HEALTH AND GROWTH: A META-REGRESSION ANALYSIS UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS Nikos...

HEALTH AND GROWTH:A META-REGRESSION ANALYSIS

UNIVERSITY OF IOANNINADEPARTMENT 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.

2015 MAER-Net Colloquium

Introduction

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.

.


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.

.


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).


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).


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.


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.


The meta-regression model:

βj = β0 + ΣαkZjk + β1sej + uj (1), where:

βj reported estimate of health coefficient of jth study,

β0 true value of health coefficient,

Zjk moderator variables which explain variation in βj,

αk MRA coefficients reflecting effect of particular study characteristics

sej standard error of coefficient of jth study

uj meta-regression disturbance term.

.


Empirical studies use varying sample sizes, econometric specifications and estimation procedures. Hence, uj are likely to be heteroscedastic.

Thus, we estimate the Weighted Least Squares (WLS) version of equation (1), by dividing it by sej:

tj = β1 + ΣγiKij + β0(1/sej) + ΣαkZjk/sej + vj (2), where:

tj is t-statistic which corresponds to the estimate βj.

Kij 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.


Figure 1: Funnel graph (n=688)

430 estimates < 0431 estimates > 0

5 estimates = 0

01

02

03

04

05

01

/se

-1 -.5 0 .5 1r = t stat/ sqrt(tstat^2+df)

Table 1: Summary statistics of the studies included in meta-regression analysis

Number of estimates

Minimum Maximum Median Standard deviation

Mean

1 Bhargava-Jamison-Lau-Murray 2001 4 0.1127893 0.2053412 0.1552227 0.0385714 0.157144

2 Lorentzen-McMillan-Wacziarg 2008 47 0.0390137 0.537778 0.2988422 0.098081 0.2962789

3 Bloom-Canning-Sevilla 2004 2 0.0910718 0.1764016 0.1337367 0.0603373 0.1337367 4 Hassan-Cooray 2012 26 -0.24428191 0.237283926 0.041048978 0.106580826 0.0275277115 Dauda 2011 25 -0.7880076 0.6300946 -0.1936881 0.3649692 -0.108819 6 Chakraborty 2004 4 0.404049 0 .6378231 0.4767651 0.1088734 0.4988506 7 Bloom-Malaney 1998 2 0.0507718 0.2687883 0.15978 0.154161 0.15978 8 Ogunleye-Eris 2008 6 -0.0677641 0.444764 0.0079311 0.2386655 0.1354641

9 Bloom-Finlay 2008 4 0.1316099 0.1756688 0.154432 0.0201957 0.1540357

10 Hamoudi-Sachs 1999 9 0.0139687 0.3960025 0.2311337 0.1354138 0.1832942

11 Bloom-Canning 2005 4 0.1293416 0.2141601 0.1653771 0.0348591 0.168564 12 Ogunleye 2011 2 -0.0375591 0.047183 0.0048119 0.0599217 0.0048119 13 Aguayo-Rico 2005 16 -0.8652772 0.0368825 -0.3162207 0.2976298 -0.3671903 14 Hartwig 2010 90 -0.36057 0.3111134 -0.2378656 0.1429659 -0.1830541 15 Aghion Howitt Murtin 2011 46 -0.4349467 0.6765192 0.2705822 0.3338576 0.2495588 16 Barro 2013 2 0.1921057 0.1958518 0.1939787 0.0026489 0.1939787 17 Barro 1996 7 -0.1638054 0.1952904 0.1909404 0.1327709 0.1296216 18 Akram 2009 6 0.2429159 0.9999934 0.9999613 0.3090633 0.8737888 19 Acemoglu-Johnson 2007 8 -0.0944395 0.5200244 0.0368381 0.2350488 0.1368345 20 Bloom-Canning-Fink 2013 12 -0.489435 0.7236468 -0.0386692 0.4037553 0.1249301 21 Grimm 2011 22 -0.6706818 0.5253376 0.1714329 0.3360617 0.019544

Number of estimates

Minimum Maximum Median Standard deviation

Mean

22 Acemoglu-Johnson 2014 16 -0.3866729 -0.1136748 -0.2973236 0.094554 -0.2742631

23 Coorey 2013 26 0.0075936 0.5271178 0.1020374 0.1435864 0.1409324

24 Pocas-Soukiazis 2013 20 -0.4323282 -0.0274014 -0.2603462 0.1081623 -0.2572113

25 Morgado 2014 4 0.3277324 0.2771657 -0.0146922 0.2912972 -0.0199878

26 Naidu-Chand 2013 2 0.0109603 0.1388662 0.0749133 0.0904431 0.0749133

27 Strittmatter- Sunde 2013 88 -0.5273883 0.1363126 -0.2149934 0.1563704 -0.2129748

28 McCarthy-Wolf-Wu 2000 12 -0.311311 -0.1195245 -0.1776583 0.0627895 -0.2056611

29 Nketiah-Amponsah 2009 1 0.369881 0.369881 0.369881 - 0.369881

30 Rivera-Currais 2004 9 -0.1359857 0.347066 0.2454038 0.154853 0.1779704

31 Kumar-Mitra 2009 2 -0.316501035 0.225946776 -0.04527713 0.383568525 -0.04527713

32 Bloom-Canning-Fink-Finley 2009

3 0.192753279 0.466802317 0.326423076 0.137038207 0.328659557

33 Magnus-Powell-Prufer 2010 23 0 0.076290879 0.038263937 0.01663281 0.035733569

34 Barro 1996 4 0.217822079 0.233188612 0.21915562 0.007267053 0.222330482

35 Cervellati-Sunde 2011 18 -0.099132404 0.046921478 -0.032648516 0.038253654 -0.035900685

36 Suhrske-Urban 2010 34 -0.27610975 0.257386953 -0.13102291 0.132438 -0.094009152

37 Hansen 2014 24 -0.369166979 0.166725802 0.009287362 0.161062829 -0.060852529

38 Acemoglu-Johnson 2013 26 -0.208281082 0.089734833 -0.067542258 0.078132284 -0.064155108

39 Afonso-Jalles 2013 8 -0.123766926 0.164756211 -0.073766192 0.104004459 -0.033609615

40 Afonso-Allegre 2007 2 -0.337897177 -0.243573802 -0.290735489 0.066696698 -0.290735489

41 Miller-Russek 1997 6 -0.248311345 -0.010049871 -0.179727019 0.083391651 -0.16170273

42 Devarajan-Swaroop-Zou 1996 26 -0.304997141 0.353743243 0.085698168 0.156852492 0.074243354

Table 2. Moderators in the Multiple Meta-Regression Analysis.Variable Description of the variable

K-variables Sample size = the size of the sampleZ-variables invse=1/standerror

Health variables Life expectancy =1 if study uses life expectancy as proxy for healthadult survival rate =1 if study uses adult survival rate as proxy for health

mortality =1 if study uses mortality as proxy for healthhealthexpend =1 if study uses health expenditure as proxy for health

Additional controls pcapital = 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 variablesschooling =1 if study uses education variable as explanatory variables

Effect measurement var. loghealth =1 if study uses log of health variable as explanatory variableData variables earlyyear First year of sample

lastyear Last year of sampleobs Sample size

Quality public =1 if study is published in academic journalSpecification variables independent = number of independent variables

ols =1 if study uses OLS estimationIV =1 if study uses IV estimation

panel =1 if study uses panel data in the sampleCountry moderators OECD =1 if study uses OECD countries in the sample

- Assuming that all αk and γi are zero ,we employ the Funnel Asymmetry Test or FAT for publication selection:

tj =β1 +β0(1/sej) + ej (3)

Table 3: FAT – PET tests


Variables OLS OLS-cluster REML FE WLS FE-WLSinvse 0.9745795***

(0.0083094)0.9745795***(0.0483479)

0.9745817***(0.0083095)

0.9878064***(0.0054442)

1.024898***(0.0019008)

0.9993504***(0.0011459)

constant -13.56022***(0.4564856)

-13.56022***(1.815613)

-13.56075***(0.4565126)

-16.92275***(2.115023)

-22.08949***(0.6732306)

-17.23793***(2.112692)

R-squared 0.9525 0.9525 95.24% (Adjusted)

0.9938 0.9976 0.9998

Ramsey RESET test

F(3, 683) = 3151.78Prob > F = 0.0000

F(3, 683) = 3151.78Prob > F = 0.0000

- F(3, 642) = 689.17Prob > F = 0.0000

F(3, 683) = 6935.32Prob > F = 0.0000

F(3, 642) = 979.92Prob > F = 0.0000

Table 4: Multiple MRA (General-to-Specific)Moderators OLS OLS-cluster REML FE WLS FE-WLS

invse -4.954885(5.81282)

-4.75473***(1.131392)

-38.67527***(11.03288)

-6.521372***(2.00681)

-144.245***(26.11726)

-162.2581**(69.41389)

lifexpectancyse

0.5728103***(0.025954)

0.5246986***(0.1119178)

0.3785084***(0.0482872)

0.5248614**(0.2292944)

0.2569807***(0.02415730)

0.0087589**(0.0035193)

asrse 0.6004023***(0.0354863)

0.6876609***(0.1248938)

0.4857136***(0.0818584)

0.4678013**(0.02253665)

0.2878495***(0.1045601)

mortalityse 0.4786432***(0.0378257)

0.5174235***(0.1125756)

0.382556***(0.0483545)

0.4387827*(0.2321043)

0.265295***(0.0558326)

0.0047896***(0.0012525)

healthexpendse

0.4554834***(0.0360014)

0.5185342***(0.1134509)

0.37558***(0.0482951)

0.4355875*(0.2311656)

0.2552161***(0.0505899)

pcapitalse 0.1187521**(0.0597831)

politicalse -0.0962391*(0.0580775)

-0.0725786*(0.0413745)

-0.1140728**(0.0553672)

fiscalse 0.1527059***(0.0437664)

0.0814832*(0.0420769)

demogsepcgdpse 0.1109274***

(0.0300665)0.1543276**(0.0564921)

0.0634092*(0.0351573)

0.059688**(0.024953)

openessse 0.0168764*(0.0090839)

schoolingse -0.00661672**(0.0333537)

-0.01704737***(0.0435144)

-0.0147475**(0.0720169)

-0.157932***(0.0420475)

-0.207941***(0.0581286)

Moderators OLS OLS-cluster REML FE WLS FE-WLSloghealthse 0.0853329***

(0.0288474)0.3804074***(0.0496019)

0.2622761**(0.1263145)

0.6536539***(0.0522448)

0.6496928***(0.0997399)

loggdpse 0.0566082**(0.0265359)

0.0840303*(0.043673)

-0.0567871*(0.031742)

-0.0559627*(0.031624)

publicse -0.218693***(0.053403)

-0.130664**(0.0598798)

-1.285459***(0.1025633)

-1.487794***(0.3016201)

earlyyearse 0.0017318***(0.0003361)

0.0023478***(0.0005704)

0.0020655**(0.00086)

0.0034352***(0.0009158)

0.0050321***(0.0010224)

0.0066438***(0.0017439)

lastyearse -0.0098592**(0.0039211)

obsse 0.0005302***(0.0000812)

0.0004833***(0.0000625)

0.000717***(0.0000836)

0.0005416***(0.0000815)

Independentse

0.0281693***(0.0043363)

0.020515***(0.0052838)

0.0268547*(0.0148301)

0.0145058**(0.0057576)

0.0208934*(0.0104596)

olsse 0.0881661***(0.0260463)

0.1123361***(0.0272101)

0.1251389***(0.0242303)

0.1498104**(0.0560428)

ivse 0.0797895***(0.0258407)

0.0772722***(0.0270671)

0.0871327***(0.0257016)

0.1161138**(0.0522497)

panelse -0.0870967***(0.0262189)

-0.102632***(0.0320081)

0.0639528*(0.0365299)

0.1726486***(0.0354127)

0.1701726***(0.0530974)

oecdse -0.0962151*(0.0508852)

obs -0.035752***(0.0042325)

-0.037476***(0.0036561)

-0.0448501***(0.0038293)

-0.035272***(0.0070162)

-0.004252***(0.0015215)

constant -2.820381***(0.3331904)

-2.711719***(0.7327533)

-2.13143***(0.5409898)

-2.419682**(1.158048)

-2.12159***(0.5020711)

-1.295005(1.138706)

R-squared 0.9535 0.9535 95.29% adjusted 0.9948 0.9986 0.9999


Meta-regression results – Conclusions (1)

1. We find evidence of downward publication selection bias in

the empirical health-economic growth literature.

2. All methods indicate a positive health effect on growth after

correction for publication selection.

3. Differences across studies can be partially attributed to

differences in terms of their characteristics.


Meta-regression results – Conclusions (2)

• The inclusion of ols/iv vs other estimation methods, life

expectancy, adult survival rate, pcgdp, many independent

vars, log(health) and mortality tend to make the estimated

impact of health education on growth, corrected for

publication bias, positive.

• The use of schooling, publication in academic journals and

political variables tend to lower the estimated growth impact

of health.

• Thus, the variation in reported estimates is attributed to

differences in health measurement and study characteristics

(model specification, type of data used, and the quality of

research outlets where studies are published, e.g. academic

journals vs. working papers).


Thank you very much for your attention

HEALTH AND GROWTH: A META-REGRESSION ANALYSIS UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS Nikos...

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