Stuff I Say at School – Part X: Direct Effects of Trade on Poverty

This is part of the Stuff I Say at School series.

The Assignment

A critical literature review of trade openness on poverty. This post consists of section on direct effects of trade on poverty as well as the conclusion.

The Stuff I Said

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The majority of studies on openness and poverty concentrate on trade’s effects on economic growth and, consequently, growth’s effects on poverty. As Panagariya (2019, pg. 136) notes, this means that “the literature directly linking trade openness and poverty is sparse.” Nonetheless, a few more recent studies have attempted to look at the direct linkage between trade liberalization and poverty.

Measuring trade openness by the trade-to-GDP ratio and average tariffs, Aisbett, Harrison, and Zwane (2008) confirm previous studies in a cross-country analysis showing a strong link between trade and increased aggregate income growth. However, when the direct link between trade and poverty is measured, the tie is weakened considerably. Nonetheless, the direct association between trade and poverty remains positive, if not always statistically significant. The authors recommend complementary domestic policies related to good governance and institutions in order to make trade optimal for the poor. However, a more recent study finds a stronger direct tie between trade and poverty. Updating Aisbett et al.’s (2008) data with more recent years and the World Bank’s new poverty headcount ratio, Devashish Mitra (2016, pg. 65) shows that in the period of 1981-2013, “a single percentage point increase in trade leads to a poverty decline of 0.149 percentage points.”

Petia Topalova (2007, pg. 293) explores the effect of trade liberalization—measured by the weighted tariff average—on various districts within India from the late 1980s throughout the 1990s and comes to more pessimistic conclusion: “trade liberalization led to an increase in poverty rate and poverty gap in the rural districts where industries more exposed to liberalization were concentrated.” However, a response article by Hasan, Mitra, and Ural (2007) actually reverses her results after more robust measurements are taken into consideration (i.e., the inclusion of non-tariff barriers, the avoidance of nontradable employment weights, better sampling data from state-level measures). They “find that states whose workers are on average more exposed to foreign competition tend to have lower rural, urban and overall poverty rates (and poverty gaps), and this beneficial effect of greater trade openness is more pronounced in states that have more flexible labor market institutions” (2007, pg. 75). A follow-up study by Cain, Hasan, and Mitra (2012) updates Hasan et al. (2007) with the latest available data and comes to the same conclusions, determining that 38% of the poverty reduction between 1987 and 2004 was due to international trade.

Maelan Le Goff and Raju Jan Singh (2014) examine a panel of African countries between 1981 and 2010 and find that trade openness increases poverty after controlling for GDP per capita, education, and institutional quality, indicating the need for complementary reforms. Andreas Bergh and Therese Nilsson (2014) analyze 114 countries from 1983 to 2007, breaking the poverty data down into four five-year periods. In order to test economic globalization’s causality, they control for (1) the number of years McDonalds has been in the country and (2) the preceding average level of economic globalization of the neighboring countries. They discover that while trade flows lead to reductions in poverty, the significance disappears once they control for income and growth. However, even after those controls, liberalized trade restrictions have a large poverty-reducing effect (along with information flows).

Despite some mixed results, this handful of studies seems to support the conclusions of the previous section that international trade ultimately leads to reduced poverty. Even still, complementary domestic policies are necessary for countries to reap the full benefits of trade.

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In her recent book, Kimberly Clausing (2019, pg. 65-66) writes, “Openness to the world economy has played an important role in one of the most encouraging developments in human history: the dramatic increase in worldwide living standards in recent years…International trade is not solely responsible for these impressive achievements, but it has played a key role.” This literature review fully supports Clausing’s view. Trade has done an enormous amount of good for the poor worldwide and will continue to do so as long as policymakers and the public steer clear of populist-fueled protectionism.

Stuff I Say at School – Part IX: Indirect Effects of Trade on Poverty

This is part of the Stuff I Say at School series.

The Assignment

A critical literature review of trade openness on poverty. This post consists of part of the introduction and the section on trade and economic growth.

The Stuff I Said

As the The Economist (2013) reports, “The world’s achievement in the field of poverty reduction is, by almost any measure, impressive.” The United Nations’ “aim of halving global poverty between 1990 and 2015 was achieved five years early…The [Millennium Development Goals] may have helped marginally, by creating a yardstick for measuring progress, and by focusing minds on the evil of poverty. Most of the credit, however, must go to capitalism and free trade, for they enable economies to grow—and it was growth, principally, that has eased destitution.” This last statement is at times controversial in the popular press. In order to engage the controversy, this review will survey the academic literature on the effects of trade liberalization on poverty. This will be explored through two main channels. First, through trade’s indirect effects on poverty via economic growth. Most research on trade liberalization and poverty is focused on the relationship between trade and growth. Other possible avenues associated with trade, growth, and poverty—such as innovation or institutional change —will largely be ignored. Only work that focuses specifically on the connection between trade and growth will be reviewed in this section. The final section will mine the scant research on direct effects of trade liberalization on poverty.

Economist and trade expert Jagdish Bhagwati (2004, pg. 64) argues that “freer trade is associated with higher growth and…higher growth is associated with reduced poverty. Hence, growth reduces poverty.” However, empirically establishing this connection between growth and poverty reduction is necessary, seeing that it is theoretically possible for the benefits of economic growth to not be distributed to the poorest segments of society. Using a sample of 92 countries over a 40-year period, David Dollar and Aart Kraay (2002, pg. 219) find that economic growth on average increases “the income of the poor to the same extent that it increases the income of the other households in society.” Kraay (2006) finds that the main explanation for cross-country differences in poverty shifts over time is the growth in average incomes: 70% in the short-run and 97% in the long-run, respectively. In a follow-up study, Dollar, Kleineberg, and Kraay (2016, pg. 81) look at a dataset of 121 countries over four decades and come to the same conclusion: “Incomes of the bottom 20 percent and bottom 40 percent of income distribution generally rise equiproportionally with mean incomes as economic growth proceeds.” In a book-length treatment on the economic reforms in their home country of India, Bhagwati and Arvind Panagariya (2013) find that the growth since the 1990s has reduced poverty nationwide in both rural and urban regions alike and among socially disadvantaged groups. These studies confirm that the connection between economic growth and poverty reduction is solid.

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According to Bhagwati (2004), trade openness produces growth through various channels, including specialization, economies of scale, increased competition (and, consequently, decreased domestic monopolies), promotion of macroeconomic stability, and increased foreign direct investment. Much of the empirical evidence supports this view that trade openness results in growth. David Dollar’s (1992) early analysis of 95 developing countries between 1976 and 1985 concludes that trade openness (what the author calls an “outward-orientation”) and per capita GDP growth are highly correlated. Those countries in the most open quartile experienced a per capita growth rate of 2.9 percent, while those in the most closed quartile languished at -1.3 percent. Similarly, Harvard’s Jeffrey Sachs and Andrew Warner (1995, pg. 45) show in a cross-country analysis that, between 1970 and 1989, “being open to international trade has been sufficient to achieve growth in excess of 2 percent for developing countries.” However, Sachs and Warner (1995, pg. 45, fn. 61) acknowledge that their “indicators of openness are associated with other market-based reform policies, which makes it difficult to identify the precise contributions of trade as compared to other policies.” Using the portion of total trade that relies on geographical factors as an independent variable, a study by Jeffrey Frankel and David Romer (1999) finds that a one percentage point increase in the ratio of trade to GDP raises income per person between 0.5 and 2 percent. Measuring trade openness by means of tariff revenues, nontariff barriers, and other liberalization indicators, Romain Wacziarg (2001) discovers a positive effect of trade openness on economic growth in 57 countries between 1970 and 1989. Halit Yanikkaya (2003, pg. 57) provides continual support for the idea that trade stimulates growth, finding a “strong and positive relationship between trade intensity ratios and growth.” However, contrary to previous studies, Yanikkaya also finds that trade barriers can promote growth under particular conditions. William Cline (2004) questions Yanikayya’s latter findings, noting their contradiction with previous scholarship and the likelihood of his measurements either understating or misgauging the effects of protection. On the flip side, Francisco Rodriguez and Dani Rodrik (2001) argue that many of the measurements used by Sachs & Warner (1995) as well as Frankel & Romer (1999) are flawed in their openness measurements, fail to establish causality, and ignore other complementary policies necessary to promote and sustain growth. Perhaps surprisingly, T.N. Srinivasan and Bhagwati (2001) also find methodological problems with various cross-country regressions. However, in their view, this undermines many of Rodriguez and Rodrik’s criticisms due to their heavy reliance on these kinds of studies. After examining the evidence from several country-specific studies, Srinivasan and Bhagwati determine that Rodriguez and Rodrik’s criticisms fall flat and that trade and growth go hand-in-hand. Nonetheless, in a later paper, Bhagwati and Srinivasan (2002, pg. 182) acknowledge the cross-country regressions’ “interesting” findings that “practically no country that has been close to autarkic has managed to sustain a high growth performance over a sustained period.” A follow-up study by Frankel & Andrew Rose (2002) addresses many of Rodriguez and Rodrik’s concerns, controlling for small city-states, geographical distance, and institutional quality. They determine, “In every case, regardless of whether the other controls are included or not, the openness variable retains most of its magnitude and all of its statistical significance in the presence of each of the three Rodriguez-Rodrik modifications” (2002, pg. 451; italics original). On the other hand, Rodrik, Subramanian, and Trebbi (2004, pg. 135) find that when the impact of geography, global integration (international trade), and institutional quality are compared, “the quality of institutions trumps everything else.” Yet, they also find that institutions and integration positively influence each other: “A unit increase in institutional quality increases the trade share by 0.45 units, while a unit increase in trade increases institutional quality by 0.22 units” (2004, pg. 143). Conversely, Francisco Alcala and Antonio Ciccone (2004, pg. 638) control for both geography and institutional quality and measure “real openness (imports plus exports in exchange rate U.S. dollars relative to GDP in purchasing power parity US$).” Their results show that trade has a significant and robust positive (and causal) effect on productivity. Marta Noguer & Marc Siscart (2005) also control for geography and institutional quality, finding that a 1% increase in the trade share of GDP leads to a similar increase in income per capita. Dollar & Kraay (2004) look at decade-by-decade changes in trade volume across 100 countries and find that within-country changes in trade volume have a strong positive relation with changes in growth. This results in increased income for the poor. Nonetheless, Dollar and Kraay recommend complementing open trade with strong safety nets; nets that are in turn better funded by trade-induced growth.

Research over the last decade continues to support these earlier findings. Wacziarg & Welch (2008, pg. 212) find that between 1950 and 1998, “countries that liberalized their trade regimes experienced average annual growth rates that were about 1.5 percentage points higher than before liberalization.” Vlad Manole & Mariana Spatareanu (2010) use data from 131 developed and developing countries and find that reductions in trade protections lead to higher levels of income per capita. Expanding the data from Sachs & Warner (1995) and Wacziarg & Welch (2008), David Weil (2013) finds that the average growth rate of income among more open countries was significantly higher (3.1% per year) than that of closed countries (1.5% per year). Antoni Estevadeordal & Alan Taylor (2013) explore the outcomes of liberalized trade policies in countries during two time periods: 1975-1989 and 1990-2004. Those countries that liberalized during these periods had about one percentage point higher growth rates compared to non-liberalized countries.

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Maureen Were (2015) performs a cross-country analysis of 85 countries from 1991 to 2011. In agreement with most of the literature, she finds that trade has a positive and significant effect on economic growth. However, among the Least Developed Countries (LDCs)—most of which are in Africa—the statistical significance disappears. Nonetheless, she notes that trade’s effects on both domestic and foreign direct investment (FDI) are positive and significant. However, Markus Brueckner & Daniel Lederman’s (2015, pg. 1318) study focuses specifically on sub-Saharan Africa and finds that “a 1 percentage point increase in the ratio of exports plus imports over GDP is associated with a short-run increase in GDP per capita growth of approximately 0.5% in a given year,” while the long-run effect reaches about 2 percent. Pam Zahonogo (2016) argues for a Laffer Trade Curve among sub-Saharan Africa. He finds that for most measures, the thresholds are virtually non-existent. However, when imports make up for more than 33.16% of GDP, the positive effects of trade on growth begin to decline. He suggests complementary policies that promote new investments, improve institutional quality, and develop human capital. Yet, it is feasible that higher barriers on imports may harm the poor. In their analysis, Pablo Fajgelbaum & Amit Khandelwal (2016, pg. 1116) find “a propoor bias of trade in every country. On average, the real income loss from closing off trade is 63% at the 10th percentile of the income distribution and 28% for the 90th percentile.” This is due to low-income consumers spending more on traded sectors compared to high-income consumers, who spend more on non-traded services. Furthermore, Furceri, Hannan, Ostri, & Rose (2019) examine a dataset of 151 countries from 1963 to 2014. According to their results, tariff increases negatively impact output, productivity, employment, and consumption. The authors conclude, “All this seems eminently sensible and bolsters the arguments that mainstream economists make against tariffs; our results can be regarded as strong empirical evidence for the benefits of liberal trade” (2019, pg. 28). Perhaps most impressively, Arvind Panagariya (2019, pg. 98) has compiled “data on per capita incomes, good and services exports, goods and services imports, and goods and services exports as a proportion of GDP in constant 2005 U.S. dollars for more than two hundred countries over a period of fifty-four years between 1960 and 2013” (broken into three smaller periods: 1961-1975, 1976-1994, and 1995-2013). With incredible detail, he demonstrates a causal relation between trade and per capita income: those countries that experienced intensive growth in these various periods always maintained a high and/or expanding trade-to-GDP ratio.

Overall, the empirical literature seems to indicate that trade openness has a positive effect on economic growth. Growth in turn reduces poverty. Multiple literature reviews and book-length treatments have drawn similar conclusions. For example, Joshua Lewer and Hendrik Van den Berg’s (2003) review of the literature finds that, on average, studies point to a 1/5 (or more) percentage point increase in real GDP for every percentage point increase in trade. Winters, McCulloch, and McKay (2004) are slightly more cautious, but ultimately admit that the preponderance of evidence suggests that trade openness increases economic growth and income levels within countries. Alan Winters and Antonio Martuscelli (2014, pg. 498) review the more recent literature and conclude that “the evidence is very strong that greater openness is generally associated with higher levels of income and, equivalently, that trade liberalization is associated with temporary increases in growth. The relationship appears to be causal but is not absolutely invariable.” Douglas Irwin’s (2015, pg. 197) survey of the evidence finds that “greater trade openness—marked by rising trade and low or declining trade barriers—has been a feature of virtually all rapid-growth developing country experiences in the past fifty years.” Examining countries such as China, India, South Korea, Chile, and Vietnam, Irwin concludes that liberalized trade has been associated with greater growth and, consequently, declining poverty. Panagariya (2019) performs a similar analysis, dedicating extensive attention to economic “miracles” such as Hong, Kong, Singapore, Taiwan, South Korea, India, and China. He then turns his attention to other successes throughout Asia and Africa (and even moderate ones in Latin America). He writes, “I have shown that in each case, trade liberalization and expanding trade are integral parts of the success story” (2019, pg. 322).

While complementary domestic policies (e.g., improvements in institutional quality) are necessary to reap the full benefits of international trade, there appears to be no evidence that suggests trade has anything other than positive effects on growth. The majority of studies support the claim that trade reduces poverty through increased economic growth. Panagariya (2019, pg. 125) concludes, “Given this set of facts, any advice to the developing countries to opt for protectionist policies can only be viewed as purely ideological.”

What Were the Results of the Washing Machine Tariffs?

As reported by The Washington Post,

When economists at the University of Chicago and the Federal Reserve studied the 2018 duty on washing machines, they found the expected rise in retail prices from foreign manufacturers such as Samsung and LG. Surprisingly, though, these brands also increased dryer prices. Then domestic manufacturers followed suit, simply because they could.

All told, the research shows, U.S. consumers are spending an additional $1.5 billion a year on washers and dryers as a result of the tariffs. That’s an extra $86 for each washing machine and $92 for each dryer, the authors estimate. And less than 10 percent of that goes to the U.S. treasury — about $82.2 million — the study showed…Foreign manufacturers are passing some costs on to consumers, while domestic ones are simply pocketing extra profits, according to the study.

…Manufacturers also capitalized on buyer habits when they bumped up the price of dryers, which were not subject to the tariffs. “Many consumers buy these goods in a bundle,” Tintelnot said. “Part of the price increase for washers was hidden by increasing the price of dryers.”

In sum, “U.S. consumers shouldered 125 to 225 percent of the costs of the washing-machine tariffs. And the duty was mostly a dud on the job-creation front,” costing consumers about $815,000 for every one of the 1,800 jobs created.

That’s exciting. Looks like tariffs are exactly what they are cracked up to be.

Do Minimum Wage Hikes Drive Some Restaurants Out of Business?

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From a recent NBER paper (quoting from an earlier draft): 

As theory would suggest, we find robust evidence that the impact of the minimum wage depends on how close a restaurant is to the margin of exit, proxied by its rating. Looking at city-level minimum wage changes in the San Francisco Bay Area (the “Bay Area”), we present two main findings. First, at all observed minimum wage levels, restaurants with lower ratings are more likely to exit, suggesting that they are less efficient in the economic sense. Moreover, lower rated restaurants are disproportionately affected by minimum wage increases. In other words, the impact of the minimum wage on exit is most pronounced among restaurants that are closer to the margin of exit. 

…Our results suggest that a $1 increase in the minimum wage leads to an 14 percent increase in the likelihood of exit for the median 3.5-star restaurant, but no impact for five-star restaurants (the point estimate is in fact negative, suggesting that the likelihood of exit might even decrease for five-star restaurants, but the estimate is not statistically different from zero). These effects are robust to a number of different specifications, including controlling for time-varying county characteristics that may influence both minimum wage policies and restaurant demand, city-specific time trends to account for preexisting trends, as well as county-year fixed effects to control for spatial heterogeneity in exit trends.

…Overall, our findings shed on the economic impact of the minimum wage. Basic theory predicts that the minimum wage will cause firms that cannot adjust in other ways to cover their increased costs to exit the market. We find that lower rated firms (which are already closer to the margin of exit) are disproportionately impacted by the minimum wage. After a minimum wage increase, they are more likely to exit the market altogether and more likely to raise their prices (pg. 2-5).

This matches previous research, which finds that labor-intensive restaurants tend to exit and make room for capital-intensive restaurants. 

Who Bears the Cost of the Minimum Wage?

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From a forthcoming article in the American Economic Review (quoting from the draft version) on Hungarian minimum wage hikes:

Most firms responded to the minimum wage by raising wages instead of destroying jobs. Our estimates imply that out of 290 thousand minimum wage workers in Hungary, around 30 thousand (0.076% of aggregate employment) lost their job, while the remaining 260 thousand workers experienced a 60% increase in their wages. As a result, firms employing minimum wage workers experienced a large increase in their total labor cost that was mainly absorbed by higher output prices and higher total revenue. We also estimated that firms substituted labor with capital and their profits fell slightly. These results suggest that the incidence of the minimum wage fell mainly on consumers. Given the relatively small effect on employment, our results also suggest that minimum wages can redistribute income from consumers to low-wage workers without large efficiency losses. Our findings also indicate that the optimal level of the minimum wage is likely to vary across industries,cities and countries. In countries where low-wage jobs are concentrated in the local service sector (such as Germany or the U.S.) raising the minimum wage is likely to cause limited disemployment effects or efficiency losses. Moreover, in cities where mainly rich consumers enjoy the services provided by low wage workers this redistribution will be from rich to poor. The heterogenous responses across industries also underline the advantages of sector-specific minimum wage polices used in some European countries such as Italy or Austria. For instance, setting a higher minimum wage in the non-tradable sector than in the tradable sector can push up wages relatively more where it will generate more modest disemployment effects (pg. 23-24).

Passing the costs on to consumers fits with previous evidence. This also makes evident that the kind of industry (e.g., tradable vs. non-tradable) also matters when it comes to positive/negative effects of the minimum wage.

Stuff I Say at School – Part VIII: The Impact of Openness

This is part of the Stuff I Say at School series.

The Assignment

1. Do you feel that a country can thrive in an insular or isolated capacity? Is exchanges needed for a country to be successful? Do you see any examples of countries who have been reluctant to adopt new ideologies or integration?

2. What did we learn from the Columbian exchange that would be applicable to modern day society?

The Stuff I Said

1. While I think a country can thrive to some extent in isolation depending on a number of factors, it will not thrive as much as it could have had it been integrated into a larger exchange network. An extreme historical case is Tasmania: when the island was cut off from the mainland by rising sea levels, the population not only failed to progress, but actually regressed. Anthropologist Joseph Henrich surveyed the archaeological evidence and found that the isolation caused Tasmanians to lose a number of skills and technologies they had once possessed, including bone tools, cold-weather clothing, nets, fishing spears, barbed spears, etc. Even their canoeing skills and technologies worsened. Beyond comparative advantage, trade leads to innovation (what author Matt Ridley calls “ideas having sex”). And it is innovation–technological innovation in particular–that truly transforms standards of living. 

Protectionism and isolationism have had a bit of a global resurgence lately, but these positions fly in the face of the expert consensus as far as economic welfare is concerned (check out the survey data on tariffs at the bottom of the post). This populist backlash to globalization led to a string of recent academic books empirically and philosophically defending economic openness:

2. I’ll rely on Nobel laureate Angus Deaton for the next question:

The historian Ian Morris has described how increased trade around the second century CE merged previously separate disease pools that, since the beginning of agriculture, had evolved in the West, South Asia, and East Asia, “as if they were on different planets.” Catastrophic plagues broke out in China and in the eastern outposts of the Roman Empire. The Columbian exchange after 1492 is an even better-known example. Many historical epidemics started from new trade routes or new conquests.

…Yet globalization also opens its routes to the enemies of disease. We have already seen how the germ theory of disease–a set of ideas and practices developed in the North–spread rapidly to the rest of the world after 1945. Knowledge about drugs to control high blood pressure spread rapidly across the world after 1970, producing…synchronized declines in mortality…That cigarette smoking caused cancer did not have to be rediscovered country by country. While the origins of HIV/AIDS are in dispute, there is no dispute about its rapid spread from one continent to another. The scientific response–the discovery of the virus, the deduction of its means of transmission, and the development of chemotherapy that is transforming the disease from a fatal to a chronic condition–was extraordinarily rapid by historical standards, although hardly rapid enough for the millions who died as they waited. Today’s understanding of the disease, although still incomplete, has underpinned the response–not just in the rich world–and in the worst affected African countries rates of new infection have fallen in the past few years, and life expectancy is beginning to rise again (The Great Escape, pg. 150-151).

From Gregory Mankiw’s Principles of Economics, 7th ed. (pg. 32).

From the IGM Economic Experts Panel, University of Chicago

Stuff I Say at School – Part VII: The Importance of Institutions

This is part of the Stuff I Say at School series.

Summary & Commentary on Week’s Readings

Acemoglu et al argue that inefficient institutions persist for a number of major reasons. First, the lack of third-party enforcement of commitments prevents elites from relinquishing their monopoly on political power. Furthermore, the beneficiaries of the economic status quo are usually unwilling to risk their economic welfare through competition. This leads them to promote protectionism and further engage in rent-seeking activities. Institutions that encourage these kinds of activities fail to grow. We see this kind of conflict manifest in various areas of the economy, from labor and financial markets to regulations in pricing. The more institutions concentrate political power in the hands of the few, the more incentives are warped and distort paths to economic growth.

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In their book Why Nations Fail: The Origins of Power, Prosperity, and Poverty, Daron Acemoglu and James Robinson distinguish between inclusive and extractive institutions, with the former creating the conditions for prosperity. “Inclusive economic institutions,” they write,

…are those that allow and encourage participants by the great mass of people in economic activities that make best use of their talents and skills and that enable individuals to make the choices they wish. To be inclusive, economic institutions must feature secure private property, an unbiased system of law, and a provision of public services that provides a level playing field in which people can exchange and contract; it also must permit the entry of new business and allow people to choose their careers…Inclusive economic institutions foster economic activity, productivity growth, and economic prosperity (pg. 74-75).

On the other hand, extractive economic institutions lack these properties and instead “extract incomes and wealth from one subset of society to benefit a different subset,” empowering the few at the expense of the many (pg. 76).

The importance of getting institutions right is highlighted by Rodrik and Subramanian’s study. Three theoretical culprits have been blamed for the vast income inequality between countries: (1) geography, (2) integration (globalization, international trade), and (3) institutions. Regression analyses indicate that institutions trump all other explanations. This is also shown from the outset of Acemoglu and Robinson’s Why Nations Fail, in their story of Nogales, Arizona (United States of America) and Nogales, Sonora, (Mexico). Acemoglu and Robinson lay out their archetype story of two towns with the same essential culture, geography, and relative free trade (NAFTA), in most ways they are the same place. The only reason they are two towns is an institutional barrier between two separate countries. Yet one is rich and one is poor because of institutions. The direct effects of geography are weak at best, while there were no direct effects from integration. However, there were indirect effects of integration: institutions have significant, positive effects on integration, while integration has a positive impact on institutions. This, in some sense, creates a virtuous, growth-enhancing cycle. Rodrik and Subramanian point out that the institutional factors emphasized the most have largely been market-oriented (e.g., property rights, enforceable contracts). Yet, factors such as regulation, financial stabilization, and social insurance also matter in getting institutions right.

The interaction between political and economic institutions is an important insight. For example, even though most research finds that seemingly liberal political institutions like democracy have no direct impact on economic growth, more recent evidence from Acemoglu and colleagues suggests that they may in fact contribute to growth. What’s more, the evidence strongly suggests that economic openness—particularly international trade—contributes to growth. A 2010 study used data from 131 developed and developing countries and found that reductions in trade protections led to higher levels of income per capita. A World Bank study found that between 1950 and 1998, “countries that liberalized their trade regimes experienced average annual growth rates that were about 1.5 percentage points higher than before liberalization. Postliberalization investment rates rose 1.5-2.0 percentage points, confirming past findings that liberalization fosters growth in part through its effect on physical capital accumulation…Trade-centered reforms thus have significant effects on economic growth within countries” (pg. 212). A 2016 IMF paper found that trade liberalization boosts productivity through increased competition and greater variety and quality of inputs. All this suggests that Sachs and Warner were correct when they found “that open policies together with other correlated policies were sufficient for growth in excess of 2 percent during 1970-89” (pg. 45; fn. 61). Their findings also suggest “that property rights, freedom, and safety from violence are additional determinants of growth” (pg. 50). Acemoglu and Robinson in a 2005 paper found “robust evidence that property rights institutions have a major influence on long-run economic growth, investment, and financial development, while contracting institutions appear to affect the form of financial intermediation but have a more limited impact on growth, investment, and the total amount of credit in the economy” (pg. 988).

In short, inclusive institutions are necessary to fully reap the benefits of an open economy.

Is Student Loan Forgiveness for the Marginalized?

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I saw this floating around Facebook recently with the news of Elizabeth Warren’s student loan plan. For those unfamiliar with what Mayfield is referencing, here’s the entry from the HarperCollins Bible Dictionary:

As another Bible dictionary clarifies, “Though Leviticus 25 does not explicitly discuss debt cancellation, the return of an Israelite to his land plus the release of slaves implies the cancellation of debts that led to slavery or the loss of land.”

So does Warren’s plan benefit “the marginalized”?

According to Adam Looney at the Brookings Institution, Warren’s proposal is “regressive, expensive, and full of uncertainties…[T]he top 20 percent of households receive about 27 percent of all annual savings, and the top 40 percent about 66 percent. The bottom 20 percent of borrowers by income get only 4 percent of the savings. Borrowers with advanced degrees represent 27 percent of borrowers, but would claim 37 percent of the annual benefit.”

E Warren Distribution of benefit

He continues,

Debt relief for student loan borrowers, of course, only benefits those who have gone to college, and those who have gone to college generally fare much better in our economy than those who don’t. So any student-loan debt relief proposal needs first to confront a simple question: Why are those who went to college more deserving of aid than those who didn’t? More than 90 percent of children from the highest-income families have attended college by age 22 versus 35 percent from the lowest-income families. Workers with bachelor’s degrees earn about $500,000 more over the course of their careers than individuals with high school diplomas. That’s why about 50 percent of all student debt is owed by borrowers in the top quartile of the income distribution and only 10 percent owed by the bottom 25 percent. Indeed, the majority of all student debt is owed by borrowers with graduate degrees.

Drawing on 2016 data from the Federal Reserve’s Survey of Consumer Finances, Looney’s final analysis

shows that low-income borrowers save about $569 in annual payments under the proposal, compared to $900 in the top 10 percent and $2,653 in the 80th to 90th percentiles. Examining the distribution of benefits, top-quintile households receive about 27 percent of all annual savings, and the top 40 percent about 66 percent. The bottom 20 percent of borrowers by income get 4 percent of the savings…[W]hile households headed by individuals with advanced degrees represent only 27 percent of student borrowers, they would claim 37 percent of the annual savings. White-collar workers claim roughly half of all savings from the proposal. While the Survey of Consumer Finances does not publish detailed occupational classification data, the occupational group receiving the largest average (and total) amount of loan forgiveness is the category that includes lawyers, doctors, engineers, architects, managers, and executives.  Non-working borrowers are, by and large, already insured against having to make payments through income-based repayment or forbearances; most have already suspended their loan payments. While debt relief may improve their future finances or provide peace of mind, it doesn’t offer these borrowers much more relief than that available today.  

The Urban Institute’s analysis has similar findings (though their tone is more optimistic):

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I’m not sure whether or not Warren’s plan is a good one (I’m skeptical, especially given some of the results abroad). But I’m not big on acting like college graduates in a rich country are the marginalized of society.

Does Good Management Produce More Equal Pay?

Nicholas Bloom–whose research on the economics of management I’ve relied on in my own work–and colleagues have an interesting article in Harvard Business Review:

For 2010 and 2015, the U.S. Census Bureau fielded the Management and Organizational Practices Survey (MOPS) in partnership with a research team of subject matter experts, including one of us (Nick), as well as Erik Brynjolfsson and John Van Reenen. The MOPS collects information on the use of management practices related to monitoring (collecting and analyzing data on how the business is performing), targets (setting tough, but achievable, short- and long-term goals), and incentives (rewarding high performers while training, reassigning, or dismissing low performers) at a representative sample of approximately 50,000 U.S. manufacturing plants per survey wave. We refer to practices that are more explicit, formal, frequent, or specific as “more structured practices.” From the MOPS and related data, researchers have demonstrated just how important the use of these structured management practices is for companies and even entire economies, since firms that implement more of these practices tend to perform better.  We wanted to know what effect these management practices have on workers.

We found that companies that reported more structured management practices according to the MOPS paid their employees more equally, as measured by the difference between pay for workers at the 90th (top) and 10th (bottom) percentiles within each firm.

The authors fully admit, “To be honest, it surprised us…If anything, we expected the opposite…We hypothesized that more structured management would lead to rewarding high-performers over others, therefore leading to a rise in inequality inside of the firm. As the chart above shows, the reality is exactly the reverse – and that remains true even after controlling for employment, capital usage, firm age, industry, state, and how educated the employees are.” They continue,

Our research finds that the negative correlation between structured management and inequality is driven by a strong negative correlation between the use of structured monitoring practices and inequality. By contrast, higher usage of structured incentives practices was positively correlated with inequality, albeit weakly. In other words, our finding seems to suggest that companies that collect and analyze specific and high-frequency data about their businesses tend to have a smaller gap between the earnings of workers at the top of the income distribution and the earnings of workers at the bottom of the distribution.

The authors offer several possible explanations:

Previous research shows that firms with more structured management practices are more profitable on average, and there’s long been evidence that when companies make extra profits they share some of them with workers. Perhaps companies with more structured practices allocate these profits such that less well-paid workers get more of the pie.

The relationship could also result from increased efficiency. Maybe firms with more structured practices have more efficient low-paid workers, as a response to training or monitoring practices, and their pay reflects that extra efficiency.

Finally, it could be that firms with more structured practices are more focused on specific tasks and rely more on outsourcing. More and more companies are outsourcing tasks like cleaning, catering, security, and transport. If outsourcing is more common for firms that use more structured practices, workers performing tasks outside of the companies’ core tasks would no longer be on those companies’ direct payrolls. If the jobs that are outsourced are lower-paying than the jobs that are held by employees, the companies’ pay data will become more equal.

Other research finds that paying employees higher wages

  • Motivates employees to work harder.
  • Attracts more capable and productive workers.
  • Lead to lower turnover
  • Enhance quality and customer service
  • Reduce disciplinary problems and absenteeism
  • Require fewer resources for monitoring
  • Reduces poor performance caused by financial anxiety

Looking forward to Bloom et al.’s published work.

How and Why to Rate Books and Things

Here’s the image that inspired this post:

Now, there’s an awful lot of political catnip in that post, but I’m actually going to ignore it. So, if you want to hate on Captain Marvel or defend Captain Marvel: this is not the post for you. I want to talk about an apolitical disagreement I have with this perspective.

The underlying idea of this argument is that you should rate a movie based on how good or bad it is in some objective, cosmic sense. Or at least based on something other than how you felt about the movie. In this particular case, you should rate the movie based on some political ideal or in such a way as to promote the common good. Or something. No, you shouldn’t. ALl of these approaches are bad ideas.

That's not how this works

The correct way to rate a movie–or a book, or a restaurant, etc.–is to just give the rating that best reflects how much joy it brought you. That’s it!

Let’s see if I can convince you.

To begin with, I’m not saying that such a thing as objective quality doesn’t exist. I think it probably does. No one can really tell where subjective taste ends and objective quality begins, but I’m pretty sure that “chocolate or vanilla” is a matter of purely personal preference but “gives you food poisoning or does not” is a matter of objective quality.

So I’m not trying to tell you that you should use your subjective reactions because that’s all there is to go on. I think it’s quite possible to watch a movie and think to yourself, “This wasn’t for me because I don’t like period romances (personal taste), but I can recognize that the script, directing, and acting were all excellent (objective quality) so I’m going to give it 5-stars.”

It’s possible. A lot of people even think there’s some ethical obligation to do just that. As though personal preferences and biases were always something to hide and be ashamed of. None of that is true.

The superficial reason I think it’s a bad idea has to do with what I think ratings are for. The purpose of a rating–and by a rating I mean a single, numeric score that you give to a movie or a book, like 8 out of 10 or 5 stars–is to help other people find works that they will enjoy and avoid works that they won’t enjoy. Or, because you can do this, to help people specifically look for works that will challenge them and that they might not like, and maybe pass up a book that will be too familiar. You can do all kinds of things with ratings. But only if the ratings are simple and honest. Only if the ratings encode good data.

The ideal scenario is a bunch of people leaving simple, numeric ratings for a bunch of works. This isn’t Utopia, it’s Goodreads. (Or any of a number of similar sites.) What you can then do is load up your list of works that you’ve liked / disliked / not cared about and find other people out there who have similar tastes. They’ve liked a lot of the books you’ve liked, they’ve disliked a lot of the books you’ve disliked, and they’ve felt meh about a lot of the books you’ve felt meh about. Now, if this person has read a book you haven’t read and they gave it 5-stars: BAM! You’re quite possibly found your next great read.

You can do this manually yourself. In fact, it’s what all of us instinctively do when we start talking to people about movies. We compare notes. If we have a lot in common, we ask that person for recommendation. It’s what we do in face-to-face interactions. When we use big data sets and machine learning algorithms to automate the process, we call them recommender systems. (What I’m describing is the collaborative filtering approach as opposed to content-based filtering, which also has it’s place.)

This matters a lot to me for the simple reason that I don’t like much of what I read. So, it’s kind of a topic that’s near and dear to my heart. 5-star books are rare for me. Most of what I read is probably 3-stars. A lot of it is 1-star or 2-star. In a sea of entertainment, I’m thirsty. I don’t have any show that I enjoy watching right now. I’m reading a few really solid series, but they come out at a rate of 1 or 2 books a year, and I read more like 120 books a year. The promise of really deep collaborative filtering is really appealing if it means I can find is valuable.

But if you try to be a good citizen and rate books based on what you think they’re objective quality is, the whole system breaks down.

Imagine a bunch of sci-fi fans and a bunch of mystery fans that each read a mix of both genres. The sci-fi fans enjoy the sci-fi books better (and the mystery fans enjoy the mystery books more), but they try to be objective in their ratings. The result of this is that the two groups disappear from the data. You can no longer go in and find the group that aligns with your interests and then weight their recommendations more heavily. Instead of having a clear population that gives high marks to the sci-fi stuff and high-marks to the mystery stuff, you just have one, amorphous group that gives high (or maybe medium) marks to everything.

How is this helpful? It is not. Not as much as it could be, anyway.

In theoretical terms, you have to understand that your subjective reaction to a work is complex. It incorporates the objective quality of the work, your subjective taste, and then an entire universe of random chance. Maybe you were angry going into the theater, and so the comedy didn’t work for you the way it would normally have worked. Maybe you just found out you got a raise, and everything was ten times funnier than it might otherwise have been. This is statistical noise, but it’s unbiased noise. This means that it basically goes away if you have a high enough sample.

On the other hand, if you try to fish out the objective components of a work from the stew of subjective and circumstantial components, you’re almost guaranteed to get it wrong. You don’t know yourself very well. You don’t know for yourself where you objective assessment ends and your subjective taste begins. You don’t know for yourself what unconscious factors were at play when you read that book at that time of your life. You can’t disentangle the objective from the subjective, and if you try you’re just going to end up introducing error into the equation that is biased. (In the Captain Marvel example above, you’re explicitly introducing political assessments into your judgment of the movie. That’s silly, regardless of whether your politics make you inclined to like it or hate it.)

What does this all mean? It means that it’s not important to rate things objectively (you can’t, and you’ll just mess it up), but it is helpful to rate thing frequently. The more people we have rating things in a way that can be sorted and organized, the more use everyone can get from those ratings. In this sense, ratings have positive externalities.

Now, some caveats:

Ratings vs. Reviews

A rating (in my terminology, I don’t claim this is the Absolute True Definition) is a single, numeric score. A review is a mini-essay where you get to explain your rating. The review is the place where you should try to disentangle the objective from the subjective. You’ll still fail, of course, but (1) it won’t dirty the data and (2) your failure to be objective can still be interesting and even illuminating. Reviews–the poor man’s version of criticism–is a different beast and it plays by different rules.

So: don’t think hard about your ratings. Just give a number and move on.

Do think hard about your reviews (if you have time!) Make them thoughtful and introspective and personal.

Misuse of the Data

There is a peril to everyone giving simplistic ratings, which is that publishers (movie studios, book publishers, whatever) will be tempted to try and reverse-engineer guaranteed money makers.

Yeah, that’s a problem, but it’s not like they’re not doing that anyway. The reason that movie studios keep making sequels, reboots, and remakes is that they are already over-relying on ratings. But they don’t rely on Goodreads or Rotten Tomatoes. They rely on money.

This is imperfect, too, given the different timing of digital vs. physical media channels, etc. but the point is that adding your honest ratings to Goodreads isn’t going to make traditional publishing any more likely to try and republish last years cult hit. They’re doing to do that anyway, and they already have better data (for their purposes) than you can give them.

Ratings vs. Journalism

My advice applies to entertainment. I’m not saying that you should just rate everything without worrying about objectivity. This should go without saying but, just in case, I said it.

You shouldn’t apply this reasoning to journalism because one vital function of journalism for society is to provide a common pool of facts that everyone can then debate about. One reason our society is so sadly warped and full of hatred is that we’ve lost that kind of journalism.

Of it’s probably impossible to be perfectly objective. The term is meaningless. Human beings do not passively receive input from our senses. Every aspect of learning–from decoding sounds into speech to the way vision works–is an active endeavor that depends on biases and assumptions.

When we say we want journalists to be objective, what we really mean is that (1) we want them to stick to objectively verifiable facts (or at least not do violence to them) and (2) we would like them to embody, insofar as possible, the common biases of the society they’re reporting to. There was a time when we, as Americans, knew that we had certain values in common. I believe that for the most part we still do. We’re suckers for underdogs, we value individualism, we revere hard work, and we are optimistic and energetic. A journalistic establishment that embraces those values is probably one that will serve us well (although I haven’t thought about it that hard, and it still has to follow rule #1 about getting the facts right). That’s bias, but it’s a bias that is positive: a bias towards truth, justice, and the American way.

What we can’t afford, but we unfortunately have to live with, is journalism that takes sides within the boundaries of our society.

Strategic Voting

There are some places other than entertainment where this logic does hold, however, and one of them is voting. One of the problems of American voting is that we go with majority-take-all voting, which is like the horse-and-buggy era of voting technology. Majority-take-all voting is probably much worse for us than a 2-party system, because it encourages strategic voting.

Just like rating Captain Marvel higher or lower because your politics make you want it to succeed or fail, strategic voting is where you vote for the candidate that you think can win rather than the candidate that you actually like the most.

There are alternatives that (mostly) eliminate this problem, the most well-known of which is instant-runoff voting. Instead of voting for just one candidate, you rank the candidates in the order that you prefer them. This means that you can vote for your favorite candidate first even if he or she is a longshot. If they don’t win, no problem. Your vote isn’t thrown away. In essence, it’s automatically moved to your second-favorite candidate. You don’t actually need to have multiple run-off elections. You just vote once with your full list of preferences and then it’s as if you were having a bunch of runoffs.

There are other important reasons why I think it’s better to vote for simple, subjective evaluations of the state of the country instead of trying to figure out who has the best policy choices, but I’ll leave that discussion for another day.


The idea of simple, subjective ratings is not a cure-all. As I noted above, it’s not appropriate for all scenarios (like journalism). It’s also not infinitely powerful. The more people you have and the more things they rate (especially when lots of diverse people are rating the same thing), the better. If you have 1,000 people, maybe you can detect who likes what genre. If you have 10,000 people, maybe you can also detect sub-genres. If you have 100,000 people, maybe you can detect sub-genres and other characteristics, like literary style.

But no matter how many people you have, you’re never going to be able to pick up every possible relevant factor in the data because there are too many and we don’t even know what they are. And, even if you could, that still wouldn’t make predictions perfect because people are weird. Our tastes aren’t just a list of items (spaceships: yes, dragons: no). They are interactive. You might really like spaceships in the context of gritty action movies and hate spaceships in your romance movies. And you might be the only person with that tick. (OK, that tick would probably be pretty common, but you can think of others that are less so.)

This is a feature, not a bug. If it were possible to build a perfect recommendation it would also be possible to build (at least in theory) an algorithm to generate optimal content. I can’t think of anything more hideous or dystopian. At least, not as far as artistic content goes.

I’d like a better set of data because I know that there are an awful lot of books out there right now that I would love to read. And I can’t find them. I’d like better guidance.

But I wouldn’t ever want to turn over my reading entirely to a prediction algorithm, no matter how good it is. Or at least, not a deterministic one. I prefer my search algorithms to have some randomness built in, like simulated annealing.

I’d say about 1/3rd of what I read is fiction I expect to like, about 1/3rd is non-fiction I expect to like, and 1/3rd is random stuff. That random stuff is so important. It helps me find stuff that no prediction algorithm could ever help me find.

It also helps the system over all, because it means I’m not trapped in a little clique with other people who are all reading the same books. Reading outside your comfort zone–and rating them–is a way to build bridges between fandom.

So, yeah. This approach is limited. And that’s OK. The solution is to periodically shake things up a bit. So those are my rules: read a lot, rate everything you read as simply and subjectively as you can, and make sure that you’re reading some random stuff every now and then to keep yourself out of a rut and to build bridges to people with different tastes then your own.