Sustaining Growth of the People's Republic of China

This paper reviews economic growth theory in the framework of economic development and explores the possibility of sustained growth in the People's Republic of China (PRC) in the long run. We argue that the PRC has the potential to sustain relatively high growth rates. First, since the technological gap with major developed countries still exists, the PRC can continue to enjoy its “advantage of backwardness” in the near future. Second, large-scale infrastructure investment, which began several decades ago, may possibly extend to the future and provide the country a basis for further growth. Third, structural readjustment, which is needed in many areas, should similarly be able to support the Chinese economy. This paper argues that to sustain long-term growth in the PRC, a number of general preconditions need to be fulfilled—these include well-functioning markets, a minimum amount of investment, continued structural upgrading, and effective government.

I. Introduction
Economic development of the People's Republic of China (PRC) over the past 30 years has been an intriguing phenomenon. The country provides an interesting case for the study of economic growth, especially the sources of growth and expectations about future performance. With the PRC's declining growth rate during the past 4 years, there have been heated debates recently on the potential growth rate of the country in the medium and long run. Lin (2012a) argues that the Chinese economy has the potential to grow by 8% annually for another 20 years beginning in 2008.
This paper attempts to provide answers to the question of whether or not the PRC can indeed maintain a relatively high growth rate in the coming decades. We first present a review of the history of growth performance over the past 35 years including the recent slowdown, which had triggered concern about long-run growth potential. We also provide a review of the various theories on sustaining growth in the long run, which serve as the basis for subsequent analysis of the PRC's growth potential. Finally, we discuss the preconditions for sustained growth.

II. Historical Review of the PRC's Growth Performance
The PRC has been experiencing extraordinary growth in the past 3 and a half decades, with annual gross domestic product (GDP) growth averaging 9.8% over the 35-year period since 1978. No country has ever grown as fast or for as long a time as the PRC during the past 35 years. Before the transition from a planned economy to a market economy in the late 1970s, the country had been trapped in poverty for centuries. However, it is now an upper-middle-income economy, with GDP per capita of over $6,000.
Prior to the late 1970s, the PRC failed to achieve dynamic growth, as it applied a strategy that prioritized the development of heavy industries. From the 1950s to the 1970s, the Chinese government subsidized the firms in priority sectors through various price distortions and protections. Such a strategy was clearly inconsistent with the comparative advantage of the country during the period. As a result, resources became misallocated, total factor productivity (TFP) remained low, and growth was driven mainly by input increases. Firms in the priority sectors became unviable in an open and competitive market. Despite annual GDP growth of 6.1% and the establishment of a complex system of advanced industries during 1952-1978, household consumption grew by only 2.3% annually during the period (Lin 2012a).
The PRC has achieved rapid growth since the start of economic reform in the late 1970s, with the extraordinary performance partly due to the adoption of a gradual, dual-track transition strategy. On one hand, the government provided protection in the form of transitory subsidies to prioritized heavy industry sectors to maintain stability. On the other hand, it liberalized entry into sectors where the country had comparative advantage based on factor endowments.
The PRC recently experienced a decline in economic growth, which had fallen from 10.4% in 2010 to 7.7% in 2013. This is the longest period of growth deceleration since the country's transition to a market economy in 1979 and has naturally raised concern about the sustainability of growth. However, the slowdown had not been due to overinvestment, financial repression, the state-led growth model, or other internal structural problems frequently mentioned by observers predicting a crash in the Chinese economy. 1 The recent slowdown of the Chinese economy in fact was mainly caused by external and cyclical factors-specifically, the slow recovery of advanced economies after the 2008 global financial crisis, which has repressed export growth, and the slowdown of the country's investment growth due to the completion of projects supported by the fiscal stimulus in the aftermath of the 2008 crisis ( Figure 1).
These same factors triggered growth deceleration in other emerging market economies and in high-income economies. As seen in Figure 2, the slowdown in the PRC had been relatively mild compared to that in other countries.
Facing the potential of a "new normal" in high-income countries (i.e., protracted low growth), export growth will likely be subdued in the coming years. The PRC's growth will therefore have to depend on domestic demand-investment as well as consumption. How high the country's growth will be in the coming years meanwhile will depend on its potential growth rate.

III. Theories on Sustaining Growth in the Long Run
The development of theories of economic growth in the long run has been remarkable in the past half century. In this section, we review the basic features of some of the major theories in a framework of economic development. The neoclassical growth model, developed in the mid-20th century, is an exogenous growth model of long-run growth set within the framework of neoclassical economics. In these models, the long-run growth rate is exogenously determined by either the savings rate (the Harrod-Domar model) or the rate of technical progress (the Solow model). Solow's model assumes that factors of production are paid according to the value of their marginal products. The marginal product of capital will fall if capital rises faster than labor, which is normal for a growing economy, leading to a fall in the growth rate of output.
Neoclassical growth theory therefore indicates a convergence or catch-up process in which developing countries acquire techniques and learn how to use them efficiently. This supports arguments for the PRC's long-run growth slowdown.
In the late 1980s, a number of economists began to analyze the nature and role of the residual element in neoclassical growth theories by making it endogenous (Romer 1986, Lucas 1988. In endogenous growth theory, knowledge (technology) is the instrument of production that is not subject to diminishing returns. The theory tries to explain the lack of convergence by introducing the role of human capital, maintaining that economic growth is primarily the result of endogenous forces-e.g., investment in human capital, innovation, and knowledge-and not external forces.
Economic growth and convergence will be faster the more the economy is invested in human capital. Aghion and Howitt (1992) developed a model of growth through creative destruction in which innovations serve as the underlying source of growth. In their model, the amount of research in any period depends on the expected amount of research next period, which comes from created destruction. The growth rate and the variance of the growth rate are increasing functions of the size of innovation and skilled labor force, and the productivity of research.
Development economics uses growth theory to analyze economic growth in developing countries. The theories have come in three waves since the 1950s. The first wave involved structuralism (Rosenstein-Rodan 1943, Prebisch 1950, Singer 1950. Based on this thinking, governments were advised to develop industries that were too far advanced compared to their corresponding countries' level of development and that went against comparative advantage. Firms were thus nonviable in competitive markets and needed government policy support for initial investment and continuous operation (Lin 2009).
The second wave of development thinking, neoliberalism, was encapsulated in the Washington Consensus. The term originally referred to the reform package put together by financial institutions based in Washington, DC and recommended to developing countries mired in crisis in the late 1990s. It has since been used to refer to a more general orientation toward a strongly market-based approach.
The Washington Consensus set up a standard for developing countries based on the successful practices of developed economies. It opposed governments playing a proactive role in facilitating entry of firms into sectors even if consistent with a country's comparative advantage. It also failed to recognize that distortions in developing countries may be endogenous and advised governments to immediately eliminate them, causing the collapse of previously prioritized sectors.
The third wave involved what is known as the new structural economics. This development theory argues that each country needs to adjust its economic structure in the process of development according to its comparative advantage as determined by factor endowments (Lin 2012b). It applies a neoclassical economics approach to understand the determinants of economic structure and its evolution. Industrial structure is assumed to be endogenous to the endowment structure, which is given at any specific time and changes over time.
New structural economics argues that, following comparative advantage, which is determined by the endowment structure, developing industries is the best way to upgrade the endowment structure and sustain industrial upgrading, income growth, and poverty reduction. If economic structure upgrading lags behind changes in endowments, the economy will be less competitive, and growth will be lower than the potential growth rate. Improvement of the economic structure provides new sources of growth by reducing the inefficiency within and between sectors. Structural upgrading needs a competitive market system and a facilitating state.
New structural economics also postulates that industrial policy is desirable, and that target industries should be in line with an economy's latent comparative advantage. The theory points out that as long as its government chooses the right set of policies, a developing country has the potential to grow continuously at 8% or more for several decades and to become a middle-income (or even high-income) country in one or two generations.
A review of growth theory in the framework of development tells us that sustaining growth depends on many factors, not just the productivity of physical capital. It is influenced as well by human capital, technology, and structural change in the economy. All these factors help to determine growth at the dynamic path.

IV. The PRC's Potential for Sustained Growth in the Long Run
By adopting correct policies, the PRC has the potential to sustain dynamic growth in the coming decades. This section presents possible reasons for the country's sustained long-run performance. These are the "advantage of backwardness," the effect of capital investment, and structural readjustment.

A. The Advantage of Backwardness
A backward country has an advantage in economic development because it can borrow from a large backlog of technological innovations from advanced countries and adopt the latest technology without facing resistance from users of old technologies (Gerschenkron 1962). It can thus reduce the costs and risks of technological innovation, allowing it to grow faster than advanced countries. Since the late 1970s, the PRC has achieved high growth by tapping into this advantage of backwardness.
After 35 years of dynamic growth, does the PRC still have the potential to grow dynamically? The answer depends partly on whether or not the advantage of backwardness still exists. One way to measure this is by the gap between per capita GDP of the PRC and of the United States (US). Per capita GDP represents a country's average labor productivity, which in turn reflects the country's average technological level.
The PRC's per capita GDP measured in purchasing power parity (PPP) in 2008 as a percentage of that of the US was similar to that of Japan in 1951, the Republic of Korea in 1977, andTaipei,China in 1975 (Table 1). Annual GDP growth reached 9.2% in Japan during 7.6% in the Republic of Korea during 1977-1997, and 8.3% in Taipei,China during 1975-1995 After 20 years of growth, Japan's per capita income measured in PPP was 66% that of the US in 1971. The corresponding percentage for the Republic of Korea was 50% in 1997, while that for Taipei,China was 54% in 1995. Figure 3 shows the per capita GDP of Asian economies relative to the US per capita GDP from the year these economies started to take off-1951, 1975, 1977, and 1980, respectively, for Japan; Taipei,China; the Republic of Korea; and the PRC. Table 2 meanwhile indicates the degree of the advantage of backwardness  in terms of stocks of capital and human capital per person (that is, by disentangling the extent that output per worker might be driven by differences in these stocks or other features). We extend growth accounting analysis for the PRC using the Penn World Table (PWT), which decomposes growth in GDP per worker according to various sources-namely, growth of physical capital per worker k, human capital per worker hc, and TFP A. This can be represented by the following equation: (1)  We use the average of the capital coefficient in the past 5 years estimated by the PWT for the period 2015-2028 and calculate the growth of GDP and its components from 2012 to 2028 assuming zero growth rate in the number of workers during the forecasting period (see Table 3).
We assume the following in our economic analysis: a zero growth rate for the number of workers during 2015-2028, which is consistent with the population trend and age structure of the PRC, and a 1%-1.2% increase in human capital per person during 2015-2028, or slightly lower than the actual annual growth of human capital of 1.2% during 1952-2011.
We also assume that the country's capital stock will keep growing albeit at a moderate rate based on the assumptions of continued structural upgrading and discovery of new investment areas. The increase will be 6.8%-8.8% during 2015-2020 and 5.5%-7.5% during 2021-2028, lower than the actual growth rate of capital stock during 2001-2011 (11.8%) and 2013 (8.8%).
TFP growth will similarly decline slightly during the forecast period, as the PRC approaches the world technological frontier. We assume a growth rate of 1.7%-2.3% during 2015-2028, lower than the actual growth rate of 3% during 2001-2011.
Our growth accounting analysis shows that the GDP growth rate of the PRC will be in the range of 6.1%-8.0% before 2020 and 5.4%-7.6% during 2021-2018 if capital stock increases by 6.8% to 8.8% during 2015-2020 and by 5.5% to 7.5% during 2021-2028, human capital per person increases by 1% to 1.2% during 2015-2028, and TFP increases by 1.7% to 2.3% during 2015-2028. The assumptions are reasonable and conservative compared to growth rates in previous periods, which are also shown in Table 3.
To support our forecasts, we calculate using PWT data the average annual growth rates of physical capital, human capital, and TFP in Japan; the Republic of Korea; and Taipei,China during the same development stage that the PRC finds itself in currently. The results are shown in Table 4. Since the PRC is in a similar development stage as the Republic of Korea during 1977-1997and Taipei,China during 1975-1995 it is likely that the country's capital stock and TFP will grow at similar rates in the near future.
To prevent mistakes in forecasting, our assumptions about the growth rates of the PRC's capital stock are tailored to be more conservative than the actual growth rates of capital stock in the Republic of Korea during 1977-1997 (11.8%) and Taipei,China during 1975-1995.
If the PRC maintains an 8% growth in the next 2 decades, by 2030, the PRC's per capita income measured in PPP may reach about 50% of that of the US. The exploitation of the advantage of backwardness has and will allow the PRC to achieve extraordinary economic growth by reducing the costs of innovation, industrial upgrading, and social and economic transformation.

B. Large-Scale Infrastructure Investment
Public investments, say in infrastructure, may have a positive influence on long-run growth if it raises the marginal productivity of private capital. Barro (1990) develops a closed-economy endogenous growth model where government service is viewed as an input in private producers' production function. The effects of public investment on private producers have also been analyzed in many empirical studies. Aschauer (1989), for instance, studied the US economy during 1949-1985 and found that public investment in infrastructure had maximum explanatory power for the productivity of private capital.
Continued large-scale infrastructure investment, which began when the Chinese economy started to reform and open up, could be another possible factor behind sustained growth in the near future. The PRC has made large-scale infrastructure investments over the past several decades, which updated the country's infrastructure in all sectors and raised productivity, with continued positive effects on future growth. Table 5 shows the gross capital formation in the PRC from 1991 to 2013.
A rough comparison with the US shows that the PRC's gross investment during 2010-2013 measured in US dollars and using official annual average exchange rates was greater than the US gross domestic investment during the period. Although the share of physical investment in GDP declined slightly in recent years, it will remain relatively high in the near future. The quality of physical investment also remains quite high in terms of operating profit, although it declined slightly in recent years.
Real estate investment has been an important part of investment in the PRC in the past 20 years. Table 6 shows the operating statistics of real estate firms in the country. The share of taxes and profits increased dramatically beginning in 2005 and continued to be relatively high until 2012. This suggests that investments in real estate and physical capital will keep growing at a comparatively high rate in the near future.
From the most recent data, we expect that returns on investment will gradually fall but will stay at a level high enough to attract investment. Based on this, we forecast that investment in physical capital will continue to grow at a rapid pace in the near term.
Many economists worry that the PRC's economic growth relies too heavily on saving and investment. They posit that a large contribution of capital means a small contribution to technological progress, a condition that may not be sustainable. However, none of the existing methods measuring technological progress has successfully accounted for the technological progress embedded in capital. Capital investment brings about new approaches of production as well as new teams of workers, which together increase productivity and bolster growth. There may also be a time lag between capital investment and the functioning of capital stock. The potential role of physical capital in development depends on the relationship between investment and technical progress. Scholars working on growth, especially those focusing on technology diffusion and TFP, have been studying this potential role. Caselli and Wilson (2004) found strikingly large differences in investment composition across countries, with the composition of capital accounting for large observed differences in TFP across countries. They also found that the differences were based on each equipment type's degree of complementarity with other factors whose abundance differs across countries (Caselli and Coleman 2001). Comin and Hobijin (2004) observed a trickle-down mechanism in the diffusion of technologies across countries, which meant that although most of the technologies originating from advanced economies were adopted there first, other countries that lag economically could still benefit subsequently. The determinants of the speed at which a country adopts technologies include the country's human capital endowment, type of government, degree of openness to trade, and adoption of predecessor technologies. Jorgenson and Vu (2010) introduced a new framework for projecting the potential growth of the world economy, emphasizing the contribution of information technology. Investment in information and communications technology (ICT) raises the amount of capital available for labor, thus increasing labor productivity, likely increasing economic growth. It also introduces new technologies into the production process. Inklaar and Timmer (2013) discuss new measures of capital stocks in PWT version 8.0, while Aghion (2014) notes the positive relation between ICT share (in value-added) and economic growth, using panel data. The contribution of investment in ICT has increased in all regions of the world, but especially in industrialized economies and developing Asia.
To explore the sustainability of the PRC's future growth in physical capital investment based on the composition of capital, we collected data on the growth of ICT capital services. Data show that ICT capital services grew at a more rapid pace in the country than in Japan, the Republic of Korea, and the US during 2000-2013 (Table 7).
This suggests that the potential exists for further increases in productivity and capital investment in the PRC in the near future. The average annual contribution of ICT capital services to overall growth in capital services in the country was 1.06 percentage points from 2004 to 2013, compared with 5.57 percentage points for non-ICT capital services during the same period. In addition to rapid physical capital accumulation, the PRC has seen largescale human capital accumulation. The country has been improving its stock of human capital over the past 30 years, owing to government commitment and increasing returns to education. Education spending by the Chinese government in terms of the share of total government spending is lower than that in the US but higher than in the United Kingdom and Japan (Bai, Wang, and Qian 2010).
Higher education in the PRC has grown at a faster pace than both primary and secondary education. College admission and enrollment has increased tenfold, with 6.4 million bachelor's and 514,000 master's and PhD degree holders produced by Chinese universities in 2013 (Table 8). The quality of human capital investment in the country has also improved through the years. The number of college students graduating as a percentage of the total has risen in the 2010s compared to the 1990s. Table 9 similarly shows that the number of graduates from college and senior secondary schools in the PRC has increased dramatically in recent years. This trend, which has greatly improved the country's quality of education, is expected to continue in the near future. As Aghion (2014) and other authors have pointed out, education is important in bolstering economic growth.

C. Structural Readjustment
The PRC has been experiencing structural changes in the past 3 decades. Structural readjustment, which allows a transfer of resources from sectors with    1985-1989 27 1990-1994 17 1995-1999 15 2000-2004 20 2005-2009 19 1985-2009 19 Source: Xie and Zhang 2014. lower productivity to sectors with higher productivity, serves as another source of economic growth. Brandt, Hsieh, and Zhu (2008) decomposed the PRC's growth according to agriculture and non-agriculture sources during 1978-2004 and found that labor reallocation from agriculture to non-agriculture contributed 24.6% to the country's overall growth. They attribute this to the large productivity gap between agriculture and non-agriculture sectors during the period.
Theoretically, GDP growth in a multisector model can be divided into four components-the contributions of capital, labor, pure technical progress (weighted average of technical progress in all sectors), and structural change.
The current Chinese economy is still far from its long-term steady state. Large productivity gaps still exist between sectors of the economy, while endowments continue to change as income rises. The production structure clearly needs to change to follow the changes in endowments. These structural readjustments should be able to provide new sources of economic growth and raise the potential for a relatively high growth rate in the medium to long run.
Heterogeneity at the firm level is a source of productivity growth. Aghion (2014) points out that the heterogeneity between firms is much larger than that between sector averages. In the US, the productivity of the top 10% most productive firms is twice that of the bottom 10%. Hsieh and Klenow (2009) find the corresponding multiple to be as high as 5 times in the PRC. This huge difference shows the country's potential for structural improvements in the future.
The enormous size of the Chinese economy benefits structural readjustment, as a wide enough industrial spectrum makes an update of the industrial structure and a widening of product variety possible. The huge product space makes it possible for the country to upgrade its industrial structure at a wide scale. This feature differentiates the PRC from mid-sized countries such as Japan and the Republic of Korea.
Aside from well-functioning markets and continued structural upgrading, there are other factors that contribute to the structural improvement of the Chinese economy and that help drive future growth. These include private sector development and privatization of state-owned enterprises to spur greater competition, greater protection of intellectual property rights and other legal support to encourage R&D and innovation in the manufacturing sector, bilateral investment treaties and free trade zones to foster greater openness of the economy, and deregulation and introduction of greater competition to improve productivity in the services sector.
Innovation is a key driver of long-run growth, one that is particularly important for middle-income countries such as the PRC. Xie and Zhang (2014) find that domestic Chinese firms have become increasingly more innovative in terms of patent applications, and that private firms, rather than state-owned firms, have been the engine of innovation. Facing increasing wages, the firms have had to invest more in technologies to substitute the more expensive labor. The annual growth rate of patents granted during 1985-2012 averaged at 19%, almost twice the annual growth rate of GDP during the same period (Xie and Zhang 2014).
Overall, on one hand, the PRC is still far behind the world technology frontier and will continue to enjoy the advantage of backwardness. On the other hand, it is closer to the world technology frontier than at any time previously and possesses greater capital and skilled labor than it did 30 years ago. This provides a basis for updating industries and further economic development.

V. Preconditions for the PRC's Sustained Growth and Policy Suggestions
The PRC has the potential to sustain a relatively high growth rate in the long run provided government adopts the right policies. There are a number of general preconditions or prerequisites that must be met in order to achieve this goal.
The PRC needs to narrow income disparities and fight corruption to maintain social stability. Greater income disparity and corruption are the results of the dual-track transition strategy where the government continues to provide subsidies and protection to nonviable firms in prioritized capital-intensive sectors, creating distortions in factor markets and monopolies in certain service sectors while allowing or facilitating the entry of new firms to the labor-intensive sector that had been previously repressed. Such an approach allows the country to achieve stability and dynamic growth simultaneously.
However, distortions in factor markets and monopolies in telecommunications and financial sectors create rents and encourage rent-seeking, leading to greater income disparity and corruption. Many capital-intensive firms in the old prioritized sectors have become viable because of the change in the endowment structure, which was a consequence of rapid growth and capital accumulation over the past 35 years. It is time for the country to eliminate these distortions and complete the transition to a well-functioning market economy, allowing prices to play a decisive role in resource allocation to improve efficiency and eliminate the root of income disparity and corruption (Lin 2012a).
The PRC needs to continue investing in physical and human capital in order to change its endowment structure. It also needs to continue upgrading technologies, transforming its industrial structure, and tapping latecomer advantages. As developments continue, factor endowments and industrial structure will keep changing, and the country will need to update its infrastructure accordingly. The inconsistency between the two means leaving the equilibrium path and lowering trend growth.
Finally, the Chinese government also needs to play an enabling role in future economic development. It needs to help firms reduce transaction costs by improving both soft and hard infrastructure. A sound fiscal position, at both central and local government levels, will be beneficial to economic growth. An enabling government is also essential to overcome inherent externalities of technological innovation and industrial upgrading. State effort is also needed to improve the country's environmental quality and achieve environmental sustainability.

VI. Concluding Remarks
In sum, the PRC can sustain a relatively high growth path over a long period based on three basic arguments. First, since a substantial technological gap still exists between the PRC and major developed countries, it can continue to enjoy the advantage of backwardness. Second, the long period of large-scale infrastructure investment that began several decades ago can still continue and provide the country a basis for further growth. Third, structural readjustment, which is needed in many fields, should be able to bolster economic growth in the long run. These arguments are supported by growth accounting analysis.
We argue that to realize the potential for sustained growth, a number of general preconditions or prerequisites need to be fulfilled. These include well-functioning markets, an appropriate rate of investment, continued structural upgrading, and an enabling government that can facilitate the appropriate structural transformation. If the economic adjustments are done correctly and in a timely manner, sustained growth should be possible in the medium-and long-term.


Prediction of Financial Crisis with Artificial Neural Network: An Empirical Analysis on Turkey

Prediction of economic crisis, financial distress or bankruptcy has attracted great deal of attention in financial literature and in many other fields among the researchers over the past few decades. Although there are a variety of different methods that can be used to predict the future financial crisis, due to the complexity of the existing factors, prediction of financial crisis is a very difficult case. With the advent of Artificial Neural Networks (ANNs), researchers had the chance to solve various problems in finance. ANN approach is the application of artificial intelligence, which has been improved by the simulation of cognitive learning process of human brain. ANNs are commonly used in recent years, due to major advantages that they offer such as their ability to perform nonlinear statistical modeling that provides new alternative to other statistical methods and to learn directly from examples without needing or providing an analytical solution to the problem. In this study, a monthly dataset covering the period of 1990 and 2014 that belong to the Turkish economy will be used. The purpose of this study is to develop an early-warning system to predict financial crisis. To realize this aim, multi-layered feedforward neural networks (MLFNs) will be used. By using monthly data of 7 key macroeconomic and financial indicators of Turkish economy during 1990 and 2014, we find that predictive power of ANN is quite striking. Our out-of-sample forecasts indicate that the Turkish economy remains at high risk due to major negative developments and potential political instability between 2014 and 2016.

Introduction
The integration of financial markets and the globalization all around the world exposed policy makers to new challenges as a results of shortening of the time period that policy makers should respond to global financial crises. Past crises indicated that financial markets are reasonably poor at realizing a crisis approaching. Therefore, as Cheng et al. (2006) point out, prediction of economic crisis, financial distress or bankruptcy has attracted great deal of attention in financial literature and in many other fields among the researchers over the past few decades. The recent financial crisis of 2008 has revealed great weaknesses in financial system and inadequacy of financial regulations. After big corporate scandals and the demise of giant companies like Arthur Anderson, Enron or Worldcom, global economies and people all around the world have been extremely cautious about the potential early warning signs of financial system. Since the beginning of the recent financial crisis in 2008, investors and lending institutions experienced enormous financial lossess.
A financial crisis is a certain economic state in which economic, social and political deteriorations create uncertainty and chaos in the society. Many factors can be specified as causes of financial crisis including excessive risk taking, poor management practices, inadequate internal control mechanisms, and interest rate volatility (Ecer, 2013). While the people and societies, who cannot predict the financial crisis had to suffer from unemployment, poverty and a variety of other economic problems, those who can anticipate the crisis can use it to their advantages and transform the potential drawbacks into opportunities. Therefore, prediction of financial crisis and developing an evolutionary approach sufficient for handling with dynamic financial circumstances are utmost important among policymakers, governments, regulators, researchers and global investors. If they can succeed, by utilizing the early warning systems, governments can monitor the financial system and take precautions to protect their societies from new financial crisis.
Additionally, an early warning system might also be useful in determining the vulnerability of a group of countries, which would be more difficult to evaluate. By identifying which countries are more vulnerable in a period of global financial crises, policy makers would be able to focus their attention on the countries that need radical policy adjustments to prevent a crisis. Thus, early warning systems can support regulators and policymakers to protect their markets from economic crisis.
Empirical research on developing early warning systems has been relatively new. Although there are a variety of different methods that can be used to predict the future financial crisis, due to the complexity of factors, prediction of financial crisis is still a very difficult case. As Yildirim et al. (2011) state, researchers generally benefit from a variety of different statistical methods to analyze data, establish models and make decisions based on their estimations to conduct their reseraches. In the last few decades, as studies aimed at predicting the financial crisis have become more common, machine learning has also been widely used among researchers to realize this goal.
With the advent of Artificial Neural Network (ANN) researchers had the chance to analyze various problems in finance such as prediction of potential financial crisis. ANNs have been frequently used in the field of finance. Recently, they have become popular tools to assist financial decision making. As Oztemel (2003) points out, there are many different areas of research in finance in which ANNs are used such as credit authorization and screening, credit risk assessment, project management, financial and economic forecasting, fixed income investments, prediction of default and bankruptcy and credit card manipulations. Additionally, use of ANNs can significantly improve the following corporate finance applications such as financial simulation, investor behavior prediction, portfolio management, pricing initial public offerings, determining optimal capital structure, financial evaluation and credit approval (Bahrammirzaee, 2010).
The reminder of the study is organized in the following way. We discuss the literature focusing on the studies based on the ANNs and the alternative techniques in section 2. We describe the data set and methodology in Section 3. In this section, we also give brief information about the structure of ANN. In section 4, we present the results concerning the relationship between the real values and the obtained values from the ANN. Section 5 is the conclusion part of the study.

Literature Review
Since the beginning of 1990s, ANNs have been frequently used to solve complicated problems and assist many business applications successfully. They can be utilized as efficient tools to analyze critical issues in business decisions. As Ecer (2013) states, real world financial data have often been nonlinear recently. However, thanks to latest developments in computing technology, nonlinear models can also be used in modelling and forecasting myriad of financial relationships. In this context, ANNs also help to solve problems in finance with poorly defined models.
Although developing a common model for all types of financial crisis is very difficult, it is possible to build an early warning system that would pick up different variables. Many researchers prefer to utilize ANN to conduct their studies. However, only limited number of researchers use ANN in their empirical investigations to identify financal distress or predict financial crisis. In one of the earliest studies, Bell et al. (1990) employed BPN technique by using eleven predictor variables to classify whether selected banks are bankrupt or not. They compared the performance of a logit model with the ANN performance and concluded that ANN was better than logit in determining marginally distressed banks. Tam and Kiang (1992) compared the BPN technique with multivariate discriminant analysis (MDA) and logit to predict bankruptcy of selected Texas banks by using nineteen financial ratios to develop their models. Their results indicate that BPN technique is more accurate in predicting bankruptcy rates of banks. Brockett et al. (1994) also utilized BPN technique and multivariate discriminant analyssis (MDA) to identify early warning signals of insurer insolvency. They conclude that BPN technique has a higher success in classifying insolvent or solvent firms.
The most important difficulty, which may be experienced in the prediction of financial crises is on the determination of which variables should be selected among a great number of data to be included into the models. Kaminsky, Lizondo and Reinhart (1998) have picked up the most useful variables to be used in the prediction of the financial crises by seperating out 16 variables out of 103 indicator variables. For all of the variables, the indicator on a given month was defined as the percentage change in the level of the variable with regard to its level in the previous month. At the same time, these indicator variables can be evaluated as a signal for anticipating crisis. Genarally, a signal that is followed by a crisis within 24 months is named as a good signal, whereas a signal not followed by a crisis within the mentioned period of time is called a false signal. Nag and Mitra (1999) used the ANNs to establish an early warning system to detect exchange rate crisis over the period of 1980-1998 in Indonesia, Malasia and Tayland. They employed sixteen variables for their study. They compared their results with the results obtained by the signal approach. They found that the ANN model has a superior ability of prediction as compared to the signal approach. Frank and Schmied (2003) utilized the ANNs in the prediction of speculative attacks. They investigated the speculative attacks in Russia and Brazil experinced in 1998 and 1999. According to them, trying to determine the nonlinear relationships by using linear models weakens the possibility of the prediction of financial crisis. For that reason they state that they used ANNs in their study and conclude that ANN has a more accurate performance as compared to the logit models in the prediction of speculative attacks. Oh et al. (2005) used the ANN model and the method of Nonlinear Programming (NLP) together to predict the 1997 financial crisis within the framework of Korean example bu using the early warning systems. The major difference of the study from the previous studies is that the researchers used daily data and established alarm zones by using financial crisis prediction variables. Peltonen (2006) aimed to predict monetary crisis in developing countries by using ANN and probit models. In the study, the monthly data for twenty four emerging market countries over the period of 1980 and 2001 were used. They obtained 45% of successful prediction levels in both models prior to financial crisis. Fioramanti (2008) in his study, tried to predict the debt crisis in less developed countries by using the data that belong to the period of 1980 and 2004 by employing the ANN model. He asserts that the ANN model can obtain more accurate results as compared to other early warning systems thanks to its high flexibility and its ability to analyze the nonlinear relationships. In his study, he used yearly data of 46 developing countries that belong to 34 independent variables over the period of 1980 and 2004. Similar to the results of Nag and Mitra (1999) and Franck and Schmied (2003), Fioramanti (2008) observed that the ANN model provided more accurate and successful results.
Implementation of ANN models in Turkey also provided similar successful results. Various researchers used ANNs such as Yıldız (2001) and Benli (2002) to predict financial failures; Çelik and Karatepe (2006) to predict banking crises; Tektaş and Karataş (2004) to predict stock prices; Benli (2005) to predict bank failures; and Erilli et al. (2010) to predict inflation rates.

Artificial Intelligence Techniques
Artificial intelligence techniques are used in a variety of fields and new techniques are being developed every day. As Oztemel (2003) classifies major artificial intelligence techniques include:  Expert Systems: They are computer systems which can produce results to the problems just as a human expert can do. They are equipped with knowledge of expertise to solve complex problems. Inference mechanisms make decisions by establishing relationships between data.  Artificial Neural Networks (ANNs): They are a type of statistical learning algorithms which resemble biological neural networks. They are used to estimate functions that can depend on a large number of inputs and are generally unknown.  Genetic Algorithm: They are adaptive heuristic search algorithm developed to solve problems which cannot be solved with the traditional optimization technology. They are based on the ideas of natural selection and genetics. They benefit from historical information to direct the search to obtain beter performance in the search space. They are based on the philosophy of combining solutions of the problems to provide beter solutions.  Fuzzy Logic: It is a type of logic that recognizes more than simple true and false values. With fuzzy logic, propositions can be represented with degrees of truthfulness and falsehood, which eases decision making process.

The Structure of Artificial Neural Networks (ANNs)
The neural networks principally match inputs to the outputs by utilizing layers and neurons to create a complex learned algorithm. The artificial nevre cells come together and compose the artificial neural networks. They do not join together randomly. Usually, the nevre cells come together in three layers, in paralel with each other in each layer and they constitute the network. (Oztemel, 2003) Thus each of the layers is comprised of many neurons that are connected to the neurons in the preceding and subsequent layers. These layers are (Muller et al., 2009):

Empirical Results
In this study, we assess the relative performance of forecasting model based on the multi-layered feedforward neural network (MLFN). MLFN, which is also known as "backpropagation network" is one of the most widely used ANN in financial researches. Basically, the structure of MFLN consists of an input layer, one or more hidden layers depending on the result of the study and the output layer of processing units. One of the major decisions that the developer of a neural network may encounter is the number nodes in a hidden layer. No theory or a formula exists to determine the optimal number and therefore, network developers need to use the method of trial and error to find the optimal number of hidden nodes.
The layers in the structure are interlinked with each other that the processing units in the input layer are connected to the units in the hidden layer. The units in the hidden layer are connected to the units in the output layer respectively. For any structure with multiple hidden layers, the first hidden layer which transmits information from the input layer is linked to the second hidden layer. Rest of the hidden layers are connected similarly and consequently, the last hidden layer sends the processed information to the output layer. MLFN with supervised learning can be used to develop nonlinear models to indicate the relationship between the independent and the dependent variables. (Chen and Leung, 2005) In this paper, the data of 298 months between January 1990 and September 2014, obtained from the internet sites of Electronic Data Distribution System of the Turkish National Bank (EDDS) and The World Bank, were used. In the empirical analysis part, seven different variables, which are US Dollar (USD), gold prices (GP), Borsa Istanbul (BIST) 100 Index, wholesale price index (WPI), money supply (MS), domestic debt stock (DDS), composite leading economic indicators index (CLEI) were used. In the prediction of the next 24 months of crisis indicator data in Turkey, we prefer to use feed-forward, backpropagation, multi-layered ANN. Testing was performed by the JAVA programming language. Obtained models have investigated whether the data will show a decrease or increase in comparison with previous or latter month.
In this study, in terms of applied method, a feed-forward and multilayered ANN has been used which is composed of 7*12=84 inputs, 7 outputs and 2 hidden layers in which 25 neurons are included in each layer. In the layers of the network, the sigmoid activation function has been used. The artificial neuron output value, which depends on the selected sigmoid activation function, is calculated with Eq. 1. The generated output is sent via the connections of the network to the other cells (Sagiroglu et al., 2003)  In addition to this, the resillient propogation learning algorithm has been used for the learning of the network. In this learning proces, the error value of 0.1 % has been chosen as a target value which provides a limit between the desired values. In the structure of a multi-layered ANN, there are mainly three layers. An input layer with artificial neural cells, which are connected to each other in different ways, a hidden layer (intermediate layer) and an output layer. In Figure 1, a three layer network structure, artificial neurons and inter-layer relations have been demonstrated schematically. The input layer which is composed of 84 (7*12) data is the first layer and is responsible for receiving incoming data to the ANN. It also serves to distribute the incomimg data to the intermediate or hidden layer. The

Figure 1. A brief depiction of the proposed methodology for ANN
Because of easiness in its utilization, time series which take part in ENCOG framework, have been given preference in the Temporal Neural Dataset class of software that are constituted for the data sets. Additionally, we have benefited from several strategies in order to assist coding in the framework such as "If certain number of iterations have been passed in the training of the network, restart to learning" or If the error value does not progress a significant proportion, restart to learning.
The normalization and the denormalization of data have been done with the help of built-in functions found in framework. While the network is being trained, the twelve-month lagged values of data were given as prologue and forecasting of the thirteenth month has been demanded. In the same manner, it has been tried to be estimated starting from the 36th month of 299 months that are in the data set. For each month to be predicted, ANN has been trained iteratively with its previous three-year data. For the 300th and the following months, again its previous three-year data has been given to the ANN as training set and the missing months have been filled by the prediction values obtained in previous iterations.
In an economic environment in which capital movements have been liberalized, Turkey experienced the crises of 1994 and 2001. Both crises had few common points such as large amounts of capital inflows and capital outflows before and after both crises. In the aftermath of 1994 crisis, financial risks in banking sector increased rapidly due to the state guarantee on bank deposits. The overvaluation of Turkish Lira and the rapid rise in interest rates could not be evaluated adequately and this resulted in the 1994 financial crisis. During the period of 1995-1999, the potential crises and disparities were tried to be avoided by taking a variety of measures of precautions. However, risk perceptions in Turkey also changed in paralel to the financial crises experienced in different parts of the world in 1998 and capital outflows also accelarated.
When the graphics presented in Figure 2 are reviewed in detail, especially, as a consequence of the interest rates discounted by government in 1993, funds tended to move towards foreign currency and stock exchange. Consequently, foreign exchange rates have risen in parallel to this. Besides, DDS attained to its the peaking limit for the first time in its history. It reached to a point that tax revenues could not defray the payments of domestic borrowings.
All the negative developments in economy such as the overvaluation of Turkish Lira, the boom of gold prices, the jump of the stock market, the overshooting of wholesale price index (especially, on December of 1999), the rise of USD in the final half of 1999, and political uncertainities all caused the most severe financial crisis in the history of Turkey, the economic crisis of 2001. Analyzing the predicted data in terms of financial crisis in Turkey, especially during the 2001 financial crisis, we see that indicator variables either sharply increase or decline. These fluctuations clearly reveal the impacts of the 2001 financial crisis on the Turkish economy. The period of 2002 and 2007 is a stability period, thanks to structural reforms applied in Turkish economy and the financial system, and the positive course of events in the global economy. This stable period provided high growth rates in Turkish economy until 2008. In this time period, the average growth rate of Turkish economy was 7% and the avreage increase in exports was 23%. The relation between the values calculated with the prediction model concerning the training, validation and test data and the real values can be seen in Figure 2. And the prediction of the data from 2014.12 to 2016.11 is given in Table 1. Figure 2 shows that the predicted ANN model has a stronger explanation abilities for the 2001 and 2008 crises. This figure also clearly indicates that the rapid economic growth during the periods between 2002-2004 and 2005-2007 have been accompanied by accelerated increases in macroeconomic and financial risks.
2008 financial crisis, the first global financial crisis of the world, has had a deep negative affect initially on developed countries and later on emerging countries. Turkey is one of these developing countries which have been affected considerably from this crisis. As a result of the approaching crisis, the high growth rates in the economy slowed down and the Turkish economy had a negative growth rate in 2009. Undoubtedly, 2008 financial crisis had negative affects on the growth rates of all economies. However, the level of contraction in the economy has been extensive on the European countries and on Turkey. The crisis was severely felt in the emerging economies, mainly because of reductions in the amount of capital flows from developing to the emerging markets. As a consequence of falling demands from the developed countries, the exports of emerging countries like Turkey to the developed markets sharply declined. Unemployment rate, one of the most important indicator of the financial crisis, remains as a social and economic problem that should be solved in developed countries as well as in emerging countries.

Conclusion
It is difficult to forecast the share prices in countries like Turkey, which have many manipulative changes in its economic structure. Especially in less developed or developing countries like Turkey, political and economic uncertainities affect macroeconomic politics intensely. In spite of the fact that some researchers claim that they are successful in predicting financial crisis, the remarkable differences among the incidences of such crisis in the world suggest that these incidences are hardly predictable in a systematic manner.
This paper is one of the first applications of the ANNs for the literature of prediction of financial crisis. Furthermore, factors affecting financial crises were evaluated with a special focus on the economic fundamentals obtained from the financial crises theories, as well as factual exchange rate regime and contagion effects. By using monthly data of 7 key macroeconomic and financial indicators of Turkish economy during 1990 and 2014, we find that predictive power of ANN is quite striking. Our out-of-sample forecasts indicate that the Turkish economy remains at high risk due to major negative developments and potential political instability between 2014 and 2016. Although we have obtained some interesting results, we are aware that prediction in a complex and dynamic social system is indeed a very difficult task that calls upon more advanced and sophisticated techniques of computational intelligence. In addition, how to incorporate qualitative data such as contagion, political disturbances and moral hazard into intelligent decision systems remains an open question.
Although, the number of related studies on the prediction of financial crises is highly limited in Turkey, as a suggestion for further research, such studies could be conducted for other business sectors, too. As such, managers, policymakers and researchers can develop long-term plans and make better decisions in critical conditions. Consequently, long-term forecasting will contribute positively to the Turkish economy as a first step of stability in economy and in business.
To sum up, as many studies in the literature indicate, the ANN has superior performance in the prediction of financial crises to develop an early warning system. It is noteworthy that certain economic variables that are associated with the financial crises of 1990s and the economic fundamentals derived from the financial crisis theories were capable of explaining the threshold of the financial crises. In contrast, in the 2000s, other factors, such as the contagion effect and the instable foreign exchange regimes, seem to have played a larger role in the occurrence of these crises. This reinforces the view that developing a stable model that could predict or even explain financial crises by using ANN or other alternative methods can be challenging but also invaluable for the global economy in general.


extfieldsofstudy: Biology Medicine

text:
Inequalities in Global Trade: A Cross-Country Comparison of Trade Network Position, Economic Wealth, Pollution and Mortality

In this paper we investigate how structural patterns of international trade give rise to emissions inequalities across countries, and how such inequality in turn impact countries’ mortality rates. We employ Multi-regional Input-Output analysis to distinguish between sulfur-dioxide (SO2) emissions produced within a country’s boarders (production-based emissions) and emissions triggered by consumption in other countries (consumption-based emissions). We use social network analysis to capture countries’ level of integration within the global trade network. We then apply the Prais-Winsten panel estimation technique to a panel data set across 172 countries over 20 years (1990–2010) to estimate the relationships between countries’ level of integration and SO2 emissions, and the impact of trade integration and SO2 emission on mortality rates. Our findings suggest a positive, (log-) linear relationship between a country’s level of integration and both kinds of emissions. In addition, although more integrated countries are mainly responsible for both forms of emissions, our findings indicate that they also tend to experience lower mortality rates. Our approach offers a unique combination of social network analysis with multiregional input-output analysis, which better operationalizes intuitive concepts about global trade and trade structure.

Introduction
Economic globalization refers to flows of trade and capital among and between countries. Within sociology, a common, critical view on economic globalization is that the historical forces influencing how and when a given country becomes 'integrated' into global trade (and the world economy as a whole) conditions the potential paths of development open to that country (e.g. [1]). Here, trade and other relations between countries act as structural mechanisms enabling wealthier, more core countries to maintain favorable terms of trade, which in turn negatively impacts less developed, more peripheral ones in a variety of ways [2,3,4].
In this paper, our primary interest is in tracing how countries' level of integration in international trade gives rise to between-country inequalities. By integration, we mean the extent to which countries are embedded in the global network of international trade, and we use network analysis to capture this level of embeddedness. A number of scholars (e.g. [5,6,7,8]) use network analysis for measuring countries' level of integration as an alternative to measures based on exports and/or imports over GDP, noting that such network measures better incorporate implicit notions of 'economic integration,' namely, the number and volume of trading ties, as well as the structure of regional trading, that demonstrate the level of connectivity, and hence 'integration' in the world economy.
By between-country inequalities, we are interested in emissions, wealth and mortality. For emissions, we make use of multiregional input-output (MRIO) analysis to distinguish between two forms of emissions, these being emissions produced within a country via that country's manufacturing activities (referred to as production-based emissions), and emissions that is triggered by a country's purchase by accounting for all emissions that is triggered throughout the whole global production chain and then allocated to the final consumer (referred to as consumption-based emissions). As means of an example, when a person purchases a toothbrush in the USA, this purchase triggers a supply-chain of production around the entire globe. Some parts hail from Asia, others from Europe, and all get shipped to Northern America for assembly [9]. At each step in this global supply-chain, some form of air polluting emissions occurs, be it through manufacturing, assembly, or transportation processes. In a production based accounting approach, the emissions occurring within a specific country's border, as it relates to the toothbrush's production, would be assigned to that country. In contrast, with a consumption-based approach, all emissions along the global commodity chain of the toothbrush would be allocated to the country where the toothbrush was purchased. By accounting for emissions from these two approaches, we are able to distinguish between countries that generate emissions through their production activities, and those that trigger emissions through their consumption. In combination with the use of network analysis, which captures structural features of global trade networks, we see our use of MRIO analysis as providing a more powerful framework that reflects the globalized nature of emissions-intensive commodities.
In addition to better understanding the globalized nature of emissions, we are interested in how emissions and wealth work together to affect countries' mortality rates. Whereas production-based emissions are often experienced as a 'burden' that local populations must endure in exchange for participation in global trade [10], economic growth and wealth are often seen as the main, potential benefits of such participation [11,12,13,14]. These trade-offs come together differently for different countries, such that the potential impacts of emissions experienced by local populations' may be 'buffered' by the benefits associated with more wealth. We consider whether such wealth indeed translates into a buffer against emissions impacts by considering how countries' share of global production-based emissions stands in relation to their share of global value added. Efficiency is often seen as one sign of a country's level of development, in that more economically advanced societies have accumulated more wealth to invest in cleaner, more efficient technologies (e.g. [15]) such as better filter technologies, less emissions intensive production structures, and a less polluting fuel mix.
Finally, in looking at how emissions impacts countries' populations, we have chosen to use a regional pollutant, in this case sulfur dioxide (SO2). This toxic gas is emitted via the combustion of fossil fuels in power plants and manufacturing facilities, and evidence suggests that local exposure to SO2 is linked to respiratory illnesses such as bronchitis or terminal lung cancer in both children and adults [16,17,18,19]. For these reasons, we make use of SO2 (as opposed to a more global pollutant such as carbon dioxide) for our study.
Taken together, our paper makes a number of contributions to the literature on international trade and emissions allocation. Although we are not the first to untangle productionbased from consumption-based emissions [20,21,22] a common shortcoming of this work is that it remains largely descriptive, demonstrating the regional and between-country emission disparities without attempting to explain them statistically (although see [23] for an exception to this trend). In addition, we are aware of no other paper that simultaneously attempts to uncover the drivers of these different forms of emissions, as well as trace their effects on countries' populations.
The rest of the paper is structured as follows: we offer a review on economic and sociological literature on global trade, economic globalization and emissions. This is followed by a description of our longitudinal data, which includes country-by-country trade data on the sector level, as well as a number of country-level attributes. We discuss our methods, which include social network analysis (SNA), multi-regional input-output (MRIO) analysis, and panel data regression models and estimation techniques. We conclude with a discussion and reflection of the study, highlighting our methodological and substantive contributions to ongoing discussions pertaining to the WS and the environment.

Global Trade, Emissions, and the Effects on Mortality
International trade is often described as a system of increasing interdependent economic relations [6,14]. These economic relations form patterns, giving rise to structural features that shape characteristics and outcomes for countries. A number of studies exist that adopt a network approach to studying international trade relations [5,12,24,25,26]. Some of these studies use network measures to describe the entire network structure, and in doing so, attempt to gauge the extent to which the global economy has become integrated overtime [5,12,27]. Other studies consider how individual countries are conditioned by their position within this global trade network [24,25,26], and/or their level of centrality [28]. Here, an important distinction is made between countries that are positioned in the network 'core' versus its 'periphery.' In network terminology, a core-periphery structure refers to a two-class partitioning where the core consists of a set of actors (or nodes) that are densely connected to one another and central to the entire network, i.e. they form a well-integrated block and share a similar set of ties to others in the network. In contrast, the periphery refers to a class of countries that are more or less isolated from one another and linked to the rest of the network mainly via ties to the core.
Within this core-periphery structure, countries situated within the core are seen as being more integrated into the overall global trade network, and consequently accruing larger benefits, principally in the form of economic growth and/or development [26]. Here, the well-integrated core is understood to exploit the periphery in an unequal exchange, such that financial investment and/or high-value goods flow from the core to the periphery, in exchange for undervalued goods produced in, or extracted from that region [29,30,31]. Such unequal exchanges, moreover, prompt higher levels of emissions and resource exploitation in these less-developed, more peripheral nations [26,30,32,33,34,35]. In addition, less-integrated, lessdeveloped countries are looking for opportunities for economic growth, which often includes attracting foreign investment and/or the relocation of certain manufacturing activities from core-based transnational companies. As such, these countries' regimes take a number of measures to attract this economic activity, such as relaxing labor laws and/or environmental regulations, with the consequential result that more environmental degradation is often experienced in these regions [10,35,36,37,38,39].
Another body of research suggests, however, that populations of well-integrated, core countries tend to emit high amounts of emissions [23,28,40]. Here, emissions are seen as rising because of the high presence of energy/pollution-intensive activities found within wealthier, more integrated countries. Although agents within these countries may indeed invest and develop more efficient, less polluting technologies [15], the gains in efficiency are understood to lead to increases in the overall rate of consumption, thus leading to increases in overall resource use and the pollution emissions associated with this [41]. In a similar way, research that adopts a consumption-based approach to pollution suggests that wealthier, more countries are the biggest emitters [20,21,26,42]. Here, wealthier, more integrated countries are seen as consuming a larger global portion of goods and services, and thus, triggering a disproportionately higher amount emissions through their consumption of goods and services. As such, they are seen as being more accountable for a larger share of global emissions.
Taken together, the contrasting narratives presented above offer an unclear picture regarding the extent to which being integrated in global trade conditions the distribution of emissions among countries. Are core countries generating most of the emissions? Or have they managed to decrease levels of production-based emissions through externalization, thus prompting the same (or more) amounts of emissions in other countries? Finally, are the use of cleaner technologies truly capable of combating the negative environmental impacts of high-consuming countries? Part of the confusion in understanding just how much emissions are caused by these more integrated countries may lie in the fact that most studies fail to disentangle, within the context of the same study, pollution that is produced within a countries' own boundaries (i.e. production-based emissions) and pollution emissions that are triggered through countries' purchases and consumption habits (i.e. consumption-based emissions). In this paper, we contrast both types of pollution emissions and examine the extent to which being integrated, or 'core' conditions these two forms of pollution. In doing so, we seek to offer a better view of the intertwined, interdependent nature of pollution burdens and pollution responsibilities.
In addition to disentangling which countries are the main producers of SO2, and which ones trigger the most SO2 through consumption, we are equally concerned with identifying who is most affected by this pollution, and how a country's level of integration might help mitigate the impacts of this pollution. As core countries tend to accumulate more wealth than lessintegrated ones (e.g. [24,26,26]), the fungible nature of this wealth should help societies adjust and potentially buffer the harmful effects of emissions.
To assess whether such wealth translates into an economic buffer that lowers the negative impacts of emissions, we consider how countries' share of global production-based emissions (SO2prod) stands in relation to their share of global value added. In doing so, we are essentially looking at countries' relative efficiency scores, and asking to what extent such efficiency can help mitigate negative impacts of emissions. In assessing the negative impacts of emissions, we consider countries' mortality rates, in particular, child and infant mortality, as infants and children are generally seen as the most vulnerable segments of a society [43], and hence, the ones most vulnerable to air pollution occurring within non-core countries. Research has shown that infants and children born in low socioeconomic conditions have lower access to resources and health services, and at the same time higher exposure to pollutants, thus increasing their risk for preterm births and premature death [44]. In addition, research on water pollution and infant mortality [45] and children's health [46] has shown that the periphery suffers higher mortality rates than the core, and such high mortality rates are generally seen as resulting from the multiple structural disadvantages found within the periphery, namely weaker institutions and less environmental safe-guards [35,46,47,48].
Taken together, we predict that more efficient countries would be ones that would also hold lower mortality rates, regardless of how much that country might actually pollute. Stated differently, we argue that countries with higher shares of wealth (in relation to shares of emissions) have an economic buffer that would help mitigate the harmful impacts of pollution on infant and child mortality.

Material and Methods
This section describes our dataset, our variables and measures, the methods for constructing these measures, and panel regression techniques.

Data and Data Transformations
Our trade data were extracted from the Eora database. Eora is a multi-region input-output database that provides a time series of high resolution input-output (IO) tables with matching environmental and social satellite accounts for 186 countries [49]. The MRIO tables from Eora contains trade flows, production, consumption and intermediate use of commodities and services for 26 sectors, both within and between 186 countries (see http://www.worldmrio.com/ for more details). Our data covers a 20 year time span .
The benefit of using the global MRIO data, (combined with the SO2 data described below), is that we are able to calculate how SO2 is triggered by both consumption and production processes along the entire global production chain. As part of our intention here is to distinguish between consumption-based and production-based emissions on the global level, such detailed economic trade data are necessary.
Although Eora contains data on 186 countries and regions, we were not able to find corresponding data for our other variables of interest for all countries in this dataset, as some countries in Eora have been aggregated into supranational regions. In spite of this constraint, we managed to gather health data for 172 of the 186 cases (see the full listing in the SI).
For calculating value-added (VA) and consumption-based SO2, we made use of the full multi-regional input-output (MRIO) database without any transformations. For calculating countries' level of integration, however, we transformed the dataset, following guidelines established by previous sociological work on global trade networks [24,50]: first, we aggregated all 26 sectors to form one country-by-country trade matrix consisting of valued data. A single, valued trade network dataset is often preferred for operationalizing ideas of coreness or integration, as a country's position is determined not only through the quantity and patterning of trading ties to others, but also considers the trade volume of those ties [28]. In addition, as rows in the trade matrix correspond to exports and columns to imports, we took the trade matrix and its transpose, then summed the two together to arrive at a symmetrized matrix that combines, for each country, information on that country's exports and imports. By summing the export matrix to its transpose (i.e. the import matrix), we focused attention on the structure of trade, as opposed to the directionality of trade ties (18,22). Lastly, to smooth out any skewness, we took the square root of each cell in the symmetrized matrix.
Our SO2 emission data are at the sector level, and were also collected from the Eora database.
One thing to note about these SO2 data is that they refer to the emissions themselves, and not to concentrations of SO2 emissions. Concentrations of SO2 take into account whether or not air filters and other cleaning technologies are used to trap the emissions and lessen the amounts that escape into the atmosphere. Analyses of SO2 and economic or health implications are often limited to examining SO2 emissions [51,52] rather than their concentrations. Concentrations would be better correlated to health impacts but less so to production or consumption based emissions due to other ambient and atmospheric factors and cross-boundary pollution. As SO2 concentrations data are not available for many countries in our sample, we are left with examining SO2 emissions.

Measures
A number of analysts have used social network analysis (SNA), a methodological approach for analyzing relational data [53], to measure structural ideas of global trade (e.g. [24,54,55,56,57]). Although no single network measure has been widely adopted for assessing countries' level of integration in global trade (see [58] for review), our use of SNA is in keeping with recent research focusing on global trade networks (e.g. [24,26,50]). In particular, we made use of the continuous coreness procedure [59] for measuring countries' level of integration within the world trade network. This procedure fits a core/periphery model to an observed network to identify the extent to which the observed network approaches an ideal core/periphery structure. In an ideal core/periphery structure, the 'core' is a block of actors who are tied to one another, and in addition, have ties with many other actors in the network. To be core, then, is to be highly central in one's own right, as well as part of a dense block of other highly central actors. In contrast, peripheral actors form a second block in which members are largely isolated from one another, and any ties they do hold are with the core. The coreness procedure introduced by Borgatti and Everett [59] proceeds in determining what type of partitioning of actors (in our case, countries) in the observed network most closely brings that network towards an ideal core/periphery partitioning. The procedure results in a vector of scores assigned to countries, which range between 0 and 1, the higher values indicating a country being more core, and lower values indicating the country being more peripheral. The advantage of this procedure is that it results in a ratio-scaled vector of scores, thus enabling a higher degree of precision in making cross-country comparisons. We refer to this resulting vector of scores as our level of 'integration' measure for countries. A full listing of all countries and their integration/coreness scores can be found in S1 Dataset.
Our production-based SO2 measure consists of the data collected from the Eora database. Our consumption-based SO2 measure was constructed using MRIO Analysis (see [60] for an additional example). At its core, MRIO analysis is an accounting procedure relying on national economic input-output (I-O) tables (the global MRIO dataset consists of 186 such national I-O tables) and international trade matrices, depicting the flows of money to and from the various sectors of the national and international economies, thus revealing each sector's entire supply chain. This method has been applied to global trade studies on land use [61], water consumption [62,63], CO 2 emissions [64,65], materials use [66], and biodiversity loss [67]. The process begins with the Leontief inverse of the MRIO matrix, which essentially involves multiplying an economy's input requirements with each other by an infinite number of times, thus representing infinite rounds of production layers triggered by the previous set of inputs to fulfill production for final consumption. As such, computing the matrix inverse accounts for all the direct and indirect inputs triggered by final demand for a consumption item in any given country. Second, we calculated the total input requirements to satisfy final demand by multiplying the inverse matrix by final demand of a particular consumption item in any country. Finally, to account for total consumption-based SO2 emissions, direct pollution coefficients for each sector and each economy, showing the amount of pollution that is created for the production of each unit of economic output, are multiplied with the total inputs that are triggered by final demand. Technical details on consumption-based emissions can be found in S1 Supporting Information.
For measuring mortality, we used child and infant mortality data (each per 1000 live births) downloaded from UNICEF's Millennium Development Goals database (please visit http:// mdgs.un.org/unsd/mdg/Data.aspx for details). We included these measures to capture the potential impacts of SO2 and a country's level of integration on vulnerable segments of a country's population, i.e. children under the age of 5 and 1. In our regressions, we used the natural logarithm of these two mortality variables in order to normalize their distributions.
To measure countries' efficiency levels, we divided countries' share of global SO 2 emissions (%SO 2 ) by their global share of value-added (%VA), such that (NEM = %SO 2 /%VA). As such, this NEM essentially translates as a normalized measure of a country's efficiency, as all countries are being assessed in relation to global totals. This enables us to see how countries compare in terms of global shares of emissions and wealth, as opposed to comparisons based on individual country attributes, as is found in more traditional efficiency measures (e.g. SO 2 / GDP).
Our major control variables include population size (in 1,000,000s) and urbanization (percentage of population estimated to live in urban areas in a country), both taken from the World Bank's database (http://www.worldbank.org). Research has shown that population size is positively linked to forms of environmental degradation, including air pollution [68,69]. Similarly, research has shown a positive link between a country's level of urbanization and environmental degradation [29,69,70]. Urbanization has also been shown to be positively linked to infant mortality in the periphery [46], although in the core, urban centers have historically been places where wealth and other key resources necessary for a higher quality of life become concentrated (e.g. [71]), thus potentially mitigating the health threats of air pollution. In addition, we have also included countries' health expenditure as percentage of GDP and countries' fertility rates as control variables for regression models predicting infant and child mortality. Data for both these variables was downloaded from the World Bank (http://www. worldbank.org).
All variables' means and standard deviations are shown below in Table 1.

Regression Analyses
For assessing which countries are causing SO 2 pollution, we regressed countries' integration scores against our two SO 2 pollution measures, controlling for population size and urbanization. For assessing the link between countries' efficiency (NEM), and their level of integration, we regressed countries' integration scores on their NEM, with the same controls. Finally, to gauge the extent to which countries suffer from their pollution costs, we regressed mortality rates on levels of integration, NEM, and production-based SO 2 , alongside other controls. Our actual regression technique was a time-series cross-sectional Prais-Winsten estimation technique with panel-corrected standard errors (PCSE) as used by Jorgensen and Clark (2012). This technique allows for disturbances that are heteroskedastic and contemporaneously correlated across panels. The PCSE correction is capable of avoiding extreme overconfidence often associated with the popularly-used feasible generalized least-square estimator in the case of panel data sets in which the total time period T is smaller than total sections N. We control for common first-order autocorrelations of the disturbance terms and both period-specific and unit-specific disturbances. Because there are missing values in different places across variables, we employed 'pairwise' option associated with the xtpcse command in Stata (ver. 13) to include all available observations with non-missing pairs, thus maximizing the number of observations in the unbalanced panels.
Prior to running regression models, we plotted our main predictor (countries' level of integration) against our main outcome variables. These scatterplots are found below in Fig 1.

Results
To begin, we offer a global map (Fig 2) showing countries' level of integration according to year 2010, where darker shades indicate higher levels of integration. The pie charts depict wellintegrated countries' total imports, again for year 2010.
Next, we present our regression results, starting with results for production-based SO 2 and consumption-based SO 2 , as shown below in Table 2.
Here, we see that, for both emission outcome measures, 'integration' holds a positive, highly significant coefficient, even after the controls are entered into the model. This finding suggests that the level of countries' SO 2 pollution is positively associated with their level of integration in global trade. In addition, consumption-based SO 2 is more strongly correlated to integration than production-based SO 2 . In particular, models 2a and 4a show that the integration coefficient (elasticity) is much stronger (i.e. more than doubled) in relation to consumption-based SO 2 than production-based SO 2 , once we control for population size and urbanization. Finally, the explanatory power of the models increases once the control variables are introduced, as reflected in the R 2 values. As such, we see evidence that core countries are responsible for larger pollution emission levels, relative to less core countries, both through their at-home manufacturing activities, and also, through their consumption habits. Our findings thus suggest support for both our narratives: core, well-integrated countries are both the larger emitters of SO 2 emissions via manufacturing, and they also appear to be the main 'externalizers' of emissions, in this case via consumption.
To assess whether being more integrated translates into attaining a higher 'buffer', we turn to results presented in Table 3. Here, we note the significant, positive relationship between levels of integration and NEM scores, both with and without controls.
These findings indicate that as countries become more core, they gain larger shares in global SO 2 than shares of wealth (regardless of how much they might actually emit). Thus, there appears to be a tendency for more integration leading to less efficiency, i.e. not developing an economic buffer against emissions.
To what extent does an increase in efficiency have a pay-off, in terms of mortality rates, for individual countries? Integration does not seem to go hand in hand with a lower NEM score, but would lower NEM scores translate into an economic buffer to help reduce mortality rates for infants and children? Table 4 below shows regression model results exploring this question.
For both sets of models in Table 4, we see that countries' level of integration holds a negative and statistically significant relationship with mortality rates, suggesting that more core countries have lower mortality rates. In addition, countries' NEM scores have a positive and statistically significant relationship to mortality, suggesting that countries with higher shares of emissions in relation to shares of VA suffer higher rates of mortality. In short, it appears that more core countries with higher economic buffers (in the form of lower NEMs) experience lower mortality rates. Interestingly, levels of production-based SO2 is not in any way linked to mortality, implying that countries might potentially have high levels of SO2 emissions within their own boundaries, but this is insignificant if the same countries are well-integrated, and have a low NEM score. As such, more core countries may have managed to 'cloak' the potential negative impacts of SO 2 emissions through making a number of adjustments (a lower NEM score, more urbanization, more spending on health, and so on), and these adjustments act as causal mechanisms to buffer the real impacts of SO 2 on the population.
We also note that our controls operate largely as expected: i) the urbanization coefficient is negative and significant, suggesting that countries with higher portions of an urban population experience lower mortality rates; ii) the health expenditure coefficient is also negative and statistically significant, implying that higher spending on health per unit of GDP coincides with fewer deaths, and iii) fertility rates are positively and significantly linked to mortality, implying that countries with more births tend to suffer more deaths.
Taken together, when we ask ourselves who is most affected by SO2 emissions, our findings offer a complex picture. More integrated countries experience lower mortality rates, yet they appear to do so via a number of mechanisms. One such mechanism is a country's NEM score. Low NEM scores (implying greater efficiency) appear to help reduce mortality, and thus, it appears that higher levels of integration help countries acquire a stronger economic buffer to mitigate the negative impacts of SO 2 on mortality. In addition, higher levels of integration coincide with higher levels of urbanization, higher health expenditures and fertility rates. All these mechanisms thus appear to be working together to give more core, integrated countries an advantage over less-core ones with regards to mortality rates. Thus, the simple answer to the question, 'who is most affected by pollution emissions?' is that less core countries are the ones most affected. Yet it is via a number of causal mechanisms associated with one's position in the global trade network (as implied by Tables 3-4) that this disadvantage arises.
As robustness tests to these findings, we re-ran the same models shown in Tables 2-4 as stepwise regression models (see Tables 5-7 below).  The stepwise regression model results largely replicate our findings presented in Tables 2-4. The main differences between the results/models include the fact that the 'pairwise' regression option (Tables 2-4) includes all available observations with non-missing pairs, thus maximizing the number of observations in the unbalanced panels. In contrast, the standard stepwise regression models (Tables 5-7) exclude those observations with missing values. In addition, the t-test in stepwise regression is less efficient, making the results slightly less reliable than those from the pairwise option. As such, although production-based SO2 appears as a significant predictor of mortality outcomes in Table 7, we return to our more conservative result(s) found in Table 4, which show production-based SO2 being an insignificant predictor of mortality outcomes. Other than these noted differences, the patterns in the data results are largely the same.

Discussion and Conclusion
We began our paper with a distinction between two ways of accounting for SO 2 emissions: production-based and consumption-based SO 2 . We showed that both ways of accounting for  emissions hold a positive, linear relationship with countries' level of integration. Well integrated countries not only emit larger quantities of SO 2 than less integrated ones, they also are the ones who trigger the most SO 2 throughout global supply chains through their consumption habits. Our second main research aim was assessing, 'who is most affected by emissions?' Answering this question involved two steps. First, we looked at the relationship between integration and NEM scores. The literature suggests that being core gives countries a number of 'spill over' benefits, and thus, we argued, it is not so much that more integrated countries stop polluting, but rather, that their shares of wealth should exceed their shares of emissions, and such an excess in wealth should be perceived as an economic buffer that outweighs pollution costs. Our results showed that, contrary to what we expected, higher integration levels corresponded with higher NEM scores.
Although more integrated countries tended to have higher NEM scores, our next round of analyses indicated to us that more core, integrated countries with stronger economic buffers appeared to be in a better position to mitigate the negative impacts of pollution, the so-called 'costs' of SO 2 emissions. In particular, an increased economic buffer (in the form of a low NEM) coincided with more urbanization and higher spending on health, all of which could help mitigate the negative impacts of emissions in the form of lower mortality rates. Thus, our findings suggest that being well-integrated works in conjunction with a number of mechanisms-these being countries' NEM scores, their level of urbanization and amount of health expenditure-to effectively lower mortality rates. In other words, countries' position within the larger trade network implies a number of spill-over benefits, as suggested by the literature (e.g. [72]).
As a robustness check to our analyses, we re-ran the same set of regression models for predicting air pollution and mortality outcomes, replacing level of integration with two alternatives. These two alternatives included i) a categorical variable for trade network integration and ii) GDP per capita. The categorical measure was the same used in other trade network studies (e.g. [7,55]), and GDP per capita was used to replicate other globalization studies focused on the relationship between emissions and wealth (e.g. [69,73,74]). The details of these variables, their measurements, and the regression results can be found in our SI (see Tables A-D in S1 Supporting Information File). In sum, the results for these alternative models largely replicated those presented here in Tables 2-4, and the R 2 values were very similar. The main difference between results presented in Tables 2-4 and those based on the two alternative measures is that the coefficients for the alternative measures proved weaker than those using integration.
In terms of our contribution to the literature: although we are not the first scholars to disentangle consumption-based from production-based emissions (e.g. [75,76,77]), we are unaware of any research that has used such an approach to make theoretical arguments about emissions inequalities and mortality outcomes resulting from trade network position in the way we have demonstrated here. By doing so, we have shown, quite clearly, evidence for well-integrated, core countries being both major polluters as well as a major externalizers of emissions. Further, our focus on SO2 has enabled us to see how global trade patterns can have real local impacts in the form of mortality rates. As such, we have moved beyond fundamental concerns pertaining to inequalities resulting from globalization (e.g. wealth and emissions) to show how such inequalities translate into real 'life or death' issues for given societies.
Our study has, through a series of analytical steps, slowly developed a picture of the role core countries have on both emissions and mortality outcomes. As data becomes available, future research will track these processes over a broader range of environmental outcomes, such as land displacement, water usage, and other forms of emissions. When thinking about environmental justice, such comparisons between consumption-based forms of environmental degradation and degradation occurring within a country's boarders are needed for deepening our understanding of who is responsible for and who suffers from environmental harm within the global trade system.