KEY MACROECONOMIC FACTORS AFFECTING THE UNEMPLOYMENT RATE

Аннотация

This study investigates the macroeconomic factors affecting unemployment in the Shanghai Cooperation Organization (SCO) member countries over the period 2004–2023, based on data extracted from the World Bank database. We constructed a model and conducted a regression analysis using the Ordinary Least Squares (OLS) method to explore the relationships among thevariables. Thefindings are presented below. Keywords: Unemployment, Inflation, Foreign Direct Investment (FDI), STATA-14 software, OLS method, VIF indicators, Breusch-Pagan test, Breusch-Godfrey test. 1. Introduction. In today’s context, unemployment has become one of the most frequently debated issues. The global rise in unemployment has led many people in various countries and cities to seek employment abroad rather than in their own countries. Moreover, in developed nations, unemployment has been associated with increased crime rates and heightened social tensions. Let us first define the term “unemployment.” Unemployment is a socio-economic phenomenon that reflects the underutilization of a portion of the available labor force within a country. Individuals who are able to work and willing to be employed but are not currently working are considered unemployed. Unemployment can be classified into several types, including structural, frictional, institutional, technological, regional, hidden, stagnant, and cyclical unemployment. Among the primary causes of unemployment are the transitional periods during which workers leave one job for another or require time to acquire new skills and adapt to a new workplace. These factors mainly account for structural and frictional unemployment, which together constitutenatural unemployment. In addition, factors such as a decline in aggregate demand, reduction in production, underdevelopment or inefficiency of enterprises that generate jobs, and the automation of labor, including the adoption of robotics and information technologies, also contribute to rising unemployment levels. Furthermore, enterprise managers may not be able to lay off all workers at once. As a result, some employees may not be fully employed during the workday or workweek, leading to hidden unemployment. In some countries, there are individuals who are physically able to work but have lost their jobs and have no interest in seeking employment or receiving unemployment benefits. Such conditionsgive rise to stagnant unemployment. Among the key macroeconomic factors influencing unemployment, the most prominent are inflationand investment,along withother economic variables. Inflation refers to the depreciation of a currency due to the supply of money exceeding the demand for goods and services, leading to a general increaseinprices. Investment, on the other hand, is the allocation of financial resources for the restoration and expansion of fixed and working capital, as well as for the enhancement of production capacity. Investments can take the form of cash, bank loans, shares, and other securities. Nominal https://ijmri.de/index.php/jmsi volume 4,issue6,2025 41 investment refers to investment in monetary terms, while real investment refers to the actual acquisitionof investment resources with that money. Based on this data and the World Bank Databank, we constructed a model to study the influence ofinvestment and inflation onunemployment usingSTATA-14 software. Methodology .As mentioned above, we collected relevant data from the World Bank Databank. Specifically, we used thefollowing indicators: “Inflation, consumer prices (annual %)” “Unemployment, total (% oftotal labor force) (modeled ILO estimate)” “Foreign direct investment,net inflows (% ofGDP)” From these, wedeveloped an econometric model. Ourdependent variable (Y) is: Unemployment, total(% oftotal labor force)(modeled ILO estimate) Theindependent variables (X1 and X2)are: X1 =Inflation X2 =Investment Thus, webriefly labeled our variables: Y=Unemployment X1 =Inflation X2 =Investment Unemployment it= B0+ B1*Inflation it+ B2*Investment it+ uit These statistical data were obtained from the World Bank’s World Development Indicators (WDI) database. The data were collected for the eight member countries of the Shanghai Cooperation Organization (SCO). These countries include Azerbaijan, Uzbekistan, the Russian Federation, the Islamic Republic of Iran, China, Kyrgyzstan, Tajikistan, and Kazakhstan. The dataset covers theperiod from 2004to 2023. After inputting our model into STATA, we tested for the presence of multicollinearity, heteroskedasticity, and autocorrelation issues. The results revealed the presence of heteroskedasticity in the model. Therefore, we transformed the variables by applying logarithmic conversion. Following that, webegan ouranalysis byconducting descriptive statistics onthemodel. To do this, we used the sum command in STATA-14, which generated the following statistical indicators: Variable Obs Mean Std.Dev. Min Max lnunemployment 160 1.770793 0.3999066 0.8175747 2.615935 lninf