Social programs and socioeconomic variables: Their impact on Peruvian regional poverty (2013-2022)

Hinojosa Pérez, J. Adolfo; Briceño Ávalos, Hernán Ricardo; Vargas Salazar, Ivonne Yanete; Carrasco Mamani, Sergio Christian Article Social programs and socioeconomic variables: Their impact on Peruvian regional poverty (2013-2022) Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Hinojosa Pérez, J. Adolfo; Briceño Ávalos, Hernán Ricardo; Vargas Salazar, Ivonne Yanete; Carrasco Mamani, Sergio Christian (2024) : Social programs and socioeconomic variables: Their impact on Peruvian regional poverty (2013-2022), Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 8, pp. 1-19, https://doi.org/10.3390/economies12080197 This Version is available at: https://hdl.handle.net/10419/329123 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Adolfo, Hernán Ricardo Briceño Avalos, Ivonne Yanete Vargas Salazar, and Sergio Christian Carrasco Mamani. 2024. Social Programs and Socioeconomic Variables: Their Impact on Peruvian Regional Poverty (2013–2022). Economies 12: 197. https://doi.org/10.3390/economies 12080197 Academic Editors: Camelia Teodorescu, Ana-Irina Lequeux-Dincă and Florentina-Cristina Merciu Received: 13 April 2024 Revised: 10 July 2024 Accepted: 11 July 2024 Published: 29 July 2024 Copyright: © 2024 by the authors. 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/). economies Article Social Programs and Socioeconomic Variables: Their Impact on Peruvian Regional Poverty (2013–2022) J. Adolfo Hinojosa Pérez 1,* , Hernán Ricardo Briceño Avalos 1, Ivonne Yanete Vargas Salazar 1and Sergio Christian Carrasco Mamani 2 1 Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru; [email protected] (H.R.B.A.); ivar[email protected] (I.Y.V.S.) 2Escuela de Economía, Facultad de Ciencias de la Empresa, Universidad Continental, Huancayo 12000, Peru; [email protected] *Correspondence: [email protected] Abstract: The aim of this research is to establish the extent to which social programs and socioeconomic variables have been influencing poverty in the 24 Peru regions (2013–2022). The study is quantitative, non-experimental, and correlational. We use secondary data obtained from official sources such as the National Institute of Statistics and Informatics, Ministry of Economy and Finance, as well as the Peruvian Institute of Economics. For estimations, we use the Generalized Method of Moments System and dynamic panel data. The results indicate that Juntos, Pensión 65, Qali Warma, and Trabaja Perúsocial programs, with p-values of 0.383, 0.715, 0.681, and 0.870, respectively, have not had favorable impacts on reducing poverty. On the contrary, negative coefficients for human capital and physical infrastructure mean that improving them will reduce poverty at the regional level. A year more in schooling for the population aged over 15 years reduces poverty between 1.7% and 1.2%. Increasing 10% of the proportion of national roads in paved condition reduces poverty levels between 1.9% and 2.4%. Keywords: social programs; infrastructure; paved roads; human capital; poverty; economic and social development; dynamic panel data 1. Introduction Since the implementation of social programs, they have been controversial, epitomized by the famous phrase “giving fish or teaching to fish”. Generally, social programs are accused of perpetuating the vicious cycle of poverty among vast sectors of the population instead of promoting entrepreneurship and stimulating individual initiative and personal development. Conversely, other perspectives argue that these programs do not perpetuate the poverty cycle; they serve as temporary protection mechanisms for the less privileged social sectors, against the highest income inequalities generated by imperfectly competitive markets. In recent decades, social programs have become the main instruments of public policy to fight against poverty and extreme poverty worldwide. This is evident in the “2000 Millennium Goals”, endorsed by 189 nations under the United Nations (UN) “The Member States of the UN commit to reducing extreme poverty manifestations: hunger, disease, gender inequality, lack of education, and access to basic infrastructure” (UN 2022a, para. 1). Similarly, in 2015, all UN member states approved the 2030 Agenda for Sustainable Development Goals (SDGs), calling for “universal action to end poverty, protect the planet, and improve the lives and prospects of people everywhere” (UN 2022b, para. 1). In this context, the Peruvian government, since the early 1990s, has been strengthening and expanding social programs, creating more than thirty to address social inequities. Currently, the Ministry of Economy and Finance (MEF 2022) divides social programs into those by their nature, such as the Universal Program and the Targeted Program; it also classifies them by their geographic location, as they are applied at the national or Economies 2024,12, 197. https://doi.org/10.3390/economies12080197 https://www.mdpi.com/journal/economies Economies 2024,12, 197 2 of 19 district level. Finally, they are separated by their benefits, i.e., whether they are individual or collective. According to the National Institute of Statistics and Information (INEI 2022a,2022b), between 2011 and 2021, the budget executed by the General Government for programs to reduce poverty, extreme poverty, and social inclusion programs steadily increased. While in 2011, the total amount allocated to these programs was S/ 7655 million, in 2021, this amount had increased to S/ 17,126 million, representing an increase of more than 123.7% in just a decade. However, overall, social programs accounted for 8% of the total national budget for 2021. In this sense, our general problem of research is to what extent have social programs and socioeconomic variables influenced the evolution of regional poverty levels in Peru (2013–2022)? Our objective is to determine how these programs and variables have influenced the evolution of regional poverty. Finally, the general hypothesis is as follows: Social programs have positively influenced the evolution of regional poverty. These issues have not been studied profoundly. Most of the time, governments wrongly believed that increasing social programs would reduce poverty. This research is structured as follows. In the next section, we review some authors’ literature who have worked on these poverty-related topics and split it into three subsections. In the Section 3, the methodology of this working paper and the theoretical framework are developed for applying the econometric model, also we assess the data for our empirical analysis. In the Section 4, we evaluate the explanatory variables behavior on poverty and contrast the main statistical results under econometric GMMS models. In the following section, we discuss our econometric results compared with other research results. Finally, this working paper includes conclusions, recommendations to reduce poverty rates, and, naturally, some limits in our research. 2. Literature Review 2.1. Social Programs Quispe Quispe (2017) assessed the impact of social programs on reducing monetary and extreme monetary poverty (2009–2015). Durand Gonzales (2021), using a quantitative methodology, analyzes the impact of social programs on child malnutrition in Peru. Public spending on social programs is significantly related to chronic child malnutrition, as 1% budget increases for social programs reduced the malnutrition rate by 0.07% during the study period. Rahmer et al. (2022) analyzed the causal relationships between cash transfers and poverty in Cartagena de Indias using Bayesian autoregressive vectors, concluding that the effects of family subsidies on monetary income are almost null. Regarding the analysis of four social programs. The National Program of Direct Support to the Poorest (JUNTOS) implemented by 2005, is a conditional cash transfer program aimed at reducing poverty in the country by stimulating the demand for health and education services, offering 200 soles every two months for both rural and urban populations (MIDIS 2022). Maco (2022) highlights discrepancies within the Ministry of Development and Social Inclusion (MIDIS). Most experts believed that social policy could not solve rural poverty, suggesting that this program’s deficiencies affect the target population in rural areas. Pensión 65 provides S/ 250 bi-monthly monetary transfer to individuals aged 65 and over who are in extreme poverty due to high levels of labor informality, with noncontributory pension programs aiming to address the survival issues of individuals who could not generate a pension fund due to remaining in informality. Orco Díaz et al. (2020), citing studies commissioned by the MEF about the Pensión 65 effects, argued that their impacts were positive on several levels, such as the fact that 4% of older adults stopped working after being admitted to this program. Additionally, households with at least one program beneficiary increased their consumption by 40%, and the level of psychological depression among the target population decreased by 9%. Orco for Peruvian case reveals that being a beneficiary of this program increases the likelihood of not being poor by 0.10597 Economies 2024,12, 197 3 of 19 compared to those who are not beneficiaries of the program. Also, living in urban areas reduces the probability of being poor by 0.19433 compared to those living in rural areas. Rivera Medrano (2023), a microeconomic study, researches the impact of the Pensión 65 on the well-being of users in Chupaca (Junín), finding a non-direct correlation between the effectiveness of this program and the quality of life. This suggests that the program had neither positive nor negative impacts, meaning it did not alter the quality of population life. The Qali Warma was implemented by MIDIS since 2012, replacing the National Food Assistance Program (PRONAA), aimed at providing daily food to children in all public schools. This program receives the largest budget from the National Treasury in 2021, about S/ 1777 million. It has the particularity of operating under a “co-management” pattern; it involves the participation of the beneficiary population, such as parents, who are directly engaged in the management of the program that benefits their children. Francke and Acosta (2021) revealed no impacts of this program on reducing anemia and chronic child malnutrition, children received much more iron and protein content at home than in schools. Citing Lavado and Barrón(2019), they found that Qali Warma contributed only 16.7% to their iron intake and that 93% of beneficiaries received an iron intake lower than expected. Ayala Beas (2022) stated that the school feeding received by children through this program has no significant effect on improving learning in reading comprehension or mathematics. The author questions the effectiveness and efficiency of this program for children’s learning. Trabaja Perú(Lurawi Perú) was implemented in 2011, with the aim of addressing employment issues by creating temporary inclusive social employment for underemployed and unemployed people, both in rural and urban areas, poverty, and extreme poverty areas. This program received S/ 74 million in 2021 and S/ 390 million in 2020. Considering that the Peruvian problem is not so much unemployment as underemployment, precarious employment affected almost 80% of the EAP. Briceño Avalos (2017) indicates that during the period 2009–2014, the product–employment elasticity fluctuated only between 0.211 and 0.263, reflecting the high informality in the labor market. Tumi (2015), at a macro-evaluation of this program, reveals that this program contributed significantly and increasingly to the creation and promotion of employment for the target population in rural and urban areas, having generated, according to the author, in the period 2011–2014, 122,148 jobs with a total of 2700 projects, including river defenses, roads, sanitation, and irrigation infrastructure, with a total investment of approximately S/ 364 million, most of the beneficiaries were women. Chung Vergara (2022) at the micro level argues for San Juan Bautista (Loreto), there is a positive relationship between Trabaja Perúand the quality of population life, finding a coefficient of 0.744 with a (bilateral) significance of 0.000 p-value. In turn, it coincides with Martínez (2018), for the case of Amarilis (Huánuco), in which the author finds a 0.966 correlation and a zero p-value. This means that there is a favorable relationship bewteen the Trabaja Perúand the quality of life. All of which confirms that this program has been favorable to fight against underemployment and unemployment at the macro and micro levels. On the other hand, when analyzing the first variable, namely unemployment and its relationship with poverty, the link between both has been a central concern in the field of economics, so this study aims to explain the dynamics between unemployment and poverty, or the lack of it. 2.2. Socioeconomic Variables According to Karnani (2009), generating stable employment opportunities with reasonable wages is the best way to lift people out of poverty. This requires two major efforts: creating jobs and increasing employability. Sánchez Torres (2020), the relationship between poverty and the labor market in developing countries is closer when analyzing low labor incomes and the quality of employment. For Colombia, this author distinguishes that poverty is mainly associated with informal jobs; labor formalization policy would have significant effects on improving incomes and reducing poverty. Specifically, formalizing Economies 2024,12, 197 4 of 19 workers would reduce the number of poor people about 40%. Therefore, improving labor market conditions (reducing informality) would have a significant effect on reducing poverty and would also contribute to better income redistribution. Finally, Castillo and Huarancca (2023) built a multidimensional poverty indicator to assess the Peruvian poverty trajectory (2007–2020) and indicate that eliminating precarious employment could decrease the incidence of multidimensional poverty by 10.4%. Regarding human capital, its investment and development are also essential for poverty reduction because they enhance economic growth through their effect on total factor productivity, in addition to having positive effects on creating equal opportunities for citizens. Human capital is the skills and knowledge acquired by which people improve their economic productivity, includes not only formal education but also on-the-job training, work experience, self-learning, and any other type of investment that improve a person’s ability to perform their job (Becker 1975). These investments are rationally taken by evaluating the cost–benefit relationship, so future gains obtained through higher wages and greater employment opportunities are weighed. Investment in human capital takes significant importance; it guarantees a real and significant reduction in poverty; therefore, it becomes a primary objective for developed and developing economies. In this sense, Mincer (1991) maintains that higher education has positive effects on maintaining people’s incomes while simultaneously reducing unemployment risk. Olopade et al. (2019) in a study for the 12 OPEC countries, where theoretically the resource curse hypothesis could be an obstacle to economic growth and have implications for poverty, determine that there is a positive interaction between human capital and poverty reduction. They recommend that investment should be made in improving the quality of human capital through education and health. In this regard, according to the same author, OPEC members that allocated significant percentages for education have achieved a reduction in the poverty rate (1.47%). Regarding infrastructure, it is a main channel for agricultural, industrial, commercial, and service production, as infrastructure supports the development of the country’s economic activities. Efficient public infrastructure boosts competitiveness in local and regional markets and, therefore, the national market, thereby promoting productivity and competitiveness. In this way, Rozas and Sánchez (2004) state that: “Transport networks ( . . . ) constitute a central integration element of the economic and territorial system, making transactions possible within a specific geographic/economic space, and with the outside” (p. 8). Therefore, these authors highlight the favorable relationship between infrastructure and poverty reduction, as the results show that at the regional (Latin American) level there is a negative relationship ( − 0.023071). Thus, investments in infrastructure improve the population’s access to markets and enable coverage of services such as education, health, housing, transport, employment, etc. Currently, there is a significant gap in infrastructure. “Closing the infrastructure gap for the period 2016–2025 would imply an average annual investment of 8.27% of GDP (i.e., USD 15,955 million annually)” (Bonifaz et al. 2015). 2.3. The Peruvian Regional Poverty Ariza and Retajac (2020) point out that poverty is a socioeconomic situation where there is vulnerability and a lack of necessary resources for subsistence, such as food, health, education, relationships, and productivity. Therefore, Borga and D’Ambrosio (2021) specify that in the face of the existence of chronic poverty and underemployment, it is necessary to implement public work programs with adequate remuneration and duration that would make it possible to accumulate economic capital. For this reason, regarding public expenditure, Hlasny et al. (2022) indicate that its amount and efficacy are important for achieving a reduction in inequality as part of the Sustainable Development Goals (SDGs). Therefore, poverty reduction requires effective management of public resources allocated to social programs that demonstrate their significant contribution to improving the well-being of the most vulnerable population segments in the short, medium, and long terms. Economies 2024,12, 197 5 of 19 Poverty affects most of the world’s population, experiencing a decline over the last 25 years. However, starting with the COVID-19 pandemic, the decreasing trend was interrupted (Banco Mundial 2022). Similarly, the behavior of the poverty rate in Peru has followed the same trend, with better results compared to other countries; since poverty in general as a percentage of the total population in 2001 rose to 54.8% (INEI 2023), it stood at 20.2% in 2019, rising again in 2022 to 27.5% due to COVID-19. It is important to highlight that the decreases in the poverty rate have taken place, thanks to the good performance of the country’s macroeconomic variables, particularly highlighting the economic growth that began in 2002, until before the pandemic. “Countries that managed to reduce their poverty did so by increasing the average income associated with economic growth. Therefore, the decrease in the regional poverty rate occurred in the context of widespread economic growth” (León Mendoza 2019, p. 4). Chapa et al. (2022), in their studies on the impact of social programs on poverty levels, have developed a social matrix for Mexico in 2008, which is square and presents income and expenses, divided by economic sector, production factor, and institution. They work on a linear general equilibrium model based on three social programs: Oportunidades, Procampo, and Adulto Mayor. In this way, they find a favorable impact of social programs on reducing Mexican poverty, (as stated above) decreasing its level by 9% and generating direct and indirect effects that improve income distribution. Borga and D’Ambrosio (2021) evaluate the impact of three social programs: The Productive Safety Net Program in Ethiopia (PSNP), National Rural Employment Guarantee Act in India, and Juntos Program in Peru. These authors use panel data provided by five surveys from 2002 to 2016. Different poverty thresholds are considered to analyze the distributive impact of the programs and their influence on poverty levels in three dimensions: living standards, health, and education. The program’s impact is econometrically evaluated, estimating difference-in-differences (DID) models with propensity score matching (PSM) methods; after estimating the first-round logit model that considers household wealth, household members, rural or urban location, and mother’s characteristics. Borga and D’Ambrosio (2021) analyzed three or more indicators in the PSNP and Juntos Program, establishing that they do not present a statistically significant impact on reducing the poverty incidence, but their intensity is reduced. In this sense, participating in social programs is associated with a decrease in the indicators of poverty dimensions analyzed. In this way, the effect is greater for people with a higher level of poverty due to the multiple deprivations they face. And for the specific case of the Juntos Program, there is a higher reduction in poverty, where the multidimensional poverty index decreases from 0.57 (2006) to 0.17 (2016), with a decrease in the incidence of poverty of 54% and a decrease in the poverty intensity of 18% over a decade. Ariza and Retajac (2020) evaluate the factors associated with urban poverty in Colombia (2002–2018), using indicators of incidence, gap, and severity, which are based on income, poverty threshold, and poverty situation. Furthermore, a logit model is estimated considering as dependent variables poverty and explanatory sociodemographic variables of size, composition, educational level, and characteristics within households, as well as unemployment, non-labor income, and external support. Stampini et al. (2016) used synthetic panels, considering data from the Harmonized Database of Household Surveys of Latin America and the Caribbean (Sociometer-IDB), to evaluate the vulnerability of the poor and middle class from twelve countries, including Peru. They established that chronic poverty comprises 91% of extreme poverty and 50% of moderate poverty, finding that chronic poverty conditions have a very low level of human capital and few opportunities for employment due to their residence in rural areas. Therefore, it is difficult for strategies to improve the capacity to generate income in this population segment to be successful. The authors also find that 14% of the middle class has experienced a situation of poverty over a decade. They consider that to fight against chronic poverty, long-term social policies must be combined with those of the extremely rural poor. Economies 2024,12, 197 6 of 19 Whereas, for transitory poverty, they point out that short-term policy measures are required with flexible entry and exit assistance from the social program for urban residents. In this context, the country’s rural sector needs to be prioritized in academic reflections, considering that these households are the most likely to suffer the impact of international variation in food prices. Paradoxically, a part of the peasant’s food diet comes from imported supplies, affecting their food security. The support of social programs is necessary to reduce this effect, contribute to their resilience, and reduce their vulnerability (Osabohien et al. 2024). Social support encourages families to adopt better strategies to cope with periods of crisis, avoiding greater adverse effects on the rural population, such as decreased access to education and health (Abay et al. 2023). In addition, it is necessary to provide help to address demographic challenges such as the migration of young people and the population aging that affect agricultural activities in the different regions (Lindoso et al. 2018). Therefore, in Peru, vigorous and comprehensive social programs are needed to support the poor rural population and marginal urban areas, who need access to opportunities to reach a better quality of life (Quispe-Mamani et al. 2022). It would be pertinent to incorporate digital technologies in favor of beneficiaries to overcome geographical limitations, improve access to information, broaden their employment prospects, and increase their personal development (Polo Escobar et al. 2023). Likewise, it is important to boost financial education through the Pilot Program for the Promotion of Savings in the Juntos Program (Daher et al. 2022), aimed at beneficiaries in rural areas, so that they have a savings account and access to financial services that allow them greater economic and social inclusion. Daher et al. (2022) point out that these types of programs have a significant impact on development, empowerment, and female entrepreneurship. Finally, Juntos aims to interrupt the intergenerational cycle of Peruvian rural poverty (Santos et al. 2021). It has been focusing on actions related to health and education, considering that economic vulnerability affects the conditions of well-being and economic and social development of rural areas in the regions. 3. Methods, Econometric Specification and Data 3.1. Methods Our method is quantitative, non-experimental, and correlational, based on secondary data obtained from official sources, such as the Instituto Nacional de Estadistica e Informatica (INEI) and the user-friendly query of the Ministry of Economy and Finance (MEF), as well as data obtained from the Peruvian Institute of Economics (IPE). The information for the period 2013–2022 at the level of the 24 departments/regions was considered. We use the system generalized method of moments (SGMM) to estimate the econometric dynamic panel data models, in order to obtain information over a long period of time and across entities (regions). We studied the four most representative social programs: The National Program of Direct Support to the Poorest Juntos, National Solidarity Assistance Program Pension 65, National School Nutrition Program Qali Warma, and Program for the Generation of Social Inclusive Employment “Trabaja Perú”. Additionally, socioeconomic variables, such as unemployment, gross Domestic Product per capita (income), human capital, physical infrastructure, and rotated expenditure were analyzed. Also, dummy variables on the occurrence of COVID-19 and the “El Niño” phenomenon (FEN2017) were included. A dynamic panel data model was used, considering the various advantages over other econometric techniques; it corrects the problem of endogeneity, allows control of constant unobservable heterogeneity, temporal effect control, modeling of dynamic effects, and avoids aggregation bias, among others. This problem arising from the individual effect tends to be eliminated when working with series in first differences as instruments. Economies 2024,12, 197 7 of 19 3.2. Econometric Specification Under the SGMM econometric methodology, our dynamic model was constructed and specified: Yit =c+∝Yi,(t−1)+βX′it +εit εit =µi+ϑit E(ui)=E(ϑit)=E(µiϑit)=0 The following general equation has been considered: Y(t)=c+∝Y(t−1)+β1X1+β2X2+β3X3+β4X4+β5X5+β6X6 +β7X7(t−1)+β8X8(t−1)+β9X9(t−1)+β10 X10 +ε(t) Y:Poverty X1: Income X2: “El Niño” phenomenon X3: Unemployment X4: Human Capital X5: Infrastructure X6: COVID 19 X7: Juntos X8: Pensión65 X9: Qaliwarma X10: Trabaja Perú The following general poverty equation is proposed: Pobreza(t)=c+ ∝Pobreza(t−1)+β1Ingreso +β2FEN2017 +β3Desempleo +β4Capital Humano +β5In f raestructura +β6Covid19 +β7Juntos(t−1)+β8Pensin65(t−1) +β9Qaliwarma(t−1)+β10 Trabaja Per +ε(t) In the next section, seven (07) SGMM econometric estimations are obtained from this equation, where the influence of socioeconomic variables and the impact of social programs implemented in Peru to reduce poverty are econometrically analyzed. 3.3. Data Data for our empirical analysis has been obtained from different Peruvian official sources. Table 1shows the variables name, following by definition and sources. They included social programs, socioeconomic variables and two dummy variables such as phenomenon El Niño and the pandemic of COVID-19. Table 1. Socioeconomic variables. Variable Name Definition Source Poverty % of population in poverty conditions Peruvian Institute of Economics (IPE) Poverty(−1) % of population in poverty conditions, lagged by one period Peruvian Institute of Economics (IPE) Income Real GDP per capita Central Reserve Bank of Peru (BCRP 2022) FEN2017 El Niño Phenomenon of 2017 Dummy Variable Unemployment Unemployment rate as a percentage of the economically active population National Institute of Statistics and Informatics (INEI) Human Capital Average years of education achieved by the population aged 15 and over National Institute of Statistics and Informatics (INEI) Economies 2024,12, 197 8 of 19 Table 1. Cont. Variable Name Definition Source Infrastructure Percentage of existing road infrastructure that is paved (national highway) Ministry of Transport and Communications COVID-19 COVID-19 pandemic in 2020 Dummy Variable Juntos Social program Ministry of Economy and Finance Pensión 65 Social program Ministry of Economy and Finance Qali Warma Social program Ministry of Economy and Finance Trabaja PerúSocial program Ministry of Economy and Finance Own elaboration. After correlations were obtained, Figure 1shows a negative relationship between poverty levels and human capital, infrastructure, per capita income. On the other hand, Figure 2, shows a positive relationship between poverty rates and the different social programs spending, including Juntos, Pensión 65, Qali Warma, and Trabaja Peru. Economies 2024, 12, x FOR PEER REVIEW 8 of 19 Human Capital Average years of education achieved by the population aged 15 and over National Institute of Statistics and Informatics (INEI) Infrastructure Percentage of existing road infrastructure that is paved (national highway) Ministry of Transport and Communications COVID-19 COVID-19 pandemic in 2020 Dummy Variable Juntos Social program Ministry of Economy and Finance Pensión 65 Social program Ministry of Economy and Finance Qali Warma Social program Ministry of Economy and Finance Trabaja Perú Social program Ministry of Economy and Finance Own elaboration. After correlations were obtained, Figure 1 shows a negative relationship between poverty levels and human capital, infrastructure, per capita income. On the other hand, Figure 2, shows a positive relationship between poverty rates and the different social programs spending, including Juntos, Pensión 65, Qali Warma, and Trabaja Peru. Figure 1. Relationship between poverty and per capita GDP, unemployment, human capital, infrastructure, and social programs. Figure 1. Relationship between poverty and per capita GDP, unemployment, human capital, infrastructure, and social programs. Economies 2024,12, 197 15 of 19 On the other hand, the Qali Warma school feeding program also does not have a significant effect on poverty levels, with its effect being marginal (coefficient of 0.0012 and p-value of 0.027) in the SGMM 5 model, while in the SGMM7 model, where all social programs are considered, a non-significant value with a p-value of 0.681 is observed. The results are in accordance with what was found in Mexico, where the School Breakfast Program of Mexico City, according to the study by Sánchez Vargas et al. (2019), determined that the results of the estimates suggest that the school feeding program does not lead to significant changes in grades, observing that participation in the School Breakfast Program shows a positive, but not significant relationship with academic score. Thus, being a participant in the program would raise the score only by 2.3% in fourth-grade primary students; however, this coefficient lacks statistical significance since its p-value is 0.321, meaning the program would not have collateral effects on reducing poverty in the long term. These results are aligned with what Francke and Acosta (2021) find when studying the impact of the Qali Warma program on anemia and malnutrition in children aged 3 to 5 years, determining that the program would not have effects on anemia or chronic malnutrition, at least in the age range studied. Because, according to them, the probability that children do not have anemia if they benefit from the program is just 1.8%. Regarding the employment assistance program Trabaja Perú, evaluated in the SGMM6 and SGMM7 regressions, it is observed that it is also not significant to explain the reduction in poverty in the country since the p-value is greater than 0.10. These results coincide with those of Abramo et al. (2019), in a study of social programs for Latin America, in which they indicate that while the poor or extremely poor population may manage to be employed, they do so in low-productivity jobs. Since 74.4% of men and women in 2016 were employed in low-productivity sectors for the first income quintile, and for a second quintile, 60.8% of this same type of population were also employed in low-productivity sectors. In this sense, the beneficiary population of this program focuses on low-skilled jobs, which limits the program’s impact, besides being designed as a temporary solution and not addressing the structural factors that contribute to reducing poverty in the long term. The limited capacity of social programs to alleviate poverty is in line with Phan et al. (2017), where they do not find a statistically significant relationship between poverty reduction and spending on programs that are designed to benefit people living in poverty or economic vulnerability. In this regard, the p-value of the coefficients found in the estimation exceeds the threshold of 5%. This result would reflect the complexity of the management of these social programs, adding that the implementation of the program can present filtering problems, so that the benefits of these programs are captured by non-poor groups, thus damaging the effectiveness of the program. In addition, another element of inefficiency could be due to corruption. Olken (2006) points out that redistributive programs, especially in developing countries, can promote corruption and consequently generate economic losses that exceed the benefits received by recipients. 6. Conclusions, Recommendations and Limitations There is a negative impact/indirect relationship bewteen income, human capital, and infrastructure on poverty levels, while a direct relationship is observed between poverty and spending on social programs such as Juntos, Pensión 65, Qali Warma, and Trabaja Perú. These results were obtained using the System Generalized Method of Moments, which established solid and reliable foundations. Thus we are contributing to the body of knowledge existing in the economic literature to improve the quality of social spending and public budget management. Social programs implemented by Peruvian governments in recent decades aimed at reducing poverty paradoxically have not been having a significant impact on reducing poverty at the regional and national levels. They would be weak public policy instruments to generate a structural change in the inequality levels present in Peruvian society. Therefore, their main objectives would only be aimed at mitigating the effects of inequality on poverty levels. Economies 2024,12, 197 16 of 19 On the other hand, socioeconomic variables such as per capita income, employment, human capital, and productive infrastructure have a significant impact on reducing regional poverty in Peru. It is recommendable for governments to increase investment in infrastructure, such as tunnels, more roads, ports, trains, bridges, telecommunications, the Internet, etc., in regions to allow rural and poor inhabitants to connect with national and international markets, get productive jobs, and increase their income. Unfortunately, social programs have not had favorable results in the recent decade, even with increasing public financing. However, the best way to escape poverty is through better training/qualification of human capital, substantial improvement of physical infrastructure, and income generation through well-paid formal employment, where the worker deploys all their productive capacities, obtains social benefits, and contributes effectively to their personal development, regional development, and the country in general. The system generalized method of moments was demonstrated as being a good instrument to analyze regional poverty in Peru. The results are consistent with others previously obtained by different authors. Social programs implemented in Peru in the last few decades have not had clear results in reducing poverty. It is also fair to point out that COVID-19 has increased the poverty rate in the last few years. However, we can be sure that human capital, infrastructure, employment, and income can help significantly reduce poverty rates. To reduce poverty sustainably in the long term, a comprehensive policy should be implemented, focusing on addressing the structural causes of poverty, which should combine the promotion of economic growth, the strengthening of human capital, and the development of infrastructure. Incentives for investment and job creation should be established through a favorable environment for private and foreign investment through tax incentives, simplification of public bureaucracy, and proper management of social conflicts. It should also seek to improve education and job training by investing in quality education accessible to all and developing job training programs to improve the employability and productivity of the workforce. Finally, develop basic infrastructure and modernize transport infrastructure to facilitate access and connectivity to national and international markets. Among the limits of our research is the System Generalized Method of Moments (SGMM), which constitutes a contribution to the explanation of regional poverty in Peru. However, it can be mentioned that socioeconomic and geographical differences between regions can introduce unobserved heterogeneity, that the SGMM does not capture adequately, and that it could lead to biased estimates. Likewise, there may be unobserved, region-specific effects, such as cultural or public policy factors, that cannot be measured with the variables included in the model. Our theoretical-mathematical explanation can be improved by employing techniques that incorporate the analysis of multidimensional poverty, capturing more completely the shortcomings that people face beyond monetary poverty. It is recognized that monetary poverty may not capture all dimensions of poverty, some deficiencies in health or education are not necessarily reflected in the monetary poverty measure. Therefore, impact evaluations with experimental methods could help identify the effects of social programs, by comparing treatment and control groups. As for socioeconomic variables, such as unemployment, human capital, and physical infrastructure, it can be pointed out that their measurement does not internalize the quality of employment or underemployment, which also affects poverty. The average years of education considered as human capital only reflect the number of years that people have spent in the formal education system; it does not capture its quality or the skills acquired. The physical infrastructure variable, measured as a percentage of asphalted road infrastructure, could be improved by building an indicator that includes railways, airports, and ports, which would provide a more complete idea of the connectivity and accessibility of the region. Economies 2024,12, 197 17 of 19 Author Contributions: Conceptualization, J.A.H.P. and I.Y.V.S.; methodology and software H.R.B.A.; validation, J.A.H.P., I.Y.V.S. and S.C.C.M.; formal analysis, H.R.B.A.; investigation, J.A.H.P., I.Y.V.S., S.C.C.M. and H.R.B.A.; resources; data curation, S.C.C.M.; writing—original draft preparation, J.A.H.P., I.Y.V.S., H.R.B.A. and S.C.C.M.; writing—review and editing, H.R.B.A.; visualization, J.A.H.P., I.Y.V.S., H.R.B.A. and S.C.C.M.; supervision, J.A.H.P.; project administration, J.A.H.P.; funding acquisition, J.A.H.P. All authors have read and agreed to the published version of the manuscript. Funding: We acknowledge Universidad Nacional Mayor de San Marcos for funding this publication. Informed Consent Statement: Not applicable. Data Availability Statement: Data supporting reported results can be found at Instituto Nacional de Estadística e Informatica (www.inei.gob.pe), Reserve Central Bank of Peru (www.bcrp.gob.pe); Ministry of Economy and Finance (www.mef.gob.pe); and Instituto Peruano de Economia (www.ipe. org.pe). Acknowledgments: We acknowledge the comments and recommendations from our colleagues in the “VLI Reserve Central Bank of Peru Meeting”, Lima, Peru (October, 2023). Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References Abay, Kibrom A., Guush Berhane, John Hoddinott, and Kibrom Tafere. 2023. COVID-19 and Food Security in Ethiopia: Do Social Protection Programs Protect? Economic Development and Cultural Change 71: 373–402. 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