ResearchPad - economic-growth Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[The association between national income and adult obesity prevalence: Empirical insights into temporal patterns and moderators of the association using 40 years of data across 147 countries]]> At a country level, population obesity prevalence is often associated with economic affluence, reflecting a potential adverse outcome concomitant with economic growth. We estimated the pattern and strength of the empirically observed relationship between national income and adult obesity prevalence, and the moderating role of countries’ macro-environments on this relationship.MethodsWe assembled data on national obesity prevalence, income and a range of variables that characterize macro-environments related to 147 countries from multiple international organizations and databases. We used a Bayesian hierarchical model to estimate the relationship (elasticities) between national income (using Gross Domestic Product Per Capita, GDPPC) and adult obesity prevalence, and the moderating effects of five different dimensions (globalization orientation, demographic characteristics, economic environment, labor market characteristics, and strength of health policies) of countries’ macro-environments on the income elasticities. Using the latest (2019–2024) available national income growth projections from the International Monetary Fund, we forecast future global trends in obesity prevalence.FindingsOver the 40-years 1975–2014, adult obesity prevalence increased at a declining rate with GDPPC across the 147 countries. The mean income elasticity estimates were 1.23 (95% credible interval 1.04–1.42) for males and 1.01 (0.82–1.18) for females. The elasticities were positively associated with the extent of political globalization and negatively associated with urbanization and share of agriculture in the national GDP. Income based projections indicate that obesity prevalence would continue to grow at an average annual rate of 2.47% across the studied countries during 2019–2024.ConclusionsPopulation obesity prevalence exhibits a positive relationship with national income and there is no evidence that the relationship, while weakening, actually turns negative at higher income levels (“obesity Kuznets curve”). Based on current trends, global obesity prevalence will continue to increase during 2019–2024, with the rate of growth higher in low- and middle-income countries. As most people currently live in low- and middle-income countries with rising incomes, our findings underscore the urgent societal imperatives for effective policy initiatives, especially those that target the concomitant “nutrition transition” process with economic affluence, to break or at least further weaken the positive relationship of population obesity prevalence with national income. ]]> <![CDATA[Spatiotemporal characteristics and driving forces of construction land expansion in Yangtze River economic belt, China]]>

With rapid economic and population growth, construction land expansion in Yangtze River economic belt in China becomes substantial, carrying significant social and economic implications. This research uses Expansion Speed Index and Expansion Intensity Index to examine spatiotemporal characteristics of construction land expansion in the Yangtze River economic belt from 2000 to 2017. Based on a STIRPAT model, driving forces of construction land expansion are measured by Principal Component Analysis and Ordinary Least Square regression. The results show that: (1) there is a clear expansion pattern regarding the time sequence in provinces/cities of the Yangtze River economic belt, with rapid expansion in the initial stage, moderate expansion in the middle stage and rapid expansion in the later stage. (2) Spatial analysis demonstrates first expansion in the lower reaches in the early stage, rapid expansion of the upper reaches in the middle and later stage, and steady expansion of the middle reaches throughout the research period. (3)There are statistical significant correlations between construction land expansion and GDP, social fixed asset investments, population at the end of the year, population urbanization rate, per capita road area, and number of scientific and technological professionals as well as secondary and tertiary industry values. Of these factors, GDP, social fixed asset investments, population urbanization rate and second industry value are important common driving forces of construction land expansion in this region. The research findings have significant policy implications particularly on coordinated development of urban agglomerations and sustainable industry upgrading when construction land expansion is concerned.

<![CDATA[The quest for an optimal alpha]]>

Researchers who analyze data within the framework of null hypothesis significance testing must choose a critical “alpha” level, α, to use as a cutoff for deciding whether a given set of data demonstrates the presence of a particular effect. In most fields, α = 0.05 has traditionally been used as the standard cutoff. Many researchers have recently argued for a change to a more stringent evidence cutoff such as α = 0.01, 0.005, or 0.001, noting that this change would tend to reduce the rate of false positives, which are of growing concern in many research areas. Other researchers oppose this proposed change, however, because it would correspondingly tend to increase the rate of false negatives. We show how a simple statistical model can be used to explore the quantitative tradeoff between reducing false positives and increasing false negatives. In particular, the model shows how the optimal α level depends on numerous characteristics of the research area, and it reveals that although α = 0.05 would indeed be approximately the optimal value in some realistic situations, the optimal α could actually be substantially larger or smaller in other situations. The importance of the model lies in making it clear what characteristics of the research area have to be specified to make a principled argument for using one α level rather than another, and the model thereby provides a blueprint for researchers seeking to justify a particular α level.

<![CDATA[Asymmetric relationship of urbanization and CO2 emissions in less developed countries]]>

Understanding the relationship between carbon dioxide (CO2) emissions and the urbanization of national populations has been a key concern for environmental scholars for several decades. Although sophisticated modeling techniques have been developed to explore the connection between increases in urban populations and CO2 emissions, none has attempted to assess whether declines in urbanization have an effect on emissions that is not symmetrical with that of growth in urbanization. The present study uses panel data on CO2 emissions and the percentage of individuals living in urban areas, as well as a variety of other structural factors, for less-developed countries from 1960–2010, to empirically assess whether the effect of growth in urban populations on emissions is symmetrical with the effect of decline. Findings indicate that the effect of growth/decline in urban populations on CO2 emissions is asymmetrical, where a decline in urbanization reduces emissions to a much greater degree than urbanization increases emissions. We hypothesize that this is at least in part because deurbanization is connected with disruptions to the production and distribution of goods and services and/or access to electricity and other energy sources. Our finding suggests that not only the absolute level of urbanization of nations matters for emissions, but also how the patterns of migration between rural and urban areas change over time. Future research should be mindful of the processes behind deurbanization when exploring socioeconomic drivers of CO2 emissions.

<![CDATA[Linear and nonlinear causal relationship between energy consumption and economic growth in China: New evidence based on wavelet analysis]]>

The energy-growth nexus has important policy implications for economic development. The results from many past studies that investigated the causality direction of this nexus can lead to misleading policy guidance. Using data on China from 1953 to 2013, this study shows that an application of causality test on the time series of energy consumption and national output has masked a lot of information. The Toda-Yamamoto test with bootstrapped critical values and the newly proposed non-linear causality test reveal no causal relationship. However, a further application of these tests using series in different time-frequency domain obtained from wavelet decomposition indicates that while energy consumption Granger causes economic growth in the short run, the reverse is true in the medium term. A bidirectional causal relationship is found for the long run. This approach has proven to be superior in unveiling information on the energy-growth nexus that are useful for policy planning over different time horizons.

<![CDATA[Modeling the Pre-Industrial Roots of Modern Super-Exponential Population Growth]]>

To Malthus, rapid human population growth—so evident in 18th Century Europe—was obviously unsustainable. In his Essay on the Principle of Population, Malthus cogently argued that environmental and socioeconomic constraints on population rise were inevitable. Yet, he penned his essay on the eve of the global census size reaching one billion, as nearly two centuries of super-exponential increase were taking off. Introducing a novel extension of J. E. Cohen's hallmark coupled difference equation model of human population dynamics and carrying capacity, this article examines just how elastic population growth limits may be in response to demographic change. The revised model involves a simple formalization of how consumption costs influence carrying capacity elasticity over time. Recognizing that complex social resource-extraction networks support ongoing consumption-based investment in family formation and intergenerational resource transfers, it is important to consider how consumption has impacted the human environment and demography—especially as global population has become very large. Sensitivity analysis of the consumption-cost model's fit to historical population estimates, modern census data, and 21st Century demographic projections supports a critical conclusion. The recent population explosion was systemically determined by long-term, distinctly pre-industrial cultural evolution. It is suggested that modern globalizing transitions in technology, susceptibility to infectious disease, information flows and accumulation, and economic complexity were endogenous products of much earlier biocultural evolution of family formation's embeddedness in larger, hierarchically self-organizing cultural systems, which could potentially support high population elasticity of carrying capacity. Modern super-exponential population growth cannot be considered separately from long-term change in the multi-scalar political economy that connects family formation and intergenerational resource transfers to wider institutions and social networks.

<![CDATA[A measurement model for real estate bubble size based on the panel data analysis: An empirical case study]]>

Employing the fundamental value of real estate determined by the economic fundamentals, a measurement model for real estate bubble size is established based on the panel data analysis. Using this model, real estate bubble sizes in various regions in Japan in the late 1980s and in recent China are examined. Two panel models for Japan provide results, which are consistent with the reality in the 1980s where a commercial land price bubble appeared in most area and was much larger than that of residential land. This provides evidence of the reliability of our model, overcoming the limit of existing literature with this method. The same models for housing prices in China at both the provincial and city levels show that contrary to the concern of serious housing price bubble in China, over-valuing in recent China is much smaller than that in 1980s Japan.

<![CDATA[The Impact of Services on Economic Complexity: Service Sophistication as Route for Economic Growth]]>

Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy. By combining tools from network science and econometrics, a robust and stable relationship between a country’s productive structure and its economic growth has been established. Here we report that not only goods but also services are important for predicting the rate at which countries will grow. By adopting a terminology which classifies manufactured goods and delivered services as products, we investigate the influence of services on the country’s productive structure. In particular, we provide evidence that complexity indices for services are in general higher than those for goods, which is reflected in a general tendency to rank countries with developed service sector higher than countries with economy centred on manufacturing of goods. By focusing on country dynamics based on experimental data, we investigate the impact of services on the economic complexity of countries measured in the product space (consisting of both goods and services). Importantly, we show that diversification of service exports and its sophistication can provide an additional route for economic growth in both developing and developed countries.

<![CDATA[How Did Urban Land Expand in China between 1992 and 2015? A Multi-Scale Landscape Analysis]]>

Effective and timely quantification of the spatiotemporal pattern of urban expansion in China is important for the assessment of its environmental effects. However, the dynamics of the most recent urban expansions in China since 2012 have not yet been adequately explained due to a lack of current information. In this paper, our objective was to quantify spatiotemporal patterns of urban expansion in China between 1992 and 2015. First, we extracted information on urban expansion in China between 1992 and 2015 by integrating nighttime light data, vegetation index data, and land surface temperature data. Then we analyzed the spatiotemporal patterns of urban expansion at the national and regional scales, as well as at that of urban agglomerations. We found that China experienced a rapid and large-scale process of urban expansion between 1992 and 2015, with urban land increasing from 1.22 × 104 km2 to 7.29 × 104 km2, increasing in size nearly fivefold and with an average annual growth rate of 8.10%, almost 2.5 times as rapid as the global average. We also found that urban land in China expanded mainly by occupying 3.31 × 104 km2 of cropland, which comprised 54.67% of the total area of expanded urban land. Among the three modes of growth—infilling, edge expansion, and leapfrog—edge expansion was the main cause of cropland loss. Cropland loss resulting from edge expansion of urban land totalled 2.51 × 104 km2, accounting for over 75% of total cropland loss. We suggest that effective future management with respect to edge expansion of urban land is needed to protect cropland in China.

<![CDATA[The Research Focus of Nations: Economic vs. Altruistic Motivations]]>

What motivates the research strategies of nations and institutions? We suggest that research primarily serves two masters–altruism and economic growth. Some nations focus more research in altruistic (or non-economic) fields while others focus more research in fields associated with economic growth. What causes this difference? Are there characteristics that would suggest why a nation is more aligned with altruism or economic growth? To answer this question, we have identified nine major fields of research by analyzing the publication activity of 4429 institutions using Scopus data. Two fields of research are clearly altruistic (there is relatively little involvement by industry) and two fields are clearly aligned with economic growth. The altruistic vs. economic nature of nations based on their publication profiles across these fields is correlated with national indicators on wealth, education, capitalism, individualism, power, religion, and language. While previous research has suggested that national research strategy is aligned with national wealth, our analysis shows that national wealth is not highly correlated with the tradeoff between altruistic and economic motives. Instead, the tradeoff is largely captured by a culture of individualism. Accordingly, implications for national research strategies are discussed.

<![CDATA[Market Structure, Financial Dependence and Industrial Growth: Evidence from the Banking Industry in Emerging Asian Economies]]>

In this study, we examine the role of market structure for growth in financially dependent industries from 10 emerging Asian economies over the period of 1995–2011. Our approach departs from existing studies in that we apply four alternative measures of market structure based on structural and non-structural approaches and compare their outcomes. Results indicate that higher bank concentration may slow down the growth of financially dependent industries. Bank competition on the other hand, allows financially dependent industries to grow faster. These findings are consistent across a number of sensitivity checks such as alternative measures of financial dependence, institutional factors (including property rights, quality of accounting standards and bank ownership), and endogeneity consideration. In sum, our study suggests that financially dependent industries grow more in more competitive/less concentrated banking systems. Therefore, regulatory authorities need to be careful while pursuing a consolidation policy for banking sector in emerging Asian economies.

<![CDATA[Does Economic Growth Reduce Childhood Undernutrition in Ethiopia?]]>


Policy discussions and debates in the last couple of decades emphasized efficiency of development policies for translating economic growth to development. One of the key aspects in this regard in the developing world is achieving improved nutrition through economic development. Nonetheless, there is a dearth of literature that empirically verifies the association between economic growth and reduction of childhood undernutrition in low- and middle-income countries. Thus, the aim of the study is to assess the interplay between economic growth and reduction of childhood undernutrition in Ethiopia.


The study used pooled data of three rounds (2000, 2005 and 2010) from the Demographic and Health Surveys (DHS) of Ethiopia. A multilevel mixed logistic regression model with robust standard errors was utilized in order to account for the hierarchical nature of the data. The dependent variables were stunting, underweight, and wasting in children in the household. The main independent variable was real per capita income (PCI) that was adjusted for purchasing power parity. This information was obtained from World Bank.


A total of 32,610 children were included in the pooled analysis. Overall, 11,296 (46.7%) [46.0%-47.3%], 8,197(33.8%) [33.2%-34.4%] and 3,175(13.1%) [12.7%-13.5%] were stunted, underweight, and wasted, respectively. We found a strong correlation between prevalence of early childhood undernutrition outcomes and real per capita income (PCI). The proportions of stunting (r = -0.1207, p<0.0001), wasting (r = -0.0338, p<0.0001) and underweight (r = -0.1035, p<0.0001) from the total children in the household were negatively correlated with the PCI. In the final model adjustment with all the covariates, economic growth substantially reduced stunting [β = -0.0016, SE = 0.00013, p<0.0001], underweight [β = -0.0014, SE = 0.0002, p<0.0001] and wasting [β = -0.0008, SE = 0.0002, p<0.0001] in Ethiopia over a decade.


Economic growth reduces child undernutrition in Ethiopia. This verifies the fact that the economic growth of the country accompanied with socio-economic development and improvement of the livelihood of the poor. Direct nutrition specific and nutrition sensitive interventions could also be recommended in order to have an impact on the massive reduction of childhood undernutrition in the country.

<![CDATA[Classifying patents based on their semantic content]]>

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information.

<![CDATA[p-Curve and p-Hacking in Observational Research]]>

The p-curve, the distribution of statistically significant p-values of published studies, has been used to make inferences on the proportion of true effects and on the presence of p-hacking in the published literature. We analyze the p-curve for observational research in the presence of p-hacking. We show by means of simulations that even with minimal omitted-variable bias (e.g., unaccounted confounding) p-curves based on true effects and p-curves based on null-effects with p-hacking cannot be reliably distinguished. We also demonstrate this problem using as practical example the evaluation of the effect of malaria prevalence on economic growth between 1960 and 1996. These findings call recent studies into question that use the p-curve to infer that most published research findings are based on true effects in the medical literature and in a wide range of disciplines. p-values in observational research may need to be empirically calibrated to be interpretable with respect to the commonly used significance threshold of 0.05. Violations of randomization in experimental studies may also result in situations where the use of p-curves is similarly unreliable.

<![CDATA[Factors Contributing to Maternal and Child Mortality Reductions in 146 Low- and Middle-Income Countries between 1990 and 2010]]>


From 1990–2010, worldwide child mortality declined by 43%, and maternal mortality declined by 40%. This paper compares two sources of progress: improvements in societal coverage of health determinants versus improvements in the impact of health determinants as a result of technical change.


This paper decomposes the progress made by 146 low- and middle-income countries (LMICs) in lowering childhood and maternal mortality into one component due to better health determinants like literacy, income, and health coverage and a second component due to changes in the impact of these health determinants. Health determinants were selected from eight distinct health-impacting sectors. Health determinants were selected from eight distinct health-impacting sectors. Regression models are used to estimate impact size in 1990 and again in 2010. Changes in the levels of health determinants were measured using secondary data.


The model shows that respectively 100% and 89% of the reductions in maternal and child mortality since 1990 were due to improvements in nationwide coverage of health determinants. The relative share of overall improvement attributable to any single determinant varies by country and by model specification. However, in aggregate, approximately 50% of the mortality reductions were due to improvements in the health sector, and the other 50% of the mortality reductions were due to gains outside the health sector.


Overall, countries improved maternal and child health (MCH) from 1990 to 2010 mainly through improvements in the societal coverage of a broad array of health system, social, economic and environmental determinants of child health. These findings vindicate efforts by the global community to obtain such improvements, and align with the post-2015 development agenda that builds on the lessons from the MDGs and highlights the importance of promoting health and sustainable development in a more integrated manner across sectors.

<![CDATA[Network structure impacts global commodity trade growth and resilience]]>

Global commodity trade networks are critical to our collective sustainable development. Their increasing interconnectedness pose two practical questions: (i) Do the current network configurations support their further growth? (ii) How resilient are these networks to economic shocks? We analyze the data of global commodity trade flows from 1996 to 2012 to evaluate the relationship between structural properties of the global commodity trade networks and (a) their dynamic growth, as well as (b) the resilience of their growth with respect to the 2009 global economic shock. Specifically, we explore the role of network efficiency and redundancy using the information theory-based network flow analysis. We find that, while network efficiency is positively correlated with growth, highly efficient systems appear to be less resilient, losing more and gaining less growth following an economic shock. While all examined networks are rather redundant, we find that network redundancy does not hinder their growth. Moreover, systems exhibiting higher levels of redundancy lose less and gain more growth following an economic shock. We suggest that a strategy to support making global trade networks more efficient via, e.g., preferential trade agreements and higher specialization, can promote their further growth; while a strategy to increase the global trade networks’ redundancy via e.g., more abundant free-trade agreements, can improve their resilience to global economic shocks.

<![CDATA[Is Decoupling GDP Growth from Environmental Impact Possible?]]>

The argument that human society can decouple economic growth—defined as growth in Gross Domestic Product (GDP)—from growth in environmental impacts is appealing. If such decoupling is possible, it means that GDP growth is a sustainable societal goal. Here we show that the decoupling concept can be interpreted using an easily understood model of economic growth and environmental impact. The simple model is compared to historical data and modelled projections to demonstrate that growth in GDP ultimately cannot be decoupled from growth in material and energy use. It is therefore misleading to develop growth-oriented policy around the expectation that decoupling is possible. We also note that GDP is increasingly seen as a poor proxy for societal wellbeing. GDP growth is therefore a questionable societal goal. Society can sustainably improve wellbeing, including the wellbeing of its natural assets, but only by discarding GDP growth as the goal in favor of more comprehensive measures of societal wellbeing.

<![CDATA[Trading Off Global Fuel Supply, CO2 Emissions and Sustainable Development]]>

The United Nations Conference on Climate Change (Paris 2015) reached an international agreement to keep the rise in global average temperature ‘well below 2°C’ and to ‘aim to limit the increase to 1.5°C’. These reductions will have to be made in the face of rising global energy demand. Here a thoroughly validated dynamic econometric model (Eq 1) is used to forecast global energy demand growth (International Energy Agency and BP), which is driven by an increase of the global population (UN), energy use per person and real GDP (World Bank and Maddison). Even relatively conservative assumptions put a severe upward pressure on forecast global energy demand and highlight three areas of concern. First, is the potential for an exponential increase of fossil fuel consumption, if renewable energy systems are not rapidly scaled up. Second, implementation of internationally mandated CO2 emission controls are forecast to place serious constraints on fossil fuel use from ~2030 onward, raising energy security implications. Third is the challenge of maintaining the international ‘pro-growth’ strategy being used to meet poverty alleviation targets, while reducing CO2 emissions. Our findings place global economists and environmentalists on the same side as they indicate that the scale up of CO2 neutral renewable energy systems is not only important to protect against climate change, but to enhance global energy security by reducing our dependence of fossil fuels and to provide a sustainable basis for economic development and poverty alleviation. Very hard choices will have to be made to achieve ‘sustainable development’ goals.

<![CDATA[Rich and Poor Cities in Europe. An Urban Scaling Approach to Mapping the European Economic Transition]]>

Recent advances in the urban science make broad use of the notion of scaling. We focus here on the important scaling relationship between the gross metropolitan product (GMP) of a city and its population (pop). It has been demonstrated that GMPY Ypopβ with β always greater than 1 and close to 1.2. This fundamental finding highlights a universal rule that holds across countries and cultures and might explain the very nature of cities. However, in an increasingly connected world, the hypothesis that the economy of a city solely depends on its population might be questionable. Using data for 248 cities in the European Union between 2005 and 2010, we found a double GMP/pop scaling regime. For West EU cities, β = 1 over the whole the period, while for post-communist cities β > 1 and increases from ∼1.2 to ∼1.4. The evolution of the scaling exponent describes the convergence of post-communist European cities to open and liberal economies. We propose a simple model of economic convergence in which, under stable political conditions, a linear GMP/pop scaling is expected for all cities. The results suggest that the GMP/pop super-linear scaling represents a phase of economic growth rather than a steady, universal urban feature. The results also suggest that relationships between cities are embedded in their political and economic context and cannot be neglected in explanations of cities, urbanization and urban economics.

<![CDATA[Complex Economies Have a Lateral Escape from the Poverty Trap]]>

We analyze the decisive role played by the complexity of economic systems at the onset of the industrialization process of countries over the past 50 years. Our analysis of the input growth dynamics, considering a further dimension through a recently introduced measure of economic complexity, reveals that more differentiated and more complex economies face a lower barrier (in terms of GDP per capita) when starting the transition towards industrialization. As a consequence, we can extend the classical concept of a one-dimensional poverty trap, by introducing a two-dimensional poverty trap: a country will start the industrialization process if it is rich enough (as in neo-classical economic theories), complex enough (using this new dimension and laterally escaping from the poverty trap), or a linear combination of the two. This naturally leads to the proposal of a Complex Index of Relative Development (CIRD) which shows, when analyzed as a function of the growth due to input, a shape of an upside down parabola similar to that expected from the standard economic theories when considering only the GDP per capita dimension.