“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“A full-throated defense of economic dynamism.” - The Wall Street Journal
“Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
“Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
“Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
“…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
AI Summary.Wage gaps between poorer and richer U.S. cities narrowed at about 1.4% a year between 1940 and 1980, but this regional convergence then stopped. Using a dynamic spatial equilibrium model that matches observed wage, skill-ratio, and migration patterns, the authors attribute much of the change in cross-city wage differences to skill-biased technical change and agglomeration economies.
Elisa GiannoneUniversity of Chicago
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Prior to 1980, US wage gaps btw poorer & richer cities were shrinking at 1.4% annual rate. Post-1980, regional convergence ceased due to skill-biased technical change & agglomeration economies.
Why did wage convergence between poorer and richer U.S. cities stop?
Core argument: The wage gap between poorer and richer U.S. cities narrowed at roughly 1.4% a year between 1940 and 1980, but regional convergence ceased after 1980, attributed largely to skill-biased technical change and agglomeration economies.
Between 1940 and 1980, the wage gap between poorer and richer U.S. cities was shrinking at an annual rate of ~1.4%. However, post-1980, regional convergence ceased, largely due to skill-biased technical change (SBTC) and agglomeration economies. A dynamic spatial equilibrium model was developed to analyze these shifts, successfully matching the quantitative features of U.S. regional wage convergence and reproducing changes in skill ratios and migration patterns across cities. The model's counterfactual analysis indicates that SBTC significantly contributed to the change in cross-city wage differentials, highlighting its role in halting the convergence trend.
Takeaways by Macro Roundup® AI
The wage gap between poorer and richer U.S. cities narrowed at roughly 1.4% a year between 1940 and 1980, but regional convergence ceased after 1980, attributed largely to skill-biased technical change and agglomeration economies.
A dynamic spatial equilibrium model matches the quantitative features of U.S. regional wage convergence and reproduces observed changes in skill ratios and migration patterns across cities.
Counterfactual analysis of the spatial equilibrium model indicates that skill-biased technical change contributed substantially to the change in cross-city wage differentials and to the halt in regional convergence.
“…. Between 1940 and 1980 the wage gap between poorer U.S. cities and richer ones was shrinking at an annual rate of roughly 1.4%. After 1980, however, there was no further regional convergence overall. This paper quantifies the contributions of skill-biased technical change (SBTC) and agglomeration economies to the end of cross-city wage convergence within the U.S. between 1980 and 2010. I develop and estimate a dynamic spatial equilibrium model that looks at the causes of regional convergence and divergence. The model choice is motivated by novel empirical regularities regarding the evolution of the skill premium and migration patterns over time and across space. The model successfully matches the quantitative features of the U.S. regional wage convergence. Moreover, the model also reproduces changes in the skill ratio across U.S. cities, as well as, migration patterns after 1980. Finally, the counterfactual analysis suggests that SBTC explains much of change in cross-city wage differentials….”
AI Summary.Income convergence across US states has slowed markedly alongside rising housing prices in high-income areas, which has redirected low-skill migration away from those regions. The authors' model points to housing supply regulations as a critical factor, with returns to moving to high-income places diverging by skill level.
Peter Ganong and Daniel ShoagJournal of Urban Economics
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US regional income convergence has declined over the past 30 years, coinciding with rising housing prices in high-income areas, deterring low-skill migration. @PeterGanong @DanielShoag
Do housing supply regulations explain slowing income convergence across states?
Core argument: The rate of income convergence across US states has declined significantly over the past 30 years, coinciding with a marked rise in housing prices in high-income areas.
Over the past 30 years, the rate of income convergence across US states has significantly declined, coinciding with a marked increase in housing prices in high-income areas. This trend has redirected low-skill migration away from these regions, as rising housing costs deter such movement. The divergence in skill-specific returns to moving to high-income places further exacerbates this issue. A model developed to analyze these dynamics highlights the critical role of housing supply regulations in this process. The data underscores how these factors collectively contribute to slowing regional income convergence, as low-skill workers face barriers to relocating to areas with higher economic opportunities.
Takeaways by Macro Roundup® AI
The rate of income convergence across US states has declined significantly over the past 30 years, coinciding with a marked rise in housing prices in high-income areas.
Rising housing costs in high-income regions have redirected low-skill migration away from those areas, while returns to moving to high-income places diverge across skill levels.
A model of these dynamics points to housing supply regulations as playing a critical role in slowing regional income convergence, as low-skill workers face barriers to relocating.
"...The past thirty years have seen a dramatic decline in the rate of income convergence across states and in population flows to high-income places. These changes coincide with a disproportionate increase in housing prices in high-income places, a divergence in the skill-specific returns to moving to high-income places, and a redirection of low-skill migration away from high-income places. We develop a model in which rising housing prices in high-income areas deter low-skill migration and slow income convergence. Using a new panel measure of housing supply regulations, we demonstrate the importance of this channel in the data...."
AI Summary.Wages vary widely across US metropolitan areas: high school graduates earn $14.1/hr in the median metro, with a 32% gap between the 10th and 90th percentile metros ($12.5 vs. $16.5) and a 60% gap between the 1st and 99th. Dispersion is larger for college graduates.
Enrico MorettiNational Bureau of Economic Research
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Metro wage gaps reveal stark geography effect: High school grads see 60% max spread ($11.9-$19/hr); college grads 112% ($18.1-$38.5/hr). Top metros pay 2x lowest for same education level.
How much do wages for similar workers vary across metros?
Core argument: High school graduates in the median US metro area earn $14.1/hr, with the 10th and 90th percentile metros at $12.5 and $16.5/hr (a 32% gap) and the 1st and 99th percentiles at $11.9 and $19.0/hr (a 60% gap).
Labor costs exhibit significant variation across US metropolitan areas, with high school graduates in the median metro area earning $14.1/hr. The 10th and 90th percentiles earn $12.5 and $16.5/hr, respectively, a 32% difference, while the 1st and 99th percentiles earn $11.9 and $19.0/hr, a 60% difference. Notably, high school graduates in Stamford, CT or San Jose, CA earn wages two times higher than those in Brownsville, TX or McAllen, TX, despite similar education levels. This disparity is even more pronounced among college graduates, with a 41% difference between the 10th and 90th percentiles ($20.5 vs. $28.5/hr) and a 112% difference between the 1st and 99th percentiles ($18.1 vs. $38.5/hr). These variations highlight the substantial impact of geographic location on wage levels, underscoring the economic challenges faced by low-skilled workers in lower-wage cities compared to their counterparts in high-wage urban centers.
Takeaways by Macro Roundup® AI
High school graduates in the median US metro area earn $14.1/hr, with the 10th and 90th percentile metros at $12.5 and $16.5/hr (a 32% gap) and the 1st and 99th percentiles at $11.9 and $19.0/hr (a 60% gap).
High school graduates in Stamford, CT and San Jose, CA earn wages twice as high as those in Brownsville, TX and McAllen, TX, despite similar education levels.
Geographic wage dispersion is wider for college graduates, with a 41% gap between the 10th and 90th percentile metros ($20.5 vs. $28.5/hr) and a 112% gap between the 1st and 99th percentiles ($18.1 vs. $38.5/hr).
“….The figure indicates that labor costs vary significantly across US metropolitan areas. The average high school graduate living in the median metropolitan area earns $14.1 for each hour worked. The 10th and 90th percentile of the distribution across metropolitan areas are $12.5 and $16.5, respectively. This amounts to a 32% difference in labor costs. The 1st and 99th percentile are $11.9 and $19.0, respectively, which amounts to a 60% difference. While some of these differences may reflect heterogeneity in skill levels within education group, differences across metropolitan areas conditional on race, experience, gender, and Hispanic origin are equally large…. The distribution of the average wage of college graduates across metropolitan areas is even wider than the distribution of the average wage of high school graduates. The 10th and 90th percentile of the distribution for college graduates are $20.5 and $28.5. This amounts to a 41% difference in labor costs. The 1st and 99th percentile are $18.1 and $38.5, respectively, which amounts to a 112% difference…High school graduates living in Stamford, CT or San Jose, CA earn an hourly wage that is two times as large as workers living in Brownsville, TX or McAllen, TX with the same level of schooling…”
AI Summary.The authors estimate that workers without college degrees moved from a housing-inclusive urban wage premium of over 10% in 1970 to an urban penalty of over 10% by 2015, driven by rising housing costs and falling relative wages. College-educated workers continued to earn a substantial urban wage premium over the same period.
Daniel ShoagAmerican Enterprise Institute
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Non-college workers have seen a decline in urban wage premium, turning into a housing-inclusive urban wage penalty of nearly 20% btw 2000 & 2015. @DanielShoag
Have housing costs turned the urban wage premium into a penalty?
Core argument: Non-college workers moving from low- to high-density U.S. areas earned a housing-inclusive wage premium above 10% in 1970, but faced an urban penalty of over 10% by 2015.
Over the past 50 years, non-college workers have experienced a significant decline in the urban wage premium, turning into a housing-inclusive urban wage penalty of nearly 20% between 2000 and 2015. In 1970, these workers could expect a housing-inclusive wage premium of over 10% when moving from low to high-density areas, but by 2015, this shift resulted in an urban penalty of over 10%. This trend is driven by escalating housing costs and declining relative wages, making high-density areas less attractive for non-college workers. Conversely, college-educated workers continue to enjoy a substantial urban wage premium, with hourly wages increasing by approximately 50% over the same period. The disparity highlights the economic challenges faced by non-college workers in urban settings, as housing costs negate potential wage benefits.
Takeaways by Macro Roundup® AI
Non-college workers moving from low- to high-density U.S. areas earned a housing-inclusive wage premium above 10% in 1970, but faced an urban penalty of over 10% by 2015.
Between 2000 and 2015, the housing-inclusive urban wage penalty for non-college workers reached nearly 20%, attributed to rising housing costs and declining relative wages.
College-educated workers retained a substantial urban wage premium over the same 50 years, with hourly wages rising roughly 50% when moving to high-density areas.
"....Have workers stopped moving to the highest-density, highest-productivity places in the country because of a decline in the urban wage premium, or because the rent is too high? We analyze how important these two explanations are by studying them in one and the same empirical analysis. We find that that non-college workers now effectively face a housing-inclusive urban wage penalty, while workers with college education continue to face a significant urban wage premium.....Our results suggest that the decline in the urban wage premium for non-college workers has been especially steep once housing costs are taken into account. This has had dramatic consequences for the attractiveness of high-density areas:for non-college workers, there is now, on average, an urban wage penalty after accounting for the cost of shelter....find that non-college workers now effectively face a housing-inclusive urban wage penalty, while college workers continue to face a significant urban wage premium...For college workers, shown in blue, the steep gradient of the slope relating wages to prior density shows that the wage premium has remained large over the past half century. A back-of-the-envelope calculation may be helpful in interpreting the size of the wage premium here: whether one moves from 2.7 to 3.1 on the log scale, as in the 1970 panel, or from 2.9 to 3.3, as in the 2015 panel, hourly wages increase by some 50%. On the other hand, workers with only a high school degree or less, shown in red, have seen their wage premium in dense areas decline, especially after the year 2000, from about 35% in 1970to 10% in recent years. This decrease in high-density wages for low-skilled workers makes migration toward high-density areas less attractive for such workers, ceteris paribus...Incorporating housing costs does not change the steep urban wage premium facing workers with at least some college much. However, non-college workers saw that housing-inclusive premium starting to erode during the 1980s, before watching it turn into an urban wage penalty of almost 20% between 2000 and 2015. This means that not only have relative wages decreased dramatically for non-college workers in dense areas, their housing costs have escalated as well...Panel B shows that, in 1970, non-college workers could expect a housing inclusive wage premium of over 10 percent when moving from the 25th percentile of commuting-zone density to the 99th percentile of commuting-zone density. In 2015, that same move would instead be associated with an urban penalty of over 10 percent. The wage gradient is not only negative in 2015, but at all densities non-college workers are worse off in real terms in 2015 than at any other point on the table...."
New from your friend Stan Veuger (and Shoag of Ganong/Shoag) looks at the urban wage premium, supports Ganong/Shoag and finds that non-college workers are now net negative if you include housing costs.
Ed Comment:“I took a first read of your paper over the weekend (a topic that interests me greatly) and love where you are heading. Congrats. Two aspects of the data have always given me pause—differential real estate appreciation by relevant neighborhood/demographic (i.e., high-skilled vs low-skilled) and migration of non-native-borns. Obviously, an influx of high-skilled workers raises real estate prices on average, especially real estate desirable to high-skilled workers. Increased demand for real estate from an influx of high-skilled workers surely spills over into low-skilled neighborhoods at the margin. Although given the share of high-skilled workers (say 5%) to low-skilled workers (66%), one might suspect the spillover is smaller than the sky-is-falling media admits. We care more about the dynamics of low-skilled housing price appreciation relative to low-skilled wages, and why an increase in low-skilled real estate prices lowers low-skilled after-rent wages (rather than proportionately raising low-skilled wages), especially when real estate restricts the supply of low-skilled labor. We know from Moretti that low-skilled wages are significantly higher in faster growing cities (i.e., compensated for some of the higher costs). You would also think that as low-skilled wages and real estate prices rise, low-skilled workers would find it increasing economical to commute longer distances to capture higher wages and avoid higher priced real estate. Given these dynamics, I can’t help but wonder if an influx of non-native-born low-skilled workers willing to accept lower after-rent wages has squeezed low-skilled after-rent wages rather than an influx of high-skilled workers. It’s not hard to imagine low-skilled migration patterns being further exacerbated by ethnic non-native born immigrants finding ethic low-skilled neighborhoods (and schools) more desirable than native-borns, and vice versa.”
Ed Comment: “Housing expenditures as a share of income segregated by income level solves my concern about segregating the housing data.I’m still left looking for reasons/evidence why low-skilled urban after-rent wages have fallen, especially if the supply of real estate isn’t restricted by something other than replacement cost (i.e. unrestricted other than the cost of commuting). I suppose one reason could be changing preferences where people might spend a greater share of their income on housing (and healthcare) as other things grow cheaper (or they price the growing benefits of urbanization into real estate). But that doesn’t explain why native-borns are/seem to be affected differently (i.e. migrating away instead of to urban areas).One answer is native-borns owned houses in urban areas and are now selling them to consume their imbedded equity (probably as part of a retirement strategy) rather than suffering lower after-rent wages. But I still have a sneaking suspicion that without the non-native born low-skilled migration, we are missing an important part of the picture. What other than a growing demand for urban jobs and low-skilled urban real estate (in ethnic neighborhoods) would drive down low-skilled urban after-rent wages significantly?Whenever I try to hire domestic employees, they insist on working off the books unless I pay them their taxes. I suppose if they can work next door for more, I would have to pay the difference. But if I have to pay their taxes, why wouldn’t I have to equilibrate all the additional costs necessary for them to urbanize? It’s hard to imagine urban labor markets as monopsonies. Again, it leads you to believe the demand for urbanization must growing despite the increased cost—a contradictory conclusion if native-borns are moving away.”
Stan Comment:“Thanks for the note, Ed. We’re planning on looking at the migration issues a little more closely. Keep in mind though that there are still quite a few places where housing costs don’t exceed construction costs by much. I’m less concerned about residential segregation by income/skill because we use actual spending numbers. A separate issue I’m worried about is cost of living heterogeneity (besides housing) for college workers. Not quite sure yet how to think about that. See you soon Stan”
AI Summary.Across 204 cities, the tendency for high-quality workers and high-quality firms to cluster together has strengthened, with the elasticity of this co-location rising from 0.457 to 0.884 between the earlier and later periods studied. The authors argue this sorting contributes to widening wage gaps between large and smaller cities.
Wolfgang DauthCenter for Economic and Policy Research
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Assortative matching btw high-quality workers and high-quality firms is driving divergence between cities.
Does clustering of top workers and firms widen city wage gaps?
Core argument: Across 204 cities, the elasticity of co-location between high-quality workers and high-quality firms nearly doubled from 0.457 in 1985–1991 to 0.884 in 2008–2014.
The phenomenon of assortative matching, where high-quality workers and firms co-locate, is intensifying in large cities, driving economic divergence. Data from 204 cities show that the elasticity of this co-location pattern nearly doubled from 0.457 in 1985-1991 to 0.884 in 2008-2014. Large cities like Munich exhibit stronger assortative matching within local labor markets, with a correlation between worker and employer quality increasing from 0.038 to 0.061 over the same periods. This trend contributes to widening wage gaps between large cities and smaller ones, as high-quality workers and firms cluster in urban areas, enhancing productivity and economic growth.
Takeaways by Macro Roundup® AI
Across 204 cities, the elasticity of co-location between high-quality workers and high-quality firms nearly doubled from 0.457 in 1985–1991 to 0.884 in 2008–2014.
In large cities such as Munich, the correlation between worker quality and employer quality within local labor markets rose from 0.038 to 0.061 between 1985–1991 and 2008–2014.
The clustering of high-quality workers and firms in large cities contributes to widening wage gaps between large and smaller cities while enhancing productivity and economic growth.
just a FYI new Moretti blogpost on his paper from the fall, charts are better then the ones from the paper
“…Assortative matching matters in two different ways. First, there is the tendency of high-quality workers and plants to co-locate in the same local labour markets. The top two panels in Figure 2 show the positive relationship between average worker and plant quality across the 204 cities for two separate time windows. The figure suggests that the strength of this co-location pattern almost doubled over time: the elasticity is 0.457 in the period 1985-1991 and 0.884 in 2008-2014. The places were high-quality workers and plants cluster together tend to be the large West German cities like Munich, Hamburg or Frankfurt. This is shown in the bottom two panels of Figure 2 where we find positive associations between local population size and both average quality measures. But large cities like Munich do not only exhibit increasingly better pools of workers and plants than small cities like Uelzen. The second key mechanism is stronger assortative matching within those local labour markets. Inside Munich, matches are formed where the qualities of employers and employees are tightly aligned. The relatively best-paid persons tend to work for the relatively best paying local firms. Within Uelzen, by contrast, matching is far less assortative; who works for whom is closer to random. To show this more specifically, we compute the correlation of the worker fixed effects and the corresponding plant fixed effects of their employers in the first year of the respective time window. Figure 3 shows that this correlation tends to be higher in larger cities. Moreover, the association between local labour market size and within-city assortativeness has become considerably stronger over time: the elasticity is 0.038 in the period 1985-1991 and 0.061 in 2008-2014. In other words, job matches in Munich were more assortative than in Uelzen already in 1985-1991, but even more so in 2008-2014. This has contributed considerably to the rising wage gap between Munich and Uelzen….” Wolfgang Dauth, Sebastian Findeisen, Enrico Moretti, Jens Südekum, “Assortative matching in cities,” Center for Economic and Policy Research, January 6, 2019, https://voxeu.org/article/assortative-matching-cities
Ed Comment:“I took a first read of your paper over the weekend (a topic that interests me greatly) and love where you are heading. Congrats. Two aspects of the data have always given me pause—differential real estate appreciation by relevant neighborhood/demographic (i.e., high-skilled vs low-skilled) and migration of non-native-borns. Obviously, an influx of high-skilled workers raises real estate prices on average, especially real estate desirable to high-skilled workers. Increased demand for real estate from an influx of high-skilled workers surely spills over into low-skilled neighborhoods at the margin. Although given the share of high-skilled workers (say 5%) to low-skilled workers (66%), one might suspect the spillover is smaller than the sky-is-falling media admits. We care more about the dynamics of low-skilled housing price appreciation relative to low-skilled wages, and why an increase in low-skilled real estate prices lowers low-skilled after-rent wages (rather than proportionately raising low-skilled wages), especially when real estate restricts the supply of low-skilled labor. We know from Moretti that low-skilled wages are significantly higher in faster growing cities (i.e., compensated for some of the higher costs). You would also think that as low-skilled wages and real estate prices rise, low-skilled workers would find it increasing economical to commute longer distances to capture higher wages and avoid higher priced real estate. Given these dynamics, I can’t help but wonder if an influx of non-native-born low-skilled workers willing to accept lower after-rent wages has squeezed low-skilled after-rent wages rather than an influx of high-skilled workers. It’s not hard to imagine low-skilled migration patterns being further exacerbated by ethnic non-native born immigrants finding ethic low-skilled neighborhoods (and schools) more desirable than native-borns, and vice versa.”
Ed Comment: “Housing expenditures as a share of income segregated by income level solves my concern about segregating the housing data.I’m still left looking for reasons/evidence why low-skilled urban after-rent wages have fallen, especially if the supply of real estate isn’t restricted by something other than replacement cost (i.e. unrestricted other than the cost of commuting). I suppose one reason could be changing preferences where people might spend a greater share of their income on housing (and healthcare) as other things grow cheaper (or they price the growing benefits of urbanization into real estate). But that doesn’t explain why native-borns are/seem to be affected differently (i.e. migrating away instead of to urban areas).One answer is native-borns owned houses in urban areas and are now selling them to consume their imbedded equity (probably as part of a retirement strategy) rather than suffering lower after-rent wages. But I still have a sneaking suspicion that without the non-native born low-skilled migration, we are missing an important part of the picture. What other than a growing demand for urban jobs and low-skilled urban real estate (in ethnic neighborhoods) would drive down low-skilled urban after-rent wages significantly?Whenever I try to hire domestic employees, they insist on working off the books unless I pay them their taxes. I suppose if they can work next door for more, I would have to pay the difference. But if I have to pay their taxes, why wouldn’t I have to equilibrate all the additional costs necessary for them to urbanize? It’s hard to imagine urban labor markets as monopsonies. Again, it leads you to believe the demand for urbanization must growing despite the increased cost—a contradictory conclusion if native-borns are moving away.”
Stan Comment:“Thanks for the note, Ed. We’re planning on looking at the migration issues a little more closely. Keep in mind though that there are still quite a few places where housing costs don’t exceed construction costs by much. I’m less concerned about residential segregation by income/skill because we use actual spending numbers. A separate issue I’m worried about is cost of living heterogeneity (besides housing) for college workers. Not quite sure yet how to think about that. See you soon Stan”