It is difficult to measure poverty

Measuring poverty is very difficult. First, it is a relative concept and requires the definition of a standard or threshold. Second, as people are usually not normally distributed, any single measure misses some aspect of the distribution. Third, the item whose distribution is measured may not be the appropriate one to represent poverty. Most of the time this is income, but temporary low income is very different from permanent low income, and in both cases, purchasing power may differ dramatically on location, social policies and period. All these difficulties have lead to a plethora of poverty measures. In fact, if you look at the program of any economic inequality conference, there will be plenty of papers on new measures by authors hopeful that their names will stick to a new index or coefficient.

Walter Bossert, Satya Chakravarty and Conchita d'Ambrosio come up with a new measure that emphasizes the persistence of poverty. They are very careful in making their measure following three axioms: the measure corresponds to static poverty in the one period-case, a measure is worse is poverty spells are longer ans spells out of poverty are shorter, and two decomposability axioms too complex to describe here.

The measure they propose is a weighted sum of per period poverty measures, where weight are proportional to the current poverty spell. Using the European Community Household Panel, they find that their measure does not change rankings much whether poverty spell weights are used or not. But I bet they would change quite a bit for the US.

The iPhone must have an exclusive carrier

Aren't you angry that the particular mobile phone you prefer has an exclusive contract with a carrier? This limitation of carrier choice seems anti-competitive, if not frustrating. US anti-trust authorities seem to be getting interested in these arrangements and may intervene. It turns out that maybe they should not.

Robert Hahn and Hal Singer say exclusivity contracts are in fact the best thing that could happen for consumer welfare. Indeed, they spur competition through innovation, and the fact that the smart phone industry is innovative is hardly an understatement. Indeed, the exclusive contracts allow manufacturers to share the risk with the carrier, they make sure that both want the success of the new phone, and thus insure better reception and coverage. All this taken together induces manufacturers to take more risk and go for even faster and bolder innovations, which ultimately benefits the consumer.

Studying migration with experiments

One of the big difficulties with Economics is that one cannot perform controlled experiments like in the physical sciences. While experiments exist, they all have major drawbacks: they may be of too small a scale (although this does not deter medical research to have ridiculous samples), the stakes in the experiment may not be real-life-relevant, controls are to come by, or results may not be easy to generalize. And the larger the problem, the more difficult it is. Think macro-policy, for example.

So I am surprised to find a paper that reviews the literature on migration experiments. You cannot randomize people on whether they migrate or not, or where to. But, as David McKenzie and Dean Yang point out, visa lotteries are exactly that (and decisions of visa officers seem just as random to me...). But only few of those lotteries have been studied, Natural experiments are more common, but about migration per se, rather its impact. For example, the Asian crisis led sharp and unexpected changes in the value of migrant remittances, which impacted families at home. But, of course, one can have questions about how random the "assignment" is across migrants.

As to field experiments, again it is about the impact of remittances. McKenzie and Yang cite as an example a randomized trial where some migrants had access to a savings account in their home country in their name only. This experiment corroborated that migrants have a much stronger savings motive than their families back home. The survey also mentions a paper I discussed before about seasonal famines and migration in Bangladesh.

These are not the experiments I had first in mind, but an interesting introduction nonetheless.

Is the US a third world country?

Of course it is not. But for a European who sets foot on the continent for the first time, several observations would lead to this conclusion:

1. Urban ghettos, homeless people, trailer parks are certainly not a showcase of an advanced and rich civilization.
   
2. High criminality and violence.
   
3. Rampant corruption, which is even legal. Politicians can be bought openly, and few people see a problem with that.
   
4. Crumbling infrastructure. Roads are full of potholes, in particular urban highways.
   
5. Telephone and electricity are still supplied through overhead cables that are high in maintenance.
   
6. Cellphones cannot be used to pay in stores.
   
7. Credit cards do not have chips.
   
8. No sidewalks in many places.
   
9. A quarter of all homes still use septic tanks.
   
10. Idle police officers watching every construction site on roads.
    

Growth success in Africa: firms become smaller

How could one characterize a developing economy with little growth? Large informal sector, small firms, lots of red tape in the formal sector. As the informal sector typically has low productivity (before red tape), a typical prescription for growth is to move its activity into the formal sector. This can be achieved, for example, by reducing regulation in the formal sector.

Justin Sandefur looks at Ghana, which has recently experienced solid growth following some deregulation, and remarks that average firm size was halved over a 17 year period, while the share of the informal sector has increased. Using a manufacturing survey covering 1987 to 2003, Sandefur finds that aggregate growth did not come from firm growth, but from firm creation. These microenterprises stay tiny until they die, while the existing big firms stay as big.

While the growth experience of Ghana seems encouraging, one needs to realize that small informal firms stay small and informal. Thus once all entry opportunities have been used, growth will petter out.

Why so few drug innovations?

Research and development has an inherent tendency to have a decreasing growth rate. As the pool of things to discover continuously shrinks, it becomes harder to innovate. But we a groundbreaking discovery is made, this opens a lot of new opportunities and one should see a lot of new innovation. But with molecular biology and genomics, the pharmaceutical industry should have seen a burst of innovation, and in particular a jump in innovation productivity. Yet the contrary happened. One argument could be similar to the one that has been made about the productivity slowdown of the seventies, that an groundbreaking innovation like information technology needs time and resources to be understood.

Fabio Pammolli, Massimo Riccaboni and Laura Magazzini claim that this effect is very important. They observe that all the low hanging fruit have been picked in pharmacology and that first have shifted their investment portfolio towards more difficult problems. They suggest that one particular reason to do so is that improving current drugs is not profitable as generics are close substitutes and little rents can be extracted. Thus new classes of molecules are sought.

I would add another development in the field of R&D in general. It has become increasingly difficult and costly to file patents, as the field is littered with "predators" who file vague patents to prevent other from innovating, or to claim royalties. Not only does this increase the cost of innovating, it also increases its uncertainty, as any discovery can be subject to litigation even if it was a genuine discovery. This also encourages laboratories to find new molecules that are much different from existing ones.

The Great Depression: demand or supply shocks?

The fact that we are in a big recession has renewed interest in the Great Depression, and this has revived the questions about its origin. In particular, the eternal question on whether demand or supply shocks have driven it is back.

This time it is asked by Mark Weder, who runs a horse race between tow versions of a real business cycle model: one with only shocks to total factor productivity (supply shocks) as measured by Solow residuals, one with only preference shocks (demand shocks), measured as residuals of an Euler equation. The latter, though, are not associated with monetary or fiscal variables. Both types of shocks are the evaluated in their ability to forecast what happened to GDP, and none is a clear winner.

But is this really the best one could do? Clearly the models are way to simple to 1) forecast anything, 2) to capture the changing policy environment during this period, as highlighted by Milton Friedman, Anna Schwartz, Harold Cole, Lee Ohanian and many others. Also, the relative importance of the two shocks may have shifted over time, something that would have been worthing looking at.