February 9, 2010
This figure is my own representation of an exchange ― reconstructed from interviews with people working in insurance companies― between a possible user and an insurance seller who works for one of the health insurance firms in Chile. Probably this encounter happens after the salesperson, interested in increasing her client portfolio, contacts a possible user who has accepted to have an introductory meeting. When they meet, the seller asks certain socio-demographic information (sex, age, family number, income) from which it is possible to suggest the array of insurance policies available for the prospective user, and the premiums and type of coverage in each case. If the potential user is still interested; she will be asked to fill a ‘medical declaration’ which, for the most part, focuses on her previous medical history. The meeting finishes here. At the next meeting, the salesperson plays a different role; now her work is informing the outcomes of the medical declaration. There are three main options: accepted without restrictions; accepted but with a restricted policy; or not accepted. Restrictions and rejections are connected to the user’s medical history, or what is called ‘pre-existences’, that is, past medical events that suggest potential future medical expenses which insurers are legally allowed to not cover. This is not so different to many other commercial interactions we face every day, that are generally seen only interesting for the experts directly involved in these industries. However, I think, this opens at least three different research agendas of social studies of finance.
First, like other risk screening processes studied by Deville and Poon, this is an exchange that apart of human beings, involves many other type of actors, such as forms, objects, affects, and so on. In this case, when the seller gathered socio-demographical information and proposed certain policies, she was referring to an already assembled network. Here the main actor is the actuarial department. This department is in charge of developing new information systems by matching the available statistical information and potential costs of medical provisions. In order to do that, they produce a virtual object, namely, a population’s potential health situation and their potential costs. These are virtual because they are not material (a tendency in statistical software) yet they are regarded as objects because from the moment they are produced they are assumed as real, and cause a real impact upon the next stages of the network. The medical declaration is evaluated by a different section known as ‘medical comptrollers’. By using previous epidemiological information, they can predict the future risk of new users, determining the existence of relevant pre-existences. Here two virtual objects are produced: the past medical history of a potential user and her possible future health. What is produced in both cases is not just virtual but multiple. The medical history developed by medical comptrollers is not the same as the medical history presented by the seller or to the way in which past medical events are conceived by the prospective user. At the same time, medical history will change depending on the kind of formulas used to merge medical statistics, on the form that registers this information, or in the event of changes taking place in the statistical information at hand.
Second, this exchange is embedded in wider processes. As discussed in a previous post, a long chain of events, including economists as main actors, had been relevant in shaping the form of this exchange. At the same time, as the director of one of the first private health insurance companies in Chile explained to me, from the beginning of the system in the early eighties, the statistical information that is available (and its ability to predict future events) has dramatically increased, changing the landscape of this industry. In fact, this is not just a matter of available technologies, but actuaries themselves have been a very scarce resource. This is not a professional degree offered at Chilean universities; therefore, insurance firms mostly hire experts from Argentina, where this profession is one of the specialisations in schools of business and economics. And finally, like car insurance, these are compulsory insurance policies which are much more regulated than other type of policies. In this sense, the clear division between actuarial department and medical comptroller has to do with that that this system’s regulation allows considering only two factors in their pricing tables: age and sex (of course: the formula used to connect these two variables with the potential health cost is owned by each firms). Then, instead of being included in the premium, medical history has been connected to potential exclusions.
Finally, this exchange also opens new social connections to be followed. As scholars inspired by the late Foucault have shown insurance is a technology of risk, and as such, what it does is pooling or connecting people under a common fund. This is made in different layers. First, in their risk screenings, new users are included in statistical populations that allow estimating their potential health expenses. Second, in case the insurance policy is bought, users are connected with other usersof the same type of policy. Most probably, they don’t know you, but your monthly withdrawn can help to pay their hospital in case they have an accident or they can help you if you are the one sick. In this sense, insurance is really important in producing what Durkheim called “organic solidarity”, or the modern situation that tie us to those that we don’t know. However private insurance does not work in the same logic than national welfare regimes. Pooling is not about building a national population, but about producing more delimited funds. We are not connected to all the costumers of our chosen insurer, but with those that are in our same group (for instance: men, young, etc.). We are actually connected to others, but we cannot join our “colleagues” because we cannot really who they are, or even the categories that tie us together. In this sense, this third potential stream of research, is not just about how this insurance exchange is embedded in wider political events or entangled in heterogeneous networks, but about following how it is central in assembling new collectives and social categories.
January 31, 2010
One year ago I met Daniel Beunza in an economic sociology event at Goldsmiths. He told me that I could post sometimes here. The same January I had my PhD thesis viva, and since then I have been quite busy by teaching and writing a research fund application to follow the consumer credit industry in Chile. Now before being overwhelmed by this new research I’m finally trying to write some articles out of my PhD thesis. The thesis attempted to understand how the private health insurance in Chile ended up having it actual shape. There are a couple of ideas connected with this case, but I think also of more general interest, that I would like to share here and in two other posts.
Perhaps one of the main issues in social sciences in Latina America in the last decade or so has been the “ubiquitous rise” of economists and economics in the sub-continent. Said very simple, this literature has aimed to explain their role in three main phenomena: the technocratization of governmental elites, the institutional isomorphism centered on market liberalization, and the production of a sharp boundary between economy and those elements that are within the reach of direct government intervention. Of course, existing research combines these elements in different forms, some of my favorites are: Babb, Cárcamo-Huechante, Fourcade&Babb, Mitchell, Neiburg, and Valdés. The case of Private Health Insurance (PHI) in Chile, I studied in my PhD research (and particularly in a chapter that I would be happy to circulate), touches few elements from these different types of questions, however, it also illustrates another dimension of the multiple parts played by economists in Latin America recent history, that I would like to highlight here.
The creation of PHI in 1981, in the context of the Chicago Boys reforms in Pinochet’s Chile, followed one basic assumption: a combination between free choice consumers and competition between insurers would produce insurance policies that would optimize efficient health expenses and good protection to users. However, talking with economists experts in this system today, it is easy to realize that this equation turned to be quite problematic. Just to mention three of the most controversial issues: (i) ten years after it was created most of health policies were covering highly probably but not very expensive events, leaving users finally unprotected; (ii) risk screening -and the exclusion of pre-existing medical events- in new insurance policies made an important group of users unable to actually choose between the available goods; and (iii) the amount of choices in this market is so large that rational calculation is almost impossible. In order to solve these problems different solutions had been figured out: today each insurance policy includes a catastrophic coverage, contracts are aimed to be long lasting, and there is agreement that the range of insurance policies in this market needs to be simplified.
Economists see this story as a matter of lack of knowledge. When the system was created the sub-section of economics particularly interested in this type of issues (health economics) was not very developed, and concepts that are today so influential in framing this type of discussions (such as moral hazard, adverse selection) were not widely available. In other words, there is now new information that would allow a better market design. I think, however, this is also a very particular case of performativity of economics. Perhaps, economists would agree that when the PHI was developed members of very few professions would imagine a new market as a solution for health policies, but, at the same time, the role played by this expertise would decrease together with the development of this industry. Nevertheless, after the unexpected consequences of this development, there is a consensus on that the PHI market needs to be regulated to fulfill its original aims: efficient health administration and protection. Regulation, here has specifically meant that the thing traded in this market – the insurance policy- has been standardized, and, competition today is less about singularizing each policy, and more about the prestige –or other properties – of the insurers.
Borrowing a metaphor used by Harrison White in his book on markets, I think there is a one-way mirror in this case. The shape of the product exchanged is not just the outcome of the interaction between supply and demand – and other elements highlighted by economic sociologists such as political struggles or networks – but it also reflects economics. However, those who represent this market – and are those who almost exclusively regulate it – economists, cannot see the role their knowledge play in the development of this industry. I believe this case shows the relevance to expand the discussion about economists and economics in Latin America to analyzing their role as market makers, but at the same, that it is also needed to increase the attention to the dynamic relationship between economics and the economy in those markets that has been created as a form of policy making.
June 12, 2009
I have just received from COST US, a Google group dedicated to corporate sustainability, links to articles about technologies that may reshape how investors and consumers politically engage with companies.
The first one, from the corporate blog of Hitachi, discusses the happy marriage between the Global Reporting Initiative and XBRL language. The GRI is a non-profit that advocates a system for environmental and social reporting, and XBRL is a new format for electronic reporting. This natural union could be one of those happy combinations of content and platform, like mp3s and the ipod.
It’s clear that by providing preparers and users of data with the means to integrate financial and so-called nonfinancial data (i.e., that which discloses a company’s environmental and social performance), XBRL offers exciting possibilities. The potential for XBRL to provide the users of corporate sustainability performance data with the leverage to push and pull information that meets their requirements is certainly there. That was the thinking behind the first version of an XBRL taxonomy for GRI’s sustainability reporting guidelines, released in 2006.
The partners’ solution: a volunteer army of finance geeks. Their project, Freerisk.org, provides a platform for investors, academics, and armchair analysts to rate companies by crowdsourcing. The site amasses data from SEC filings (in XBRL format) to which anyone may add unstructured info (like footnotes) often buried in financial documents. Users can then run those numbers through standard algorithms, such as the Altman Z-Score analysis and the Piotroski method, and publish the results on the site. But here’s the really geeky part: The project’s open API lets users design their own risk-crunching models. The founders hope that these new tools will not only assess the health of a company but also identify the market conditions that could mean trouble for it (like the housing crisis that doomed AIG).
These are exciting developments for sociologists of finance. As Callon has argued, it is the tools that market actors use to calculate that end up shaping prices. There are politics in markets, but they are buried under the device. Following the controversy as it develops during the construction of the tools is the key way to unearth, understand and participate in it. This is of course, a favorite topic of this blog, of several books and of an upcoming workshop, “Politics of Markets.”
One open question, as Gilbert admits, is whether the “open source” approach and tool building will take up.
So, how many companies are tagging their sustainability disclosures in this way? The answer is: surprisingly few. Why is this? Perhaps companies are unaware of the ease with which it can be done. As previous contributors to this blog have noted, XBRL is not that hard an idea to get your head round, and implementing the technology involves very little in terms of investments in time or cash.
An alternative model is Bloomberg’s efforts at introducing environmental, governance and social metrics on their terminals (a worthy topic for another post).
March 13, 2009
Bernie Madoff was sent to jail yesterday after he pleaded guilty to operating the world’s largest Ponzi scheme. His investment advisory firm had provided unusually and consistently high returns on clients’ investments, which turned out to be bogus, meaning that he wasn’t in fact investing the money, but instead paid out the money he got instantly to others who had trusted their money to him earlier.
Pension schemes are sustainable as long as there is more money flowing in than flowing out.* The technique is to take the money from the new contributors and give it to the old ones.
Madoff is in jail. European governments are on trial.
1. Spot the difference.
2. Is such a scheme necessarily wrong? Under what moral and calculative circumstances is it right?
3. Compare with the current bank meltdown. Under what circumstances is a bank a Ponzi scheme? These schemes don’t invest the money into anything, instead they churn it right back out, but not to the same people who gave it. There is nothing on the asset side, only liabilities. Banks also take people’s money and give it to other people to use)? Banks that are going bankrupt today did invest, but their assets turned out to be worth next to nothing, so in the end they couldn’t satisfy their creditors. Is there a lesson here for financial intermediation and the length of financial circuits?
*Pay-as-you-go pension systems work by collecting social security contributions from active employees and use it to pay out pensions that are currently due.
Banks realized that although electronic banking is cheaper than maintaining branches, they don’t need to deter clients from seeking personal contact. In fact, they might get the most out of the branch by not trying to figure out how they could “herd” different people to different communication channels (branch, internet, telephone) for different services, as one of my interviewees said. Rather, they can let them come for whatever reason—once you have the customer’s willing attention, you can achieve a lot.
The problem is how to have effective interactions with a large volume of small retail clients who are largely unknown to the clerks serving them. The problem of banking as a mass financial service is how to singularize an already standardized product to an ever-growing mass of mobile clients. First, this is different from “high finance”—the topic of much research in the social studies of finance—where products are custom-developed by investment banks for large corporate clients (see Vincent Lepinay’s work in the volume Market Devices). Second, the Hungarian bank I studied wanted to be in the niche of quality services, but had to deal with an exploding customer base. A single clerk typically does not personally recognize the clients who show up at her branch. Third, we shouldn’t forget that clients had just been raised by banks to be mobile—the movement towards electronic banking and telephone services meant that you could break away from your local branch and complete transactions anywhere, with or without human assistants.
Banks’ return to the personal is via the technologies that personalize. The technology Customer Relationship Management is spreading to support the clerk’s work of serving clients, providing personal banking for the masses. CRM refers to both an information and communication technology (SAP, Oracle, and Salesforce are leaders of this market) and the strategy of “relational marketing,” which is based on the idea that the firm should be organized around the customer instead of the product. Instead of selling as many products as possible to any number of people over the short term, the customer-centered strategy is to treat each customer longitudinally and focus on selling multiple products to that customer throughout the lifetime. This means that each interaction with the client has a dual goal: it counts towards the relationship long-term, while it should also be used to attempt a sale. Pitching products and building a relationship can both be done in a very personalized way because the bank has had information about the client and knows her habits well.
CRM as an information system makes instant familiarity with an individual client possible because it tracks clients by pooling data from all corners of the bank, and renders it legible for the bank staff who have live interactions with clients. When clients use the bank’s services they leave a trace, but these myriad points of data about a customer are scattered around the organization in heterogeneous formats. More precisely, in order for tracking technologies to work, there must be traces to piece together, so the bank has to make sure that each contact is recorded in specific form that is acceptable to the technology that will integrate it. Customer Relationship Management integrates all that data, analyzes it, compiles, updates, and delivers it to the screen of any clerk at any branch of the bank.
At the bank I studied, Customer Relationship Management software did more than tracking: it tailored the product offer to each (existing) customer. When the customer visits the branch next time, the clerk pulls up the profile and immediately sees the list of products that match the profile, and she is supposed to be offering the customer some of those products. Amazon is the prime example of mass customization, with its technique of collaborative filtering that compares customers’ behavior as it is visible within the system (what items they have purchased) and based on that calculates what new products to recommend. The CRM that I observed in action at the bank branch relies partly on similar calculations that relate one customer’s behavioral pattern to the rest, in a dynamic evolving fashion that recalculates the best offers when new data are entered.
Ironically then, banks are bringing in impersonal technology to turn personal encounters with customers into a more individualized experience. In the next post, I will describe CRM in action, based on my observations of the situation between the client and the clerk. The fact that in this version of dynamic and personalized product recommendations, there is someone between my “Amazon screen” and myself, and that someone selects the product they think is appropriate for me–what’s more, I am not aware that there is an Amazon screen–has all kinds of consequences for what kind of exchange can be accomplished between me and the bank.
In thinking about financial innovation, retail banking is not the first thing that comes to mind. Derivatives, global deals, complex portfolios — yes. But ATMs? Bank branches? As an ex-banker from Citigroup used to say to me years ago, “branches are a dead weight.”
Branches, however, are what makes the work of Zsuzsanna Vargha so interesting. Zsuzsi is finishing her Ph.D. in sociology at Columbia, and her dissertation examines a massive shift that has occurred across the Atlantic. She writes
Banks, after pursuing electronic banking, are once again
investing in the face-to-face encounter. This re-personalization of
finance is particularly well observable in post-socialist Hungary.
Banks individualize services to keep their share of profitable
clients, and reach out to new populations in person.
What do these changes mean for the future of retail finance? To address that, Zuzsi conducted ethnographic research on the Customer Relationship Management (CRM) software used to recommend products to bank clients. She then examined a savings and loan-type bank that used a direct selling organization, not unlike the “thundering herd” of salespeople that Merrill Lynch used to have. Her first findings are available in the first paper to come out her dissertation, “Markets from interactions: the technology of mass personalization in consumer banking.”
Zsuzsi’s study comes at a particularly timely juncture. Now that proprietary trading no longer seems to an option for American banks, these institutions need to reimagine their identity as something exciting and innovative … but distinct from Wall Street. Zsuzsi’s conception of retail banking as a high-tech service could well be the way to go.
We’re lucky to have Zsuzsi as guest blogger at Socializing Finance for the coming week.
The $75 billion dollar Homeowner Affordability and Stability Plan announced by Obama today promises to relieve between 4-5 million responsible households who are facing foreclosure. Included in the plan are provisions to work with mortgages that are not yet in default. For some well framed examples of the types of calculations for refinancing the Administration expects the plan to enable see here. It is noteworthy that the plan does not dispute the price of the house nor does it compensate owners who have paid down value that has now been lost; it simply permits the readjustment of loan amount, where ratio rules might have impeded modifications, to the current value of the property. In other words, it provides access to refinancing.
A key word in the plan is responsible which is repeated again and again in the Administration’s Fact Sheet. There is a strong populist emphasis on demonstrating that unscrupulous speculators will not be rescued or rewarded. We won’t know how eligable homeowners will be defined until guidelines are announced. This is expected to occur on Wed. March 4th. In watch points, look for the use of FICO scores, in the the determination of individual responsibility…