rural poverty in Indonesia could not be comprehensively understood solely through income-based measures.
By intersecting three poverty frameworksAi WB.
BPS, and UUFMAithis research identified six nuanced categories of poverty integrating both monetary and non-monetary dimensions.
These categories have offered a more accurate ISSN: 2407-814X .
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Journal of Agribusiness 14 AGRARIS:
and Rural Development Research representation of deprivation in rural areas and highlighted significant inconsistencies in the targeting of social assistance.
With a 35.
99% misalignment rateAiwhere many eligible individuals were excluded, and many ineligible individuals benefitedAithis study underscored the limitations of current poverty targeting systems.
The findings have reinforced the structural nature of poverty, shaped by institutional failures and data system deficiencies.
a practical response, this research proposed the implementation of a participatory, villagebased data collection model and the development of differentiated social protection interventions customized to each poverty category.
By offering a context-specific, multidimensional classification framework, this study has made both conceptual and operational contributions to the improvement of poverty alleviation strategies in IndonesiaAos rural areas.
Future studies should validate this classification model in other regional contexts and examine its potential integration into national poverty reduction and social protection Acknowledgments: We would like to thank the Center for Educational Financial Services (PUSLAPDIK Indonesi.
, the Education Fund Management Institute (LPDP Indonesi.
, the Precision Village Data Laboratory.
Faculty of Human Ecology.
IPB University, and the Rural Sociology Study Program.
Department of Communication Science and Community Development.
Faculty of Human Ecology.
IPB University.
The authors would also like to thank the Center for Education Financial Services (PUSLAPDIK Indonesi.
and the Indonesia Endowment Funds for Education (LPDP Indonesi.
for providing a grant for this Author contributions: LH: conceptualization, methodology, data collection, data analysis, writing original draft, writing review editing, and validation.
LMK.
SS, and RAK:
conceptualization, methodology, validation.
SS: conceptualization, methodology, supervision, and validation.
Conflict of interest: Authors declare no conflict of interest
REFERENCES