Dynamics between multidimensional and monetary poverty in Brazil: From deprivation to freedom

da Cunha, Marina Silva Article Dynamics between multidimensional and monetary poverty in Brazil: From deprivation to freedom Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: da Cunha, Marina Silva (2025) : Dynamics between multidimensional and monetary poverty in Brazil: From deprivation to freedom, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 5, pp. 1-17, https://doi.org/10.3390/economies13050142 This Version is available at: https://hdl.handle.net/10419/329422 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Robert Czudaj Received: 24 March 2025 Revised: 13 May 2025 Accepted: 14 May 2025 Published: 21 May 2025 Citation: Cunha, M. S. d. (2025). Dynamics Between Multidimensional and Monetary Poverty in Brazil: From Deprivation to Freedom. Economies, 13(5), 142. https://doi.org/10.3390/ economies13050142 Copyright: © 2025 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Dynamics Between Multidimensional and Monetary Poverty in Brazil: From Deprivation to Freedom Marina Silva da Cunha Department of Economic, State University of Maringa, Maringa 87020-900, Brazil; [email protected] Abstract: Poverty is a global problem associated with deprivation; it is marked by the lack of access to the minimum social needs for people’s integration and well-being. This work aims to measure the relationships between multidimensional poverty and unidimensional poverty in Brazil from 2016 to 2022. The research methodology used microdata from the Continuous National Household Sample Survey of the IBGE, multidimensional and unidimensional poverty measures, and multinomial logit regression. The results show a reduction in poverty in its different approaches. However, in 2022, 2.5% of the Brazilian population still lived in chronic poverty, 0.8% in structural poverty, and 25.7% in situational poverty, while the rest enjoyed their basic freedoms. Women, children and young people, non-whites, those with less education, and those living in the North and Northeast regions are more vulnerable. Based on the research results, it is recommended to enhance public policies to housing, education, employment inclusion, and food stability. Keywords: poverty; well-being; basic services; chronic poverty; public policies; Brazil JEL Classification: D63; I32; I28; H51 1. Introduction In 2022, it was estimated that 1.1 million people worldwide were experiencing multidimensional poverty, which includes indicators related to health, education, and standard of living. Out of the total number, around 50% were in sub-Saharan Africa, 33 million were in Latin America and the Caribbean, and 7.9 million were in Brazil (UNDP & OPHI,2023). Considering a poverty line of USD 3.65, from the World Bank for lowand middle-income countries, there were 26.7 million people in a situation of monetary poverty in Brazil. However, when the poverty line of BRL 606,00 is considered, this totals 63.4 million people, which corresponds to around 30% of the Brazilian population in 2022 (IBGE,2023). Hence, poverty has consequences for people across the globe. This problem is concerning, and addressing it fully is the primary objective of the first Sustainable Development Goal outlined in the 2030 Agenda, which was established during the 2015 United Nations Sustainable Development Summit held at the headquarters in New York (ONU,2015). Essentially, poverty means the absence of basic personal freedoms. Seeing it in this light, the growth of a nation would revolve around the advancement of these freedoms, the absence of which constrains opportunities, choices, and accomplishments. Access to essential amenities for well-being has beneficial effects on both individuals and society (Sen & Anand,1997;Sen,2000). Previous studies in this area have aimed to quantify poverty using both unidimensional (Chen & Ravallion,2013) and multidimensional approaches (Alkire & Foster,2009; Alkire et al.,2017;Alkire & Fang,2019;Bárcena-Martín et al.,2020), yet there is a lack of Economies 2025,13, 142 https://doi.org/10.3390/economies13050142 Economies 2025,13, 142 2 of 17 research delving into the interplay between these two approaches. In this method, the population is divided into distinct categories of deprivation, aiding in the detection of social vulnerabilities (Alkire & Santos,2010;Alkire et al.,2015;Valente,2023). Considering these studies for some countries, this research aims to add to this subject by providing new data for Brazil. The interaction between these two approaches creates four population categories. This study seeks to assess both multidimensional and unidimensional poverty and explore their interconnections in Brazil between 2016 and 2022. Information from the IBGE’s Continuous National Household Sample Survey, measures of multidimensional and unidimensional poverty, and multinomial logit regression are used to identify the main determinants of poverty, considering both personal and spatial characteristics. Furthermore, in addition to per capita household income, which measures monetary poverty, eleven other indicators related to education, health, basic services, and housing conditions are used to calculate multidimensional poverty. Thus, we seek to carry out an empirical analysis, identifying the population’s situation as chronic poverty, structural poverty, situational poverty, or nor-poverty, which contributes to a better targeting of public policies, as well as their monitoring and evaluation. Besides unidimensional measures, multidimensional poverty analysis improves the understanding of the well-being status of a population, identifies the recipients of public policies and services, and aids in tracking and assessing the effectiveness of public policies and programs. Moreover, researchers and public managers are interested in the theoretical insights of Amartya Sen and the wealth of information from a multidimensional viewpoint (Alkire & Foster,2009). Public social policies need to focus not only on taking people out of poverty but also on preventing them from falling back into it (A. M. R. Silva et al.,2011). This work is organized into five sections and this introduction. The upcoming section explores theoretical and empirical factors related to poverty. Section 3it discusses the research methodology related to the database and techniques employed. It proceeds by discussing the measures and trends of multidimensional and unidimensional poverty in Brazil. Additionally, this section covers the connections between multidimensional and unidimensional poverty, presenting regression estimates that aim to describe the population experiencing deprivation. Section 5discusses the main findings of the study, considering the literature on the subject. Section 6presents the key findings of the study. 2. Theoretical and Empirical Aspects Classical economic literature defines poverty based on unfulfilled survival requirements like food and shelter, as determined by income levels. Townsend (1993) proposed that poverty could be objectively defined by examining the relative deprivation experienced by individuals, families, or groups within a population. Poverty can be defined as not having enough resources to buy food and maintain a standard of living that is considered normal, which results in significant exclusion from community activities and traditions. The method of tackling poverty outlined here stems from utilitarian economic principles, which prioritize income or consumption as the most reliable measures of individual welfare. Defining basic necessities and determining the acceptable threshold are essential steps in operationalizing this comprehensive concept of poverty. As a result, poverty can be quantified either absolutely or relatively. Salama and Destremau (1999) point out that absolute poverty refers to having enough income to sustain an individual or family, whereas relative poverty places them within the societal context. In this method, poverty is defined as the lack of income and is referred to as the monetary or unidimensional viewpoint. Rocha (2006) argues that in contemporary economies, where people satisfy their basic needs through commercial transactions, it is common for the assessment of these needs to be carried out indirectly by looking at income levels. Chen and Ravallion (2013) Economies 2025,13, 142 3 of 17 stated that the poverty line is influenced by the consumption and income levels of a society, which are determined by the overall well-being and social norms. Sen (2000) argues that wealth’s value is in what it can accomplish and acquire, whereas Bourguignon and Chakravarty (2003) view poverty as a multidimensional phenomenon with income serving as a mere indicator. Towards the end of the 20th century, there was a shift in focus within research on poverty, looking at the fulfillment of basic needs or adopting a multidimensional viewpoint. According to Salama and Destremau (1999), the perspective of Unsatisfied Basic Needs (UBNs) encompasses two key elements: the first one is tied to biological requirements, whereas the second is associated with the availability of public resources and services. Rocha (2006) also views basic needs, such as food, education, sanitation, and housing, from this standpoint. Following Amartya Sen’s ideas, a second multidimensional viewpoint known as capabilities arises, which, rooted in principles of social justice, integrates freedom of choice regarding the opportunities available to each person. From this standpoint, the notions of functioning and capabilities are integrated. Individuals value certain functioning for their well-being, whereas capabilities refer to the functioning that individuals can actually achieve. Hence, empowerment denotes a kind of independence, enabling individuals to engage in different sets of functioning (Sen,1993;Sen,2000). Hence, unidimensional or monetary indicators of poverty are considered indirect measures of poverty, whereas multidimensional indicators are considered direct measures. Therefore, poverty could stem from factors beyond income, which is reflective of the opportunities acquired over a person ´ s lifespan. Other elements come into play, like the investments made in health and education, which are crucial for economic success. In the beyond-income perspective, poverty is defined as the attainment of valued goals or outcomes by individuals, which are accomplished using income or a set of goods. Furthermore, certain authors suggest assessing the connections between multidimensional and unidimensional methods. (Alkire & Santos,2010;Alkire et al.,2015;Alkire & Fang,2019;Valente,2023). The interaction between these two approaches creates four population categories, which are detailed in Table 1. The first one is when individuals are affected by both monetary poverty and multidimensional poverty, called chronic poverty. In this scenario, people lack enough income and are denied access to necessities. Therefore, this group consists of the most susceptible individuals in the population. Another type of poverty, known as structural poverty, is observed when a population faces multidimensional deprivation rather than just monetary hardships. This group comprises individuals who earn enough to meet their basic needs yet lack access to minimum living conditions. Table 1. Multidimensional and unidimensional poverty dynamics. Categories Multidimensional Poverty Poor Non Poor Unidimensional poverty Poor Chronic poverty Situational poverty Non-poor Structural poverty Socially integrated Source: Prepared in the research. In contrast, individuals may face situational poverty if they lack income but can still access basic goods and services. Therefore, even though the basic needs are being fulfilled, the income of this group falls below the threshold for monetary poverty, resulting in a state of recent poverty. In conclusion, there is a group within the population that does not fall into either monetary poverty or multidimensional poverty, referred to as the non-poor. This group of individuals is socially integrated because it has its basic needs fulfilled and enough income to maintain a decent quality of life. Economies 2025,13, 142 4 of 17 International studies have deepened the discourse on multidimensional poverty by examining its relationships in a more holistic manner, departing from the narrow, unidimensional viewpoint. Regarding monetary poverty, the work of Chen and Ravallion (2013) stands out when they analyzed information for 125 countries, from 1981 to 2008. They confirmed a decrease in poverty and found that economic growth has occurred alongside the decrease in absolute poverty, while relative measures are more influenced by inequality than by economic growth. Scholars have investigated how poverty is measured using a multidimensional approach, both across different countries (Alkire & Santos,2010;BárcenaMartín et al.,2020) and within individual countries (Alkire & Foster,2009;Alkire et al., 2017;Alkire & Fang,2019). Various studies in the empirical literature for Brazil have utilized the multidimensional perspective. Bourguignon and Chakravarty (2003) observed a decrease in educational deprivation but a rise in income deprivation among the rural population in Brazil during the 1980s. Over the following few decades, data from the National Household Sample Survey also shows a decrease in multidimensional poverty in the country (Hoffmann & Kageyama,2006;Barros et al.,2006;Fahel et al.,2016;Marcelino & Cunha,2024). In turn, Neves and Silva (2023) showed a reduction in multidimensional poverty from 2004 to 2008, but an increase from 2016 to 2019. The authors attribute this performance to the greater economic growth in the initial period and the allocation of funds to social programs. During the second period, the economy experienced a decrease in dynamism, a rise in unemployment, and a decline in social well-being. By examining data from the 1991, 2000, and 2010 Demographic Censuses, Brambilla and Cunha (2021) and Brites et al. (2022) demonstrated a decline in poverty within the country’s municipalities. Additionally, Rosa et al. (2023) researched the North region and noted a trend of poverty becoming more concentrated in metropolitan areas from 1991 to 2010, followed by a reversal in the following decade. Studies have examined different regions of the country, such as the North and Northeast (A. F. Silva et al.,2017;J. J. Silva et al.,2020), and specific studies have focused on individual states (Albuquerque & Cunha,2012;Fahel & Teles,2018;Vieira et al.,2017). Pereira et al. (2020) examined poverty in Brazilian youths, while Belkiss et al. (2021) focused on the COVID-19 pandemic period. Reviewing both theoretical and empirical literature on poverty emphasizes its intricate nature and the necessity of addressing it to foster a more equitable society and sustainable development. Overall, there are signs of advancement, which are notable on a global scale as well as in Brazil. Finally, there is still scarce evidence on the relationships between unidimensional and multidimensional poverty, following the approach of Alkire and Foster. In this sense, for China, in 2008, the evidence points to 1.82% of the population in situational poverty, 11.6% in structural poverty, 0.87% in chronic poverty, and 85.71% in non-poverty (Alkire & Santos,2010). In this empirical literature, there are studies that follow the approach of Bourguignon and Chakravarty (2003), which include income as an additional indicator in the elaboration of a multidimensional poverty index (Hoffmann & Kageyama,2006;Barros et al.,2006; Albuquerque & Cunha,2012;Bárcena-Martín et al.,2020;Brambilla & Cunha,2021;Brites et al.,2022;Rosa et al.,2023;Neves & Silva,2023). On the other hand, there are studies that follow the approach of Alkire and Foster (2009) and Alkire et al. (2015), which analyze unidimensional and multidimensional poverty separately (Fahel et al.,2016;Vieira et al., 2017;Fahel & Teles,2018;Pereira et al.,2020;Belkiss et al.,2021;Marcelino & Cunha,2024). As in the present study, in general, the studies that follow the latter approach use three dimensions, with social well-being indicators related to education, health, public services, and housing conditions. Economies 2025,13, 142 5 of 17 3. Methodology 3.1. Data and Measurement of Multidimensional and Unidimensional Poverty This work uses information from the Continuous National Household Sample Survey (PNADC) of the Brazilian Institute of Geography and Statistics (IBGE), from 2016 to 2022. It considers households with accessible per capita income, adjusting their values with the National Consumer Price Index (INPC) for the last quarter of 2022. Moreover, in order to keep the comparison consistent across PNADC years and standardize the databases, observations were included that contained all the relevant information for the study. The Alkire and Foster method was utilized to assess multidimensional poverty, taking into account indicators, dimensions, weighting procedures, and aggregation techniques (Alkire & Foster,2009;Alkire et al.,2015). Thus, suppose there are nindividuals and d dimensions. Considering a given indicator, its achievement for individual iin dimension jis represented by x ij . To avoid being classified as deprived, an individual must achieve at least the minimum performance specified by z j . Therefore, if x ij is smaller than zj, individual iis private in dimension jand has a g0 ij equal to 1; otherwise g0 ij equals zero. In order to create a poverty index, it is necessary to give a weight to each indicator to show its relative importance, denoted as w j , where the total sum from j = 1, . . . , to d must be equal to 1. The deprivation level of individual iis determined by summing up ci=∑d j=1wjg0 ij , which falls between zero (indicating no deprivation) and one (signifying deprivation in all dimensions). A deprivation score or poverty threshold needs to be established, indicating the value (k) at which an individual is classified as poor (c i≥ k). In this study, a score of 1/3 or higher, in line with existing research, was used to classify someone as experiencing multidimensional poverty. The multidimensional poverty index (M0) is calculated by multiplying the percentage of individuals living in poverty (H) by the degree of poverty (A). The percentage of individuals in poverty is determined by dividing the number of identified deprived individuals by the total number of individuals, based on the chosen score for k. Additionally, the severity of poverty is calculated specifically for individuals living in poverty and indicates the proportion of hardships that they experience, denoted as A=1 q∑n i=1ci(k) . Thus, the multidimensional poverty index is the product: M0 = H × A, where M0, Hand Acan vary from 0 to 1. In this study, three dimensions are examined, each carrying an equal weight of 1/3, as suggested by previous research. Eleven dimensions of poverty are analyzed, assigning a value of one in cases of deprivation and zero in cases of non-deprivation. Hence, Table 2 displays the indicators, dimensions, and weights utilized. Three indicators of deprivation are taken into account for the education dimension. Deprivation in terms of school attendance refers to the presence of at least one schoolaged child (between six and seventeen years old) in the household who is not enrolled in school. Regarding academic delay, not completing secondary education was viewed as a deprivation for at least one individual aged between nineteen and twenty-four. Households were classified as educationally deprived if they did not have any adult aged twenty-five or older with a complete primary education. Four indicators are present in the dimension of health and basic services. Water deprivation happens when a household does not have access to running water from a general distribution network, well, or spring in at least one room. When garbage is not collected by a cleaning service, it indicates a deficiency in these services. Households that do not have main electricity, generators, or solar lighting are in a precarious position. In this context, the lack of proper sewage systems linked to either a network or a river denotes deprivation. Economies 2025,13, 142 6 of 17 In the dimension of housing conditions, there are a total of four indicators. Having three or more residents per bedroom is a form of deprivation, just like having insufficient ceiling material in the house. Using cooking fuel other than electricity or gas suggests a lack of resources, as well as not having two or more durable goods. Thus, the indicators seek to capture deprivation in households and the absence of social well-being. However, despite being widely used as a measure of access to goods and services, they measure their presence or absence, but not their quality, which may represent a limitation in measuring poverty. Table 2. Dimensions, indicators, description, and weight for multidimensional poverty. Dimension/Indicators Description Weight Education 1/3 School attendance Homes with at least one child or teenager between six and seventeen years old who is not in school. 1/9 Schools delay Household that includes a young person who has not yet graduated from high school. 1/9 Years of study Household where no adult resident has finished primary school. 1/9 Health and basic services 1/3 Water supply A residence without access to running water in at least one room supplied by a general distribution network, well, or spring. 1/12 Garbage destination Household that does not use a cleaning services for garbage collection. 1/12 Electricity Household lacking main electricity, generator, or solar lighting. 1/12 Sanitary sewage Household with a toilet that is not connected to the sewage or rainwater network. 1/12 Housing conditions 1/3 Density A household where three or more people share a bedroom. 1/12 Ceiling material Household with a roof primarily made of materials other than tiles, concrete slabs, or construction-grade wood. 1/12 Fuel Household that cooks without gas or electricity. 1/12 Durable goods Household that does not have more than one of the following items: refrigerator, television (color or black and white), telephone (landline or cell phone), washing machine, personal computer, and automobile. 1/12 Source: Prepared by the author. Furthermore, the proportion of poor individuals is utilized as a metric for unidimensional poverty, with two poverty thresholds determined by the minimum wage (SM) of 2022 and the per capita household income. In order to identify extreme poverty, the poverty line was set at BRL 303.00, equivalent to a quarter of the minimum wage (SM), whereas to assess poverty, it was set at BRL 606.00, corresponding to half of the minimum wage (SM). 3.2. Multinomial Logit Regression Our goal was to define the characteristics of individuals in different segments by conducting regressions with the multinomial logit model. The dependent variable was a discrete polychotomous variable with multiple values. The multiple choices logit model is a generalization of binary models. The unordered nominal dependent variable is modeled by the multinomial logit. Economies 2025,13, 142 7 of 17 The general structure of this model, as described by Greene (2018), is as follows: pij =Prob(Yi=j|wi) = exp(w′ iαj) ∑J j=0w′ iαjj=0, . . . , j;e i=1, . . . , n. (1) where idenotes the individual, jdenotes the state, and w i represents a vector with the control variables for individual iand denotes the probability of individual ibeing in state j. In this study, each estimated model has a value of three for J, with a total of four categories. As such, every conceivable link between multidimensional and unidimensional poverty was investigated, encompassing chronic, situational, and structural poverty, with the individual placed in a non-poor situation. According to Greene (2018), since the probabilities total one, estimating Jvectors is sufficient to determine the J+ 1 probabilities. Estimates are presented in the form of odds ratios, calculated as p ij /p i0 , to illustrate the likelihood of being in state jcompared to the reference state j= 0. According to Cameron and Trivedi (2005), the natural logarithm of the odds ratio, ln[p ij /p i0 ], is linear with the regressors, and the model takes the form of relative risk. Therefore, if the coefficient estimates for all control variables are above one, the probability of being in state iis higher than in the reference state. Chances are reduced compared to the reference state when the value is less than one. Concerning the control variables, a binary variable was incorporated to differentiate between sex, assigning a value of one for women. Another variable was introduced to distinguish race or skin color, assigning a value of one for non-white individuals, that is, black, brown, and indigenous people. To differentiate the age of the population, seven binary variables were included for age groups from 0 to 5 years, from 6 to 10 years, from 11 to 14 years, from 15 to 17 years, from 18 to 24 years, from 25 to 29 years, and from 30 to 59 years. Six binary variables based on different education levels to analyze the impact of education were used: those with no education or less than 1 year of study, incomplete elementary school, complete elementary school, incomplete secondary education, complete secondary education, and incomplete higher education. Three binary variables were included to differentiate between nuclear, extended, and compound household units in which the individual lives. A binary variable with a value of one to distinguish rural households from urban ones was used. A binary variable was used to separate individuals living in metropolitan areas from those living in non-metropolitan areas, with a value of one indicating residence in metropolitan areas. To account for the country’s regional variations, four binary variables were incorporated, representing the North, Northeast, South, and Central-West regions. Thus, for the binary variables included, men, white people, individuals aged 60 or over, people with a higher education degree or more, and residents of single-person households in urban, metropolitan areas, and the Southeast region were considered as reference categories. 4. Results 4.1. Multidimensional and Unidimensional Poverty Measures from 2015 to 2022 At first, the multidimensional poverty index was obtained by gathering indicators related to education, health, basic sanitation, and housing conditions, as outlined in Table 3. In 2016, the adult population in households experiencing multidimensional poverty had the highest level of deprivation in terms of years of study, that is, the absence of at least one adult with complete primary education in the household, which was equal to 91.6%. In terms of education, the second most severe deprivation for young individuals is the delay in completing secondary education, which stands at 62.2%. This is followed by school Economies 2025,13, 142 8 of 17 attendance in basic education, with deprivation of 21.2%, which considers both primary and secondary education. In 2022, high levels of educational deprivation persisted among those in multidimensional poverty, reaching 15.6% in school attendance, 54.9% in academic delay among young people, and 86% in years of study in the adult population. Table 3. Deprivation in households in situations of multidimensional poverty, 2016–2022. Dimension/Indicattor 2016 2017 2018 2019 2022 Education School attendance 21.22 21.03 19.37 17.47 15.66 School delay 62.22 62.05 61.84 58.21 54.88 Years of study 91.61 90.38 89.96 88.67 85.99 Health and basic sanitation Water supply 27.65 27.18 27.84 26.18 29.81 Garbage disposal 70.32 70.30 70.08 71.32 72.58 Electricity 4.03 4.21 4.50 4.64 4.22 Sanitary sewage 85.52 86.80 89.39 88.52 88.85 Housing conditions Density 52.67 52.19 54.12 51.20 49.83 Ceiling material 13.40 14.49 13.17 13.29 16.29 Fuel 0.80 1.23 1.32 20.82 18.56 Durable goods 4.02 4.06 3.65 4.35 3.79 Source: Research information, obtained based on PNADC/IBGE. People living in multidimensional poverty experience significant deficiencies in health and basic sanitation. Among the indicators analyzed, sewage represented 85.5% in 2016. It became even more intense in 2022, when it reached 88.85%. The reason for this outcome is the building of houses in regions lacking proper infrastructure. The indicator for waste disposal ranked second in terms of deprivation in this dimension. It rose over the period, reaching 72% in 2022. About 4% of the population surveyed did not have access to electricity, which was one of the indicators with the highest level of availability. In terms of housing conditions, the indicator that shows the highest level of deprivation is household density, which refers to households with three or more residents sharing the same room, standing at approximately 50% in 2022. This indicator saw a decrease of 2% during the period. The percentage of inappropriate ceiling material rose to 16% during this period. In 2022, 19% of the population in multidimensional poverty used inappropriate cooking fuel. In the period studied, the ownership of goods showed the lowest level of deprivation, at approximately 4%. Consequently, individuals living in poverty exhibited considerable shortcomings across various indicators, as illustrated in Table 3. The country still faces significant challenges in the dimensions studied. When it comes to education, Federal Law 12,796 of 2013 set out new directives and foundations for the national education system, such as increasing the age for free and compulsory basic education from 4 to 17 years of age (Brasil, 2013). The 2014 National Education Plan (PNE) laid out principles, objectives, and tactics for education in the country from 2014 to 2024 (Brasil,2014). Therefore, the PNE aimed to have at least 50% of children under 3 years old enrolled in daycare centers by 2024, as well as provide universal access to preschool for 4and 5-year-olds by 2016. By 2024, the aim was for at least 95% of students aged 6 to 14 to finish primary education at the appropriate age. By 2016, the aim was to ensure that all individuals aged 15 to 17 would have access to secondary education, and by 2024, the goal was to raise the net enrollment rate to 85%. Data from the 2022 IBGE Demographic Census reveal that approximately 24.3% of the Brazilian population still lacked proper sanitation (IBGE,2024). In 2020, the Federal Economies 2025,13, 142 15 of 17 6. Conclusions This study focused on both multidimensional and unidimensional poverty and examined how these two approaches are interconnected. This led to the division of the population into categories of chronic, structural, situational, and socially integrated poverty. It analyzed PNADC data from 2016 to 2022, along with poverty measures, and conducted regressions to identify the key factors influencing poverty levels in the Brazilian population. Based on the findings presented, it was evident that poverty metrics decreased, both from 2016 to 2019 and from 2019 to 2022. The exception was situational poverty, which increased from 2019 to 2022. In 2016, approximately 32% of the population faced some form of deprivation, compared to around 29% in 2022. This still accounts for a significant segment of the population. In terms of multidimensional poverty, a lack of access to education had the greatest impact, followed by inadequate health and sanitation, and lastly, poor housing conditions. The majority of people living in poverty are women, non-whites, young individuals, those with lower education levels, those living in rural or non-metropolitan areas, and those in the North and Northeast regions. Poverty rates are the highest in the state of São Paulo, followed by Bahia, Minas Gerais, Pará, and Rio de Janeiro. Hence, the study outcomes imply that poverty does not result from a shortage of wealth but from the notably unequal distribution of wealth across the country. Another important point to consider is the high rate of poverty affecting children, teenagers, and young adults in the nation, which stands at approximately 40%. This hinders the possibilities of advancing socially and escaping poverty in the future. These individuals require protection but face daily struggles with basic needs, such as food insecurity. This issue could be addressed through access to daycare centers and full-time education programs. For future work, we suggest to analyzing the impact on the measurement of multidimensional poverty when the weights used for the dimensions and for the level of deprivation necessary to be considered in a situation of poverty are changed. The inclusion of new indicators is also suggested, such as those associated with health, such as food and nutritional security and infant mortality, which are not available at the household level for the years analyzed. Furthermore, a limitation of the study is the availability of indicators on the quality of goods and services, not just their quantity. Hence, the findings from the study enable us to describe the nation as having significant socioeconomic disparities. A significant portion of the population, about 30%, remains inadequately integrated into society, meaning they are unable to access their basic freedoms to engage in education. In this scenario, it is recommended to enhance public policies such as health services, education from daycare up to high school graduation, housing, employment inclusion, and food security. Poverty is marked by the lack of access to minimum social necessities for integration and well-being, basic for the sustainable progress of succeeding generations. Funding: The author receives a research productivity grant from CNPq during the study. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The research data is available at IBGE (https://www.ibge.gov.br/ accessed 10 February 2024). Conflicts of Interest: The author declares no conflict of interest. Economies 2025,13, 142 16 of 17 References Albuquerque, M. R., & Cunha, M. S. (2012). 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