Spare change?

Every weekday morning he stations himself on the sidewalk beside the park that lies right in front of the Main Complex of the World Bank in Washington DC. Even though there are other seemingly homeless people occupying the park, he’s usually the only one trying his luck along that strip of side walk in the morning, as though he’s been assigned a shift according to a custom among the park’s residents.

“Spare change?” yells the short shabby black man who greets me and everyone else who passes him on their way to work. This particular gentleman is well spoken, polite and seems capable. His park mates are much less so.

This request presents a dilemma to those of use working in the field of economic development. To give or not to give? The economist will opt for the latter, citing disincentives to work. And yet, what if there is no work available that they could do? And even there were, who would interview them, much less hire them?

I really don’t like giving money to pan handlers- the economist in me wins out. I think that if they can ‘earn’ enough by begging, they will continue to do so and not search for work. At the same time, some of these beggars have no hope of finding work.

To assuage my guilt for not giving, I propose an NGO with the following mandate: To offer the homeless and panhandlers clean up services (shower, shave, haircut), mental health service, interview counseling (how to speak interact with a potential employer), interview clothes, and job placement (and possibly training) services. The job training and placement could be geared toward taxi/truck driving, warehouse work, bricklaying, etc. There are services like this for the unemployed non-homeless, but I think the homeless need services tailored specifically for them. This NGO would hold the hand of a homeless person, taking him or her through the process to employability. It would be a one-stop-shop.

Now I don’t expect a huge uptake among the homeless for this kind of service. This is more for the benefit of people like me who don’t want to give money to someone when we can’t be sure that this person actually needs it, or will spend it appropriately. We could at least then justify our seeming callousness by donating to this NGO instead; we could then say - to ourselves, the panhandler and the world – that there are better options available to the homeless, which we support. Hopefully, it would have the added benefit of bringing some homeless people into the formal, productive economy.

Is the average DC man a jerk?

A good female friend of mine (lets call her K) recently made the following post on facebook: "Men in DC, Disaster..."

I think that there is a selection bias here that has contaminated the results of this experiment. Let us examine the average treatment effect as follows:

If we let: D equal the treatment indicator, where treatment is defined as romantic exposure of a DC guy to K; Y equal the outcome variable, which is K's happiness, then we can define the average treatment effect ATE as:

ATE = E[Y1-Y0] =
( αE[Y1|D=1] + (1-α)E[Y0|D=0] ) + ( (1-α)E[Y1|D=0]−(α)E[Y0|D=1] )

where the first term is the observed outcome: 
αE[Y1|D=1] + (1-α)E[Y0|D=0]

and the second term is the selection bias: 
(1-α)E[Y1|D=0]−(α)E[Y0|D=1].

So that:

(observed outcome) = ATE - selection bias

The first term in the selection bias is the expected happiness of K had she dated DC men that she did not date, and the second term is the expected happiness of K had she not dated DC men that she did date. α is the proportion of DC men that K did in fact date.

Within the selection bias term, I argue that the former term is likely much higher than the latter term, making the overall selection bias highly positive, thus leading to the very negative observed outcome despite the true average treatment effect (romantic exposure to DC men) being actually highly positive.

Why does the selection bias lead to such poor observed outcomes? Because the DC men that get romantic exposure to K are on average worst of all DC men (perhaps she tends to scare the good ones off?)

To eliminate the selection bias, K needs to do some RANDOMIZATION - i.e. she should date a random selection of men and then measure her happiness after each date. The results may very well redeem DC men in general.

Note that this research project is actually self funding (even the jerks will pay for dinner/coffee), which would help in a pitch to the MIT poverty action lab to run the experiment. Good luck to her!

An upgrade to growth diagnostics

One component of the growth diagnostics framework of Haussman, Rodrik, and Velasco involves examining enterprise surveys as part of the search for what is constraining entrepreneurship in an economy. However, as Ricardo Haussman emphasizes in his PED-309 lectures, these surveys will at best give you a clue as to factors constraining enterprises that currently exist, as opposed to factors that prevent enterprises that do not exist from existing in the first place (does anyone have suggestions on how to phrase this better?)

So, for example, surveying construction firms, textile manufacturers, and paint producers will not give you much insight as to why there is no domestic software industry. This is because the needs of the software industry are, for the most part, different from those of the construction, textile and paint industries. So then how do you assess constraints to industries that do not exist?

Traditionally, you look at what you think the requirements of these industries are and look at their shadow values or what else is going on with them. Since software is intensive in human capital, you would run a mincer regression on household survey data to look at returns to education. If they are high, you'd conclude that human capital is relatively scarce and so human capital might be a constraint. There is much more you would do, which may include looking at: the other requirements of a software industry; looking specifically at returns to education in high-tech fields, outward migration rates by specialization, etc. This is all very data intensive, rigorous and requires resourcefulness and creativity. Yet, in the end, how persuasive can your conclusions be? You're trying to find out, using indirect ways, why no software or other higher-productivity industry exists.

Why not try a more direct route, like surveying firms that do not exist?

Here's my contribution: why not survey decision makers in firms that have expanded operations outside of their borders and ask them the following:

1. Besides the country in which you chose to expand your operations, which other countries did you consider, and why did you decide against them?
2. Why did you not consider countries X, Y, Z, etc.?
3. How did you go about getting information that you needed to make your decision?

I think the answers to question #1 above will be the most helpful, even if they are at odds with reality. For example, suppose that Mr. Decider at Octopus Corporation says: we considered expanding to Jamalastan, but we didn't think we could find enough computer scientists. If there was indeed a scarcity of computer scientists, then its likely that you've found a constraint to at least one industry. If it turns out that there a many computer scientists driving taxis, then you've found a different constraint: misconceptions about the country.

For question #2, it is unlikely that you'd get answers that are the result of thorough investigations. But they still might be useful. Question #3 could yield interesting information in that it could tell you what firms consider reliable sources of information (e.g. Heritage Index, Corruption Perceptions, Doing Business Indicators, use of personal networks, conversations with local entrepreneurs and bankers). This could help local governments focus on a set of performance indicators.

I would like to see a survey of hundreds or perhaps thousands of firms every few years asking the above questions. The survey could include firms that have expanded domestically or made acquisitions domestically also.

What other questions would you add to such a survey?