The Indian blackouts & oDesk

A nationwide blackout in India has left some 600 million people without electricity. Given that a large number of the contractors on oDesk are from India, I assumed that effects of the blackout would show up readily in the oDesk data. This evening, I wrote a query to get the hours worked each day by Indian contractors during the last month and the number of applications sent. I divided these counts by the respective totals for that day for all of oDesk. From this time series, we can get a sense of what was supposed to happen today and compare it to what actually happened. The time series for applications (top) and hours worked (bottom) are plotted below [1], with today annotated in red. Each percentage estimate has a 95% confidence interval.  

Some observations

• There is a very easy to detect drop-off in the hours worked---my eyeball calculation says they should have been responsible for around 22% of the hours worked today, while the actual number is closer 17.5%. This is far less of a fall-off than we would naively predict from the "1/2 of Indians without power" headline. Presumably many contractors have access to private generators, or perhaps oDesk is over-represented in parts of the country that were less affected by the blackout. 
• There is no corresponding obvious drop-off in the fraction of applications. I don't have a good explanation for this, but perhaps non-affected Indian contractors have made up the difference and exploited the now-thinner market. If I can get some data on what parts of the country are actually being affected by the blackout, I could test this notion since I do have contractor locations down to the city level. 
• Indian contractors take weekends off, both in terms of working and job finding (or at least more so than their oDesk counter-parts from other countries). Remember that this time series is the fraction for a given day, so there's no reason for a strong weekend/weekday pattern. See oDesk Country Explorer for more of this kind of data.   
• Indian contractors are generally over-represented in the application pool, making up ~25% of applications but only about ~20% of hours worked, though this could easily reflect differences in the kinds of categories Indian contractors work in---there is a great deal of variance in the average number of applications per opening across the different job categories. 

Code for the plots (done in ggplot2):

Digitization of the supply side of the labor market

Note: This blog post also contains a short review of Google's new Consumer Surveys service. See the end of the blog post for details. 

On most electronic commerce sites, information about the supply side is digitized and publicly available while information about the demand side is generally not: Amazon, Expedia, iTunes, Etsy etc., all collect and display detailed data about the items for sale, but there is generally little or no information about the consumers with the demands. If we look at the labor market, the reverse us true, in that it is the demand side that's digitized. On online job boards like CareerBuilder, Monster.com, Indeed, SimplyHired etc., vacancies are described via detailed textual descriptions about the nature of the work, skills required, location and approximate salary, but the job seekers---the sellers---generally do not create profiles that describe themselves to the marketplace. 


While we might think that there are some fundamental reason for this difference, I don't think this is the case for the simple reason that in the case of labor markets, the supply side is being digitized, primarily though LinkedIn (in a big way) and through sites like oDesk (in a comparatively smaller, but more comprehensive way). On these sites, workers create permanent, searchable profiles for employers that containe rich, employment-relevant data about themselves.

With the rise of LinkedIn, we are witnessing an unprecedented, voluntary data collection and digitization of the supply side of the labor market. On LinkedIn, individuals can create public profiles and list their education, professional credentials, associations, skills, current and past work experiences and, critically, their other professional connections (indicated by approved links to other LinkedIn users).  As of yesterday (July 24th, 2012), approximately 19% of the US-based Internet using population had a LinkedIn profile [* see note below for interesting background for this 19% figure]. According to LinkedIn, as of March 12, 2012, over 160 million people have created profiles, and in many industries, a LinkedIn profile is expected of all applicants. I talked recently to oDesk's corporate recruiter, asking her how many candidates had LinkedIn profiles. She responded: 

I'd say it is close to 100% (and certainly 100% for viable candidates).   I can't think of an example of someone who I have screened who didn't have a profile on LinkedIn. 

I think this supply digitization is likely to prove consequential, because once the supply side of the labor market is digitized, platforms can begin making data-driven, highly contextualized recommendations to both sides of the market. The recommendations made by a platform can have the advantage of being potentially informed by the platform's holistic perspective on the marketplace. In computer-mediated marketplaces, by necessity essentially every piece of data that goes into or is generated by the marketplace is captured in an electronic database that could conceivably used to make recommendations. 

Of course, job board do try to make recommendations by suggesting vacancies to workers, but they are limited to conditioning those recommendations on whatever search terms and perhaps geographic and/or salary constraints a job-seeker enters in a relatively brief search session. The platform cannot condition its recommendations on a worker's employment history, educational background, skills, current employment status, professional connections, certifications, personality, test scores and other match-relevant factors, nevermind try to balance recommendations to navigate the twin shoals of market thinness and market congestion. 

Unfortunately, I think a lot of this work on recommendations will happen within companies in a state of semi-secrecy, but hopefully enough will be made public that others can contribute, ala the Netflix challenge. It's a little sad that to date society has expended more machine learning research effort trying to predict taste in moves rather than fit for jobs, despite the enormous welfare consequences of the labor market. However, I predict this will change and expect a lot more work on this topic from computer scientists and market designers in the coming years. 

[*] The Origin of "19% of the US Population has a LinkedIn profile" Number

In writing this blog post, I wanted to get an accurate number for what fraction of the US population has a LinkedIn profile. This number was proving hard to come by, so I decided to try a relatively new service launched by Google called Google Consumer Surveys. For 10 cents an answer, you can pose questions to a supposedly representative sample of US-based Internet users. You also get some of the respondent's basic demographics, such as inferred age, gender and income. I launched a one question survey and got 1511 responses in less than a day. The screenshot below shows the main results, but it also includes some neat tools for looking at the data in different ways. I made the survey public---check it out here. I'm quite pleased with the service and plan to use it again.  

Shrimponomics, Complements & BPOs

Most relevant image available from doing a
Google Image search for "Shrimp using a computer"

A few years ago, there was a Freakonomics post about how people reason about economic situations and phenomena. The phenomenon in question was shrimp consumption: the amount of shrimp people eat in the US per capita  tripled between 1982 and 2007. When asked to explain this rise, non-economists mainly give demand reasons (changes in preferences), while economists are more likely to also give supply reasons (improved fishing efficiency, rise of aquaculture etc.). 

If I had to offer an explanation for this focus on demand explanations, my guess it that demand explanations come more easily to us because it is the side of the market that is more familiar to us : most of us have eaten shrimp & bought shrimp---very few of us have worked in commercial fishing. So when asked "why are people consuming more shrimp?" we start with "why might I consume more shrimp?" and although price is certainly a reason (and a path of thought that would help lead to a demand explanation), it's not as salient or even as interesting as things like changing tastes, health trends, exciting new shrimp-based dishes etc.     

So this blog post isn't about shrimp and it isn't about supply & demand. It's about complements and substitutes. I think there is a similar psychological tendency to focus on goods-as-substitutes than goods-as-complements.  At the individual level where we are making choices, we are usually thinking in terms of substitutes: do I want coffee or tea? Should I take a vacation to Las Vegas or Hawaii? Mac or PC? It's a bit more subtle to think about "if I had X, would it make Y more useful to me" which is at the heart of all complementarity stories. 

This is a long-winded introduction to my real topic, which is that in my last blog post, I made the argument that online work could disrupt the BPO industry by serving as a substitute for what BPOs offer. A point I didn't think of---but in retrospect seems pretty obvious---is how just as easily complementarity could be the dominate effect. After my blog post, my CTO at oDesk, Odysseas, emailed me with his thoughts:  

The primary benefit of BPOs is not that of labor cost arbitrage. Thats typically the motive/benefit for offshore staff augmentation firms - but BPOs are business process outsourcers. BPO is ADP [Automated Data Processing] that outsources your payroll or a business that outsource your HR process etc... We often tend to think of BPOs as an offshore firm that does a little bit of everything having as sole pivot point its lower cost of labor - thats true, but its an abuse of the term and I would agree there that the particular type of business is going to be affected in the years to come from online labor.

This part is basically my substitutes story---now the complements part:

However, the more interesting effect would be the effect of online labor to the real BPOs..

There BPOs will not be negatively affected - the opposite. The availability of online labor would allow BPOs to become more flexible lower their overall fixed costs force them to become more automated and streamline (their virtual nature will require that), allowing them to lower even the cost per customer, allowing them to focus on smaller projects, smaller customers allowing to address smaller/different market segments.  They will become less relying on an enterprise sales force customer acquisition model which is dramatically affecting their cost structure.

We are seing examples of what the new BPOs will become in companies that outsource the process of testing (uTest) of seo writing (Mediapiston) etc.

He's of course exactly right---and he's a CS PhD, not an economist, so shame on me :). If you think of true BPOs in the sense that Odysseas is talking about, then the complementarity story becomes more important. These true BPOs would be big buyers in the inputs market and would benefit greatly from a liquid, efficient market for labor.

Will online labor markets disrupt the traditional BPO firm?

Today I spoke on a panel on something called "impact sourcing" at the BPO World Forum. The idea of impact sourcing, in a nutshell, is that online work is a tool for development and that for-profit firms outsourcing some part of their business should look beyond traditional BPO firms and consider non-profits like Samasource and Digital Divide Data. It was a good audience for this pitch, as many of attendees were CIOs from big companies that are accustomed to signing multi-million dollar IT outsourcing deals with the likes of traditional BPO firms like Wipro, Infosys, Tata Consultancy etc.

After the panel, I was at a reception where I talked to someone fairly high up in a traditional BPO. When I described my elevator pitch version of oDesk's business---clients post jobs, contractors make bids, clients make a hire, we intermediate the work and take a percentage---he said, literally "what are you doing here at this conference? You guys are like the Antichrist." What he meant (in a half joking, half serious way) is that oDesk and similar companies threaten the model of the BPO. 

My perception is that the traditional BPO model is possible because of two facts: (a) the enormous, purely placed-based differences in wages and (b) the difficulty of actually arbitraging those differences without help. BPOs stand ready to help companies reap the benefits of (a) by giving the help necessitated by (b). The word is still very far away from (a) no longer being true, but if oDesk and similar companies can radically lower the barriers to arbitraging differences by making it easy to hire, manage and pay workers regardless of geography, then (b) starts to become less true. If we get to the point where the qualitative differences of online remote and in-person work diminish and assessing and hiring workers is simple and easy, it would obviate the need for much of what the BPO firm is selling. 

This is not to say that there isn't still a huge space for IT consulting---outsourcing an entire process is hard and BPOs with lots of experience have something very valuable to offer. Furthermore, besides purely cost level, one of the motivations for business process outsourcing is ability to change cost structure, namely by turning a fixed cost into a variable cost. But these caveats aside, on the margin, the mediation aspect of the BPO role seems likely to get less attractive over time as technology improves and online labor markets mature.       

Resources for online social science

The Economist recently had an article about the growing use of online labor markets as subject pools in psychology research; ReadWriteWeb wrote a follow-up. If you've been following this topic, there wasn't very much new, but if you're a researcher that would like to use these methods, the articles were pretty light on useful links. This blog post is an attempt to point out some of the resources/papers available. This is my own very biased, probably idiosyncratic view of the resources, so hopefully people will send me corrections/additions and I can update this post.

To start, let's have this medium pay tribute to itself by running through some blogs and their creators. 

Blogs 

• There is the "Follow the Crowd" blog which I believe is associated with HCOMP conference. It's definitely more CS than Social Science, but I think it's filled with good examples of high-quality research done w/ MTurk and with other markets. 
• There's Gabriel Paolacci's (now at Erasmus University) "Experimental Turk" blog which was mentioned in the article and is probably the best resource for examples of social and psychological science research being done with MTurk.  
• Panos Ipeirotis (at NYU and who is now academic-in-residence at oDesk) has a great blog "Behind-enemy-lines" that's basically all things relating to online work 
• The defunct "Deneme blog" by Greg Little (who also works at oDesk) and Lydia Chilton (at University of Washington). 

Guides / How-To (Academic Papers)

A number of researchers have written guides to using MTurk for research. I think the first stop for social scientists should be the paper by Jesse Chandler, Gabriel Paolacci and Panos Ipeirotis:

Chandler, J. Paolacci, G. and Iperiotis, I. Running Experiments on Mechanical Turk,
Judgement and Decision Making (paper) (bibtex)

My own contribution is a paper with Dave Rand (who will still be starting as new assistant professor at Yale) and Richard Zeckhauser (at Harvard). The paper paper contains a few replication studies, but the real meat and the part I think is most important is the part discussing precisely why/how you can do valid causal inference online (I'm stealing this write-up/links of the paper from Dave's website):

Horton JJ, Rand DG, Zeckhauser RJ. (2011) The Online Laboratory: Conducting Experiments in a Real Labor Market. Experimental Economics. 14 399-425. (PDF) (bibtex)

Press: NPR's Morning Edition Marketplace [audio], The Atlantic, Berkman Luncheon Series [video], National Affairs, Crowdflower, Marginal Revolution, Experimental Turk, My Heart's in Accra, Joho blog, Veracities blog

Software 

Unfortunately there hasn't been too much sharing of software for doing online experiments. Since a lot of the experimentation is done by computer scientists who do not feel daunted by making their own one-off, ad hoc applications, there are a lot of one-off, ad hoc applications. Hopefully people know of other tools that are out there that the can open source / they can share links to.

"Randomizer"

Basically, it lets you provide subjects one link that will automatically redirect them (at random) to a collection of URLs you've specified.I made the first really crummy version of this and then got a real developer to re-do it so it runs on Google App Engine.



"QuickLime"
This is a tool for quickly setting up an Limesurvey (an open source alternative to Qualtrics & Surveymonkey) on a new EC2 machine. This was made courtesy of oDesk research. I haven't fully tested it yet, so as with all this software, caveat oeconomus.


"oDesk APIs"
There haven't been lot of experiments done on oDesk by social scientists, but there's no reason it cannot be done. While it currently is not as convenient or as low-cost as doing experiments on MTurk, I think long-term oDesk workers would make a better subject pool since you can more carefully control experiments, it's easier to get everyone online at the same time to participate in an experiment, there are no spammers etc. If you're looking for some ideas or pointers, feel free to email me.

"Boto"
This is a python toolkit for working with Amazon Web Services (AWS). It's fantastic and saved me a lot of time when I was doing lots of MTurk experiments.

"Seaweed"
This was Lydia Chilton's masters thesis. The idea was to create tools for conducing economics experiments online. I don't think it ever moved beyond the beta stage, but if you (a) have some grant money and (b) are thinking about porting z-tree to the web, you should email Lydia and see where the codebase is & if anyone is working on it.

Here's a little javascript snippet I wrote for doing randomization within the page of an MTurk task.  

People 

I'm not doing to try to do a who-is-who of Crowdsourcing, but if you're looking for some contacts of other people (particularly those in CS) who are doing work in this field, you can check out the list of recent participants at "CrowdCamp" which was a workshop prior to HCI.

History

Probably the first paper I'm aware of that pointed out that experiments (i.e., user studies) were possible on MTurk was by Ed Chi, Niki Kittur and Bongwon Suh.  As far as I know, the first social science done on MTurk was Duncan Watts and Winter Mason's paper on financial incentives and the performance of crowds.

The Innovation of StackOverflow

So as I write this, there is an egg timer ticking away next to me, set with 10 minutes of time. What am I waiting for? 10 minutes is how much time I predicted it would take to get my programming question answered on StackOverflow (SO):


http://stackoverflow.com/questions/10860020/output-a-vector-in-r-in-the-same-format-used-for-inputting-it-into-r

The back story was that I was writing some R code and I got to a point where I was stuck: there was something I wanted to do and I remembered that there was a built-in function that could accomplish my goal. Unfortunately, I couldn't remember that function's name. After some fruitless googling, I posted the question on SO.

So, how long did it actually take to get the right answer? About 6 1/2 minutes. As I write this sentence, I'm waiting for some more time to elapse so I can actually approve the answer:  

 

This has been my general experience with SO---amazingly high-quality answers delivered almost immediately. I feel sheepish that I haven't been able to answer as many questions as I've asked, but one of the animating ideas of the community is that asking high-quality, answerable questions is a way of contributing. 

What's interesting to me is that SO is an example of a primarily social---as opposed to technological---innovation. There's nothing really technically innovative about SO: the site is fast, search works well, tagging works well etc., but lots of sites have those things. What's special about SO is that through a carefully designed system of incentives and policies, they have created a community that is literally---and I think profoundly---changing how people program computers.  

 

The reason I point about the social nature of the innovation is that it's become popular to lament the shallowness or perceived frivolity of many start-ups that are built around social rather than technological innovations (e.g., Facebook, Twitter, Instagram etc.). The idea seems to be that if you aren't making solar panels or cancer-curing drugs, you're not doing something socially useful. I personally don't share that bias, but if we are going to judge companies on the basis of some more "serious" metric like productivity or social surplus, then SO is a great example how a purely social innovation can succeed spectacularly on those metrics.  

Data openness by private firms

The New York Times has a story today about social scientists working with company data and being unable or unwilling to make it public. The story begins:

When scientists publish their research, they also make the underlying data available so the results can be verified by other scientists.

I think the first sentence is probably more a description of how we'd like the world to be than how it actually is right now, especially in the social sciences. The main so-what of the story is that private companies are collecting enormous amounts of high quality data that lets you do fascinating social science, but companies are understandably reluctant to make this data public, primarily for privacy reasons (and probably also because they are afraid of giving up some competitive advantage).

I think the options for any organization that does or might do research are:

1) Do research for business purposes. Make neither the findings nor the data public.
2) Do research for business purposes. Make the findings but not the full data public.
3) Do research for business purposes. Make the findings and data public.  
4) Do research. Make findings and data public.

Most companies probably aren't interested in (4) and this is probably academia's biggest comparative advantage. Barring (4), I think from a social perspective, privacy issues aside, the best outcomes in order are (3) > (2) > (1).   I can understand (1) in some cases, but at least in the kind of companies I'm familiar with, the advantages of keeping everything secret probably aren't that great. 

The advantages of (2) or (3) over (1):  

a)  If you're a software company and you release a feature that works, it will probably get copied anyway, regardless of whether you publish a paper, so you might as well get the thought leadership credit for coming up with the idea in the first place. This paper is/was the basis for Google's secret sauce---posting it to the InfoLab servers back in 1999 didn't doom the company and probably did a lot to increase the perceptions that they were doing something smarter (even though there were antecedents of this idea going back many years---including in Economics, by my academic grandfather).  

b) If you give them access and them publish, you can get outside academics to work on your problems for free (the Netflix prize is an obvious example).  You can recruit those academics to come work for you, or at least get their grad students to come work for you. 

c) If you let your internal researchers publish, you can get them to work at reduced cost or get researchers you otherwise wouldn't be able to attract (see Scott Stern's paper on scientists "paying" to do science).

On (2) versus (3), I think there is a real dilemma: openness and privacy concerns are in tension. Furthermore, just releasing more aggregated or somehow obfuscated versions of the data is not risk free: there's actually an emerging literature in Computer Science on how to release data in ways that are guaranteed to still have the right privacy properties (CMU UPenn professor Aaron Roth recently taught a course on the topic). The fact that smart people are working on it is exciting, since they might figure out provably risk-free ways to release data publicly, but it's also evidence that this isn't a trivially easy problem---seemingly innocuous data disclosures would let someone unravel the obfuscation.  

As a coda, I have a personal anecdote to share about this story. One of the people discussed in the article is Bernardo Huberman: 

The chairman of the conference panel — Bernardo A. Huberman, a physicist who directs the social computing group at HP Labs here — responded angrily. In the future, he said, the conference should not accept papers from authors who did not make their data public. He was greeted by applause from the audience. 

When I was a grad student, I taught a course to Harvard sophomore economics majors called "Online Labor" (syllabus).  I assigned some of Huberman's papers on motivation. I emailed him to ask for the data from one of his papers. He wrote back: 

Dear Dr. Horton:
Thank you for your interest in my work and I certainly feel pleased when I learn that you liked my paper enough to assign it to your class.
As to your request, let me talk with the person who now handles the youtube data (we lately used it to uncover the persistence paradox) and I'll get back to you.
Incidentally if you are interested in the role that attention and status (its marker) play among people I could send you a paper that reports on a experiment (as opposed to observational data) that elucidates it quite cleanly across cultures.
Best,
Bernardo

I got the data within days---I can state that he privately practices what he preaches publicly.

Update: I incorrectly stated that Aaron Roth was a professor at CMU---he did his PhD at CMU. He's a professor at UPenn. Apologies.