• Published: 18 January 2021

The impact of economic, social, and political globalization and democracy on life expectancy in low-income countries: are sustainable development goals contradictory?

  • Arif Eser Guzel   ORCID: orcid.org/0000-0001-5072-9527 1 ,
  • Unal Arslan 1 &
  • Ali Acaravci 1  

Environment, Development and Sustainability volume  23 ,  pages 13508–13525 ( 2021 ) Cite this article

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The 17 Sustainable Development Goals announced by the United Nations are important guides for the development processes of developing countries. However, achieving all of these goals is only possible if the goals are consistent with each other. It has been observed in the literature that possible contradictions between these goals are ignored. Therefore, the main purpose of this study is to investigate whether two sustainable development goals (SDGs) of the UN are contradictory or supporting each other in low-income countries. These SDGs are “Good Health and Well-Being” (SDG3) and “Partnerships for the Goals” (SDG17). For this purpose, the role of globalization and democracy in life expectancy is empirically investigated in 16 low-income countries over the period 1970–2017. While globalization has been used as an indicator of the partnership between countries, democracy has been used as an indicator of accountability and cooperation between governments and societies. According to estimations of the continuous-updated fully modified (CUP-FM) and bias-adjusted ordinary least squares (BA-OLS), globalization and its subcomponents such as economic, social, and political globalization affect life expectancy positively. Democracy also increases life expectancy in those countries. The GDP per capita is also used as a control variable. Our results show that a higher level of per capita income is positively associated with higher levels of life expectancy. In conclusion, no contradiction was found between SDG3 and SDG17 in those countries. Achieving a healthier society requires economic, social, and political integration between governments and societies.

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1 introduction.

The main problem of economics is to increase economic development and social welfare. Increasing the social welfare level is a complex process that depends on economic and non-economic factors. Achieving economic development or increasing the level of welfare depends on achieving and sustaining the main objectives in political, economic, and social areas. Today, development is no longer a process that can be realized through policies implemented by governments alone. It requires cooperation between governments and societies. While cooperation between different countries requires globalization in the economic, social, and political fields, democracy is the way to ensure cooperation between governments and societies.

Health is one of the most important indicators of social welfare. Besides being one of the indicators of development, it is one of the determinants of human capital formation which is necessary for economic development. Individuals living in developed countries live a healthier life compared to those living in less developed countries. While the differences between the levels of development of countries determine the health conditions, at the same time, improvement of public health paves the way for economic development. Healthy people have higher opportunities to earn a higher income than unhealthy people. Individuals with higher incomes can benefit from better nutrition and access to health services. Therefore, economic development and improvement of health conditions represent a two-way process. In this context, the determination of the variables that will enable the achievement of the goal of a healthier society is especially important in explaining the economic differences between developing countries and developed countries. Because of its importance, health-related goals have an important place both among the Millennium Development Goals (MDGs) and the Sustainable Development Goals (SDGs) announced by the United Nations.

The world leaders with the support of international funding organizations announced the Millennium Declaration in September 2000 at the United Nations Headquarters in New York. They committed their nations to a new international partnership to achieve some development targets having with the final deadline of 2015. The Millennium Development Goals (MDGs) consist of 8 goals, 21 targets, and 60 related indicators covering a wide spectrum of development areas such as “End Poverty and Hunger (MDG 1),” “Universal Education (MDG 2),” “Gender Equality (MDG 3),” “Child Health (MDG 4),” “Maternal Health (MDG 5),” “Combat HIV/AIDS (MDG 6),” “Environmental Sustainability (MDG 7),” and “Global Partnership (MDG 8).” As we see, three of the goals are directly associated with the health status of the people. In the deadline of 2015, according to “Health in 2015: From MDGs to SDGs” report of the World Health Organization (WHO), there are improvements in health-related targets such as child health, maternal health, and combat with HIV/AIDS. Globally, HIV, tuberculosis, and malaria targets have been met. Also, the child mortality rate was reduced by 53% and maternal mortality by 43% (WHO 2016 ). On a global view, although health-related problems are largely resolved, the situation is not as good for low-income countries. As shown in Fig.  1 , significant differences exist between developing countries and developed countries in achieving health-related goals.

figure 1

Source Halisçelik and Soytas (2015)

World Bank Income Groups’ MDGs Index Values in 2015.

According to MDGs, indexes in the context of health status show that the goals desired in terms of health are not attained in low-income countries compared to other income groups. After the deadline of MDGs, the United Nations has announced 17 SDGs, and “Good Health and Well-Being” takes its place as the third goal. Since achieving these goals requires the cooperation of countries and societies, “Partnership for the Goals” is determined as the seventeenth SDG. According to the United Nations ( 2019 ), the main indicators of global partnerships are trade, foreign direct investments, remittances, financial integration technology transfers, data monitoring and accountability, internet usage, and political integration among countries. In our study, while globalization is used as a proxy indicator of global cooperation, democracy is an indicator of cooperation between societies and governments. Democracy also refers to accountability levels of governments.

Globalization can simply be defined as the process of international integration which has economic, social, and political dimensions (Dreher 2006 ). Many countries have adapted to this process and have enjoyed the welfare effects of globalization by implementing necessary economic and institutional transformation. However, some countries still suffer from poor adaption to global markets. According to the KOF Globalization Index published by the Swiss Economic Institute ( 2020 ), low-income countries have the lowest globalization level compared to other income groups. They also suffer from bad health conditions such as low life expectancy, communicable diseases, and high mortality rates according to MDG indexes given above. At this point, the literature is divided into two parts. The first one blames globalization and argues that poverty and as a result of this, low life expectancy derives from the inequality created by globalization itself (Buss 2002 ). The second group mostly focuses on the benefits of free trade, capital mobility, and technology transfers (Rao and Vadlamannati 2011 ). The low-income countries also suffer from low institutional quality in the context of democracy and political rights. According to Freedom House’s list of electoral democracies, the countries without electoral democracy are mostly the low-income countries in the Middle East, North Africa, Sub-Saharan Africa, and Southeast Asia (Freedom House 2019 ).

The main question of our study is to determine whether the problem of low life expectancy in low-income countries is due to the low levels of globalization and weak political institutions in these countries. To answer this question, the role of economic, social, and political globalization and democracy in life expectancy in those countries is empirically investigated. This study provides several contributions to previous literature. First, we provide a new perspective in the context of sustainable development goals. Previous studies mostly focused on how to achieve SDGs, while possible conflicts between the goals were mostly ignored especially in the context of health. Such conflicts between sustainable development goals in the literature have mostly focused on the impact of economic growth and globalization on the sustainable environment (Ulucak and Bilgili 2018 ; Zafar et al. 2019a ). Those studies are mostly addressed the relationship between SDG7, SDG8, SDG13, and SDG17 (Zafar et al. 2019b ). To the best of our knowledge, it is the first study that investigates the relationship between SDG3 and SDG17. It is also important to examine this relationship in low-income countries since they still suffer from low levels of life expectancy, less adaptation to globalization, and poor democratic institutions compared to other income groups. Previous works mostly provide global evidence, while only a few studies focus on less developed countries. Achieving these 17 goals put forward by the United Nations at the same time is possible only if these goals do not conflict with each other. Second, empirical works in previous literature consist of traditional estimation methods called first-generation tests. In the analysis of panel data, the estimators considering cross-sectional dependence are called the second-generation estimators. Cross-sectional dependency simply refers to the situation when the shock that occurs in one country affects other countries as well. The source of this problem encountered in panel data analysis is the economic, financial, and political integration among countries (Menyah et al. 2014 ). The ignorance of cross-sectional dependence results in biased and inconsistent estimates and wrong inferences (De Hoyos and Sarafidis 2006 ; Chudik and Pesaran 2013 ). Low-income countries are mostly African countries where there is a rising trend in terms of integration to global markets and institutions (Beck et al. 2011 ). Using estimation techniques that consider cross-sectional dependence in those countries prevents misleading results. As the literature is divided into two parts about the effects of globalization on human well-being, fresh evidence via robust estimation methods is required in order to provide proper policy implications. To fill this gap, our work provides second-generation estimations.

2 Literature review

To improve the health conditions of a country, the welfare of the poor should be improved as well. Poverty is detrimental to access to health services. Therefore, the positive impact of globalization on health first emerged with its positive effects on economic growth (Labonté et al. 2009 : 10). The effects of globalization on growth were mostly driven by free trade, international specialization, technology transfers, knowledge spillovers, and competitive markets. It also offers broader opportunities for entrepreneurs and paves the way for innovation (Grossman and Helpman 2015 : 101). As expected, poverty rates significantly reduced in the last two decades because of the integration of developing economies to global markets (Harrison 2006 ). When trade liberalization and income increases are considered together, people's access to treatments and medications can be easier and life expectancy may be prolonged. However, we should consider other possibilities in the context of spreading communicable diseases. As Deaton ( 2004 ) mentioned before, access to cheap and easy travel can increase the rate of spread of communicable diseases. Migration is also another fact to take into account. Particularly rising sexual tourism and migrant sex workers increase the spread of sexually transmitted diseases such as HIV/AIDS. But today there are improved treatment methods to solve these problems. Even HIV-infected people can survive with antiretroviral therapy, and it also reduces sexual transmission of the infection (Dollar 2001 ; Cohen et al. 2011 ). Due to the high cost of advanced drugs as in the case of antiretroviral therapy, it should be accepted that people in low-income countries will have trouble accessing the drugs (Buss 2002 ). There are approaches known as the unequal exchange that globalization increases inequality among countries and that developed countries are more profitable from the globalization process (Love, 1980 ). It may also increase domestic income inequality. There are a few studies that came with the conclusion that globalization rises inequality (Dreher and Gaston 2008 ; Ha 2012 ), but Bergh and Nilsson ( 2010 ) suggested a different perspective. Due to extensive R&D investments and scientific activities, developed countries can find new treatment methods and supply advanced drugs. The only way to access that knowledge and these drugs are trade and integration between developed and underdeveloped countries. Globalization can play an important role in improving the health conditions of low-income countries to the extent that it can provide these linkages. One should also notice that wider markets and higher returns are important factors that motivate entrepreneurs. Buss ( 2002 ) claimed that the intellectual property rights of advanced drugs belong to private firms in developed countries, and because of the strong protection of property rights, less developed countries have trouble accessing them. However, rising global human rights became an important step to advance public health issues against economic concerns in the trade of pharmaceutical products.

The human rights approach focuses on how globalization affected disadvantaged people worldwide (Chapman 2009 ). It is an important instrument in the suppression of the inequality created by economic globalization. Because of the pressure on the government about human rights, disadvantaged people are becoming able to meet their basic human needs. The role of political globalization on this point is forcing governments to adopt global institutions. It increases the number of international organizations in which a country is a member. This makes governments more accountable in the global area and forcing them to pay attention to protect human rights. Gelleny and McCoy ( 2001 ) also claimed that integration among countries leads to political stability. Therefore, governments' tendency to violate human rights in order to maintain their power becomes lesser. Moreover, as social dimensions of globalization expand and communication opportunities among people in different countries increase, the possibility of human rights violations being discovered by other people increases (Dreher et al. 2012 ). Governments that know the international sanctions required by these violations have to be more cautious against human rights violations. Social globalization also provides cultural integration among the world’s people, and it changes lifestyles and consumption patterns worldwide. The consequences of this change can have positive and negative effects. First, increased urban population and sedentary lifestyles may enhance prepared food consumption and reduce daily movements which result in rising obesity and diabetes (Hu 2011 ). Second, although rapidly increasing consumption options and diversity are known as welfare indicators, they also can cause stress which is known as an important determinant of many diseases both psychological and physical (Cutler et al. 2006 ). Third, due to knowledge spillovers and communication technology, people can learn about healthy nutrition and protection from communicable diseases. Thus, unhealthy but traditional consumption patterns and lifestyles may change. These days we experience the coronavirus epidemic and we see once again the importance of globalization. Countries are aware of infectious diseases in different parts of the world in a very short time and can take measures to stop the spread of the virus. The changes created by social and political globalization play a major role in this emergence. Social globalization enables people in very remote areas of the world to communicate with each other, while political globalization forces governments to be transparent about infectious diseases.

With economic globalization, increased economic activity may lead to urbanization. One may think about unhealthy conditions of an urban area such as environmental degradation, air and water pollution, higher crime rates, and stress which reduce life expectancy. However, according to Kabir ( 2008 ), people living in an urban area can benefit from improved medical care, easy access to pharmacy, and to the hospitals that use higher technology. They can also get a better education and can enjoy better socioeconomic conditions.

Democracy can be considered as another determinant of life expectancy. In order to solve the health problems of the poor, people should draw the attention of the government. Sen ( 1999 ) claimed that the instrumental role of democracy in solving problems is enabling people to express and support their claims. Thus, the attention of politicians can be attracted to the problems of the poor. Politicians who have never tasted poverty do not have the urge to take action against the problems of the poor at the right time. Another linkage can be established through accountability (Besley and Kudamatsu 2006 ). In democracies, governments have an obligation to account to citizens for what purposes the resources were used. Thus, resources can be allocated to solve important public issues such as quality of life, communicable diseases, and mortality.

Compared to theoretical discussions, previous literature provides a lack of empirical evidence. Barlow and Vissandjee ( 1999 ) examined the determinants of life expectancy with cross-sectional data available in 1990 for 77 developed and developing countries. According to regression results, per capita income, literacy rate, and lower fertility are important determinants of life expectancy while living in a tropical area decreasing it. Another finding in this study shows that health expenditures in those countries failed to increase life expectancy. Following this study, Or ( 2000 ) analyzed the determinants of health outcomes in 21 industrialized OECD countries covering the period 1970–1992. This study presents gender-specific estimates separately for men and women. Fixed effects estimation results reveal a significant negative relationship between public health expenditure and women's premature death. The relationship also occurs for men, while GDP per capita dropped from the regression model due to high collinearity. Furthermore, GDP per capita and the proportion of white-collar workers reduce premature death for both men and women, while alcohol consumption increases it.

Franco et al. ( 2004 ) analyzed the impact of democracy on health utilizing political rights data of 170 countries. Empirical results show that people living in democracies enjoy better health conditions such as longer life expectancy, better maternal health, and lower child mortality. Following this, Besley and Kudamatsu ( 2006 ) investigated the nexus between democracy and health outcomes utilizing panel data from the 1960s to the 2000s. In their study, they used life expectancy at birth and child mortality variables for 146 countries as indicators of health outcomes. According to results, democracy has a positive and significant effect on life expectancy at birth and it also reduces child mortality. Safaei ( 2006 ) also investigated the impact of democracy on life expectancy and adult and child mortality rates with the data of 32 autocratic, 13 incoherent, and 72 democratic countries. According to the OLS estimation results, improving democratic institutions increases life expectancy and reduces child and adult mortality rates. Another finding of the study is that socioeconomic factors such as income, education, and access to health care services are important determinants of health status.

Owen and Wu ( 2007 ) found a positive relationship between trade openness and health outcomes using a panel of 219 countries. Health outcome measures of this study are infant mortality and life expectancy. Trade openness is one of the most important dimensions of globalization.

Kabir ( 2008 ) analyzed the determinants of life expectancy in 91 developing countries. Empirical results obtained are the opposite of the expected. According to results, per capita income, literacy rate, per capita health expenditure, and urbanization have no significant impact on life expectancy. On the other hand, the number of physicians has a positive and significant impact on life expectancy, while malnutrition reduces it. As a dummy variable, living in Sub-Saharan Africa is another factor that reduces life expectancy due to communicable diseases like HIV, malaria, etc.

Bergh and Nilsson ( 2010 ) used a panel of 92 countries in the period 1970–2005 to investigate the relationship between globalization and life expectancy. They used social, political, and economic globalization data separately, and the results show a significant positive effect of economic globalization on life expectancy at birth. But no significant relationship was found between social globalization, political globalization, and life expectancy. They also used average years of education, urban population, the number of physicians, and nutrition as control variables and the effect of economic globalization was still positive and significant.

Welander et al. ( 2015 ) examined the effects of globalization and democracy on child health in their panel data analysis for 70 developing countries covering the period 1970–2009. According to the results, globalization significantly reduces child mortality. In addition, democracy improves child health and it also increases the beneficial effects of globalization on child health. Following this study, Tausch ( 2015 ) analyzed the role of globalization in life expectancy in 99 countries. The results of OLS estimates show that globalization leads to inequality, and therefore, it reduces health performance in terms of life expectancy and infant mortality. These results are contradictory to positive views on the role of globalization in public health. However, in 19 of 99 countries, globalization increases public health performance. Ali and Audi ( 2016 ) also analyzed the role of globalization in life expectancy in Pakistan. According to ARDL estimation results, life expectancy is positively associated with higher levels of globalization. Another study on the Pakistan case proposed by Alam et al. ( 2016 ) concluded that foreign direct investment and trade openness which are important indicators of economic globalization affects life expectancy positively.

Patterson and Veenstra ( 2016 ) concluded that electoral democracies provide better health conditions compared to other countries. Their analysis includes annual data from 168 countries covering the period 1960–2010. Empirical results show democracy has a significant positive impact on life expectancy and it reduces infant mortality.

In their recent study, Shahbaz et al. ( 2019 ) investigated the impact of globalization, financial development, and economic growth on life expectancy. The authors used nonlinear time series analysis methods utilizing the data of 16 Sub-Saharan African countries over the period 1970–2012. Their results show that globalization, financial development, and economic growth affect life expectancy positively in 14 of 16 Sub-Saharan African countries.

The previous literature provides a lack of evidence in the context of globalization, democracy, and life expectancy relationship. There are also methodological weaknesses in previous empirical studies. First, it can be observed that previous studies are mostly based on traditional estimation methods. Second, the panel data analyses are based on the first-generation estimators that assume cross-sectional independence. This assumption is hard to satisfy due to integration among countries. In addition, ignoring the cross-sectional dependence results in inconsistent estimations. Particularly in empirical work in the context of globalization which refers to economic, political, and cultural integration among countries, considering the cross-sectional dependence becomes more important. Therefore, in order to make a methodological contribution to previous literature, we used second-generation panel time series methods considering cross-sectional dependence.

3 Methodology and data

According to the United Nations, achieving sustainable development goals requires global cooperation and partnership. Therefore, “partnerships for goals” has taken its place as the 17th sustainable development target. However, it was emphasized that some sub-goals should be realized in order to reach this goal. These include improving international resource mobility, helping developing countries to attain debt sustainability, promoting the transfer of information and technology between developed and developing countries, an open and rule-based free trade system, encouraging public–private and civil society partnerships, increasing transparency and accountability, and high quality and reliable data (United Nations 2019 ). In our empirical work, economic, social, and political globalization and democracy variables were used as proxies of the subcomponents of SDG17. In addition, the life expectancy at birth variable that mostly used in related literature as a proxy of health status and well-being, it is used in our study as a proxy of SDG3. In this study, we investigated the role of globalization and democracy in life expectancy in 16 low-income countries. Footnote 1 Following Barlow and Vissandjee ( 1999 ) and ( 2000 ), GDP per capita is used as a control variable in order to mitigate omitted variable bias. Our dataset is covering the period 1970–2017. Following the related literature, we present our model as follows:

where lex is life expectancy at birth which refers to the average number of years a newborn is expected to live. Life expectancy at birth data is provided by World Bank ( 2019 ) World Development Indicators. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. The dataset is consisting of a weighted average of collected data from several co-founders. In Eq.  1 , X refers to the KOF Globalization Index developed by Dreher ( 2006 ). This index has been used in previous literature as a proxy of SDG17 (Saint Akadiri et al. 2020 ). The current version of the data published by the Swiss Economic Institute is revised by Gygli et al. ( 2019 ). The globalization variables are between 0–100, and 100 refers to the highest globalization level. In our analysis, we used subcomponents of globalization index such as economic (EC), social (SOS), and political (POL) globalization in addition to overall globalization (GLB). Due to high collinearity, the effects of different types of globalization are analyzed separately. Models 1, 2, 3, and 4 represent the estimations with overall, economic, social, and political globalization indexes, respectively. The democracy variable ( dem ) is provided from the Polity IV project dataset (Marshall and Jaggers 2002 ). While the increases in this indicator represent a more democratic regime, the decreases represent a more autocratic regime. Finally, gdp is real GDP per capita (constant 2010 $) and it is provided from World Bank World Development Indicators. All variables transformed to the logarithmic form except democracy due to negative values. In the estimation of the model, the panel data analysis methods are used.

3.1 Cross-sectional dependence

Traditional panel data methods are based on the assumption that no cross-sectional dependence exists among cross section units. However, this assumption is hard to satisfy due to rising economic, social, and political integration between countries. The estimations do not take this process into account may cause inconsistent results. Such results may also lead to incorrect inferences (Chudik and Pesaran, 2013 ). The existence of cross-sectional dependence in variables and the error term is obtained from the model analyzed with Pesaran ( 2004 ) \({\text{CD}}_{{{\text{LM}}}}\) and Pesaran et al. ( 2008 ) bias-adjusted LM test. These techniques are robust whether N > T and T > N. Therefore, \({CD}_{LM}\) and bias-adjusted LM ( \({LM}_{adj})\) tests are found to be appropriate and their test statistics can be calculated as follows:

Equation  2 shows the calculation of Pesaran ( 2004 ) \({CD}_{LM},\) and Eq.  3 is Pesaran et al. ( 2008 ) bias-adjusted LM test statistic. \({V}_{Tij}\) , \({\mu }_{Tij}\) , and \({\widehat{\rho }}_{ij},\) respectively, represent variance, mean, and the correlation between cross section units. The null and alternative hypothesis for both test statistics; \({H}_{0}\) : No cross-sectional dependence exist; \({H}_{1}\) : Cross-sectional dependence exist.

In the selection of stationarity tests and long-run estimators, the existence of cross-sectional dependence will be decisive. If the null of no cross-sectional dependence is rejected, second-generation methods that assume cross-sectional dependence should be used in order to provide unbiased and consistent estimation results.

3.2 Slope homogeneity

Pesaran and Yamagata ( 2008 ) proposed a method to examine slope heterogeneity in panel data analysis based on the Swamy ( 1970 )’s random coefficient model.

The calculation of the test statistic of Swamy’s model is given in Eq.  4 .

In Eq.  4 , \({\stackrel{\sim }{\beta }}_{i}\) and \({\overbrace{\beta }}_{WFE},\) respectively, indicate the parameters obtained from pooled OLS and weighted fixed effects estimation, while \({M}_{T}\) is the identity matrix. The test statistic obtained from Swamy’s model is improved by Pesaran et al. ( 2008 ) as follows:

where \(\stackrel{\sim }{S}\) is the Swamy test statistic and k is a number of explanatory variables. \({\stackrel{\sim }{\Delta }}_{adj}\) is a bias-adjusted version of \(\stackrel{\sim }{\Delta }\) . \({\stackrel{\sim }{Z}}_{it}\) =k and \(Var\left({\stackrel{\sim }{Z}}_{it}\right)=2k(T-k-1)/T+1\) . The null and alternative hypothesis for both test statistics is given below.

The rejection of the null hypothesis shows that slope coefficients of Eq. 1 are heterogeneous. In the selection of panel data estimation methods, the results of those preliminary analysis are taken into account.

3.3 Unit root test

Pesaran ( 2006 ) suggested a factor modeling approach to solve the cross-sectional dependency problem. This approach is simply based on adding cross-sectional averages to the models as proxies of unobserved common factors. The Cross-sectionally Augmented Dickey–Fuller (CADF) unit root test developed by Pesaran ( 2007 ) is based on that factor modelling approach. This method is an augmented form of Augmented Dickey–Fuller (ADF) regression with lagged cross-sectional average and its first difference to deal with cross-sectional dependence (Baltagi, 2008 : 249). This method considers the cross-sectional dependence and can be used, while N > T and T > N. The CADF regression is:

\({\stackrel{-}{y}}_{t}\) is the average of all N observations. To prevent serial correlation, the regression must be augmented with lagged first differences of both \({y}_{it}\) and \({\stackrel{-}{y}}_{t}\) as follows:

After the calculation of CADF statistics for each cross section ( \({CADF}_{i}\) ), Pesaran ( 2007 ) calculates the CIPS statistic as average of CADF statistics.

If the calculated CIPS statistic exceeds the critical value, it means that the unit root hypothesis is rejected. After the preliminary analysis of unit root, the existence of a long-run relationship between the variables in our model will be investigated via Westerlund and Edgerton ( 2007 ) cointegration test. After this, the long-run coefficients will be estimated using the continuous-updated fully modified (CUP-FM) estimator developed by Bai and Kao ( 2006 ) and Bias-adjusted OLS estimator developed by Westerlund ( 2007 ).

3.4 Cointegration test and long-run relationship

In this study, the cointegration relationship was investigated by Westerlund and Edgerton ( 2007 ) LM bootstrap test. This method considers cross-sectional dependence and provides robust results in small samples (Westerlund and Edgerton, 2007 ). This method is based on the following equation

where \({n}_{ij}\) is an independent and identically distributed process with zero mean and var( \({n}_{ij})\) = \({{\sigma }_{i}}^{2}\) . Westerlund and Edgerton ( 2007 ) suggested following LM test in order to test the null of cointegration

where \({S}_{it}\) is partial sum process of the fully modified estimate of \({z}_{it}\) and \({\widehat{w}}_{i}^{-2}\) is the estimated long-run variance of \({u}_{it}\) conditional on \(\Delta {x}_{it}^{^{\prime}}\) . If the calculated LM statistic is below the critical value, the null of cointegration will be accepted. The critical values will be provided using the bootstrap method in order to prevent cross-sectional dependence.

In the estimation of long-run coefficients, the CUP-FM estimator was used and this method is based on the following regression

where \({\widehat{\lambda }}_{i}^{^{\prime}}\) refers to the estimated factor loadings and \(\hat{y}_{{i,t}}^{ + } = y_{{i,t}} - \left( {\lambda _{i} ^{\prime } \hat{\Omega }_{{F \in i}} + \hat{\Omega }_{{\mu \in i}} } \right)\hat{\Omega }_{{ \in i}}^{{ - 1}} {{\Delta }}x_{{i,t}}\) indicates the transformation of the dependent variable for endogeneity correction. According to Bai and Kao ( 2006 ), CUP-FM estimator is robust under cross-sectional dependence. However, the assumption that the number of common factors (k) is known cannot be satisfied in practice (Westerlund, 2007 ). Therefore, Westerlund ( 2007 ) suggested a bias-adjusted estimator (BA-OLS) following the methodology of Bai and Kao ( 2006 ) except in the context of determining the number of common factors. The author suggested the estimation of k using an information criterion as

where \(IC\left(k\right)\) is the information criterion. In this study, we determined the number of common factors via the Bayesian information criterion (BIC) as follows.

In the equation above, V(k) is the estimated variance of \({\widehat{u}}_{it}\) based on k factors. By minimizing the BIC, we obtain \(\widehat{k}\) . Westerlund ( 2007 ) showed that the estimation of k provides better results compared to CUP-FM estimator assuming k is known. Both of the estimators require cointegrated variables in the long run.

3.5 Empirical results and discussion

The results of Pesaran ( 2004 ) \({CD}_{LM}\) and Pesaran et al. ( 2008 ) bias-adjusted LM tests are given in Table 1 .

The results given in Table 1 show that the null of no cross-sectional dependence is rejected at 1% according to both \({CD}_{LM}\) and \({LM}_{adj}\) test statistics in all variables. In addition, in the error terms obtained from models 1, 2, 3, and 4 the null of no cross-sectional dependence is rejected at 1%. These results show that the methods to be used in the analysis of the stationarity of the variables and the determination of the long-run relationship should consider the cross-sectional dependence.

The results of homogeneity tests developed by Pesaran and Yamagata ( 2008 ) are given in Table 2 . According to the results, the null of homogeneity is accepted at %1 in all models. Therefore, estimators assume parameter homogeneity are used in our analysis.

After the preliminary analysis of cross-sectional dependence, the CADF unit root test developed by Pesaran ( 2007 ) is found to be appropriate for our model because of its robustness under cross-sectional dependence. The results of the CADF unit root test are given in Table 3 .

In the analysis of unit root, constant and trend terms are both considered at level, while only constant term is added at first difference. Maximum lag level is determined as 3, while optimum lag level is determined by F joint test from general to particular. According to results, the null of unit root is accepted for all variables, while calculated CIPS statistics of first-differenced variables exceed 1% critical value. All variables have a unit root, and their first differences are stationary ( \({I}_{1})\) . Therefore, in order to determine the existence of a long-run relationship, we applied Westerlund and Edgerton ( 2007 ) panel cointegration test. This method considers cross-sectional dependence and can be used, while the series are integrated in the same order. The results are shown in Table 4 .

Constant and trend are both considered in the analysis of cointegration, and critical values are obtained from 5000 bootstrap replications. The results show that the null of cointegration is accepted for all models. There is a long-run relationship between life expectancy, globalization, democracy, and GDP per capita. After determining the cointegration relationship, we estimated long-run coefficients utilizing CUP-FM and BA-OLS estimators proposed by Bai and Kao ( 2006 ) and Westerlund ( 2007 ), respectively.

The long-run estimation results given in Table 5 show that overall, economic, social, and political globalization are positively associated with life expectancy at 1% significance level according to both CUP-FM and BA-OLS estimators. The results show that a 1% increase in globalization index increases life expectancy %0.014 and %0.015 according to CUP-FM and BA-OLS estimators, respectively. The impact of economic, social and political globalization indexes is 0.013%, 0.011%, and 0.015% according to CUP-FM estimation results while 0.014%, 0.012%, and 0.017% according to both estimators, respectively.

Our results confirms the findings of Owen and Wu ( 2007 ), Ali and Audi ( 2016 ), and Shahbaz et al. ( 2019 ) who found a positive relationship between globalization and life expectancy. Our empirical work also supports the evidence of Bergh and Nilsson ( 2010 ) in terms of positive effect of economic globalization on life expectancy. While the authors found no significant impact of social and political globalization on life expectancy, our results show that life expectancy is positively associated with both social and political globalization. The results we found contradict Tausch ( 2015 )’s evidences in 80 of 99 countries. However, according to his results, in 19 of 99 countries, globalization affects health positively. When these countries are examined, it is seen that 14 of them are countries in the low and lower-middle income groups. In this sense, it can be said that the evidence we found for low-income countries is in line with the author's evidence. As Dreher ( 2006 ) mentioned, despite its possible inequality effects, the net effect of globalization on development is mostly positive and our empirical work supports that idea. The effect of democracy on life expectancy is also positive and significant at 1% which confirms the findings of Franco et al. ( 2004 ) and Besley and Kudamatsu ( 2006 ). In electoral democracies, people living in poverty and suffering from health problems can easily attract the attention of policymakers compared to autocracies. This leads to the reallocation of resources to solve the primary problems of the society. In the context of sustainable development goals, our results show that there is no conflict between SDG3 (good health and well-being) and SDG17 (partnerships for the goals). The improvement of the health conditions of the poor countries depends on global partnership and economic, social, and political integration among countries. In addition, democracy is an important tool in achieving the goal of a healthy society, as it fosters accountability, transparency, and partnership between governments and the societies they rule. As stated in the introduction section, low-income countries show low performance in terms of health-related sustainable development goals, and their connections with global markets are weak compared to other countries. At the same time, democratic institutions are not developed. Our work supports the idea that in order to achieve SDG3, global partnership and democracy are required.

The GDP per capita that used as a control variable has a positive impact on life expectancy at a 1% level. These results support the evidence of Barlow and Vissandjee ( 1999 ), Or ( 2000 ), and Shahbaz et al. ( 2019 ). Individuals living in countries with high per capita income are expected to have higher welfare and have a longer life expectancy (Judge, 1995 ). In low-income countries where people still suffer from having difficulty in meeting basic human needs, increasing per capita income may lead to better nutritional status, easier access to advanced treatment methods and technology.

4 Conclusion

In this study, the effects of globalization and democracy on life expectancy are empirically investigated in low-income countries. While globalization and democracy indexes are used as proxy indicators of “Partnerships for the Goals (SDG 17),” life expectancy used a proxy of “Good Health and Well-Being (SDG 3).” With this, it is aimed to examine the existence of contradiction between those SDGs. In the estimation of the long-run relationship between the variables, second-generation panel data analysis methods that consider cross-sectional dependency are used. According to the results, the globalization index and its subcomponents such as economic, social, and political globalization are important instruments to achieve a healthier society. In addition, higher levels of democracy lead to higher levels of life expectancy. Finally, GDP per capita growth improves health status of countries.

The findings obtained from our study show that economic, social, and political integration of countries and democracy accelerate the process of achieving a healthier society. Therefore, it is seen that SDG3 and SDG17 targets are compatible with each other. In order to achieve SDG3, economic, social, and political integration between countries should be encouraged and democratic institutions should be improved. Policy makers should remove the barriers on globalization, and they should promote participation on international organizations and public–private and civil society partnerships.

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Guzel, A.E., Arslan, U. & Acaravci, A. The impact of economic, social, and political globalization and democracy on life expectancy in low-income countries: are sustainable development goals contradictory?. Environ Dev Sustain 23 , 13508–13525 (2021). https://doi.org/10.1007/s10668-021-01225-2

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  • The Concept of “Political Globalization” and Global Challenges

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Political globalization is a dynamic, nonlinear, global process of increasing and complicating the interdependence between all elements of the global political system. Global political processes are processes taking place in the context of the political aspect of globalization, resulting in the structural transformation of the world system of international relations and the emergence of new global political actors, an increase in the political interconnection and interdependence between them, and the creation of a global political architecture and hierarchy. This article reveals the trends in the development of global political processes, including: the formation of a new structure and architecture for the global world; the emergence of new actors in the global political system; the manifestation of two models of modernization of the countries of the global world (the western [Atlantic] and continental); the growth of instability at the global level; the inefficiency of global governance; the conflict between globalization and the national interests of states; and the growing role of the countries of the “global periphery” in world politics. Global political challenges are potential points of political bifurcation that must be prevented. If ignored, they can develop into global political problems, and if they are not resolved, will evolve into global political risks. This article presents a classification of global political challenges and examines the causes affecting their dynamics. The author describes possible scenarios for the development of the world political system depending on the reaction of society to global political challenges and the consequences of the implementation of each.

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Philosophical aspects of globalization: a multidisciplinary inquiry.

Cover Philosophical Aspects of Globalization: A Multidisciplinary Inquiry

  • Interdisciplinary Sciences
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  • Preliminary Material
  • Copyright Page
  • Foreword: Philosophy of Globalization in Today’s Dialogue of Civilizations
  • Notes on Contributors
  • Globalization from the Philosophical Point of View
  • Globalization as a Holistic Process
  • The Age of Competing Globalizations
  • Globalization and Global Processes: The Algorithm of Development
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  • The Role of Global Studies in the Development of a Sustainable Security System
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  • The Globalized World under the Prism of COVID -19
  • The Twilight of Neoliberal Globalization
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  • Harmonization of the Biosphere and the Technosphere as a Global Problem of Modernity
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  • Towards a Theory of Global Security
  • Modern Challenges of Global Sports Development
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The impact of economic, political and social globalization on overweight and obesity in the 56 low and middle income countries

Yevgeniy goryakin.

a Health Economics Group, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK

b UKCRC Centre for Diet and Activity Research (CEDAR), Institute of Public Health, Cambridge, UK

Tim Lobstein

c World Obesity Federation, London, UK

W. Philip T. James

d London School of Hygiene and Tropical Medicine, London, UK

Marc Suhrcke

e Centre for Health Economics, University of York, York, UK

Associated Data

Anecdotal and descriptive evidence has led to the claim that globalization plays a major role in inducing overweight and obesity in developing countries, but robust quantitative evidence is scarce. We undertook extensive econometric analyses of several datasets, using a series of new proxies for different dimensions of globalization potentially affecting overweight in up to 887,000 women aged 15–49 living in 56 countries between 1991 and 2009. After controlling for relevant individual and country level factors, globalization as a whole is substantially and significantly associated with an increase in the individual propensity to be overweight among women. Surprisingly, political and social globalization dominate the influence of the economic dimension. Hence, more consideration needs to be given to the forms of governance required to shape a more health-oriented globalization process.

  • • Globalization as a whole is significantly associated with individual overweight risk among women.
  • • Social and political globalization have strong positive association with overweight probability among women.
  • • Women in the most economically globalized countries are less likely to be overweight.

1. Introduction

Globalization has often been blamed for the rapid rise in obesity in much of the developing world ( Hawkes, 2006; Popkin, 2006; Zimmet, 2000 ). The existing evidence for this claim does, however, rest primarily on case studies and simple ecological comparisons of national conditions. A notable exception is a recent study by De Vogli et al. (2013) who explored the influence of economic globalization (e.g. foreign direct investment or trade) on obesity world-wide. Arguably, the scarcity of quantitative data amenable to statistical analysis relates to the difficulty in quantifying the complex, multi-faceted nature of globalization. Economists were among the first to try to quantify the different components of globalization in their attempt to assess its impact on economic growth ( Dollar and Kraay, 2004; Dreher, 2006 ). Indeed, the measures of globalization commonly employed have been exclusively economic, commonly proxied by e.g. total imports and exports or foreign direct investment, expressed as a share in GDP. Yet, globalization is not solely an economic process, and even if it were, there is more to economic globalization than the mere flow of goods and capital.

More recent efforts at measuring globalization were built on the conceptualisation by Keohane and Nye (2000) of three different relevant dimensions of globalization: (1) economic: long distance flows of goods, capital and services as well as information and perceptions that accompany market exchanges, (2) political: the diffusion of government policies internationally, and (3) social: the spread of ideas, information, images, and people ( Dreher, 2006 ). Dreher et al. (2008a) have developed the so-called KOF index of globalization to capture each of these dimensions (as well as additional sub-dimensions). For all dimensions, this index was created using comprehensive data collected annually, from 1970 to 2013. In this paper we make use of this new measure and its various components, to arrive at a more detailed and nuanced assessment of the impact of different dimensions of globalization on overweight in low- and middle-income countries.

All three of these components of globalization might have contributed to obesity in low- and middle-income countries, and because they capture different dimensions and – as will be shown further below – are at best imperfectly correlated with each other, it is important to examine the influence of each sub-dimension separately. Taken together, globalization may be contributing to obesity by stimulating increased calorie consumption, and/or smaller energy expenditure. While there exists a considerable literature which considers the role of technological change in affecting energy expenditure and consumption (e.g. ( Finkelstein et al., 2005; Huffman and Rizov, 2007; Lakdawalla and Philipson, 2009 ; Tomas Philipson, 2001 ; TJ Philipson and Posner, 2003b; Swinburn et al., 2011 ), the literature that considers the potential globalization & overweight/obesity nexus from the point of view of how globalization affects energy imbalance is quite limited. Nevertheless, as globalization may be both a product and a driver of technological change, they may have similar causal links with overweight through a set of factors collectively known as the “nutritional transition” ( Popkin, 2001; Popkin et al., 2012 ). Specifically, both globalization and technological change may be associated with urbanisation (with living in the cities offering a greater choice of food at lower prices), increasing use of cars and of mechanical aids (resulting in a decline in physical activity), and with a general increase in fat and sugar intake both of which, probably through their effects on energy density, contribute to weight gain ( Amine et al., 2002; Hooper et al., 2012; Te Morenga et al., 2013 ). Thus both technological change and globalization may lead to a lower cost of calorie intake, as well as to the higher opportunity cost of expending calories, resulting in the higher probability of obesity/overweight (TJ Philipson and Posner, 2003a ). In the case of globalization, the nutritional transition may also be facilitated by the importation of cheaper, higher energy density foods from the industrialized world, rather than from the countries' internal production.

The most readily recognized manifestation of economic globalization is the opening of markets to foreign trade and investment in the second half of the last century, which entailed a substantial increase in agribusiness-related foreign direct investment (FDI) ( Hawkes, 2006 ). Much of this investment went into food processing ( Popkin et al., 2012; Thow, 2009 ), thus potentially accelerating the nutritional transition and leading to a greater obesity burden ( Popkin, 2001, 2006; Popkin et al., 2012 ).

Political factors relating to the formation of regional trade blocks, or participation in various international treaties, may also have played a role, by acting as a precursor to greater economic integration via the opening of food markets to free trade and consequent nutritional change associated with overweight. On the one hand, greater political integration on a regional level is likely to lead to deeper regional cooperation (e.g. in the form of trade blocks), while on the other hand it may also create mechanisms, for instance, trade barriers, designed to protect participating countries from outside economic competition ( Dreher, 2006 ). While the precise impact of such manifestations of political integration on overweight in developing countries is hard to predict, it may at least be conceivable that political globalization acts independently of (or as a facilitator of) purely economic forces. Differential effects of political vs. economic globalization have, for instance, been found in recent research examining the impact of globalization on economic growth ( Dreher, 2006 ).

Social and cultural globalization, involving cross-border movement of cultures and openness of media, may also have increased a population's perception of the supposed benefits of foreign lifestyles (e.g. in the form of greater car use, decreasing calorie expenditures) as well as of foreign diets (e.g. which may lead to greater calorie consumption through intake of fast food rich in fats and sugars). The effect of social globalization on overweight may therefore be akin to the effect of urbanization on various technologies potentially associated either with the reduction in energy expenditure over time ( Monda et al., 2007; Popkin, 1999; Rivera et al., 2002; Swinburn et al., 2011 ), or with more abundant supply and consumption of cheaper, higher calorie foods ( Drewnowski and Popkin, 1997, 1999; Popkin and Gordon-Larsen, 2004 ).

In addition to examining the importance of these different components of globalization, a further unique feature of our analysis consists of the integration of the various indicators of globalization into a world-wide dataset containing individual-level information up to 887,000 individuals. This allows us to a) utilise information on the (objectively measured) overweight status of each individual and b) to control for relevant individual-level covariates (e.g. education, age, residence, household size) – a feature that should increase analytical precision, compared to the analysis of country-level data alone (which was used by De Vogli et al. (2013) ). To better isolate the effect of the various manifestations of globalization, it is important to control for a range of country-level factors that may simultaneously affect individual overweight risk and the country-level indicators of globalization, including the total GDP as a proxy of the size of the market, the Human Development Index, as well as the Index of Economic Freedom from the Heritage Foundation, which measures the quality of economic and legal institutions. Through this analysis we aim to find out whether overall globalization indeed increases the individual likelihood of overweight, and whether the different dimensions of globalization – economic, political and social – play a greater or lesser part in raising the risk of overweight.

2. Methods and their rationale

2.1. definition and measurement of the component variables of globalization.

Globalization is our independent variable of primary interest. We seek to capture both the influence of globalization as a whole as well as its relevant sub-components: economic, social and political globalization dimensions.

  • 1) Total globalization is measured using the KOF total globalization indicator ( Dreher, 2006 ), which is an aggregation of three sub-components, described below.
  • 2) Economic globalization: Our primary measure of economic globalization is the relevant KOF sub-index, which is a composite measure comprising the following variables: trade (in percent of GDP); foreign direct investment (FDI) stocks (in percent of GDP), portfolio investment (in percent of GDP), income payments to foreign nationals (in percent of GDP), hidden import barriers, mean tariff rate, taxes on international trade (in percent of current revenue) and capital account restrictions.
  • 3) Political globalization: We take advantage of the political KOF index mentioned above, which is a composite measure including information on the following four components: number of foreign embassies in a given country; membership in International Organizations; participation in U.N. Security Council missions; number of signed international treaties ( Dreher et al., 2008a ). This component is designed to measure the degree of a country's international political engagement ( Dreher, 2006 ). It was used, for instance, in studies examining the influence of globalization on partisan politics ( Potrafke, 2009 ) and government expenditure patterns ( Dreher et al., 2008b ).
  • 4) Social globalization: Our main measure of this type of globalization is the social KOF globalization index, which is based on the following variables: telephone traffic transfers (percent of GDP); international tourism foreign population (in percent of total population); international letters (per capita); internet users (per 1000 people); TVs (per 1000 people); trade in newspapers (percent of GDP); number of McDonald's restaurants (per capita); number of Ikea (per capita); trade in books (percent of GDP).

2.2. Econometric specifications

Starting with the most parsimonious model, we are primarily interested in how individual risk of overweight is affected by various manifestations of globalization:

where Y cit is a dummy for being above normal weight (i.e. either overweight or obese), for individual i living in country c at time t ; X ct is a vector of country-level covariates measuring various dimensions of globalization with the corresponding parameter vector β ; C cit captures individual-level control variables with the corresponding parameter vector γ ; D t is a time effect allowing us to control for potential time dependence or for any world-wide factors (e.g., global economic crises) that could affect our associations of interest, and e cit is an error term assumed to be uncorrelated with X (i)ct . To account for potential spatial correlation of the error term, all our standard errors are clustered according to cluster IDs provided in the dataset. In the rest of the paper we shall refer to “overweight” when we mean ‘being above normal weight’, i.e. either overweight or obese.

Our data came from several sources. Outcome and individual control variables were obtained from the Demographic and Health Surveys (DHS) collected in a total of 56 countries over the period 1991–2009 (variable definitions, as well as the full list of countries and survey years used is provided in the online Annex Supplementary material ). The DHS surveys have been extensively described elsewhere (S. Subramanian et al., 2011 ). Country level control variables came from World Development Indicators collated by the World Bank. Globalization indices were taken from the KOF globalization index of globalization prepared at the Swiss Federal Institute of Technology ( Dreher, 2006 ). Finally, Economic Freedom Index from the Heritage Foundation was used as an additional control variable.

The outcome variable of interest (i.e. being overweight) was defined as having a body mass index (BMI) greater or equal to 25 kg/m 2 . The BMI was calculated by dividing each person's weight in kilograms by height squared in meters. In order to trim outliers, observations for women whose BMI was above 50 kg/m 2 , or whose weight was either greater than or equal to 220 kg, were excluded. In addition, observations for women whose height was recorded as either greater than or equal to 2.2 m, were also excluded. Overall, in the pooled sample, data on BMI were available for about 72% of the full sample of 1,225,816 women. We restricted the sample for the analysis to non-pregnant women only, aged 15–49 years. Although the original sample contained women who were older than 49, for the vast majority of observations the anthropometric data was collected only in the 15–49 group. The actual sample size used in the regression analysis varied between 756,000 and 887,000.

As an alternative to using overweight as a dependent variable, some studies have employed the continuous variable BMI ( De Vogli et al., 2013 ; S. Subramanian et al., 2011 ). We decided not to follow this approach, since increases in BMI associated with various independent variables may have very different implications, depending on the initial BMI value. For example, a change in BMI from 18 to 19 (i.e. from being malnourished to having normal weight – i.e. a desirable outcome) can hardly be compared to an increase in BMI from 24 to 25 (i.e. from having normal weight to being overweight – i.e. an undesirable outcome). Measuring the association between covariates and BMI does not capture this difference, while measuring the effect of covariates on overweight (treated as a dummy variable) does.

Globalization-related indicators contained in vector X (i)ct were defined for one overall proxy of globalization as well as three sub-dimensions: economic, political and social ( Dreher, 2006 ), as discussed above. For each index, and for each year, we split the values for each country into four quartiles to enable a more intuitive interpretation of the resulting parameters on the relationship between overweight and globalization, rather than using the un-transformed or log-transformed KOF-scores. For example, a positive parameter on the second dummy (assuming the first dummy serves as a reference) would suggest an increase in the risk of overweight for people living in a country that is located in the second globalization quartile, relative to other 55 countries in any given year. Using quartiles also allows us to capture potential non-linearities in the relationship between globalization and overweight.

As countries compete for more investment by becoming more open relative to others in a given year ( Asiedu, 2002 ), we have chosen a year-specific categorization for globalization categories. Alternatively, we could have categorized countries based on their position relative to all other countries in all years combined, but that would answer a different question: how becoming more globalized not only relative to each other – but also relative to some long-term average globalization level – is related to overweight risk.

In addition, vector C cit contains individual-level covariates expected to improve precision in estimating the main vector of parameters, β . The vector includes indicators for various levels of education, for different age groups, for living in a city, for occupational status, as well as for family size. Education was defined using DHS dummies for six levels, i.e. for people with no, incomplete primary, complete primary, incomplete secondary, complete secondary and higher education. The occupational status for a woman depended on self-reporting her current employment status, and separate dummies were defined for being unemployed, working in the services sector (professional and managerial; clerical; sales; household and domestic; services), in agriculture (agriculture employed and self-employed) and in a manual (skilled manual; unskilled manual) occupation.

The main requirement for consistent parameter estimation in model (1) is that the error term is uncorrelated with the covariates. This is unlikely to be a reasonable assumption, as both overweight and globalization may be driven by some other unobserved factors not included in model (1). In principle, our dataset allows us to include country fixed effects (CFE), which should control for any time-invariant unobservable drivers of globalization and overweight. However, with country fixed effects included, only across time variation (i.e. “within-variation”) in country-level indicators will be used. For 19 out 56 countries (including the largest country-India), only one year of data was collected, so with the addition of country fixed effects, these countries would drop out of the analysis. Moreover, even in countries that had more than one year of data (n = 37), only a few had any variation in the globalization quartiles across years. In the specifications that include both individual and country controls, only 9–10 countries (out of 56) per globalization dimension had any variation in the globalization quartiles, resulting in a big drop-out of countries from the analysis (including some very large ones, e.g. Brazil, Turkey, Egypt, India, Nigeria, Bangladesh, Ethiopia and the Philippines). As this reduction in the sample is attributable to our transformation of the globalization indices into dummy variables (which we adopted for ease of interpretability of the resulting coefficients), this can be remedied by avoiding the transformation of the globalization indicators and using them as un-transformed variables. Hence, when it comes to the (important) comparison between the OLS- and FE-based results, we will revert to the use of the untransformed variables.

As a first step, we deal with the confounding problem by including a set of country-level covariates contained in vector C 2 ct , as in specification (2) below.

The choice of the country-level confounders was informed by the existing literature on the factors which facilitate movement of trade and investment between countries, and therefore are drivers of globalization. In addition, these variables are expected to be related to overweight risk. They include the size of the market ( Asiedu, 2006; Bevan and Estrin, 2004 ), measured here as total GDP (taken from WDI). The size of a country's GDP is also likely to be related to the level of economic development, and thus, in turn, may affect the obesity risk ( Goryakin and Suhrcke, 2014 ). In addition, foreign investors may consider it more worthwhile to invest in countries with higher overall levels of education and socioeconomic development ( Asiedu, 2006; Walsh and Yu, 2010 ). The Human Development Index (HDI) developed by UNDP is a well-known metric which takes into account not only living standards as measured by GDP per capita, but also two other important components: life expectancy at birth and the literacy rate. Globerman and Shapiro (2002) , for example, found that HDI and FDI were significantly correlated in specifications which did not control for governance institutions and infrastructure indicators. Likewise, it was found in several studies (e.g. Dinsa et al., 2012; Goryakin and Suhrcke, 2014; C. Monteiro et al., 2004b ) that socioeconomic status and development may be related to overweight and obesity, and therefore controlling for Human Development Index appears to be important in this case.

In addition, another important determinant of globalization (and potentially of economic and social development, which in turn may affect overweight prevalence independently of globalization) is the quality of economic and legal institutions ( Asiedu, 2006; Obwona, 2001; Walsh and Yu, 2010 ). In this paper, we utilize the Index of Economic Freedom from the Heritage Foundation. It takes into account a number of factors potentially important in the decision-making by foreign investors to engage in economic relationships with countries, such as: a quantitative measure of the ability to start, operate, and close a business; absence of tariff and non-tariff barriers; measure of the tax burden imposed by government; security of property rights; freedom from corruption; flexibility of the labour markets. Therefore this indicator is likely to be particularly valuable in our search for relevant proxies for drivers of country-level globalization.

As we mentioned above, although the above approach is designed to control for a range of potentially important confounders, not taking advantage of the within-country variation, when such option is in principle available, would be too costly. Therefore, as a final check, we also conduct country fixed effects estimations on the untransformed globalization scores. Although parameter interpretation is more difficult in this case, there is much more within-variation when untransformed scores are used, and this allows us to test whether findings from the OLS estimation will also hold when controlling for potential time invariant, unobserved country-level confounding.

The authors of the study did not have to obtain ethical approval, as they only analysed secondary, fully anonymized individual-level data from the publicly available Demographic and Health Surveys, as well as some country-level data.

3.1. Description of main variables

In the annex Table S1 , we present overweight prevalence by country and year ( Supplementary material ). In most countries where there were at least two years worth of observations, overweight prevalence tended to increase over the years, although at different rates. Overweight prevalence was generally considerably higher in Eastern Mediterranean countries, and was the lowest in Africa and South East Asia.

Table S2 shows how the total globalization score varied in each country by year ( Supplementary material ). In almost all countries, the value of the score increased, although again, the rate of change did differ. In Table S3 , the relative ranking of the countries by year is set out, according to the same score ( Supplementary material ). It is evident that the most globalized countries (e.g. Turkey, Brazil, Egypt, Jordan) tended to remain the most globalized in most years, while the same consistency was true for the least globalized countries (e.g., Central African Republic, Congo Democratic Republic, Chad). There appeared to be more variation in relative ranking for countries that were in between these two extremes, although in most cases the rate of change in the score was modest.

Finally, Figs. 1–4 show local regression graphs plotting non-parametric relationships between each globalization score and overweight prevalence in each country. These figures reveal that the relationship appears positive, quite pronounced and mostly linear for the social globalization score. On the other hand, it appears considerably weaker for the economic score. For total and political scores, the relationship seems quite strong, but mostly non-linear. In the former case, it seems that the association is flat for the least globalized countries, before becoming strongly positive. For the political dimension, it appears that there is no relationship to overweight for the majority of countries, except for the most globalized ones, for which we observe a strongly positive association.

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Lowess, unconditional association between overweight and total globalization index, 1991–2009. Source: DHS dataset; KOF index. Bandwidth = 0.8.

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Lowess, unconditional association between overweight and economic globalization index, 1991–2009. Source: DHS dataset; KOF index. Bandwidth = 0.8.

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Lowess, unconditional association between overweight and social globalization index, 1991–2009. Source: DHS dataset; KOF index. Bandwidth = 0.8.

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Lowess, unconditional association between overweight and political globalization index, 1991–2009. Source: DHS dataset; KOF index. Bandwidth = 0.8.

3.2. Overall globalization

Table 1 sets out the association between overweight and the overall globalization index, split into 4 quartiles. In the first column, not controlling for any covariates except for time dummies and the Sub-Saharan Africa dummy, we find that living in the countries which are in the top quartile for this metric is related to a 29.2 percentage points (p.p.) greater risk of being overweight, compared to the reference category of living in countries with the lowest quartile of the total globalization index. There is also a visible gradient: each higher total globalization quartile is associated with a greater overweight risk, with the shape suggesting a convex pattern. However, as this association may in part be driven by country-level confounding, it is also important to consider its robustness by including relevant controls. In column 2, the adding of individual control variables improves the precision of the estimates, while also somewhat reducing the magnitude of the association. What matters more, however, is the addition of the country level controls: results in column 3 demonstrate that their addition further reduces the magnitude of the association, although the parameters for the globalization dummies remain significant and positive.

Table 1

The relationships between the index of total globalization and overweight in women aged 15–49, Ordinary least squares (OLS) regression results.

Cluster-robust standard errors in parentheses. Sample restricted to women aged 15–49. No controls (except time dummies and Saharan African dummy) are included in the baseline specification. Reference categories for each of the sets of dummy variables: living in the least globalized quartile of countries, women with higher education, aged 35–49, having 6 or more children, being unemployed, and living in a rural location. All specifications contain time dummies. ***p < 0.01, **p < 0.05, *p < 0.1.

Looking at the effect of the main control variables ( Table 1 , column 3), women with no education are significantly less likely to be overweight than women with the most education; the risk of being overweight increases with age; women that are unemployed or in service occupations and reside in urban areas are more likely to be overweight. Women with no children are less likely to be overweight than women with 6 or more children, whereas women with 1–5 children were more overweight than those with 6 or more children. Moreover, an increase in the size of the market (i.e. total GDP) by 1 billion dollars is associated with an about 0.02 p.p smaller risk of overweight. With HDI ranging from 0 to 1, an increase by 0.1, for example, is associated with about an 8 p.p. greater risk of being overweight. Interestingly, better economic and legal institutions have an opposite effect: an increase of the score by 1 is related to an about 0.5 p.p. smaller overweight risk, suggesting that our proxies for economic and social development on the one hand, and for the quality of economic and legal institutions the other hand, are controlling for two distinct sources of potential confounding.

3.3. Sub-components of globalization

Prior to entering into the regression results, we determined whether each of the sub-components of globalization indeed captured distinct phenomena. As shown by the cross-correlation matrix, the sub-components are not too closely correlated with each other ( Table 2 ), except for economic and social components. The correlation is particularly weak between political and economic globalization (r = 0.15), underlining the need to “unpack” the overarching concept of globalization into its constituent parts.

Table 2

Correlation matrix of each dimension of globalization, 1991–2009.

3.3.1. Economic globalization

The first three columns in Table 3 assess the influence of economic globalization on overweight. The results in column 1 without controls for any factors except time dummies and a sub-Saharan African dummy, indicate that greater economic globalization is associated with a greater risk of being overweight. Adjusting for individual covariates, however, reduces the magnitude of the association. The biggest impact on parameter sign, however, occurs after adding country controls: now the relationship becomes concave, with people living in the most economically globalized countries having lower probability of being overweight, although this finding needs to be seen in the light of the very small magnitude of this association (i.e. only about 1p.p. lower probability).

Table 3

The relationship between economic, political and social globalization and overweight in women aged 15–49 years, OLS regression results.

Cluster-robust standard errors in parentheses. Sample restricted to women aged 15–49. No controls (except time dummies and sub Saharan African dummy) are included in the baseline specification (columns 1, 4, and 7). In columns 2, 5 and 8, controls also include education, age, number of children, occupation and urban residence dummies. In columns 3, 6, 9, 10, the following controls are also added: total GDP (constant 2000 dollars); Human Development Index, Economic Freedom score.

***p < 0.01, **p < 0.05, *p < 0.1.

3.3.2. Political globalization

Columns 4–6 in Table 3 provide the results on the role of political globalization in affecting individual chances of being overweight. In the basic specification, (column 4), the relationship appears convex, with a fall in the probability of being overweight in the second and third quartile, before an increase for the most politically globalized countries (column 4). However, the addition of individual, and especially country level controls, leads to a more pronounced association: column 6 shows that people living in the most politically globalized countries have a 13.5p.p. greater risk of being overweight, compared to people living in the least globalized countries. This is also true for people living in the third quartile, although the increase in the probability of overweight is considerably smaller.

3.3.3. Social globalization

In columns 7–9 of Table 3 we consider the association between social globalization and overweight. It appears that this dimension has the most stable and pronounced association with overweight across dimensions, as adding different sets of control variables changes the magnitude of the association only slightly. People living in the most socially globalized quartile have an about 18 p.p. greater risk of being overweight, compared to the least globalized group.

3.4. All globalization indices combined

Next, we consider the association between overweight and all globalization indices taken together. One potential disadvantage of this approach is some collinearity between different sub-components (especially between social and economic dimensions, as shown in Table 2 ). On the other hand, putting these scores together in the same model may help ensure an additional degree of control for residual confounding. As the results in column 10 of Table 3 reveal, this approach turns the negative association between economic globalization and overweight into a more pronounced one, while making little difference for the political and social components.

3.5. Robustness checks

Some of our findings may be partly driven by the differences in sample size across specifications. For example, the sample size of the basic specifications in Table 3 is up to 887,000, while it is 765,000 in the most adjusted specifications. To check the robustness of the results we estimated the regression parameters for identical samples in Table 4 , for each of the three globalization types. Comparing the estimates from Tables 3 and 4 confirms that there is little difference in the size of the economic and social globalization parameters, implying that changes in parameter size across specifications are not due to the differences in sample size. On the other hand, for political globalization, the relationship with overweight becomes uniformly positively signed in all 3 specifications in the identical samples (columns 4–6 in Table 4 ).

Table 4

Robustness checks: identical sample size across specifications.

Cluster-robust standard errors in parentheses. Sample restricted to women aged 15–49. No controls (except time dummies and sub Saharan African dummy) are included in the baseline specification (columns 1, 4, and 7). In columns 2, 5 and 8, controls also include education, age, number of children, occupation and urban residence dummies. In columns 3, 6 and 9, the following controls are also added: total GDP (constant 2000 dollars); Human Development Index, Economic Freedom score.

Earlier in the paper, the analysis with OLS using globalization scores transformed into quartiles was presented as this allowed a more intuitive interpretation of results. However, we recognize that this approach is costly, as it effectively precludes a country fixed effects analysis (which would allow controlling for an important source of unobserved confounding) due to a very small within-variation. So to complete our analysis, both OLS and FE estimates (with the same set of control variables as in columns 3, 6, 9 in Table 4 ) are presented using the original, untransformed globalization scores. Even though interpretation of our key parameter estimates now becomes less clear, this comparison is useful in that it allows us to examine whether the OLS findings continue to hold when the assumption of no correlation between globalization scores and time-invariant unobservables is relaxed.

From Table 5 , we can see that that when the globalization dimensions are combined in column 5 – arguably the most comprehensive specification – the signs are identical in both OLS and CFE models, and the magnitudes of the effects of economic and political globalization are at least close to each other. We also see that the magnitude of the CFE associations remains substantive. For example, a 50 percentage point (p.p.) increase in overall globalization score entails an about 15 p.p. greater overweight risk. This compares with an about 16.8 p.p. greater overweight risk for the most globalized countries relative to the least globalized ones ( Table 1 , column 3). This change in magnitude is not dramatically different, when comparing the results between Tables 5 and 3 for other dimensions.

Table 5

Robustness checks: estimating the relationship between overweight and globalization using original globalization scores.

Cluster-robust standard errors in parentheses. Sample restricted to women aged 15–49.

The following controls are added in all specifications: age, number of children, occupation and urban residence dummies, Saharan African dummy, total GDP (constant 2000 dollars); Human Development Index, Economic Freedom score.

We also estimate overweight as a quadratic polynomial function of globalization dimensions (results not shown here, but available on request). In order to ensure better interpretability and to mitigate the multicollinearity problem, we centred our estimation on the mean values of the globalization dimension scores. We found the main parameters to be virtually identical for all dimensions. In addition, there appears to be a convex relationship between total and political globalization and overweight, a mostly linear negative relationship between economic globalization and overweight, and a mostly linear positive association between social globalization and overweight.

4. Discussion

While most of the existing literature focussed on the relationship between economic globalization and obesity, specific quantitative measures of the range of potentially very different globalization-related drivers involved have not been examined previously. In this analysis we find that the relationship between overweight and globalization depends on the specific dimension of globalization. Thus, while both political and (especially) social globalization dimensions appear strongly positively related to the greater overweight risk, the same is not apparent for economic globalization.

More concretely, comparing different dimensions of globalization and including suitable adjustments for confounders and covariates we find for the first time that political and social globalization consistently show a positive association with the individual odds of overweight: in our preferred specification (i.e. with country controls), the risk of being overweight among women is about 13.5 p.p. (or 17.8 p.p.) greater in the most politically globalized group (or in the most socially globalized group) compared to the least globalized group. This finding is also confirmed in the models using the untransformed globalization scores, although the magnitude of the association is notably smaller for the social (but not for the political) dimension in the CFE compared to the OLS model. Although arguably the biggest attention has so far been directed at the impact of economic globalization, we have found that living in the most economically globalized quartile of countries predicts a 1 p.p. smaller overweight risk than in the least economically globalized ones. This is a rather surprising finding, given the focus of most of the literature on the potential link between obesity and economic globalization ( Hawkes, 2006 ), and the scant attention paid to other dimensions. This finding is also slightly at odds with recent results by De Vogli et al. (2013) , who found – using aggregate cross-country level data rather than individual level data – that national BMI (as opposed to overweight) was significantly positively related to the KOF index for economic globalization in 127 countries. Having said that, the parameter sign for the economic dimension was quite sensitive to the inclusion of country-level controls. This appears to be consistent with the hypothesis that at least part of the relationship between economic globalization and overweight may be driven by country-specific factors such as economic development and infrastructure, education, attractiveness of economies to investors, as well as the size of the market.

Inevitably, our study suffers from several limitations. Most of them are data-related, and are thus similar to those faced by other studies which also used DHS data to examine correlates of obesity ( Goryakin and Suhrcke, 2014; Monteiro et al., 2004a; Subramanian et al., 2010 ). For example, the sample was necessarily restricted to women only, and mostly of child-bearing age. Therefore, generalizing our findings to women of all age groups, let alone to both genders, is not possible. Nevertheless, since the age group of 15–49 represents the most productive group of women, who also typically have a number of dependants, focussing attention on this demographic segment may be warranted for informing policies to tackle overweight. Most importantly, we are limited in drawing major causal claims about our findings, especially in relation to 19 countries that were only present in the sample for one year (and thus could not provide any within-variation for the fixed effects analysis).

There are a few other intrinsic data-related concerns which call for caution when interpreting the findings. Thus, by the nature of the research frame, most sampled women were mothers with at least one child under 5 years of age ( Monteiro et al., 2004a ). This is potentially problematic in that such women may more likely be overweight, although the reverse may be true in the lowest income countries, where both pregnancy and breastfeeding may lead to large energy needs relative to family resources (and thus potentially to malnourishment). Nevertheless, keeping with Monteiro et al. (2004a) assessment, this should not make a substantial difference in terms of the association between overweight and the extent of globalization, especially given that we are controlling for the number of children in our analysis as well as for the educational level of mothers.

Another problem is that very few countries stayed in the sample for all periods, given the nature of the DHS data collection. Whereas in some countries (e.g. Egypt, Ghana) data was collected every five years or even more frequently, in many others it was collected for no more than two years. In 19 countries, data was only available for one year. There was also very little variation in our categorical globalization variable across years, which prevented us from undertaking country fixed effects analysis using the globalization indicator dummies. Nevertheless, when using untransformed globalization scores as exposure variables, our country fixed effects findings were mostly in line with our earlier OLS estimates presented in Tables 1, 3 and 4 .

It remains possible, however, that some time-varying variables (which country fixed effects cannot control for) may still be a source of bias for our results. For example, availability of infrastructure, wars, economic shocks and famine may affect both the extent of globalization and overweight risk. However, although we are not controlling for these factors explicitly, we nevertheless control for the Human Development Index, as well as the Index of Economic Freedom (which proxies for the quality of economic and legal institutions). Both of these variables, in our view, should to a large extent account for such confounders.

Finally, in this paper, we have only considered the contemporaneous association of globalization with overweight/obesity. It is possible that it may operate with some time lag, but there was little variation across time for globalization indices, and therefore the effect of time lags is unlikely to be estimated with any precision, if the distributed lag model (as seems appropriate) is used.

While these results cannot be given a causal interpretation, they do provide evidence of statistically significant positive association between some dimensions of globalization and overweight. If more robust statistical evidence were found on the causal link between globalization and obesity, what might appropriate policy responses be? It bears emphasising that such evidence would not imply that it would be appropriate to halt or slow down the progress of globalization, but the challenge would be to find ways of limiting and countering the adverse health consequences of globalization while preserving its beneficial effects. Having said that, not all types of globalization appear to affect the risk of obesity equally: the economic dimension, for example, appears to do less harm than previously thought and social and other changes stemming for politically related factors seem of greater importance.

These conclusions have two implications. First, more research is needed to understand the ways in which social and political globalization – as well as economic – influence overweight. The composite elements within the globalization indices could be examined to identify those which are most closely related to overweight risk. For example, it would be useful to know if the increase in McDonald's outlets, an arguably more direct index of the availability of energy dense diets, is more closely associated with the development of overweight than the increase in IKEA outlets, and if the former retains its association after controlling for the latter. Similarly, what are the key political factors – are they related to market freedoms or to democratic expression or to the adoption of current Western political attitudes – and how do these interact with economic and cultural/social factors? Open societies and cultural globalization go hand in hand with open markets and open media, with rapid penetration of advertising and brand promotion by global corporations, together with the depiction of supposedly desirable Western lifestyles which in turn help create a merging of food environments and food cultures as globalization progresses.

Secondly, with greater clarity about the key aspects of globalization becoming available, the challenge to public health policy becomes better focused. For example, if it is shown that fast food outlets are closely associated with overweight prevalence, then what are the policy implications? Is the fast food chain itself a problem, or does it simply reflect the composite effects of FDI policy and cultural openness to advertising and brand promotion, or a more direct effect of a closely related factor, such as a rise in soft drinks consumption ( Basu et al., 2013 )? Increasing attention is being paid by health promoters to the role of transnational corporations ( Hastings, 2012 ) and accumulating evidence that the rate of increase in consumption of unhealthy food products parallels that of tobacco and alcohol and is fastest in low- and middle-income countries ( Stuckler et al., 2012 ). This has led to public health policy analysts calling for public regulation and market intervention to prevent the harm caused ( Moodie et al., 2013 ), and international agencies have, for example, made recommendations to limit children's exposure to the advertising of unhealthy foods ( PAHO, 2011; World Health Organization, 2010 ). While these policy proposals are widely discussed in the public health arena, they remain marginal to the larger discussions on economic growth and global development. Thus there was no expression of the need to tackle the negative health effects of globalization in the Millennium Development Goals ( UN, 2000 ) which are due to expire in 2015. The High Level Panel steering the post-2015 Sustainable Development programme has yet to specify their target areas for action, but of the 27 international members, 10 have economics, trade and finance backgrounds, three have private sector experience (including one with experience of working for Unilever and Nestlé) but none appears to have public health experience or health qualification ( UN, 2013 ). If globalization in at least some of its dimensions is having a significant impact on the risk of excess weight, then there is indeed a need for stronger governance mechanisms able to take responsibility for protecting health during the globalizing process as recently highlighted by the Oslo/Lancet Commission ( Ottersen et al., 2014 ).


The authors were supported by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, and the Wellcome Trust (087636/Z/08/Z ESRC: ES/G007462/1), under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged.

Appendix A. Supplementary data

The following is the supplementary data related to this article:

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Globalization: A Very Short Introduction (5th edn)

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4 (page 64) p. 64 The political dimension of globalization

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Political globalization refers to the intensification and expansion of political interrelations across the globe. ‘The political dimension of globalization’ raises political issues relating to state sovereignty and the question of whether the nation-state will survive globalization. Growing social, economic, and cultural interconnectedness has facilitated migration in large numbers and permeated borders. Contemporary globalization has put pressure on traditional forms of global governance by fostering the growth of supraterritorial social spaces and institutions that unsettle both familiar political arrangements and cultural traditions. The worldwide intensification of cultural interactions makes greater accommodation and tolerance possible, but it is just as likely to increase political resistance and opposition.

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