Econometrics-Term paper The relationship between GDP, labor force and health expenditure in European countries Student: Nguyen Thu Ha
Contents 1. Background:... 2 2. Discussion:... 2 3. Regression equation and data:... 3 4. Panel data analysis:... 4 4.1 Pooled OLS with dummy variables:... 4 4.2 Fixed effect model:... 5 4.3 Random effect model:... 6 4.4 Comparison:... 6 5. Implications:... 7 References:... 8 1
1. Background: In recent years, considerable attention has focused on aggregate health care spending. The government is responsible for the education, health and social development of the people. Labor, along with capital and the technological level, is considered a major factor in the models of economic growth. The labour force participation rate is the measure to evaluate working-age population in an economy, refers to the total number of people or individuals who are currently employed or in search of a job. This is an important metric when the economy is not growing or is in the phase of recession. Health expenditures are defined on the basis of their purpose of improving health, regardless of the primary function or activity of the entity providing or paying for the associated health services. Health expenditure consists of all expenditures or outlays for medical care, prevention, promotion, rehabilitation, community health activities, health administration and regulation and capital formation with the predominant objective of improving health. Health-related expenditures also include expenditures on health-related functions such as medical education and training, and research and development. 2. Discussion: This paper analyzes the effect of labor force and health expenditure on the economy in 15 European countries. The labor force participation rate is an economic statistic regarding the supply and demand of labor in the overall economy and is one component of many used in the evaluation of overall economic growth and contraction. The share of the health expenditures & GDP in developed countries is often more than developing countries, therefore as the level of development increases health expenditures increase too. It is believed that there are positive correlation between labor force, health expenditure and GDP of those 15 countries. 2
3. Regression equation and data: gdp = β0 + β1 health + β2 labor + u In which: gdp: GDP per capita (current US$): GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. health: Health expenditure per capita (current US$): Total health expenditure is the sum of public and private health expenditures as a ratio of total population. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation. Data are in current U.S. dollars. labor: Labor force participation rate, total (% of total population ages 15+) (national estimate): Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Data: The data used in this paper was taken from Worldbank Databank, of 15 European countries from 2005 to 2014. Pooled OLS, random effect model and fixed effect model will be used for panel data analysis. 3
4. Panel data analysis: 4.1 Pooled OLS with dummy variables: The dummy variable g1 is set to 1 for year 2005 and zero for other years. The variables g2 to g10 are coded in the same way for each year.. reg gdp health labor g1 g2 g3 g4 g5 g6 g7 g8 g9 g10 note: g1 omitted because of collinearity Source SS df MS Number of obs = 150 F(11, 138) = 380.84 Model 8.7711e+10 11 7.9737e+09 Prob > F = 0.0000 Residual 2.8894e+09 138 20937369.4 R-squared = 0.9681 Adj R-squared = 0.9656 Total 9.0600e+10 149 608052940 Root MSE = 4575.7 gdp Coef. Std. Err. t P> t [95% Conf. Interval] health 9.308721.171865 54.16 0.000 8.968892 9.64855 labor 100.9415 60.00434 1.68 0.095-17.70527 219.5883 g1 0 (omitted) g2 832.5201 1670.979 0.50 0.619-2471.514 4136.554 g3 1932.877 1673.117 1.16 0.250-1375.383 5241.137 g4 1958.54 1677.209 1.17 0.245-1357.812 5274.891 g5-1739.127 1675.714-1.04 0.301-5052.523 1574.269 g6-944.7499 1676.944-0.56 0.574-4260.577 2371.077 g7-848.7546 1684.682-0.50 0.615-4179.882 2482.373 g8-1351.803 1681.342-0.80 0.423-4676.326 1972.719 g9-1693.359 1685.051-1.00 0.317-5025.217 1638.499 g10-1644.498 1686.833-0.97 0.331-4979.878 1690.882 _cons -1271.374 3621.65-0.35 0.726-8432.476 5889.728 This model accounts for 96% of the total variance, in which the health variable is significant and the labor variable is not statistically significant, which means labor force has no effect on GDP in these 15 countries. 4
0 0 20000 40000 60000 80000 gdp 20000 40000 60000 80000 gdp 100000 100000 0 2000 4000 6000 8000 10000 health 40 50 60 70 80 labor 4.2 Fixed effect model:. xtreg gdp health labor, fe Fixed-effects (within) regression Number of obs = 150 Group variable: countrynum Number of groups = 15 R-sq: Obs per group: within = 0.8534 min = 10 between = 0.9615 avg = 10.0 overall = 0.9552 max = 10 F(2,133) = 387.26 corr(u_i, Xb) = 0.5964 Prob > F = 0.0000 gdp Coef. Std. Err. t P> t [95% Conf. Interval] health 7.298282.2623669 27.82 0.000 6.779331 7.817234 labor 425.9193 166.702 2.55 0.012 96.1892 755.6495 _cons -14226.03 10288.78-1.38 0.169-34576.84 6124.78 sigma_u 6178.9229 sigma_e 2155.737 rho.89148717 (fraction of variance due to u_i) F test that all u_i=0: F(14, 133) = 39.25 Prob > F = 0.0000 This model accounts for 95% of the variance with significant variables: Gdp = -14226.03 + 7.29health + 425.91labor Health and labor had significant positive weight, indicating that countries with higher health expenditure and labor force participant rate are expected to have higher GDP. 5
4.3 Random effect model:. xtreg gdp health labor, re Random-effects GLS regression Number of obs = 150 Group variable: countrynum Number of groups = 15 R-sq: Obs per group: within = 0.8534 min = 10 between = 0.9632 avg = 10.0 overall = 0.9567 max = 10 Wald chi2(2) = 1125.77 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 gdp Coef. Std. Err. z P> z [95% Conf. Interval] health 7.704833.2357385 32.68 0.000 7.242794 8.166872 labor 419.4923 118.5161 3.54 0.000 187.205 651.7797 _cons -15280.53 7242.038-2.11 0.035-29474.66-1086.397 sigma_u 4254.2691 sigma_e 2155.737 rho.79569153 (fraction of variance due to u_i) This model accounts for 95% of the variance with significant variables: Gdp = -15280.53 + 7.70health + 419.49labor Health and labor had significant positive weight, indicating that countries with higher health expenditure and labor force participant rate are expected to have higher GDP. 4.4 Comparison: - Breusch and Pagan test to compare pooled OLS and random effect model:. xttest0 Breusch and Pagan Lagrangian multiplier test for random effects gdp[countrynum,t] = Xb + u[countrynum] + e[countrynum,t] Estimated results: Var sd = sqrt(var) gdp 6.08e+08 24658.73 e 4647202 2155.737 u 1.81e+07 4254.269 Test: Var(u) = 0 chibar2(01) = 313.67 Prob > chibar2 = 0.0000 6
Reject null hypothesis, OLS method is not appropriate. - Hausman test to compare fixed effect and random effect model:. hausman fe re Coefficients (b) (B) (b-b) sqrt(diag(v_b-v_b)) fe re Difference S.E. health 7.298282 7.704833 -.4065505.1151683 labor 425.9193 419.4923 6.427011 117.2326 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(2) = (b-b)'[(v_b-v_b)^(-1)](b-b) = 21.94 Prob>chi2 = 0.0000 Reject H0, fixed effect model is preferred over random effect model. 5. Implications: Gdp= -14226.03 + 7.2982health + 425.9193labor There is a significant positive correlation between health expenditure and health on gdp. This result can be explained by the benefits of health spending on the economy. Increased health care spending improves and increases access to new technologies, providing both new options of treatment and treatment for a greater number of individuals, which lead to better health and higher labor productivity. Health care spending growth also creates health care jobs, raises incomes for health care workers and increases demand for related goods and services. Beside health expenditures, labor force participation rate also has a positive correlation with GDP. When working-age people are entering the labor force at a faster rate than those exiting, the labor force participation rate increases. As a result, economic productivity and output rises, causing an increase in the gross domestic product. 7
References: 1. Worldbank Databank 2. Miniar Ben Ammar Sghari, Sami Hammami (2013) Relationship between Health Expenditure and GDP in developed countries, OSR Journal Of Pharmacy. 3. Mohsen Mehrara et al (2012) The Relationship between Health Expenditures and Economic growth in Middle East & North Africa (MENA) Countries. 4. Victor R. Fuchs (2013) The Gross Domestic Product and Health Care Spending. 5. Muhammad Aamir Ali, Muhammad Ismat Ullah, Muhammad Afzal Asghar (2017) Effect of Health Expenditure on GDP, a Panel Study Based on Pakistan, China, India and Bangladesh. 8