Archive for the ‘Measures, Statistics & Technicalities’ Category

Did AGOA work? Identification and export incentives

Sunday, August 15th, 2010

The former USTR-Africa who designed the African Growth and Opportunity Act (AGOA) preferential trade scheme declares it a “phenomenal success“:

Rosa Whitaker: I think it’s been a phenomenal success. Has it been a panacea for everything in Africa? No, it wasn’t designed to do that. But if you look at the return on the investment, it’s been amazing. It costs the American taxpayer very little – about $2 million a year. In under a decade, exports from AGOA-eligible countries grew over 300% from $21.5 billion in 2000 to $86.1 billion in 2008…

AGOA helped develop an automobile industry in South Africa. In 2000, that industry was exporting about $148 million; it has increased to $1.9 billion in 2008. Car parts exported to the U.S. had an 18-25% tariff. When those tariffs came off for Africa, the assembly part of that manufacturing process moved to South Africa. There are plenty of other examples. Lesotho was exporting $139 million in apparel in 2000; now it’s over $340 million: a 143% increase. Kenya’s cut flower industry expanded from $34 million in 2001 and to exports over $240 million now. Swaziland was exporting $85,000 in jams and jellies in 2000; today it’s $1.6 million. For a small country like Swaziland, that’s important. Then you have Tanzanian coffee and other products. I could go on and on.

Policymakers frequently evaluate programs using this approach — they compare circumstances before and after legislation passed and judge the program based on the difference in outcomes over time. But of course, correlations aren’t very informative about causal relationships.

Economists are interested in the counterfactual — what impact did the program make relative to what would have happened without the program? The most obvious problem with a before-and-after comparison is that steady growth creates improvements over time, regardless of policy changes. For example, Singapore’s Business Times touted that US-Singapore trade had grown nearly 20% since the US-Singapore preferential trade agreement took effect, but US-Malaysia trade grew the very same amount during that period without any US-Malaysia PTA.

Similarly, telling us that African export volumes grew from 2000 to 2008 isn’t very informative, because we naturally expect exports to grow over time as economies grow. (If one wants to suggest that AGOA encouraged greater African openness, the appropriate measure would be the exports-to-GDP ratio.) Identifying the causal impact of AGOA requires a method that distinguishes the increase in exports due to the trade preferences from the counterfactual scenario. (A 300% increase in exports is big, so I’m not suggesting that AGOA necessarily had zero impact. The question is: what share of the increase was due to AGOA?)

In such circumstances, economists often turn to an identification strategy known as “differences in differences“. This involves comparing differences across countries in their differences across time. For example, only some African nations are AGOA-eligible. If African economies receiving preferential tariff treatment from the United States experienced export volume growth that was faster than export volume growth in ineligible economies, we might think that this suggests that AGOA spurred greater exports. However, such a comparison doesn’t constitute valid causal inference in the case of AGOA, because AGOA eligibility was determined according to governance and policy criteria that likely make a difference in economic and export growth. Countries with characteristics making them AGOA-eligible may grow faster than their neighbors due to those characteristics, even without any preferential market access.

Paul Collier and Tony Venables tackled this by taking what is akin to a differences-in-differences-in-differences approach: they looked at the value of a country’s apparel exports to the US relative to its apparel exports to the EU (World Economy, 2007). The thrust of their story is captured by their Figure 1:

Collier & Venables (2007) Figure 1.

Collier & Venables (2007) Figure 1.

African apparel exports to the US increased dramatically faster than such exports to the EU in the early 2000s (even though the EU’s Everything But Arms initiative, which is similar to AGOA, launched in 2001). Collier and Venables also present econometric results in which AGOA apparel eligibility is associated with significantly greater relative exports to the US. A glance at the data on South African automobile exports also suggests that Rosa Whitaker’s story is meaningful in comparative terms: auto exports to the US jumped while exports to the UK and Germany fell slightly.

Period Trade Flow Reporter Partner Code Trade Value
2000 Export South Africa Germany 87
$538,728,295
1
2000 Export South Africa USA 87
$190,767,522
1
2000 Export South Africa United Kingdom 87
$158,073,103
1
2008 Export South Africa USA 87
$1,867,615,402
1
2008 Export South Africa Germany 87
$485,841,841
1
2008 Export South Africa United Kingdom 87
$139,980,048
1

Yet such evidence need not imply that AGOA caused a significant increase in exports by eligible countries. The AGOA trade preferences raised both the incentive to export and the relative incentive to export to the US. It is possible that AGOA-eligible countries would have experienced significant export increases even in the absence of the preferential program and the tariff advantages of AGOA only induced them to direct their sales to the US instead of the EU. Such a claim is compatible with the two pieces of evidence discussed thus far: (1) African exports to the US increased significantly after AGOA came into force and (2) AGOA-eligible economies export more to the US relative to the EU.

Collier and Venables (2007) and Frazer and Van Biesebroeck (2007) address such concerns to some degree. For example, the latter show that:

The impact of AGOA on E.U. imports is in column (6). The effects for most product categories are not significantly different from zero. Perhaps surprisingly, where the effect is significant, it is positive. For example, E.U. imports of GSP-Manufactured products, are found to increase by 4%. A potential explanation (among many) could involve spillover effects from the increased U.S. imports.

Note that though this evidence makes the alternative story about export diversion suggested in my previous paragraph rather unlikely, it cannot completely rule it out (perhaps the relative magnitudes aligned so that the size of the total export increase offset the change in relative shares, leaving exports to the EU constant). This demonstrates one of the difficulties of doing causal inference in a non-experimental setting. We have highly suggestive evidence, but, with enough effort, one can conceive of an alternative explanation.

So was AGOA a success? Probably. Economists have both theoretical reasons to expect it would improve African exports and empirical evidence that suggests that it did. Policymakers and other commentators would be more persuasive if they cited comparisons (in the spirit of Figure 1 from Collier and Venables) rather than just presenting the time series of US imports from Africa – say something like “AGOA-eligible countries’ exports to the US  grew 300% in the last eight years, substantially more than their exports to Europe”. Better (if imperfect) efforts at identifying the counterfactual distinguish the studies analyzing AGOA from meaningless statistics cited in support of other trade policies.

[I've tried to informally convey some ideas about empirical identification issues in the context of AGOA. For a proper introduction to the topic, start with a paper or book that mentions the Rubin causal model, such as Angrist and Pischke's Mostly Harmless Econometrics or Imbens and Wooldridge (JEL, 2009).]

How big is China?

Monday, April 12th, 2010

If you want to think about urban bias in price indices, index number problems, measuring GDP from the expenditure side vs the output side, and the relative size of nations, check out “How Big is China? And Other Puzzles in The Measurement of Real GDP” by Feenstra, Ma, Neary, and Rao.

“Terms-of-Trade Gains, Tariff Changes, and Productivity Growth” (NBER 15592)

Sunday, April 4th, 2010

The NBER Digest on the work of Robert C. Feenstra, Benjamin R. Mandel, Marshall B. Reinsdorf, and Matthew J. Slaughter:

In the past decade, the U.S. economy clearly enjoyed faster productivity growth than in previous time periods. The authors suggest that the magnitude of this acceleration has been overstated, with a sizable share of the gains actually being accounted for by the benefits of international trade. Their findings indicate that from 1995 through 2006, the actual average growth rates of the price indexes for U.S. imports are 1.5 percent per year lower than the growth rate of price indexes calculated using official methods. Thus, properly measured terms-of-trade gains can account for close to 0.2 percentage points per year, or about 20 percent, of the apparent increase in productivity growth for the U.S. economy over this period.

ISO country codes for USITC DataWeb output

Thursday, March 18th, 2010
The US International Trade Commission’s Interactive Tariff and Trade DataWeb provides detailed data describing aggregate trade flows between the United States and other economies. It reports country names without reporting a country code. Many data sources (e.g. the IMF’s World Economic Outlook database) report both a country name and a standardized ISO three-letter country code. Due to the use of unofficial names (Burma vs Myanmar, East Timor vs Timor-Leste, etc) and incon- sistent formatting (“Grenada Island” vs “Grenada”, “Saint” vs “St” vs “St.”, etc), merging using country names rather than standardized country codes is unreliable.
I’m making available a correspondence between USITC DataWeb country names and ISO country codes that I built in the course of my research. You can download it as a tab-delimited text file and a Stata data file from my website. The (very brief) documentation is here.

The US International Trade Commission’s Interactive Tariff and Trade DataWeb provides detailed data describing aggregate trade flows between the United States and other economies. It reports country names without reporting a country code. Many data sources (e.g. the IMF’s World Economic Outlook database) report both a country name and a standardized ISO three-letter country code. Due to the use of unofficial names (Burma vs Myanmar, East Timor vs Timor-Leste, etc) and inconsistent formatting (“Grenada Island” vs “Grenada”, “Saint” vs “St” vs “St.”, etc), merging using country names rather than standardized country codes is unreliable.

I’m making available a correspondence between USITC DataWeb country names and ISO country codes that I built in the course of my research. You can download it as a tab-delimited text file and a Stata data file from my website. The (very brief) documentation is here.

The schoolboy error that will not die

Tuesday, March 9th, 2010

In a special report on managing information, the Economist writes that Wal-Mart’s “revenue last year, around $400 billion, is more than the GDP of many entire countries.”

This is an apples-to-oranges comparison that means nothing. GDP measures value-added. Revenue measures gross value. Please never print such a comparison again.

Martin Wolf tackled this in a FT column in 2002. Jagdish Bhagwati took it on in In Defense of Globalization in 2004. And Paul De Grauwe and Filip Camerman even devoted 15 pages to measuring the size of companies correctly. Yet this “elementary howler” keeps rearing its head, time after time.

Addendum (22 March): My very brief letter to the Economist on this point appeared online.

Ravallion on Pinkovskiy and Sala-i-Martin

Monday, March 8th, 2010

Martin Ravallion is open to the idea that African poverty has been improving to the last 15 years, but he is cautious regarding the quality of our data and methods:

Maxim Pinkovskiy and Xavier Sala-i-Martin (PSiM herafter) have confidently claimed that “The conventional wisdom that Africa is not reducing poverty is wrong” and that “African poverty is falling and is falling rapidly.” This sounds like good news. But is it right?

We must first be clear about what we mean when we say “poverty is falling”. What many people mean is falling numbers of poor. However, PSiM refer solely to the poverty rate—the percentage of people who are poor. (There is no mention of this important distinction in their paper.)…

Here we agree: aggregate poverty rates have fallen in Sub-Saharan Africa (SSA) since the mid-1990s.  Shahoua Chen and I came to exactly the same conclusion in our research, for the World Bank’s global poverty monitoring effort, although our methods differ considerably and (no surprise) I prefer our methods.

However, Chen and I also point out that the decline in the aggregate poverty rate has not been sufficient to reduce the number of poor, given population growth…

Two points to note here: (i) Chen and I show that the poverty decline in SSA tends to be larger for lower poverty lines (in the region $1-$2.50 a day) and (ii) PSiM’s method attributes the entire difference between GDP and household consumption to the current consumption of households, and they assume that its distribution is the same as in the surveys. These assumptions are very unlikely to hold, and they give an overly optimistic picture.

In effect, PSiM are using a lower poverty line than us…

PSiM do not tell readers just how few survey data points they have actually used after 1995. Indeed, readers of their paper may be surprised to hear that there is any uncertainty about the trend decline since the mid-1990s; their main graph has 30 annual data points since 1995. But these are not real data points in any obvious sense; rather they are synthetic (model-based) extrapolations based on national accounts and growth forecasts.

We have national household surveys for all but 10 of the 48 countries in SSA since 1995. However, for only 18 countries do we have more than one survey since 1995; for 30 countries, there are is at most one survey since 1995.

As we warn explicitly in our paper, this is not yet sufficient survey data to be confident about the (promising) downward trend for Africa’s aggregate poverty rate that PSiM have announced with such confidence.

Hopefully we will see a confirmation of the emerging downward trend for Africa in the years ahead, as more (genuine) data emerge.

HT: Larry W-S.

Addendum: Blattman beat me to it and has more thoughts.

Measuring protectionist actions during the crisis: What’s the counterfactual?

Friday, January 15th, 2010

Dani Rodrik:

The GTA’s latest report identifies no fewer than 192 separate protectionist actions since November 2008, with China as the most common target. This number has been widely quoted in the financial press. Taken at face value, it seems to suggest that governments have all but abandoned their commitments to the World Trade Organization and the multilateral trade regime.

But look more closely at those numbers and you will find much less cause for alarm. Few of those 192 measures are in fact more than a nuisance. The most common among them are the indirect (and often unintended) consequences of the bailouts that governments mounted as a consequence of the crisis. The most frequently affected sector is the financial industry.

Moreover, we do not even know whether these numbers are unusually high when compared to pre-crisis trends. The GTA report tells us how many measures have been imposed since November 2008, but says nothing about the analogous numbers prior to that date. In the absence of a benchmark for comparative assessment, we do not really know whether 192 “protectionist” measures is a big or small number.

Finicelli, Pagano, and Sbracia: “Trade-revealed TFP”

Monday, January 4th, 2010

To the extent that you’re willing to believe in a particular model, you can pull off some interesting exercises, such as “trade-revealed TFP“:

We introduce a novel methodology to measure the relative TFP of the tradeable sector across countries, based on the relationship between trade and TFP in the model of Eaton and Kortum (2002). The logic of our approach is to measure TFP not from its “primitive” (the production function) but from its observed implications. In particular, we estimate TFPs as the productivities that best fit data on trade, production, and wages. Applying this methodology to a sample of 19 OECD countries, we estimate the TFP of each country’s manufacturing sector from 1985 to 2002. Our measures are easy to compute and, with respect to the standard development-accounting approach, are no longer mere residuals. Nor do they yield common “anomalies”, such as the higher TFP of Italy relative to the US.

Via Agent Continuum.

Pierce & Schott: A Concordance Between HTS & SIC/NAICS

Tuesday, December 1st, 2009

This paper looks like it may be helpful to applied empirical researchers:

This paper provides and describes concordances between the ten-digit Harmonized System (HS) categories used to classify products in U.S. international trade and the four-digit SIC and six-digit NAICS industries that cover the years 1989 to 2006. We also provide concordances between ten-digit HS codes and the five-digit SIC and seven-digit NAICS product classes used to classify U.S. manufacturing production. Finally, we briefly describe how these concordances might be applied in current empirical international trade research.

Measuring distance

Wednesday, November 11th, 2009

Neat:

The CEPII has built and made available two datasets providing useful data for empirical economic research including geographical elements and variables. A common use of these files is the estimation by trade economists of gravity equations describing bilateral patterns of trade flows…

Distance calculation requires information on geographical coordinates of at least one city in each of the country. The simplest measure of geodesic distance considers only the main city of the country, reported here with the English and French names, latitude and longitude. In most cases, the main city is the capital of the country. However, for 13 out of the 225 countries, we considered that the capital was not populated enough to represent the “economic center” of the country. For these countries, we propose the distances data calculated for both the capital city and the economic center…

There are two kinds of distance measures: simple distances, for which only one city is necessary to calculate international distances; and weighted distances, for which we need data on the principal cities in each country. The simple distances are calculated following the great circle formula, which uses latitudes and longitudes of the most important city (in terms of population) or of its official capital. These two variables incorporate internal distances based on areas provided in the geo_cepii.xls file. The two weighted distance measures use city-level data to assess the geographic distribution of population inside each nation. The idea is to calculate distance between two countries based on bilateral distances between the largest cities of those two countries, those inter-city distances being weighted by the share of the city in the overall country’s population.