Population health status and

economic growth in Chinese

provinces: some policy implications

Vasudeva N. R. Murthya and Albert A. Okunadeb,*aDepartment of Economics and Finance, Creighton University, Omaha, NE68178, USAbDepartment of Economics, Office 450BB (The FCoBE), The University ofMemphis, Memphis, TN 38152, USA

Using recent cross-sectional, public use, data set on 31 Chinese provinces, weempirically model the core determinants of life expectancy (population healthstatus) using the Ordinary Least Squares (OLS) method, instrumental variablesestimation and the relatively more efficient Hubert robust estimator. The empiri-cal regression model results and diagnostic tests indicate that the core determi-nants of life expectancy are the real GDP per capita, illiteracy rates and dailyvisits to physicians. Using results of the robust regression estimator (mimic theinstrumental variables model estimation), the statistically significant elasticitiesof life expectancy are 0.033 (t-ratio = 2.45) with respect to per capita real GDP,0.41 (t-ratio = 2.54) with respect to daily visits to the physicians and is 0.026(t-ratio = 2.26) with respect to the illiteracy rate. That is, income and daily visitsto physicians are positively linked to life expectancy while the illiteracy ratestifles life expectancy production. Our findings are consistent with receivedtheories. Policy implications are explored.

Keywords: maternal mortality rate; robust regression; economic growth; lifeexpectancy

JEL Classification: I12

I. Introduction

Life expectancy at birth is an important and a widely usedindicator of population health status throughout the world.The World Bank (2010) defines life expectancy at birth asthe number of years a new born infant would live ifprevailing patterns of mortality at the time of its birthwere to stay the same throughout its life. The link ofhealth human capital to income productivity is wellknown (Liu et al., 2008). Consequently, a large numberof countries in the past 50 years have attempted to raisepopulation life expectancy (see, Kabir, 2008) through

health care systems improvements, and most haveachieved considerable gains.Since 1979, China in particular has embarked on a

number of economic reforms aimed at reducing regionalinequities in population health. The nation has succeededin raising its average life expectancy of 35 years in 1949to 73 years in 2008 (World Bank, 2010) but large dis-parities persist in health status among and within itsprovinces, autonomous regions and municipalities(Fang et al., 2010; Xinming et al., 2010). For instance,in 2005, the mean life expectancy at birth in Shanghaiwas 80 years while Tibet had a mean life expectancy of

*Corresponding author. E-mail: aokunade@memphis.edu

Applied Economics Letters, 2014Vol. 21, No. 6, 377382, http://dx.doi.org/10.1080/13504851.2013.859369

2013 Taylor & Francis 377

67 years (Tang et al., 2008; Fang et al., 2010). Moreover,across Chinas regions that include 31 provinces, auton-omous regions and municipalities (hereafter, provinces),life expectancy gains for decades are uneven with theeastern and coastal regions witnessing remarkable pro-gress while the western and inland regions achievingconsiderably less gains. Throughout China, large ruralurban health inequities prevail (Chou and Wang, 2009)and the inequities vary across regions or provinces (Panand Liu, 2012). The New Cooperative Medical Scheme(NCMS), a public health insurance program for ruralChina, has recently increased preventive care and physi-cal examinations (Lei and Lin, 2009). Despite this,within rural China, the persistence of pro-rich inequitiesin health insurance coverage is largely due to the greatercapacities of the better-off villages to prevent the col-lapse of their insurance schemes (Wagstaff, 2005). Onthe other hand, the 2007 Urban Resident Basic MedicalInsurance (URBMI) has been more beneficial to theurban poor and those with chronic diseases or inpatienttreatments (Lin et al., 2009). The Chinese authorities,recognizing this problem of health inequities, chargedthe Ministry of Health to declare health as the cor-nerstone of comprehensive human developmentassur-ance of health equity is now regarded as the keyparameter for social justice and fairness in the country,accessibility of basic medical and health care services isa basic right of the people (Chen and Gao, 2008).Consequently, to reduce the large health status inequi-

ties, as measured by life expectancy at birth, knowledge ofthe key determinants of life expectancy is essential for theChinese policy-makers and the external observers of theChinese economy. Although there is a timely need for suchstudies, no rigorous econometric studies exist in the litera-ture on the determinants of life expectancy using theChinese provincial level data. A survey of the existingliterature of interest reveals a small number of papers onhealth inequity in general and life expectancy in Chineseprovinces (see, Congdon, 2007; Tang et al., 2008; Fanget al., 2010; Liu et al., 2010; Xinming et al., 2010). Amongthese, Liu et al. (2010), using 2006 mortality data extra-polated from the 1990 and 2000 census files and disabilitydata, studied regional variations in disability-free lifeexpectancy (DFLE) among older adults in China. Withthe use of a multiple regression model, they found a con-siderable degree of variation in DFLE at age 60 amongChinese provinces and per capita GDP, the proportion ofurbanization and access to health care to explain thesevariations. Fang et al., (2010) employed factor analysisand canonical correlation analysis to show that a highdegree of health disparity existed in Chinese provincesduring 2005. The reviewed studies are welcome contribu-tions to Chinese population health economics; however,they did not undertake a detailed and a robust econometricinvestigation.

Therefore, to extend the economic literature, this cur-rent study attempts to empirically identify some coredeterminants of life expectancy at birth in 31 Chineseprovinces using 2005 cross-sectional sample data set.The lack of availability of detailed long-run series on allthe relevant variables of interest hampers an analysisbased on panel unit root testing and cointegration. Therest of this work proceeds as follows. Section II integratesliterature review, theoretical model specification and thedata. Section III focuses on the empirical regressionmodelestimation results and Section IV concludes with implica-tions of the study findings for policy.

II. Model Specification and the Data

Consistent with the relevant economic theory, previousliterature reviewed and availability of the required datameasures, the following estimable double-log model isspecified:

LFEi 1 2INCOMEi 3DAILYVISITi 4ILLITERACYi i

(1)

where LFE, INCOME, DAILYVISIT and ILLITERACY arelogs of life expectancy at birth, per capita income, dailyvisits per doctor and illiteracy rates, respectively and icaptures the disturbance term associated with the model.The subscript i refer to cross-sectional units, the 31Chinese provinces, included in the study. Equation 1 isestimated using the Ordinary Least Squares (OLS)method. If outliers exist in the model, the comparativelymore efficient robust estimator, such as that proposed byHuber (see, e.g. Judge et al., 1985), is then used instead.Theoretically, a positive sign for 1, 2 and 3 and anegative sign for 4 is expected. That is, a rise in the percapita real income and number of visits per doctor areexpected to raise life expectancy, and a reduction in illit-eracy rate is expected to raise life expectancy.Equation 1 has the advantage of capturing any non-

linear relationship between the dependent and explanatoryvariables, and the regression coefficients themselves arethe elasticities of life expectancy with respect to eachdeterminant. We require the disturbance terms in themodel to be homoscedastic as the underlying sample iscross-sectional data. To test for constant residual variance,we conduct Whites heteroscedasticity and BrucePagantests. Since the explanatory variables are expected toorthogonal, we evaluate the statistical significance of theVariance Inflation Factors (VIF) for the independentvariables.The explanatory variables in the specified Equation 1

are chosen based on received economic theories and pastrelated studies (see, Kabir, 2008). The famous Preston

378 V. N. R. Murthy and A. A. Okunade

curve phenomenon in economics highlights the impor-tance of income in life expectancy production [see,Preston (1975); Pritchett and Summers (1996) andBloom and Canning (2007)]. Preston observed incometo be an important explanatory variable because improvedeconomic growth increases improvements in populationnutritional intake, wider access to clean drinking water,better sanitation, diverse public health programs, diseaseprevention and higher levels of investment in life.Moreover, Jones and Hall (2007) demonstrated theoreti-cally, and through simulations, for the US, incomeincreases diminish the marginal utility for nonhealthdemand, the marginal utility of life extension throughconsumption of health care commodities, with a prefer-ence for mortality risk reduction, may lead to a higherhealth spending share in real GDP. To quote (Jones andHall, 2007, p. 48), [T]he health share rises over time asincome grows if the marginal utility of consumption fallssufficiently rapidly to the joy of living an extra year andthe ability of health spending to generate that extra year.As a consequence, we expect a positive and significantincome elasticity of life expectancy in a rapidly develop-ing country, such as China.The economic literature confirms that literacy rate and

education play important roles in prolonging life expec-tancy (Lleras-Munley, 2005; Cutler and Lleras-Muney,2010). Education extends life expectancy throughmechanisms such as improved health behaviours and lifestyles. There is ample empirical evidence indicating thaton average the educated smoke less, exercise more, payincreased attention to diet and nutrition, consume lessalcohol and maintain a better lifework balance. In theUS, for 1960, an additional year of schooling raised lifeexpectancy at age 35 by 1.7 years (Lleras-Munley, 2005).The better educated population segment is more receptiveand responsive to medical information and more likely touse new health knowledge, medical technology tools andequipments (Link and Phelan, 1995). Consequently, weexpect the elasticity of life expectancy with respect toeducation or literacy rate, or the education gradient, tohave significantly positive effect on life expectancy atbirth.Finally, as the daily visits per physician rise, population

health status improves. The mechanism through whichthis improvement occurs include more frequent visits to

the physicians resulting in early disease diagnosis andprevention, health maintenance, increased use of prescrip-tion drugs, frequent follow-ups and screening and bettermonitoring of the health. These, taken together, wouldfinally lead to greater life expectancy. Therefore, the elas-ticity of life expectancy with respect to daily visits tophysicians is expected to be positive. This effect is highlyimportant in developing countries that have poor healthliteracy and high disease (e.g. tuberculosis, cholera,typhoid, polio, diarrhoea, etc.) incidence and prevalencerates.

III. Empirical Results

Table 1 contains summary statistics of the variables toimplement in our empirical model. The data are from thestatistical tables in Appendices AD of Fang et al. (2010),pp. 2324). The data on life expectancy at birth in years(LFE) were collected from China Provincial HealthStatistical Reports and the data on all other variableswere obtained from the China Health Statistics 2006(Ministry of Health, China) and the China Statistics2006 compiled by the State Statistical Bureau. (See, fordetails, Fang et al., 2010). Standard deviations of the lifeexpectancy and daily visits do not reflect a high degree ofdata variability. However, the JarqueBera normality testindicates that these data measures are normally distribu-ted. Standard deviations of the income and illiteracy vari-ables reveal a very high degree of variability and theJarqueBera normality test could not reject the normalityof distribution assumption for income and illiteracyvariables.Table 2 presents the empirical results of Equation 1, the

double-log model. The estimated model has a high degreeof explanatory power based on the adjusted R2 and itsstatistical significance as captured with the overall modelF statistic. Specifically, the three independent variablesjointly explain about 70% of the variation in life expec-tancy, which is a fairly good fit for the cross-sectional datamodel. The regression model parameter estimates of allthe explanatory variables have the theoretically expectedsigns. The standardized coefficients reveal income as themost important determinant, followed by illiteracy rateand daily visits to the doctors. The regression parameter

Table 1. Descriptive statistics of the data

Variable Mean Maximum Minimum SD JarqueBera (JB) test

Life expectancy 73.011 80.130 67.000 3.110 0.494Income 16098.030 51583.0 5222.0 1081.0 31.613Daily visit 5.132 9.500 2.600 1.810 3.789Illiteracy 12.371 44.840 3.920 8.195 69.795

Note: Authors calculation using the data sources (Op. cit.).

Health and economic growth in Chinese provinces 379

estimates for INCOME, DAILY VISIT and ILLITERACYare statistically significant at the 5% level. These theore-tically consistent results suggest that increases inINCOME and DAILY VISIT are important mechanismsfor raising population life expectancy at birth in China.As income grows, the population can afford and there-

fore, consume more and better health care commodities toachieve higher longevity as health care in the aggregate isa luxury good in most developing countries. Moreover,other things being equal, more frequent doctor visitsimprove the tracking and monitoring of health conditionsas well as compliance to clinical regimens. These activ-ities would eventually yield improved health status andraise longevity. The negative coefficient sign for the illit-eracy rate suggests that its reduction is a potentially potentstrategy for raising life expectancy. In other words,improved population literacy is a significant determinantof greater life expectancy because the more educated areincreasingly aware of the benefits of better health andmedical information. Moreover, the better educated aremore open to demand innovative medical care technolo-gies and related processes of care.Table 3 contains the results of the linear variant of

Equation 1. They are broadly in line with those arrayed

in Table 2, although the magnitude and statistical signifi-cance are higher. The sample data consisting of provincesof different sizes raises the possibility for outliers. As aconsequence, we applied the Huber robust M-estimator, amaximum likelihood estimator (see, for details, Judgeet al., 1985, pp. 8302), to the double-log Equation 1.The robust estimation1 results are in Table 4. Again, theempirical findings are consistent with those in Table 2 asto the magnitude and statistical significance of the regres-sion coefficients.Recently, a number of researchers have contended that

life expectancy affects economic growth positively; thatis, the higher the level of life expectancy, the greater thelevel of economic growth or real per capita income (see,Acemoglu and Johnson, 2007). Therefore, there might bea possible simultaneity problem between the dependentvariable LLFE and INCOME as an explanatory variable.In order to control for this tendency, the Two-Stage LeastSquares (2SLS) method is used to estimate Equation 1.The instruments are per capita health care expenditure andthe access to drinking water. The 2SLS regression estima-tion results are reported in Table 5. We further performedthe Hausman specification test of endogeneity of theinstruments and the results indicate that the explanatory

Table 2. Regression results of the double-log model

Variable Regression coefficient t-Statistics p-Value Beta coefficient

INCOME 0.034 2.65**a 0.013 0.426DAILY VISIT 0.039 2.55** 0.017 0.309ILLITERACY 0.026 2.41** 0.023 0.342CONSTANT 3.967 30.79* 0.000

Notes: Adj. R2 = 0.70; FA = 24.564* (p-value = 0.000); nR2 = 7.417 (p-value, 2 = 0.594); Mean VIF = 2.037.

a* and ** denote statistical significance at the 1% and 5% levels, respectively.

Table 3. Regression results of the linear model

Variable Regression coefficient t-Statistics p-Value Standardized beta coefficient

INCOME 0.0001 4.915*a 0.000 0.519DAILY VISIT 0.369 2.009** 0.079 0.215ILLITERACY 0.131 5.401* 0.004 0.345CONSTANT 3.967 30.79* 0.000

Notes: Adj. R2 = 0.72; FA = 27.02* (p-value = 0.000); nR2 = 5.653 (p-value, 2 = 0.774); Mean VIF = 1.54.

a* and ** denote statistical significance at the 1% and 5% levels, respectively. The computed t-statistics are basedon Whites heteroscedasticity-consistent SEs.

1 Hubers (see, Hampel et al., 1986) extension of his results on estimation of a location parameter to linear regression involvescomputation of weighted least-squares estimates (redefined iteratively) of the form wi = min {1, c/|ri}, where ri is the ith residual andc a positive constant. The weights depend on the estimate (i.e. they are not fixed). Huber proposedM-estimators Tn, where (Tn) =min{()|}, where () =

Pni1 ((yi x

Ti)/) for some function : RR

+ and for a fixed . Assuming has a derivative (/r)(r) = (r),Tn satisfies the system of equations (with the p-vectors xi)

Pni1 ((yi x

TiTn)/)xi = 0. The Huber-estimator defined by the weight wi

above is a maximum likelihood when the residuals are distributed according to the distribution with density proportional to exp(c(r)).

380 V. N. R. Murthy and A. A. Okunade

variable is not correlated with the error term. The resultsare largely similar, regarding the signs, magnitudes andstatistical significance of the regression coefficients, tothose obtained by the OLS2 technique. They clearly indi-cate that a large per cent of the variation in life expectancyin Chinese provinces is captured by changes in income,daily visits to the doctors and illiteracy rate.

IV. Conclusion

This article, using regression models and the most recent,publically available, data of 31 Chinese provinces for theyear 2005, has identified important determinants of popu-lation health status as measured by life expectancy atbirth. We found that, in these provinces, increases in percapita real GDP and daily visits to physicians are statisti-cally important drivers of life expectancy. Our findingsreinforce the tendency for high illiteracy rates to reducelife expectancy. This implies that life expectancy wouldrise with more years of formal schooling in the population,a finding in line with those of related past studies on lifeexpectancy production.This study is innovative as the first attempt to identify

the determinants of life expectancy in Chinese provincesusing rigorous econometric testing procedures and therecent, publicly available, cross-sectional data of 31Chinese provinces. Some major policy implications areclear from our findings. First, to improve the level ofpopulation health status in these provinces, as measured

by life expectancy at birth, the Chinese authorities mightconsider crafting out long-term policy strategies aimed atsustaining high-economic growth rate, invest in educa-tional policies to eradicate illiteracy, implement subsidiesto reduce high out-of-pocket health care cost burdens onrural residents (under the NCMS) and on the less-educatedsegment of the urban population (under the URBMI) and,as earlier echoed in Wagstaff et al. (2009) and Sun et al.(2009), ease the currently rigid policies on provider pay-ments and intergovernmental fiscal linkages. These policyactions, taken together, can be highly potent and effectiveif implemented in the provinces that havesignificantly lagged behind in achieving higher lifeexpectancies.

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382 V. N. R. Murthy and A. A. Okunade

AbstractI. IntroductionII. Model Specification and the DataIII. Empirical ResultsIV. ConclusionReferences