# Is health care a necessary or luxury product for Asian countries? An answer using panel approach

- S. M. Abdullah
^{1}Email author, - Salina Siddiqua†
^{2}and - Rumana Huque†
^{1}

**Received: **12 September 2016

**Accepted: **6 January 2017

**Published: **25 January 2017

## Abstract

A number of studies have estimated the income elasticity of health care expenditure to identify whether health care is a necessary or luxury product. However, the issue has received less attention in developing countries, especially in Asian economies. The current study for the first time has used the panel data covering 36 Asian countries for the period 1995–2013 for revealing the nature of health care as a product. Along with conventional econometric techniques we have addressed the issue of cross section dependence and used Westerlund (2007) panel cointegration test which is robust against cross section dependence and heterogeneity for detecting the presence of panel cointegration. By applying Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) it was found that the long run elasticity of Health Care Expenditure (HCE) with Gross Domestic Product (GDP) is less than unit implying that the health care can be regarded as necessary in nature for these countries.

## Keywords

## JEL Classification

## Background

Macro level health spending has significant beneficial effects on health outcomes [1–3]. Data from 47 African countries [4] and 133 low and middle income countries [1] showed that increased health spending led to reduced infant and under-5 child mortality rates. Hence, the share of health expenditure, the determinants of resources a country devotes to medical care, and the relationship between national income and national health care expenditures have drawn attention of health economists worldwide. A large number of studies have estimated the income elasticity of health care expenditure to identify whether health care is a necessary or luxury product, and found varying results. While income elasticity coefficient of health care expenditure was found unity in 21 Organization of Economic Cooperation and Development (OECD) countries reflecting health care not a luxury [5], it was found less than unity in 13 MENA (Middle East and North Africa) countries characterizing health care as a necessary product [6]. However, majority of these studies have focused mainly on developed countries, including OECD countries [5, 7–9], United States [10], Middle East and African countries [6, 11], and European Countries [12, 13].^{1} The issue has been discussed little in developing countries, especially in Asian economies. In recent years only Hassan et. al [14] analysed this relation using South Asian Association for Regional Cooperation (SAARC) countries’ experience. One major limitation of many of the previous studies is that of using a single year of data to obtain cross-country estimates of the natural correlation [11, 15–17], making the regression results spurious. More recently, researchers have used panel data on Health Care Expenditure (HCE) and Gross Domestic Product (GDP) measured across countries and across time [18–23], which offers a number of advantages over cross-sectional study. Using multiple years of data enables researchers to include country-specific fixed effects for each country, thereby controlling for a wide range of time-invariant country characteristics, This avoids potential bias for the estimated relationship between HCE and GDP while retaining the cross sectional dependence and panel heterogeneity as important issues. In addition, the stationarity of the concerned variables are of vital importance when dealing with long panel. Considering these drawbacks of earlier studies, this study examined the impact of income on health care expenditure at macro level using a long panel of 36 Asian countries^{2} for the period 1995 to 2013 while addressing the issue of heterogeneity and cross sectional dependence.

The paper is organized in the following sections: section 2 describes the source of data and methodological procedures applied in this paper. In Section 3, results of econometric estimations have been discussed and section 4 concludes.

## Methods

where, i = 1, 2, −----- N and t = 1, 2, −----- T indexes cross section and time series units respectively. *lnHCE*
_{
i,t
} is the natural logarithm of health care expenditure (measured in current US $) for country i at time t and *lnGDP*
_{
i,t
} is the natural logarithm of GDP (measured in current US $) and used as proxy of income. *HSI*
_{
i,t
} stands for health status improvement of country i at time t which is proxied by using infant mortality and life expectancy for all countries. Finally, *ε*
_{
i,t
} is the error term with all unobserved factors.

*β*

_{1}is the coefficient which measures the impact of income on health care expenditure. As the higher the income the higher is the health care expenditure, this coefficient is expected to carry a positive sign. However, its magnitude determines whether health care is a necessary or luxury product. More specifically, as the standard theory of microeconomics suggests if,

### Data and statistical software

We have used two secondary data sources to develop the panel data set, namely World Development Indicators (WDI) and Health Nutrition and Population Statistics (HNPS) from World Bank database. A total of 36 Asian countries have been studied for the period of 1995 to 2013. The variables that we have used are, Health Care Expenditure, Gross Domestic Product and Health Status Improvement measured through Infant Mortality (IM) and Life Expectancy (LE). We used EViews 9 and STATA 14 softwares to carry out the econometric analysis.

### Cross section dependence test

In order to test the above hypothesis, we applied four different tests namely Breuch – Pagan Lagrange Multiplier (LM) [24], Pesaran Cross Sectional Dependence (CD), Pesaran Scaled LM [25] and Baltagi, Feng and Kao Bias Corrected Sclaed LM [26]. However, Pesaran CD is regarded as the most general one as it is suitable for stationary and as well as non – stationary panels. It also consists of reasonable small sample properties.

### Panel unit root test

then the appropriate null hypothesis for testing the panel unit root would be *H*
_{0} : *α*
_{
i
} = 0, *for all i*. In the above regression *y*
_{
it
} denotes the variable of concern for which stationary property would be tested and *x*
_{
i,t
} stands for other control variables.

### Panel cointegration test

Several panel cointegration tests are available, including Pedroni [31, 32], McCoskey and Kao [33], Kao [34] and Westerlund [35]. We employed Pedroni [31, 32] and Westerlund [35] panel cointegration test for detecting the existence of cointegrating relationship among the variables. The reason is that the first one allows for heterogeneity while the second one is robust against heterogeneity and cross correlation in panel.

### Pedroni panel coinetgration test

In particular panel statistic is concerned with homogeneous alternative while group statistics is concerned with the other. However, all these statistics are distributed as asymptotically normal.

### Westerlund panel cointegration test

*“no cointegration”*. It occurs due to the failure of

*“common – factor restriction”*(Banerjee et. al [36]). By depending on structural dynamics (rather than error dynamics) Westrelund [35] have developed a set of panel cointegration test which do not require any common factor restriction. These tests are general enough to be robust against heterogeneity and cross section dependence. The cointegration test assumes the following data generating process:

Where, i = 1, 2, −----- N and t = 1, 2, −----- T indexes cross sectional units and time series. In the above process, *x* is a vector of independent variables that includes GDP and HSI measured in terms of infant mortality and life expectancy and finally *d*
_{
t
} contains deterministic components. Here, *α*
_{
i
} is referred to as error correction parameter. If *α*
_{
i
} < 0 then there will be error correction and hence cointegration while if *α*
_{
i
} = 0, reflects that the error correction is absent and consequently there is no cointegration.

### Estimation of cointegrating relationship

In our data GDP and HSI i.e. infant mortality and life expectancy can become endogenous and also the error terms can be serially correlated which would result in the dependence of OLS estimators on nuisance parameters. In order to solve these problems two estimators namely FMOLS and DOLS can be introduced. Phillips and Hansen [37] proposed a semi parametric correction for the problem of long run correlation among cointegrating equation and stochastic regressors innovations resulting in FMOLS estimators. It is asymptotically unbiased. On the other hand Saikkonen [38] and Stock and Watson [39] advanced an asymptotically efficient estimator which eliminates the feedback in the cointegrating system by augmenting the cointegrating regression with lags and leads of independent variables. The resulting estimator is known as DOLS estimator.

*HCE*

_{ i,t }is the health care expenditure (an I(1) process),

*β*is (

*2**1) vector of parameters,

*α*

_{ i }are intercepts and

*u*

_{ i,t }are the stationary disturbance terms. Here

*x*

_{ i,t }are assumed to be (

*2**1) vector of independent variables (GDP and HSI measured with infant mortality and life expectancy) which are I(1) for all cross section units. It is assumed that it follows an autoregressive process of following form:

*w*

_{ i,t }= (

*u*

_{ i,t },

*ε*

_{ i,t }) ~

*I*(0) the variables are said to be cointegrated for each members of the panel with cointegrating vector

*β*. The asymptotic distribution of the OLS estimator is condition to the long run covariance matrix of the innovation vector. The FMOLS estimator is derived by making endogeneity correction (by modifying variable

*HCE*

_{ i,t }) and serial correlation correction (by modifying long run covariance of innovation vector,

*w*

_{ i,t }). The resulting final estimator is expressed as follows:

## Results

With a view to determining the appropriate estimation method, we need to check the stationary of the variables and also their order of integration. However, cross sectional dependence or cross sectional correlation of the variables is a fact that should be detected for the variables to decide which panel unit root test would be applied.

Test Results for Cross Sectional Dependence of the Variables

Variables and Test Names | Breusch - Pagan LM | Pesaran - Scaled LM | Bias Corrected Scaled LM | Pesarn CD | |
---|---|---|---|---|---|

| |||||

GDP (Current US $) | Statistic | 10994.69 | 291.99 | 290.99 | 104.58 |

Prob. | 0.000 | 0.000 | 0.000 | 0.000 | |

Health Care Expenditure (Current US $) | Statistic | 10541.35 | 279.22 | 278.22 | 102.40 |

Prob. | 0.000 | 0.000 | 0.000 | 0.000 | |

Infant Mortality | Statistic | 10563.79 | 279.85 | 278.85 | 100.50 |

Prob. | 0.000 | 0.000 | 0.000 | 0.000 | |

Life Expectancy | Statistic | 11418.19 | 303.92 | 302.92 | 106.79 |

Prob. | 0.000 | 0.000 | 0.000 | 0.000 |

Therefore, we have used IPS panel unit root test to detect the stationarity of the variables along with some other tests e.g. Levin, Lin and Chu (LLC) test (Levin et al., [29]), ADF - Fisher Test and PP – Fisher test (Choi, [30]).

*“Panels Contain Individual Unit Root”*except LLC that tests the null hypothesis of

*“Panel Contains Common Unit Root”*. The tests have been carried out with two different test regression specifications; one with constant and the other with constant and trend. It is evident from the test results that GDP and HCE are difference stationary i.e. I(1) variable according to all tests. With regards to infant mortality, it is also found to be difference stationary in intercept and trend specification under IPS, ADF – Fisher and PP – Fisher test. Thus it can be treated as an I(1) variable. Life expectancy was found to be difference stationary in intercept specification under IPS, in intercept and trend specification under LLC and in both specification under ADF - Fisher test. Since all the variables have been found to be integrated of a unique order we have identified the long run relationship among them by establishing the panel cointegration.

Panel Unit Root Test Results of the Variables

Variables | Im – Pesaran – Shin (IPS) Test for Panel Unit Root | |||
---|---|---|---|---|

| ||||

Intercept | Intercept and Trend | |||

IPS W – Stat | Prob. | IPS W - Stat | Prob. | |

GDP (Current US $) | 20.06 | 1.000 | 6.15 | 1.000 |

D(GDP) | −4.79 | 0.000 | −12.43 | 0.000 |

Health Care Expenditure (Current US $) | 21.23 | 1.000 | 6.07 | 1.000 |

D(Health Care Expenditure) | −3.70 | 0.000 | −12.61 | 0.000 |

Infant Mortality | −20.81 | 0.000 | 0.36 | 0.642 |

D(Infant Mortality) | −2.18 | 0.014 | −5.04 | 0.000 |

Life Expectancy | −1.62 | 0.052 | −10.72 | 0.000 |

D(Life Expectancy) | −15.08 | 0.000 | −20.25 | 0.000 |

In order to check the existence of cointegration among the variables along with Pedroni [31, 32] Engle Granger based panel cointegration test, we applied Westerlund [35] error correction based panel cointegration test. The later one is already established in the literature for its robustness against panel with heterogeneity and cross sectional dependence. Hence, application of this test allowed us to check issue of existence of cointegartion among health care expenditure and income while controlling for health status improvement measured with infant mortality and life expectancy in a more comprehensive manner. Both the tests have been performed with three different deterministic specifications.

*“no cointegration”*was rejected by all 11 statistic under Pedroni [31, 32] test. All the 4 statistic of Westerlund [35] test have also found to be significant. In the same specification when cointegration was checked among health care expenditure, income and life expectancy a total of 7 statistic in Pedroni [31, 32] and 2 among 4 statistic of Westerlund [35] was found to be statistically significant. When the deterministic specification was changed to allow the presence of constant, only 7 and 6 of 11 statistic for infant mortality and life expectancy respectively have found to be able to reject the non existence of cointegration under Pedroni [31, 32] test. By using the similar deterministic specification Westerlund [35] test was able to reject the null for both variables under 2 statistic out of 4. The conclusion of Westerlund [35] test has remained the same when the deterministic specification allows for both the constant and trend. While Pedroni [31, 32] test was carried out by using the later deterministic specification, 4 statistic have found to be significant when panel cointegration was checked with infant mortality while the number was 6 if it was tested with life expectancy. Thus, it can be argued that there might exists a long run cointegrating relationship among health expenditure, income and health status improvement which is substituted by using infant mortality and life expectancy.

Westerlund Panel Cointegration Test

Westerlund (2007) ECM Panel Cointegration Test, | ||||
---|---|---|---|---|

HCE, GDP and IM | HCE, GDP and LE | |||

Statistic | Stat. | Prob. | Stat. | Prob. |

Deterministic Specification: No Constant & Trend | ||||

Gt | −2.702 | 0.000 | −2.788 | 0.000 |

Ga | −7.807 | 0.015 | −6.778 | 0.148 |

Pt | −16.120 | 0.000 | −12.352 | 0.000 |

Pa | −11.134 | 0.000 | −2.579 | 0.461 |

Deterministic Specification: Constant Only | ||||

Gt | −2.912 | 0.000 | −3.074 | 0.000 |

Ga | −7.555 | 0.934 | −6.475 | 0.994 |

Pt | −18.933 | 0.000 | −18.24 | 0.000 |

Pa | −6.287 | 0.326 | −6.512 | 0.244 |

Deterministic Specification: Constant & Trend | ||||

Gt | −2.921 | 0.003 | −3.096 | 0.000 |

Ga | −5.376 | 1.000 | −4.293 | 1.000 |

Pt | −18.059 | 0.000 | −16.477 | 0.001 |

Pa | −5.223 | 1.000 | −5.665 | 1.000 |

With a view to estimate the cointegrating vector we have applied two different methods, FMOLS and DOLS. Each of the methods provides three different estimators namely, pooled, pooled weighted and grouped mean. The pooled FMOLS estimator is the extension of Phillips and Hansen [37] FMOLS estimator offered by Phillips and Moon [40] which provides the estimators after correcting deterministic components in regressand and regressors. In order to allow different long run variances across the cross section for heterogeneous panels, Pedroni [41] and Kao and Chiang [42] proposed pooled weighted FMOLS. Finally the grouped mean FMOLS estimator developed by Pedroni [41, 43] is derived by averaging the individual cross section FMOLS estimates. In contrast to FMOLS, augmentation of model with lags and leads of differenced regressand and regressors in DOLS helps it to overcome the problem of asymptotic endogeneity and serial correlation. Kao and Chiang [42], Mark and Sul [44, 45] and Pedroni [43] extended the standard DOLS estimation developed by Saikkonen [38] and Stock and Watson [39]. Kao and Chiang [42] proposed pooled DOLS where the augmented cointegrating regression allows the short run dynamics to be cross section specific. By allowing heterogenous long run variance, Mark and Sul [44, 45] developed pooled weighted DOLS. Finally, Pedroni [43] developed the grouped mean DOLS estimates by averaging the individual cross section DOLS estimates.

Estimation of Cointegrating Regression with Infant Mortality as proxy for Health Status Improvement

Variables | FMOLS | DOLS | ||||
---|---|---|---|---|---|---|

Pooled | Weighted | Grouped | Pooled | Weighted | Grouped | |

Cointegrating Regression | ||||||

Log of GDP | 0.832 | 0.730 | 0.816 | 0.742 | 0.807 | 0.676 |

(0.040) | (0.009) | (0.042) | (0.081) | (0.025) | (0.127) | |

Log of IM | −0.366 | −0.431 | −0.675 | −0.477 | −0.389 | −0.861 |

(0.087) | (0.001) | (0.190) | (0.136) | (0.054) | (0.758) | |

Wald Test, H | ||||||

t – Stat. | −4.166 | −28.637 | −4.355 | −3.155 | −7.666 | −2.534 |

| 17.357 | 820.080 | 18.973 | 9.957 | 58.782 | 5.719 |

Estimation of Cointegrating Regression with Life Expectancy as proxy for Health Status Improvement

Variables | FMOLS | DOLS | ||||
---|---|---|---|---|---|---|

Pooled | Weighted | Grouped | Pooled | Weighted | Grouped | |

Cointegrating Regression | ||||||

Log of GDP | 0.924 | 0.841 | 0.924 | 0.774 | 0.917 | 0.831 |

(0.033) | (0.009) | (0.031) | (0.085) | (0.027) | (0.100) | |

Log of LE | 0.912 | 1.489 | 5.464 | 3.906 | 1.416 | 10.419 |

(0.629) | (0.001) | (1.459) | (1.496) | (0.673) | (3.530) | |

Wald Test, H | ||||||

t – Stat. | −2.20 | −16.091 | −2.436 | −2.632 | −3.023 | −1.674 |

| 4.883 | 258.944 | 5.938 | 6.930 | 9.140 | 2.805 |

Thus, unlike many OECD and developed countries such as USA, Canada, Germany and Italy where health care expenditure has been identified as luxury good, for Asian Countries it is revealed to be a necessary one. The findings contradict Hassan et. al [14] but in line with what have been found in Dreger and Reimers [5], Mehrara et. al [6] and Penas et. al [8].

## Discussion and conclusion

By exploiting data for the period of 1995 to 2013, the study finds that long run elasticity of health care expenditure in relation to income is less than unit in 36 Asian countries, ensuring that the health care can be treated as a necessary product as a whole for the sample countries. In general the responsiveness was found to be higher when life expectancy was used instead of infant mortality as proxy of health status improvement. Our finding is different from that of Hassan et. al [14] which suggested health care as luxury products for South Asian Association for Regional Cooperation (SAARC) countries. However, the latter study has ignored the issue of cross correlation which may mislead the findings. Moreover, the way the coefficients had been analyzed made the reasoning and policy implications rather weak.

The contribution of this paper to the literature on the relationship between health care expenditure and income is twofold. First, it has covered almost all the countries in Asia, and analyzed the issue in a more rigorous manner in the sense of addressing cross correlation and heterogeneity problem that potentially exists in the panel and brought the findings in front to realize how health care should be treated in those countries. Second, from methodological point of view, as the study has addressed the issue of cross sectional correlation and panel heterogeneity, the findings are more reliable. The current work has examined the existence of long run relationship between health care expenditure and income using a panel cointegration technique which is robust against cross sectional correlation and panel heterogeneity along with conventional panel cointegration test. The estimation technique- FMOLS and as well as DOLS- has been used which is robust against asymptotic endogeneity and serial correlation.

The study has a number of areas to improve. As cross section dependence was detected it would have been better if second generation panel unit root tests were used when identifying the integration order of the variables. However, demeaning of data has taken care of the severity of problem to some extent. Throughout the analysis the parameters have been assumed to be stable, thus should there be any structural instability the findings may vary. Further research is required addressing the above issues.

List of Countries: Bahrain, Bangladesh, Bhutan, Brunei, Cambodia, China, India, Indonesia, Iran, Israel, Japan, Jordan, Kazakhstan, Korea Rep., Kuwait, Kyrgyzstan, Laos, Lebanon, Malaysia, Mongolia, Maldives, Nepal, Oman, Pakistan, Philippines, Qatar, Saudi Arabia, Singapore, Srilanka, Tajikistan, Thailand, Turkmenistan, United Arab Emirates, Uzbekistan, Vietnam and Yemen

## Declarations

### Acknowledgement

Authors of the paper are greatly indebted to Professor Joakim Westerlund and Dr. Damiaan Persyn for sharing their codes to perform econometric exercise which made the journey of completion of the work easier.

### Authors’ contributions

All the authors in the current work have contributed uniformly. SMA developed the research problem formulated the model design and performed the econometric exercise. SS took the responsibility to do the survey of existing literature and finding the research gap and contributed to the result explanations. RH synthesized research gap with the methodology and have given effort to bring the issue into perspective and contributed to prepare the draft. All authors have read and approved the manuscript.

### Competing interests

The authors declare that they have no competing interests.

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## Authors’ Affiliations

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