Quality of Government, Democracy, and Well-Being as Determinants in Achieving the Sustainable Development Goals

Abstract

Recent reports have indicated a slowdown in global progress towards compliance with the 2030 Agenda and a setback in some objectives. This has prompted the development of research to identify the factors contributing to some countries moving faster than others in achieving the goals. Until now, the literature has emphasized the role of economic and institutional factors in achieving the 2030 Agenda, making it necessary to investigate the effects that other political or social factors may generate. To contribute to this purpose, this article aims to identify the effect of the quality of government, democracy, and well-being on aggregate compliance with the 2030 Agenda. Through a quantitative analysis that uses the level of achievement of the 2030 Agenda as a dependent variable and six independent variables related to the quality of government, democracy status, and well-being, we found that the effectiveness of government, the welfare regime, subjective well-being, and democracy status positively influence the achievement of sustainable development objectives. These findings have practical implications, as they suggest that countries with solid and effective government institutions, social safety networks, high subjective well-being, and healthy democracy have greater potential for meeting the goals of the 2030 Agenda, emphasizing the urgency of our collective efforts.

1. Introduction

In 2015, the member countries of the United Nations (UN) signed the 2030 Agenda, which established 17 Sustainable Development Goals (SDGs) to leave no one behind [1]. The 2030 Agenda seeks to generate shared prosperity in a world where everyone can lead productive, vibrant, and peaceful lives on a healthy planet [2]. Unfortunately, the feasibility of achieving the objectives set out in the 2030 Agenda is increasingly low due, among other factors, to the adverse effects of the COVID-19 pandemic and the war conflicts of recent years [3]. Sachs et al. [4] warn that the SDGs are far from being met, projecting only 72% global compliance with the Agenda for 2030.

The comparative panel shown by Sachs et al. [4], allows us to visualize the change in the achievement of each of the 17 SDGs between 2022 and 2023, identifying with an arrow whether the indicator increased, decreased, or was maintained. In addition, it uses a traffic light system to reflect compliance (green, yellow, orange, and red). This dashboard shows that, at a global level, in the year 2023, none of the 17 SDGs has been met, which is worrying, with only six years remaining until the deadline for compliance with the 2030 Agenda. Even more so, when comparing the level of achievement of the SDGs in countries grouped by income level, stark disparities emerge: high-income countries, such as Finland, Sweden, and Denmark, have aggregate compliance exceeding 85%, while low-income countries, such as Africa, South Sudan, and Central African Republic, are struggling with less than 40% achievement [4]. This means that the group of low-income countries has 13 of the 17 SDGs in red (i.e., not met) and only one SDG showing improvements between 2022 and 2023. At the other extreme, the group of high-income countries has four SDGs in red and three with improvements in the same period [4]. The above allows us to observe that, as has been revealed from the literature [5,6,7], the income level of a country and its economic growth are fundamental in achieving the SDGs. Studies conducted by Lee and Lin [8] and Chien et al. [9] point out that economic growth combined with appropriate financial policies promote the achievement of the SDGs.

However, Kotzé y Rakhyun [10] warn that achieving the 2030 Agenda requires limiting economic growth and making progress in eliminating development disparities between rich and poor countries to not affect planetary integrity and justice. Therefore, although economic growth has been considered an essential factor for achieving the SDGs, there is growing agreement among experts on the need to abandon gross domestic product (GDP) as the leading indicator of national prosperity. For instance, the European Commission’s ’Beyond GDP’ initiative advocates for the use of development indicators that also include the environmental and social aspects of progress, reflecting a shift in how we measure and prioritize economic success [11]. Along these lines, Besley [12] suggests that traditional macroeconomic metrics should be complemented with other indicators, such as subjective well-being, that is, how people perceive their well-being and experience their lives.

But what factors other than economic ones contribute to some countries moving faster towards sustainable development? One way to identify them is to resort to theories that seek to explain the determinants of the development of countries, where it is possible to determine, among others, three currents. First is the Theory of Institutions, in which authors such as Douglas North [13] and Daron Acemoglu [14] have highlighted the importance of institutions in economic development. Second, the Theory of Democracy, with authors such as Adam Przeworski [15] and Robert Barro [16] affirming that democracy tends to be associated with more stable and sustained economic growth. Third, other approaches related to well-being economics, such as that of Joseph Stiglitz [17], or Amartya Sen’s Theory of Capabilities [18], point out that promoting equity and social well-being allows achieving genuinely human sustainable development.

When analyzing the previous literature on the determinants of achieving the 2030 Agenda, it is observed that the first two currents have been addressed. Various studies focus on exploring the institutional factors linked to institutional capacity and governance at the country level [19,20,21], finding that institutional quality and governance have a positive influence on achieving the SDGs [22,23,24], while other research finds that higher levels of corruption negatively affect progress toward sustainable development [25,26,27,28]. Regarding the influence of the political component, studies carried out by [21,22,29,30,31] show that democracy and government stability contribute significantly positively to the SDGs.

With respect to the third current, even though some authors identify the theoretical relevance of human well-being for the 2030 Agenda [17,32], there are no studies that investigate the effect of well-being on sustainable development, since in this area, research has, instead, analyzed the impact of sustainable development on improvement in well-being [33,34,35].

Recognizing the gaps in the literature, this article builds a theoretical framework that allows exploration of other institutional, political, and social factors that can influence the achievement of the SDGs. Specifically, the study aims to identify the effects of quality of government, democracy, and well-being on aggregate compliance with the 2030 Agenda.

The study conducts a multiple linear regression analysis using a quantitative methodology and data from public databases for 163 countries. The results show that government effectiveness, the welfare regime, subjective well-being, and democracy status positively influence the achievement of the SDGs. However, a negative relationship is found between less corruption and freedom in accomplishing the 2030 Agenda. Thus, the findings suggest that countries that consistently have solid and effective government institutions have social safety nets to compensate for social risks, generate a higher level of happiness and quality of life for their inhabitants, and have a healthy democracy; these have a greater possibility of meeting the goals associated with the 17 SDGs.

This article makes three contributions. First, the study uses recent data to identify the institutional, political, and social characteristics that influence the achievement of the SDGs. Secondly, it advances exploration of the effect that well-being generates on the 2030 Agenda, finding that both subjective well-being and the existence of a well-being state positively affect the achievement of the SDG goals. Thirdly, it enriches previous literature, highlighting the importance of government effectiveness and strong and participatory democratic institutions for sustainable development.

The rest of the article is structured as follows: Section 2 explores the relevant literature on possible determinants of achieving the 2030 Agenda and develops the research hypotheses. Section 3 describes the variables, data sources, and hypothesis-testing methods. Section 4 provides the results of the hypothesis testing. Section 5 presents the discussion and implications. Finally, Section 6 presents the conclusions, and considers study limitations and directions for future research.

2. Theoretical Framework and Research Hypothesis

Seeking to contribute new factors related to the achievement of the 2030 Agenda, authors such as Biermann et al. [19], Arenilla [20], and Gyimah et al. [21] point out that the success of the SDGs depends on a series of institutional factors, such as the degree to which states formalize their commitments, generate collaborative actions between the government and civil society, strengthen global governance agreements, translate global ambitions in national contexts, and implement policies taking into account behavior, well-being, and emotions. The relevance of institutions dates back to North [13], who argued that effective and well-designed institutions are crucial to fostering innovation, investment, and economic growth. Later, Acemoglu and Robinson [14] added that inclusive institutions promote innovation, growth, and the long-term prosperity of countries.

Likewise, regarding the achievement of the 2030 Agenda, for Arenilla [20], a determining factor in achieving the 2030 Agenda is the design of a solid and integrative institutional architecture. In this sense, the concept of institutional capacity is defined as the ability of a state or organization to effectively and efficiently respond to environmental challenges, utilizing its skills and technical, logistical, human, or financial resources. For their part, Gyimah et al. [21] point out that by promoting accountability, transparency, and participation in decision-making processes and by combating corruption, governance contributes to reducing global warming and to sustainability. Therefore, the state must assume the roles of regulator, monitor, and coordinator of collective action, creating an environment conducive to achieving the SDGs [29].

Some studies have shown that both the institutional and political aspects are relevant to the achievement of the SDGs [21,22,23,24,36,37]. For instance, Glass and Newig [22] estimate multiple regression models with information from 41 high- and upper-middle-income countries for 2015. They use this approach to identify the influence of institutional and political factors (such as governance and the existence of democratic institutions) on the achievement of each of the 17 SDGs, concluding that both democratic institutions and citizen participation, as well as economic power, education, and geographical location, influence the achievement of the SDGs. Similarly, Barbier and Burgess [23] analyze information from low-income and developing countries from 2000 to 2018, relating the Worldwide Governance Indicators (as a measure of institutional quality) and the Country Risk Index with indicators for each of the 17 SDGs. The results of Barbier and Burgess [23] show that progress towards achieving the SDGs is slightly correlated with institutional quality and highly correlated with a lower country risk index. The study of Baciu [37] estimates a panel data linear regression model for 2005 to 2021, considering 165 countries. Her results show that higher values in each of the six components of the Worldwide Governance Indicators are associated with more significant sustainable development. Likewise, political stability is a relevant factor for achieving the 2030 Agenda. According to Gyimah et al. [21], the rule of law allows the institutions responsible for environmental policies to make decisions without fear or interference, favoring the fight against global warming. Through a cluster analysis of 110 countries with data from 2019, Çağlar and Gürler [24] relate the progress of the SDGs to the socioeconomic structure and the political-cultural structure. The socioeconomic structure is characterized by independent variables, such as GDP per capita, the classification according to the income level of the World Bank, and human capital; they find that both GDP per capita and human capital are significantly and positively related to achieving the SDGs. To assess the political-cultural structure, the authors use the World Governance Index and the Human Freedom Index, and they also find a positive and significant relationship with achieving the SDGs, measured through the SDG Index proposed by Sachs et al. [38]. This highlights the importance of political stability in creating an environment conducive to sustainable development. By contrast, when there is no institutional capacity, there is government inefficiency and poor regulatory quality, the rule of law and the fight against corruption are put at risk, which hinders progress towards the goals of the 2030 Agenda [26] because corruption leads to the circumvention of laws and regulations, an increase in public spending, and a decrease in tax revenues [27]. By analyzing ten former European colonies classified as highly corrupt, Mombeuil and Diunugala [26] find that the lack of capacity of governments to reduce corruption hinders the achievement of the SDGs and affirm that the goals of the 2030 Agenda appear to be out of reach of the poorest economies. Similarly, Ahmed and Anifowose [27] analyze the impact of corruption on sustainable development through various ordinary least squares models considering a pool of data from 2017 to 2021 for 42 African countries. The authors relate the Corruption Perception Index with the SDG Index, finding that countries with a high prevalence of corruption have lower SDG compliance rates. Subsequently, Sani et al. [28] analyze the case of Kogi State and show that high corruption negatively affects sustainable development. It is then observed that the weakness of government institutions and corruption continue to be two critical obstacles to achieving the SDGs. For example, in health, corruption negatively impacts SDG 3 by preventing people’s access to quality health services and safe and effective medications [25]. Indeed, the 2030 Agenda itself, through SDG 16, highlights the importance of the fight against corruption and proposes that one of its goals is substantially reducing corruption and bribery in all its forms [2]. The above allows us to postulate a first hypothesis, $H_1$: Better quality of government positively affects aggregate compliance with the 2030 Agenda. When examining the components of the 2030 Agenda, it is observed that its implementation requires democratic governments, understood as that form of government that establishes peace and justice between people and sustainable development as its primary objectives. Democratic regimes are associated with more stable and sustained economic growth, providing a stable political environment that encourages investment and innovation [16] and offering greater long-term stability and economic resilience [15]. Regarding the SDGs, governments must seek a balance between democracy and sustainable development. Following Sahu [31], in a democratic government, development must occur to promote a just and egalitarian society. Hence, democracy makes no sense if it does not contemplate balanced social, environmental, and economic well-being. Even so, the relationship between democracy and sustainability is not entirely clear. For some, democracy is a necessary condition to advance sustainability, being able to provide higher levels of coherence of public policies with sustainable development [29,30]. For its part, the 2030 Agenda modifies social and economic public policies priorities [20]. Other authors are less optimistic because, although they consider that democracy can be beneficial for producing public goods in the long term, it is unlikely to promote environmental policies in the short term if the majority of the population does not support it, and remains subject to social preferences [29]. In this sense, even though Sachs et al. [4] point out that political leadership and the coherence of public policies are decisive for achieving the SDGs, they recognize that, to date, this integration remains slight. However, with an increasingly empowered citizenry, the role of democracy in achieving the 2030 Agenda becomes more critical. Evidence shows that those countries that are consistently ranked as highly democratic are the same ones that are recognized as more sustainable [29], so it is proposed as a second research hypothesis that $H_2$: A better quality of democracy positively affects aggregate compliance with the 2030 Agenda. Other authors [17,32,33,39] point out that social and cultural factors are relevant to achieving the 2030 Agenda. The behavioral sciences highlight concepts such as subjective well-being and happiness [29], considering that happiness is essential for everyone around the world and across generations; so, today’s decisions should attach due importance to the well-being of current and future generations [40]. At the same time, some suggest that subjective well-being (measured from national or international public opinion surveys) can be used as a dependent variable with respect to achievement of the 2030 Agenda [33,34,35]. Others argue that the SDGs cannot be achieved unless there is global sustainable human well-being [17,32] since human well-being is not separable from the well-being of other living entities and the non-human world [41]. The above is in line with what Stiglitz [42] stated regarding the need to implement policies that promote equity, social well-being, and economic growth to achieve truly sustainable human development. It also aligns with the Capabilities Theory, which focuses on the importance of expanding human capabilities, such as education, health, political freedom, and access to opportunities, to move toward fair development [18].

Even though the 2030 Agenda is expressed in terms of goals, deadlines, human rights, and sovereign responsibilities, it is evident that it reflects a tacit conception of human well-being, maintaining that it will be promoted through a comprehensive agenda that encompasses economic, social and environmental policies, as opposed to a narrow agenda focused solely on economic growth [34]. Therefore, it is valid to investigate how happiness can influence achieving the 2030 Agenda and produce economic benefits by increasing productivity and reducing health spending [29]. Furthermore, the primary purpose of the economy is to contribute to the greater well-being of people. In that case, it is necessary to use well-being metrics together with economic, social, and environmental indicators to capture the changes in the quality of life associated with the development of the industry and design better policies that promote sustainable development [39]. Besley [12] points out that there is a consensus to adopt broader approaches to social progress, incorporating subjective elements related to people’s perceptions of different aspects of their lives and objective information about their conditions. From the above, the third research hypothesis is established, $H_3$: Greater well-being has a positive effect on aggregate compliance with the 2030 Agenda. Given the three hypotheses proposed, the article’s contribution lies in using recent data to identify understudied relationships, such as the influence of democracy and well-being on compliance with the SDGs, considering a large number of countries.

3. Methodology

This study’s research is explanatory since it seeks to determine the relationship between variables that affect each other. The design is non-experimental, and the method is quantitative cross-sectional. The observation units are the United Nations member countries committed to the 2030 Agenda. The sample selection is determined by the availability of information for the variables included in the statistical model. The research methodology consists of three stages, as shown in Figure 1.

3.1. First Stage: Operationalization of Variables and Preparation of Database

Initially, the independent and dependent variables are defined and operationalized using secondary sources of information, corresponding to open-access databases, such as the Sustainable Development Report and The Quality of Government Institute. The relationship between these variables is visualized through a conceptual model that facilitates the formulation of hypotheses. Next, a database is built using IBM SPSS Statistics 26 software.

3.2. Second Stage: Descriptive and Correlational Analysis

Once the database has been prepared, the variables are analyzed descriptively to identify minimum, maximum, and average values and the N available for each one. Given the heterogeneity in the income levels of the countries analyzed, a descriptive analysis is carried out according to income group to identify the existence of a positive relationship between the variables under study and income level. A significant statistical correlation between the variables is also verified. These analyses are developed with IBM SPSS Statistics 26 software.

3.3. Third Stage: Estimation and Validation of the Statistical Model

At this stage, a multiple linear regression model is specified; this type of analysis is considered the most appropriate when seeking to control the simultaneous effect of many observed variables on the variable of interest or dependent variable [43,44]. Furthermore, a multiple linear regression model allows controlling other factors that simultaneously affect the dependent variable [44], enabling testing of theories or evaluation of effects when the data used are not experimental [44,45], as is the case in the present research. This statistical analysis technique can be used in cross-sectional research, where the information considered corresponds to a single moment in time [46].

The generic equation of a multiple regression model is stated as follows:

$$Y_i={\beta }_0+{\beta }_1{X}_{1i}+...+{\beta }_k{X}_{ki}+\mu ,$$

(1)

where $Y_i$ is the dependent variable, $X_{1i},...,X_{ki}$, are the explanatory variables, and ${\beta }_0,{\beta }_1,...,{\beta }_k$ are the coefficients.

Subsequently, the model is estimated using IBM SPSS Statistics 26 software, and its results are validated to determine its consistency and to assess the goodness of fit between the hypothesized model and the observed data. Robustness is evaluated based on the significance of the t and F statistics. The non-existence of multicollinearity that could affect the estimation of the regression coefficients is also verified [43,44], for which VIF values measure the speed with which variances and covariances increase. If a VIF value is greater than 10, it indicates that multicollinearity is present. The non-autocorrelation of residuals is also evaluated using the Durbin–Watson d statistic [43]. Following Gujarati and Porter [43] and Wooldridge [44], heteroscedasticity is evaluated, which can arise when the variance of the errors is not constant, causing the t- and F-statistics to be invalid. The White test is used to detect the presence of heteroscedasticity, where if the p-value obtained is greater than the selected significance level, the null hypothesis of homoscedasticity is accepted. Otherwise (presence of heteroscedasticity), some corrective measures must be taken.

The proposed hypotheses are tested or refuted once the robustness and validity tests have been conducted.

It should be noted that this research does not require the approval of an ethics committee since it does not use personal data and is based on publicly available information.

4. Results

4.1. Operationalization of Variables and Preparation of the Database

The statistical analysis considers the relationship between six independent variables and the aggregate achievement of the 2030 Agenda as a dependent variable. This variable was operationalized from the SDG Index corresponding to the year 2022 with information from the Sustainable Development Report [47]; this index was chosen since it is considered superior to others due to its broad coverage of countries [48].

The SDG Index is measured on a scale from 0 to 100 and can be interpreted as a percentage of aggregate achievement of the 17 sustainable development goals. The difference between 100 and the value obtained in the SDG Index of each country is the distance, in percentage points, that must be overcome to achieve the SDG goals [47]. To calculate this index, the country’s scores on each SDG are estimated using the arithmetic mean of the SDG indicators; then, these scores are averaged across the 17 goals to obtain the SDG Index for each country [47]. The six independent variables were obtained from the 2023 Standard Dataset of The Quality of Government Institute (QOG) [49]. Two variables were used to operationalize the quality of government. The first is government effectiveness, a variable from The Worldwide Governance Indicators data set, whose most recent information corresponds to 2019. Government effectiveness measures factors such as the quality of the provision of public services, quality of bureaucracy, competence of the public officials, independence of public administration, and credibility of the government’s commitment to policies, being a good approximation to Arenilla’s [20] concept of institutional capacity. The index has the name wbgi_gee and takes values from −2.5 to 2.5. For this analysis, the variable was recoded to have only positive values from 0 to 5, resulting in a new index through the formula gov_effect = wbgi_gee + 2.5. The second variable is the corruption perceptions index from Transparency International, which measures corruption in the public sector, understanding it as the abuse of a public office for private benefit. This indicator, called ti_cpi, ranges between 0 and 100; 0 is the highest level of perceived corruption, and 100 is the lowest. The data correspond to the year 2021. Two indices were used to approximate the quality of democracy in each country. Democracy status (bti_ds) belongs to the Bertelsmann Transformation Index data set, which ranges between 1 and 10. This variable measures five criteria: stability, political participation, rule of law, stability of democratic institutions, and political and social integration. The other index is personal autonomy and individual rights (fh_pair), which Freedom House calculates. The fh_pair variable assesses the degree of state control over citizens’ travel, the right to own property and establish private businesses, and the freedom of private enterprise from undue influence from government officials, political parties, and security forces. The index classifies countries on a scale between 0 and 16, where 0 represents the minimum freedoms and 16 the greatest freedoms. For both variables, the most recent information corresponds to the year 2019. Finally, well-being was operationalized through two variables. The welfare regime, belonging to the Bertelsmann Transformation Index data set, evaluates the existence of a social risk compensation system in the country through the questions: “To what extent do social safety nets compensate for social risks?” and “To what extent does equality of opportunity exist?” This variable, called bti_wr, ranges on a scale from 1 to 10, with 10 being the best value. The information corresponds to the year 2019. The other variable considered is subjective well-being, which measures people’s perception of quality of life. The variable, called whr_hap, is calculated based on the Cantril ladder that asks people to rate, from 0 to 10, the quality of their current life, including satisfaction with their life as a whole, their feelings at a particular moment, or the degree to which they feel their lives have meaning or purpose [50]. It belongs to the World Happiness Report database for the year 2020. Although the measurement of subjective well-being focuses on what people believe and say they feel, it can be related to objective living conditions [50]. The six independent variables related to the quality of government, democracy status, and well-being correspond to measurements from years before 2022, while the dependent variable, the SDG Index, considers the value of the year 2022. In this way, it is possible to attribute a causality relationship between them by relating each country’s previous state (year t − x) to the independent variable with a subsequent state (year t) regarding aggregate compliance with the SDG goals. Figure 2 shows a relational scheme between the dependent variables that account for quality of government, quality of democracy, and well-being, and the dependent variable related to achieving the SDGs.

Once the independent variables were defined with which the concepts of quality of government, democracy, and well-being were approximated, a database was built with information for 163 of the 193 countries belonging to the United Nations. However, when considering the information available for the six independent and dependent variables, complete records were obtained for 120 countries, so the linear regression model considers a valid N = 120.

The research hypotheses were restated as follows:

Quality of Government

• $H_{1a}$. Greater government effectiveness positively affects aggregate compliance with the 2030 Agenda.
• $H_{1b}$. A lower perception of corruption positively affects aggregate compliance with the 2030 Agenda.

Democracy

• $H_{2a}$. A better democracy status positively affects aggregate compliance with the 2030 Agenda.
• $H_{2b}$. The existence of personal autonomy and individual rights has a positive effect on aggregate compliance with the 2030 Agenda.

Welfare

• $H_{3a}$. A welfare regime positively affects aggregate compliance with the 2030 Agenda.
• $H_{3b}$. Greater subjective well-being positively affects aggregate compliance with the 2030 Agenda.

A multivariate linear regression model is estimated to test these hypotheses, the results of which are shown in Section 4.3. In the following section, a descriptive and correlational analysis is carried out.

4.2. Descriptive and Correlational Analysis

Table 1. Descriptive statistics considering all 163 countries.

	SDG Index 2022	gov_effect	ti_cpi	bti_ds	fh_pair	bti_wr	whr_hap
N	163	163	162	129	163	129	147
Minimum	39.0	0.1	9.0	1.5	0.0	1.0	2.4
Maximum	86.5	4.7	87.0	9.9	16.0	9.5	7.8
Mean	67.2	2.5	43.2	5.6	9.2	5.0	5.5
Standard deviation	10.21	0.99	18.91	2.01	4.14	1.83	1.16

shows the results of the descriptive analysis carried out with the IBM SPSS Statistics 26 software, considering information valid for 163 countries.

It is observed that:

• The average value of the SDG Index 2022 is 67.2; South Sudan has the lowest value of 39.0, while Finland obtained the highest value of 86.5.
• Regarding the government effectiveness(gov_effect), the average is 2.5; South Sudan appears again with a minimum value of 0.1, while Singapore has a maximum value of 4.7.
• For the corruption perception index (ti_cpi), the average is 43.2, Somalia having a minimum value of 9.0, while Denmark and New Zealand share the maximum value of 87.0.
• Democracy status (bti_ds) has an average of 5.6, where the minimum value is 1.5 in countries such as Somalia and Yemen and the maximum value is 9.9 in Uruguay.
• The variable personal autonomy and individual rights(fh_pair) has an average of 9.2. The minimum value of 0.0 is shared by the Central African Republic, Somalia, South Sudan, and Syria, while Canada, Ireland, Luxembourg, Holland, Norway, and Sweden share a maximum value of 16.0.
• Regarding the welfare regime (bti_wr), the average is 5.0, with 1.0 being the minimum value shared by the Democratic Republic of the Congo, Somalia, and Sudan. However, the maximum value is obtained by the Czech Republic with a value of 9.5.
• Subjective well-being (whr_hap) ranges from a minimum value of 2.4 in Afghanistan to a maximum of 7.8 in Finland, with an average of 5.5.

Table 2. Descriptive statistics for low-income countries.

Low-Income Countries	SDG Index 2022	gov_effect	ti_cpi	bti_ds	fh_pair	bti_wr	whr_hap
N	24	24	24	24	24	24	22
Minimum	39.0	0.1	9.0	1.5	0.0	1.0	2.4
Maximum	60.2	2.6	53.0	6.9	10.0	5.5	5.2
Mean	51.7	1.7	25.8	4.4	4.8	3.0	2.4
Standard deviation	5.63	0.61	10.3	1.73	3.19	1.24	0.77

,

Table 3. Descriptive statistics for lower-middle-income countries.

Lower-Middle-Income Countries	SDG Index 2022	gov_effect	ti_cpi	bti_ds	fh_pair	bti_wr	whr_hap
N	46	46	45	44	46	44	40
Minimum	50.3	0.5	18.0	2.9	3.0	2.5	2.7
Maximum	75.7	2.9	68.0	7.8	13.0	6.5	6.5
Mean	62.6	1.9	32.7	5.1	7.4	4.3	5.5
Standard deviation	7.10	0.46	8.83	1.51	2.44	1.08	0.88

,

Table 4. Descriptive statistics for upper-middle-income countries.

Upper-Middle-Income Countries	SDG Index 2022	gov_effect	ti_cpi	bti_ds	fh_pair	bti_wr	whr_hap
N	45	45	45	41	45	41	41
Minimum	60.3	0.8	16.0	2.8	2.0	3.5	3.5
Maximum	77.7	3.5	61.0	9.1	14.0	8.0	7.0
Mean	69.9	2.4	38.6	6.0	9.1	5.8	5.5
Standard deviation	4.46	0.55	10.32	1.77	2.69	1.04	0.74

and

Table 5. Descriptive statistics for high-income countries.

High-Income Countries	SDG Index 2022	gov_effect	ti_cpi	bti_ds	fh_pair	bti_wr	whr_hap
N	48	48	48	20	48	20	44
Minimum	60.4	2.5	40.0	2.5	2.0	4.5	5.6
Maximum	86.5	4.7	87.0	9.9	16.0	9.5	7.8
Mean	76.7	3.7	66.0	7.0	13.1	7.5	6.7
Standard deviation	5.87	0.55	13.77	2.64	3.64	1.43	0.6

also allow us to visualize how the study variables are related to the countries’ income levels. It is observed that all the variables show an increasing trend as countries’ income increases. Thus, for example, the SDG Index averages 51.7 in low-income countries, 62.6 in lower-middle-income countries, 69.9 in upper-middle-income countries, and 76.7 in high-income countries. The above supports what Mombeuil and Diunugala [26] stated regarding poor countries’ difficulty in achieving the SDGs, making them a practically utopian challenge.

Table 6. Correlation analysis.

	SDG Index 2022	gov_effect	ti_cpi	bti_wr	whr_hap	fh_pair	bti_ds
SDG Index 2022	1.000	0.818	0.723	0.480	0.740	0.806	0.760
gov_effect		1.000	0.932	0.578	0.788	0.855	0.751
ti_cpi			1.000	0.599	0.777	0.781	0.686
bti_ds				1.000	0.876	0.715	0.365
fh_pair					1.000	0.773	0.692
bti_wr						1.000	0.599
whr_hap							1.000

The correlation is significant at the 0.01 level (bilateral) for all variables.

shows the results of the correlation analysis using the Pearson coefficient. All correlations are significant at a level of 0.01 and have the expected sign. However, it is observed that the variables gov_effect and ti_cpi have a high correlation (0.932), so it will be necessary to verify that there is no multicollinearity once the model is estimated. The information in Table 6 allows us to affirm that the aggregate fulfilment of the SDGs is positively and significantly related to the effectiveness of government, perception of corruption, democracy status, personal autonomy and individual rights, welfare regime, and subjective well-being. However, these findings are insufficient to verify the research hypotheses, so a multivariate linear regression model will be specified and estimated to relate a dependent variable with independent variables of the same nature [51].

In this study, the regression equation is expressed as follows:

$$SDGIndex2022={\beta }_0+{\beta }_{1}gov\_effect+{\beta }_{2}ti\_cpi+{\beta }_{3}bti\_ds+{\beta }_{4}fh\_pair+{\beta }_{5}bti\_wr+{\beta }_{6}whr\_hap+\mu$$

(2)

4.3. Estimation and Validation of the Statistical Model

Table 7. Regression model. Dependent variable: SDG Index 2022.

	Non-Standardized $\mathit{\beta }$	Standardized $\mathit{\beta }$	Significance	VIF
Constant	40.489		0.000
gov_effect	4.143	0.354	0.008	8.262
ti_cpi	−0.223	−0.317	0.005	5.723
bti_wr	0.734	0.272	0.017	5.979
whr_hap	−1.478	−0.310	0.004	5.281
fh_pair	3.553	0.679	0.000	5.623
bti_ds	1.525	0.160	0.011	1.820

presents the results of the multiple linear regression model, estimated using the IBM SPSS Statistics 26 software for 120 countries. All explanatory variables, representing different aspects of a country’s governance and socio-economic conditions, were significant according to the t-statistic, indicating their importance in the model. As shown in

Table 8. Goodness of fit of the regression model.

R	R-Squared	Adjusted R-Squared	F	Sig.	Durbin-Watson (d)
0.873	0.762	0.749	4.759	0.000	2.072

, this robust model is significant as a whole (F = 4.759) and explains 74.9% of the variation in the SDG Index (adjusted $R^2$ = 0.749). The serial correlation was verified with the Durbin–Watson d-statistic, which obtained a value of 2.072, falling within the acceptance zone of the non-autocorrelation hypotheses (1.803 < d < 2.197, with $d_l$ = 1.55 and $d_u$ = 1.803, K = 6, N = 120). The VIF values, which indicate the absence of multicollinearity, were all less than 10, even when there was a high correlation between two independent variables. Regarding heteroscedasticity, the White test showed a p-value = 0.309, which indicates that the null hypothesis of homoscedasticity is not rejected (p > 0.05) [43].

The estimated equation is expressed as follows:

$$\mathit{SDGIndex}\mathit{2022}=40.489+4.143gov\_effect-0.223ti\_cpi+0.734bti\_ds-1.478fh\_pair+3.553bti\_wr+1.525whr\_hap+\mu$$

(3)

Figure 3 shows the results of the model schematically. It visually represents the relationships between the variables and the SDG Index. In the context of the established hypotheses, which are theoretical assumptions about the relationships between the variables, the figure shows the following:

• Government effectiveness has a high (${\beta }_1$ = 4.143) positive and significant effect on achieving the 2030 Agenda, fulfilling what is established in $H_{1a}$.
• Contrary to what is established in $H_{1b}$, the perception of corruption has a low (${\beta }_2$ = −0.223), negative, and significant effect on achieving the 2030 Agenda.
• Democracy status has a high (${\beta }_2$ = 0.734), positive, and significant effect on achieving the 2030 Agenda, fulfilling what is established in $H_{2a}$.
• Autonomy and individual rights have a high (${\beta }_4$ = −1.478), negative, and significant effect on achieving the 2030 Agenda, contrary to what is established in $H_{2b}$.
• The welfare regime has a high (${\beta }_5$ = 3.553), positive, and significant effect on achieving the 2030 Agenda, fulfilling what is established in $H_{3a}$.
• Subjective well-being has a high (${\beta }_6$ = 1.525), positive, and significant effect on achieving the 2030 Agenda, fulfilling what is established in $H_{3b}$.

The interpretation of the results indicates that government effectiveness, the welfare regime, and subjective well-being are the factors that contribute the most to the achievement of the SDGs. The SDG Index, a measure of a country’s progress towards the Sustainable Development Goals, increases by 4.143 for every one-point increase in government effectiveness. Similarly, a one-point increase in the welfare regime and subjective well-being leads to a 3.553 and 1.525 increase in the SDG Index, respectively. On the other hand, democracy status has a smaller effect (0.734), suggesting that it is less influential in driving SDG progress. Contrary to expectations, less corruption and an increase in personal autonomy and individual rights reduce the value achieved by the SDG Index, indicating that these factors may hinder SDG progress.

5. Discussion

The results from the descriptive statistical analysis show a positive relationship between the countries’ income levels and the achievement of the 2030 Agenda. This underscores the immense challenges that poorer countries face in achieving the SDGs, even if they strive to emulate the progress of high-income countries. Given the prevailing conditions in these nations, it is a daunting task [26]. Therefore, following the premise of the “Beyond GDP” initiative, this article has sought to contribute to the field of study that explores the factors that contribute to achieving the 2030 Agenda by incorporating factors from the social sphere, specifically, the welfare variable. Assuming that the 2030 Agenda embodies an implicit theory of human well-being [34], it is relevant to consider whether a higher level of well-being impacts progress toward the SDG goals [12].

The multiple linear regression analysis allowed us to assess the effects of various institutional, political, and social factors on achieving the 2030 Agenda. The research results reinforce findings from previous studies that have highlighted the importance of quality of government [20,22,23,24,26,36] and democracy [22,30,31], but differ from others which find positive effects of human freedoms on the SDGs [24]. The research carried out shows that the existence of greater government effectiveness and a solid democracy promote the sustainability of the country. At the same time, autonomy and individual freedoms would have a negative effect when combined with other variables. This may occur because the results of the institutional capacity and democracy status have a direct relationship with the results that the government generates on social, economic, and environmental dimensions related to the 17 SDGs. The specific indicator of autonomy and human freedoms probably only influences goals such as SDG 10-Reduced Inequalities and SDG 16-Peace, Justice and Strong Institutions.

On the other hand, the study advances exploration of the effect that well-being has on the 2030 Agenda, while previous research, instead, analyzes the influence of the SDGs on well-being [33,34]. The findings of this research allow us to affirm that subjective well-being and the perception of quality of life turn out to be of high relevance for the achievement of the 2030 Agenda; thus, the more satisfied with their lives the population of a country are, the higher the level of compliance with SDGs it is expected that this country will have. If it is assumed that the 2030 Agenda as well as the protection of planetary integrity should not be a means to an end but rather an end in itself [10], then it is necessary to consider how the perception of a better quality of life can affect the achievement of the SDGs and not only see how the quality of life improves by advancing the accomplishment of the goals of the 2030 Agenda. Likewise, there are positive effects of the welfare regime on the fulfilment of the SDGs, showing that a country with social safety networks to compensate for social risks also ensures equal opportunities and promotes more significant progress towards the goals of the 2030 Agenda.

Even though the relationship between lower perception of corruption and compliance with the SDGs is statistically significant, it does not have the expected sign since, according to the estimated model, an increase in transparency has a negative effect on the achievement of the 2030 Agenda. These results are contrary to those of previous studies, such as those of Mackey et al. [25], Mombeuil and Diunugala [26], and Ahmed and Anifowose [27]. A possible explanation is that when analyzing its effect with other variables, such as government effectiveness, its relative importance decreases, even becoming negative. However, more research is needed to understand this dynamic fully. In this regard, it is important to note that in the correlation analysis carried out, both variables are positively and significantly related with a value of 0.723, so the explanation for the negative coefficient in the estimation of the multiple regression model reflects interactions with the other regressor variables.

Implications

Three of the implications of these findings for countries facing more significant challenges in achieving the 2030 Agenda can be highlighted. Firstly, the incorporation of social factors as explanatory of the achievement of the 2030 Agenda highlights the need to prioritize well-being and improve quality of life since these can contribute to sustainable development, given that empirical evidence demonstrates a strong and significant positive reationship between the well-being regime and the achievement of the SDGs, as well as between subjective well-being and the SDGs.

Second, this study adds a new perspective to the existing literature, which has already highlighted the importance of the government’s capacity to implement effective policies and programs and the presence of strong and participatory democratic institutions in fostering sustainable development. The study’s contribution is that it reveals a positive and significant relationship between government effectiveness, the status of democracy, and the achievement of the SDGs.

Third, the discovery of unexpected effects of both perceived corruption and individual autonomy and freedoms raises intriguing questions that urgently deserve further exploration. For example, the interaction between less corruption and government effectiveness distorts the effect that less corruption alone can have on achieving the SDGs. Similarly, the results suggest potential tensions between certain aspects of autonomy and individual rights with the SDGs, highlighting the need for more effective strategies to promote equitable and sustainable development.

In summary, these results emphasize the importance of several factors, such as government effectiveness, democracy, and social well-being, in achieving the SDGs by 2030. The above is relevant for decision-making processes at the government level, so it is suggested to continue advancing the institutional capacity of countries, especially those with lower levels of development [20], to rebuild citizen trust with democratic institutions as well as the appreciation and defence of democracy at a global level, and to design and implement policies and programs that address issues of subjective well-being, happiness, and mental health. These improvements can be enhanced by establishing collaborative governance as an option to revitalize trust in the government through the co-production of public policies and services [52].

6. Conclusions and Limitations

6.1. Conclusions

The study’s results underscore the complexity of the factors influencing progress toward the 2030 Agenda. Importantly, they suggest a series of practical actions that can be taken to improve progress towards these goals. These include enhancing the quality of government, strengthening democracy, promoting well-being, and adopting collaborative governance approaches, all of which have direct implications for policymakers and practitioners.

The study employed a multiple linear regression analysis, using the level of achievement of the 2030 Agenda measured for the year 2022 as a dependent variable. It also included six independent variables related to the quality of government, democracy status, and well-being. We conclude that countries with consistently strong and effective government institutions also have social security networks to compensate for social risks, generating a higher level of happiness and quality of life for their inhabitants, and that having a healthy democracy is associated with a greater possibility of meeting the 17 SDGs. This robust methodology and the clear findings it produced strengthen the credibility of our research.

These findings are a significant contribution to the literature that has sought to identify factors that affect the achievement of the 2030 Agenda, which has mainly focused on the economic and political structure. Thus, the importance of this research lies in incorporating new variables, such as subjective well-being and the well-being regime, and in using recent data to identify the institutional, political, and social characteristics that influence the fulfilment of the SDGs. Furthermore, the findings confirm that variables related to well-being have a positive and significant effect on achieving the goals of the 2030 Agenda, providing a new perspective and understanding in this field.

6.2. Limitations and Future Research

This study has the limitation of using cross-sectional data since it does not seek to explore evolution over time but rather to identify causality between explanatory variables measured at a time t-x and a response variable at a later time t. This limitation could be addressed in future research by incorporating longitudinal data to capture the dynamic nature of the variables. On the other hand, the indicators approximate complex concepts, such as the quality of government, democracy status, and well-being, capturing only a part of such multidimensional concepts. For this reason, it was decided to use indicators from data sources that are widely accepted and recognized by the academic and political world, such as The Worldwide Governance Indicators, Transparency International, the Bertelsmann Transformation Index, Freedom in the World, and the World Happiness Report. These indicators are calculated periodically for many countries worldwide, which allowed the construction of a database with observations for 163 countries, strengthening the analysis. These indicators may not fully capture the nuances of the concepts they represent, and future research could explore alternative measures or develop new indicators to overcome this limitation. The research has revealed that government effectiveness, the welfare regime, subjective well-being, and democracy status emerge as crucial factors for progress toward the 2030 Agenda. However, it is necessary to continue delving into the relationship between these variables and the achievement of each of the 17 SDGs separately so that future research could address specific analyses for each objective of the 2030 Agenda, fostering a deeper understanding and contributing to the ongoing academic discourse.