تحليل العلاقة بين التضخم والنمو الاقتصادي باستخدام النماذج الإحصائية دراسة ميدانية على المستهلكين في العراق
Analysis of the relationship between inflation and economic growth using statistical models: A field study on consumers in Iraq
Mots-clés :
Inflation, economic growth, consumer spending, financial literacy, consumersRésumé
This study aimed to analyze the impact of the relationship between inflation and economic growth through a set of intermediate variables represented by (consumer financial awareness, confidence in economic policy, consumer spending, increased cost of goods, and loss of purchasing power). This was based on field questionnaire data for a sample of (61) individuals
from Basra Governorate. The researcher used the multiple regression analysis method to estimate the statistical model and test the hypotheses. The results showed that the independent variables as a whole were able to explain the change in economic growth at an acceptable rate, as the value of the impact coefficient reached (F=7.190), indicating that the five independent variables explain a significant portion of the variance in economic growth, and the coefficient of determination reached (R²=0.395), meaning that the variables explain 39.5% of the change in economic growth. The unexplained portion of the variance, i.e., the residuals, accounted for 60.5% of the variance. This is likely due to other variables outside the model, such as oil price fluctuations, unemployment rates, foreign investment volume, and tax and financial policies. This opens the door for future studies to incorporate these factors into more comprehensive models using advanced statistical tools.
It also revealed that the loss of purchasing power was the most influential factor, with a positive effect, while financial awareness showed a negative and significant effect. The other independent variables did not achieve any statistical significance. The study recommends strengthening purchasing power and directing financial awareness towards investment, suggesting the use of more complex statistical models in the future.