R E S E A R C H A N D A N A LY S I S The Corruption Footprints of Nations Yanyan Xiao, Manfred Lenzen, Catherine Benoˆıt-Norris, Gregory A. Norris, Joy Murray, and Arunima Malik Keywords: Supporting information is linked to this article on the JIE website Summary In this study, we innovatively apply multiregional input-output analysis to calculate corruption footprints of nations and show the details of commodities that use the most employment affected by corruption (EAC), as they flow between countries. Every country’s corruption footprint includes its domestic corruption and the corruption imported by global supply chains to meet final demand. Our results show that, generally, the net corruption exporters are developing countries, with the exception of Italy where corruption is likely to be more affected by political and cultural factors than economic factors. China is the largest gross corruption exporter, and India follows close behind, with clothing as one of the industries in which the most people are affected by corruption. This is because: (1) China and India are major clothing exporters, thus many workers are employed in the clothing industry within the country as well as in countries providing intermediate commodities by supply chains, and (2) corruption is high in China and India. Our results can be useful to identify where regulations to combat corruption can have the greatest impact. More important, the method we use can be applied to link corruption to other economic and social aspects of trade, such as working conditions, thus making it possible to find avenues for tackling the problem that are not usually considered in anticorruption strategies. Introduction Corruption, as one of the biggest global problems (Transparency International 2010), has attracted a great deal of attention worldwide. It exists in all countries and at all levels of society and government. Corruption is a multidimensional phenomenon caused by a combination of economic, political, and social factors. In turn, corruption affects a country’s economic, political, and social performance, exacerbating problems such as inequality and poverty (Chetwynd 2003). Economic performance has been identified as being influenced by corruption (Lambsdorff 2006). For example, corruption is viewed as the major obstacle to economic development (Kaufmann 1997; Lambsdorff 2004): World Bank Group President Jim Yong Kim declared that corruption is “public enemy number one” in developing countries (World Bank 2013). International trade is a primary accelerator of economic growth (PACI 2008; De Jong and Bogmans 2011), but it is generally hampered by corruption. In addition, a relationship between international trade and corruption seems to exist, because trade openness can affect corruption levels within countries (Krueger 1974; Gatti 2004; Tanzi 1998; Jong-Sung and Khagram 2005). Knorich (2004) finds that the corruption levels in different countries are correlated, indicating that corruption occurring in one country may influence corruption in other countries, for example, through businesses being exposed to one another’s corrupt practices. Such cross-country spillovers might be facilitated by international trade, and thus interactions between trade and corruption could mean that domestic corruption levels can be influenced through trading with more or less corrupt countries. Recent studies have explored social impacts of internationally traded commodities in articles addressing, for example, bad labor (Simas et al. 2014a, 2014b), social inequality (Alsamawi et al. 2014; Wiedmann 2016), and occupational health and Address correspondence to: Manfred Lenzen, ISA, School of Physics A28, 501, The University of Sydney, NSW, AUS 2006. Email: [email protected] © 2017 by Yale University DOI: 10.1111/jiec.12537 Editor managing review: Klaus Hubacek Volume 22, Number 1 68 Journal of Industrial Ecology www.wileyonlinelibrary.com/journal/jie Printed by [Duke University Libraries Box 90187 - 152.003.102.254 - /doi/epdf/10.1111/jiec.12537] at [11/11/2020]. corruption footprint industrial ecology input-output analysis (IOA) international trade multiregional input-output tables social science R E S E A R C H A N D A N A LY S I S Norris et al. 2014) with the ultimate consumers of products produced in corrupt environments (see explanations of Social Hotspot Database [SHDB] in Methods and Data Sources). For example, in China, Foxconn Technology is a huge contract electronics manufacturer that makes Apple iPhones for the American company, Apple Inc. In 2014, several managers at Foxconn Technology were arrested for soliciting millions of U.S. dollars in bribes from suppliers of iPhone parts (ZDnet 2014), which meant that the final product was tainted by this corruption; or, in other words, the corruption became “embodied” in the product. Such a supply-chain perspective is commonly understood as a footprint (Wood et al. 2014; Ivanova et al. 2015; Wiedmann 2016). These iPhones would have been shipped to Apple Inc., then distributed all over the world, thus carrying a corruption footprint to the rest of world. Our work makes no causal links between trade and corruption. In saying that links can be made between corrupt producers, such as Foxconn in China and final consumers elsewhere in the world, we are not suggesting that iPhone consumers cause corruption in the way that, say, consumption of goods will cause emissions from energy use along the supply chain. Emissions from energy derived from use of fossil fuels are inevitable; corruption in the production of goods and services is not. As customers, we can reduce our consumption of goods and services to decrease energy use and reduce emissions; however, we cannot simply apply the same method to combat corruption. One does not cause the other; however, knowingly purchasing goods and services from a corrupt country may tacitly endorse, or at least demonstrate tolerance for, its way of doing business. Knowingly purchasing that country’s exports with high levels of embodied employment, such as clothing or food (illustrated in our results), forgoes a potential to benefit the lives of thousands of workers. For example, rather than turning a blind eye, high-profile clothing brands or grocery chains can accept some responsibility for investigating corruption in the supply chain. They can, for example, challenge illegal clothing factories that thrive on bribery of building inspectors as in the case of the Rana Plaza fire (see below) (Transparency International 2014b) or nonenforcement of chemical handling regulations as in the case of banana growers (Robinson 2010). They can accept some responsibility for EAC embodied in their supply chains and work with government bodies, industry regulators, and nongovernmental organizations (NGOs) to improve, for example, inspection of working conditions and enforcement of regulations. As consumers, we can join with others to pressure global brands and governments to combat corruption wherever it is found in the supply chain. This already happens in some instances, supported by media publicity or individual case studies. For example, according to news reports, people in Ukraine are keen to encourage the world’s leading furnishing chain IKEA to open stores in their country because of its reputation for combatting corruption. The Ukraine government hopes that IKEA will help the spread of anticorruption attitudes and strategies throughout local industries (Newsweek 2015). Our study will assist in such campaigns by providing information about where Xiao et al., The Cor r uption Footprints of Nations 69 Printed by [Duke University Libraries Box 90187 - 152.003.102.254 - /doi/epdf/10.1111/jiec.12537] at [11/11/2020]. safety (Alsamawi 2015). In a similar vein, our study offers a comprehensive picture of corruption embodied in globalized trade. In this work, we link employment affected by corruption with centers of consumption by using multiregional input-output (MRIO) analysis (Leontief and Strout 1963). This method has been used previously at multiple scales (Hubacek et al. 2014) for analyzing the supply-chain effects of international trade on various environmental and social indicators, such as carbon dioxide emissions (Feng et al. 2013; Kanemoto et al. 2014), scarce water (Lenzen et al. 2013a), water use (Feng et al. 2011), air pollutants (Kanemoto et al. 2014), land use (Hubacek and Giljum 2003; Yu et al. 2013), material use (Giljum and Hubacek 2004; Wiedmann et al. 2015), species threats (Lenzen et al. 2012b; Moran et al. 2016), bad labor practices (Simas et al. 2014a, 2014b), social inequality (Alsamawi et al. 2014; Wiedmann 2016), occupational health and safety (Alsamawi, 2015), civil war (Moran et al. 2015), and global value chains (Los et al. 2015). It is used in our study to analyze international supplychain effects in terms of corruption. Using the detailed Eora MRIO database (Lenzen et al. 2012a, 2013b), we calculate corruption footprints for 189 countries. Every country’s corruption footprint includes its domestic workers affected by corruption and the workers affected by corruption embodied in goods imported by global supply chains to meet final demand. In the following discussion, we simplify the term workers affected by corruption embodied in goods, instead using the short form: corruption embodied in goods. We investigate commodities flowing along trade routes between corrupt countries and to the rest of the world. We detail those commodities that embody the highest levels of employment from the most corrupt countries. We refer to these commodities as commodities embodying high levels of employment (or numbers of workers) affected by corruption (EAC) (see the explanation of EAC in Construction of Corruption-Weighted Employment Conversion Matrix). In using the term affected by corruption, we imply that corruption has consequences for workers. The consensus seems to be that, in its broadest terms, corruption has economic consequences. According to Dal Bo´ and Rossi (2007), there are economic consequences associated with inefficiencies at the firm level, which, in turn, affect a country’s overall prosperity. This is compounded by the damage that corruption does to the country’s reputation, which, in turn, inhibits foreign investment, trade, and, ultimately, economic growth (Corruption Watch 2016). Other consequences may be more localized and may have highly adverse effects on individual workers and communities. For example, an Organization for Economic Cooperation and Development (OECD) issues paper on corruption and economic growth (OECD 2013) also discusses impacts of corruption on development of human capital, public services, welfare, distribution of income, and disregard for the environment. Below, we also cite instances of bribery that has catastrophic effects on the health and safety of workers. The particular novelty of our work is that we link the occurrence of EAC with the global trade routes of commodities that are being produced in corrupt countries. Thus, we are able to connect corruption in social hotspot1 countries (Norris 2014; R E S E A R C H A N D A N A LY S I S Methods and Data Sources Basic Input-Output Theory and Extended Input-Output Analysis Input-output (I-O) analysis (IOA) is an economic technique developed by Russian-born economist Wassily Leontief in the 1930s (Leontief 1936). It shows the complex interdependencies between production and consumption across all sectors of an M economy. The equation x = (I − A) −1 y1 gives all direct and indirect links between an economy’s gross output x and its final demand y (see the Supporting Information available on the Journal’s website for detailed I-O algebra). I-O accounts can be extended with “satellite” accounts to include a range of nonmonetary indicators (Leontief and Ford 1970). Extended IOA has been intensively applied to investigate the relationships between nonmonetary and monetary flows throughout the entire global economy (Lenzen et al. 2012b, 2013a; Moran and Geschke 2013; Wiedmann et al. 2015; Hoekstra 2010). Such relationships can be used to illuminate social and environmental issues in the supply chain. The satellite account K is sized S×N, with elements Khj (h = 1, 2, . . . , S) that represent nonmonetary indicators’ input for the production in economic sector j. Defining q = Kx̂ −1 , where q (also sized S×N) is the direct physical indicator intensity. The direct and indirect flows through the entire economy can be enumerated in terms of the nonmonetary indicators by equation (1): K = qx = q(I − A)−1y1 M = qLy1 M , (1) where qx represents the production-side breakdown of total indicators use K, and qLy represent its consumption-side breakdown. Corruption Satellite and Footprint Account In this study, the satellite account K holds information on the level of corruption for 189 countries included in the Eora country list (see table S2 in the supporting information on the Web) and a combined 15,979 industry sectors. With x holding 70 Journal of Industrial Ecology gross output for the same country and sector set, the corruption footprint K can be calculated as shown by equation (2): K = Kx̂−1 (I − A)−1y1 M (2) Every country’s corruption footprint is defined from a consumption-based perspective, which includes its domestic corruption and the corruption imported from all other countries, embodied in goods and services required for meeting its final demand.2 Net corruption trade in one country is defined as its corruption embodied in imports minus corruption embodied in exports. Data Sources Three types of databases are used in this work. 1. Corruption databases: Corruption perception indices for countries are obtained from Transparency International (1995); control of corruption indices for countries are obtained from the World Bank (2015); irregular payment and bribe indices are obtained from the World Economic Forum (2011). 2. Employment databases: The employment data measured in full-time equivalent (FTE) person-years by sectors and countries are gained from the International Labor Organization’s (ILO) LABORSTA database (ILO 2012) and the United Nations System of National Accounts Official Country database (UNSD 2011). 3. MRIO database: Interindustry transaction data are downloaded from the Eora global Multi-Region Input-Output (MRIO) database (www.worldmrio.com). The corruption-weighted employment in one country is estimated by multiplying employment data with the corruption index. Then, it is subjected to the demand-pull calculus of an extended I-O model to calculate corruption footprints. These calculations are carried out for the time period 1995–2011. However, in this article, we only present the corruption footprints using the latest data for the year 2011. Construction of Corruption Index Conversion Matrix The commonly accepted definition of corruption is “the abuse of public power for private benefit” (Lamsdorff 2007; Kaufmann et al. 2011). Since corruption cannot be measured directly (Rohwer 2009), some international organizations have created indicators to assess the corruption status in different countries. The three most widely used corruption indices are the Corruption Perception Index (CPI), the Control of Corruption Index (CCI), and the Irregular Payments and Bribe Index (IPB). The comparison of these three indices is shown in table 1. The CPI is an aggregate index, collected from a wide and various range of business and expert views. It covers both the administrative and political aspects of corruption, but only focuses on the public sectors (Transparency International Printed by [Duke University Libraries Box 90187 - 152.003.102.254 - /doi/epdf/10.1111/jiec.12537] at [11/11/2020]. to look for high levels of employment affected by corruption and hence where to concentrate efforts to have the greatest effect on the most workers. The attribution of corruption to consumers has not previously been considered anywhere in the existing literature. Our results provide a comprehensive picture of employment affected by corruption embodied in internationally traded commodities, which shows the number of workers who are suffering effects of corruption, such as bribery, because of international trade. We reveal where to look for high levels of employment affected by corruption and hence where to concentrate efforts to uncover the details of corruption. Our work can therefore benefit anticorruption campaigns that involve governments, NGOs, and national and international law enforcement agencies (Huther and Shah 2000). R E S E A R C H A N D A N A LY S I S Table 1 Comparison of three most widely used corruption indices Database Source Country coverage Time coverage Definition Transparency International (1995) 41-183 1995–2014 Before 2012: to a scale of 0 to 10, where 10 equals an entirely clean country and 0 equals a country where business transactions are entirely dominated by kickbacks, extortion, etc. No country scores either 10 or 0. After 2012: to a scale of 0 to 100, where 0 equals the highest level of perceived corruption and 100 equals the lowest level of perceived corruption. Control of Corruption Index World Bank (2015) 184-212 1996–2013 Estimate of governance (ranges from approximately –2.5 [weak] to 2.5 [strong] governance performance). Irregular Payment and Bribe World Economic Forum (2011) 139-148 2010–2014 In every country, how common is it for firms to make undocumented extra payments or bribes connected with (a) imports and exports; (b) public utilities; (c) annual tax payments; (d) awarding of public contracts and licenses; (e) obtaining favourable judicial decisions. In each case, the answer ranges from 1 (very common) to 7 (never occurs). 1995). The CCI is also an aggregate index, captured from multiple sources, including enterprise, citizen, and expert survey respondents (World Bank 2015). The CCI also measures the corruption of private sectors, which covers more than the commonly accepted definition of corruption (Rohwer 2009). Both the CPI and CCI are often combined (June et al. 2008), and there exists an extremely high correlation between the CPI and CCI (Donchev and Ujhely 2007). The IPB is a single-source index, gained from a large number of business leaders’ perceptions on undocumented extra payments or bribes in their firms. This index is used in the calculation of the Global Competitiveness Index (GCI), whereas the GCI is on the source list for both CPI and CCI calculations (World Economic Forum 2011). As shown in table S1 in the supporting information on the Web, the three indices use different scales to measure corruption. The CCI and IPB use one scale each, whereas the CPI has changed its scale from 0 to 10 to 0 to 100 since 2012. Given that the CPI is the most widely used corruption database (Tansparency International 2014a), we use CPI data as our main data source and fill in missing data using the CCI and IPB indices (table 2). To this end, we estimate a mapping function that we use to translate CCI and IPB indices into the CPI scale (figure 1). In order to use corruption data on the same scale, the CPI’s 0 to 100 scale is adopted in our study. First, CPI data before 2012 are converted to a 0 to 100 scale by multiplication of 10; second, the correlation functions between CCI and CPI, as well as IPB and CPI, are found by polynomial curve fitting, respectively (R2 is used to test the goodness of fit for the curve fitting functions; see table S3 in the supporting information on the Web); third, according to the correlation functions, CCI Table 2 Data source contribution for corruption index conversion matrix Corruption index CPI CCI IPB Extrapolation No. of data points Percentage 2,637 874 1 728 62.2 20.6 0.02 17.2 Note: CPI = Corruption Perception Index, and CCI = the Control of Corruption Index; IPB = Irregular Payments and Bribe Index. and IPB data are converted to the CPI’s 0 to 100 scale. Data missing in all databases have been estimated by extrapolation. All CPI, CCI, and IPB data are shown at the national level and as time series. Take figure 1 (top panel) as an example: The CPI has a country coverage of 41 to 183 countries and a time coverage from 1995 to 2014, whereas the CCI has a country coverage of 184 to 212 countries and a time coverage from 1996 to 2013. The overlapping data in CPI and CCI represent a CPI-to-CCI pair in a certain year. The correlation function is thus estimated based on many such overlapping data points, thus making the conversion quite accurate. Construction of Corruption-Weighted Employment Conversion Matrix Because of its illicit nature, corruption can only be measured indirectly. The most widely accepted approach to corruption measurement is that of perceptions of corruption. All three indices used in this study subscribe to this approach. By Xiao et al., The Cor r uption Footprints of Nations 71 Printed by [Duke University Libraries Box 90187 - 152.003.102.254 - /doi/epdf/10.1111/jiec.12537] at [11/11/2020]. Corruption Perception Index R E S E A R C H A N D A N A LY S I S definition, corruption perception indices are subjective and thus cannot depict accurate corruption levels in every economic sector across a country’s entire economy. We use corruptionweighted employment K = E * κ to measure corruption levels. In this equation, employment data E have sector details, with the unit in FTE person-years, corruption perception index can be used as national corruption weights κ for scaling E in order to obtain the estimation of corruption-weighted employment in every economic sector in all countries. These sectoral results in corruption-weighted employment can be interpreted as EAC. Thus, EAC is the unit in our research. The bigger the values, the higher the extent of corruption harming people. It should be noted that, in our research, the corruption level in a given sector is defined according to the number of workers in that sector and the country’s overall corruption risk; more workers in the sector means a greater number of people suffering from the effects of corruption, for example, of bribery to avoid build- 72 Journal of Industrial Ecology ing regulation compliance. This is an imperfect measure; when detailed sectoral corruption data are available in the future, they can be combined with the current results to show the real corruption variations among sectors. The equation we use above follows the practice of the World Health Organization (Driscoll et al. 2004) on the estimation of disease risks and the practice of the SHDB (Norris 2014; Norris et al. 2014) on the social risk estimations for over 100 different indicators. In the estimation of disease risks, disability-adjusted life years, which are generated from each country’s population with occupational exposure for certain disease, is used for showing disease risk levels, whereas in SHDB, work hours, obtained by multiplying employment-hours and social risk levels, is used for expressing absolute social risk. In our research, we borrowed the idea both from the disease estimation and social impacts estimation. We treat the social risk, corruption, as a “disease” in the society and use EAC (the unit is the same as full-time Printed by [Duke University Libraries Box 90187 - 152.003.102.254 - /doi/epdf/10.1111/jiec.12537] at [11/11/2020]. Figure 1 CPI-to-CCI (top) and CPI-to-IPB (bottom) correlations. CPI = Corruption Perception Index, and CCI = the Control of Corruption Index; IPB = Irregular Payments and Bribe Index. R E S E A R C H A N D A N A LY S I S –1.0 0.1 0.3 0.5 0.7 0.9 1.1 Net corruption imports / 1000 cap Figure 2 Top ten importers (upper bars) and bottom ten exporters (lower bars) of corruption. Although Hong Kong is a Special Administrative Region of China, it is treated as a country in this study. cap = capita. equivalent and expressed in work hours) as the measurement unit. The corruption index acts as a “disease risk”; thus, the number of workers who are suffering from corruption in a sector can be expressed by multiplying the corruption index by the employment data. Given that corruption data are only available at a national scale, we assume that there is no difference in corruption risk exposure among sectors in one country, and then use employment in every sector as the currency to convert corruption indices into an additive quantity. Note that although Hong Kong is a Special Administrative Region of China and not a nation, corruption data are available for Hong Kong and it is treated as a country in this study. The databases on corruption hold indices that characterize the degree of corruption in various countries. Such indices are not additive, because, as with other relative quantities, adding two numbers on corruption degrees does not make sense. However, a Leontief-type IOA demands that the quantities in a satellite account be additive, in order to enable the accumulation of impact along supply-chain transactions. In order to become additive, corruption indices must be used as weights for an additive “currency” quantity. The usage of such currencies is also known from other I-O studies. For example, Lenzen and colleagues (Lenzen 2013a) use water use in liters to enable an IOA of water scarcity where scarcity data were available only as indices. Similarly, Alsamawi and colleagues (2014) use employment as currency for their I-O study of Ginitype inequality indices. Therefore, the rationale for converting corruption indices into an additive quantity is that corruption indices can approximately be treated like percentage measures, so, in our study, the EAC in every sector is proportional to employment. In other words, if one sector has twice as many employees as in another sector, the employment affected by corruption in this sector is twice as high as in the other. The assumption that all the sectors in one country have the same exposure risk to corruption is one of the limitations of our research. We made this assumption for three main reasons: First, although the three sets of raw corruption perception data that we have used (CPI, CCI, and IPB) are the most reliable and acceptable data we can obtain with a world scope, they are only obtainable at a national level, but not at a sector level. Second, there exists a large regional disparity in corruption perception (Song and Cheng 2012), with the exposure risk perception in one region generally not being applicable to another region. Third, in many countries, corruption is both pervasive and/or arbitrary (Lee and Oh 2007; Varese 2000), thus lending some credibility to the assumption of a uniform distribution across all sectors in each individual country. Results and Discussion The top net corruption exporters and importers are shown in figure 2. This result shows a clear distinction in net corruption per capita for developed and developing economies. The top ten net corruption importers tend to be the developed countries, whereas the top ten net corruption exporters are developing countries. This is mainly because the developed world’s imported goods and services are mostly produced in developing countries, where corruption levels are much higher. Consumption of imported goods and services that have corruption embodied in their production affects the corruption footprint in countries that are not affected by corruption or affected to a lesser extent. Xiao et al., The Cor r uption Footprints of Nations 73 Printed by [Duke University Libraries Box 90187 - 152.003.102.254 - /doi/epdf/10.1111/jiec.12537] at [11/11/2020]. Hong Kong Kuwait United Arab Emirates Switzerland Australia Norway Singapore Qatar USA United Kingdom Vietnam Guatemala Malaysia China Uganda Thailand Tanzania Cambodia Tajikistan Madagascar R E S E A R C H A N D A N A LY S I S The world’s demand for goods and services with the highest embodied corruption (figure 3) shows that China is the biggest corruption exporter. It occupies seven spots in the top ten gross corruption flows. Further, China is identified as the exporter in the top three gross corruption flows: China to Hong Kong; China to United States; and China to Japan. One understandable reason to explain this trend is the magnitude of manufacturing exports from China. China has been named factory of the world, a name given to it by the Japanese Ministry of Economy, Trade and Industry (METI) in 2001 (METI 2002). A large proportion of the working population of China works in manufacturing. Though the corruption index for China is in the middle status among all countries, the multiplication of this corruption index and the employment value is extremely large, which inevitably means that a high level of employment affected by corruption embodied in goods made in China flows to regions that are not corrupt, or corrupt to a lesser extent, through international trade. India is the second-biggest corruption exporter. It ranks fourth place with exports to the United States and ninth place with exports to United Arab Emirates. Corruption is a pervasive phenomenon in India, perceived to exist at all levels of the political and administrative system (Chêne et al. 2009). One historical root of India’s corruption is that a business needs to give bribes to obtain or renew various licenses and permits from different goverments and organizations (Deloitte 2014; Xu 2014). It seems hard to tell where corruption originates in this complex situation. The method outlined above distributes the country’s corruption throughout the economy according to each sector’s employment numbers. The results that follow provide details of dominant exports with the highest levels of employment from the most corrupt countries, thereby providing a picture of where to find the largest concentrations of employment affected by corruption. These countries and commodities 74 Journal of Industrial Ecology will be an indication of where strategies to combat corruption will have the greatest effect for good. We have investigated supply chains with high concentrations of employment affected by corruption to provide further information about corruption embodied in international trade (tables S4 and S5 in the supporting information on the Web). Our investigation shows that clothing, leather products, petroleum, transportation construction and equipment, food, and alcohol are the most corruption-intensive commodities. In particular, clothing is the dominant export product from Bangladesh, China, and Italy, whereas textiles, the raw material for clothing, is the dominant export product from Pakistan. Transparency International (2012) has reported that corruption and weak law enforcement are significant problems in most of the countries that manufacture clothing. Bangladesh, India, and Vietnam are the main suppliers of low-priced, ready-made clothing. They also supply the primary products needed for apparel and accessories used in popular Western brands. European exporters, such as Italy and Spain, are the main consumers of low-priced primary products; however, by contrast, they are the main suppliers of high-quality and high-priced apparel. The link between clothing manufacture and corruption can be illustrated using Bangladesh as an example. Fires are a serious risk in clothing factories, particularly, it seems, in Bangladesh (The Guardian 2013). They have injured or killed hundreds of workers over the years. Though fire seems to be the direct cause of deaths, deep-rooted corruption is the driver of these human tragedies. For example, 1,135 people were killed and over 2,500 people were injured in Bangladesh’s worst garmentfactory accident—the Rana Plaza fire on 24 April 2013 (Transparency International 2014b). The factories involved were illegally constructed. Safety inspectors prioritized good relations with managers and gave them advance notice before inspections. Such long-standing corruption resulting in unsafe and Printed by [Duke University Libraries Box 90187 - 152.003.102.254 - /doi/epdf/10.1111/jiec.12537] at [11/11/2020]. Figure 3 Employment affected by corruption (EAC) flow embodied in commodities traded along international supply routes in 2011. Bre = bread; Car = cars; Clo = clothing; Coc = cocoa; Fab = fabrics; Fis = fish; Fju = fruit juices; Fur = furnitures; Jew = jewellery; Lea = leather; Mac = machines; Med = medicine; Mpa = motor parts; Pak = packing; Pet = petroleum; Ric = rice; Sta = starch; Tea = tea; Tex = textiles; Tob = tobacco; Tou = tourism; Toy = toys; Tur = turbines; TV = televisions. R E S E A R C H A N D A N A LY S I S Conclusions In this study, we provide a new perspective on how to account for corruption through global trade routes. By applying MRIO analysis, we offer a comprehensive picture of global corruption footprints. Our results indicate that top net corruption per capita is generally from developing countries to wealthy developed countries. China is the biggest gross corruption exporter and India is close behind. The fact that Italy is the only wealthy developed country that exports corruption embodied in commodities can perhaps be attributed to political and cultural factors rather than economic factors. All of society is, in some way, responsible for the existence of corruption, because, knowingly or unknowingly, people are direct or indirect “sellers” or “buyers” of the world’s products embodying employment affected by corruption (Langseth 1999). It is unlikely that final consumers willingly support corruption. However, by our consumption, the industry in which workers are suffering from the effects of corruption, such as bribery, remains prosperous, thus feeding the corruption. Based on the corruption footprints of nations calculated by our work, we have identified the relationships between employment affected by corruption and final demand through supply chains. For every specific transaction between countries, the corruption-free country can help its trading partner to ameliorate corruption using commercial strategies in international trade. Revealing the commodity details with the highest EAC means that commercial anticorruption strategies can be targeted to export industries where intervention will affect the lives of the highest number of workers. Nevertheless, commercial anticorruption strategies do not simply mean that we stop buying the products with high EAC. To do so may be to cut off the income of someone who desperately needs it to feed a family. The argument is the same as that for controlling the illegal phenomenon of child labor in export industries in developing countries, reducing investment in the sector may force unemployed children to seek even moreprecarious employment in informal sectors (Panagariya 2001). Likewise, boycotting the clothing industry may be of little help in controlling corruption that harms the workers employed in this sector, but would punish poor workers who consequently lose their jobs. Instead of asking big international retailers, such as WalMart, Gap, and H&M, to cancel their orders from the clothing industry of Bangladesh with its high rate of employment affected by corruption, consumers can support these multinational companies to work with governments on legislation enforcement in controlling corruption. Consumers can use their purchasing power to collectively pressure these global companies to combat corruption. These global companies then can pressure local government and the industries in which most people are affected by corruption to develop and enforce anticorruption laws and regulations. In fact, fighting corruption through global brands is already being implemented in Bangladesh. Since the Rana Plaza fire, many multinational companies have signed up to the Bangladesh Fire and Building Safety Accord, which has resulted in the inspection to date of 675 factories (Transparency International 2014b). These improvements are a powerful guarantee against bribes for skipping regular safety inspections. In addition, some international anticorruption organizations, such as Transparency International, World Bank, and Corruption Watch, are performing their duties in monitoring and assisting anticorruption progress in corrupt countries. The corruption footprint can also be used by governments for anticorruption policy development, for example, to design demand-side policies and initiatives that can support domestic governance in ameliorating corruption. In addition to assisting in the fight against corruption, this corruption footprint measure has significance for corruption research. One is that we provide a new method to measure Xiao et al., The Cor r uption Footprints of Nations 75 Printed by [Duke University Libraries Box 90187 - 152.003.102.254 - /doi/epdf/10.1111/jiec.12537] at [11/11/2020]. overcrowded buildings makes fires almost inevitable. The high corruption embodied in these factories’ products is exported to the world through multiple and complex supply chains. China produces miscellaneous clothing, which includes top-, middle-, and low-priced products. Its clothing industry also has a high level of corruption embodied in its exports because of high employment in the clothing supply chain and the country’s overall corruption rating. The high imported corruption footprint embodied in raw materials for clothing from some corrupt countries, such as Bangladesh and Pakistan, is another explanation for China’s high corruption footprint. Unlike other wealthy developed European countries, Italy is not a net corruption importer, but an exporter. Generally, the view is that there exists an inverse relationship between corruption and development status (Gray and Kaufmann 1998; Paldam 2002), which suggests that the more corrupt the country is, the more poverty the country has. Obviously, Italy is an exception to this view. Del Monte and Papagni (2007) have investigated the determinants of corruption in Italy over nearly 40 years and conclude that the corruption in Italy is much more affected by political and cultural factors than economic factors. This is in agreement with Treisman’s (2000) cross-national corruption analysis. More-recent reports also demonstrate that the weakness of Italian governments and the long history of corruption have helped to embed it in Italian society (Independent 2015; Open Democracy 2015). As mentioned above, our work has merit for the research field and civil society. However, it also has limitations. First, we cannot provide the extent of corruption at the detail of individual establishments, which means that information on corrupt companies in certain sectors needs to be investigated further in order to inform consumers about individual cases. Second, IOA shows uncertainties in several aspects, for example: raw data errors; assumption deviations; and mischaracterization of causative sectors (e.g., the clothing industry only refers to clothing in some countries, whereas in other countries the clothing industry includes textiles, footwear, and accessories production) (Moran et al. 2016). Third, the assumption we made that every sector in a country faces the same corruption risk (see explanations in methodology above) can be improved when new data become available on corruption risk disparity among sectors. R E S E A R C H A N D A N A LY S I S Acknowledgments We thank A. Geschke (University of Sydney) for help in topic selection and A. Alsamawi (University of Sydney) for technical assistance in calculations. Notes 1. Social hotspot countries are the countries of the inputoutput LCI database Social Hotspot Database (SHDB; http://socialhotspot.org). 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