Edward Conard

Top Ten New York Times Bestselling Author

  • “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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…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
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
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Friday, November 5, 2021

China, India and Other Developing Nations Seek $1.3 Trillion a Year in Climate Finance

AI Summary. Developing nations including China, India, and Indonesia are asking wealthy countries for $1.3tn a year in climate finance, with half directed to renewable energy projects and half to protection against climate impacts. Developed nations fell $20bn short of their earlier $100bn annual pledge, fueling frustration among developing countries and complicating negotiations.

Matthew Dalton Wall Street Journal
Date Posted:
November 5, 2021
Is Database:
Database

Developing nations demand $1.3tn in annual climate finance by 2030 to meet Paris Agreement goals, citing unmet $100bn pledge from wealthy countries. Half the funding would support renewables, half climate resilience. Shortfalls in current commitments complicate COP26 negotiations.

Developing nations demand $1.3tn in annual climate finance by 2030 to meet Paris Agreement goals, citing unmet $100bn pledge...

How much annual climate finance are developing nations demanding from wealthy countries?

Core argument: Developing nations including China, India, and Indonesia are asking wealthy countries for $1.3tn a year in climate finance starting in 2030, with half directed to renewable energy projects and half to protection against climate impacts.

Developing nations, including China, India, and Indonesia, are urging wealthy countries to provide $1.3tn annually in climate finance starting in 2030, as discussed at the COP26 summit. This demand highlights the significant investments needed to meet the Paris accord's climate targets, aiming to limit global warming to well below 2°C and aspire to 1.5°C. Half of the requested funds would support renewable energy projects, while the other half would protect against climate impacts. Despite a previous commitment by developed nations to provide $100bn annually from 2020-2025, they fell $20bn short in 2020 and are unlikely to meet this target until 2023. The shortfall has fueled frustration among developing countries and complicated negotiations. Western nations are hesitant to set post-2025 targets due to challenges in meeting current commitments, indicating a complex path ahead for climate finance discussions.

Takeaways by Macro Roundup® AI

  1. Developing nations including China, India, and Indonesia are asking wealthy countries for $1.3tn a year in climate finance starting in 2030, with half directed to renewable energy projects and half to protection against climate impacts.
  2. Developed nations fell $20bn short of their $100bn annual climate finance pledge in 2020 and are unlikely to meet the target until 2023, fueling frustration among developing countries and complicating negotiations.
  3. Western nations are reluctant to set post-2025 climate finance targets given their difficulty meeting the existing $100bn commitment covering 2020–2025.

"...Most of the world’s developing countries have backed a demand for wealthy nations to channel at least $1.3 trillion in climate finance to them annually starting in 2030, the opening salvo in one of the most contentious negotiating topics at the COP26 climate summit. African nations and a group called the Like-Minded Developing Countries, which includes China, India and Indonesia, said in a document they submitted to the United Nations at the summit that half the money should go toward funding renewable energy in the developing world and half toward protecting these countries from the effects of global warming. Developed nations have long pledged to help pay for developing nations to respond to climate change. That promise was crucial to sealing the Paris accord in 2015, when the U.S., Europe and other wealthy countries agreed to provide $100 billion a year from 2020 through 2025...."

Matthew Dalton, "China, India and Other Developing Nations Seek $1.3 Trillion a Year in Climate Finance,"Wall Street Journal, November 4, 2021, https://www.wsj.com/articles/climate-finance-china-india-11636039142

China, India and Other Developing Nations Seek $1.3 Trillion a Year in Climate Finance

GLASGOW—Most of the world’s developing countries have backed a demand for wealthy nations to channel at least $1.3 trillion in climate finance to them annually starting in 2030, the opening salvo in one of the most contentious negotiating topics at the COP26 climate summit.

African nations and a group called the Like-Minded Developing Countries, which includes China, India and Indonesia, said in a document they submitted to the United Nations at the summit that half the money should go toward funding renewable energy in the developing world and half toward protecting these countries from the effects of global warming.

Developed nations have long pledged to help pay for developing nations to respond to climate change. That promise was crucial to sealing the Paris accord in 2015, when the U.S., Europe and other wealthy countries agreed to provide $100 billion a year from 2020 through 2025.

The $1.3 trillion target for mobilizing funds reflects the huge investments that will be needed after that to reach the climate targets of the Paris accord, the paper says.

“The post 2025 mobilization goal must reflect the ambition, progression and the collective agreement to stay well below 2 (degrees) Celsius and aspire to stay within the 1.5 degree Celsius temperature goal,” the paper says.

Developed nations didn’t hit that target in 2020, falling $20 billion short, and aren’t likely to meet it until 2023, climate negotiators said in a report in October. The shortfall has angered developing nations and complicated the talks in Glasgow.

Western officials say they aren’t ready to set a target for climate finance post-2025, given how difficult it has been for them to hit the $100 billion target. They will only begin the talks at COP26 on a goal for post-2025.

“We’re not feeling particularly capable now,” said one European official. “It’s really not the right time.”

Delegates from China, India and Africa didn’t immediately respond to requests for comment.

The European Union said on Thursday that there had been a good start to climate negotiations in Glasgow.

“It’s a bit early to say whether we are on track for a fully successful COP but the early signs seem reasonably good,” said Jacob Werksman, the EU’s lead negotiator.

One of the top priorities for the ongoing meeting is finalizing the rulebook behind the 2015 Paris Agreement. Outstanding issues include defining rules for international carbon trading, whether countries should update their carbon emission reduction plans more frequently than the current five-year cycle, and rules on the reporting of those goals.

Earlier this week some climate negotiators said they had made progress toward establishing a framework for international carbon trading, after Brazil said it was ready to make concessions, having blocked any agreement in the past.

Also on Thursday, two groups that include dozens of countries agreed to stop funding new coal-fired power plants and overseas oil and natural gas projects.

The two agreements fit into a pattern of deals inked by subsets of like-minded countries pursuing their own, limited emissions-reducing ambitions at COP26, which they hope can pressure other countries to act more aggressively.

“These announcements are raising the bar for everyone, including India and China,” said Lauri Myllyvirta, lead analyst at think tank Centre for Research on Energy and Clean Air. “It has a knock on effect,”

The two deals come as a broader U.N.-backed agreement on specific, binding emissions-reductions commitments evades delegates so far. Big polluters like China, India and Russia have pledged emissions cuts, but not to the levels that Western nations, like the U.S., the U.K. and members of the EU have insisted are necessary to limit global warming.

Ending coal financing has been a key focus of the U.K. government in its role as host of the climate talks. Signatories to the coal deal have agreed to phase out unabated coal power—meaning projects that don’t have technology to capture carbon dioxide emissions—in major economies that have signed on in the 2030s, and for the rest in the 2040s. The U.K. government estimates the deal could result in the closure of 40 gigawatts of coal power, equivalent to over half the U.K.’s electricity-generating capacity.

But Thursday’s deal falls significantly short of a global pledge. Leading coal consumers China, India and the U.S. aren’t part of the 40-member group that signed the pledge. Meanwhile, a separate promise by the U.S. and the U.K. to end public funding of oil and gas investments overseas included about 20 signatories.

In that deal, government-backed banks in the U.S., Canada and the U.K. said that they would stop financing unabated coal, oil and natural gas projects abroad by the end of next year. Priority will instead be given to clean energy projects, according to a statement issued by 20 countries and institutions. The group said they currently spend about $15 billion annually financing such fossil-fuel projects.

The largest funders of fossil-fuel development abroad, including South Korea, Japan and China, weren’t part of Thursday’s oil and gas agreement. Europe was split. Portugal and the European Investment Bank, a lending arm of the EU, signed up. Italy, Germany and the European Bank of Reconstruction and Development, a government funded development bank, did not.

On Wednesday, the private sector vowed to help the world transition to cleaner energy. Global banks, major investors and insurers, and financial regulators pledged to incorporate carbon emissions into their most fundamental decisions.

  • Global Warming

Differences in Rent Growth by Income 1985-2019 and Implications for Real Income Inequality

AI Summary. From 1985 to 2019, rent rose 81% for the top income quintile versus 72% for the bottom, a minor differential, while the general price index rose 136% for the top quintile and 143% for the bottom. The authors conclude rent growth was not a major factor in widening income inequality.

Daryl Larsen FEDS Notes
Date Posted:
November 5, 2021
Is Database:
Database

Rent increased by 81% for top income quintile and 72% for bottom quintile btw 1985-2019.

Has rent growth been a major driver of widening income inequality?

Core argument: Rent rose 81% for the top income quintile and 72% for the bottom quintile from 1985 to 2019, a minor differential indicating that rent growth was not a major factor in widening nominal income inequality.

From 1985 to 2019, rent increased by 81% for the top income quintile and 72% for the bottom quintile, reflecting minor rent growth differentials across income groups. Despite these differences, the general price index rose by 136% for the top quintile and 143% for the bottom, indicating that rent growth was not a major factor in widening nominal income inequality. Only 12% of households in the top quintile were renters in 2019, suggesting that rent may not fully capture housing costs for this group. Lower-income households spent a larger fraction of their consumption on housing, which contributed to a higher general inflation rate for this group. Overall, the dispersion in rent growth was minor, and real income inequality expanded substantially over the period, driven more by factors other than rent growth.

Takeaways by Macro Roundup® AI

  1. Rent rose 81% for the top income quintile and 72% for the bottom quintile from 1985 to 2019, a minor differential indicating that rent growth was not a major factor in widening nominal income inequality.
  2. The general price index rose 143% for the bottom income quintile versus 136% for the top from 1985 to 2019, with lower-income households spending a larger fraction of consumption on housing, contributing to their higher inflation rate.
  3. Only 12% of top-quintile households were renters in 2019, suggesting rent may not fully capture housing costs for that group, while real income inequality expanded substantially, driven more by factors other than rent growth.

New Fed research looks at changes in rent across income groups from 1985-2019, "...We find that rent growth differentials across groups were fairly minor overall. Rent growth was slightly faster for households at the top of the income distribution from 1985 to 2001, but this differential disappeared in the second half of the sample. Cumulating over the entire period, rent increased by 81 percent at the top quintile, and by 72 percent at the bottom quintile....Cumulating over the entire period, the general price index rose by 136 percent for the top income quintile and by 143 percent for the bottom quintile. These differences are fairly minor relative to the widening in nominal income inequality during this period. Thus, on the whole, we find that accounting for differences in the price of housing services faced by households at different income levels does not change the conclusion that real income inequality expanded substantially over the past three decades....."

"...As shown by Figure 1, only 12 percent of households in the top income quintile in 2019 were renters. Because renting is so uncommon for this group, the rents that are observed may be a poor proxy for the price of housing services of owner-occupied homes in this group. Indeed, research has found that the ratio of rent to prices tends to be lower for high-value homes, signaling that these two types of units may not be comparable for high-income households (Garner and Verbrugge 2009, Heston and Nakamura 2009). That said, because the average growth rate of owners' equivalent rent was almost the same as the average growth rate of tenants' rent over our sample period, we maintain that changes in the rents paid by high-income households should provide a reasonable proxy for the changes in the price of housing services at the top of the income distribution, at least for the purpose of calculating long-run average growth rates in a CPI-type framework...."

Differences in Rent Growth by Income 1985-2019 and Implications for Real Income Inequality: Extended Excerpt Image 1


"... Figure 4 shows rent growth experiences in the AHS broken out by quintile of household income. We report median rent growth because the distribution of rent growth has a long upper tail, so average rent growth does not reflect the experience of most households in that quintile. In most years prior to 2003, rent growth was generally a little higher for the higher income groups, but this difference disappears in the second half of the sample. Table 2 reports that on average, the annualized rent growth rate for the fifth income quintile was 0.4 percentage points higher than the first quintile from 1985 to 2001. From 2001 to 2019, rent growth at the top was slightly lower than rent growth at the bottom. Cumulating over the entire 34-year period, rent rose by only 9 percentage points more for the top quintile than for the bottom quintile. Meanwhile, rent growth for the middle three quintiles was in between the growth rates for the top and bottom quintiles. Thus, on net the dispersion in rent growth across income groups was pretty minor over our sample period...."

Differences in Rent Growth by Income 1985-2019 and Implications for Real Income Inequality: Extended Excerpt Image 2


"..After calculating a housing expenditure share for each household, we take averages by year and income quintile. Figure 5 shows these expenditure shares. Lower income households spend a larger fraction of consumption on housing, and all households have spent a growing fraction of consumption on housing over time...."

Differences in Rent Growth by Income 1985-2019 and Implications for Real Income Inequality: Extended Excerpt Image 3


"..Table 3 reports the general inflation estimates by household income group. In the first half of the sample, the estimates turn out to be quite similar across groups. Even though the higher income groups tended to experience higher rent growth, lower income groups tended to spend a larger fraction of their consumption on housing. Since rent growth even for the bottom quintile was faster than inflation for other goods and services, the higher consumption share for the bottom quintile boosts the general inflation rate for that group by more than the top income quintile. This effect turns out to roughly offset the higher rent growth faced by the higher income quintile during this period. In the second half of the period, the general inflation rate is a bit higher for lower income groups, owing to both their slightly faster rent growth and their higher share of housing services in consumption. Cumulating over the entire sample period, the differences across income groups are relatively small...."

Differences in Rent Growth by Income 1985-2019 and Implications for Real Income Inequality: Extended Excerpt Image 4


Daryl Larsen, and Raven Molloy, "Differences in Rent Growth by Income 1985-2019 and Implications for Real Income Inequality," FEDS Notes, November 5, 2021, https://www.federalreserve.gov/econres/notes/feds-notes/differences-in-rent-growth-by-income-1985-2019-and-implications-for-real-income-inequality-20211105.htm

  • Inequality
  • Comparisons
    • Historical
  • GDP
    • Housing
  • Workforce

Proximity to the frontier, markups, and the response of innovation to foreign competition

AI Summary. Evidence from Chile indicates that increased import competition from China was associated with declines in research and development, process and product innovation, and quality among low-productivity firms, with larger declines where markups fell, while firms near the technological frontier saw gains in product innovation and quality, especially where markups rose.

Ana Cusolito, Alvaro Garcia Marin and William Maloney Center for Economic and Policy Research
Date Posted:
November 5, 2021
Is Database:
Database

Low-productivity firms in Chile saw decline in innovation across all measures in face of import competition, while frontier firms saw gains. @ACusolito @AlvaroGarciaMar @WilliamMaloney

Does foreign competition boost innovation only at frontier firms?

Core argument: Evidence from Chile indicates that increased import competition from China reduced R&D, process innovation, product innovation, and quality among low-productivity firms, with declines larger where markups fell.

Evidence from Chile indicates that increased import competition from China negatively impacted innovation across all measures for low-productivity firms, while frontier firms experienced gains. Specifically, research and development (R&D), process innovation, product innovation, and quality declined for laggards, with effects worsening when markups fell. Conversely, product innovation and quality improved for leaders, especially when markups rose. This supports the view that proximity to the technological frontier and higher rents are crucial for innovation. The findings suggest that competition's positive impact is greater when a larger share of firms are near the frontier, highlighting the need for policies that enhance firm capabilities and access to resources.

Takeaways by Macro Roundup® AI

  1. Evidence from Chile indicates that increased import competition from China reduced R&D, process innovation, product innovation, and quality among low-productivity firms, with declines larger where markups fell.
  2. Chilean frontier firms responded to Chinese import competition with gains in product innovation and quality, especially where markups rose.
  3. The authors argue that proximity to the technological frontier and higher rents matter for innovation, so competition's benefits are larger when more firms sit near the frontier, pointing to policies that build firm capabilities and access to resources.

Using evidence from Chile Ana Cusolito, Alvaro Garcia Marin and William Maloney find Chinese competition had a net negative impact on local innovation, though local frontier firms became more competitive, "...This column analyses the impact of increased import competition from China on innovation by Chilean firms. It finds a negative overall impact of competition on innovation indicators. Low-productivity firms in particular see declines across all innovation measures, while the most productive firms experience improvements in product innovation and product quality...We illustrate this in Figure 1, where we report the competition-innovation elasticity dividing the sample by leaders and laggards, and then by whether markups rose or declined with the increase in Chinese imports for four of the innovation variables. Research and development (R&D), process innovation, product innovation, and quality all fall for laggards, and the effect is exacerbated when markups are falling. Product innovation and quality rise for leaders, and more so if markups are increasing…Overall, we find a negative impact of competition on innovation across all measures. But we also find important heterogeneity....We illustrate this in Figure 1, where we report the competition-innovation elasticity dividing the sample by leaders and laggards, and then by whether markups rose or declined with the increase in Chinese imports for four of the innovation variables. Research and development (R&D), process innovation, product innovation, and quality all fall for laggards, and the effect is exacerbated when markups are falling. Product innovation and quality rise for leaders, and more so if markups are increasing. Thus, Aghion etal.’s(2005) view of the importance of proximity to the frontier, as well as the classical Schumpeterian view of the importance of rents, both receive support..."

Ana Cusolito, Alvaro Garcia Marin and William Maloney, "Proximity to the frontier, markups, and the response of innovation to foreign competition," Center For Economic And Policy Research, November 4, 2021, https://voxeu.org/article/proximity-frontier-markups-and-response-innovation-foreign-competition

Proximity to the frontier, markups, and the response of innovation to foreign competition

The widely accepted relationship between competition and growth sits somewhat uncomfortably with the inconclusive literature on the relationship between competition and innovation (for reviews, see Cohen 2010 or Gilbert 2006), and this is becoming increasingly relevant to the debate over trade openness and growth. Despite an extensive literature suggesting that trade liberalisation increases productivity, recent evidence from the US, Canada, and Europe generally finds negative or unclear impacts of rising import exposure on innovation (e.g. Autor et al. 2017, 2020, Bloom et al. 2016, Campbell and Mau 2021, Kueng et al. 2016).

There are conceptual reasons to think that, in practice, the effects could vary greatly with country context. Aghion et al. (2005) offer a synthesis of the view that competition is necessary to get entrepreneurs out of bed in the morning, and Schumpeter’s argument that higher rents are needed to increase innovation. They accept that firms that are far from the frontier may behave à la Schumpeter and retrench with greater competition, but they argue that firms that are closer to it may see innovation precisely as a way to escape from competition (Akcigit et al. 2018). They find supportive evidence for an inverted U-shaped relationship between industry-level innovation and competition in the UK (Hashmi and Van Biesebroeck 2016), although other evidence (e.g. Hashmi 2013, Gorodnichenko et al. 2010) has been more ambiguous. This raises the question of whether the net impact of competition on innovation might be more detrimental in emerging countries which have fewer globally competitive firms than developed economies.

To shed light on the debate, in a recent paper (Cusolito et al. 2021), we revisit this question exploiting the China shock to study the impact of increased competition on innovation in a prominent upper middle-income country, Chile, which is, perhaps, uniquely suited as a case for two important reasons. First, we are able to draw on a matched firm production-innovation panel dataset that contains information on input and product prices at the firm level and therefore allows us to generate measures of markups and efficiency (physical total factor productivity) that correspond more closely to the concepts of rents and technological leadership envisaged in the Schumpeterian literature. Second, the Chilean data permit us to study the effect of foreign competition not just on patenting, as has been the traditional focus, but on a broader range of plant performance and innovation outcomes than has been possible previously, including quality.

Chile offers a clean experiment to explore the effects mentioned above. It is the iconic ‘textbook’ well-run open economy with few micro distortions that, as elsewhere, saw levels of competition shocked by a major increase in import penetration from China. But, distinct from many trade liberalisation episodes, this shock was not accompanied by other sector-specific reforms. Hence, the effects we see are likely to be purely due to differential exposure to increased foreign competition across sectors.

Overall, we find a negative impact of competition on innovation across all measures. But we also find important heterogeneity along the lines of Aghion et al. (2005). We illustrate this in Figure 1, where we report the competition-innovation elasticity dividing the sample by leaders and laggards, and then by whether markups rose or declined with the increase in Chinese imports for four of the innovation variables. Research and development (R&D), process innovation, product innovation, and quality all fall for laggards, and the effect is exacerbated when markups are falling. Product innovation and quality rise for leaders, and more so if markups are increasing. Thus, Aghion et al.’s (2005) view of the importance of proximity to the frontier, as well as the classical Schumpeterian view of the importance of rents, both receive support.

Proximity to the frontier, markups, and the response of innovation to foreign competition: Extended Excerpt Image 1


These results are consistent with other recent findings. In contemporaneous work, also exploiting the canonical China shock, Aghion et al. (2021) find a detrimental effect on French firms’ sales and patenting from Chinese competition in output markets - with the negative impact being concentrated in low-productivity firms, defined as being below the median level of revenue total factor productivity (TFP). In a somewhat different experiment, Aghion et al. (2009) find the entry of greenfield foreign firms raises patenting for sectors close to the technology frontier but has a weak or even negative effect in laggard industries, again, where leaders are defined as the top 50%.

What is striking in the Chilean case is that when leaders are defined similarly, there is no positive impact on innovation. Sequentially running our specification with different cut-offs across the physical total factor productivity distribution (TFPQ), we find positive effects only when leaders are defined as the top 10% of the distribution, which accounts for 25% of industrial value added. Intuitively, this is consistent with countries further from the frontier having few firms near the frontier. But it also means that the impact of competition on incumbent innovation will be lower than in advanced countries and, potentially, net negative, as appears to be the case in Chile.

Our findings return us to our original paradox - how to reconcile that plant productivity often appears to increase with trade liberalisation, even in developing countries, but innovation does not. We propose two possible explanations. The first one may arise from the fact that the revenue TFP (TFPR) measure commonly used in the literature to define leaders and laggards combines efficiency, quality, and rents. Thus, if greater trade exposure actually leads to higher margins, as shown by De Loecker and Goldberg (2014), then this will show up as increased ‘productivity’. Our ability to exploit product price data to work with a cleaner measure of proximity to the efficiency frontier, TFPQ, allows us to abstract from mark-ups. The second explanation may arise from the fact that it is possible that increased competition leads to one-off adjustments - shedding excess workers, for example - but does not lead to dynamic increases arising from innovation.

Clearly, a finding of limited incumbent rises in innovation and perhaps lesser confidence in previous findings of increased productivity with trade liberalisation does not dictate reducing competition. Competition works through other margins, such as the reallocation of resources from low-productivity plants to high-productivity plants and through the entry of more productive plants and the exit of less productive ones (Melitz and Redding 2021). For the same period covered in the present study, Cusolito and Maloney (2018) show that over 60% of the gains in TFPQ in Chile arose precisely from entry and exit. Liu (1993) finds that in the early phases of the Chilean reforms, much productivity growth precisely occurred along the extensive margin, which rings true given the extraordinary levels of protection and distortions being unwound at the time.

But the evidence does suggest that the positive impact of competition is likely to be greater, the larger the share of frontier-proximate firms. Hence, raising the capabilities of firms and their access to resources may be an important complement to pro-competition policies. This might include extending managerial consulting programs, strengthening local innovation systems, supporting standards compliance, fostering technology adoption, and ensuring access to longer term finance, among other firm-level initiatives.

References

Akcigit, U, S T Ates and G Impullitti (2018), “Innovation, trade policy, and globalization”, VoxEU.org, 02 July.

Aghion, P, A Bergeaud, M Lequien, M Melitz and T Zuber (2021), “Opposing firm-level responses to the China shock: horizontal competition versus vertical relationships?”, Unpublished manuscript, Harvard University.

Aghion, P, N Bloom, R Blundell, R Griffith and P Howitt (2005), “Competition and Innovation: An Inverted-U Relationship”, The Quarterly Journal of Economics 120(2): 701-728.

Aghion, P, R Blundell, R Griffith, P Howitt and S Prantl (2009), “The effects of entry on incumbent innovation and productivity”, The Review of Economics and Statistics 91(1): 20-32.

Autor, D, D Dorn, G H Hanson, G Pisano and P Shu (2017), “Competition from China reduced innovation in the US”, VoxEU.org, 20 March.

Autor, D, D Dorn, G H Hanson, G Pisano and P Shu (2020), “Foreign Competition and Domestic Innovation: Evidence from U.S. Patents”, American Economic Review: Insights 2(3): 357-374.

Bloom, N, M Draca and J Van Reenen (2016), “Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, IT and Productivity”, Review of Economic Studies 83(1): 87-117.

Campbell, D and K Mau (2021), “On ‘Trade Induced Technical Change: The Impact of Chinese Imports on Innovation, IT and Productivity’”, The Review of Economic Studies, forthcoming.

Cohen, W M (2010), “Fifty years of empirical studies of innovative activity and performance”, Handbook of the Economics of Innovation 1: 129-213.

Cusolito, A P, A Garcia-Marin and W F Maloney (2021), “Proximity to the Frontier, Markups, and the Response of Innovation to Foreign Competition: Evidence from Matched Production-Innovation Surveys in Chile”, Policy Research Working Paper Series 9757, The World Bank.

Cusolito, A P and W F Maloney (2018), Productivity revisited: Shifting paradigms in analysis and policy, Washington, DC: World Bank Publications.

De Loecker, J and P K Goldberg (2014), “Firm Performance in a Global Market”, The Annual Review of Economics 6: 201-227.

Gilbert, R (2006), “Looking for Mr. Schumpeter: Where are we in the competition-innovation debate?”, Innovation policy and the economy 6: 159-215.

Gorodnichenko, Y, J Svejnar and K Terrell (2010), “Globalization and innovation in emerging markets”, American Economic Journal: Macroeconomics 2(2): 194-226.

Hashmi, A R (2013), “Competition and Innovation: The Inverted-U Relationship Revisited”, The Review of Economics and Statistics 95(5): 1653-1668.

Hashmi, A R and J Van Biesebroeck (2016), “The Relationship between Market Structure and Innovation in Industry Equilibrium: A Case Study of the Global Automobile Industry”, The Review of Economics and Statistics 98(1): 192-208.

Kueng, L, N Li and M-J Yang (2016), “The Impact of Emerging Market Competition on Innovation and Business Strategy”, NBER Working Papers 22840.

Liu, L (1993), “Entry-Exit, Learning, and Productivity Change Evidence from Chile”, Journal of Development Economics 42(2): 217-242.

Melitz, M J and S J Redding (2021), “Trade and Innovation”, VoxEU.org, 28 July.

  • Productivity
    • Innovation/Research
    • Investment
  • GDP
    • Trade (not deficits)

Tuesday, November 9, 2021

The Returns to Colleges: Estimating Value Added and Match Effects in Higher Education

AI Summary. A study of Texas students finds college value added is not strongly linked to selectivity: only 55% of students admitted to multiple colleges enroll at the most selective option, and among top-decile high school students, 45% choose non-flagship campuses. The authors suggest factors such as STEM degree attainment may drive college value.

Jack Mountjoy University of Chicago
Date Posted:
November 9, 2021
Is Database:
Database

College value add seems unrelated to selectivity, while STEM degree attainment proves to be valuable.

Does college selectivity determine how much value a college adds?

Core argument: Only 55% of students admitted to multiple colleges enroll at the most selective option available to them, with an average selectivity range across their admitted choices of 116 SAT points.

The study reveals that college value add is not strongly linked to selectivity, as only 55% of students admitted to multiple colleges choose the most selective option, with an average selectivity range of 116 SAT points. Among those admitted to UT-Austin, the most selective campus, only 58% enroll, and even when not admitted to Texas A&M, 40% still opt for less selective schools. This trend is evident among Top Ten Percent students, with only 30% enrolling at UT-Austin and 25% at Texas A&M, leaving 45% choosing non-flagship campuses. These findings suggest that factors beyond selectivity, such as STEM degree attainment, may drive college value.

Takeaways by Macro Roundup® AI

  1. Only 55% of students admitted to multiple colleges enroll at the most selective option available to them, with an average selectivity range across their admitted choices of 116 SAT points.
  2. Among students admitted to UT-Austin, the most selective campus in the study, 58% enroll, and 40% of those not admitted to Texas A&M still choose less selective schools.
  3. Among Top Ten Percent students, 30% enroll at UT-Austin and 25% at Texas A&M, leaving 45% at non-flagship campuses, which the study suggests reflects factors beyond selectivity, such as STEM degree attainment, driving college value added.

Jack Mountjoy and Brent Hickman, "The Returns to College(s): Estimating Value Added and Match Effects in Higher Education," University Of Chicago, January 2020, https://bfi.uchicago.edu/working-paper/the-returns-to-colleges-estimating-value-added-and-match-effects-in-higher-education/

Overall the paper does look at admitted “elite” students choosing less selective schools however I don’t see a carveout for those students and STEM selection, "...While Figure 2 shows that admission portfolio controls nearly eliminate imbalances in observable student potential acrosscollege treatments, a lingering concern with the matched applicant approach is that it relies on “the small share of students who make what is a very odd choice” (Hoxby, 2009): enrolling in a less selective college when admitted to a more selective alternative. Table 2 directly investigates the nature of this identifying variation in our sample, and finds that such choices are actually far more common than the concern supposes. The first section of Table 2 shows that among students in our identifying sample, i.e. those admitted to at least two colleges, only 55% end up enrolling in their most selective option, with the average admission portfolio spanning a selectivity range of 116 SAT points (SD 78). The second section of Table 2 shows that this phenomenon is not confined to students choosing solely among less selective institutions. Of the 27% of students in our identifying sample who are admitted to UT-Austin, the most selective campus in our students’ choice sets, only 58% choose to enroll there. And the remainder are not simply sidestepping en masse over to the other flagship: conditional on being admitted to UT-Austin and not being admitted to Texas A&M, still only 60% of students choose UT-Austin, meaning 40% forgo it in favor of a less selective, non-flagship school. Very similar patterns also arise among Top Ten Percent students in our sample, who are guaranteed admission to every Texas public university they apply to. Only 30% of them end up enrolling at UT-Austin, and 25% at Texas A&M, leaving fully 45% who decline to exercise their flagship entitlement and instead fan out across the 28 non-flagship campuses in our sample...."

Ed Comment:I read the paper. I would start by asking WTF? The paper is straightforward. Conceptually, it does exactly what I thought it should do: regress outcomes against pre-college characteristics, most importantly test score, and then analyzes the residuals. For all intents and purposes, it finds that colleges add very little value beyond what is predicted by a student’s demonstrated pre-college success. Beyond that, the insights are pretty thin. Schools that make the effort to get a higher share of their students to graduate produce higher earning students relative to the earnings their pre-college success would predict. And students that earn stem degrees earn more than students who don’t. No surprise, learning valuable things, like STEM, is valuable. So, no surprise, schools that produce a greater share of students who earn stem degrees add more value than schools that don’t. The signally value of college selectivity fades quickly. There also doesn’t seem to be much effect from “isms” in college or the 8 years after, even in Texas, since the results are essentially the same for black vs whites, men vs women, and rich vs poor. Had you written this summary in response to my questions, I wouldn’t have had any questions. And I wouldn’t have needed to read the paper. Scratching my head why it isn’t that easy.

Ed Comment:Are you sure they have individual scores and not just a pool of students who turned down school X? With the latter, they can compare that turn-down pool's average earnings to the average earnings of the pool of students from the school they turned down? Is 14 based on the pool of people who turned down x for x- or a larger pool? I don’t understand how completion factors into the above. That seems like different data. If a student starts in STEM and ends up graduating in not-STEM, how are they measured and what does it mean? If you have the scores of individuals, why all the rigmarole? Wouldn't you start with how much more does pedigree of school add over and above test score, and how much more does degree (STEM vs not-STEM) add on top of that? Then you would incrementalism from there and explain any differences. My guess, not havening read the paper, is that they are solving for the fact that they don’t have scores.

Ben Comment:I don’t really know what you mean by rigmarole. That said - I think they are doing what you’re suggesting, I think it’s just more complicated a procedure than you’re suggesting it would be. The first they they do in the paper (section 3) is calculate the value add of the colleges. In section 3 they discus selection bias (3.1 and 3.2) and the assumptions that need to hold for their model of value added to be correct. 3.3 Looks at matching, sorting, and support to try to get at whether or not the value added at different schools depends on the student as well as the school (going to Michigan might mean something else for me and for one of my classmates). So once they’ve solved for these things they have something they’re calling the schools value added - it’s a kind of residual from taking out the characteristics of the students at the school. They do other analysis first but then get to section 7 where we find Fig 14. Fig 14 uses all the schools and students and looks at how the value added (of earnings) of the schools compares to the value added of completing certain degrees from particular schools. If a student starts in stem but ends up an English major, they’ve completed their degree as an English major and are non-stem completion. I don’t think they’re tracking every major switch through each person’s entire college career. From my reading, non-completion has to do with drop outs, not people who switched majors. I am sure they have the scores.

Ed Comment:By rigmarole I mean.: Regressing earning against test scores and other pre-college credentials, and then regressing the residuals against some measure of the colleges' prestige/selectiveness is an obvious and straightforward starting point that goes a long way toward assesses the value added of a college and college_degree combination over and above the student's pre-college credentials. If they did that, report it, I'd like to see it, because it's the obvious starting point. If they didn't, explain why they didn't. Then explain to me what additional information/refinement they gained (or thought they gained) by looking at the sub pool of people who turned down a more prestigious/selective college. If they didn't have scores and other pre-college credentials, they would have gain a lot. But remember, they claim colleges don't add much value so the turned down pool shouldn't be much different than the not-turned down pool. So if you have the analysis in the first paragraph (and why wouldn't you if you had the data?....which is my question.) Then you haven't learned much more. You're just confirming what you learned in the first paragraph. You only learn that colleges don't add much value IF you don't have the analysis in the first paragraph. Then explain what we are learning in the rest of the paper (presumably fig 14). It doesn't seem to build on the analysis above. Rather it goes off in another direction. And I don't understand the insight. If they have the analysis in paragraph 1, then the analysis in paragrpah 2 seems like rigmarol if it's doing more than just confirming 1. And not doing the analysis in 1 if they have the data to do it seems like rigmarol. And going off on a tangent to fig 14 seems like rigmarol if it doesn't yield a valuable and unambiguous insight --an insight so far I don't understand. The goal here is to 1) teach me the insight while saving me the time of having to read the paper myself, which so far you haven't done, 2) don't make me pull teeth to achieve my objective, which is to save my time 3) do it in a way that I enjoy.

They find that things like lots of STEM majors and less selective schools really helps value add (Figure 14), or supporting students in a way that helps them get towards graduation (this one is a little more complicated and I don’t buy what they’ve done). “

They go on to test this assumption (and it’s fairly good). Looking back at the graph I showed you, any time a horizontal red line crosses the vertical lighter-red line, that is a college making no difference - they’re using 95% confidence intervals. A school like Texas Southern is a no results because they fail to reject the null, but it’s close. After seeing that many colleges don’t provide value add (but some do!), they try to look at what the colleges that do add value do to add value. Presumably, if you believed this analysis, you could use the next sections of the paper to improve colleges’ value add by adopting the characteristics of schools that do add value.

This graph shows the earnings increase a student would get from going to a college relative to what that student would earn relative to UT Austin. The red dots are the point estimate and the red lines are the confidence intervals. The triangles show the same calculation using a different estimator (I am unfamiliar with Bayes Shurnk Forecasts but can look them up if you would like). The main point of the using the admissions portfolio as a control is econometric. In order to discover something about the colleges, the authors need to net out the effects of the students. Typically you would do this with a fixed effect specification. They’ve found that they cannot use student fixed effects this way because so they assume the admissions portfolio is a decent proxy for the individual.

Ben Comment:They wouldn’t have done it stepwise as you’re suggesting because of the econometric issues they discuss in section 3 of the paper (I can give you more info here, but I’m not sure I can do that without “jargon”). Instead, they came up with the estimation strategy they’re employing. This paper is about the colleges themselves so, other than some summary statistics about the sample, they do not present any data about the students. In the data appendix they do show the graph of the value added by college:

Ed Comment:It’s still basically impossible to figure out what insights the paper has from your description of it. I believe that technical expertise (STEM as a surrogate here) is valuable for smart people to learn. So if the conclusion is schools that have more technical grads add more value, ok but I need more. For example, how have they removed selection bias from students earning technical degrees? If they haven’t accounted for it, they haven’t really accounted for anything. So if your summary doesn’t explain that, it doesn’t explain their insight (if they have one). I explained my concern up front. That students who couldn’t get into to Michigan STEM, went to Michigan State STEM rather than Michigan Non-STEM. They were smarter on average than Michigan State grads and as smart as Michigan no-STEM grads. So, they ended earning (how do they measure results?) like Michigan not-STEM grads. What does that prove? I don’t think it proves that Michigan doesn’t add value even though I don’t believe it does. If they’re going to make points about STEM, they have to factor out selection bias. By degrees. Further, statements like “This paper is about the colleges themselves so, other than some summary statistics about the sample, they do not present any data about the students” leave me scratching my head. I’m not asking about students. I’m asking about colleges. At the end of the day, aren’t the results of the college the summation of the results of its students? I’m asking about the summation of the results of the colleges (and degrees). I have no clue what they could tell me about students that would be of interest other than the effects colleges have on them on average. I’m not saying a step wise regression is necessarily the way to do the analysis. I’m trying to explain what it is about your summary of the paper that confuses me by explaining my perspective on the paper taken from your summary, so that you can write a summary that addresses my concerns. I often feel that economists start with their assumptions so lose that they end up way over complicate things from the get-go. That’s fine for an ending point. It’s not so great as a stating point. So I look at things and say what additional insight/refinement/precision did the complications (i.e. rigmarole) add relative to a more straightforward approach. Here, as a practical businessman, I would sort students into STEM and not-STEM graduates and regress earning against test scores and then see if the exclusiveness of the school as measured by the school’s (divided into STEM and non-STEM average test scores (or average earnings of their students) adds any additional predictive value. My guess is surely they did that and compared their results to that even if they didn’t report it. I specifically asked you: why do they need all the complications? I think your answer was: read section three—not an answer to my question. The summary should start with the bottom line. Do colleges add a lot of value or not? I thought your summary said no. usually you don’t get published for not finding something statistically significant, so then explain what adds value/is statistically significant. I also don’t know what “fixed effects” are unless you explain them. if you mean test score fro example, say “test score.”

Steve Comment: The paper doesn’t address individuals degrees, but goes carve out the impact of STEM majors. "....We next unpack BA completion by STEM versus non-STEM degrees to explore whether value-added on completing different majors has different predictions for earnings effects. The top two panels of Figure 14 shows that a college’s value-added on completing a STEM degree (left) has a strong correlation with its earnings VA, while value-added on non-STEM completion (right), in contrast, has almost no bivariate relationship with earnings VA. The middle panel shows that STEM and non-STEM VA are negatively correlated, however, suggesting that colleges may face tradeoffs across fields in boosting degree completion. Exemplifying this to the extreme, UT-Austin at the (0,0) origin has the lowest value-added on STEM completion, as we also saw in Section 6, but simultaneously has the highest VA on non-STEM degree completion. Simple bivariate correlations like the top right panel of Figure 14 may thus underestimate the earnings gains of producing more non-STEM degrees, since this is correlated with fewer STEM degrees in the cross-section. The bottom right panel of Figure 14, consistent with this hypothesis, shows that the relationship between earnings VA and non-STEM VA becomes significantly more positive when controlling for STEM VA. On the other hand,STEM VA becomes an even stronger predictor of earnings VA when controlling for non-STEM VA (bottom left panel of Figure 14). The partial slope coefficients imply that a 10 percentage point controlled increase in STEM VA predicts roughly twice as much earnings VA ($3,550) as the same controlled increase in non-STEM VA ($1,741)....”

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