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2018-07-01
Consumer credit in comparative
perspective
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Citation (published version): Akos Rona-Tas, Alya Guseva. 2018. "Consumer Credit in Comparative Perspective." Annual Review of Sociology, Volume 44, pp. 55 - 75. https://doi.org/10.1146/annurev-soc-060116-053653
https://hdl.handle.net/2144/39179 Boston University
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Consumer Credit in Comparative Perspective Akos Rona-Tas1
Alya Guseva2
1 Department of Sociology, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0533; email: [email protected]
2 Department of Sociology, Boston University, 100 Cummington Mall, Boston MA 02215, National University-Higher School of Economics, 20 Myasnitskaya, Moscow, Russia, 101000; email:
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Keywords: debt, consumption, welfare, social ties, health, globalization Abstract:
We review the literature in sociology and related fields on the fast, global growth of consumer credit and debt and explanations for this expansion. We describe the ways people interact with the strongly segmented consumer credit system around the world, more specifically, the way they access credit and the way they are held accountable for their debt. We then report on research on two areas where consumer credit is consequential, its effects on the social relations, and on physical and mental health. Throughout the article, we point out national variations and discuss explanations for these differences. At the end, we briefly discuss the future tasks and challenges of comparative research on consumer credit.
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I. Consumer Credit Growth as a Global phenomenon
Recent decades have seen a rapid expansion of consumer credit all over the world only briefly thwarted by the financial crisis of 2007/8 (Leon 2017). In the US, real median household debt increased 179% between 1989 and 2007 (Fligstein and Goldstein 2015). This growth was mirrored in other regions – Europe, Canada, Australia, emerging markets of East and Central Europe, Latin America, and Africa. Even the Middle East despite the Islamic prohibitions on interest saw a dramatic increase in consumer credit (Abdul-Muhmin 2008). In some countries, like the United States and the United Kingdom consumer credit growth started earlier, in other countries like Germany, Russia, Korea or Brazil, it took off later. One may expect a considerable degree of convergence in consumer credit due to the global nature of financial industry, shared lending technologies and diffusion of consumer cultures. Recent studies indeed found evidence of
convergence in the prevalence of consumer credit (Leon 2017, Bahadir and Valev 2017, Jappelli et al 2013): in countries with less consumer debt, the growth of lending is more intense than in those with more as laggards are catching up with leaders. Yet the convergence in scale is not the same as growing similarity in the way people borrow -- door-step lenders are common in the U.K. and Hungary, but not in the U.S. or Germany, and mortgages are much more prevalent in Denmark than in Italy.
On the borrowers’ side, consumer lending remains a surprisingly local affair. Consumer credit is rarely available in a foreign country as lenders are reluctant to give consumer loans directly to foreign applicants. It is still very uncommon for people to shop for mortgages abroad or to get a credit card in a country where they don’t reside, partly because consumer credit histories are stored in national databases. Countries have their own laws, regulations and policies on lending dictated to a large extent by the country’s economic and political conditions, culture and history. Regulated lenders belong to their own national organizations, they are supervised by national agencies, and national governments are the main sources of aggregate data on lending.
A sizeable literature in economic geography breaks out of the confines of national economies, and offers a flexible approach to space in finance by introducing the notion of
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the interplay of global, regional and local factors (Harvey 1982, Coppock 2013, Corrado and Corrado 2015, French et al 2009, Rankin 2013, Searle and Koeppe 2017). For instance, Gallmeyer and Roberts (2009) identify neighborhood characteristics that are attractive to payday lenders in Colorado, such as a high percent elderly, military and foreign born. And Aalbers (2009) reminds us that the mortgage crisis must be viewed through the intricate connections of neighborhoods to local and national governments, as well as to global financial centers. Nevertheless, the nation-state still carries unique importance as a spatial unit because it plays a key role in buffering global impacts and setting parameters for local processes. But the geographic perspective is a powerful reminder that consumer credit is simultaneously shaped by factors operating at multiple levels.
In what follows, we review a sprawling literature on the causes and some of the
consequences of the growth in consumer lending, on the types of lenders and credit products, and on the way people interact with the consumer credit industry. Our review draws not just on the sociological literature but also on research with a sociological bent produced in other disciplines. Our view of consumer credit is dominated by the American experience. This is due partially to American provincialism but also to the enormous hegemonic power of American financial and consumer culture. In this review, we try to bring a wide geographic focus and put the American experience in a deeper dialogue with literature focused on consumer credit in other national contexts. Due to space limitations, we omit the large body of literature on Islamic finance (Pitluck 2013).
II. Causes: Why Consumer Credit Has Emerged A. Supply side explanations
The growth of consumer borrowing is often referred to as the “democratization” of credit (Prasad 2012, Ryan et al 2011, Sevim et al 2012) or – less charitably -- of debt (Hodson et al 2014), a phrase that foregrounds the growing availability of credit, both in terms of its quantity and its social reach. There were a series of changes that made it easier for people to borrow. The rise of financial markets turned corporations and governments from bank loans to securities, forcing banks to recoup lost business in retail lending (Lapavitsas 2009:126, Erturk and Solari 2007: 377-378, Karacimen 2014:172). These pressures were coupled with new opportunities. Deregulation – most importantly of interest rates and capital requirements -- made consumer lending more lucrative and created more competition among lenders to expand markets (Montgomerie 2006, Carruthers and Kim 2011, Kus 2015, Japelli et al 2013). At the same time, legislation and regulation in other areas,
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such as disclosure requirements, lenders’ rights and licensing formalized market rules, addressed market failures and stabilized expectations (Mann 2006, Rona-Tas and Guseva 2014, Wang et al. 2017, Durkin et al 2014). Innovations in the lending business also played an important role, from credit cards that simplified borrowing to tools of high finance like securitization of consumer debt (Mandell 1990, Engelen et al 2010, Durkin et al 2014, Quinn 2017). These efforts were boosted by improvements in information technology – data management, computational algorithms and electronic communication – that made the delivery of financial services faster and cheaper and allowed to standardize products, pool and calculate risk and price accordingly (Evans and Schmallensee 2005, Marron 2007, Ryan et al 2011).
The diffusion of these processes, -- losing corporate customers, regulation/deregulation, industry innovations, and technological advances, -- however, has been uneven, negotiating different histories and local conditions. In Japan and South Korea, for instance, the move away from
corporate customers did not happen (Fuller 2015). Regulation, too, played out differently in
different countries: interest rates are capped in Germany and France, but not in Denmark or Austria (although usury is regulated in a variety of ways, see Maimbo and Gallegos 2014); consumer debt is securitized in Spain but not in Italy (Aalbers and Engelen 2015); and in Vietnam, automation of teller services turned out to be too expensive and had to be halted (Rona-Tas and Guseva 2014: 210). Moreover, Trumbull’s (2014) historical comparison of consumer credit in France and the U.S. has demonstrated not just that states can frame consumer credit very differently, the French
emphasizing consumer protection and Americans access to credit, but early policy decisions have lasting effects as they reflect and reinforce deeper power relations. In the U.S., powerful banks offered consumer credit much earlier than in France, where strong unions much more skeptical about consumer credit held sway.
B. Demand side explanations
Another set of explanations, often presented as complementary to those discussed above, draw attention to changes in the needs and wants of households, and focuses on socio-cultural processes. Based on the U.S. experience, one such explanation highlighted the role of the culture of consumerism. Drawing on Veblen’s classic work, The Theory of the Leisure Class (1899), scholars argued that the spread of consumer credit was the result of status competition and rising consumer expectations (Schor 1998, Lissowska 2015). As inequalities widened, particularly at the top, middle and upper class consumers’ reference groups “stretched vertically”: no longer did they compare their
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lifestyles to those in their own “general earning category, or even to those one rung above [them] on the ladder. Today a person is more likely to be making comparisons with, or choose as a ‘reference group’ people whose incomes are three, fourth, or five times his or her own,” resulting in “new consumerism” of upscale spending (Schor 1998: 4) and driving consumer indebtedness up. Consumerism and competitive consumption are pervasive around the world, particularly among younger people (Carr et al 2012), who are eager to go into debt to satisfy their consuming desires (Mathur 2010).
One strand of literature addresses positional goods, the value of which is perceived in relative rather than absolute terms. Families strive to own homes that are bigger than other homes on the block or in the town, or those that are located in neighborhoods with better public schools; which usually means homes that more expensive than they can afford (González 2015a). The reason for this is not the envy of “new consumerism”, but the fear of falling behind, not aspirational but defensive consumption (Frank 2013). Context -- the kinds of homes one sees around and the kinds of educational opportunities the children of other families have -- is perceived as a norm, and families, particularly middle-class families, strive to exceed it to be ahead of others. If all families do this, they end up contributing to positional goods arms races and “expenditure cascades,” where everyone is spending more than necessary without much change to their relative positioning (Frank 2013, Curran 2017). Overspending on positional goods leads to overborrowing: families are willing to go deep into debt if that means buying homes in an unaffordable neighborhood so that their children be eligible for high-quality schools or reverse-mortgage their homes to finance college.
Competitive consumption is further fueled by advertisements and other cultural messages (Schor 1998). Some of these messages are taking advantage of cognitive biases and limitations (Bar-Gill 2003, Kamleitner et al 2012), and poor financial literacy. A large 55-country study found that controlling for other factors financial literacy is worse where social provisions are more generous, suggesting that people who are better shielded from the market feel less need to think about their personal finances (Jappelli 2010). Comparative studies have generally agreed that financial literacy is quite poor everywhere, even in countries with the highest average scores (Atkinson and Messy 2012, Nicolini et al 2013). In all of the countries, educated, affluent, middle-aged men seem to be most knowledgeable about everyday finance. Yet large survey comparisons struggle with the context-dependent nature of financial knowledge (Atkinson and Messy 2012, Lazarus 2016). Consequently, results are very sensitive to sampling and measurement differences (World Bank 2013), and are often
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contradictory. Atkinson and Messy (2012:13), for instance, find that people in postcommunist countries understand inflation much better than people from places like Germany, while Lusardi and Mitchell (2014:14) report the exact opposite. In one study, Japan is deemed to be a high literacy country (Jappelli 2010), but does much worse in another (Lusardi and Mitchell 2014:13).
Another body of research shifts the emphasis away from consumerist culture and positional goods to wider political and economic forces. It locates the engine lifting household indebtedness in the retrenchment of the welfare state and the striving of the middle-class households to preserve their lifestyles (Rajan 2010, Krippner 2011, Prasad 2012, Kus 2015).
Two explanations that commonly underpin the welfare-debt substitution argument is privatized Keynesianism and the debtfare state. Privatized Keynesianism (Crouch 2009) contends that capitalism based on Fordist mass production had to be reconciled with democracy and that had been achieved through mass consumption. But as capitalist wages did not sustain the necessary purchasing power the state had to step in to manage demand partially by redistribution through taxes and, partially, through borrowing. The neoliberal turn of the late 1970s-1980s was hostile to taxes, state intervention and government deficit. Curtailing social programs and government
spending amounted to pushing the problem of demand management onto the households who had no other choice but to deepen their reliance on consumer credit. It is now private borrowing not public debt that keeps the economy running. Soederberg (2014) develops the argument of the debtfare state through the comparative study of the U.S. and Mexico. Here the fundamental flaw of capitalism is overaccumulation (too much capital and labor, not absorbed by markets). Capitalism has spatial fixes for this problem by moving excess capital to other countries (Harvey 1982). Debt is a
temporal fix that raises incomes of surplus workers by letting them borrow against their future earnings so they can spend more in the present. Debt then creates a secondary form of exploitation and an entire industry profiting from indebtedness.
Households that experience rollback of welfare provisions will likely turn to borrowing as a compensatory strategy (Fligstein and Goldstein 2015). In the US, households have been increasingly relying on housing wealth as a way to build their retirement income or pay for their children’s college expenses (Conley and Gifford 2006). In the emerging economies of the postcommunist region, where generous social provisions have been curbed dramatically starting in the 1990s, consumer credit grew to help access goods and services that previously were free or heavily subsidized such as education, healthcare, consumer durables, and recreation (Stenning et al 2010,
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Guseva 2008, Rona-Tas and Guseva 2014). But in other cases, high rate of borrowing may, in fact, correlate with generous welfare provisions. In Finland, consumer debt increased substantially while the country did not change its commitment to robust welfare policies (Oksanen et al 2015:.232). France experienced a bigger housing bubble prior to the 2008 financial crises, as well as more volatility, and more short-terminist mortgage borrowing than the US, despite having a much stronger welfare state (Schelke 2012). The rise in welfare provisions in Korea, Brazil and South Africa (Yoon 2009:342, Lobato 2016:92) coincided with a parallel expansion of credit (Kim et al 2014, Lima-Marques and Benjamin 2009, Schraten 2014). In fact, there are good reasons to believe that generous welfare provisions can actually encourage more borrowing. Prasad (2012:228) points out, that a “smaller welfare state implies lower taxation, and this should increase purchasing power, lowering consumers’ need to turn to credit. A welfare state can itself raise the level of credit in an economy if welfare funds are invested in credit instruments. And […] people should be more willing to take on high levels of debt if they do not feel it necessary to save for their own future health care and pension needs.” For this reason, weak welfare states can stifle borrowing rather than
encouraging it, like in Italy, where both the household debt and the level of state services people receive are low (Casolaro et al 2006:103-14). Russia saw considerable increase in the level of
household indebtedness over the past 20 years, particularly in small consumer loans and credit cards (Guseva 2008), but the overall indebtedness remains relatively low with only one third of
households reporting any debts (Kuzina 2013). Households may borrow for a variety of reasons: some because of competitive consumption, and others under pressure to make ends meet, but economic uncertainty and weak welfare protections can also dampen the willingness to borrow while strengthening inclinations to save (Zavisca 2012, Ibragimova 2011).
III. Consumer Credit as a Process: How Consumer Credit Operates A. Credit Tiers
Consumer credit coheres into highly segmented markets. These are different worlds with different lenders that tend to follow different rules and serve different clienteles. The most common distinction made in the literature is between formal and informal lending. Formal lending is much more visible, and most of the reports of recent growth in consumer borrowing are calculated from the ledgers of formal financial institutions. Yet a lot of people borrow informally, from family and friends, or even from local loan sharks. The scope of these other kinds of borrowing often remains hidden: lending within the family is often not even perceived as credit, just part of traditional care
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and mutual support (Heath and Calvert 2013), and the illegality of usury makes collecting data on loan sharks complicated. As a result, we don’t have a good understanding of whether formal credit is replacing informal borrowing, and to what extent it adds to the total debt burden of households (Guérin 2014:145), even though this substitution is often an explicit policy goal of promoting financial inclusion among the poor (Tsai 2014). As the formal segment has expanded, it bifurcated further into a primary or mainstream and a secondary or fringe market (Fourcade and Healey
2013:564, Stoesz 2014). The four tiers – formal mainstream, formal fringe, informal borrowing from family/friends and loansharking/usury -- are easy to recognize even if in practice the distinctions are not always water tight.
Formal lending is subject to regulations, legal contracts, formal accounting and reporting etc. In the mainstream tier most lenders are intermediaries, i.e., they are not lending their own money but that of depositors, investors or other lenders and consequently, they are under some pressure to be transparent. Their business model aspires to economies of scale and scope. The primary formal institutions are retail banks and credit cooperatives. They service upper, middle and working class clienteles with a wide selection of credit products.
The secondary formal market is often called “fringe lending” or “alternative financial services.” It specializes in a narrow set of products that tend to lend small amounts on short term and high interest. With products like payday loans, home credit (doorstep lending), deferred
payment purchase loans and pawn brokering they often target those who the primary lenders refuse to serve (Fuller and Mellor 2008, Leyshon et al 2006, Agarwal and Bos 2014). Fringe lenders can be small, one-person enterprises or large multi-national companies, and are often depicted as predatory and deceptive. While there is little published empirical research in sociology on this topic, there is an extensive literature in economics, especially on payday lending (Campbell et al. 2011, Stoesz 2014, Durkin et al. 2014: 386-395, Karlan and Zinman 2010, Skiba and Tobacman 2011, Melzer 2011). That literature is divided on the harms and benefits of regulated fringe lending. Much of it arguably suffers from a narrow, marginalist approach focusing on the choice of customers among poor alternatives that are taken for granted (Banks et al. 2015).
Informal lending between family and friends remains ubiquitous despite the recent growth in formal consumer lending worldwide. According to recent World Bank surveys, the majority of people in the developing world borrow from family and friends, and in the emerging economies of Europe, Latin America and Asia households borrow from friends and family and formal institutions
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simultaneously (Kuzina 2013, Wilkis 2015, González 2015b). Even in countries like Switzerland and Austria, people turn more frequently to informal sources of credit than to institutional lenders (World Bank 2013). In many families, wealth is passed down between generations in forms of loans (Lewin-Epstein and Semyonov 2016, Heath and Calvert 2013, Druta and Ronald 2017). Rotating savings and credit associations or ROSCAs are extending informal ties beyond family and kin (see Pham and Lensink [2007] on Vietnam, Zanotelli [2014] on Mexico, Johnson [2014] on Kenya), and they can be entry points to the formal tier (Kear 2016). The complete replacement of informal with formal lending is, therefore, but a distant reality. On the contrary, it is apparent that households combine formal and informal loans in new and creative ways, for instance indirectly and informally accessing formal loans through friends and family who qualify (Ossandon 2012, Wilkis 2015). But having social ties with resources does not mean they can be turned into informal loans. Ties facilitate the asking, but they do not necessitate the giving. Wherry et al. (2017) explore relational work
strategies of a sample of low and moderate-income Americans that are faced with a difficult task: how to say no to an informal request for a loan without appearing callous and damaging the relationship? The study underscores the ubiquity of informal credit, even in urban US, and illustrates that a form of pre-screening regulates access to informal loans, too.
Usury or loan sharking is a type of informal lending where the lender is an individual (or a family) who lends relatively small amounts of money to people in need at very high interest. Loan sharking is a term coined in the U.S. in the late 19th century but this kind of lending is pervasive around the world and carry similar traits. Be it Roma usurers in Hungary, Slovakia or Bulgaria (Durst 2015, Hrustič 2015, Kojouharov and Rusev 2016), Chinese “vest-pocket” lenders in Holland
(Soudijn and Zhang 2012), mashonisas – the neighborhood money lenders – in South Africa (James 2014), or the Italian, Jewish, Chinese and African American mafia in American cities (Light 1977, Steffenmeier and Ulmer 2006), the loan sharks are local to the point where they often fight for physical territory. This type of lending is embedded enough in social ties so that the lenders can know and sanction their clients, but lending happens through some boundary – ethnic or family – so that the charging of high interest is not perceived as a violation of norms. In fact, the terms of this type of loan depends to a large extent on social distance. Durst (2015) describes the delicate balance of social connectedness and distance between usurers and borrowers in a Gypsy community in Hungary. The threat of coercion and physical violence is always present even if rarely enacted (Ellison et al 2010, Kempson and Whyley 1999, Hrustič 2015). Loan sharks often target people with
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stable albeit insufficient income flows from the legal economy. In this way, loan sharking and usury, informal and illegal, are parasitic on formal bureaucratic organizations. The original urban loan sharks in U.S. cities were often referred to as “salary-lenders” or “salary-buyers” as they targeted people with regular paychecks from public or large private employers (Carruthers et al. 2012). Mashonisas in South Africa depend on state salaries of their debtors and Roma usurers feed off of their clients’ welfare or pension payments, just as do Australian fringe lenders (Banks et al 2015:40, Marston and Shevellar 2014: 162-3). Ironically, “financial inclusion” policies, which provide the poor with bank accounts, can assist the sharks’ collection because they can just demand an ATM card and its PIN to the client’s account (Hrustič 2015, James 2014).
B. Credit Access
Within each tier, accessing credit requires passing a screening process, a selection, as lenders must decide who will and who won’t be given credit. Knowing who to lend to is often one of the main skills lenders believe they have, a skill that saves them from losses by borrowers’ default, and that make their business profitable and competitive (or sustainable, if we are talking about lending to family and friends). How this screening happens varies: for instance, banking is much more
personalized in Russia or France, where central credit registries are weak, than in the US or the UK where they are strong (Guseva and Rona-Tas 2001, Lazarus 2012). It also varies by tiers of credit: unlike formal lending, which is more likely to use statistical algorithms, fringe lending depends heavily on collateral (payday checks or pawn shops) or on intensive personal assessment (home credit).
1. Credit Registries
In mainstream lending the assessment of potential borrowers has gone through an enormous transformation in the last few decades from reliance on community ties and lenders’ own judgment (Carruthers 2013, Laferté et al 2010) to the use of credit registries and statistical tools. Credit registries accumulate past and current information about borrowers. In the 2000s, the World Bank made a strong effort to persuade all countries to build them (Miller 2003, Matuszyk and Thomas 2008, Han et al 2013, Jappelli et al 2013), and it currently tracks expanding registry coverage
worldwide. Registries vary from country to country. In some cases, they are created by government action and run by the authorities supervising banks as in China, Belgium and Portugal. In others, they are products of lenders’ associations as in South Korea, Mexico or Germany, yet in others, like the U.S. and Argentina, they are privately owned and run. Registries can contain only negative
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information (black lists) as in France or Finland, or full information (both positive and negative) as in most countries including the U.S., Hungary, China, the UK or Sweden. How much information they collect varies enormously depending on the threshold for the loan amount to be reported and the kind of variables that registries gather and keep about borrowers. Full information registries have not just more information about people but they provide additional incentives to borrow and “build credit,” as not having a credit history when one really needs a loan is a disadvantage, in contrast to black lists, where not having a history in the system is a sign of good behavior.
Registries have been of interest to sociologists as they seem to have ushered in a new form of governmentality (Leyshon and Thrift 1999, Wainwright 2011). They aid lending not only because they are predictive but also because they act as disciplining devices, as they punish non-payers by excluding them from future credit or making it more expensive for them to borrow. Comparative studies conducted prior to the financial crisis found that credit information sharing made it easier for customers to switch between lenders because their “reputations” were portable. Particularly where lenders can use registry data for marketing purposes, credit information sharing intensified
competition and increased lending volume (Jappelli and Pagano 2002, Miller 2003, Djankov et al. 2007, de Janvry et al. 2010), turning credit bureaus into “debt factories” (Manzerolle 2010). Jappelli and Pagano suggest that overall default rates decrease somewhat after credit bureaus are introduced (2002: 2035). Yet credit expansion and low default rates are in tension. The deeper the lenders reach, the more likely they lend to people who may do just fine under normal circumstances but fold quickly if they suffer a large external shock. The financial crisis revealed that some of the countries worst hit by consumer credit defaults, like the US or the UK, were those with full reporting registries with strong traditions, while countries with skimpy black lists, such as Finland, France and Denmark suffered much less turmoil (Rona-Tas 2015). With the data revolution, creditors are looking for other data sources, including social media and internet use, with the slogan “all data is credit data” (Hurley and Adebayo 2016).
2. Credit scores, credit assessment, personalized lending
Credit scoring is a statistical prediction of the future behavior of a loan applicant based on how people like her behaved in the past. Originally, credit scoring as an industrywide practice was introduced in the U.S. to reduce discrimination and bias associated with individual discretion of lenders (Pager and Shepherd 2008). By contrast, in the European Union, fully automated decision making is banned as dehumanizing and it is demanded that credit assessments are ultimately made
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by human judgment (Mendoza and Bygrave 2017). While this law has been circumvented in practice by most countries (Rona-Tas and Guseva 2013), in some, like Germany, it is a contentious political issue (Metz 2012).
Scoring takes a person out of her social context (Aalbers 2005:48) and disaggregates her into a finite number of variables, making her a “dividual” (Deleuze 1995, Lazzarato 2012, Langley 2014). These characteristics can include a wide variety of socio-economic, demographic and other attributes or only those related to credit histories. Then through statistical manipulation she is connected by a series of comparisons to a large group of strangers equally disaggregated into a handful of variables. Finally, she is reassembled into a single number or category (Pridmore and Zwick 2011:271). Studies of credit scoring always treat the statistical manipulation as a black box because the scoring
algorithms are proprietary secrets. The opacity of the algorithm is not just a methodological obstacle but a sociological fact. On the one hand, lenders hide their algorithms from their competition, as they hope to get an edge over other lenders by predicting customers’ behavior better. On the other hand, lenders shield algorithms from customers to prevent them from gaming the system (Rona-Tas and Hiss 2010). There is also an intrinsic opacity to algorithmic processes as, in many instances, the technical details of the mathematical model simply cannot be interpreted with respect to the lending process (Lepri et al 2017).
The lack of transparency of scoring generated legal challenges. In Brazil, there have been hundreds of thousands of lawsuits filed against the practice (Doneda 2016:154). One question that judges had to tackle is this: given that information in credit databases must be accessible to
individuals whom it concerns, does this also include the scoring methodology? To put it differently, if one can access all the information that goes into a scoring model, why can’t one access the model? The decision was that the model is not data, but “just” a mathematical equation, it adds no new information, only organizes existing data. (For a similar debate in Germany, see Spindler [2016]).
There are a series of human decisions that go into setting up a mechanized system, including the choice of the predictors and their measurement, the definition of the outcome (being late vs. defaulting and the definition of either) and the cut off for turning a continuous score into a discrete decision. While lending is relational (Lacan and Lazarus 2015), as its outcome depends both on the borrower’s and the lender’s behavior, credit scores almost never consider information about the lender in their calculations.
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Comparative researchers of credit scoring rarely worry about these questions. The few comparative studies of credit scoring following the declared interest of the lending industry mostly try to find out if statistical models work at all in certain contexts (van Gool et al 2012) or how well “generic” models, developed in the U.S. or U.K. work elsewhere. Most found that generic models improve with local tweaking while others find them to work well enough across borders (Andreeva et al 2008). There are notable differences in what is allowed in those models. In the Netherlands, for instance, residential address (post-code) is a key variable (Aalbers 2005) while in the US concerns about redlining made zipcodes illegal to use. Gender is also widely used but not in the US (Krippner 2017).
Lazarus (2012) presents an analysis of the French credit scoring system through comparisons with the American one. In France, unlike in the U.S., scoring is done in-house by the lenders and the scores are not disclosed to the clients. Customers’ reputations are therefore not portable, making them more attached to their banks. While U.S. scores are based on credit history alone, French scoring uses socio-economic variables like income, age, marital status etc., most related to stability (Ducourant 2014:95). Because interest rates in France are highly regulated, higher risk clients are refused loans, while in the U.S. they can borrow, just pay more. French lenders price loan products and not customers and applicants are either accepted or rejected for the product.
Wainwright (2011) discusses how credit scoring as a market device was transported to the UK from the US. The history reveals that similar economic advantages (cheaper assessment, better organizational control), social considerations (concerns about gender discrimination) and
technological innovations (computer and communication technology, statistical tools) motivated and enabled lenders in the UK, albeit with a half decade-long delay. Yet there are certain differences: ownership of the data on which the scores are built stay with the lenders, and like in France, UK scores are built on socio-economic data not just on credit history.
Other countries, too, present interesting variation in the use of credit scoring. In Portugal, as in France, instead of relying on credit bureaus, banks gather their own data for scoring (Lopes and Marques (2011:18-20). In Chile, it is not the banks, but large department store chains (González 2015a) that led the consumer credit expansion, and, as a result, scoring had to be performed in the stores (Ossandon 2014). Because of the limitations of the data, the assessment is complemented with personal judgments and the initial credit line offered is very small but can increase over time with good behavior. In China, data used for scoring come from a much broader set of experiences,
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including keeping one’s promises and complying with legal rules, moral norms, and professional and ethical standards, and aims at measuring “social credit” or the degree of trustworthiness. These scores are also used in a much wider set of contexts. A low social credit score can result not just in denial of a loan, but can exclude people from air travel, the use of high speed trains and government employment (Chen and Cheung 2017: 10-11). When it comes to expanding the use of the credit score, the US is not far behind as it is increasingly being used “off-label” (Rona-Tas 2017) as a measure of “one’s general character” in decisions ranging from auto insurance to rentals to private sector employment (Kiviat 2017, Fourcade and Healey 2017).
In a theoretical piece, Burton (2012) lays out the case for seeing lending in a larger, cultural context and lists a series of reasons why scoring would not work well in developing countries, including the oversized role of the informal economy, problems of validating information especially income, the social norms around the responsibility for the debt, lack of credit history data,
corruption, economic and political instability, and tribal divisions. Comparative empirical evidence for these hypotheses, however, is sparse.
Mechanized predictions are by and large limited to the formal banking sector. But even there not everyone is screened by machines. Relational banking is alive and well in many community and rural lending establishments (Holmes et al 2007, Dinh and Kleimeier 2007), and even in large banks (Li et al 2009, Vargha 2017). In fact, there is a curious symmetry of personalized attention in lending. Most banks maintain private banking for high-net-worth or VIP clients serviced by dedicated
personnel who spend face-time with clients and dispense bespoke advice (Rona-Tas and Guseva 2014, Chung et al 2014). Sorting people along the dimension of sales vs. client-oriented banking creates distinctions and boundaries, and reproduce pre-existing hierarchies, as Krenn (2017) explains using the case of German banks. At the other end of the income spectrum, certain home credit or micro-finance companies, such as the British Provident or the Russian Bystrodengy, also stick to face-to-face methods (on home credit in the UK see Kempson and Whyley 1999, Leyshon et al 2006, Coppock 2013, for Poland and Slovakia see Stenning et al 2010).
Fringe lending avoids mechanized scoring and large registries. If it cannot rely on collateral as payday-lenders or pawn brokers do, fringe lending uses personal judgment and leans more heavily on aggressive collection techniques. The small personal loans of home credit are administered by loan officers, or “doorstep lenders” who visit potential customers in their homes and build a
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positive, the money is handed to the applicant physically on a second house visit. Collection is also done in person at the home of the client at mutually agreed upon times. The loan officer usually lives in the vicinity of the applicant and has local knowledge about the applicant’s circumstances. Some European countries, like France, have regulated doorstep lending out of existence, usually by imposing a stringent usury cap (Laferté and O’Connell 2015).
C. Collection and Bankruptcy
While screening and monitoring tries to prevent default, collection and bankruptcy are ways to deal with non-payment after it happened. Collection is regulated and practiced differently depending on countries and lending tiers. Roberts (2014) provides a comparative history of debt collection in regulated lending in the UK and the US, from debtor’s prisons to current day practices. In both countries, debt collection has become a large industry as lenders – often following a statistical analysis of the likelihood of recovering the debt, another example of applying mechanized scoring in consumer lending – sell the rights to specialized companies at a substantial discount. Roberts claims that collection is becoming increasingly coercive even to a point where in the US a new form of debtors’ prison emerges. People are incarcerated not for non-payment but for contempt of court when collectors file lawsuits against debtors who fail to appear.
Collection puts debtors under extreme stress (Dunn and Mirzaie 2016). Collectors perform complex emotional labor to keep debtors attached to their debt, and deploy carefully designed scripts of intimidation, moral pressure and empathy (Deville 2014). This work is often outsourced to other countries (Poster 2013). Increasingly, collectors use their own scoring techniques to guide them in finding the most promising line of action (Deville 2012). In informal or predatory lending, collection can involve violence (Ellison et al 2010, Kojouharov and Rusev 2016), and in countries like India, even legally registered lenders can resort to physical force (Guérin et al 2014).
Bankruptcy regulation aims to balance between two goals: to protect the interests of the lender, and give some reprieve to the borrower. Ramsay (2006) points out that in different cultures bankruptcy may be tied to different ideologies: while in the US, bankruptcy is a means to allow individuals to quicker get back “to the market”, in Scandinavian countries, it is perceived in the same category as welfare, so bankruptcy is limited to help people who are subject to long-term
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unemployment or illness, but not those who “overconsumed” or were “imprudent” (p.263). This leads McGregor et al (2001) when comparing insolvency in Sweden, Canada and the U.S. to note that the Swedish state takes the most pro-creditor stance, while Haber (2015) similarly documents more state regulation and state activism preventing evictions in the U.K. than in Sweden.
US bankruptcy regulation was originally framed around the importance of a “fresh start.” But if the American 1978 Bankruptcy reform act rejected moralization of failure to pay back and made the reasons behind bankruptcy irrelevant (Carruthers and Halliday 1998, Sullivan et al. 1999), the more recent 2006 amendments re-moralized bankruptcy again making it more difficult for individuals to discharge their credit card debts, strengthening the position of lenders vis-à-vis borrowers (Carruthers and Kim 2011). Ramsay (2006) finds that one third of debtors do not get a ‘fresh start” through bankruptcy because they continue to be plagued by insufficient income, and Agarwal and Boss (2014) present evidence that bankruptcy reduces future chances of borrowing from mainstream lenders and pushes borrowers into high interest fringe borrowing. Furthermore, the stigma of bankruptcy becomes a disadvantage on the labor market (Maroto 2012) which has its own financial consequences.
The US concept of the “fresh start” has influenced recent changes to the indebtedness regulation in European countries, but this effect should not be overestimated (Ramsay 2007). The fresh start idea is in stark contrast to the long-standing tradition in many European countries of life-long debt liability, and Western European countries as a rule have opposed US-style bankruptcy laws. EU harmonization pressures have brought about a proposal for a standard calculation of APR throughout Europe, but this did not extend to bankruptcy and debt adjustment procedures.
European policy makers are more focused on prevention of overindebtedness than on bankruptcy as a solution, favoring in particular “responsible lending” measures, such as credit background checks of borrowers and adequate income requirements.
Comparative literature on bankruptcy aims to explain different filing rates across national contexts. Based on variable rates of bankruptcy filings in the US, Canada, UK, Australia and Japan, Mann (2009) considers three types of explanations: legal (i.e. ease of filing, including the upfront costs), cultural (perceptions of debt, personal responsibility, etc.) and economic (exogenous shocks like unemployment), and finds that all three can explain the US’s highest filing rate, but if one adjusts for levels of debt and unemployment, the highest bankruptcy rates would be in Canada. Americans file much more simply because they on average carry more debt and are more likely to be
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burdened by job losses and unsurmountable health bills. But in Canada, filing costs and procedural hurdles are low, signaling that the ease of filing matters. Ramsay (2009) is generally skeptical of cultural explanations and argues that what may have seemed a “cultural” opposition to filing for bankruptcy in Japan, has changed once new insolvency rules were introduced. Japan currently has higher bankruptcy rates than England and Wales. More promising explanations for national
variation in filing rates are path-dependency of legal institutions and the role of interest groups that compete to shape insolvency legislation either to favor consumers or the financial industry (Ramsay 2007, Kozuka and Nottage 2009).
Focusing on German households, a group of researchers (Backert et al 2009) report that the most common micro-reasons for declaring bankruptcy are job loss, loss of financial oversight, perhaps related to bad financial decisions or lack of knowledge, and divorce/separation, but echoing other studies, they conclude that easing access to bankruptcy filing (for instance, deferring payment for filing fees) increases filing rates.
IV. Consequences A. Social Fabric
What is the effect of consumer credit on social ties? To the extent that credit helps
borrowers to honor social commitments and participate in social rituals, it is relational: it marks and differentiates social ties, and may help solidify them. In Hungary, a mortgage can alter and also clarify intimate relationships by forcing couples to discuss their future plans explicitly (Pellandini-Simányi et al 2015.) Chilean families earmark department credit cards for gift-giving: they help ease “the burden of unplanned expenses,” since birthdays and weddings do not come up regularly throughout the year and cannot be budgeted like other expenses, or to serve “as a ‘gift enhancer,’” allowing gift-givers to afford more and “ratcheting up the consumption standards and expectations of coming events” (González 2015b:792). For Bosnians, regular indebtedness to kin and local shopkeepers is inescapable, and they constantly worry about dying indebted, and yet funeral expenses are also often financed by loans (Jasarevic 2012).
Not only borrowing but also debt repayment is relational: one newlywed spouse can pay for the other’s debts or use wedding gift money to pay as the couple strives to establish their young household “with a ‘clear’ account” (Olcoń-Kubicka 2016). Borrowing and debt create new
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dependencies, or strengthen existing ones. Among the poor in Argentina, debt obligations are experienced by families as socialized collective obligations because “debt imposes its own rhythm on the circulation of money within the family.” (Wilkis 2015 773). Not repaying debts can ruin personal relationships, not only in lending circles in Argentinian slums, but also in Indian microlending groups (Sanyal 2014, Beck and Radhakrishnan 2017). Existing social ties constrain non-payment because a failure to repay by one individual can jeopardize the moral capital of the whole community and its ability to borrow in the future.
The very fact of access to credit by one household member can change intrafamily dynamics. In households where members keep their money separate, credit may be used to compensate for income differences at the intrahousehold level (Pahl 2008). Credit can have a liberating effect on the borrower’s existing relationships: it provides additional resources, and can make recipients,
particularly younger wives, less dependent on their husbands and in-laws (Sanyal 2014), at the very least because additional resources raise women’s standing in the extended family hierarchy. But credit can also generate new dependencies: servicing credit obligations may necessitate reliance on partners for help, and increase marital conflict (Kamleitner et al. 2012). Moreover, digital traces that some newer forms of credit can generate (i.e. credit cards) can in some families create unwelcome opportunities for increased surveillance and social control, but in others, on the contrary, a chance to demonstrate mutual trust if financial information is shared between partners (Olcoń-Kubicka 2016, Guseva and Rona-Tas 2017).
Do social ties have an effect on consumer credit? Unlike formal lending, getting access to informal loans is exclusively facilitated by social ties, but Lacan and Lazarus (2015) are skeptical of starkly juxtaposing network-based informal credit and impersonal and institutionalized formal lending (see also Guseva 2008:73-82). Indeed, empirical evidence suggest that formal lending, too, is often closely intertwined with social ties. In microfinance lending, social ties are used as collateral (Sanyal 2014). In post-communist Eastern Europe, in contrast to the Western part of Europe, social ties are positively correlated with financial inclusion into the formal lending tier (Corrado and Corrado 2015). In poorer urban communities in Latin America, informal ties broker the use of formal credit: Ossandon (2012) and Wilkis (2015) report frequent lending of credit cards by those who qualified for them to those that cannot access formal credit on their own. In the West,
“ubercapitalism” relies on big data valuation of individual consumers, including their social network data, classifying them for the purposes of targeted selling and lending (Fourcade and Healy 2017).
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B. Health/Mental Health
Socio-economic status is the strongest and most persistent predictor of morbidity and mortality (Phelan et al. 2010), and the poor are particularly vulnerable. Therefore, to the extent to which borrowing helps avoid poverty, it should have a positive effect on physical and mental well-being. Explanations framing debt as a rational, economic decision start from this very premise. If debt allows people to smooth income flow and cope with financial stress, as the lending industry claims, it could be thought of as a psychological buffer (Haushofer and Fehr 2014). Any negative
correlation could be attributed to reverse causality: illness jeopardizes earnings, leads to poverty and to overindebtedness. In an experiment conducted in South Africa, Karlan and Zinman (2010) randomly assigned applicants to a high interest (200% APR) short term loan, and found that compared to those that were rejected, the loan recipients were better off later not just economically but also in terms of their mental health. Dwyer et al (2011) also found mental benefits for going into debt. Their research shows that for American college students, especially those from lower and middle class background, debt promotes mastery and self-efficacy, as being deemed debtworthy confers a positive social judgment.
Keese and Schmitz (2014) analyzed a large German panel spanning ten years, and came to the opposite conclusion. Measures of subjective wellbeing, mental health and obesity deteriorate if people take on debt (see Drentea and Reynolds 2015, Sweet et al 2013). The seemingly contradictory findings can be reconciled once we consider that the health effect of debt may be highly context dependent. Karlan and Zinman’s experiment in South Africa included people who were marginally rejected by loan officers for a loan with a high rejection rate of over 50%, people in a better relative position than the typical fringe borrower in the US or UK, which raises the question of
generalizability of their results to other contexts. The positive effect in Dwyer et al. (2011) is unlikely to obtain in other countries with credit cultures different from the US (Minty 2016). Similarly, one could argue that German culture is an outlier in its negative framing of debt (the German word for “debt” – die Schuld – also translates as “guilt” or “fault”).
Context matters in whether debt is perceived as stressful or not. Not just country but the kind of debt (Kalousova and Burgard 2013, Clayton et al 2015), local social norms (Gathergood 2012), historical changes (Dunn and Mirzaie 2016, Dwyer et al 2016) and social class (Dwyer et 2011) can make a difference in how debt affects health. Qualitative studies can help disentangle the effect of cultural, historical and class contexts by identifying the frames through which people make
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sense of their indebtedness (Johnson 2014:70, Thorne and Anderson 2006, Jasarevic 2012, Kar and Schuster 2016).
V. Future Directions
There are a series of challenges for future research. While there is a large amount of data accumulating on formal credit, and even more on its various correlates, most of those are
proprietary. Doing independent research on the topic involving large datasets will be increasingly difficult. The same goes for algorithms. As algorithmic predictions proliferate, from assessing the likelihood of default and the future profitability of the loan, to the probability of the success of collection, understanding their operations will become imperative, particularly since their growing influence in multiple other contexts (employment, housing, etc.) can subject some people or groups to simultaneous burdens. Measurement of informal lending, still an important part of consumer credit, is yet another challenge, especially as new forms of peer-to-peer lending emerge with the help of the Internet. Comparative small-scale qualitative research encounters its own difficulties, yet more, well-designed comparative research grappling with practices, experiences and meaning of consumer credit in different settings is needed. To get a better grasp of how carrying debt is tied to other micro- and macro-level phenomena, like social relations and health, as well as social
inequalities and social control, we need to address circular causation, as debt turns out to be both a cause and an effect. Moreover, some technologies like credit assessment, place individuals in “classification situations,” and may act as self-fulfilling prophecies amplifying and solidifying both disadvantages and privileges (Fourcade and Healy 2013).
Finally, as all data – from what one buys to who one befriends – becomes credit data, and, at the same time, credit behavior as an indicator of one’s general character is increasingly linked to countless areas of social life far beyond borrowing money, research must connect consumer credit to its larger contexts.
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