Tuesday, October 20, 2015

Economic history

How the study of history affects economics research field?

The study of the past was neglected by economists in the early 80’s. Deirdre McCloskey surveyed the use of economic history in the 70’s and 80’s and he found that economists “believe history to be of small and diminishing interest”, he concluded that the average American economist answers “no” to the question “Does the past have useful economics?” McCloskey showed a sharp decline in the publication of economic history papers in the top economic journals (AER, QJE, JPE).

Today, economic history has reverted that trend and, although still small –compared to other fields, it has become a relevant field of study according to most of research economists.

Ran Abramitzky published last month a paper about the use of economic history by economists. He found that the top 5 economic journals have increased their share of papers related in one way or another to economic history.

This idea that the past influences the present in a path dependence process is now widespread. Many economists argue that a process of path dependence often takes place in the modern world: norms and expectations impede change, discrimination survive even in highly competitive markets, and change can be very slow.

Another point Abramitzky makes in his paper is the problem with endogeneity in economic history. Identification of a causal effect is a main challenge for economic history but that’s no different from applied economics. Over the last two decades researchers have started to take causal identification more cautiously, changing their language to more lightly words that imply but not state causality such as “effect”, “impact” and “influence,” and only claiming causality when a random or quasi-random variation is established.

Economic history has increased the use of data and quantitative tools to solve the endogeneity problem. The classic example is the AJC 2001 paper.

Abramitzky ends quoting Arrow “it will always be true that practical understanding of the present will require knowledge of the past.”

Saturday, September 5, 2015

Race against the machine

The global labour markets are constantly changing and the key demanded skills change with those. In the latest years we have seen a rising concern about the role of computers in the labour market. We have obviously seen these before (e.g. Luddites) but many say this time might be different. Machine learning and computer learning is replacing human cognitive tasks of rapidly increasing complexity.

These pattern has been analysed by many, among those; Frey and Osborne in 2013 , Wolff in 2005, and Benzell et al.

A new academic paper published in early August by David Deming tries to identify the recent trends on skills’ demand and builds a theoretical model that predicts the changes since the 80’s. It's a compelling story. The most interesting finding in the paper is the following:

“Nearly all job growth since 1980 has been in occupations that are relatively social skill-intensive. Jobs that require high levels of analytical and mathematical reasoning but low levels of social interaction have fared especially poorly.”

The argument goes like this: as more computers replace human analytical power the relative value of social skills become more important. Computers are still very poor at simulating human interaction. Reading the minds of others and reacting accordingly seems to be a very complex thing to do.

Sunday, August 9, 2015

Peru’s mining mita

Reading the notable book from Acemoglu and Robinson, "Why nations fail" I came across the concept of mita and the effect of it on economic growth. Mita was an extensive forced mining labor system in effect by the Spanish Empire in some regions of today´s Peru and Bolivia between 1573 and 1812. Fascinatingly, a paper wrote in 2011 by Melissa Dell (or here) found that:
Regression discontinuity results indicate that a mita effects lowers household consumption by 25% and increases the prevalence of stunted growth in children by around 6% points. Mita’s influence has persisted through its impacts on land tenure and public goods provision. Today, those regions are less integrated into road networks and their residents are substantially more likely to be subsistence farmers and with lower educational attainment.
Undoubtedly, it’s been known that historical institutions are key on current economic outcomes. This paper not only confirms this statement but finds the routes through which those institutions can have this persistent effect on growth. Aka public goods provision and land tenure. The key element of this research is that the mita required indigenous communities to send a constant share of their adult population to work in the silver and mercury mines and conscripts changed discretely at the boundary of the subjected region: on one side, all communities sent the same percentage of their population, while on the other side, all communities were exempt. This is probably an arbitrary and short informed decision of the regional government ef the Spanish Empire that allows the researcher to use as an external shock.

Friday, July 31, 2015

Machine learning visual experiment

R2D3 is an experiment in expressing statistical thinking or machine learning with interactive visual design. In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.
In this case the authors Stephanie and Tony design  a machine learning process in order to know if a house with unknown location is from San Francisco or New York based on other parameters such us altitude, price, sq feet,…

In most cases they seem to apply a logistic regression where high or low values of one variable such as high altitude are more likely to be from one city rather than the other. By sequentially applying this process with all variables and using recorded data the authors can get a very accurate estimate of location for any given house, provided the explanatory variables are available.

Saturday, July 4, 2015

Coloured time series

Here comes an idea (I saw it in Flowingdata.com) to graph many time series together in a well-designed and simple way.

The thing is that when you try to put many graphs together, the Y-axis becomes too short and changes are really hard to appreciate.

These kind of graphs can be converted into colour graphs which are much easier to understand. The Y-axis is fixed to a given level for all graphs. If one of the time series goes above that level, a darker area start at that point from the Y=0. Negative numbers can be drawn as reddish.

It is much clearer when a time serie is up or down by looking at colours.

Monday, June 15, 2015

Determinants of Trade in Parts and Components

There is a broad agreement that growth in world trade has outpaced the growth in global output in recent decades but the key aspect in global trade is production fragmentation. The production process has been split in an increasing number of micro-processes that rises the interaction between the different members internationally, giving rise to a sharp increase in global trade.

The question is, What factors determine trade in parts and components? This is the focus of a recent study.

Theoretically, each stage of the production process should be allocated following the comparative advantage of each country.

Empirically, the author selected parts and components trade data for 26 countries and from 1990 to 2010. From their regression results, they found that trade of parts and components are positively and significantly related to the size of the economy as well as the quality of labour. Second, infrastructure and institutional qualities matter. Third, trade in parts and components are at least somewhat different from trade in all goods. Trade in parts and components are much more sensitive to the quality of labour. In addition, the extent of the production network is responsive to a narrower subsets of measures of quality of infrastructure/institutions.

Once again human capital and institutions are the key to trade and output growth.

Tuesday, May 26, 2015


I found quite amusing to discover several papers about forecast performance and the ability to estimate future events.

It has repeatedly said that forecasting shouldn’t be an economist job. Future is unforeseeable for
everyone and none of the economist tools can help there.  Regardless of what is said, many of us
can’t stay away from forecasting. Two recent papers have been analysing the forecasting power of different people.



Aparently being open-minded, a knowledge on probabilistics, interacting with others and discuss your forecasts with other analysts increases significatively your chances of being right.