The Latest NBER Working Papers(2024-12-02)
NBER最新工作论文
2024-12-02
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目录
1. From Novelty to Norm: Uncovering the Drivers of Virtual Tour Effectiveness in Real Estate Sales
Miremad Soleymanian and Yi Qian #33204
2. The End of Oil
Ryan Kellogg #33207
3. The Political Economy of School Finance Systems with Endogenous State and Local Tax Policies
Stephen Calabrese, Dennis Epple, and Richard Romano #33212
4. Estimating Gross Output Production Functions
Markus Trunschke and Kenneth L. Judd #33205
5. Financial Conditions Targeting
Ricardo J. Caballero, Tomás E. Caravello, and Alp Simsek #33206
6. Painful Bargaining: Evidence from Anesthesia Rollups
Aslihan Asil, Paulo Ramos, Amanda Starc, and Thomas G. Wollmann #33217
7. Systemic Risk Measures: Taking Stock from 1927 to 2023
Viral V. Acharya, Markus K. Brunnermeier, and Diane Pierret #33211
8. Subsidizing Medical Spending through the Tax Code: Take-Up, Targeting and the Cost of Claiming
Gopi Shah Goda #33213
9. Dispersed Information, Nominal Rigidities and Monetary Business Cycles: A Hayekian Perspective
Christian Hellwig and Venky Venkateswaran #33215
10. Predicting College Closures and Financial Distress
Robert J. Kelchen, Dubravka Ritter, and Douglas A. Webber #33216
11. The Causal Effects of Income on Political Attitudes and Behavior: A Randomized Field Experiment
David E. Broockman, Elizabeth Rhodes, Alexander W. Bartik, Karina Dotson, Sarah Miller, Patrick K. Krause, and Eva Vivalt #33214
12. FinTech Lending to Borrowers with No Credit History
Laura Chioda, Paul Gertler, Sean Higgins, and Paolina C. Medina #33208
13. Rate-Based Emissions Trading with Overlapping Policies: Insights from Theory and an Application to China
Carolyn Fischer, Chenfei Qu, and Lawrence H. Goulder #33197
14. Unresolved Conflict in Workers' Compensation: The Impact of Legal Representation on Workers' Compensation Benefits
Bogdan Savych and David Neumark #33210
15. FinTech Platforms and Asymmetric Network Effects: Theory and Evidence from Marketplace Lending
Lin William Cong, Ke Tang, Danxia Xie, and Weiyi Zhao #33173
摘要
1. From Novelty to Norm: Uncovering the Drivers of Virtual Tour Effectiveness in Real Estate Sales
Miremad Soleymanian and Yi Qian #33204
This study examines the effectiveness of virtual tours and digital marketing strategies in enhancing real estate sales using a unique dataset combining MLS data, government-assessed property values, and agents’ marketing activities. While virtual tours are often perceived as a powerful tool to boost sales, their impact is context-dependent. Using classical econometric models and causal machine learning techniques, we find that virtual tours increase property sale prices by an average of 1%. However, the effect has declined over time, particularly post-COVID, indicating a shift from being a novel feature to a standard practice. Further analysis using causal random forests reveals significant heterogeneity in their effectiveness across property attributes, market conditions, and agent characteristics. Virtual tours are less impactful for highly differentiated properties but more beneficial in competitive markets and for less experienced agents who lack familiarity with the local market. These results suggest that real estate agents may benefit from considering property features, market dynamics, and their own experience when deciding how to use virtual tours. Our findings offer valuable insights for practitioners looking to optimize digital marketing strategies and enhance sales performance.
2. The End of Oil
Ryan Kellogg #33207
It is now plausible to envision scenarios in which global demand for crude oil falls to essentially zero by the end of this century, driven by improvements in clean energy technologies, adoption of stringent climate policies, or both. This paper asks what such a demand decline, when anticipated, might mean for global oil supply. One possibility is the well-known “green paradox”: because oil is an exhaustible resource, producers may accelerate near-term extraction in order to beat the demand decline. This reaction would increase near-term CO2 emissions and could possibly even lead the total present value of climate damages to be greater than if demand had not declined at all. However, because oil extraction requires potentially long-lived investments in wells and other infrastructure, the opposite may occur: an anticipated demand decline reduces producers' investment rates, decreasing near-term oil production and CO2 emissions. To evaluate whether this disinvestment effect outweighs the green paradox, or vice-versa, I develop a tractable model of global oil supply that incorporates both effects, while also capturing industry features such as heterogeneous producers, exercise of market power by low-cost OPEC producers, and marginal drilling costs that increase with the rate of drilling. I find that for model inputs with the strongest empirical support, the disinvestment effect outweighs the traditional green paradox. In order for anticipation effects on net to substantially increase cumulative global oil extraction, I find that industry investments must have short time horizons, and that producers must have discount rates that are comparable to U.S. treasury bill rates.
3. The Political Economy of School Finance Systems with Endogenous State and Local Tax Policies
Stephen Calabrese, Dennis Epple, and Richard Romano #33212
Beginning in the 1970’s, many state courts declared the widespread inequality in education spending across schools to violate their state’s constitution. Funding systems then emerged providing differing approaches to state and local support of education. We develop a theoretical framework and characterize outcomes under alternative systems. Our framework is distinctive in having voting over policies in both state and local elections. We also develop a calibrated computational model to compare equilibrium outcomes under the alternative school finance systems and to examine across state differences in expenditures. The model predicts that voters prefer systems with mixed state and local finance with designs mirroring those observed in practice.
4. Estimating Gross Output Production Functions
Markus Trunschke and Kenneth L. Judd #33205
This paper develops a novel method to estimate production functions. Earlier papers rely on special assumptions about the functional form of production functions. Our approach efficiently estimates all parameters of any production functions with Hicks-neutral productivity without additional exogenous variables or sources of variation in flexible input demand. We provide Monte Carlo Simulation evidence of our method’s performance and test our approach on empirical data from Chilean and Colombian manufacturing industries.
5. Financial Conditions Targeting
Ricardo J. Caballero, Tomás E. Caravello, and Alp Simsek #33206
We present evidence that noisy financial flows influence financial conditions and macroeconomic activity. How should monetary policy respond to this noise? We develop a model where it is optimal for the central bank to target and (partially) stabilize financial conditions beyond their direct effect on output and inflation gaps, even though stable financial conditions are not a social objective per se. In our model, noise affects both financial conditions and macroeconomic activity, and arbitrageurs are reluctant to trade against noise due to aggregate return volatility. Our main result shows that Financial Conditions Index (FCI) targeting—announcing a (soft and temporary) FCI target and setting the policy rate in the near future to maintain the actual FCI close to the target—reduces the FCI volatility and stabilizes the output gap. This improvement occurs because a more predictable FCI enables arbitrageurs to trade more aggressively against noise shocks, thereby "recruiting" them to insulate FCI from financial noise. FCI targeting is similar to providing forward guidance about the FCI, and in our framework it is strictly superior to providing forward guidance about the policy interest rate. Finally, we extend recent policy counterfactual methods to incorporate our model's endogenous risk reduction mechanism and apply it to U.S. data. We estimate that FCI targeting could have reduced the variance of the output gap, inflation, and interest rates by 36%, 2%, and 6%, respectively, and decreased the conditional variance of the FCI by 55%. When compared with interest rate forward guidance, it would have reduced output gap variance by 21%. We also show that a significant share of the gains from FCI targeting can be attained by an augmented version of a Taylor rule that gives a large weight to a simplified financial conditions target.
6. Painful Bargaining: Evidence from Anesthesia Rollups
Aslihan Asil, Paulo Ramos, Amanda Starc, and Thomas G. Wollmann #33217
A rollup is a series of acquisitions through which a financial sponsor consolidates ownership. Increasingly, this strategy is shaping economically important markets, but historically, it has escaped antitrust enforcement. We study this phenomenon in the anesthesia industry, site of the first rollup-based antitrust case in US history. First, we identify 18 other rollups that are observationally similar to the litigated ones. Next, we show that rollups consolidate ownership and that prices rise sharply as competing practices are acquired. Last, we estimate a structural bargaining model and simulate counterfactual equilibria under remedies that courts are likely to consider.
7. Systemic Risk Measures: Taking Stock from 1927 to 2023
Viral V. Acharya, Markus K. Brunnermeier, and Diane Pierret #33211
We assess the efficacy of systemic risk measures that rely on U.S. financial firms’ stock return co-movements with market- or sector-wide returns under stress from 1927 to 2023. We ascertain stress episodes based on widening of corporate bond spreads and narrative dating. Systemic risk measures exhibit substantial and robust predictive power in explaining the cross-section of market realized outcomes, viz., volatility and returns, during stress episodes. The measures also help predict bank failures and balance-sheet outcomes, confirming their relevance for understanding risks to the real economy emanating from banking sector fragility. Overall, market-based systemic risk measures offer a promising complement to macro-prudential and supervisory assessments of the financial sector.
8. Subsidizing Medical Spending through the Tax Code: Take-Up, Targeting and the Cost of Claiming
Gopi Shah Goda #33213
The U.S. tax code partially subsidizes out-of-pocket medical spending as itemized medical deductions (IMDs). In this paper, using detailed information in the Health and Retirement Study, I find that while a substantial share of medical spending among older Americans is deducted through the tax code, take-up is incomplete: 61.8 (50.5) percent of potential tax savings (deductions) are claimed, resulting in lost tax savings of $5.4 billion annually. Further, frictions in take-up result in diverting tax savings from higher-need populations. I investigate potential mechanisms and estimate a discrete choice model to simulate eligibility, take-up and the implied cost of claiming under different policy counterfactuals. The results indicate that subsidizing medical expenses through the tax code imposes significant economic burdens, reducing the net subsidy available to taxpayers.
9. Dispersed Information, Nominal Rigidities and Monetary Business Cycles: A Hayekian Perspective
Christian Hellwig and Venky Venkateswaran #33215
We study the propagation of nominal shocks in a dispersed information economy where firms learn from and respond to information generated by their activities in product and factor markets. We prove the existence of a “Hayekian benchmark”, defined by conditions under which imperfect information has no effect on equilibrium outcomes. This occurs under fairly general conditions when prices are flexible, i.e. without nominal frictions, informational frictions are irrelevant. With sticky prices, however, this irrelevance obtains only if there are no strategic complementarities in pricing and aggregate and idiosyncratic shocks are equally persistent. With complementarities and/or differences in persistence, the interaction of nominal and informational frictions slows down price adjustment, amplifying real effects from nominal shocks (relative to a full information model with only nominal frictions). In a calibrated model, the amplification is most pronounced over the medium to long term. In the short run, market generated information leads to substantial aggregate price adjustment, even though firms may be completely unaware of changes in aggregate conditions.
10. Predicting College Closures and Financial Distress
Robert J. Kelchen, Dubravka Ritter, and Douglas A. Webber #33216
In this paper, we assemble the most comprehensive dataset to date on the characteristics of colleges and universities, including dates of operation, institutional setting, student body, staff, and finance data from 2002 to 2023. We provide an extensive description of what is known and unknown about closed colleges compared with institutions that did not close. Using this data, we first develop a series of predictive models of financial distress, utilizing factors like operational revenue/expense patterns, sources of revenue, metrics of liquidity and leverage, enrollment/staff patterns, and prior signs of significant financial strain. We benchmark these models against existing federal government screening mechanisms such as financial responsibility scores and heightened cash monitoring. We document a high degree of missing data among colleges that eventually close and show that this is a key impediment to identifying at risk institutions. We then show that modern machine learning techniques, combined with richer data, are far more effective at predicting college closures than linear probability models, and considerably more effective than existing accountability metrics. Our preferred model, which combines an off-the-shelf machine learning algorithm with the richest set of explanatory variables, can significantly improve predictive accuracy even for institutions with complete data, but is particularly helpful for predicting instances of financial distress for institutions with spotty data. Finally, we conduct simulations using our estimates to contemplate likely increases in future closures, showing that enrollment challenges resulting from an impending demographic cliff are likely to significantly increase annual college closures for reasonable scenarios.
11. The Causal Effects of Income on Political Attitudes and Behavior: A Randomized Field Experiment
David E. Broockman, Elizabeth Rhodes, Alexander W. Bartik, Karina Dotson, Sarah Miller, Patrick K. Krause, and Eva Vivalt #33214
We study the causal effects of income on political attitudes and behavior with a field experiment. In the experiment, a non-profit gifted 1,000 low-income Americans 1,000 per month for three years tax-free, and 2,000 control participants 50 monthly. Contrary to resource models of participation, we find no effects on political participation or engagement, and rule out effects equivalent to the observational association between turnout and income. Political preferences largely do not change, with the estimates again distinguishable from the observational relationship that economic conservatism increases with income. Dispositions such as trust in government, polarization, and support for democracy also do not change. We do find effects consistent with mood misattribution: affect towards one's own racial group, other racial groups, and some politicians slightly improves. There is also some evidence that treated participants saw work as more important for individuals, society, ! or even as a requirement for accessing government programs; qualitative evidence illuminates potential mechanisms. Our findings contrast with findings from other economic shocks such as government-sponsored or taxable transfers—thereby helping clarify the mechanisms likely responsible for their effects—and underscore the durability of political predispositions.
12. FinTech Lending to Borrowers with No Credit History
Laura Chioda, Paul Gertler, Sean Higgins, and Paolina C. Medina #33208
Despite the promise of FinTech lending to expand access to credit to populations without a formal credit history, FinTech lenders primarily lend to applicants with a formal credit history and rely on conventional credit bureau scores as an input to their algorithms. Using data from a large FinTech lender in Mexico, we show that alternative data from digital transactions through a delivery app are effective at predicting creditworthiness for borrowers with no credit history. We also show that segmenting our machine learning model by gender can improve credit allocation fairness without a substantive effect on the model’s predictive performance.
13. Rate-Based Emissions Trading with Overlapping Policies: Insights from Theory and an Application to China
Carolyn Fischer, Chenfei Qu, and Lawrence H. Goulder #33197
Jurisdictions employing emissions trading systems (ETSs) to control emissions often utilize other environmental or energy policies as well, including policies to support renewable energy and reduce energy consumption. Interactions with these other policies lead to different outcomes from what might be predicted by examining the policies separately. The prior literature considering policy interactions has focused mainly on the case where the ETS is cap and trade. This paper extends the literature by examining the outcomes under a wide range of ETSs (including several forms of tradable performance standards) and overlapping policies (including various renewable subsidies and electricity consumption taxes). An analytical model demonstrates that the impacts of overlapping policies on allowance prices, emissions, and electricity output depend critically on the nature of the ETS. A numerical general equilibrium model tailored to China’s economy explores the implications for the cost-effectiveness of emissions reductions. Results indicate that overlapping policies that reduce cost-effectiveness under cap and trade can significantly enhance cost-effectiveness under tradable performance standards. The model predicts that under the current and planned designs for China’s ETS, which sets differentiated tradable performance standards for emitters, implementing renewable portfolio standards and accounting for indirect emissions from electricity consumption are both beneficial. Together they can reduce the cost of achieving the national emissions target by 20-30 percent over the interval 2020-2035. Transitioning to uniform benchmarks for emitting power generators could save another 10-15 percent. The findings highlight the importance of coordinating the designs of emissions trading systems with the overlapping policies.
14. Unresolved Conflict in Workers' Compensation: The Impact of Legal Representation on Workers' Compensation Benefits
Bogdan Savych and David Neumark #33210
We estimate the causal effect of attorney involvement on the indemnity benefits workers receive after their injuries. To address the fundamental challenge that claims and injuries may differ on unmeasured dimensions that affect both attorney involvement and benefits received, we propose and use two instruments. The first is the baseline local area attorney involvement rate derived from a subset of claims for fractures, lacerations, and contusions without permanent partial disability and/or lump-sum payments. The second instrument is a delay in the first indemnity payment. Our outcome is the total indemnity benefits that workers receive after their injuries, which captures payments to workers for time lost from work and other adverse effects of an injury. Our analysis of more than 950,000 claims with more than seven days of lost time indicates that attorney involvement substantially increases total indemnity benefits paid to workers.
15. FinTech Platforms and Asymmetric Network Effects: Theory and Evidence from Marketplace Lending
Lin William Cong, Ke Tang, Danxia Xie, and Weiyi Zhao #33173
We conceptually identify and empirically verify using marketplace lending data the features distinguishing FinTech platforms from non-financial platforms: (i) Long-term contracts introducing default risk at both the individual and platform levels; (ii) Lenders’ investment diversification to mitigate individual default risk; (iii) Platform-level default risk leading to greater asymmetric user stickiness and rendering platform-level cross-side network effects (p-CNEs), a novel metric we introduce, crucial for adoption and market dynamics. We incorporate these features into a model of two-sided FinTech platform with potential failures and endogenous participation/fees. The model predicts lenders’ single-homing, occasional lower fees for borrowers, asymmetric p-CNEs, and the predictive power of lenders’ p-CNEs in forecasting platform failures. Marketplace lending in China empirically corroborate our model predictions in this dynamic industry characterized by entries, exits, and network externalities. Specifically, lenders’ p-CNEs are empirically lower on declining or more established platforms compared to growing or new ones. Moreover, lenders’ p-CNEs predict platforms’ survival likelihood among others, even at very early stages. Our findings provide novel economic insights on multi-sided FinTech platforms for both practitioners and regulators.
