Monthly Archives: September 2013

Economics for skeptical social scientists

I recently gave a talk at the “Training school on Virtual Work,” which was held at the University of Malta. The participants were mostly graduate students and junior faculty at European universities studying some aspect of virtual work e.g., Wikipedia editors, gold farmers, Current TV contributors, MTurk workers etc. Most were coming from very different methodological background than my own and the people I usually work with—sociology, anthropology, media studies, gender studies etc. I think it is fair to say that most participants have a fairly dim view of economics.

One of the organizers felt that few participants would have encountered the economic perspective on online work. I was asked to present a kind of non straw man version of economics and present the basic tools for how economists think about labor markets. Below is the result—a kind of apologia for economics, combined with a smattering of basic labor economics. I’m not the best judge obviously, but I think it was reasonably well received.

Economics and Online Work (a slightly misleading title though – see description) from John Horton

PS – I should write more about the school later, but one of the main take-aways for me was how (a) pervasive the acceptance of the labor theory of value was among participants and (b) how this leads to very different conclusions about almost everything that matters with respect to online work. It would be interesting to try to analyze a couple of different online work phenomena using the LTV and the marginalist approach to value.

Impressions from a visit to a large call center

Paul Krugman has a piece of research advice, which is to “listen to the gentiles.” What he means is to pay attention to what smart practitioners say about their business in order to get economic ideas and insights. I recently had the chance to take a tour of large call center for a major global financial institution. It was interesting throughout and I thought I would share some of my notes.

Recruiting and Training
The company uses a tiered screening approach. They start with an online test. Some proceed to phone interviews and the finally round is in-person interviews. The HR director felt that the simulated work environment test was primary predictor of future job success—this matches up quite well with the industrial psychology literature on employer screening. He also felt that the best predictor of retention was how comfortable a worker seemed up front with the demands of shift-work.

The company made extensive use of their existing employees to recruit new ones. Referrals were highly valued because they were more likely to bring in candidates who understood the reality of shift-based call center work (and thus were less likely to turn over). It seemed to be less about bonding or reducing formal recruitment costs.

Long company-specific training period, but no general training (as Becker would have predicted). However, there are several other call centers in this region and the company does lose employees to them. The company-specific training period was surprisingly long (on the order of 3 months) and was conducted by more senior employees during low call volume periods.

The company acts like a price taker with respect to wages. Compensation for new employees was determined by doing yearly market research into what competitors were paying. The company did have very high turn-over (though about in line with the industry), but there was no mention of raising wages as a solution. Their approach seems to be to wait for people to “sort out” of the job that find that they cannot handle shift work. I meant to ask about explicit performance incentives but didn’t get a chance to. However, my impression was that rewards came through promotion and one-off bonuses rather than through relating payment to specific actions, despite performance being quite measurable.    
They had surprisingly rich amenities. Although pay was not high, amenities were reminiscent of a Silicon Valley start-up: pleasant office, cheap and free food, free gym, concierge service etc. Some of these things seemed like amenities the firm could more cheaply offer than their competitors because of their larger size, since some were club goods. In other words, they could amortize a concierge over many more employees.    

Customers are segmented by value and routed accordingly. The company is multi-national and has call centers in several locations, including Europe, Southwest Asia and East Asia. The company’s clients are segmented based on value and routed to the call center that roughly corresponds to the skill level of the workers at the call center e.g., the best customers get the European call center and low-tier do not.

They are highly sophisticated at demand and supply management. Perhaps unsurprisingly, they are good at forecasting call volumes and staffing accordingly—with all of this done semi-automatically. They can adjust supply on the fly by calling off training, meetings etc. if demand spikes via building-wide announcements of status changes.

There was little evidence that much technologically-driven productivity improvement was on the horizon. Although the tasks are highly structured, there was no evidence that significant technology-driven productivity gains were on the horizon. All the big gains from automation already occurred many years ago (e.g., the ubiquitous “Press 1 for “Accounts”). There was no talk of Watson-like automation of responses to customer queries. The one technology they really wanted—and that would radically reduce their costs—was some easy way to verify customer identities over the phone. This alone would increase their productivity by about 20-30%.

There was little evidence that this would could be easily distributed. Most of the firm’s workplace policies seemed to be driven by concerns about regulatory compliance and fear of losing sensitive customer information and required a great deal of monitoring and control. It is difficult to imagine a substantial chunk of this work being done by a geographically distributed workforce.