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        "datetime_modified":"20260311T090412",
        "datetime_start":"20260311T143000",
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        "date_start":"2026-03-11",
        "date_end":"2026-03-11",
        "time_start":"14:30:00",
        "time_end":"16:00:00",
        "time_zone":"America\/Detroit",
        "event_title":"(Artificial) Intelligence saturation and the future of work",
        "occurrence_title":"",
        "combined_title":"(Artificial) Intelligence saturation and the future of work: Ioana Marinescu, University of Pennsylvania",
        "event_subtitle":"Ioana Marinescu, University of Pennsylvania",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"Macroeconomic models typically treat AI as just another form of capital and predict a slowly evolving world, while computer science scaling laws applied to the whole economy predict explosive growth and the potential for a singularity-like event. Both views gloss over the asymmetric reality that intelligence capital or AI scales at computer-science speeds, whereas physical capital and labor do not. What\u2019s missing is a unified, parameter-driven framework that can nest assumptions from both economics and computer science to generate meaningful predictions of AI\u2019s wage and output impacts. Here we use a constant elasticity of substitution (CES) production function framework that separates physical and intelligence sectors. Whereas physical capabilities let us affect the world, intelligence capabilities let us do this as well: The two are complementary. Given complementarity between the two sectors, the marginal returns to intelligence saturate, no matter how fast AI scales. Because the price of AI capital is falling much faster than that of physical capital, intelligence tasks are automated first, pushing human labor toward the physical sector. The impact of automation on wages is theoretically ambiguous and can be non-monotonic in the degree of automation. A necessary condition for automation to decrease wages is that the share of employment in the intelligence sector decreases; this condition is not sufficient because automation can raise output enough to offset negative reallocation effects. In our baseline simulation, wages increase and then decrease with automation. Our interactive tool shows how parameter changes shift that trajectory. Wage decreases are steeper at high levels of automation when the outputs of the physical and intelligence sectors are more substitutable. After full automation, more AI and more physical capital increase wages, a classic prediction from standard production functions in capital and labor. Yet, when intelligence and physical are complementary, the marginal wage impact of AI capital saturates as AI grows large. More broadly, the model offers a structured way to map contrasting intuitions from economics and computer science into a shared parameter space, enabling clearer policy discussions and guiding empirical work to identify which growth and wage trajectories are plausible.",
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        "event_title":"Estimation in linear models with clustered data (joint work with Mikkel Solvsten and Baiyun Jing)",
        "occurrence_title":"",
        "combined_title":"Estimation in linear models with clustered data (joint work with Mikkel Solvsten and Baiyun Jing): Anna Mikusheva, Massachusetts Institute of Technology",
        "event_subtitle":"Anna Mikusheva, Massachusetts Institute of Technology",
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        "description":"We study linear regression models with clustered data, high-dimensional controls, and a complicated structure of exclusion restrictions. We propose a correctly centered internal IV estimator that accommodates a variety of exclusion restrictions and permits within-cluster dependence. The estimator has a simple leave-out interpretation and remains computationally tractable. We derive a central limit theorem for its quadratic form and propose a robust variance estimator. We also develop inference methods that remain valid under weak identification. Our framework extends classical dynamic panel methods to more general clustered settings. An empirical application of a large-scale fiscal intervention in rural Kenya with spatial interference illustrates the approach.",
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        "datetime_modified":"20260226T160621",
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        "event_title":"PPE Lecture Series: Rajiv Sethi (Barnard College, Columbia University)",
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        "combined_title":"PPE Lecture Series: Rajiv Sethi (Barnard College, Columbia University)",
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        "description":"Title:\n\"The Interpretation of Signals\"\n\nAbstract: \nWe live in a sea of signals. Words and phrases, emoticons and emojis,  pronouns in bios,  tattoos and piercings, skin color and eye shape, flags and insignia, diplomas and certificates, standardized test scores, and prices in markets \u2014 these are all carriers of information demanding attention and interpretation. We attach meanings to these messages, and they shape our actions in ways large and small. This lecture is motivated by the conviction that something useful can be said about economic and social life if one examines signals \u2014 despite their bewildering variety and complexity \u2014 from a unified perspective, with a focus on statistical inference and strategic incentives.\n\nRajiv Sethi is a Professor of Economics at Barnard College, Columbia University and an External Professor at the Santa Fe Institute. He is a 2025-26 fellow at the Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford University. Rajiv was a 2020-21 Joy Foundation Fellow at the Harvard Radcliffe Institute, and a 2008-09 Richard B. Fisher Member at the Institute for Advanced Study in Princeton. Professor Sethi has served on the editorial boards of several journals, including the American Economic Review, and is a founding associate editor of Collective Intelligence.\n\n His current research deals with information and beliefs.\n\nIn collaboration with Brendan O\u2019Flaherty, he has examined the manner in which stereotypes affect interactions among strangers, especially in relation to crime and the criminal justice system. These include interactions between victims and offenders, officers and suspects, prosecutors and witnesses, and judges and defendants. Their book, Shadows of Doubt: Stereotypes, Crime, and the Pursuit of Justice was published by Harvard University Press in 2019.\n\nWith Muhamet Yildiz, he has explored communication among individuals who consider each other to have valuable information, but also believe that others are biased to different degrees in the manner in which they process information. In deciding where to seek information, therefore, people face a trade-off between sources that are well-informed (in the sense of having precise information about the world) and those that are well-understood (in the sense of having transparent biases). In previous work they have examined public disagreement and private information flows, and in current work are exploring the implications of correlated biases within social groups.\n\nRajiv is a contributor to CORE (Curriculum Open-Access Resources for Economics), an initiative aimed at the production of high-quality resources for the teaching of economics, distributed free of charge worldwide under a Creative Commons license.",
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        "event_title":"Public Finance Seminar: Monday, March 16",
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        "event_title":"Zoning: Externalities or Misallocation?",
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        "combined_title":"Zoning: Externalities or Misallocation?: Chang-Tai Hsieh, The University of Chicago Booth School of Business",
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        "event_title":"Who and How? Adverse Selection and Flexible Moral Hazard (by Henrique Castro-Pires, Deniz Kattwinkel, and Jan Knoepfle)",
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        "description":"As corporate income tax rates have fallen across the world, other capital taxes become more important. This paper studies the choice between income taxation at the corporate and shareholder level depending on capital market openness. I develop a model of capital markets and sufficient-statistics framework to determine optimal tax reforms. The main result is that when the incidence of the corporate income tax on workers is higher than that of shareholder income taxes, lowering the former and reducing the latter is typically optimal. In a policy application, I derive optimal reform directions for corporate and shareholder income taxes for a large and a small economy.",
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        "event_title":"Sticky Discount Rates",
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        "combined_title":"Sticky Discount Rates: Kilian Huber, The University of Chicago Booth School of Business",
        "event_subtitle":"Kilian Huber, The University of Chicago Booth School of Business",
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        "description":"In standard models, expected inflation on its own does not affect the real investment decisions of firms, unless the real cost of capital or investment opportunities also change. We highlight a new mechanism, which implies that this inflation neutrality breaks down when firms\u2019 discount rates (which determine the nominal marginal product of capital) are sticky with respect to expected inflation. Sticky discount rates generate theoretical predictions that are consistent with stylized empirical facts but distinct from the New Keynesian textbook and other standard models: increases in expected inflation directly raise real investment; demand shocks generate investment consumption comovement; and the sensitivity of investment to real interest rates is low. Sticky discount rates imply monetary non-neutrality, even when all other prices are flexible. In the New Keynesian optimal monetary policy problem, the central bank steers long-run inflation expectations, even in response to temporary shocks.",
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        "event_title":"Freemium Model for Information Provision",
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        "combined_title":"Freemium Model for Information Provision: Igal Milchtaich, Bar-Ilan University",
        "event_subtitle":"Igal Milchtaich, Bar-Ilan University",
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        "description":"The paper explores a theoretical freemium model for the sale of information, drawing on mathematical tools used in the study of repeated zero-sum games and Bayesian persuasion. Unlike in standard persuasion models, the information seller (IS) is not affected by actions taken by the information buyer (IB), and is concerned solely with maximizing the revenue from selling information to the IB. Offering some information for free may increase the IB\u2019s willingness to pay for additional information. The information that the IB seeks is modeled as information about the state of the world. Initially, the IB only knows the prior distribution over possible state. The IS supplies both free and paid information through signals whose state\u2011dependent distributions determine the IB\u2019s posterior via Bayes\u2019 rule. The IB\u2019s utility is a function of the posterior. An optimal free signal is one that maximizes the IS\u2019s expected revenue from the subsequent paid signal. That revenue is equal to the IB\u2019s expected gain in utility from the posterior induced by the free signal to the one induced by the paid signal. The paper characterizes the optimal free and paid signals and derives a formula for the maximal revenue in terms of the IB\u2019s utility function. It shows that any revenue gain for the IS from providing free information is accompanied by an equally large or larger direct loss to the IB, implying that free information is never socially beneficial. Whether or not free information can increase the IS\u2019s revenue depends on the form of the IB\u2019s utility function. In the two-state case, the utility functions that allow such gains are fully characterized. In the general case, only necessary conditions are obtained. In particular, if the IB\u2019s utility function is convex, the IS can never profit from providing free information. This occurs, for example, when the IB uses the information to solve a decision problem. By contrast, when the IB is engaged in strategic interaction with a third party, the IS may benefit from providing free information. The paper presents a game\u2011theoretic example illustrating this possibility.",
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        "event_title":"SRC Seminar Series Presents: Pricing Job Amenities: A Practitioner's Manual",
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        "event_subtitle":"Stefan Szymanski, University of Michigan",
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        "description":"After WW2 the dominant professional sports in the US (baseball) and England (soccer) faced a common shock - the rapid penetration of TV ownership. Both experienced significant declines in attendance and profitability.  By 1960, approximately 300 minor league baseball teams (75%) had folded and the residual teams came under the control of the 16 major league clubs. In England, however, none of the 92 professional clubs folded despite facing comparable economic pressures. These differences are attributed to institutional structures - notably the integrated governance of soccer, compared to the fragmentation of baseball, and the promotion and relegation system of soccer. The latter offered a mechanism for clubs to control costs and the possibility of a better future.",
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        "event_title":"Counting Defiers: A Design-Based Model of a Randomized Experiment Can Reveal Evidence Beyond the Average Effect (with Neil Christy)",
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        "description":"We estimate the numbers of always takers, compliers, defiers, and never takers in the sample of people in an experiment, rather than a hypothetical population from which it was drawn, using structure from the randomization design. Our data include only a binary intervention and outcome. We develop a visualization to show that samples with defiers can sometimes generate the data in more ways than samples without defiers, yielding a higher design-based likelihood. We propose a maximum likelihood decision rule that can harness this evidence, which is not captured by standard hypothesis tests, and we provide optimality conditions. We illustrate the output of our decision rule for all possible data in samples of 50 and 200 with half in intervention, demonstrating a pattern in when the MLE includes defiers despite a positive average effect. We provide insights into effect heterogeneity in two published experiments with interventions that could plausibly backfire for some people despite statistically significant positive average effects on takeup of desirable health behaviors. In both, our 95% credible sets include the estimated Frechet bounds, demonstrating that evidence is weak. Yet, our MLE includes no defiers in one; in the other, the MLE includes a count of defiers equal to the estimated upper Frechet bound, over 18% of the sample. The MLE can support a monotonicity assumption or a specific alternative as a step toward improving the average effect of future interventions by targeting them away from noncompliers. Our dbmle package, compatible with Python and Stata, implements our statistics.",
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        "description":"This paper discusses identification, estimation, and inference on dynamic local average treatment effects (LATEs) in instrumental variables (IVs) settings. First, we show that com- pliers\u2014observations whose treatment status is affected by the instrument\u2014can be identified individually in time series data using smoothness assumptions and local comparisons of treat- ment assignments. Second, we show that this result enables not only better interpretability of IV estimates but also direct testing of the exclusion restriction by comparing outcomes among identified non-compliers across instrument values. Third, we document pervasive weak identifi- cation in applied work using IVs with time series data by surveying recent publications in leading economics journals. However, we find that strong identification often holds in large subsamples for which the instrument induces changes in the treatment. Motivated by this, we introduce a method based on dynamic programming to detect the most strongly-identified subsample and show how to use this subsample to improve estimation and inference. We also develop new identification-robust inference procedures that focus on the most strongly-identified subsample, offering efficiency gains relative to existing full sample identification-robust inference when iden- tification fails over parts of the sample. Finally, we apply our results to heteroskedasticity-based identification of monetary policy effects. We find that about 75% of observations are compliers (i.e., cases where the variance of the policy shifts up on FOMC announcement days), and we fail to reject the exclusion restriction. Estimation using the most strongly-identified subsample helps reconcile conflicting IV and GMM estimates in the literature.",
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        "description":"In this paper, we show that standard approaches for valuing non-market amenities like clean air through defensive expenditures are methodologically fragile. Estimates are highly sensitive to assumptions about household use of defensive technologies, and survey data on usage is unreliable. Moreover, because researchers convert a one-time purchase or willingness-to-pay elicitation into a flow value by dividing by assumed hours of use, lower assumed usage can mechanically imply higher valuations. To address this fragility, we develop a revealed- preference approach that estimates the flow value of clean air using high-frequency data on residential air purifier usage in Dhaka, Bangladesh. Leveraging randomized variation in the marginal cost of operation, we recover the households\u2019 trade-off between money and clean air without relying on purchase decisions or stated usage intentions. We find that the marginal willingness to pay (MWTP) for clean air is lower and much more tightly bounded than previous estimates. This low valuation is not driven by traditional market frictions like liquidity constraints or misperceptions about pollution severity and technology effectiveness, suggesting limits to environmental policies that rely on sustained complementary household action.",
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        "event_title":"Delegated Screening: Evidence from Government-Backed Loans (with Felipe Brugu\u00e9s, Thi Mai Anh Nguyen, and Sebasti\u00e1n V\u00e9lez)",
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        "combined_title":"Delegated Screening: Evidence from Government-Backed Loans (with Felipe Brugu\u00e9s, Thi Mai Anh Nguyen, and Sebasti\u00e1n V\u00e9lez): Rebecca De Simone, University of Michigan",
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        "description":"Government guarantees are a ubiquitous policy tool, leveraging intermediaries' informational advantage to allocate credit, yet they introduce a fundamental agency conflict: by insulating lenders from losses, they undermine the incentive to screen and distort product choice towards intermediaries' private benefits. This paper examines the power of dynamic incentives in mitigating both frictions. In the context of a large government-backed credit program in Colombia, we find evidence that lenders conduct less screening and take on greater risk with government-backed loans than with their traditional products. However, this tendency is mitigated by a dynamic quota-and-fee mechanism employed by the program.",
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        "datetime_start":"20260331T113000",
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        "datetime_modified":"20260213T081432",
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        "event_title":"Transport Infrastructure and Agriculture Productivity: Evidence from the Antebellum United States",
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        "combined_title":"Transport Infrastructure and Agriculture Productivity: Evidence from the Antebellum United States: Alexander Klein, University of Sussex",
        "event_subtitle":"Alexander Klein, University of Sussex",
        "event_type":"Workshop \/ Seminar",
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        "description":"This paper investigates the relationship between transport infrastructure and agricultural productivity in the Antebellum United States (1840\u20131860). Leveraging a novel dataset of county-pair transport costs, we utilize time-, region-, and direction-specific freight rates and newly digitized road network to understand how declining transit costs reshaped the agrarian landscape and agricultural production. These data allow for a rigorous evaluation of what we can call an antebellum \"place-based\" transport policies and their heterogeneous effects on antebellum regions. We use minimum spanning tree approach to construct a plausibly exogenous transportation network which is used to assess the causal impact of transportation infrastructure on antebellum agriculture.\n \nOur first set of findings reveal that the impact of canal construction was not uniform, showing considerable variation across different geographic regions. Notably, in the Middle Atlantic, the introduction of canals catalyzed a significant reallocation of resources away from wheat production, signalling a shift in comparative advantage. While canals laid the groundwork, railroads exerted the most transformative influence during the 1850s. In the final decade preceding the Civil War, the expansion of the rail network drove a broad reallocation away from traditional grain production, as improved connectivity facilitated deeper market integration and specialization. By accounting for the nuances of freight directionality and regional specificity, this study provides new evidence on how infrastructure-led cost reductions altered land use and productivity patterns in the nineteenth-century U.S. economy.",
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        "event_title":"Understanding Criminal Record Penalties in the Labor Market",
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        "combined_title":"Understanding Criminal Record Penalties in the Labor Market: Evan Rose, University of Chicago",
        "event_subtitle":"Evan Rose, University of Chicago",
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        "description":"This paper studies the earnings and employment penalties associated with criminal records using linked data on criminal justice interactions, state laws on formal labor market restrictions, and wages and employment. We estimate fixed effects models that decompose earnings into a worker's portable earnings potential and firm pay premia, both of which are allowed to shift after a worker acquires a record. We find that sorting into lower-paying firms explains a small share of the record penalty. Legal record restrictions are not concentrated in higher-paying jobs, meaning that sorting away from them contributes little to the earnings gap. Conditional on earnings potential, however, workers with a record are substantially less likely to be employed. The results suggest that criminal record penalties operate primarily by reducing employment and lowering earnings potential at every firm rather than increasing sorting into lower-paying jobs, although the bulk of the gaps can be attributed to differences that existed prior to acquiring a record.",
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        "datetime_modified":"20260330T111931",
        "datetime_start":"20260409T103000",
        "datetime_end":"20260409T120000",
        "has_end_time":1,
        "date_start":"2026-04-09",
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        "event_title":"\u201cPower, Purpose and the Color of Wealth: An Economic Vision that Centers People and the Environments in Which We Live\u201d",
        "occurrence_title":"",
        "combined_title":"\u201cPower, Purpose and the Color of Wealth: An Economic Vision that Centers People and the Environments in Which We Live\u201d: Stone Center Speaker Series: Darrick Hamilton",
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        "description":"Join the Stone Center for Inequality Dynamics as we host Darrick Hamilton\u200b, University and Henry Cohen Professor of Economics and Urban Policy at The New School. Darrick will present, \u201cPower, Purpose and the Color of Wealth: An Economic Vision that Centers People and the Environments in Which We Live.\u201d\n\nAbstract: \u201cThis talk will set the stage with a brief presentation of the Color of Wealth series, an exploration of wealth and economic well-being across seven metropolitan areas: Baltimore, Boston, Chicago, Los Angeles, Miami, Tulsa,  Washington DC. disaggregated by race and ancestry.\n\nWith this backdrop, the talk will explore the manner in which  asymmetries in power and economic agency, especially by race and other identity groupings, link to political economic structures and more macro economic deprivations that plague our economy. Ultimately, this talk will explore the concepts of purpose, power, paradigm and solidarity with a purpose of defining and determining an alternative economic vision for a human rights economy  as the point and mechanism for a well-functioning economy and multiracial democracy.\u201d\n\nDarrick Hamilton is the University Professor and Henry Cohen Professor of Economics and Urban Policy at The New School, where he also founded and directs the Institute on Race, Power & Political Economy. He additionally serves as Chief Economist at the AFL-CIO. Widely regarded as one of the nation\u2019s foremost public intellectuals, Professor Hamilton reimagines how an economy should work\u2014identifying bold opportunities to invest in our human capacity and fostering collaborations that advance economic inclusion, social equity, and civic engagement in the United States and around the world.\n\nA pioneer in the economics subfield of identity group stratification, Professor Hamilton\u2019s research has been featured in The New York Times, Mother Jones, Bloomberg Businessweek, and The Wall Street Journal. He has developed and advised on transformative policy proposals\u2014such as baby bonds, guaranteed income, and a federal job guarantee\u2014that have inspired legislation and shifted billions of dollars toward building a fair and inclusive economy.\n\nIn 2025, Professor Hamilton was named the Katherine Hampson Bessell Fellow at the Harvard Radcliffe Institute and was recognized as a Freedom Scholar by the Marguerite Casey Foundation in its 2020 inaugural class. He has advised national and global leaders on economic policy, including the U.S. Joint Economic Committee and the Senate Banking Committee and also serves on the board of directors of the National Bureau of Economic Research (NBER).\n\nBorn and raised in Brooklyn\u2019s Bedford-Stuyvesant neighborhood, Professor Hamilton earned his bachelor\u2019s degree from Oberlin College and his Ph.D. in Economics from the University of North Carolina at Chapel Hill.\n\nPlease RSVP to save your seat.",
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        "datetime_modified":"20260401T090119",
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        "datetime_end":"20260409T115000",
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        "event_title":"Improved Inference for Nonparametric Regression (joint with G.Cavaliere, M. Nielsen, and E. Zanelli)",
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        "combined_title":"Improved Inference for Nonparametric Regression (joint with G.Cavaliere, M. Nielsen, and E. Zanelli): Silvia Goncales, McGill University",
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        "event_type":"Workshop \/ Seminar",
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        "description":"Nonparametric regression estimators, including those employed in regression-discontinuity designs (RDD), are central to the economist\u2019s toolbox. Their application, however, is complicated by the presence of asymptotic bias, which undermines coverage accuracy of conventional confidence intervals. Extant solutions to the problem include debiasing methods, such as the widely applied robust bias-corrected (RBC) confidence interval of Calonico et al. (2014, 2018). We show that this interval is equivalent to a prepivoted interval based on an invalid residualbased bootstrap method. Specifically, prepivoting performs an implicit bias correction while adjusting the nonparametric regression estimator\u2019s standard error to account for the additional uncertainty introduced by debiasing. This idea can also be applied to other bootstrap schemes, leading to new implicit bias corrections and corresponding standard error adjustments. We propose a prepivoted interval based on a bootstrap that generates observations from nonparametric regression estimates at each regressor value and show how it can be implemented as an RBCtype interval without the need for resampling. Importantly, we show that the new interval is shorter than the existing RBC interval. For example, with the Epanechnikov kernel, the length is reduced by 17%, while maintaining accurate coverage probability. This result holds irrespectively of: (a) the evaluation point being in the interior or on the boundary; (b) the use of a \u2018small\u2019 or \u2018large\u2019 bandwidths; (c) the distribution of the regressor and the error term.",
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        "datetime_modified":"20260327T172407",
        "datetime_start":"20260409T130000",
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        "event_title":"The Drafted Nation: Economic and Political Legacies of Conscription (with Siddharth George and Kewei Zhang)",
        "occurrence_title":"",
        "combined_title":"The Drafted Nation: Economic and Political Legacies of Conscription (with Siddharth George and Kewei Zhang): Sam Bazzi, University of California, San Diego",
        "event_subtitle":"Sam Bazzi, University of California, San Diego",
        "event_type":"Workshop \/ Seminar",
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        "description":"This paper provides the first comprehensive evidence on the multifaceted legacy of military conscription across the world. We construct a new global database of conscription policy reforms linked to hundreds of census and survey data sources. Exploiting cohort-based eligibility cutoffs, we compare individuals just eligible for service to those just exempt. On average, conscription increases men's adult socioeconomic status, driven by higher university attainment, greater geographic mobility, and the transferability of military-acquired skills. Effects are largest where the opportunity cost of service is low and reintegration into civilian life is strong. Economic gains extend to households: women who marry conscription-eligible men experience higher living standards, and their children exhibit lower mortality, despite no direct effects on women's own education or employment. Conscription also fosters interethnic marriage, national-language use, and patriotic attachment, but simultaneously increases xenophobia and gender conservatism, revealing a tradeoff between national integration and tolerance. Effects vary widely across countries depending on political institutions, development, diversity, and conscription design. Economic and sociopolitical effects are positively correlated, suggesting that nation-building is strongest where conscription delivers greater economic returns.",
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        "datetime_modified":"20260408T122831",
        "datetime_start":"20260410T120000",
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        "time_start":"12:00:00",
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        "time_zone":"America\/Detroit",
        "event_title":"CSEAS Friday Lecture Series | Incentivizing Lending to Women Entrepreneurs: Evidence from Vietnam",
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        "combined_title":"CSEAS Friday Lecture Series | Incentivizing Lending to Women Entrepreneurs: Evidence from Vietnam: Markus Taussig, Rutgers University",
        "event_subtitle":"Markus Taussig, Rutgers University",
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        "description":"Please note: This lecture will be held in person and virtually on Zoom. The webinar is free and open to the public, but registration is required. Once you've registered, joining information will be sent to your email. Register for the Zoom webinar at: https:\/\/myumi.ch\/9p7D9\n\n In collaboration with a leading Vietnamese commercial bank, we evaluate the impact of two types of incentives to loan officers for the recruitment of women-owned or -led small- and medium-sized enterprises (WSMEs) as new borrowing clients. The average loan size in this segment is about $20,000 USD (5x GDP per capita). 50 bank branches employing 550 lending staff are randomly assigned to one of the following treatments: (1) a monthly multi-category contest; (2) a piece-rate incentive per loan; or (3) control. The multi-category contest is inclusive in that it rewards not just the very top performing agents, but also top performers among new agents (\u201crookies\u201d) and most improved performers. We find that, overall, both interventions cause an increase in WSME lending, particularly in later periods, at largely similar magnitudes. In terms of spillovers, there may be moderate negative impacts on non-WSME lending early in the treatment period, but over time there is a significant, positive spillover on non-WSME lending. Our results have the potential to inform policies to promote (W)SME lending through optimizing loan officer incentives. More broadly, they provide some of the first experimental evidence from a high-stakes, real-world setting on whether competitive or piece rate incentives better improve the performance of high-skill workers completing uncertain tasks.\n\nMarkus Taussig\u2019s research and teaching focus on international business and strategy, with a focus on emerging economies\u2014especially those in Southeast Asia. More specifically, he studies how firm performance and behavior is influenced by weak market institutions. He also has extensive experience and expertise in the global private equity and manufacturing (especially apparel) industries and in survey design and implementation of randomized controlled trials (RCTs).\n\n*Accommodation: If there is anything we can do to make this event accessible to you, please contact us at cseas@umich.edu. Please be aware that advance notice is necessary as some accommodations may require more time for the university to arrange.*",
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        "date_start":"2026-04-14",
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        "event_title":"Lock-in and productive innovations: implications for firm-to-firm innovation pass-through",
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        "combined_title":"Lock-in and productive innovations: implications for firm-to-firm innovation pass-through: Luc\u00eda Casal, Columbia Business School",
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        "description":"Firms innovate to improve efficiency and reduce their costs of production (productive innovations) and to increase customer dependency by reducing the substitutability of their products (lock-in innovations). In this paper, I quantitatively study the macroeconomic implications of lock-in innovations for aggregate productivity and market power. I develop a theoretical framework that allows firms to invest in lock-in innovations by reducing product substitutability, while also nesting standard macroeconomic models of productive innovations. A key prediction of the model is that productive innovations by suppliers increase customer firms\u2019 sales by lowering input costs, while lock-in innovations decrease customer firms' sales by allowing suppliers to charge higher prices for products that are harder to substitute. I use this theoretical insight to identify the nature of innovation in the data and calibrate the model to the U.S. economy. Informed by the observed changes in the response of customer firms' sales to their suppliers' innovations, I find that the incidence of lock-in innovations among high-markup firms has increased significantly in the post-2000 period. Moreover, had the incidence of lock-in innovations remained at pre-2000 levels, observed aggregate productivity would have been 3% higher, median markups would have stayed at pre-2000 levels, and markup dispersion would have been 9% lower.",
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        "event_title":"Screening Frontiers",
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        "combined_title":"Screening Frontiers: Frank Yang, Harvard University",
        "event_subtitle":"Frank Yang, Harvard University",
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        "description":"A principal screens an agent with an arbitrary set of allocations X. The agent\u2019s preferences over allocations are comonotonic. A subset of allocations X \u2039 \u010e X is a surplus-elasticity frontier if (i) any other allocation has a demand curve that is pointwise lower and less elastic than some allocation in X \u2039 and (ii) the allocations in X \u2039 can be ordered in terms of their demand curves such that a higher demand curve is more inelastic. We show that any surplus-elasticity frontier is an optimal menu. Moreover, if the incremental demand curves along the frontier are also ordered by their elasticities, then the frontier is optimal even among stochastic mechanisms. The result is agnostic to type distributions and redistributive welfare weights\u2014the same frontier remains optimal for a broad class of objectives. As applications, we show how these results immediately yield new insights into optimal bundling, optimal taxation, sequential screening, selling information, and regulating a data-rich monopolist.",
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        "event_title":"Same Crime, Different Time: Disparities in Judicial Outcomes for DWI Offenders",
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        "description":"Can increasing women's use of digital financial services raise their empowerment? We test this hypothesis using a randomized control trial with 152 female microfinance groups in Tanzania, where treated groups were randomly switched from cash to mobile money loan repayment. This exogenous increase in women's use of mobile money leads women to expand their use for other types of transactions, including business payments and saving. Women's control over their finances increases and they are more empowered within the household. Over time, women's businesses become more profitable. These findings highlight the benefits of shifting payments to digital for women.",
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