
Interview with Pinelopi Goldberg: Trade and Development
David A. Price serves as interlocutor in an “Interview” with Pinelopi Goldberg, subtitled “On developing countries, measuring economies by satellite, and the learning crisis” (Econ Focus, Federal Reserve Bank of Richmond, Winter 2022, pp. 22-26).
Here are a few of the points that caught my eye.
Why are COVID death rates so much lower in low-income countries?
EF: In work with Tristan Reed, you have found that COVID-19 deaths per capita were actually much lower in poorer countries than in richer ones. This seems surprising. What happened?
Goldberg: Tristan and I presented this research at a Brookings conference in June 2020 with great trepidation, because that was near the beginning of the pandemic. Most people’s reaction was that this result was just because poor countries are not connected, so COVID-19 had not arrived there yet. But there was anecdotal evidence that COVID-19 had indeed arrived there. Most capitals of low-income countries are not as isolated as people think; many of these cities are global cities. They are connected to the rest of the world. So it was surprising that the deaths were so low.
Another reaction was that this was all measurement error. … But the differences in deaths are huge — orders of magnitude apart. Just to give you one striking example, in the United States right now, the deaths per million are around 2,500. In Nigeria, the number is 14; in India, it’s 340. And it’s not easy to hide deaths. Yes, there is measurement error — probably deaths and hospitalizations are much higher in low-income countries than the statistics show — but still, there is a big difference between low-income countries and richer ones.
I think there are three reasons at work. We pointed out two of them in this initial working paper. First, everyone agrees that two of the risk factors for a serious reaction to COVID-19 leading to hospitalization and death are age and obesity. The age distribution in many low-income countries is very different from that in the United States. To mention a striking case, in Niger, the median age is 15; there, COVID-19 would probably not have very severe health effects on the population. On top of that, in low-income settings, obesity is much lower. These two factors alone could explain a lot of the difference.
In addition, many epidemiologists talk about what they call “trained immunity” for low-income countries. The idea is that people in those countries are exposed to disease all the time, so their immune systems have learned how to cope. An alternative interpretation is that there has been selection; the ones who have managed to survive the various diseases they’ve been exposed to have very strong immune systems.
It seems that all these factors have contributed. It’s still the case that the poorer the country, the lower the per capita COVID-19 deaths so far. We’ll see whether this holds in the future.
What have been the effects of the Trump administration tariffs on China imposed in 2018?
EF: In research that was published in 2020 in the Quarterly Journal of Economics, you looked at the effects of the 2018 Trump tariffs. You found that between those tariffs and the retaliatory tariffs of other countries, such as China, there was a substantial redistribution from U.S. buyers of foreign goods in favor of U.S. producers and to the government. Is this what you expected to see?
Goldberg: To a certain extent, what we didn’t expect to see is that U.S. buyers would be hurt. This is because the United States is a powerful country; to a certain extent, everyone thought that China would eat some of the tariff. What our work showed, and others’ as well, is that the tariffs were completely paid by the U.S. importing side. The other effect that some people didn’t expect is that the part of the economy that was hurt the most by the tariffs was people in Republican counties, and this is because of the retaliation by China; they targeted mainly agricultural commodities.
We have a follow-up paper where we look at how third countries were affected by the tariffs. What we show is that many countries benefited from the tariffs; trade seems to have been reallocated from the United States and China toward other countries. What did not happen is reshoring of economic activity back to the United States. …
[In] my follow-up work on the U.S.-China trade war … we focus in our new paper on bystander countries or third countries. One interesting finding of this work is that we find that the trade war didn’t simply reallocate the exports of these countries toward the United States and China, as you might expect. It also increased global exports. So, to a certain extent, it led to net trade creation, which is surprising. We don’t expect a trade war to actually lead to more trade. But it seems that happened in this case, maybe because countries decided to invest more in trade capacity, or perhaps because there are scale economies. We think it’s an interesting pattern.
In the interview, Koujianou also mentions a recent paper in the Summer 2021 issue of the Journal of Economic Perspectives (where I work as Managing Editor), co-authored with Noam Angrist and Dean Jolliffe, called “Why Is Growth in Developing Countries So Hard to Measure?” From the abstract of that paper:
Occasional widely publicized controversies have led to the perception that growth statistics from developing countries are not to be trusted. Based on the comparison of several data sources and analysis of novel IMF audit data, we find no support for the view that growth is on average measured less accurately or manipulated more in developing than in developed countries. While developing countries face many challenges in measuring growth, so do higher-income countries, especially those with complex and sometimes rapidly changing economic structures. However, we find consistently higher dispersion of growth estimates from developing countries, lending support to the view that classical measurement error is more problematic in poorer countries and that a few outliers may have had a disproportionate effect on (mis)measurement perceptions.
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