HEALTH AND GROWTH: A META-REGRESSION ANALYSIS UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS Nikos Benos and Georgios Giotis MAER-Net Colloquium, Prague 2015

-The theoretical literature on human capital and economic growth.  Human capital can be considered as those skills, abilities and knowledge embodied in individuals.  Human capital is acquired through education, health, training, migration and other investments that enhance individual productivity.  Education and health are considered to be the most significant investments in human capital MAER-Net ColloquiumIntroduction

A classification of theoretical works is based on the different roles human capital in the process of economic growth. - Growth models can be divided into two categories: exogenous and endogenous growth models.  Exogenous growth models include the Solow-Swan model and its extensions (augmented neoclassical models).  Endogenous growth models consider human capital accumulation and productive knowledge as driving forces of economic growth MAER-Net Colloquium

The empirical literature on health human capital and economic growth.  The empirical results remain controversial, since they have not always been consistent with those produced by theoretical growth models.  Health human capital proxies are often not significant or enter with negative sign in growth analysis.  Empirical evidence depends on technical problems that have to do with: I.the definition of the variables, II.the methodology used, and III.the time period over which the model is estimated MAER-Net Colloquium

MRA  Given the diversity of empirical findings on the link between health and growth, we conduct Meta-Regression Analysis (MRA).  MRA is quantitative literature review of the estimates obtained from previous regression analyses.  Meta-analysis integrates the results of several studies that share a common aspect so as to be combinable in a statistical manner (Harmon et al., 2003).  MRA aims at explaining the excess study-to-study variation in empirical results and investigates the presence of publication selection bias (Stanley, 2005) MAER-Net Colloquium

We proceed in two steps for conducting MRA: 1.Construct meta-data set, collecting empirical studies examining the link between health and growth. 2.Define meta-regression model to distinguish between numerous factors, which influence the estimated health effect on economic growth. In all steps of our analysis we follow the guidelines and protocols expressed by the Meta-analysis of Economics Research-Network (MAER-Net) MAER-Net Colloquium

We have searched EconLit, Google Scholar to find English- written articles in academic journals and working papers, estimating the health-growth nexus. The keywords used in the search process were: human capital, health and economic growth. Include only macro studies in meta-sample, which estimate the coefficient of the size effect of health on growth. Only studies providing regression results where measure of growth rate is dependent variable and at least one health measure is among the explanatory variables are included in our meta-data set MAER-Net Colloquium

We perform meta-regression analysis using data from 42 empirical studies. However, the coding of the studies is ongoing and the number of studies included in the meta-sample will be considerably higher. Include all reported estimates in each study, any potential dependence among estimates is captured by study identifiers. Given that most studies include many estimations, we use all of them as independent regressions, report a total of 688 observations. We calculated the partial correlations from each study MAER-Net Colloquium

The meta-regression model:  β j = β 0 + Σα k Z jk + β 1 se j + u j (1), where: β j reported estimate of health coefficient of j th study, β 0 true value of health coefficient, Z jk moderator variables which explain variation in β j, α k MRA coefficients reflecting effect of particular study characteristics se j standard error of coefficient of j th study u j meta-regression disturbance term MAER-Net Colloquium

Empirical studies use varying sample sizes, econometric specifications and estimation procedures. Hence, u j are likely to be heteroscedastic. Thus, we estimate the Weighted Least Squares (WLS) version of equation (1), by dividing it by se j : t j = β 1 + Σγ i K ij + β 0 (1/se j ) + Σα k Z jk /se j + v j (2), where: t j is t-statistic which corresponds to the estimate β j. K ij are additional factors correlated with publication process itself. We follow general-to-specific modeling approach for variable selection. The estimation methods we use are: i) OLS, ii) OLS-cluster, iii) REML, iv) FE, v) Weighted-Least-Squares and vi) FE-WLS MAER-Net Colloquium

Figure 1: Funnel graph (n=688) 430 estimates < estimates > 0 5 estimates = 0

Table 1: Summary statistics of the studies included in meta-regression analysis Number of estimates MinimumMaximumMedian Standard deviation Mean 1 Bhargava-Jamison-Lau-Murray Lorentzen-McMillan-Wacziarg Bloom-Canning-Sevilla Hassan-Cooray Dauda Chakraborty Bloom-Malaney Ogunleye-Eris Bloom-Finlay Hamoudi-Sachs Bloom-Canning Ogunleye Aguayo-Rico Hartwig Aghion Howitt Murtin Barro Barro Akram Acemoglu-Johnson Bloom-Canning-Fink Grimm

Number of estimates MinimumMaximumMedian Standard deviation Mean 22 Acemoglu-Johnson Coorey Pocas-Soukiazis Morgado Naidu-Chand Strittmatter- Sunde McCarthy-Wolf-Wu Nketiah-Amponsah Rivera-Currais Kumar-Mitra Bloom-Canning-Fink-Finley Magnus-Powell-Prufer Barro Cervellati-Sunde Suhrske-Urban Hansen Acemoglu-Johnson Afonso-Jalles Afonso-Allegre Miller-Russek Devarajan-Swaroop-Zou

Table 2. Moderators in the Multiple Meta-Regression Analysis. VariableDescription of the variable K-variablesSample size= the size of the sample Z-variablesinvse=1/standerror Health variablesLife expectancy=1 if study uses life expectancy as proxy for health adult survival rate=1 if study uses adult survival rate as proxy for health mortality=1 if study uses mortality as proxy for health healthexpend=1 if study uses health expenditure as proxy for health Additional controlspcapital= if study uses physical capital as explanatory variable political=1 if study uses political variable as explanatory variable fiscal=1 if study uses fiscal variable as explanatory variable demog=1 if study uses demographic variable as explanatory variable pcgdp=1 if study uses GDP pc as explanatory variable openness=1 if study uses openness variable as explanatory variables schooling=1 if study uses education variable as explanatory variables Effect measurement var.loghealth=1 if study uses log of health variable as explanatory variable Data variablesearlyyearFirst year of sample lastyearLast year of sample obsSample size Qualitypublic=1 if study is published in academic journal Specification variablesindependent= number of independent variables ols=1 if study uses OLS estimation IV=1 if study uses IV estimation panel=1 if study uses panel data in the sample Country moderatorsOECD=1 if study uses OECD countries in the sample

-Assuming that all α k and γ i are zero,we employ the Funnel Asymmetry Test or FAT for publication selection: t j =β 1 +β 0 (1/se j ) + e j (3) Table 3: FAT – PET tests 2015 MAER-Net Colloquium VariablesOLSOLS-clusterREMLFEWLSFE-WLS invse *** ( ) *** ( ) *** ( ) *** ( ) *** ( ) *** ( ) constant *** ( ) *** ( ) *** ( ) *** ( ) *** ( ) *** ( ) R-squared % (Adjusted) Ramsey RESET test F(3, 683) = Prob > F = F(3, 683) = Prob > F = F(3, 642) = Prob > F = F(3, 683) = Prob > F = F(3, 642) = Prob > F =

Table 4: Multiple MRA (General-to-Specific) ModeratorsOLSOLS-clusterREMLFEWLSFE-WLS invse ( ) *** ( ) *** ( ) *** ( ) *** ( ) ** ( ) lifexpectancy se *** ( ) *** ( ) *** ( ) ** ( ) *** ( ) ** ( ) asrse *** ( ) *** ( ) *** ( ) ** ( ) *** ( ) mortalityse *** ( ) *** ( ) *** ( ) * ( ) *** ( ) *** ( ) healthexpend se *** ( ) *** ( ) *** ( ) * ( ) *** ( ) pcapitalse ** ( ) politicalse * ( ) * ( ) ** ( ) fiscalse *** ( ) * ( ) demogse pcgdpse *** ( ) ** ( ) * ( ) ** ( ) openessse * ( ) schoolingse ** ( ) *** ( ) ** ( ) *** ( ) *** ( )

ModeratorsOLSOLS-clusterREMLFEWLSFE-WLS loghealthse *** ( ) *** ( ) ** ( ) *** ( ) *** ( ) loggdpse ** ( ) * ( ) * ( ) * ( ) publicse *** ( ) ** ( ) *** ( ) *** ( ) earlyyearse *** ( ) *** ( ) ** ( ) *** ( ) *** ( ) *** ( ) lastyearse ** ( ) obsse *** ( ) *** ( ) *** ( ) *** ( ) Independent se *** ( ) *** ( ) * ( ) ** ( ) * ( ) olsse *** ( ) *** ( ) *** ( ) ** ( ) ivse *** ( ) *** ( ) *** ( ) ** ( ) panelse *** ( ) *** ( ) * ( ) *** ( ) *** ( ) oecdse * ( ) obs *** ( ) *** ( ) *** ( ) *** ( ) *** ( ) constant *** ( ) *** ( ) *** ( ) ** ( ) *** ( ) ( ) R-squared % adjusted

2015 MAER-Net Colloquium

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