TY - JOUR
T1 - Social Programs and Socioeconomic Variables
T2 - Their Impact on Peruvian Regional Poverty (2013–2022)
AU - Hinojosa Pérez, J. Adolfo
AU - Avalos, Hernán Ricardo Briceño
AU - Salazar, Ivonne Yanete Vargas
AU - Carrasco Mamani, Sergio Christian
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/8
Y1 - 2024/8
N2 - 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 Perú social 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%.
AB - 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 Perú social 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%.
KW - dynamic panel data
KW - economic and social development
KW - human capital
KW - infrastructure
KW - paved roads
KW - poverty
KW - social programs
UR - http://www.scopus.com/inward/record.url?scp=85202477895&partnerID=8YFLogxK
U2 - 10.3390/economies12080197
DO - 10.3390/economies12080197
M3 - Original Article
AN - SCOPUS:85202477895
SN - 2227-7099
VL - 12
JO - Economies
JF - Economies
IS - 8
M1 - 197
ER -