nach oben

Empirical Economics

Erschienen in:

13.06.2020

Convergence in obesity and overweight rates across OECD countries: evidence from the stochastic and club convergence tests

verfasst von: Saadet Kasman, Adnan Kasman

Erschienen in: Empirical Economics | Ausgabe 2/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Obesity has become a serious public health problem in the last two decades and affects not only developed countries but also the developing world. To date, despite all attempts to stop its increase, obesity remains a serious social problem, and obesity-related diseases have a significant cost on the health system. In this paper, we investigate the convergence of obesity rates, overweight rates, and body mass index (BMI) across 35 OECD countries over the period 1975–2006. The empirical findings of the paper are expected to have important policy implications to better inform policymakers. In addition to the traditional convergence tests, this paper uses a newly developed LM based panel unit root test with endogenously determined structural breaks to test for the stochastic convergence. Given the shortcomings of traditional and stochastic convergence tests in light of the possibility of multiple equilibria associated with groups of countries following different convergence paths, the club convergence algorithm is also employed. Traditional cross-sectional tests show that both β- and σ-convergence of the variables of interest exist across sampled countries. Moreover, the univariate LM unit root test results provide support for convergence in the relative BMIs, obesity rates, and overweight rates for a majority of the OECD countries. The club convergence test results, however, suggest the rejection of full panel club convergence only in BMI variables and overweight rates for females and the presence of a certain number of clubs for these variables.

Vorheriger Artikel Embedding effect and the consequences of advanced disclosure: evidence from the valuation of cultural goods

Nächster Artikel “Who’ll take the chair?” Maternal employment effects of a Polish (pre)school reform

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

BMI is the most useful measure of obesity in population-level that is used commonly in the literature. However, it is an imperfect measure of obesity since it does not measure the body fat directly. Therefore, it has some drawbacks. For instance, a person’s body fat increases as the person gets older. This is not reflected by the person’s weight and height. Moreover, the correspondence between BMI and body differs with sex (see Mahadevan and Ali 2016 for details).

The updated report by the OECD in 2017 also indicates that adult obesity rates are highest in the USA (38.2%), Mexico (32.4%), New Zealand(30.7%) and Hungary (30%), while they are lowest in Japan (3.7%) and Korea (5.3%).

In convergence analysis, it is evaluated whether initial cross-country differences in obesity rates (overweight rates and BMIs) tend to vanish over time. Since convergence depends on the steady-state level the country approaches, the corresponding long-run obesity prevalence rates could be estimated using following equation: \( p^{*} = 1/\left( {1 + e^{{ - y^{*} }} } \right) \), where \( y^{*} = - \frac{\alpha }{\beta } \) since, in a deterministic steady-state equilibrium, \( y_{t} = y_{t - 1} = y^{*} = - \frac{\alpha }{\beta } \) by Eq. (1).

It should be noted that some studies in the literature investigated the convergence in health expenditures (Nghiem and Connelly 2017; Narayan 2007; Panopoulou and Pantelidis 2012; Lau et al. 2014), life expectancy and calorie intake (Hobijn and Franses 2001; Sab and Smith 2002; Mazumdar 2003; Neumayer 2003; Ram 2005; Clark 2011).

The main disadvantage of the traditional tests is that they do not allow an individual growth path of varaibles of interest across countries.

It should also be noted that in contrast to Nghiem et al. (2018) that uses BMI growth rates we use BMI rates itself.

Here, if relative obesity rates (overweight rates and BMIs) follow a stationary process (i.e., does not contain a unit root), shocks to relative obesity rates will have only a transitory effect that is critical to the convergence of obesity rates. The main objective of using unit root tests with endogenously determined structural breaks is to identify and determine the trend-shifts and utilize information on non-normal errors to further improve the power of the test.

The technical details of the test could be found in Im et al. (2005), Lee et al. (2012), and Lee and Tieslau (2019).

The Phillips and Sul (2007) algorithm was first implemented in GAUSS. Its later versions have been produced in R and Stata by Schnurbus et al. (2017) and Du (2017), respectively. We use the code of Du (2017).

We also divided the sample into three 14-year sub samples. The results of estimating Eq. (1) over three sub samples show that β-convergence appears more pronounced during the 2003–2016 period for all the variables of interest. Although the results are not reported, they are available upon request from the authors.

It should be noted that the results also indicate that σ-convergence appears more pronounced during the 1989–2002 period for all the variables of interest. The results are available upon request from the authors.

Following the suggestion of one of the referees, we also provide benchmark tests (i.e., LLC, IPS and Fisher) in Appendix 3. The test results without trend are reported in Table 10. Since the tests with trend produce similar results they are not reported but available from the authors upon request. The null hypothesis that relative obesity rates (overweight rates and BMIs) contain unit root processes (against the alternative of stationary panels) is rejected using Levin et al. (2002) test at the conventional levels significance. Im et al. (2003) test produce similar results except for BMIs. In addition to the LLC and IPS test, we also employ Fisher-type panel unit root test. The null hypothesis that all panels contain unit root processes (against the alternative that at least one panel is stationary) is rejected at the conventional levels of significance.

These empirical results support the findings of Payne et al. (2015) that investigate the convergence of per capita health care expenditures among OECD countries. They also use LM unit root tests to examine stochastic convergence of health care expenditures. The panel statistic with trend-shifts shows that the relative per capita health expenditures is stationary as a whole. However, in univariate case, they find the relative per capita health expenditures to be stationary in twelve countries. In addition, our findings also support the findings of Lee and Tieslau (2019) that examine the issue of convergence of relative per capita health care expenditures in 20 OECD countries over the period 1971–2015. They found evidence in favor of convergence across the sampled countries. The univariate LM unit root tests for each country, however, indicate that ten of the countries reject the null of nonstationarity.

One of the referees argues that it might be more plausible to explore the stationarity of differences in obesity rates (overweight rates and BMIs) of sampled countries with the USA over the sample period since the USA can be considered as the frontier country with respect to variables of interest. Following this suggestion, we consider obesity rates (overweight rates and BMIs) in each country in the sample relative to the US. For instance, the relative obesity rate used in the analysis could be expressed as follows: \( ROBE_{it} = { \ln }\left( {OBE_{it} /OBE_{US} } \right) \). Table 11 in Appendix 3 presents the univariate LM unit root test results for each sampled country, along with their optimal lag length and breakpoint locations. To save space, only the results for BMI, obesity rates and overweight rates for both sexes are presented. The results for other variables of interest are very similar and available upon request from the authors. The test results reported in Table 11 show that the null of unit root is rejected for all sampled counties. Hence, the results support the presence of stochastic convergence in relative BMI, obesity rates, and overweight rates across all the OECD countries over the sample period. Moreover, the two-break panel LM unit root test results indicate that the null of a unit root is rejected for all the variables considered. Panel LM test statistic for BMI, obesity rates and overweight rates are − 48.115, − 40.934 and − 38.788, respectively.

One of the referees argues whether a ratio could be nonstationary since BMI along with the other variables used in the paper are ratios. Several studies examine stationarity of dividend-price ratio, consumption-income ratio and the share of renewable energy supply in total energy supply in the related literatures. For instance, Solarin et al. (2018) investigate stationarity of consumption-income ratio in African countries using similar methodology. Their results show that the consumption-income ratio is stationary around structural breaks in 44 out of 50 African countries. Hence, our results are consistent with the findings of previous paper that used similar methodology.

The breaks could not be seen clearly in the figures provided in Appendix 1. This point raised by one of the referees. Hence, we randomly selected three sampled countries (Australia, Canada and Czechia) and plotted their BMI levels over the sample period so that possible break could be seen clearly. Plots are provided in Appendix 4. As can be seen in the figures, the breaks in the trend are clear and possible break dates correspond to the ones provided by the LM test. Similar analysis could be done for other countries in the sample.

The null hypothesis on the convergence of the whole sample for the remaining variables is not rejected. Hence, all obesity rates and overweight rates for male are converging across OECD countries.

These results both contradict with our earlier results from the panel convergence tests on relative BMIs and overweight rates. Our earlier results from stochastic convergence test show that relative BMIs and overweight rates are stationary as a whole, providing evidence on the convergence of relative BMIs and overweight rates across OECD countries. However, these results support the findings from the stochastic convergence test on relative obesity rates. As mentioned in previous section, to compensate the shortage of the traditional convergence test methods, Phillips and Sul (2007) propose a convergence test method that considers the heterogeneity among the countries and allows this heterogeneity to change over time. Hence, empirical analysis based on the results of the club convergence test might be more reliable.

Barro RJ, Sala-i-Martin X (1991) Convergence across states and regions. Brook Papers Econ Act 22:107–182CrossRef

Barro RJ, Sala-i-Martin X (1992) Convergence. J Polit Econ 100:223–251CrossRef

Bernard AB, Durlauf SN (1996) Interpreting tests of convergence hypothesis. J Econ 71:161–173CrossRef

Clark R (2011) World health inequality: convergence, divergence, and development. Soc Sci Med 72:617–624CrossRef

Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M (2012) Obesity and socioeconomic status in developing countries: a systematic review. Obes Rev 13:1067–1069CrossRef

Du K (2017) Econometric convergence test and club clustering using stata. Stata J 17:882–900CrossRef

Duncan R, Toledo P (2018a) Long-run overweight levels and convergence in body mass index. Econ Hum Biol 31:26–39CrossRef

Duncan R, Toledo P (2018b) Do prevalence rates of overweight and obesity converge in Europe? Res Econ 72:482–493CrossRef

Egger G, Swinburn B, Islam FMA (2012) Economic growth and obesity: an interesting relationship with worldwide implications. Econ Hum Biol 10:147–153CrossRef

Evans P (1997) How fast do economies converge? Rev Econ Stat. 79:219–225CrossRef

Finkelstein EA, Strombotne KL, Zhen C, Epstein LH (2014) Food prices and obesity: a review. Am Soc Nutr 5:818–821

Galor O (1996) Convergence? Inferences from theoretical models. Econ J 106:1065–1069CrossRef

Gomula A, Nowak-Szczepanska N, Danel DP, Koziel S (2015) Overweight trends among Polish schoolchildren before and after the transition from communism to capitalism. Econ Hum Biol 19:246–257CrossRef

Goryakin Y, Suhrcke M (2014) Economic development, urbanization, technological change and overweight: what do we learn from 244 demographic and health survey? Econ Hum Biol 14:109–127CrossRef

Goryakin Y, Lobstein T, James WP, Suhrcke M (2015) The impact of economic, political and social globalization on overweight and obesity in the 56 low and middle income countries. Soc Sci Med 133:67–76CrossRef

Grecu AM, Rotthoff KW (2015) Economic growth and obesity: findings of an obesity Kuznets curve. Appl Econ Lett 22:539–543CrossRef

Hobijn B, Franses P (2001) Are living standards converging? Struct Change Econ Dyn 12:171–200CrossRef

Hossain A (2000) Convergence of per capita output levels across regions of Bangladesh. 1982–97, IMF Working paper, 2000/121

Im KS, Pesaran H, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econ 115:53–74CrossRef

Im KS, Lee J, Tieslau M (2005) Panel LM unit root tests with level shifts. Oxford Bull Econ Stat 67:393–419CrossRef

Lakdawalla D, Philipson T (2009) The growth of obesity and technological change. Econ Hum Biol 7:283–293CrossRef

Lau CKM, Fung KWT, Pugalis L (2014) Is health care expenditure across Europe converging? Findings from the application of a nonlinear panel unit root test. Eurasian Bus Rev 4:137–156CrossRef

Lee J, Tieslau M (2019) Panel LM unit root tests with level and trend shifts. Econ Model 80:1–10CrossRef

Lee J, Strazicich MC, Meng M (2012) Two-step LM unit root tests with trend-breaks. J Stat Econ Methods 1:81–107

Levin A, Lin CF, Chu CSJ (2002) Unit root tests in panel data: asymptotic and finite-sample properties. J Econ 108:1–24CrossRef

Li X, Wang R (2016) Are US obesity rates converging? Appl Econ Lett 23:539–543CrossRef

Mahadevan S, Ali I (2016) Is body mass index a good indicator of obesity? Int J Diabetes Dev Ctries 36:140–142CrossRef

Malik VS, Willett WC, Hu FB (2013) Global obesity: trends, risk factors and policy implications. Nat Rev Endocrinol 9:13–27CrossRef

Mazumdar K (2003) Do standards of living converge? A cross-country study. Soc Indic Res 64:29–50CrossRef

Minos D, Butzlaff I, Demmler KM, Rischke R (2016) Economic growth, climate change and obesity. Econ Environ 5:441–448

Narayan PK (2007) Do health expenditures ‘catch-up’? Evidence from OECD countries. Health Econ 16:993–1008CrossRef

Neumayer E (2003) Beyond income: convergence in living standards, big time. Struct Change Econ Dyn 14:275–296CrossRef

Nghiem S, Connelly LB (2017) Convergence and determinants of health expenditures in OECD countries. Health Econ Rev 7:2–11CrossRef

Nghiem S, Vu XB, Barnett A (2018) Trends and determinants of weight gains among OECD countries: an ecological study. Public Health 159:31–39CrossRef

OECD (2010), obesity and the economics of prevention, fit not fat, 15–22

Panopoulou E, Pantelidis T (2012) Convergence in per capita health expenditures and health outcomes in the OECD countries. Appl Econ 44:3909–3920CrossRef

Payne JE, Anderson S, Lee J, Myeong HC (2015) Do per capita health care expenditures converge among OECD countries? Evidence from unit root tests with level and trend-shifts. Appl Econ 47:5600–5613CrossRef

Pesaran M (2007) A simple panel unit root test in the presence of cross-section dependence. J Appl Econ 22:265–312CrossRef

Philipson TJ, Posner RA (2003) The long-run growth in obesity as a function of technological change. Perspect Biol Med 46:87–107CrossRef

Phillips PCB, Sul D (2007) Transition modeling and econometric convergence tests. Econometrica 75:1771–1855CrossRef

Phillips PC, Sul D (2009) Economic transition and growth. J Appl Econ 24:1153–1185CrossRef

Pisa PT, Pisa NM (2017) Economic growth and obesity in South African adults: an ecological analysis between 1994 and 2014. Eur J Public Health 27:404–409

Quah D (1993) Galton’s Fallacy and tests of the convergence hypothesis. Scand J Econ 95:427–443CrossRef

Ram R (2005) Cross-country convergence in income and divergence in life expectancy: a study in reversals and contrasts during the 1990s. Economia Internazionale/Int Econ, Camera di Commercio Industria Artigianato Agricoltura di Genova 58:43–50

Sab R, Smith SC (2002) Human capital convergence: a joint estimation approach. IMF Staff Pap 49:200–221

Schnurbus J, Haupt H, Meier V (2017) Economic transition and growth: a replication. J Appl Econ 32:1039–1042CrossRef

Solarin AS, Shahbaz M, Stewart C (2018) Is the consumption-income ratio stationary in African countries? Evidence from new time series tests that allow for structural breaks. Appl Econ 12:3–4. https://doi.org/10.1080/00036846.2018.1441515CrossRef

Solow RM (1956) A contribution to the theory of economic growth. Quart J Econ 70:65–94CrossRef

Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, Gortmaker SL (2011) The global obesity pandemic: shaped by global drivers and local environments. Lancet 378:804–814CrossRef

Wang M, Wen X, Yanfeng Z, Jiang C, Wang F (2017) Is economic environment associated with the physical activity levels and obesity in Chinese adults? A cross-sectional study of 30 regions in China. BMC Public Health 17:701–710CrossRef

Windarti N, Hlaing SW, Kakinaka M (2019) Obesity Kuznets curve: international evidence. Public Health 169:26–35CrossRef

Titel: Convergence in obesity and overweight rates across OECD countries: evidence from the stochastic and club convergence tests
verfasst von: Saadet Kasman
Adnan Kasman
Publikationsdatum: 13.06.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 2/2021
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-020-01895-3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 2/2021

Bank stability and economic growth: trade-offs or opportunities?

Crises, market shocks, and herding behavior in stock price forecasts

Does high debt ratio influence Chinese firms’ performance? A semiparametric stochastic frontier approach with zero inefficiency

Can licensing induce productivity? Exploring the IPR effect

Racial demographics and cigarette tax shifting: evidence from scanner data

Horizon confidence sets

Premium Partner