Health and economic growth: new evidence from a panel threshold model

Chakroun, Mohamed Article Health and economic growth: new evidence from a panel threshold model Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Chakroun, Mohamed (2024) : Health and economic growth: new evidence from a panel threshold model, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-13, https://doi.org/10.1080/23322039.2024.2331010 This Version is available at: https://hdl.handle.net/10419/321464 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Health and economic growth: new evidence from a panel threshold model Mohamed Chakroun To cite this article: Mohamed Chakroun (2024) Health and economic growth: new evidence from a panel threshold model, Cogent Economics & Finance, 12:1, 2331010, DOI: 10.1080/23322039.2024.2331010 To link to this article: https://doi.org/10.1080/23322039.2024.2331010 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 28 Mar 2024. Submit your article to this journal Article views: 2412 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Health and economic growth: new evidence from a panel threshold model Mohamed Chakroun a,b a Department of Logistics, Tourism and Service Management (LTSM), German University of Technology in Oman, Muscat, Oman; b Faculty of Economics and Management of Sfax, Sfax University, Sfax, Tunisia ABSTRACT The paper examines the relationship between health and economic growth using a dynamic panel with threshold effect and endogeneity. The study uses a sample of 136 countries over the period of 1965–2015 and the results indicate that the relationship between health and economic growth is nonlinear. The findings suggest that the impact of health on economic growth is only positive in countries that have attained minimum benchmarks of health improvement. These findings carry significant policy implications. Low-income countries need a comprehensive approach, encompassing multiple factors, such as education enhancement, productivity boost, and entrepreneurship promotion, that go beyond health promotion to foster substantial economic growth. In countries already attaining satisfactory health outcomes, policies prioritizing improved health outcomes, through directing investments towards Research and Development can serve as an effective means to promote sustainable economic growth. IMPACT STATEMENT The paper provides an in-depth analysis of the relationship between health and economic growth. By explicitly examining potential non-linearities and thresholds, the study offers insights into how variations in health status may influence economic growth differently across different thresholds or stages of development. By identifying the key health thresholds, the research provides policymakers with actionable insights to allocate resources effectively. This guidance is critical for crafting targeted policies that maximize the impact of health investments on economic development. ARTICLE HISTORY Received 23 October 2023 Revised 22 January 2024 Accepted 10 March 2024 KEYWORDS Health; economic growth; threshold models; instrumental variables; life expectancy JEL CLASSIFICATION CODES I10; O47; C22 REVIEWING EDITOR Goodness Aye, University of Agriculture, Makurdi, Nigeria SUBJECTS Health & Development; Economics; Econometrics 1. Introduction The nexus between human capital and economic growth constitutes a longstanding focal point in economic inquiry. The trajectory set by new growth theory underscores the pivotal role of human capital accumulation as a fundamental catalyst for a nation’s wealth and prosperity (see for instance: Barro, 1991; Benhabib & Spiegel, 1994; Romer, 1990). While macroeconomists widely acknowledge the contribution of human capital to economic growth, their empirical inquiries predominantly gauge human capital through the prism of education (Bloom et al., 2001). Notwithstanding, insights from early thinkers, such as Bentham (1789) and Marx (1867) accentuated the significance of health as a pivotal component of human well-being and reproduction. Similarly, scholars like Mushkin (1962) and Schultz (1961) underscored that health constitutes a form of human capital as crucial as education. However, it was only in the 1980s and 1990s, concomitant with the development of endogenous growth theories and the emergence of health economics as a distinct field of study, that a substantial resurgence of interest in health investment, financing, and their repercussions on economic growth materialized. CONTACT Mohamed Chakroun [email protected] Department of Logistics, Tourism and Service Management (LTSM), German University of Technology in Oman, Muscat, Oman This article has been corrected with minor changes. These changes do not impact the academic content of the article. ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2331010 https://doi.org/10.1080/23322039.2024.2331010 Presently, the role of health in economic growth garners wide acceptance across both theoretical and empirical realms. In conjunction with education, health emerges as a sector encompassing the principal drivers of endogenous growth, including research and development, human capital, and public expenditure (Barro & Sala-I-Martin, 1995; Lim, 1996). Despite the growing consensus on the role of health in economic growth, a pronounced discrepancy persists between theoretical propositions and empirical investigations. Recent theoretical literature increasingly acknowledges a non-linear correlation between health and economic growth. However, this theoretical insight has not been fully translated into empirical investigations, as existing studies predominantly rely on linear models to explore the relationship between health and economic growth. This discrepancy highlights a notable gap in the current research paradigm, with the inherent complexities and potential thresholds in this relationship remaining largely unexplored or inadequately addressed in empirical studies. This underscores the necessity for more advanced methodological approaches to bridge the gap between theory and empirical exploration. In this paper, we use a panel threshold model that addresses this research gap by acknowledging and exploring nonlinearities in the relationship between health and economic growth. The advantage of such a model is that it allows for heterogeneity in the convergence and divergence regimes, in terms of growth rates and levels. It suggests that economies that differ in their initial conditions may not converge. Such an approach, considering the existence of multiple equilibria, is thus useful to allow heterogeneity in the specification of convergence equations (Bernard & Jones, 1996). Considering threshold effects in the relationship between health status and economic growth is noteworthy for several compelling reasons: different countries may exhibit varying relationships between health and economic growth. Threshold effects could differ based on initial health conditions, institutional setups, and stages of economic development. Investigating these heterogeneities helps comprehend why some countries experience rapid economic growth with incremental health improvements, while others may not, thereby providing tailored policy recommendations for different contexts. The contribution of this study to the existing literature is 3-fold. Firstly, it addresses a critical research gap prevalent in earlier empirical studies by utilizing a panel threshold model, diverging from the dominant use of linear models. Secondly, by employing this innovative approach, the paper delves deeper into the health-economic growth nexus, offering a nuanced comprehension. This exploration of potential non-line- arities and thresholds grants insights into how diverse health statuses influence economic growth, varying across distinct developmental thresholds. Such an analysis augments the existing literature by transcending oversimplified linear assumptions, enriching understanding with nuanced complexities. Thirdly, the study’s findings carry substantial policy implications by pinpointing health thresholds with significant impacts on economic growth. By illuminating these critical junctures, the research equips policymakers with actionable insights to optimize resource allocation effectively, thereby guiding the formulation of targeted and efficient policy interventions for promoting economic development through strategic health investments. The rest of the paper is organized as follows: Section 2 critically examines the current body of literature concerning the nexus between health and economic growth. Section 3 delineates the econometric model and estimation technique employed in this study. Section 4 presents and discusses the results. Finally, Section 5 summarizes the key findings, and provides recommendations for policymakers. 2. Literature review Conceptually, health can be viewed as a factor of production (Lucas, 1988; Mankiw et al., 1992). Thus, at the macro-level, it is the accumulation of health, i.e. an increase in life expectancy, that may cause economic growth. Conversely, health can be viewed as a stock (Nelson & Phelps, 1966). Consequently, it is the level of health, i.e. the average level of life expectancy, that may influence real output. Health’s impact on economic growth has been extensively explored in theoretical and empirical studies, revealing a complex and nuanced relationship. While prevailing literature often indicates a positive link between health and growth, conflicting findings challenge this consensus. Empirical examinations of the impact of health on growth often utilize cross-sectional and panel data, involving the regression of the growth rate of real GDP per capita against the level of health (Bloom & Canning, 2005). 2 M. CHAKROUN Seminal literature commonly establishes a positive correlation between health status, measured by life expectancy at birth and/or adult survival rates, and economic growth (Aghion et al., 2008,2010; Barro & Lee, 1994; Barro & Sala-I-Martin, 1995; Bloom et al., 2001,2003,2004,2014; Jamison et al., 2005; Knowles & Owen, 1995,1997; Li & Huang, 2009; Mayer, 2001a,2001b; Mayer et al., 2001; McDonald & Roberts, 2002; Lorentze et al., 2008; Sachs & Warner, 1995,1997; Sala-I-Martin, 1997,2005; Sala-I-Martin et al., 2004; Ridhwan et al., 2022). Recent empirical inquiries, utilizing state-of-the-art econometric methodologies, consistently validate the positive nexus between health indicators and economic growth. Munir and Shahid (2020) used annual panel data of four South Asian courtiers, from 1980 to 2018 and applied the panel ARDL model to analyze the long run and short run impact of life expectancy on economic growth. They reported a positive and significant relationship. Their results are confirmed by Ahmad and Nayyab (2021). He and Li (2020) examined the long- and short-term linkages between life expectancy and economic growth across 65 countries from 1980 to 2014. Employing panel cointegration analysis and causality tests, the estimations unveiled significantly positive long-run relationships between life expectancy and GDP per capita. However, these relationships exhibited variations across aging levels, with a stronger positive impact of life expectancy on economic growth observed in groups with higher levels of aging. Lawanson and Umar (2021) investigated the interplay between life expectancy, poverty incidence, and economic growth in Nigeria, utilizing the fully modified ordinary least squares method. The results underscored a positive contribution of health to economic growth. Alternative studies focused on the impact of health investments on economic growth rather than examining the correlation between health accumulation and growth. Unsurprisingly, most of these studies conclude a positive association between healthcare expenditure and economic growth. For instance, Gyimah-Brempong (1998) and Gyimah-Brempong and Wilson (2004) found a positive and significant causal relationship between public spending on health and economic growth in African countries. This result is supported by Heshmati (2001,2018) and Rivera and Currais (1999a,1999b,2003, 2004) for developed countries. Ye and Zhang (2018) scrutinized the causal relationship between healthcare expenditure and economic growth across 31 countries spanning 1986–2007, employing panel regression analysis to reveal a stimulating effect of healthcare expenditure growth on economic growth. However, recent studies investigating heterogeneity and asymmetry in the relationship between health expenditure and economic growth have challenged this positive correlation. Wu et al. (2021) used a quantile-on-quantile approach to analyze the dynamics between the quantiles of healthcare expenditure and economic growth in a pooled sample of 40 Asian countries. Their results indicate that an elevation in healthcare expenditure quantiles across these nations does not ensure a proportional escalation in the influence of healthcare expenditure on economic growth. Similarly, Zhou et al. (2021) employed a Time-Varying Parameter model to investigate the influence of healthcare expenditure on economic growth in China. The results indicate a negative relationship in the short run and a positive association in the long run, accompanied by notable heterogeneity across distinct levels of economic development within China. Indeed, the positive and monotonic association between health and growth has been challenged extensively by Acemoglu and Johnson (2007), Bhargava et al. (2001), and Hartwig (2010). Bhargava et al. (2001) found large positive effects of adult survival rates on growth rates for poor and developing countries. Conversely, for highly developed countries, the estimated effect of adult survival rates on growth rates was negative. Acemoglu and Johnson (2007) regressed per capita GDP growth against the growth in life expectancy using a sample of 59 countries, from Western Europe, Oceania, the Americas, and Asia, over the period 1940–1980. They found no evidence that the increase in life expectancy led to faster growth of income per capita. Hartwig (2010) argued that, based on OECD data, there is no evidence supporting the notion that the formation of health capital through healthcare spending or the rise in life expectancy exerts a positive Granger-causal impact on per capita GDP growth. COGENT ECONOMICS & FINANCE 3 Bloom et al. (2009) contended that the proposition suggesting a decline in income per capita due to health enhancements is counterintuitive. However, they believe that the income response to health improvements is not instantaneous but involves a time lag. Accounting for this temporal delay results in positive estimations for both the influence of prior health status and the effects of health improvements on the growth of per capita income. Becker et al. (2005) argued that countries starting with better health status are likely to grow faster than countries with a lower initial health level. Nevertheless, this relationship is subject to diminishing returns. Such an idea has been adopted by other researchers (Aghion et al., 2010; Bloom et al., 2014) who assume decreasing returns to health investments at the macro level. Decreasing returns are even advanced as a plausible explanation to the findings of Acemoglu and Johnson (2007) and Bhargava et al. (2001). Recent literature recognizes that the relation between health and economic growth changes over time (Weil, 2014) and across the process of economic development (Bloom et al., 2019; Ngwen & Kouty, 2015). A substantial body of theoretical literature posits a non-linear relationship between health and economic growth. Boucekkine et al. (2002), Cipriani (2000), De la Croix and Licandro (1999), Fuster (1999), and Tabata (2005) have derived, based on overlapping generation models, an inverted U-shaped relationship between life expectancy and economic growth. Blackburn and Cipriani (2002) developed an overlapping generation model in which the economy exhibits multiple equilibria. They conclude that there exists a critical level of human capital below which the economy ends up plunging into a poverty trap. A ısa and Pueyo (2006) demonstrate the existence of a differentiated impact of health government expenditure on economic growth. This impact appears to be positive in developing countries, while it is negative in developed countries. Kuhn and Prettner (2016) developed a growth model with overlapping generations to examine the conditions under which expanding healthcare enhances growth and welfare. They demonstrate that healthcare increases productivity, but it also diverts labor away from production and R&D. On the other hand, Reinhart (1999) derives a negative relationship between public spending on health and growth, even though the link between growth and life expectancy seems to be rather positive. The existence of such non-linearities has been highlighted in the empirical literature (Berth elemy, 2008; Kelley & Schmidt, 1995; Sachs & Warner, 1997). Sachs and Warner (1997) found that the impact of health on economic growth is positive but decreases with the stock of health capital. Similarly, Berth elemy (2008), based on a multiple regime model, shows that health is among the main factors that have contributed to keeping African countries in a poverty trap. This result is supported by Aghion et al. (2008) who consider that the low growth of developing countries is mainly due to their low initial level of life expectancy. Similarly, Bloom et al. (2009) reported that, over the period 1940–2000, countries initially characterized by high life expectancy also showed faster growth in per capita income. Cervellati and Sunde (2011) contribute to this discourse by examining the causal impact of life expectancy on per capita income growth, finding a non-monotonic effect. They assert that health improvements do not stimulate economic growth unless a country has undergone the demographic transition from high to low rates of fertility and mortality. Sirag et al. (2020) reinforce this perspective with their dynamic panel threshold methodology, highlighting a non-linear correlation between life expectancy and economic growth. They posit that life expectancy positively influences economic growth up to a certain threshold level. Yıldırım et al. (2020) applied cluster and panel threshold analyses to scrutinize the relationship between life expectancy and economic growth across 12 OECD countries during 1999–2016. The findings indicated that an increase in life expectancy at birth in countries characterized by higher health status yielded no significant impact on economic growth. However, for countries with lower health status, such an increase positively influenced economic growth. In conclusion, the literature presents a nuanced and intricate relationship between health and economic growth, marked by conceptual divergences, conflicting empirical findings, and a growing body of evidence supporting a non-linear association. These complexities necessitate a critical lens when interpreting the broader implications for health policies and economic development. 4 M. CHAKROUN 3. Empirical framework In the cross-country growth models, it has become a common practice to test the empirical linkages between growth and health using the following standard linear growth equation: Growthi¼aþbHealthiþd0Xiþei(1) where i¼1, 2, …Nis the country indicator and eiis an error term. Growth denotes the growth rate of real GDP per capita, Health is the measure of health improvements and Xirepresents a vector of control variables. To allow for nonlinearity in the relationship, we extend Equation (1) into a threshold regression model that takes the following general form: yi¼h0 1ziIq ic ðÞ þh0 2ziIðqi>cÞþei(2) where yi¼Growthi,zi¼ðHealthi,X0 iÞ0,hj¼ðb,d’Þ0,j¼1, 2. I(.) represents an indicator function that takes the value 1 if the argument in parenthesis is valid, and 0 otherwise. qiis an exogenous threshold variable used to split the data into different regimes or groups. The threshold parameter is c2C, where Ca strict subset of the support of qi:This model allows the regression parameters h1and h2to switch between regimes depending on whether qiis smaller or larger than the unknown threshold value c: Equation (2) can be rewritten in more detailed form as follows: Growthi¼b1HealthiIq ic ðÞ þb2HealthiIq i>c ðÞ þd1’XiIq ic ðÞ þd2’XiIq i>c ðÞ þei(3) Assuming that all explanatory variables are exogenous, Hansen (2000) suggests Equation (2) to be estimated using Ordinary Least Square. Conditional on the above estimators, we then can estimate the threshold value by minimizing the concentrated sum of squared errors function (SSE). However, the threshold estimation method proposed by Hansen (1999,2000) is not suitable when one or more explanatory variables are endogenous. Since we consider that the health variable is highly likely to be endogenous, we rather choose to estimate Equation (3) by applying the Caner and Hansen (2004) instrumental variable threshold regression model (IVTR). The reduced form of the endogenous variable can be defined as follows: Healthi¼gs i,p ðÞ þui(4) where pi is an unknown parameter vector, g is a linear function and uiis a random error. The vector si represents a set of instrumental variables that are not included in the growth regression, along with other exogenous variables of the model. After replacing the endogenous variable by its reduced form, Equation (3) can be written as follows: growthi¼b1gs i,p ðÞ Iq ic ðÞ þb2gs i,p ðÞ Iq i>c ðÞ þd1’XiIq ic ðÞ þd2’XiIq i>c ðÞ þvi (5) where vi ¼b1uiIðqicÞþb2uiIðqi>cÞþei The parameters are estimated sequentially. First, we estimate the reduced form parameter pand obtain the predicted value d Healthi¼^ gi¼gs i,^ p ðÞ ¼^ p’si:Second, we turn to the estimation of the threshold parameter cwhich is chosen to minimise the sum of squared residuals from a sequence of regressions of growth on the predicted value of the health variable. Third, we estimate the slope parameters (b1,b2,d1,d2Þby 2SLS or GMM on the split sample implied by ^ c: To test the existence of the threshold effect, Caner and Hansen (2004) suggest the Supremum Wald test statistic. First, we fix c2Cto any value and the equation (19) is estimated by GMM. Then, for the fixed value of c, the Wald statistic under H0 is constructed as follows: wnc ðÞ ¼^ h1c ðÞ −^ h2c ðÞ hi 0^ V1c ðÞ þ^ V2c ðÞ hi −1^ h1c ðÞ −^ h2c ðÞ hi (6) Repeating the calculation for all c2C, we can obtain the Sup statistic as the greatest value of the calculated wn: Sup W ¼sup c2s wnc ðÞ (7) COGENT ECONOMICS & FINANCE 5 The asymptotic distribution of this statistic is not standard because of the presence of the nuisance parameter c. For this reason, Caner and Hansen (2004) use a bootstrapping procedure to get the p-value. 3.1. Data description Equation (5) is estimated for 136 countries observed over the period 1965–2015. All data are averaged over periods of five years. Given that the IV threshold model doesn’t tolerate unbalanced samples, all missing observations have been dropped, reducing the sample total number of observations from 1496 to 1075 observations. Data on population, income, and physical capital are from the Penn World Tables (Feenstra et al., 2022). Health variables are from the United Nations (2022). In line with Bloom et al. (2022), health stock is proxied by adult survival rates, which measure the probability of surviving from age 15 to 60. For robustness check purposes, we use life expectancy as an alternative measure of health improvements. Data on education are from Barro and Lee (2001). Education is proxied by years of secondary schooling for the working-age population 15–64. Table A1 in Appendix A reports descriptive statistics for the estimation sample, and Table A2 reports the correlation matrix. 4. Empirical results and discussion 4.1. The linear model Table 1 reports parameter estimates of the linear IV GMM model. The results are in line with Bloom et al. (2022). Adult survival rate has the expected positive impact on economic growth and the estimated coefficient is significant at the 1% significance level. Using life expectancy as an alternative measure or population health or GDP per worker as an alternative dependent variable doesn’t alter the findings. As discussed previously, the validity of such findings relies on the fundamental assumption that the relationship between health and economic growth is basically linear. We proceed next to test this assumption using the methodology proposed by Caner and Hansen (2004). 4.2. Linearity tests The first step before estimating the threshold model is to select among the candidate threshold variables the one that most strongly rejects the linearity hypothesis. Therefore, three variables were selected: initial survival rate (SURV 0 ), initial life expectancy (LEX 0 ), and initial GDP (GDP 0 ). The choice of the first Table 1. The macroeconomic return to health: linear GMM estimates. Dependent variable Growth rate of income per capita Growth rate of income per worker (1) (2) (3) (4) Log income per capita (−1) −0.5097 −0.5981 (0.1646) (0.1924) Log income per worker (−1) −0.5807 −0.6913 (0.1873) (0.1768) Survival rate 0.04919 0.06238 (0.01356) (0.01751) Life expectancy 0.06522 0.08637 (0.01850) (0.02266) Secondary schooling 0.4551 0.5923 0.4507 0.5988 (0.1381) (0.1659) (0.1374) (0.09965) Growth of working age population 0.8923 0.9448 −0.1015 −0.05101 (0.08162) (0.07922) (0.08151) (0.08484) Population growth rate −0.1967 −0.0008222 −0.1848 (0.1381) (0.1573) (0.1395) Growth of capital per worker 0.4638 0.5166 0.4660 0.5365 (0.04674) (0.04529) (0.04678) (0.04604) Observations 1075 1075 1075 1075 Countries 136 136 136 136 J-test p-value 0.2654 0.3806 0.7128 0.9086 Note:  indicates statistical significance at the 1%, respectively. Standard errors are in parentheses. 6 M. CHAKROUN two variables is primarily intended to test the hypothesis of decreasing marginal returns of health. Thus, we would like to test whether health improvements are less likely to translate into higher growth rates in countries initially characterized by better population health conditions. The third threshold variable (GDP 0 ) has been chosen to test the hypothesis that the impact of health on economic growth changes between countries that differ in their initial economic conditions. Rejecting the linearity hypothesis ultimately means that economies that differ in their initial stock of health or in economic conditions, may not converge and may therefore be on different growth trajectories. As we can obviously observe from Tables 2 and 3, the null hypothesis of a linear relationship between economic growth and health is rejected at the 1% significance level for all the considered threshold variables. The threshold estimates for SURV 0 and LEX 0 are 78.8 and 58.4, respectively. For GDP 0 , the estimated threshold is $ 2152. Table 2. The macroeconomic return to health: IV panel threshold estimates. Threshold variable Dependent variable: growth rate of GDP per capita SURV 0 LEX 0 GDP 0 Sup W46.4394 71.2090 33.657 (0.000) (0.000) (0.000) Estimated threshold (^ c) 87.82 58.36 2152.16 qi^ cqi>^ cqi^ cqi>^ cqi^ cqi>^ c GDPCAPITA (−1) ––––0.0122 −0.0007 (0.0048) (0.0002) LOG_GDP per capita (−1) −0.4412 −0.4835 0.0353 −0.2370 –– (0.4912) (0.1737) (0.2045) (0.0925) Survival rate 0.0087 0.0508 ––0.0990 −0.1025 (0.0403) (0.0151) (0.0665) (0.0358) Life expectancy −0.0008 0.0408 –– (0.0322) (0.0132) Secondary schooling 0.9520 0.3513 0.1765 0.1984 2.7262 6.1859 (0.4274) (0.1275) (0.3329) (0.0956) (0.8517) (1.8373) Growth of physical capital 0.9068 0.9643 0.5689 0.5269 0.66291.1025 (0.1414) (0.0875) (0.0736) (0.0489) (0.3935) (0.2566) Population growth 0.6086 −0.3369 0.0305 0.0220 1.6913 3.5181 (0.4462) (0.1173) (0.1721) 0.0862 (0.4778) (1.1828) Growth of working population 0.6941 0.4959 −0.0553 −0.2781 0.4172 0.5445 0.0911 (0.0481) (0.4985) 0.1131 (0.1171) (0.0953) Observations 245 830 205 870 168 907 Note: Asterisk ,, indicate statistical significance at the 10, 5, and 1%, respectively. Standard errors are in parentheses. Table 3. The macroeconomic return to health: IV panel threshold estimates. Threshold variable Dependent variable: growth rate of GDP per worker SURV 0 LEX 0 Sup W48.1945 71.2090 (0.000) (0.000) Estimated threshold (^ c) 87.82 58.36 qi^ cqi>^ cqi^ cqi>^ c LOG GDP per worker (−1) −0.4771 −0.5494 0.0353 −0.2370 (0.5360) (0.1985) (0.2045) (0.0132) Survival rate 0.0175 0.0633 –– (0.0500) (0.0195) Life expectancy ––−0.0008 0.0408 (0.0322) (0.0132) Secondary schooling 0.9494 0.3416 0.1765 0.1984 (0.4276) (0.1253) (0.3329) (0.0956) Growth of physical capital 0.6969 0.4962 0.5689 0.5269 (0.0920) (0.0481) (0.0736) (0.0489) Growth of working population −0.0811 −0.0267 0.0305 0.0220 (0.1400) (0.0867) (0.1721) (0.0862) Population growth 0.6266 0.3324 −0.0553 −0.2781 (0.4525) (0.1164) (0.4985) (0.1131) Observations 245 830 205 870 Note: Asterisk , indicate statistical significance at the 10, 5, and 1%, respectively. Standard errors are in parentheses. COGENT ECONOMICS & FINANCE 7