Analyzing the Ecuadorian purchasing power: family income vs. basic market basket
DOI:
https://doi.org/10.51798/sijis.v5i1.724Keywords:
Purchasing Power, Basic Market Basket, Family Income, Minimum Wage, EcuadorAbstract
The basic market basket (BMB) is a set of goods and services that are essential for meeting the basic needs of a household comprising four members. The family income in Ecuador is calculated from the monthly salary of the household members, where 1.6 out of the four members contribute to the family income. Specifically, purchasing power is the ability of a person or a group as consumers to acquire a set of goods and services. The study was divided into three main parts. In the first part, we looked for correlations among the BMB, the family income, and the purchasing power of Ecuadorians. In the second part, we forecasted the price of the BMB for each remaining month of 2023. The best model was SVR, this model obtained a MAPE= 0.3666% in the testing step and a MAPE= 0.1466% in the validation step. Furthermore, the third phase involved the analysis of the monthly Ecuadorian purchasing power for 2023. In this case, the Purchasing power starts at 9.66% in April and ends at 8.07(%) in December, meaning a surplus from $74.01 to $62.69 respectively. According to the SVR predictions, an Ecuadorian family earning the minimum family income (i.e. $840 for 2023) can save $839.59 a year. This means a family has to save almost 18 years to buy a brand new cheap family car, or it takes an Ecuadorian family more than 24 years to save for a 60m2 average cheap house.
References
Bernal, J.L.C., Cuenca, L.A.B., Vásquez, J.A.C.: Variables determinantes en el crecimiento económico del ecuador función cobb-douglass 2007-2019. Sociedad & Tecnología 4(2), 109–122 (2021)
Bertuah, E., Sakti, I.: The financial performance and macroeconomic factors in forming stock return. Jurnal Riset Manajemen dan Bisnis (JRMB) Fakultas Ekonomi UNIAT 4(S1), 511–522 (2019)
Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time series analysis: fore- casting and control. John Wiley & Sons (2015)
Bure, M.C.Q., Guerrero, E.R., Aguirre, P.U., Gaona, L.B.: Evolución del precio de la canasta básica del ecuador. análisis del periodo 2000–2019. Revista Científica y Tecnológica UPSE 8(2), 59–67 (2021)
Bustamante, R.Y.S., Ruiz, G.R.S., Suarez, C.A.R., Santiana, Z.L.T.: El desempleo en el ecuador: causas y consecuencias. Polo del Conocimiento: Revista científico- profesional 5(10), 774–797 (2020)
Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I., Mrzljak, V.: Modeling the spread of covid-19 infection using a multilayer perceptron. Computational and mathematical methods in medicine 2020 (2020)
Chai, T., Draxler, R.R.: Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature. Geoscientific model de- velopment 7(3), 1247–1250 (2014)
Cisneros, F.N.G., Acevedo, C.E.R., Moran, M.F.A.: El salario mínimo y su relación con la canasta básica. Revista Electrónica de Investigación en Ciencias Económicas pp. 110–124 (2014)
Cruz, J., Maldonado, L.: Incidencia del ingreso familiar y la educación en el acceso a la canasta básica familiar en ecuador. Revista económica 3(1), 19–31 (2017)
De Myttenaere, A., Golden, B., Le Grand, B., Rossi, F.: Mean absolute percentage error for regression models. Neurocomputing 192, 38–48 (2016)
Desai, M., Shah, M.: An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (mlp) and convolutional neural network (cnn). Clinical eHealth 4, 1–11 (2021)
Dubey, A.K., Kumar, A., García-Díaz, V., Sharma, A.K., Kanhaiya, K.: Study and analysis of sarima and lstm in forecasting time series data. Sustainable Energy Technologies and Assessments 47, 101474 (2021)
Fernández, G., Guzmán, A.: La calidad de vida desde la perspectiva latinoameri- cana: Un análisis de las ponencias presentadas al iv seminario latinoamericano de calidad de vida urbana. Revista Bibliográfica de Geografía y Ciencias Sociales 242 (2000)
Karunasingha, D.S.K.: Root mean square error or mean absolute error? use their ratio as well. Information Sciences 585, 609–629 (2022)
Lin, M., Yan, C., Meng, J., Wang, W., Wu, J.: Lithium-ion batteries health prog- nosis via differential thermal capacity with simulated annealing and support vector regression. Energy 250, 123829 (2022)
Mahaputra, M.R., Saputra, F.: Determination of public purchasing power and brand image of cooking oil scarcity and price increases of essential commodities. International Journal of Advanced Multidisciplinary 1(1), 36–46 (2022)
Parbat, D., Chakraborty, M.: A python based support vector regression model for prediction of covid19 cases in india. Chaos, Solitons & Fractals 138, 109942 (2020)
Picchioni, F., Goulao, L.F., Roberfroid, D.: The impact of covid-19 on diet quality, food security and nutrition in low and middle income countries: A systematic review of the evidence. Clinical Nutrition 41(12), 2955–2964 (2022)
Qi, J., Du, J., Siniscalchi, S.M., Ma, X., Lee, C.H.: On mean absolute error for deep neural network based vector-to-vector regression. IEEE Signal Processing Letters 27, 1485–1489 (2020)
Wildan, M.A.: Macroeconomic factors affecting natural gas export management. International Journal of Energy Economics and Policy (2021).
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