Reflections of the relationship between education indicators and economic,law and human development indicators

1.1 Aim of the study

Previous studies ignored the distinction between short, medium, and long term by decomposing macroeconomic variables and human development index at different time scales.

It is now obvious that, according to the nature of education, people’s social demands such as competitive behavior and democratic rights and freedoms differ significantly. In particular, the quality of human development, global competitiveness and fairness indicators measured at regular intervals by international independent non-profit organizations provide an important source of information for policymakers in these fields.

Hung [9] said that previous studies ignored the distinction between short, medium, and long term by decomposing macroeconomic variables and human development index at different time scales. Therefore, this research aims to interpret the relationship between indicators of education, human development, economy and justice based on current indicators measured by international independent organizations. Therefore, the problem sentences of the research are expressed as follows.

How are countries classified according to education, economy, law and human development indicators?

2.1 Why education?

Education is a tool of economic and social change. Quality of education is a decisive variable in achieving the desired social order, economic power or healthcare system among other matters. For instance, the human development index (HDI) includes basic social indicators such as life expectancy, education and income [10]. Therefore, a high indicator of HDI also indicates a high level of educational process and quality. The same results were observed in several studies, including [11, 12, 13, 8].

Education was the tool for social change, though, it should be noted, without disturbing the basic structural features of the economy. Education and economy are artificial and open systems. They are artificial because they do not function as natural processes such as the solar system, biological systems, or the ecosystem but rather are designed by human beings. They are open systems because their functioning depends on elements of input, output, system and feedback. All systems have a clearly declared or defined purpose. The degree of realization of such purposes makes a system permanent. So, what is the purpose of education as an open system?

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The knowledge and skills of individuals are discovered and developed through education systems. An individual enters the education system as an input with his/her talents. The existing talents of an individual are discovered, developed or processed during the process. When the period of processing is completed, the individual becomes an output of the system as a graduate. What is expected in the element of the output is a quantitative and qualitative difference among mental, emotional and physical talents. What measures the difference between input and output is the feedback element of the system. The element of feedback provides feedback about the level of accomplishment of purposes, and the quality and quantity of input and output to maintain the system. This is very important for the innovation of a system.

At the macro level, an education system is an intentional process of acculturation based on systematic and scientific methods and techniques. This makes education systems a field of application for state policies. The process of acculturation is very important for the maintenance of social relations since enslaving or liberating a society is a part of this intentional process of acculturation.

The science of economics is based on scarcity. Individuals have unlimited needs but there are limited resources to satisfy these needs. Therefore, the purpose of the economy is to use limited resources to satisfy unlimited needs. In other words, the economy is a process of using (producing-consuming) and sharing (marketing) scarce resources. This depends on the knowledge and skills of management and administration. This means that economy is a field of application for the economy, resources, capital, consumption, production, marketing, labor, land, and efficiency. While the science of economics analyzes the balance of supply and demand in the economic activities of individuals as consumers at the micro-level, it analyzes and designs the economic activities (GDP, unemployment, growth, inflation, etc.) of countries at the macro level. So, why is it important to satisfy unlimited needs with scarce resources? Because the extent to which unlimited needs are satisfied helps to achieve a level of welfare for individuals and societies. A rise in the level of welfare is increased income at the micro-level, and economic growth at the macro level. Therefore, independent organizations keep the economy and welfare indicators of countries.

2.3 The relationship between education and economy

Individuals produce “qualified information and skills” with economic value, which are achieved by means of “education.” This leads us to the conclusion that educated people who produce qualified information and skills make the driving force of an economy [1]. Habermeier [2] emphasizes in his study that education is an obligatory precondition of the economic performance of a country. The relationship between education and economy is so clear that;

Countries that educate their people well are more successful in economic terms.

Why do creative and original ideas matter? Because the existence of creative and original ideas implies intellectual activity. What does intellectual activity mean? It means science, technology, arts, and sports. The information and skills produced in the field of science, technology, art and sports mean innovation and economic value. In short, qualified information and skills are programmed by the education system. A high-quality education system is the capital and fund of the economy. The relationship between education, economy, and human beings is shown in Fig. 2.

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As can be seen in the figure, there is a two-way interaction between human and education, human and economy, and education and economy. The variables of economy and education affect and get affected by each other. There is a two-way relationship among them. However, it is not possible to say that there is a causal relationship among these variables. Croes, Ridderstaat, Ba̧k and Zientara [14] pointed out economic growth has a positive impact on human development.

3.1 Sampling

All variables in this study were based on the data input from several official databases. Fifteen variables measured by four different organizations were selected. The coverage of this study was composed of 41 countries. These countries, their region and the income group they belong to are shown in Table 1.

51% of the countries are high-income group; 34% of them are in the upper-middle-income group and 15% of them are in the lower-middle-income group. In terms of their regions, 51% of the countries are in Western Europe, the European Free Trade Association or North America; 17% of them in Latin America or the Caribbean; 12% of them in East Asia or the Pacific; 12% of them in Eastern Europe or Central Asia and 7% of them in the Middle East or North Africa.

No measurement instruments such as questionnaires, tests or scales were used in the research. However, the data of the research was obtained from databases that are kept open to public access by international official institutions. A total of 15 indicator values were obtained from the databases of international organizations. The Global Competitiveness Index(GCI), Percentage of individuals using the Internet, Freedom of the press and Judicial independence of 140 countries from the database of World Economic Forum (WEF ) [15], WJP Rule of Law Index and Fundamental Rights indicators of 113 countries from the database of World Justice Project [16]; Human Development Index (HDI), Mean years of schooling, gross national income (GNI) per capita, Gini coefficient, Gender Inequality Index, Government expenditure on education (% of GDP) of 189 countries from the database of United Nations Development Programme (UNDP) [17] and PISA-Mathematics, Reading and Science average scores of 72 countries from the database of OECD [18] were obtained. Descriptive information about the indicators is presented in Table 2.

The number of countries for indicators derived from four different databases varies. However, a total of 41 countries, complete 15 indicators included in the research, were obtained. Descriptions and measuring ranges of the indicators are summarized in Table 3.

The fact that Freedom of the press, Gini coefficient and Gender Inequality Index measurements are close to zero indicates the ideal or the positive of the situation. In this respect, Freedom of the Press, Gini coefficient and Gender Inequality Index measurements were required to be small in quantity, while other measurements were required to be large in quantity. Particular attention should be paid to the interpretation of the correlation coefficients.

Data were analyzed using clustering, separation analysis, multidimensional scaling and Spearman order difference correlation coefficient techniques. In the first stage of the data analysis, the number of clusters was determined by performing gradual clustering analysis according to fifteen indicators of 41 countries. Due to the presence of two distinct clusters, the clustering analysis was repeated with the k-mean algorithm where the number of clusters was 2. In the second stage, the consistency and validation of the classification of the countries divided into two clusters were tested by Discriminant analysis. In the third stage, since the metrics for the indicators are not homogeneous at the metric level, all the metrics for the indicators are converted to z values based on the data sets to which they belong to. In the fourth stage, multi-dimensional scaling was performed to visualize the positions of the countries on the two-dimensional plane. In the last stage, the correlation coefficients between the indicators of human development, education, economy and law are obtained for the countries in each cluster.

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4.1 How are countries classified according to education, economy, law and human development indicators?

According to 15 indicators related to education, law, human development and economy, firstly, gradual clustering analysis was performed and it was observed that countries were divided into 2 distinct clusters. Clustering analysis was repeated according to the 2-average algorithm. Whether the 15 indicators are important in the clustering of countries is examined and the results are shown in Table 4.

According to the table, these 15 indicators were found to be significant in the division of countries into two groups ( 0.05). In addition, the normality of distance values in clustering analysis is considered sufficient rather than the assumption of normality of variables [19]. Therefore, Distance values were tested by Kolmogorov-Smirnov Test when the number of clusters was determined as 2. It was observed that the distance values corresponded to the normal distribution (Test statistic = 0.111 Sig. = 0.200). The distribution of countries by clusters is shown in Table 5.

Two clusters are formed as 28 (68%) countries in the first cluster and 13 (12%) clusters in the second one. Discriminant analysis was performed for the consistency and validation of this classification obtained by clustering analysis. Whether the discriminant function is significant, in other words, the success of assigning (separation) countries to groups, has been demonstrated by the help of Wilks’ Lambda and Chi-square test statistics given in Table 6.

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One discriminant function was found and the success of countries’ assignment to two clusters was significant according to education, law, economy and human development indicators ( 0.01). The Canonical Correlation coefficient was found to be 0.946. The division of countries into two clusters is explained by 90% of indicators of education, economy, law and human development. Wilk’s Lambda value was found to be 0.105 which is close to zero. In other words, 15 indicator values show that the effect of clustering countries is high. The assumption of the equality of covariance matrices of two clusters was found to be equal according to the Box’M test. The order of importance of indicators in the division of countries into 2 clusters is shown in Table 7.

GCI, Gross national income (GNI) per capita, Judicial independence, WJP Rule of Law Index and Human Development Index were the first five indicators that were important in the separation of clusters. The accuracy of the classification by clustering analysis is shown in Table 8.

It was observed that the countries which were divided into two clusters by clustering analysis were classified 100% correctly by discriminant analysis. Also, the locations of the countries according to the indicators with the multi-dimensional scaling technique on the two-dimensional axis are shown in Fig. 2.

Since the suitability between the original distances and the indication distances was determined by the measure of stress, the stress value between the distances of the countries was found to be 0.08. Since this value is close to zero, two-dimensional classification was deemed appropriate. RSQ coefficient was investigated for reliability of multidimensional scaling. RSQ coefficient was obtained as a high-reliability measure of 0.97.

In the field of education, law, human development and economics, the relationship between each of the 15 indicators will be calculated as a combination of 2 of 15, which is 105 for each cluster. Instead of interpreting each of these correlation coefficients, significant relationships were determined in the second cluster and non-significant relationships were determined in the first cluster since the average indicator values of the countries in the second cluster were higher than the first cluster, and the results are shown in Table 9.

Countries in the first cluster were named as “developing countries”; and ones in the second cluster were named as “developed countries”. There was a high positive correlation between developed countries’ global competitiveness and average schooling year which was 0.720 ( 0.01). However, the correlation coefficient between the global competitiveness and the average schooling year in developing countries is low as 0.263 and is not significant ( $p>$ 0.05).

In developed countries, there was a significant negative correlation between gender inequality and state investment in education, which was - 0.681 ( 0.05). In other words, as gender inequality decreases in developed countries, the investment rate of the state in education increases. In developing countries, a non-significant and negative low-level correlation was found between gender inequality and the investment rate of the state in education ( $p>$ 0.05).

In developed countries, a significant and negative correlation coefficient was found as - 0.692 between the high negative level of income distribution inequality and rule of law ( 0.01). In other words, as the rule of law increases in developed countries, income inequality decreases. In developing countries, a non-significant and negative low-level correlation was found between rule of law and income inequality ( $p>$ 0.05).

In developed countries, a middle-level positive correlation coefficient was found as 0.639 between rule of law and state investment in education. The relationship found is significant ( 0.05). In other words, as rule of law increases in developed countries, the investment rate of the state in education increases. In developing countries, a non-significant and low-level correlation was found between rule of law and the investment rate of the state in education ( $p>$ 0.05).

In developed countries, a significant and negative and very high-level correlation was found between income inequality and fundamental rights ( r = - 0.848, 0.05). In other words, as the equality of income (indicator value decreases) increases in developed countries, the indicative values of universal fundamental rights practices. In developing countries, a negative too low-level correlation and a non-significant were found between income inequality (indicator value increases) and fundamental rights (indicator value decreases) ( $p>$ 0.05).

In developed countries, a significant and negative and high-level correlation was found between income inequality and the investment rate of the state in education ( r = - 0.714, 0.05). In other words, as the equality of income (indicator value decreases) increases in developed countries, the indicative values of the investment rate of the state in education. In developing countries, a non-significant and negative too low-level correlation was found between income inequality (indicator value increases) and the investment rate of the state in education ( $p>$ 0.05).

In developed countries, a middle-level positive correlation coefficient was found as 0.671 between fundamental rights and state investment in education. The relationship found is significant ( 0.05). In other words, as practices related to fundamental rights increase in developed countries, the investment rate of the state in education increases. In developing countries, a non-significant and positive low-level correlation was found between fundamental rights and the investment rate of the state in education ( $p>$ 0.05).

In developed countries, there is a positive relationship between judicial independence and reading comprehension and science literacy averages of children of countries. These relationships found significant ( 0.01). In other words, as the indicator value of judicial independence increases in developed countries, average test scores of reading comprehension and science literacy of children of that country increase. In developed countries, a non-significant and positive correlation is found between judicial independence and reading comprehension and science literacy averages of children of countries ( $p>$ 0.05).

In developed countries, a very high positive correlation between freedom of the press and income inequality was found to be 0.823. The relationship found also is significant ( 0.01). In other words, income equality increases as freedom of the press increase in developed countries, or income inequality increases as freedom of the press are limited in developed countries. On the other hand, the positive low level of correlation between freedom of the press and income inequality in developing countries ( r = 0.256) is non-significant ( $p>$ 0.05).

In developed countries, there was a significant and negative correlation between freedom of the press and state investment in education, which was - 0.756 ( 0.01). The freedom of the press varies between 0 (good) and 100 (bad), the investment rate of the state in basic education increases as the freedom of the press increases in developed countries. In other words, as the freedom of the press is limited in countries (the indicator value increases), the investment rate of the state in basic education decreases. In developing countries, a non-significant and negative low-level correlation ( r = - 0.226) was found between freedom of the press and the investment rate of the state in education ( $p>$ 0.05).