Socioeconomic Indicators - North Macedonia

The socioeconomic indicators domain covers income distribution, employment, education, demography, and social welfare. Income inequality is a major challenge in the United States and Europe, particularly in large cities and between urban and rural areas. In addition, there are significant disparities in terms of social welfare across Europe, with some countries boasting robust and well-developed social welfare systems, while others exhibit comparatively weaker welfare structures. In Asia, progress has been made in reducing poverty in emerging economies such as China and India, but challenges remain. Each region also faces unique socioeconomic challenges. In Europe, challenges include harmonizing social welfare systems, rising youth unemployment, an aging population, and bridging the economic gap between European countries. In Asia, sustainable and inclusive growth, rural development, reducing income disparities, and, in some countries, an aging population are key concerns.

Data coverage:

The dataset encompasses data from 152 countries. The charts depict the situation of each country in six different domains. These domains are socioeconomic indicators, macroeconomic indicators, health indicators, digital and connectivity indicators, consumption indicators, as well as logistics and transport indicators. Within these domains, various segments are covered, including demography, economic measures, economic inequality, employment, consumption, health determinants, and much more.

Modeling approach:

The composition of each domain follows a comprehensive approach that combines both top-down and bottom-up methodologies, with each domain and segment being guided by a specific rationale. To evaluate the situation of these six domains within each country, we rely on pertinent indicators and data from reputable international institutions, local national statistical offices, industry associations, and leading private institutions. Additionally, we undertake data processing procedures to address issues such as missing timelines, outliers, and data inconsistency. Our data processing incorporates advanced statistical techniques, including interpolation, exponential moving weighted average, and the Savitzky-Golay filter. These methods contribute to the refinement and enhancement of data quality.

Forecasts:

In our forecasting process, a wide range of statistical techniques is utilized based on the characteristics of the markets. For example, the S-curve function is employed to forecast the adoption of new technology, products, and services, aligning the forecast model with the theory of innovation adoption. Additionally, the data is forecasted using ARIMA with and without seasonality considerations, exponential trend smoothing, and the Compound Annual Growth Rate (CAGR), with the option to incorporate adjustment factors when necessary. These techniques enable accurate and reliable forecast methods tailored to the unique characteristics of the data in each market and country.

Additional notes:

The data is updated twice per year or every time there is a significant change in their dynamics. The impacts of the COVID-19 pandemic and of the Russia/Ukraine war are considered at a country-specific level.