Goal 8 of Agenda 2030 - SDG8 The primary objective of this study is to examine the economic performance of different countries and explore the factors contributing to their diverse outcomes, focusing specifically on Goal 8 of the Sustainable Development Goals (SDG8) set by the United Nations.

Data Source The data used for this study were obtained from the UNSTATS official site, specifically from the statistics division, on April 22nd, 2023. The data can be accessed at the following link: UNSTATS Data Portal

Data Analysis Approach Given the presence of numerous missing values in the dataset, we encountered challenges in conducting a multivariate analysis. As a result, we adopted a two-step approach to address this issue.

In the first step, we utilized percentage differences to analyze the temporal changes for each year. This approach allowed us to understand how the economic indicators evolved over time.

In the second step, we used the available data to estimate the missing values by applying a simple multiplication technique [missing value = actual value * (1 + percentage difference)]. To ensure the accuracy of our estimates, we weighted the percentage difference, giving less influence to outliers.

To facilitate the analysis and ensure comparability, we divided the dataset into groups based on the clusters we previously created using GDP. This grouping ensured that the calculated percentage differences were not influenced by dissimilar countries.

Despite encountering challenges due to missing values, we implemented a two-step approach to mitigate their impact. By analyzing percentage differences and estimating missing values, we aimed to provide insights into the temporal changes and economic performance indicators.

HDI analysis Taking advantage of the HDI, or the human development index to evaluate the quality of life of the members of a country, we have divided the countries into 5 different groups (clusters):

• Slowly developing countries
• Emerging countries -Middle-income countries -Developing countries -Advanced developing countries

We repeated this analysis for each year to find in which cluster each country was in a certain year: this way we had the opportunity to see if a country was catching up in terms of HDi or if it could not keep up with the evolution:I'll show two types of analysis that we can perform on this studio: how a country behaves in terms of HDI:we can see if category changes are detected per year and we will verify if the causes are at a political, social level, etc. (e.g. Italy in 2011) you can read about it on consob.it Evaluation by year (e.g. 2008 CRISIS, we choose Bahamas, Malta, Panama, Portugal, Qatar, Russia, Saudi Arabia and the United Arab Emirates where there is no change in HDI compared to other countries (where the value is 0 i.e. the their cluster does not change but the HDI decreases)

GDP extension In the same way we have divided the countries into 4 different groups, but in this case using the GDP: -Middle-low income countries

• Middle-high income countries -High income -Low income

GDP per capita vs GDP for each employee The values in the table are obtained from the difference of the percentage of GDP per employee minus the percentage of GDP per person. If the growth rate of GDP per capita is higher than the growth rate of GDP per person employed, this could indicate that the country is generating new jobs, but at the same time the average income per worker is not rising fast. On the other hand, if the growth rate of GDP per person employed is higher than the growth rate of GDP per capita, this could indicate that the country is improving labor productivity and economic efficiency, but also that the growth of population could limit the growth of per capita GDP. (ex in Italy between 2008 and 2009 when there was an increase in the unemployment rate)

Domestic Material Consumption We have studied the correlation, for the various countries of the world, between the use of a raw material and the Gross Domestic Product (GDP). Raw materials -Petrolium -Gas -Coal -Agriculture

Number of Atm and Number of banks reflect the cluster we made for GDP (about banks is interesting to study the abnomal value of Bulgaria due to their excess of banks)

UNEMPLOYMENT Through the dataset we made on percentage differences we study the year-by-year variation of the percentage of unemployed. In particular, in RED those variations that have led to a strong growth in unemployment are marked; in GREEN we have instead marked the variations in which there was a sharp decrease in unemployment. These values represent business cycles: the 3 economic cycles are expansion , recovery and recession