Assessing Economic Development and Quality of Life: A Management Perspective on Romania and the Republic of Moldova

Abstract

This article presents a comparative analysis focusing on the economic dimensions of quality of life and explores the factors influencing economic growth and well-being between two countries located in Eastern Europe: Romania—an EU member state since 2007, and Moldova—an EU candidate status since June 2022. By examining statistical data, we assess the relationship between economic growth and development, aiming to discern patterns and dynamics within these countries’ economies. Through this comparative approach, we aim to elucidate how economic factors contribute to societal welfare and living standards. The study underscores the significance of economic management policies and structural reforms in fostering growth and enhancing the quality of life for citizens. By focusing on the economic landscapes of Romania and Moldova, we aim to offer insights into the challenges and opportunities faced by transitioning economies in Eastern Europe, shedding light on strategies for sustainable development and improved well-being.

1. Introduction

In this article, we propose a comparative analysis of the quality of life from an economic perspective, as well as an analysis of the factors that contribute to economic growth while producing positive effects on the level of economic well-being among the population. As such, we investigate the relationship between economic growth and economic development using statistical data related to the economies of Romania and the Republic of Moldova, which constitute the case study of this article.

At a theoretical level, the concept of economic development captures the qualitative and quantitative effects in terms of life satisfaction [1,2,3]. Economic well-being and development vary based on individual circumstances and aspirations [4]. However, it is essential to note that “economic” and “development” are not interchangeable terms and asserting that economic well-being is adaptable to the economic situation may stimulate many discussions, which are not included in this research.

Quality of life is a relatively new concept in the social sciences, which refers to the satisfaction felt by individuals in relation to both the microclimate (like income, expenses, employment status, access to resources, market conditions in an industry or sector, regulatory environment, and other personal or localized economic circumstances) and the social macroclimate in which they live (economic infrastructure, cultural diversity and inclusion, and social norms and values) [5,6,7,8]. Both microclimate and macroclimate factors significantly impact individuals’ overall quality of life, as microclimate emphasizes immediate, localized influences, while macroclimate pertains to broader societal-level influences.

Because humans are inherently social creatures [9], their perception of quality of life is largely shaped by their individual perspectives, which are significantly influenced by their personal goals and corresponding expectations.

At the economic level, we may associate the quality of life or social utility with the concept of economic utility. Economic utility is the satisfaction obtained by individuals from the consumption of goods and services [10,11]. Like economic utility, social utility has the same subjective nature because it is felt differently by each individual and sometimes, it is even felt differently by the same individual [12].

The existence of the individual in his social environment involves decisions, choices embodied in the consumption of a variety of tangible and intangible goods in order to satisfy needs. We must not omit that the individual, although sometimes only a consumer, is, as Mises says [13], a priori a producer. Therefore, individuals will make the best decisions to produce the means necessary to satisfy their needs [13].

The needs of individuals, social beings, are dynamic and unlimited and depend on the level of global and individual development. As societies develop, the system of needs will also diversify and become more and more evolved according to aspirations and ideals.

As a novelty, our comparative analysis is rooted in the unique context of Romania’s EU membership and Moldova’s non-EU status and, since June 2022, an EU candidate status country, offering a perspective through which to examine the impact of these divergent paths on economic development and the quality of life. Divergent paths in economic development refer to the phenomenon where countries experience different or no economic trajectories in terms of level of development and economic performance. This can be influenced by structural differences at the economic level, economic policies adopted, investments in human and physical capital, and access to technology, as well as other factors that influence economic growth. The exploration of the effectiveness of economic policies and structural reforms in this comparative framework provides a fresh perspective on the challenges faced by Moldova in the absence of an EU membership. Ultimately, this research contributes to the literature by shedding light on the potential implications of shared historical and linguistic ties for the economic trajectories of nations in Eastern Europe, offering valuable perspectives for policymakers and researchers alike. Moldova and Romania share the same historical and linguistic space, and these connections inevitably influenced their historical, cultural, and linguistic evolution [14,15,16,17]. Thus, research exploring the common historical and linguistic ties between Moldova and Romania can offer interesting perspectives on how these connections have influenced the economic trajectories of the two countries. This can be important for a deeper understanding of economic relations and potential collaboration opportunities between Moldova and Romania.

Since 2020, when the whole world was in a deep health crisis from the COVID-19 pandemic, the quality of life has deteriorated considerably in people’s perceptions. The deterioration in the quality of life has worsened in the context of the spread of the virus and the lack of means of prevention and contagion. This effect was potentiated by the limitation of social life and then by the inevitable emergence of an economic crisis (closed economies, job losses, diminishing disposable incomes, and high unemployment rates) [12,18].

2. Materials and Methods

The concept of quality of life encompasses a multifaceted examination from various perspectives, including but not limited to physical health, mental well-being, social relationships, economic status, environmental conditions, and personal fulfillment. Among these, the economic dimension stands out as paramount, exerting profound effects on the overall aspects of our existence. Extensive social and economic research consistently identifies several key factors that significantly influence the quality of life. These factors include economic resources, health status, educational attainment, financial stability, employment security, and the quality of the surrounding environment [19,20,21,22,23].

Apart from the economic and financial factors that impact quality of life, we also recognize other aspects such as emotional well-being and the fulfillment of personal ideals [24,25,26].

At the macroeconomic level, the evaluation of an economy’s development is based primarily on indicators such as the rate of economic growth, gross domestic product (GDP) per capita, and the average income per capita [27,28]. Meanwhile, at the microeconomic level, individuals’ well-being is scrutinized in relation to both economic growth rates and levels of economic development [29].

Within economic discourse, these two pivotal concepts are often delineated separately. Most of the economists are well versed in discerning between economic growth and economic development [30,31,32], understanding the nuanced distinctions and potential misconceptions that may arise if their fundamental economic implications are overlooked.

The concept of economic growth reveals changes in the market value (adjusted with the inflation rate) of goods and services produced in an economy over a period, usually one year. Conventionally, economic growth is measured as a percentage change in real gross domestic product [12].

Economic development or well-being cannot manifest in an economy without the presence of economic growth. However, it is essential to recognize that while economic growth is a prerequisite; its sustainability plays a critical role in fostering positive qualitative effects in terms of quality of life [33,34].

It is imperative to acknowledge that the process of economic development is intricate, driven primarily by economic factors and supplemented by political, social, geographical, demographic, and other variables [35,36]. A global perspective on the world economy unveils diverse values in income per capita, underscoring the multifaceted nature of economic development.

There are countries with a large volume of natural resources and, obviously, a high income per capita. But there are also nations which, although they have a lack of natural resources, also have a high per capita income [37]. There are still regions and countries that have sufficient resources but the registered income per capita is insignificant compared to similar countries or regions. What are the explanations for these differences? Is an above-average rate of economic growth enough to talk about economic development?

The factors that contribute to sustainable economic growth are obviously the same also generates economic development. Sustainable economic growth refers to the long-term increase in the production of goods and services while considering environmental, social, and economic factors. On the other hand, economic development encompasses a broader concept that includes sustainable economic growth but also focuses on improving living standards, reducing poverty, providing access to education and healthcare, promoting social equity, and fostering institutional development. In essence, sustainable economic growth is a component of economic development, but economic development goes beyond mere growth to encompass holistic improvements in the well-being of society. Although, in theory, the final goal of both processes is the same, improving the quality of life of all people, in practice, most of the time this social finality does not take place [38].

Both growth and economic development assume structural transformations involving primary resources and their quality, capital accumulation, technological and cultural progress, etc. That is why, when we talk about sustainable economic growth and sustainable economic development, we implicitly bring into discussion those factors like adopting green technologies, reducing pollution and managing waste, efficient use of resources, biodiversity conservation that contribute to the protection of the environment, and the conservation of natural resources [39].

The accurate assessment of economic development, as encapsulated in the Human Development Index (HDI), relies on several key indicators: life expectancy, the quality of education, and income per capita. The HDI represents a geometric mean of the normalized indices of these three factors, resulting in a positive and unitary value [40,41,42].

While objective measurements are more straightforward when dealing with economic factors, researchers encounter challenges when delving into aspects of people’s lives that are less quantifiable. Beyond financial metrics, analyses of quality of life encompass nuanced dimensions such as emotional well-being and the intensity of lived experiences (stress, joy, affection, sadness, etc.). Additionally, vital components of quality of life include health status, individual access to healthcare, and the effectiveness of healthcare systems [43,44].

Even some individuals assess their quality of life primarily in terms of health status and the accumulation of material possessions, we consider that it is heavily influenced by economic and financial factors, underlining its complex interplay with individual well-being [45,46,47].

Individual perceptions of quality of life are shaped by objective circumstances, which, in turn, are influenced by the overall development level of the national economy in which individuals reside. Regardless of geographic location, people are generating ideals, aspirations, and expectations for themselves and their families, which in turn shape their perception of quality of life.

It is essential to recognize that the elements that significantly impact quality of life differ across economies of varying development levels. In developed economies, factors influencing quality of life may differ from those in emerging or underdeveloped economies, highlighting the nuanced interplay between economic development and individual well-being [48,49].

Returning to the factors that influence the quality of life, they vary according to the context discussed above and can be as follows: financial security, job satisfaction, family satisfaction, health status, and social security [37]. All this can only be realized at the individual level on the basis of an adequate personal disposable income. Lots of research on quality of life have focused on the relationships between the quality of life and the rate of economic growth and its sustainability. In the context of Moldova and Romania, these aspects are significant for understanding the economic dynamics within the two countries. While some regions may possess abundant natural resources, leading to a high income per capita, others may lack such resources yet still exhibit high per capita income levels. This discrepancy suggests the presence of diverse economic drivers beyond natural resource endowment, such as human capital, infrastructure development, and industrial specialization.

Based on the above, we can say that this concept of quality of life is multidimensional and expresses the perception of individuals on their position in life in relation to their expectations and goals.

The aim of this study is to conduct a comprehensive comparative analysis of the quality of life from an economic perspective and to analyze the factors contributing to economic growth with positive effects on the economic well-being of the population. Specifically, we will explore the relationship between economic growth and economic development using statistical data related to the economies of Romania and the Republic of Moldova, serving as the case study for this research.

For our research hypothesis, we consider that our comparative analysis between Romania and Moldova is anticipated to reveal substantial disparities in the economic dimensions contributing to the quality of life.

As research questions (RQ) we mention the following:

-

Factors influencing economic growth and well-being will vary between Romania and Moldova (RQ1).

-

There will be a positive relationship between economic growth and development in both countries (RQ2).

-

Economic management policies and structural reforms play a crucial role in fostering growth and enhancing the quality of life in transitioning economies like Romania and Moldova (RQ3).

-

Insights from the economic landscapes of Romania and Moldova will highlight challenges and opportunities for sustainable development and improved well-being in Eastern Europe (RQ4).

In formulating the findings of our research, we undertook a comprehensive approach, drawing upon a blend of scholarly literature and a comparative qualitative and quantitative analysis. This multifaceted analysis aimed to provide insights into the economic landscape and quality of life indicators in Romania and the Republic of Moldova. For the empirical analysis, we collected statistical data covering a period of 12 years. The statistical data were collected from the World Bank [50] and refer to the following socio-economic indicators: GDP per capita, GNI per capita, labor force participation rate, and government expenditure on education. GDP per capita measures the total value of goods and services produced in a country, divided by the number of inhabitants. It is a general indicator of a country’s standard of living and its economic capabilities to provide goods and services.

GNI per capita, similar to GDP, but takes into account net income from abroad, as well as other international transfers. It is a measure of average per capita income and can provide a more comprehensive view of a country’s economic situation.

The labor force participation rate reflects the proportion of the active population that is employed or looking for a job. A high labor force participation rate can indicate a healthy economy and employment opportunities for citizens, contributing to economic growth and overall well-being.

Government expenditure on education reflects the government’s commitment to education and human development. Adequate spending in this field can raise living standards in the long term by providing equal opportunities for education and training, leading to a more skilled and innovative workforce.

Moreover, to capture the multifaceted nature of quality of life, composite indices such as the Human Development Index (HDI) and the Social Progress Index were employed. These composite indices offer a holistic perspective on various dimensions of well-being, encompassing factors beyond purely economic considerations. HDI is to the real sustainable economy by including indicators related to income and poverty, as well as by integrating indicators of health and education. Improving these aspects not only enhances quality of life but can also contribute to sustainable economic growth by increasing productivity. Also, SPI is connected to the real sustainable economy, as it promotes investments in areas such as health and education, which can support a sustainable and economic growth.

3. Results and Discussions

The Social Progress Index (SPI) is a valuable tool in analyzing the quality of life and social progress in a country. It measures a society’s performance in areas such as basic human, fundamental needs, well-being needs, and opportunities provided to its citizens. SPI provides a robust framework for assessing and improving the quality of life and is important in orienting policy and development efforts towards goals that lead to a more equitable, healthy, and prosperous society. According to the nonprofit organization Social Progress Imperative [51], in the period 2011–2022, Moldova and Romania occupied the following places out of a total of 169 countries (

Table 1. Social Progress Index for Romania and the Republic of Moldova.

Year	Basic Human Needs	Fundation of Well-Being	Opportunity	Social Progress Index	Rank/Tier
Romania
2011	82.64	66.88	69.97	73.16	45
2017	85.42	72.95	69.61	76	43
2022	86.7	74.78	69.2	76.89	43
Republic of Moldova
2011	77.82	57.88	63.12	66.11	70
2017	80.8	68.32	62.69	70.36	71
2022	83.13	71.98	67.46	74.19	51

Data Source: Social Progress Imperative [51].

).

As for the HDI, this index takes into account three main dimensions of human development: life expectancy at birth, education (measured by years of schooling expected and years of schooling completed), and gross national income per capita (adjusted for purchasing power). This provides a more comprehensive picture of a country’s well-being and progress than just assessing the economy (

Table 2. Human Development Index for Romania and Republic of Moldova.

Republic of Moldova Human Development Index			Romania Human Development Index
Year	HDI	Ranking	Year	HDI	Ranking
2010	0.730	86°	2010	0.807	51°
2011	0.736	84°	2011	0.808	51°
2012	0.741	86°	2012	0.805	53°
2013	0.746	87°	2013	0.810	52°
2014	0.750	87°	2014	0.811	54°
2015	0.749	88°	2015	0.813	55°
2016	0.756	87°	2016	0.816	54°
2017	0.765	84°	2017	0.823	54°
2018	0.768	85°	2018	0.827	54°
2019	0.774	84°	2019	0.832	53°
2020	0.766	81°	2020	0.824	53°
2021	0.767	80°	2021	0.821	53°

Data source: UNDP Human Development Insights [52].

).

According to UNDP reports [52], Romania and Moldova have recorded a constant improvement in HDI over the past decades, reflecting significant progress in the areas of health, education, and living standards. However, according to statistics, Romania still faces some challenges, such as income inequality. Also, in Moldova, income inequality and massive labor emigration are some of the challenges the country faces in its human development efforts.

In the following we try to demonstrate a link between GDP per capita, GNI per capita, the labor force participation rate, and government spending on education. We considered the connection between these variables to be extremely important at the level of an economy and society. We consider GDP per capita a dependent variable. They are interconnected and mutually influence economic development, living standards, and quality of life. There is a positive relationship between GDP and GNI because increases in production and income in the country will lead to increases in GDP and GNI per capita. The labor force participation rate is important for understanding the degree of involvement of the population in the economy and can be influenced by factors such as education level, job availability, and government policy. An increase in government spending on education can help build a country’s human capital, which can boost long-term economic growth. To test this hypothesis, we use econometric analysis on statistical data collected from the World Bank [50].

In this sense, we consider a linear regression in the form that follows:

$$y=\sum a_i{x}_i+u$$

(1)

where y is the dependent variable, $x_i$ are the independent variables, and $a_i$ are the coefficients (parameters) associated with the independent variables.

In what follows we note the following:

• GDP per capita—GDP/capita,
• GNI per capita—GNI/capita,
• Labor force participation rate—Lab_force,
• Government expenditure on education—Gov_exp_educ.

We look for a linear regression in the following form:

$$GDP/capita=a_1·\mathrm{G}\mathrm{N}\mathrm{I}/\mathrm{c}\mathrm{a}\mathrm{p}\mathrm{i}\mathrm{t}\mathrm{a}+a_2·Lab\_force+a_3·Gov\_exp\_educ+\mathrm{u},$$

(2)

where u is the error term (U is an error term and represents the difference between the observed value of the dependent variable and the value predicted by the regression model. In other words, it captures the part of the dependent variable that is not explained by the independent variables in the model. It is not uncertainity. U is a notation chosen by authors).

To estimate the increase in the number of w units (average growth of the variable for the analyzed period), we develop a Taylor series with the following function:

$$GDP/capita=a_1·\mathrm{G}\mathrm{N}\mathrm{I}/\mathrm{c}\mathrm{a}\mathrm{p}\mathrm{i}\mathrm{t}\mathrm{a}+a_2·Lab\_force+a_3·\mathrm{G}\mathrm{o}\mathrm{v}\_{\mathrm{e}\mathrm{x}\mathrm{p}}_{\_}educ$$

(3)

around point GNI/capita, Lab_force, and Gov_exp_educ.

We obtain the following:

$${Variation(GDP/capita(GNI/capita+w\left)\right)=a}_{1}w,$$

(4)

$${Variation(GDP/capita(Lab\_force+w\left)\right)=a}_{2}w,$$

(5)

$$Variation(GDP/capita(Gov\_\mathrm{e}\mathrm{x}\mathrm{p}\_educ+w\left)\right)=a_{3}w$$

(6)

The gross domestic product per capita expressed in standard purchasing power parity (PPP) reached 73% of the EU average in 2021.

According to data from the above table, the regression equation for Moldova is as follows:

$$GDP/capita=0.286·GNI/capita+12.639·Lab\_force+49.450·Gov\_exp\_educ-1524.955$$

(7)

Respectively, for Romania the regression equation is as follows:

$$\mathrm{G}DP/capita=0.387·GNI/capita-31.636·Lab\_force+54.884·Gov\_exp\_educ+1397.404$$

(8)

The table above (

Table 3. Regression analysis for Moldova and Romania.

Variable	R (a)	R Square	Adjusted R Square	F-Stat	Coefficient	Std. Error	t-Test
MOLDOVA
	0.996	0.991	0.988	333.098
Constant					−1524.955	608.086	−2.508
GNI/capita					0.286	0.017	16.824
Lab_force					12.639	8.791	1.438
Gov_exp_educ					49.450	32.463	1.523
ROMANIA
Constant	1	0.999	0.999	3671.02
GNI/capita					1397.404	753.490	1.855
Lab_force					0.387	0.0061	63.443
Gov_exp_educ					−31.636	13.542	−2.336
					54.884	82.260	0.667

(a) (Constant), GNI/capita, Lab_force, Gov_exp_educ.

) shows the regression analysis for Moldova and Romania with gross national income (GNI) per capita, labor force participation rate (Lab_force), and government expenditure on education (Gov_exp_educ) as predictor variables.

In the case of Moldova and Romania, the results suggest that GNI per capita significantly influences GDP per capita in both countries, while other factors such as the labor force and government spending on education may also have an impact but may not be as significant as GNI per capita.

For both countries R, R square, and adjusted R square suggest that approximately over 98% of the variance in GDP per capita is explained by the independent variables.

Also, the high value of F-statistics (333.098; 3671.02) indicates that the regression model is significant for Moldova, meaning that the independent variables have a significant influence on GDP per capita.

The negative coefficient for Gov_exp_educ (−31.636) suggests that in the case of Romania, an increase in government spending on education is associated with a decrease in GDP per capita. One possible explanation could be that, under certain circumstances, government spending on education is not efficiently utilized, or there may be other variables not included in the model that could affect the relationship between government spending on education and GDP per capita.

In the table above (

Table 4. Correlation matrix for GDP per capita, GNI per capita, labor force, and government spending on education in Moldova and Romania ***.

		Pearson Correlation				Sig. (Two-Tailed)
		GDP/ Capita	GNI/ Capita	Lab_ Force	Gov_ Exp_Educ	GDP/ Capita	GNI/ Capita	Lab_ Force	Gov_Exp_Educ
MD	GDP/capita	1.000	0.994 **	0.832 **	−0.660 *		0	0	0
	GNI/capita	0.994 **	1.000	0.817 **	−0.692 **	0		0	0
	Lab_force	0.832 **	0.817 **	1.000	−0.667 *	0	0		0
	Gov_exp_educ	−0.660 *	-0.692 **	−0.667 *	1.000	0	0	0
RO	GDP/capita	1.000	0.999 *	−0.482 **	0.682 *		0	0	0
	GNI/capita	0.999 *	1.000	−0.463 **	0.683 **	0		0	0
	Lab_force	−0.482 **	−0.463 **	1.000	−0.101 **	0	0		0
	Gov_exp_educ	0.682 *	0.683 **	−0.101 **	1.000	0	0	0

* Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed). *** N = 13.

), we analyzed the correlation coefficients for Moldova (MD) and Romania (RO), which show the Pearson correlation coefficients between the respective variables.

In the context of the correlation matrix above, values of 0 for the Sig (two-tailed) value indicate that the correlations between the GDP per capita, GNI per capita, labor force, and government spending on education are statistically significant.

For Moldova, there is a very strong and positive correlation between the GDP per capita and GNI per capita (0.994).

Additionally, there is a strong and positive correlation between the GDP per capita and the labor force (0.832).

The correlation between the GDP per capita and government spending on education is negative and moderate (−0.660), suggesting an inverse relationship between these two variables.

In the case of Romania, there is also a very strong and positive correlation between the GDP per capita and GNI per capita (0.999), but a moderate correlation between the dependent variable and government spending on education (0.682).

The correlation between the GDP per capita and the labor force is moderate and negative (−0.482).

A transitioning economy can experience structural changes, such as the shift from labor-intensive sectors to capital-intensive ones, which may lead to a redistribution of the workforce and a decrease in demand for certain types of labor.

However, the correlation of −0.101 between the labor force (Lab_force) and government spending on education (Gov_exp_educ) for Romania indicates a weak negative correlation between these two variables.

Therefore, we observe a slight tendency that when the labor force increases, government spending on education decreases, and vice versa, but this correlation is very close to zero and therefore not very strong.

In the following we briefly present the results of the regression equations generated by the econometric analysis of Romania and Moldova.

The results reveal paradoxical effects in the case of Romania for the variable Lab_force (

Table 5. The results of the regression equations.

Country	The Independent Variable	Average Growth	Effect on GDP per Capita (USD)
MOLDOVA	GNI per capita (USD)	412.754	+118.048
	Lab_force (%)	0.763	+9.648
	Gov_exp_educ (%)	0.171	+8.462
ROMANIA	GNI per capita (USD)	937.602	+362.852
	Lab_force (%)	0.339	−10.733
	Gov_exp_educ (%)	0.064	+1397.404

). There exists an inverse relationship between this variable and GDP per capita. An inverse relationship between the labor force participation rate and GDP per capita can be influenced by the existence of structural issues at the economy level or by extensive social protection policies and fiscal policies that affect income distribution and labor participation decisions.

In the table above (

Table 6. Analysis of variance for Moldova and Romania.

Model		Sum of Squares	df	Mean Square	F	Sig. (b)
	Moldova
1	Regression	2,618,440.025	3	872,813.342	333.098	15−7
	Residual	23,582.603	9	2620.289
	Total	2,642,022.628	12
	Romania
	Regression	27,819,126.625	3	9,273,042.208	3671.020	0
	Residual	22,734.110	9	2526.012
	Total	27,841,860.735	12

Dependent Variable: GDP/capita. (b). Predictors: (Constant), GNI/capita, Lab_force, Gov_exp_educ.

), which presents the ANOVA results, we observe that for both countries the F-test values are large and the associated significance is very small (<0.001), indicating that the model is significant for both countries. This means that at least one of the independent variables (GNI per capita, labor force, and government spending on education) has a significant influence on GDP per capita in Moldova and Romania.

The ANOVA results in the table support the overall significance of the multiple regression model and indicate that it is suitable for explaining the variation in GDP per capita in the two countries.

Dimension 4 has a high condition index (less than 30), suggesting potential multicollinearity issues, particularly for the constant, GNI/capita, and Gov_exp_educ variables but these are not severe (

Table 7. Collinearity diagnostics.

Country	Dimension	Eigenvalue	Condition Index	Variance Proportions
				Constant	GNI/Capita	Lab_Force	Gov_Exp_Educ
MD	1	3.344	1	74-3	76-3	0.019	0.023
	2	0.425	2.805	0.0025	0.0016	0.011	0.721
	3	0.226	3.842	0.0040	0.0062	0.822	0.022
	4	0.0045	27.209	0.993	0.991	0.148	0.234
RO	1	2.805	1	99-4	10-3	0.017	0.034
	2	0.907	1.759	24-6	11-5	0.338	0.146
	3	0.287	3.124	43-3	50-3	0.279	0.780
	4	42-3	82.133	0.999	0.999	0.366	0.040

). Variance Proportions highlight the contribution of each variable to the total variance of the dataset. This is relevant in the context of collinearity analysis as it can provide insights into the extent to which independent variables contribute to the overall variation in the data.

Regarding the collinearity test results for Moldova, we observe that the values are relatively low for all independent variables, and the condition index varies between 1 and 27,209. This also indicates a low collinearity between the independent variables. All four dimensions of the model have eigenvalue values indicating that the model has an appropriate structure without major collinearity issues.

In contrast, for Romania, the collinearity is more pronounced between the variables GNI per capita and Gov_exp_educ, especially for the fourth dimension, meaning there is a strong association between gross national income per capita and government spending on education. This may indicate that countries with a higher gross national income per capita tend to allocate more government resources to education, and this strong association can lead to significant collinearity within the regression model.

The above table (

Table 8. Residuals statistics for Moldova and Romania (a).

	Minimum	Maximum	Mean	Std. Deviation	N
MOLDOVA
Predicted Value	2231.685	3676.326	2946.394	466.925	13
Residual	−87.073	81.433	1.418	44.331	13
Std. Predicted Value	−1.531	1.563	0	1.000	13
Std. Residual	−1.996	1.805	0	1.000	13
ROMANIA
Predicted Value	7663.415	12,123.265	9700.428	1524.067	13
Residual	−77.978	65.378	−9.737	43.551	13
Std. Predicted Value	−1.337	1.590	0	1.000	13
Std. Residual	−1.567	1.725	0	1.000	13

(a). Dependent variable: GDP/capita.

) provides insights into the predicted values, residuals, and their standardized versions for each country’s GDP per capita. The standard deviation of GDP/capita is 466.925 for Moldova and 1524.067 for Romania. This tells us about the dispersion or spread of GDP per capita values around the mean. A higher standard deviation indicates greater variability in GDP per capita among the observations. A residual standard deviation of 44.331/43.551 means, on average, the observed values of GDP per capita deviate from the values predicted by the regression model by approximately 44.331/43.551 units. The predicted values for GDP per capita indicate that the model has been successful in providing consistent estimates for Moldova and Romania, considering that the predicted values do not deviate significantly from the observed values and have moderate variation around the mean. Standardizing the residuals reveals that the majority of data points fall within an acceptable range of error, as most standardized residuals have values close to zero. This suggests that the model has relatively good accuracy in estimating GDP per capita. The results indicate discrepancies between the two countries regarding predicted values and residuals. For instance, Romania has higher values for GDP per capita compared to Moldova, which may indicate significant differences in the factors influencing the economies of the two countries. Evaluating the distribution of residuals and predicted values shows that the regression model is robust and can be generalized to make estimates for other countries or time periods. If the residuals are uniformly distributed around zero value and there are no clear patterns of deviation, this indicates good generalization of the model. To identify a uniform distribution of residuals around zero, we will use the Kolmogorov–Smirnov and Shapiro–Wilk tests (Table 8). These tests are used to assess whether or not a distribution of data is normally distributed by comparing the data to a theoretical normal distribution, allowing the assessment of whether the differences between the distribution and the normal distribution are statistically significant (

Table 9. Tests of normality for Moldova.

	Kolmogorov-Smirnova			Shapiro-Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
GNI per capita	0.107	13	0.200 *	0.960	13	0.756
Labor force participation rate	0.177	13	0.200 *	0.933	13	0.368
Government expenditure on education	0.234	13	0.50	0.877	13	0.065
GDP per capita	0.125	13	0.200 *	0.954	13	0.658

* This is a lower bound of true significance; a Lilliefors significance correction.

and

Table 10. Tests of normality for Romania.

	Kolmogorov-Smirnova			Shapiro-Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
GNI per capita	0.148	13	0.200 *	0.924	13	0.283
Labor force participation rate	0.302	13	0.002	0.715	13	0.001
Government expenditure on education	0.189	13	0.200 *	0.931	13	0.352
GDP per capita	0.147	13	0.200 *	0.927	13	0.314

* This is a lower bound of true significance; a Lilliefors significance correction (The Lilliefors correction adjusts the critical values of the test to increase precision in assessing normality, especially for small sample sizes).

).

The results of normality tests for the variables GNI per capita, labor force participation rate, government expenditure on education, and GDP per capita are presented in the table above, using both the Kolmogorov–Smirnov test and the Shapiro–Wilk test. In the case of Moldova, both the Kolmogorov–Smirnov test and the Shapiro–Wilk test do not find sufficient evidence to reject the hypothesis that the data come from a normal distribution for these variables (p > 0.05). This suggests that the distributions of GNI per capita and GDP per capita, and labor force participation rate may be approximately normal. In the case of the government expenditure on education variable, the significance level is 0.005 (p = 0.005). Although the results of the Kolmogorov–Smirnov test show that there would be evidence to reject the hypothesis that the data come from a normal distribution, the Shapiro–Wilk test does not find enough evidence to reject this hypothesis.

For Romania, the normality tests indicate that there is statistical evidence that the data are not distributed according to a normal distribution for the labor force participation rate variable (p < 0.05). This suggests that the distribution of the labor force participation rate may not be normal. But for the other variables, normality tests do not find enough evidence to reject the hypothesis that the data come from a normal distribution for these variables (p > 0.05). This suggests that the distributions for the variables GNI per capita, government expenditure on education, and GDP per capita may be approximately normal.

In conclusion, most of the analyzed variables appear to be approximately normally distributed, with the possible exception of labor force participation rate, where there are some indications that the distribution may not completely conform to a normal distribution.

4. Final Remarks and Conclusions

Although the living conditions and the quality of life have improved in the last 30 years, the economy of Romania and Moldova face the same structural problems: a very large number of persons who left the two nations, a negative demographic growth, a growing budget deficit, a budgetary apparatus that is too high, a lack of investments with a real multiplier effect, etc. Romania, being a much bigger country, is less vulnerable than Moldova. All of these structural problems make a very large difference between the real output and the potential output, and make the economic growth to be unsustainable most of the time without a real economic foundation that gives the quality of life the same upward trajectory. Despite their common past within the Soviet bloc, Romania and Moldova have followed different economic and political trajectories after the dissolution of the Soviet Union. Romania pursued its own path towards democracy and a market economy, becoming a member of the European Union in 2007, while Moldova had a slower and more complex transition towards democratic and economic reforms. The comparison between Romania and Moldova (a candidate for the EU) is intended to present the advantages and benefits of the European Union pathway, showing that European integration can bring greater economic prosperity, political stability, and social and environmental improvements.

Although many indicators have improved at the level of the national economy, inequalities in terms of income distribution have deepened especially for the rural population and in the cities located in the North-East, South-East and South Muntenia development regions.

The validation of Research Question 1 confirms that factors influencing economic growth and well-being vary between Romania and Moldova. This observation stems from differences in their economic structures, government policies, and socio-cultural contexts. Romania, being an EU member state, may benefit from access to EU funding and market integration, whereas Moldova, as an EU candidate, faces different priorities and challenges in its economic development trajectory. Additionally, factors such as income inequality and labor emigration present distinct challenges for each country, underscoring the necessity for complex strategies to address these issues and promote inclusive growth and human development. Through our research technique, we have provided empirical evidence supporting the assertion that the factors influencing economic growth and well-being indeed differ between Romania and Moldova, shedding light on the nuanced complexities of their respective economic landscapes.

The econometric analyses conducted for both Romania and Moldova provide substantial evidence supporting the validation of Research Question 2, which posits a positive relationship between economic growth and development in both countries. In Romania, the regression equation yielded coefficients indicating that increases in GNI per capita and government expenditure on education correspond to significant increases in GDP per capita, while an increase in the labor force participation rate correlates with a slight decrease in GDP per capita. Similarly, in Moldova, the regression equation revealed that increases in GNI per capita, labor force participation rate, and government expenditure on education all lead to notable increases in GDP per capita.

The high empirical correlation coefficient (R = 0.996) in both cases suggests a strong positive correlation between the endogenous and exogenous variables, further reinforcing the idea of a relationship between economic growth and development. Additionally, the adjusted R square values of 0.988 for Romania and 0.991 for Moldova indicate that a significant proportion of the total variation in GDP per capita can be explained by variations in the exogenous variables, lending further credence to the hypothesis of a positive relationship between economic growth and development.

Moreover, the standard error of the estimate provides insights into the accuracy of the regression model, with relatively low values indicating that the model’s predictions closely align with the actual data. The Fisher–Snedecor statistic F, along with the significance F value, confirms that the exogenous variables significantly contribute to improving the prediction of GDP per capita, further supporting the validation of Research Question 2.

The juxtaposition of economic growth and persisting poverty rates in both Romania and Moldova underscores the complexity of their economic landscapes. Despite Romania’s economic growth surpassing the EU average, a significant portion of its population still grapples with relative poverty, with approximately 20% of citizens living at the poverty threshold. According to Eurostat [53], the rate of severe material deprivation in 2020 is the third highest in the European Union (15.2%), after Bulgaria (19.4%) and Greece (16.6%).

Similarly, Moldova faces analogous challenges, characterized by robust economic growth rates exceeding 13%, juxtaposed against a staggering poverty rate exceeding 30%. This incongruity between economic expansion and high poverty levels suggests deeper systemic issues within Moldova’s economy, necessitating a closer examination of the efficacy of economic management policies and structural reforms. While economic growth is a vital component of development, these examples underscore the importance of ensuring that growth is inclusive and translates into tangible improvements in living standards for all segments of society.

These observations resonate with Research Question 3, which posits that economic management policies and structural reforms play a pivotal role in fostering growth and enhancing the quality of life in transitioning economies like Romania and Moldova. The stark disparities between economic growth and poverty levels highlight the critical need for targeted policy interventions aimed at addressing systemic inequalities and promoting inclusive economic development.

Research Question 4 seeks to shed light on the challenges and opportunities for sustainable development and improved well-being in Eastern Europe, drawing insights from the economic landscapes of Romania and Moldova. The paradoxes observed in these economies serve as strong indicators of the complex nature of development and the importance of embracing holistic approaches that encompass not only economic metrics but also social and environmental aspects.

By examining these paradoxes and identifying potential avenues for intervention, policymakers and stakeholders can better navigate the complexities of economic transformation and work towards fostering more equitable and sustainable societies in the region [54].

As limitations of our study, we consider that despite our efforts to undertake a comprehensive analysis, it is important to acknowledge that our study focused primarily on macroeconomic indicators and composite indices. As a result, certain nuanced aspects of economic development and quality of life may not have been fully captured in our analysis. Also, variations in socioeconomic factors, cultural dynamics, and historical contexts may limit the applicability of our findings beyond the studied countries.

Our study provides insights into the economic landscape and quality of life indicators at a specific point in time. However, socioeconomic conditions are dynamic and subject to change over time. Therefore, our findings may not fully capture the long-term trends and fluctuations in economic development and quality of life in Romania and Moldova.

While composite indices such as the Human Development Index and the Social Progress Index offer a holistic perspective, they may oversimplify complex phenomena and mask variations within different dimensions of well-being. Additionally, the methodologies used to construct these indices may have inherent limitations or biases.

Both Romania and Moldova are confronted with the imperative need for a fundamental shift of their growth and economic development paradigms. This demands adopting a new approach to managing the multifaceted impacts of globalization, climate change, environmental degradation, and contemporary challenges such as pandemics, conflicts, health crises, and energy shortages, all of which exacerbate social inequalities.

Effective decision-making at the national level necessitates the formulation and implementation of sustainable development strategies that yield tangible improvements in the quality of life for all citizens, irrespective of their geographical location.

Investments in the labor factor, stimulating technological innovation, infrastructure enhancement, and the emergence of multiplier effects will foster greater financial stability and will create a favorable business environment across all sectors of the economy. Labor investments may help to achieve the economic performance of a country, as well as lead to significant changes in the quality of life for individuals and communities.

For Romania, the path towards development and implementation appears somewhat facilitated due to its membership in the European Union, affording access to financial support and advisory services. Aligned with the EU’s sustainable growth and development agenda, Romania benefits from initiatives that promote smart investments in education, research, and innovation, sustainable practices such as carbon reduction and the use of renewable energy, and inclusive measures aimed at creating jobs and reducing poverty.

In contrast, Moldova faces a more challenging trajectory, despite being granted candidate country status by the Council of Europe. As a smaller economy with limited resources, Moldova’s opportunities are comparatively constrained. However, Romania and the European Union stand in solidarity with Moldova, particularly in the current geopolitical climate marked by Russia’s conflict with Ukraine. The EU extends support to Moldova through financial assistance in the form of grants and loans, focusing on priorities such as energy security, security and defense cooperation, and economic reforms.

In navigating the complexities of socioeconomic development, Romania and Moldova stand at divergent junctures, each facing unique challenges and opportunities on their paths toward sustainable growth. Despite disparities in resources and opportunities, both nations are bound by a common pursuit of societal advancement and human flourishing. It is in this collective pursuit, nurtured by solidarity and resilience, that the seeds of enduring progress are sown, illuminating a path towards a future defined by shared prosperity and inclusive development.