It’s Just Like Telling a Joke

From the O*Net database, i learned something today — oral skills presentation is the most important attribute in an economist is oral communication. We joked about this in seminar today, but in a sense its true. Once you’ve written a paper, you must explain it to people. 

This reminds me of a joke told by Conan O’Brien when he was on Leno’s show. Conan shares something he learned from Stan Laurel (Laurel and Hardy) about telling a joke: ”Always do this. Tell the audience what you’re going to do. Do it. And then tell them it has been done.”

Tell them what you are going to do! How is the paper related to other papers? Lots of people ask this question. You want to reassure people that your work is part of the larger story of scientific advance. This is also a practical thing as well: when you want to come up with new work, you look at whats been done and think of ways to extend them.

 Do it! What is the motivation? Really good papers are of the small model kind — present stylized facts and construct a model as a way to interpret the data, and then test your interpretation. Its good to have some kind of empirical regularity at the heart of your paper. There are exceptions, such as pure theory or pure empirical/econometric work.

Tell the its been done! What is the intuitive interpretation of your model? Relate your model to the motivation you were talking about earlier, and to the results of the papers that inspired you that you copied from.

Monopolistic competition

A quick, basic overview of monopolistic competiton. Lets say there are many firms in a country, each producing a distinct variety. Firms can enter and exit freely, depending on whether they are making money. The representative consumer loves to consume all the different varieties available. For each variety available, the consumer has a demand curve. Whats key is that, for Mr. consumer, each variety has the same elasticity of substitution — preferences between good 1 and 2, are the same as good 1 and N. The demand curve is the same for all varieties. We further assume that , the elasticity of demand falls as consumption for the variety rises, which is true for linear demand curves.

On the production side, we introduce increasing returns to scale, where AC is everywhere above MC, i.e. we assume a fixed cost of production. As the firm produces of a given variety, Average Cost falls.

Dynamic Products in World Trade

Paper by Mayer, Butkevicius, Kadri and Pizarro, called “Dynamic Products in World Exports”. Its an interesting overview article about trade growth from 1980 to 2000 (henceforth, the long-term). The article asks which goods (SITC 3) have been “Dynamic Products”. The adjective dynamic needs to be defined precisely and they do so via an comprehensive Index of dynamism that includes long term annual export value growth, short term growth, volatility of long term growth and growth of the share of the product over the long term. Largely,  product dynamism is defined as average annual export value growth.

Dynamic Products, defined by long-term growth:

• SITC 75-77: Electrical and electric goods
• Textiles and labor intensive manufactures, SITC 61,65, 84
• High R&D industries other than electrical and electric goods, SITC 5, 7 (other than 75-77), 87
• Primary comodiites, cereals SITC 04, non-alcoholic beverages SITC 11

Dynamic Products, using the Comprehensive Index

• Top 4 product groups are electronics
• 8 of 20 are high R&D groups (not electronics)
• textiles and clothing, no primary commodities
• only in sitc 846 that the share of developing countries exceeds that of developed countries

Predictability

• defined as the variation in export value growth explained by AR1
• fast growing products are more predictable

Market Share Concentration

• More dynamic products exhibit lower levels of concentration, using number of countries via an HHI measure

De-Globalization

I have to make sure to link to articles by James Hamilton on De-Globalization. Here is one in the NYTimes.

Cheap oil, the lubricant of quick, inexpensive transportation links across the world, may not return anytime soon, upsetting the logic of diffuse global supply chains that treat geography as a footnote in the pursuit of lower wages. Rising concern about global warming, the reaction against lost jobs in rich countries, worries about food safety and security, and the collapse of world trade talks in Geneva last week also signal that political and environmental concerns may make the calculus of globalization far more complex.

Underestimated

I made an error with the bilateral data yesterday and underestimated the number of observations in my project. I have 150 countries, so in a bilateral dataset, thats 150*150-150=22,350 country pairs. each pair can theoretically export 772 goods, which means my bilateral trade dependent variable for one year is 17.2 million. I have 5 years of data which means i should have 86.2 million observations. wow…

Country extensive margin

Country extensive margin — how important is it? from HMR, probably not very. I should investigate this later tho…

Structural Changes

Last batch of regression replications. The magnitudes are a bit different from the big paper, but the signs are correct. Basically, the product space variables can predict what goods a country exports…

Its too big…

As part of my empirical prep, i tried to (roughly) replicate one regression in this WP. Trying to replicate should be part of every program, and i am saddened at the fact that i was not asked to replicate in any of our classes. At the very least, i was able to confirm the basic message of the paper, while at the same time question my own understanding and exercise some old fashioned doubt.

The point: each good has a ‘value’ associated by the development of the countries that specialize in them. The ‘value’ of the country’s export basket is called ExpY. The theory says that if you look at value of the export basket of a good is high, the country will exhibit subsequent economic growth.

The first practical lesson is that i coded the log of ExpY incorrectly. The numbers were too huge compared to the paper. The correct range should be from 7 to 10, in natural log scale. the regression coefficient, controlling for log of initial income is 0.4 — since a standard deviation of lnExpY is around .4 also, a country moving 1 standard deviation up will experience an increase of 0.16 in its growth rate, (simple growth rate).

Looking at the graph, you may this is making a mountain of a molehill, but the y axis is squeezed– a value of 2 is basically a doubling of real GDP per person (source, PWT 6.2). To make a more attractive graph, i log transformed growth, and the fitted line is more noticeable upward sloping:

Replication

Using sitc4, the exporter and import fixed effects are strange, but using the shared data, the specification works…

With the original data, the signs of the regressors are sometimes wrong, if we don’t include exporter and importer dummies. This implies inherent heterogeneity in the data in terms of average trade volume. That makes total sense.

UPCs

Broda and Weinstein’s new paper on international price differences tells us the source of country price dispersion between US and Canada

They study this via UPCs obtained from AC Neilsen. The first key result is that UPCs sold in BOTH the US cities and Canadian regions follow the LOOP relatively well. The other key result is that common UPCs is quite small. For the US city pairs, the highest pair with w NY-Phili, with only 30% in common. The further the city pairs are to each other, the lower the fraction of common UPCs are: about 15% for LA-NY.

For US-Canada pairs, the slope of the distance-common UPCs line is flat — no matter what the distance is, the range is from 5% to almost 10%.

For Canada pairs, the range is wider (from 33% to 55% common UPCs), and the minimum is much higher than the maximum of the US city pairs.

These results are consistent with higher price variation within the US vs Canada. Broda/Weinstein say that the fact that common UPCs are smaller for cross-border pairs is the key reason why we observe deviations from LOOP in aggregated prices cross border. This is the result of Tesar and Gorodnichenko also, although they don’t know why the variation would be different.

Broda-Weinstein move this debate one step forward — now we know why Canada deviation is different from US variation in prices. Its about what UPCs are sold where. The next question is — why is product variety so different? One guesses that this is about retailers and manufacturers price discriminating via changing the goods characteristics. Examples: eggs can be large, medium and small, come in several pack sizes, free range or not, etc. BW report that fresh eggs have 2,275 possible varieties, while the ‘typical’ group has about 2,700 varieties.

Also, among US cities, the composition of goods is different, and is more different the further away, the more different it is. For Boston-LA, about 2,700 miles, the common UPCs are only 17%, while for Boston-NY, distance of 230 miles, the common UPCs are 24%. What is the source of this non-integration/State-City Bias?