The Value of Software

Software is one of the most important assets that needs to be priced in the digital economy. It has emerged as a disruptive technology, with companies primarily valued for their software offerings growing from 2% to 13% of market share between 1996 and 2023. We document persistent anomalies in growth forecasts and stock returns for software companies, indicating significant deviations from rational expectations over multiple decades. Our findings are consistent with Bayesian investors gradually learning about software’s growing importance, highlighting how markets can be very slow to discern fundamental shifts from transient shocks in noisy data

Universal Owners and Climate Change

Universal ownership theory proposes that widely diversified investors have a financial self interest at the portfolio level in reducing market-wide risks relating to environmental or social (ES) issues. This paper sets out a double test for determining when universal owner theory justifies investor action and applies these tests to the case of climate change. When applied to the commonly adopted goal of limiting global warming to 1.5C, universal owner theory runs into problems on both tests. First, it is uncertain whether this goal is financially optimal at the portfolio level. Second, even if it were optimal, investors have limited efficacy to achieve this outcome. We consider goals a climate-concerned investor might set and the actions they could take that would be consistent with our tests. Actions best supported by evidence involves four areas of focus. First, engagement with investee companies based on realistic goals. Second, positive engagement on policy. Third, modest and bounded impact investments that can credibly be considered as reducing climate risk. Fourth, working to ensure that transition and physical risks are fully incorporated into investment models. Through targeting a more modest set of ambitions, climate-concerned investors can be more impactful while avoiding conflicts with fiduciary duties to clients

Data-Driven Investors

How does the increased use of data technologies, like machine learning, by financial intermediaries affect the allocation of capital towards innovation? I study this question in the context of startup financing by venture capitalists (VCs). While VCs adopting data technologies become better at screening startups similar to those in historical data, they tilt their investments towards this pool and become concurrently less likely to finance innovative startups that achieve rare major success. Plausibly exogenous variations in VCs’ screening automation suggest that these effects are causal. These findings highlight how investors’ adoption of data technologies can have real effects through innovation financing

Political Information and Network Effects

Why do political campaigns so often yield unexpected results? We address this question by separately estimating the direct effect of a campaign on targeted voters and the indirect effect on others in the same social environment. Partnering with a local NGO during Argentina’s 2023 presidential election, we randomized the distribution of leaflets providing an expert assessment of the likely consequences of certain proposals by the outsider candidate Javier Milei. Exploiting Argentina’s unique sub-precinct election reporting system, we show that the campaign reduced Milei’s support among directly treated voters, as expected, but increased his support among untreated voters in treated precincts, producing a backfiring, net-positive effect for Milei. A pre-registered replication confirmed these opposite-signed effects. Using theory and a survey experiment, we show that the minority of voters who disbelieved the campaign were more motivated to discuss it with peers, convincing them to support Milei. This mobilization effect appears especially likely when campaigns criticize outsider candidates. Our results highlight how campaigns aimed at anti-elite candidates can unintentionally mobilize support for them

Firms’ Real and Reporting Responses to Taxation: A Review

Taxation is a central economic policy tool, with governments increasingly using tax policy to stimulate local economic growth and also regulate multinational firms. We review the empirical literature that studies the effect of tax policies on firms’ investment, employment, and other real outcomes. Building on the neoclassical theory of corporate taxes and tangible investment, we propose an organizing framework for our review that captures the wide set of tax policies and firm responses examined in accounting research. This framework highlights four dimensions along which accounting scholars contribute to the literature: (i) documenting the role of financial reporting incentives as a moderating factor in firms’ real responses, (ii) studying firms’ reporting versus real responses, (iii) quantifying real effects of tax disclosure regulations, and (iv) improving measurement of firms’ tax status and proxies for investment and employment. We identify open questions for future research and suggest new international, federal, and local settings that may help uncover underlying mechanisms driving observed economic phenomena. Specifically, we encourage scholars to further distinguish firms’ reported and real responses to tax changes and improve measurement of these outcomes, especially in settings related to environmental taxation or settings in which tax avoidance and real outcomes are closely linked

The Click-based MNL Model: A Framework for Modeling Click Data in Assortment Optimization

We introduce the click-based MNL choice model, a framework for capturing customer purchasing decisions in e-commerce settings. Specifically, we augment the classical Multinomial Logit choice model by assuming that customers only consider the items they have clicked on before they proceed to compare their random utilities. In this context, we study the resulting assortment optimization problem, where the objective is to select a subset of products, made available for purchase, to maximize the expected revenue. Our main algorithmic contribution comes in the form of a polynomial-time approximation scheme (PTAS) for this problem, showing that the optimal expected revenue can be efficiently approached within any degree of accuracy. To establish this result, we develop several technical ideas, including enumeration schemes and stochastic inequalities, which may be of broader interest. Using data from Alibaba’s online marketplace, we fit click-based MNL and latent class MNL models to historical sales and click data in a setting where the online platform recommends a personalized six-product display to each user. We propose an estimation methodology for the click-based MNL model that leverages clickstream data and machine learning classification algorithms. Our numerical results suggest that clickstream data are valuable for predicting choices and that the click-based MNL model can outperform standard logit-based models in certain settings

Behind the Curtain of Workforce Diversity: Evidence from EEO-1 Reports

We leverage the 2023 court-ordered FOIA release of standardized Equal Employment Opportunity (EEO-1) reports to examine the workforce diversity of federal contractors. Using the released data for a sample of over 19,000 publicly traded and private firms, we provide descriptive evidence on the variation in gender and racial diversity of these companies’ workforce. We also document the existence of a racial gap between managers and lower-level employees. A substantial portion of that gap cannot be explained by industry or geographic factors, reflecting the influence of firm-level characteristics. Then, focusing on a sample of over 800 publicly traded federal contractors, we find robust evidence that the racial managerial gap is associated with firms’ decision to withhold the voluntary disclosure of their EEO-1 forms. While our findings are subject to several caveats, we provide important evidence on workforce diversity and highlight the importance of using granular, firm-level data to study diversity topics

Ask a Local: Improving the Public Pricing of Land Titles in Urban Tanzania

Information on willingness-to-pay is key for public pricing and allocation of services but not easily collected. Studying land titles in Dar-es-Salaam, we ask whether local leaders know and will reveal plot owners' willingness-to-pay. We randomly assign leaders to predict under different settings then elicit owners' actual willingness-to-pay. Demand is substantial, but below exorbitant fees. Leaders can predict the aggregate demand curve and distinguish variation across owners. Predictions worsen when used to target subsidies, but adding cash incentives mitigates this. Finally, we demonstrate that leader-elicited information can improve the public pricing of title deeds, raising uptake while maintaining public funds