Hsieh/Moretti updated their estimates on how restricting housing supply has slowed US GDP growth, suggest that GDP would be 3.7% higher in a counterfactual. add a new suggestion that if you can’t create denser population centers the US could expand transportation to ease access to the NYC/SF labor makerts

“…Specifically, we find that a major impediment to a more efficient allocation of labor across US cities is the constraint to housing supply in high TFP cities….We quantify the amount of spatial misallocation of labor across US cities and its aggregate costs. Misallocation arises because high productivity cities like New York and the San Francisco Bay Area have adopted stringent restrictions to new housing supply, effectively limiting the number of workers who have access to such high productivity. Using a spatial equilibrium model and data from 220 metropolitanareas we find that these constraints lowered aggregate US growth by 36 percent from 1964 to 2009…..We use data from 220 metropolitan areas in the United States from 1964 to 2009 to perform two calculations. First, we quantify the effect of spatial misallocation. We find that most of the increased spatial dispersion in the marginal product of labor is due to the growing spatial dispersion in housing prices. In turn, the growing spatial dispersion of housing prices is largely driven by strict zoning laws in cities such as New York and the San Francisco Bay Area with strong productivity growth. We find that the increased spatial misallocation of labor due to housing supply constraints in cities with high productivity growth rates lowered aggregate growth between 1964 and 2009 by a significant amount. In particular,we calculate that increasing housing supply in New York, San Jose, and San Francisco by relaxing land use restrictions to the level of the median US city would increase the growth rate of aggregate output by 36.3 percent. In this scenario, US GDP in 2009 would be 3.7 percent higher, which translates into an additional $3,685 in average annual earnings. Second, we calculate the contribution of each US city to aggregate US growth and compare it to an “accounting” measure based solely on the growth of the city’s GDP. The difference reflects the effect of a city’s growth on the efficiency of labor allocation across cities. While the accounting measure suggests that New York, San Francisco, and San Jose’s contribution to aggregate GDP growth between 1964 and 2009 is 12 percent, viewed through the lenses of our model, these cities were only responsible for 5 percent of growth. The difference is because the aggregate benefit of TFP growth in New York and the Bay Area was in part offset by increased misallocation of labor across cities. In contrast, for southern cities the accounting and model-based measures are the same. Due to an elastic supply of housing, much of the growth in the South took the form of employment growth, with no effect on misallocation….An alternative is the development of public transportation that links local labor markets characterized by high productivity and high nominal wages to local labor markets characterized by low nominal wages. For example, a possible benefit of the high speed train currently under construction in California is to connect low wage cities in California’s Central Valley—Sacramento, Stockton, Modesto, Fresno—to high productivity jobs in the San Francisco Bay Area. This could allow the labor supply to the San Francisco economy to increase overnight without changing San Francisco housing supply constraints. An extreme example is the London metropolitan area. A vast network of trains and buses allows residents of many cities in southern England—including far away cities like Reading, Brighton, and Bristol— to commute to high TFP employers located in downtown London. Another example is the Tokyo metropolitan area. While London and Tokyo wages are significantly above the UK and Japan averages, they would arguably be even higher in the absence of these rich transportation networks. Our argument suggests that UK and Japan GDP are significantly larger due to the transportation network…..”
Chang-Tai Hsieh and Enrico Moretti, “Housing Constraints and Spatial Misallocation,”American Economic Journal, April 2019, https://faculty.chicagobooth.edu/chang-tai.hsieh/research/growth.pdf

"... We find that inventors are significantly less likely to locate in states with higher taxes. The elasticity to the net-of-tax rate of the number of inventors residing in a state is 0.11 for inventors who are from that state and 1.23 for inventors not from that state. Inventors who work for companies are particularly elastic to taxes. Agglomeration effects appear to matter for location as well: inventors are less sensitive to taxation in a potential destination state when there is already more innovation in that state in their particular field of inventive activity. This is also true if an inventor's employer already has a record of innovation activity in that state.We confirm that firms are responsive to corporate taxes when choosing where to locate by estimating a location choice model at the individual R&D lab level. Our main findings can therefore be summarized as follows. Taxation in the form of both personal income taxes and corporate income taxes matters for innovation along the intensive and extensive margins, and both at the micro and macro levels. Taxes affect the amount of innovation, the quality of innovation, and the location of inventive activity. The effects are economically large especially at the macro state-level, where cross-state spillovers and extensive margin location and entry decisions compound the micro, individual-level elasticities. Not all the effects of taxes at the macro-level are accounted for by cross-state business stealing or spillovers. Corporate inventors are most sensitive to taxation; and positive agglomeration effects play an important role, perhaps in offering a type of compensating differential for taxation.......We have studied the effects of personal and corporate income taxes on innovation in the United States during the 20th century using a series of newly constructed datasets. Our data is sufficiently wide-ranging that we can consider both inventors and firms engaged in inventive activity over a long time period, and we can exploit the numerous changes to the U.S. tax code taking place over the 20th century. We document the effect of taxes at the macro (state) and micro (inventors and firm) levels and attempt to identify the estimates empirically. We find that both personal and corporate taxes matter for innovation. The quantity, quality, and the location of innovation are all affected by the tax system and the effects are quantitatively important. In addition to being able to document and identify these important responses to taxation, our estimates can help calibrate the tax elasticities needed in optimal tax formulas for labor or capital income (see Saez (2001) and Saez and Stantcheva (2018)), as well as quantify the efficiency costs of taxation, which are traded off against the revenue gains. Furthermore, our empirical evidence provides a sense of how firms and inventors respond to the net return to innovation, and not only to tax rates, which are merely a component of that economic calculation. In future work, it would be fruitful to compare the U.S. experience to other countries, historically and contemporaneously. That would require a major data collection effort, as we have undertaken for the U.S., but our analysis highlights the benefits of such investments. Currently we know very little about the impact of taxation on innovation over long time horizons, and our analysis is therefore an important first-step in building a better understanding of a relationship that is critical in policy discussions. While our estimates show that the state-level effects of taxes are not purely due to zero-sum business-stealing, it is still an open question as to how the federal tax rate affects national-level innovation in the U.S., when taking into account the international mobility of inventors, firms and intellectual property. An answer to that question is central to a fuller understanding of a tax regime’s real impact...."
Ufuk Akcigit, John Grigsby, Tom Nicholas and Stefanie Stantcheva, "Taxation and Innovation in the 20th Century," National Bureau of Economic Research, September 2018, http://www.nber.org/papers/w24982

Stantcheva is a frequent Saez collaborator