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2The Relationship Between Economic Growth and Human Development in RA: An Econometric Analysis
Ghazaryan Romik A.
PhD Student of the Chair of Economic-Mathematical Methods of the Armenian State University of Economics (Yerevan, RA)
rom.ghazaryan96@gmail.com
UDC: 330.35: 330.43: 519.237.5; EDN: ZNNJYX
Keywords: human development, economic growth, living standard, education, health
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rom.ghazaryan96@gmail.com
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Взаимосвязь между экономическим ростом и человеческим развитием в РА:
эконометрический анализ
Казарян Ромик А.
Аспирант кафедры Экономико-математических методов Армянский Государственный Экономический Университет (Ереван, РА)
rom.ghazaryan96@gmail.com
Аннотация. Согласно концепции человеческого развития, развитие является конечной целью человеческой деятельности, а экономический рост — скорее средством для достижения этой цели. Тем не менее, эти два показателя имеют очень сильную взаимозависимость. Воздействие человеческого развития на экономический рост будет отражаться в той мере, в какой возможности и свободы людей улучшат экономические показатели. А обратный эффект зависит от того, насколько увеличение доходов расширит возможности и выбор населения и правительства.
Данная статья направлена на выявление взаимосвязей между человеческим развитием и экономическим ростом в Армении. Для этого были построены две регрессионные модели, одна из которых характеризует влияние экономического роста на динамику человеческого развития, а вторая - противоположное влияние. Модели были оценены с использованием метода ARDL, а оценки были выполнены с использованием программного пакета EViews 10.
Результаты оценки показывают, что экономический рост и человеческое развитие в Армении действительно имеют взаимоположительный эффект. Более того, влияние человеческого развития на экономический рост больше, чем противоположный эффект, а это означает, что улучшения в области здравоохранения и образования оказывают более сильное влияние на рост доходов, чем рост доходов на улучшения в области здравоохранения и образования.
Ключевые слова: человеческое развитие, экономический рост, уровень жизни, образование, здоровье
Introduction. To assess the standard of living of the population of any country, indicators such as GDP per capita, consumer price index, employment or unemployment levels are generally examined. These indicators are of great importance, and their growth (decrease) rates generally characterize the trends of economic development of the country and improvement of the population's standard of living. The growth of the population's standard of living is often equated with economic growth, but the standard of living is a much broader concept, therefore, in order to assess it comprehensively, it is necessary to analyze other, no less important indicators characterizing the quality of life and well-being. Nobel Prize laureate Amartya Sen states, that there are many fundamentally different ways of seeing the quality of living, and quite a few of them have some immediate plausibility. You could be well off, without being well. You could be well, without being able to lead the life you wanted. You could have got the life you wanted, without being happy. You could be happy, without having much freedom. You could have a good deal of freedom, without achieving much. We can go on [6, p. 1].
Human development has recently been advanced as the ultimate objective of human activity in place of economic growth. Human development reflects the expansion of the freedoms and choices of each individual. Human development is about expanding human freedoms and opening more choices for people to chart their own development paths according to their diverse values rather than about prescribing one or more particular paths. The human development approach reminds us that economic growth is more means than end. Unlike income or economic growth, health and education are not just means but ends in themselves. Human development is an ongoing journey, not a destination. Its center of gravity has always been about more than just meeting basic needs. It is about empowering people to identify and pursue their own paths for a meaningful life, one anchored in expanding freedoms [9, p. 6].
Literature review. It is obvious that there exists a very strong connection between human development and economic growth. On the one hand, economic growth provides sufficient resources that can promote sustainable human development, and on the other hand, human development leads to an improvement in the quality of the workforce, which in turn is a powerful stimulus for economic growth. The results of some studies document that countries that pursue policies that promote human development are much more successful than those that prioritize economic growth. This finding implies that, although both human development and economic growth should
be jointly promoted, human development should be given sequential priority [5, p. 1]. Economic growth affects human development mainly through the activities of households and government, as well as various public organizations. The same level of GDP can affect human development in different ways, which is due to the efficiency of GDP distribution, as well as the behavior of households and the effectiveness of policies conducted by the government and non-governmental organizations.
The inequality of income distribution, the level of household income, as well as the control of expenses within the economy have a decisive role in the acquisition of goods that contribute to human development (such as healthy food, education, health, etc.). Low levels of per capita income, high levels of poverty or inequality in income distribution lead to low levels of household consumption of goods that promote human development. Households can increase their consumption of these goods, promoting human development, if they receive additional income, which means that reducing poverty and inequality contributes to human development. A number of studies have shown that in countries with low levels of inequality, enrollment in secondary education is significantly higher [11], and increasing household income has a significant impact on the demand for health services [1, p. 85].
The government should promote human development by competent management of public expenditures. The share of the Government's total expenses directed to human development areas, as well as the distribution of these expenses in the given areas, have a significant impact. It is also important to precisely define the priority areas, the funds directed to which should be a proper part of the country's GDP. A big problem for the government can be high demand in areas that don't contribute to human development at all (such as expenses for the acquisition of military equipment). The activities of public organizations are mainly aimed at promoting human development: creating additional income for the poor, homeless, orphanages or schools, implementing various health programs, etc.
As a result of human development, people become better fed, healthy, educated and as a result contribute more to economic growth. Achieving a high level of human development is not an end in itself, but it has a direct impact on economic growth, as it increases people's capabilities and productivity, making them more creative. Secondary and vocational education contributes to the acquisition of skills, health increases productivity, and higher education develops science. Many studies show that income increases are associated with additional
years of schooling, with income levels varying by level of education [4, p. 1328]. The higher the educational level of the labor force, the higher the overall productivity, because highly educated workers can introduce certain innovations and thus affect the productivity of all workers. In other words, overall productivity increases as the average level of education increases [3, p. 37].
Data and model specification. In order to study the mutual relationship between economic growth and human development in Armenia, two regression models were constructed, one of which characterizes the impact of economic growth on the dynamics of human development, and the second one, the opposite. The models have the following appearance:
lnHDIt =a1+a2 ln GDPt +a3 lnFDIt +a4 lnMYSt +a5TRt+a6LFt+st t=1990, 2021 (1) ln GDPT =ß1+ß2 lnHDIMT +ß3YEMPT+ß4SSET+uT t=1991, 2020 (2)
Descriptions of and details about variables are in Table 1.
GDP
Table 1. Measurement units of variables, definition and data collection source
Variable Definition Measurement unit Source
HDI human development index unit UNDP Data Center
real GDP per capita
constant 2017 dollar, PPP
World Bank WDI
FDI foreign direct investment (net inflows) US dollar World Bank WDI
TR annual import and export the sum of shares of GDP World Bank WDI
MYS mean years of schooling for adults ages 25 years and older year UNDP Data Center
LF labor force participation rate as a percentage of the population aged 15-64 World Bank WDI
HDIM modified human development index unit UNDP Data Center and author's calculations
YEMP_employment rate among youth_poprapationaged Q 5^24_World Bank WDI
SSE secondary school enrollment ratio of actual and expected World Bank WDI
values
Table compiled by author
FDI and trade stimulate the labor market and economic growth [7, p. 53], provide an opportunity to obtain durable goods by promoting import and export in the region, create new employment opportunities in the society, contributing to human development [10]. As already stated, education has a significant impact on both economic growth and human development. In order to evaluate the achievements of human development, as an indicator describing education, the level of adult literacy (mean years of schooling for adults ages 25 years and older) was chosen, which is widely used as a measure of human capital in the country [2]. The desire to be employed by the healthy, educated and workable population of any country reflects the high level of human development. What are the employment opportunities in the given country is a different matter. In this context, the labor force participation rate among the working-age population, which includes people who are either employed or looking for work, was also selected as an explanatory variable. The expected relation
between all explanatory variables and HDI is significant and positive.
The modified Human Development Index is calculated using the same methodology as the HDI, but includes only non-income components. That is, only health and education indices were calculated, and then their geometric mean. This modified index expresses human development without taking into account income growth, so it can perfectly characterize the achievements in the dimensions of health and education. In order to assess the impact on the dynamics of human development, the level of labor force participation was chosen as an explanatory variable, but only those who are engaged in any type of employment create a result in the country. Therefore, to study the dynamics of economic growth, the employment level was chosen as an explanatory variable. And since the people who ensure the development of the country in the coming decades are mainly young people, so we found it appropriate to take only the indicator characterizing the level of employment of young
people. The third explanatory variable is the level of participation in secondary education. The basis for the selection of this explanatory variable was the fact that people with secondary education have the greatest weight in the labor resources in Armenia (41% in 2020) [12, p. 31]. In this case too, a significant and positive effect is expected for all selected variables.
Results and discussion. To evaluate the constructed models, the stationarity of the series was first checked, for which the unit root (ADF) test according to Akaike's criterion was applied, the results of which show that all included variables are I(0) or I(1) processes (Table 2), which means that the models can be estimated using the autoregressive distributed lag (ARDL) method [8, p. 79].
Table 2. Results of unit root test
Group unit root test: Summary
Series: HDI, GDP, FDI, MYS, TR, LF, HDIM, YEMP, SSE Sample: 1990 2021
Exogenous variables: Individual effects Automatic selection of maximum lags Automatic lag length selection based on AIC: 0 Newey-West automatic bandwidth selection and Bartlett kernel
Method
Statistic
Prob.
Cross-sections
Obs
Null: Unit root (assumes common unit root process)
Levin, Lin & Chu t -9.60630 0.0000 Null: Unit root (assumes individual unit root process)_
242
Im, Pesaran and Shin W-stat -8.77519 0.0000 9 242
ADF - Fisher Chi-square 104.452 0.0000 9 242
PP - Fisher Chi-square 119.730 0.0000 9 242
Source: author's calculations
Thus, the following ARDL models were estimated:
pi P2
p3
p4
A ln HDIt =Ci+^ aHA ln HDIt-i + ^ a2lA ln GDPt-i + ^ a3lA ln FDIt-i + ^ a4lA ln MYSt-i
i=1 i=0 i=0 i=0 p5 P6
+ ^ a5iATRt-i + ^ a6iALFt-i ln HDIt-1 ln GDPt-1 +X3 ln FDIt-1+X4 ln MYSt-1 +
+
i=0
+X5TRt_1+X6LFt_1+st
q1
q2
t=1990, 2021
q3
(3)
q4
A ln GDPT =C2+
^ ß1j A ln GDPT.j + ^ ß2jA ln HDIMT.j + ^ ß3jAYEMPT.j + ^ ß
j=1 j=0 j=0 j=0
^4jASSEx-j +
+S1 ln GDPT-1 +ô2 lnHDIMT-1 +ô3YEMPT.1+ô4SSET.1+uT T=1991, 2020
(4)
9
where a1-6 i and P1-4 ■ are the short-term
coefficients, ^1-6 and S1-4 are the long-term
coefficients, p1-6 and q1-4 are the number of lags of
relevant variables, C1 and C2 are the free members of the corresponding models, and s and u are the error terms of the corresponding models.
The first parts of the equations with a and P represent short-run dynamics of the models, and the second parts with A and S represent long-run relationships. The null hypothesis here is non-existence of long-run relationship.
Estimation of model (3). The optimal number of lags for each variable in the equation was determined by applying Akaike's information criterion. F-Bounds test was performed in order to check the cointegration between the selected variables. The results (Table 3a) of this test indicate that the value of the F statistic is greater than the critical value of I(1) at all levels of significance. This means that we have to reject the null hypothesis and accept the alternative hypothesis, which means that there is cointegration in the model, or, in other words, there is long-term dependence between the variables.
Table 3. Results of tests for cointegration in models
a b
F-Bounds Test Null Hypothesis: No levels relationshi F-Bounds Test Null Hypothesis: No levels relationship
Test Statistic Value Signif. I(0) I(1) Test Statistic Value Signif. I(0) I(1)
Asymptotic: n=1000 Asymptotic: n=1000
F-statistic 11.31027 10% 2.08 3 F-statistic 7.136588 10% 2.37 3.2
k 5 5% 2.39 3.38 k 3 5% 2.79 3.67
2.5% 2.7 3.73 2.5% 3.15 4.08
1% 3.06 4.15 1% 3.65 4.66
Source: author's calculations
After confirming the existence of cointegration, the error correction model (ECM) was estimated, which has the following form:
Pl P2
P3
P4
A ln HDIt =Cj+^ aHA ln HDIt.1 + ^ a2lA ln GDPt.1 + ^ a3lA ln FDIt.1 + ^ a4lA ln MYSt-i +
i=1 1=0 1=0 1=0 P5 P6
+ ^ a51ATRt.1+ ^ a61ALFt.1 +ro1ECTt-1+8t t=1990, 2021 (5)
1=0
where ECTt-1 is the error correction term obtained from the residuals of the estimated model, and is the speed of adjustment. The results of this model estimation show that the error correction term is
significant and negative (-0.679), which means that 68% of the deviation from the long-run equilibrium is adjusted within a year. Results of long-run dynamics are shown in Table 4a.
Table 4. Results of the long-run dynamics of the models
b
ARDL Long Run Form and Bounds Test Dependent Variable: DLOG(HDI) Selected Model: ARDL(1, 1, 2, 0, 0, 2) Sample: 1990 2021 Included observations: 26
Levels Equation Case 2: Restricted Constant and No Trend
Variable
Coeff1c1ent Std. Error t-Statistic Prob.
LOG(GDP) LOG(FDI) LOG(MYS) TR LF C
0.092767 0.012349 0.325344 0.000342 0.001049 -2.262405
0.005742 0.002260 0.134621 0.000126 0.001061 0.227061
16.15694 5.464256 2.416745 2.716517 0.988570 -9.963863
0.0000 0.0001 0.0299 0.0167 0.3397 0.0000
EC = LOG(HDI) - (0.0928*L0G(GDP) + 0.0123*L0G(FDI) + 0.3253*L0G(MYS) + 0.0003*TR + 0.0010*LF -2.2624)
ARDL Long Run Form and Bounds Test Dependent Variable: DLOG(GDP) Selected Model: ARDL(1, 0, 1, 1) Sample: 1991 2020 Included observations: 18
Levels Equation Case 2: Restricted Constant and No Trend
Variable
Coefficient Std. Error t-Statistic Prob.
LOG(HDIM) YEMP SSE C
14.87784 0.061265 0.016820 9.956119
1.144539 0.018775 0.006037 0.734731
12.99898 0.0000
3.263157 0.0076
2.786289 0.0177
13.55070 0.0000
EC = LOG(GDP) - (14.8778*LOG(HDIM) + 0.0613*YEMP + 0.0168*SSE + 9.9561)
Source: author's calculations
This study uses the Correlogram Q-statistics to check autocorrelation, Serial correlation LM test to check the serial correlation and Breusch-Pagan-Godfrey test to check heteroscedasticity in the
residuals. According to the results (Table 5) of the tests we have to accept the null hypotheses, which means that there is no autocorrelation, no serial correlation and the residuals are homoscedastic.
a
_Table 5. Residual diagnostics for model (3)_
Sample: 1990 2021 Included observations: 26
Q-statistic probabilities adjusted for 1 dynamic regressor
Autocorrelation Partial Correlation AC PAC Q-Stat Prob.
i : i i : i
11= i 1 i
11= i =i i i i i [ i □ i i
i c i i i= i
i : i ii= i i [ i ] i i
i [ i i L i
i c i □ i i i ] i : i
i -0.102 -0.102 0.3007 0.583
2 -0.238 -0.251 2.0133 0.365
3 -0.276 -0.357 4.4233 0.219
4 0.293 0.156 7.2638 0.123
5 0.017 -0.084 7.2737 0.201
6 -0.193 -0.228 8.6255 0.196
7 -0.304 -0.320 12.178 0.095
8 0.315 0.087 16.197 0.040
9 0.105 -0.098 16.671 0.054
10 -0.065 -0.129 16.864 0.077
ii -0.115 0.084 17.504 0.094
12 0.198 0.100 19.546 0.076
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.890557 Prob. F(2,12) 0.4359
Obs*R-squared 3.360321 Prob. Chi-Square(2) 0.1863
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 1.678865 Prob. F(11,14) 0.1792
Obs*R-squared 14.78879 Prob. Chi-Square(11) 0.1924
Scaled explained SS 4.348454 Prob. Chi-Square(11) 0.9586
Source: author's calculations
As the obtained results show, the labor force participation rate among the population aged 15-64, contrary to our expectations, has a positive but not significant effect on human development in the long-run. A 1% increase in GDP per capita leads to a 0.1% increase, and a 1% increase in foreign direct investment leads to a 0.01% increase in human development index. A 1 percentage point increase in the share of imports and exports in GDP leads to a 0.035% increase, and a 1% increase in the duration
of education leads to more than a 0.3% increase in human development index.
The CUSUM and CUSUM2 were conducted to check the model's stability. The diagnostic test confirmed that the adopted model is well established and that the calculated results are reliable for policy implications, with the blue lines of both CUSUM and CUSUM2 lying between the critical boundaries at the 5% level of significance, as shown in Figures 1 and 2, respectively, which confirms the accuracy of the model and the long-run parameters.
Figure 1. The plot of cumulative sum of the recursive residual at 5% significance
06 07 08 09 10 11 12 13 14 15 16 17 18 19
CUSUM -----5% Significance
Figure 2. The plot of cumulative sum of the square of the recursive residual at 5% significance
06 07 08 09 10 11 12 13 14 15 16 17 18 19
CUSUM of Squares -----5% Significance
Source: author's calculations
1.6
0.0
-0.4
Estimation of model (4). As in the case of the The presence of cointegration in the model was
previous model, here also the optimal number of confirmed by the F-Bounds Test (Table 3b). Then
lags for each variable in the equation was the error correction model was evaluated, which has
determined using the Akaike's information criterion. the following form:
q1 q2 qs % A ln GDPT =C2+ ^ PtjAln GDPT.j + ^ P2jA lnHDIMT.j + ^ P3jAYEMPT_j + ^ P4jASSET.j +
j=1 j=0 _j=0
+ro2ECTx-1+ux t=1991, 2020 (6)
The results of this model estimation show that within a year. Results of long-run dynamics are
the error correction term is significant and negative shown in Table 4b. Residual diagnostics in Table 6
(w2 = —0.742), which means that 74% of the confirms that there is no autocorrelation, no serial
deviation from the long-run equilibrium is adjusted correlation and the residuals are homoscedastic.
Table 6. Residual diagnostics for model (4)
Sample: 1991 2020 Included observations: 18
Q-statistic probabilities adjusted for 1 dynamic regressor
Autocorrelation Partial Correlation AC PAC Q-Stat Prob.
: i i : i i 0.115 0.115 0.2785 0.598
[ i i [ i 2 -0.052 -0.066 0.3395 0.844
] i i ] i 3 0.056 0.072 0.4157 0.937
c i i i= i 4 -0.190 -0.214 1.3477 0.853
1= i i c i 5 -0.232 -0.183 2.8438 0.724
c i i c i 6 -0.122 -0.116 3.2867 0.772
: i i □ i 7 0.107 0.144 3.6641 0.818
] i i i i 8 0.069 0.027 3.8367 0.872
L i 1 c i 9 -0.119 -0.195 4.4033 0.883
1= i ll— i 10 -0.257 -0.395 7.3775 0.689
□ i 1 =i i 11 0.195 0.292 9.3383 0.591
1= i 1 1= i 12 -0.229 -0.295 12.475 0.408
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.158427 Prob. F(2,9) 0.8558
Obs*R-squared 0.612156 Prob. Chi-Square(2) 0.7363
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 1.028135 Prob. F(6,11) 0.4566
Obs*R-squared 6.467459 Prob. Chi-Square(6) 0.3729
Scaled explained SS 1.351739 Prob. Chi-Square(6) 0.9687
Source: author's calculations
As the results in Table 4b show, a 1% increase in the modified human development index (ie, health and education gains) leads to a 14.9% increase in GDP per capita. A one percentage point
increase in the youth employment rate leads to a 6.3% increase, and a unit increase in the secondary school enrollment rate leads to a 1.7% increase in GDP per capita.
Figure 3. The plot of cumulative sum of the recursive residual at 5% significance
-CUSUM -----5% Significance
Source: author
CUSUM and CUSUM2 diagnostic tests, as shown in Figures 3 and 4, respectively, confirmed the accuracy of the model and the long-run parameters.
Conclusions and Recommendations.
Summarizing the evaluation results of the two models, we can state that economic growth and human development in Armenia really have a mutually positive effect. Moreover, the effect of human development on economic growth is greater than the opposite effect, which means that improvements in health and education have a more intensive effect on income growth than income growth on improvements in health and education.
FDI provides society with capital and advanced technologies, creates a favorable tax environment, facilitates the exchange of resources, and thus promotes human development. Therefore, the government should improve the tax environment in the country, as well as achieve peace with the neighboring countries quickly and under the most favorable conditions for us, which will be a great incentive for increasing the flow of investments. Trade also has a positive impact on human development, so the government should develop trade ties with partner countries, as well as create new contact edges with potential partner countries. The adult literacy rate (expressed as the average duration of education) has a positive and significant impact on human development, so the government should increase the share of expenditure on education in GDP, creating favorable conditions that ensure the continuity of learning both in high schools and universities. This can be expressed by increasing the number of free places in universities, more flexible mechanisms for compensation of tuition fees, increasing the quality and quantity of educational hostels, etc.
The results of the second model indicate that young people have a great role in economic growth,
Figure 4. The plot of cumulative sum of the square of the recursive residual at 5% significance
CUSUM of Squares 5% Significance
's calculations
so in order to promote it, the government and private companies should create favorable conditions to increase the employment rate of young people. The universities also play a big role here, which, by improving the environment of cooperation in the public and private sectors, they will provide their graduates with professional and highly paid work. Enrollment in secondary education also has a positive effect on economic growth, so the government should make it possible for all people of the appropriate age to attend school. The results of the households' integrated living conditions survey in 2020 showed that the main reason for not attending school in the case of the 6 and older age group, and in the case of persons aged 16-20 not continuing their education, was that they simply don't want to do it (they studied as much as they wanted) [13, p. 110].
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