Volume 11 (2022): Issue 1-2 (December 2022)

Confronted with ageing and depopulation, Romania needs to identify the main factors that attract immigrant population. This paper tackles the matter of regional income disparities as a key factor for temporary immigration in Romania. The quantitative approach includes a spatial panel data analysis based on a dataset retrieved from the Romanian National Institute of Statistics and our data consists of 378 observations on 42 counties in Romania over a time span of nine years starting with the year 2012. Our findings suggest that temporary immigration in Romania is shaped by location, but does not follow the labour market characteristics. Higher salaries and job opportunities are not the main factors of attraction to temporary immigration and it does not seem to be influenced by regional disparities.

Romanian Agency for Quality Assurance in Higher Education evaluates, on demand or on its own initiative, the higher education providers and study programmes. This paper presents the results of a large-scale survey on the perception of the Romanian students in the field of Economic Sciences on the quality of the educational programs they follow. The themes covered are: the teaching resources, the educational process, the evaluation and communication, the teaching and learning, the infrastructure, the learning outcomes and the relevance for the labor market. Using a large sample of 8120 respondents, the results show that in general the perceptions are quite positive with scores between 8 and 9, on a range from 1 to 10. We identified differences between students by their seniority, by specific universities and their admission grades.

Sentiment analysis refers to the analysis of human opinions and sentiments that are expressed in written text, being also a part of the Natural Language Processing (NLP) tasks. Sentiment analysis can be applied in different domains, especially in the corporate marketing and sales, the healthcare system or the financial market analysis. In this paper we aim to highlight how data mining is able to extract the sentiment score from a financial platform that shows the major headlines regarding stocks, in order to highlight the publications’ positive or negative opinion over a stock. In order to gain the sentiment score we have scraped text data from the platform Finviz from which the polarity of the opinion may be extracted. We have also used Valence Aware Dictionary for Sentiment Reasoning (VADER), by running a Python script using the BeautifulSoup library. After that we have used Pandas (Python Data Analysis Library) to analyse and obtain a sentiment score on the article headlines. Results show that the script is able to generate the sentiment score for various selected stocks, while also showing graphical diagrams for the past and future trend of the stock, in terms of overall opinion on the stock performance.

The insurance industry has an important role in the economy, being constantly focused on diversifying product portfolios and dispersing risks. Since the uncertainty, the asymmetric information, the current economic and social-political challenges affect the economic performance and competitiveness on the insurance market, it is necessary to focus on the evaluation of the technical efficiency of the players. One of the most complex analytical research tools with increased utility that can be applied to measure the efficiency is the Data Envelopment Analysis (DEA). Our work is designed to analyze the performance of a sample made up of the ten main players in the insurance industry in Romania. Assuming a predefined set of five inputs (total expenses, provisions, average number of employees, total placements and intangible assets) and one output (total income) selected from the firms’ balance sheets, we calculate the efficiency scores with the help of DEA techniques for each year from 2016 to 2020. Our results show that Allianz and City are the most efficient firms regardless of the model type VRS or CRS, while Groupama and Omniasig fail to operate at an optimal level in any of the analyzed periods.

The circular economy has increasingly attracted the attention of regulators as a result of the emergence of the challenges associated with climate change and the need to increase the lifetime of goods in order to reduce waste and high consumption of resources. Therefore, the paper aims to identify the current state of the circular economy in Romania by analyzing waste recovery in territorial profile. Also, another objective of the work is to identify the gaps in the territorial profile of the recovery rate of collected waste. To achieve the goals of the research, appropriate statistical methods were used in this study, such as: the Jenks algorithm and the Gini Coefficient. The main results of research suggest there are a lot of gaps in territorial profile from recycling rate perspective and a low concern about circular economy in Romania.

This paper examines the reaction of the household indebtedness to various shocks in the economy between 2011Q1 and 2021Q4, using a Structural Vector Autoregressive model. The results of the econometric model indicate that average net wage was the driver of the loans granted by credit institutions to individuals’ variation after their own innovations at all time horizons. This result was achieved in the conditions that households’ resilience to shocks has improved significantly in the period 2011-2020 from lei 243 billion to lei 480 billion. The evolution of the economy starting from March 2020 was influenced by the emergence of the COVID-19 pandemic and the imposition of restrictions to prevent the spread of the disease. In this situation, between March 2020 and September 2021, the new standard mortgage loans recorded an average growth rate of 2.2 percent in nominal terms and 1.91 percent in real terms, standing above the pre-pandemic level (1.34 percent, respectively 1.17 percent). The low interest rates and general household income growth (the minimum wage in the economy increased 14 times between 2011 and 2021) were responsible for high household debt. The rise in household indebtedness growths its vulnerability to shocks in the economy, potentially having a negative impact mainly on the creditors’ balance sheet.

The article provides the impact of different averages (feature importance) within the admission exam for the national military high schools using and testing three supervised machine learning algorithms: logistic regression, K-Nearest Neighbors, and random forest. For this purpose, I have used the list with the results of candidates compounded by 430 rows, an unclassified document posted on the national military high school website, with details about: the final admission grade, the general grade for graduating of the secondary school, the general grade obtained at the national assessment, the mark obtained at admission test from Romanian language and mathematics items, etc. From the machine learning perspective, I have built a Jupyter notebook, a Python code using the specialized ML libraries (numpy, pandas, matplotlib, sklearn). In conclusion, the logistic regression algorithm identified the ‘feature importance’ (how each variable contributes to the predicted model) for admission in the national military high school: the mark obtained at admission test from Romanian language and Mathematics items - 4.821834, the general average obtained at the national assessment - 0.584434, the general average for graduating of the secondary school - 0.285446, etc. These are the expected results based on the admission methodology for the national military high schools.

This study aims to investigate the potential existence of a cointegration relationship between Romania’s gross domestic product (GDP), credit interest rates charged by financial institutions, and the benchmark interest rate set by the National Bank of Romania (NBR). The identification of a long-term relationship between these variables is considered to be of significant importance as it may provide a deeper understanding of the interactions between interest rates and GDP dynamics in Romania. To achieve this objective, we employed a robust econometric methodology, utilizing well-established and widely-used techniques for capturing long-term statistical relationships. Careful consideration was given to the manipulation of macroeconomic data in order to ensure the validity and reliability of the analysis. The results of our analysis reveal that there is no statistically significant evidence of a long-term relationship between GDP, credit interest rates, and the benchmark rate set by the NBR. This finding suggests that the interaction between interest rates and GDP in Romania is complex and may be influenced by other variables. Further research should focus on these other factors in order to gain a more comprehensive understanding of the relationship between GDP, credit interest rates and benchmark rate set by NBR.