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Lack of privacy due to surveillance of personal data, which is becoming ubiquitous around the world, induces persistent conformity to the norms prevalent under the surveillance regime. We document this channel in a unique laboratory---the widespread surveillance of private citizens in East Germany. Exploiting localized variation in the intensity of surveillance before the fall of the Berlin Wall, we show that, at the present day, individuals who lived in high-surveillance counties are more likely to recall they were spied upon, display more conformist beliefs about society and individual interactions, and are hesitant about institutional and social change. Social conformity is accompanied by conformist economic choices: individuals in high-surveillance counties save more and are less likely to take out credit, consistent with norms of frugality. The lack of differences in risk aversion and binding financial constraints by exposure to surveillance helps to support a beliefs channel.
Supranational supervision
(2022)
We exploit the establishment of a supranational supervisor in Europe (the Single Supervisory Mechanism) to learn how the organizational design of supervisory institutions impacts the enforcement of financial regulation. Banks under supranational supervision are required to increase regulatory capital for exposures to the same firm compared to banks under the local supervisor. Local supervisors provide preferential treatment to larger institutes. The central supervisor removes such biases, which results in an overall standardized behavior. While the central supervisor treats banks more equally, we document a loss in information in banks’ risk models associated with central supervision. The tighter supervision of larger banks results in a shift of particularly risky lending activities to smaller banks. We document lower sales and employment for firms receiving most of their funding from banks that receive a tighter supervisory treatment. Overall, the central supervisor treats banks more equally but has less information about them than the local supervisor.
Industry concentration and markups in the US have been rising over the last 3-4 decades. However, the causes remain largely unknown. This paper uses machine learning on regulatory documents to construct a novel dataset on compliance costs to examine the effect of regulations on market power. The dataset is comprehensive and consists of all significant regulations at the 6-digit NAICS level from 1970-2018. We find that regulatory costs have increased by $1 trillion during this period. We document that an increase in regulatory costs results in lower (higher) sales, employment, markups, and profitability for small (large) firms. Regulation driven increase in concentration is associated with lower elasticity of entry with respect to Tobin's Q, lower productivity and investment after the late 1990s. We estimate that increased regulations can explain 31-37% of the rise in market power. Finally, we uncover the political economy of rulemaking. While large firms are opposed to regulations in general, they push for the passage of regulations that have an adverse impact on small firms.
Resolving financial distress where property rights are not clearly defined: the case of China
(2022)
We use data on financially distressed Chinese companies in order to study a debt market where property rights are crudely defined and poorly enforced. To help with identification we use an event where a business-friendly province published new guidelines regarding the administration and enforcement of assets pledged as collateral. Although by no means a comprehensive reform of bankruptcy law or property rights, by instructing courts to enforce existing, albeit rudimentary, contractual rights the new guidelines virtually eliminated creditors runs and produced a sharp increase in the survival rate of financially-distressed companies. These changes illustrate how piecemeal reforms of property rights and their enforcement may have a significant impact on economic outcomes. Our analysis and results challenge the view that a fully fledged system of private property is a precondition for economic development.
This study examines the recent literature on the expectations, beliefs and perceptions of investors who incorporate Environmental, Social, Governance (ESG) considerations in investment decisions with the aim to generate superior performance and also make a societal impact. Through the lens of equilibrium models of agents with heterogeneous tastes for ESG investments, green assets are expected to generate lower returns in the long run than their non- ESG counterparts. However, at the short run, ESG investment can outperform non-ESG investment through various channels. Empirically, results of ESG outperformance are mixed. We find consensus in the literature that some investors have ESG preference and that their actions can generate positive social impact. The shift towards more sustainable policies in firms is motivated by the increased market values and the lower cost of capital of green firms driven by investors’ choices.
This paper provides a review of the development of the non-fungible tokens (NFTs) market, with a particular focus on its pricing determinants, its current applications and future opportunities. We investigate the current state of the NFT markets and highlight the perception and expectations of investors towards these products. We summarize and compare the financial and econometric models that have been used in the literature for the pricing of non-fungible tokens with a special focus on their predictive performance. Our intention is to design a framework that can help understanding the price formation of NFTs. We further aim to shed light on the value creating determinants of NFTs in order to better understand the investors’ behavior on the blockchain.
Biased auctioneers
(2022)
We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates’ informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers’ prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
This paper examines how the implementation of a new dark order - Midpoint Extended Life Order on NASDAQ - impacts financial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high-frequency order book data and apply panel regression analysis to estimate the effect of M-ELO trading on market stability and liquidity provision. The results suggest a predominance of a speed bump effect of M-ELO rather than a darkness effect. We find that the introduction of M-ELO increases market stability by reducing the average number of mini-flash crashes, but its impact on market quality is mixed.
The right to ask questions and voice their opinions at annual general meetings (AGMs) represents one of the few avenues for shareholders to communicate directly and publicly with the firm’s management. Examining AGM transcripts of U.S. companies between 2007 and 2021, we find that shareholders actively express their concerns about environmental, social and governance (ESG) issues in accordance with their specific relationship with the company. Further, they are also demonstrably more vocal about ESG issues at AGMs of firms with poor sustainability performance. What is more, we show that this soft engagement translates into a more negative tone which, in turn, results in lower approval rates for management proposals. Shareholders' soft engagement at AGMs is hence an effective way to "walk the talk".
The issuance of sustainability-linked loans (SLLs) has grown exponentially in recent years. Using a scoring methodology, we examine the underlying key performance indicators of a large sample of SLLs and analyze whether their design creates effective incentives for improving corporate sustainability performance. We demonstrate that the majority of loans fails to meet key requirements that would make them credible instruments for generating effective sustainability incentives. These findings call into question the actual sustainability impact that may be achieved through the issuance of ESG-linked debt.
Consumers purchase energy in many forms. Sometimes energy goods are consumed directly, for instance, in the form of gasoline used to operate a vehicle, electricity to light a home, or natural gas to heat a home. At other times, the cost of energy is embodied in the prices of goods and services that consumers buy, say when purchasing an airline ticket or when buying online garden furniture made from plastic to be delivered by mail. Previous research has focused on quantifying the pass-through of the price of crude oil or the price of motor gasoline to U.S. inflation. Neither approach accounts for the fact that percent changes in refined product prices need not be proportionate to the percent change in the price of oil, that not all energy is derived from oil, and that the correlation of price shocks across energy markets is far from one. This paper develops a vector autoregressive model that quantifies the joint impact of shocks to several energy prices on headline and core CPI inflation. Our analysis confirms that focusing on gasoline price shocks alone will underestimate the inflationary pressures emanating from the energy sector, but not enough to overturn the conclusion that much of the observed increase in headline inflation in 2021 and 2022 reflected non-energy price shocks.
We propose a new instrument for estimating the price elasticity of gasoline demand that exploits systematic differences across U.S. states in the pass-through of oil price shocks to retail gasoline prices. These differences, which are primarily driven by variation in the cost of producing and distributing gasoline, create cross-sectional dispersion in gasoline price growth in response to an aggregate oil price shock. We find that the elasticity was stable near -0.3 until the end of 2014, but subsequently rose to about -0.2. Our estimates inform the recent debate about gasoline-tax holidays and policies to reduce carbon emissions.
This paper investigates retirees’ optimal purchases of fixed and variable longevity income annuities using their defined contribution (DC) plan assets and given their expected Social Security benefits. As an alternative, we also evaluate using plan assets to boost Social Security benefits through delayed claiming. We determine that including deferred income annuities in DC accounts is welfare enhancing for all sex/education groups examined. We also show that providing access to well-designed variable deferred annuities with some equity exposure further enhances retiree wellbeing, compared to having access only to fixed annuities. Nevertheless, for the least educated, delaying claiming Social Security is preferred, whereas the most educated benefit more from using accumulated DC plan assets to purchase deferred annuities.
We investigate consumption patterns in Europe with supervised machine learning methods and reveal differences in age and wealth impact across countries. Using data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS), we assess how age and (liquid) wealth affect the marginal propensity to consume (MPC) in the Netherlands, Germany, France, and Italy. Our regression analysis takes the specification by Christelis et al. (2019) as a starting point. Decision trees are used to suggest alternative variable splits to create categorical variables for customized regression specifications. The results suggest an impact of differing wealth distributions and retirement systems across the studied Eurozone members and are relevant to European policy makers due to joint Eurozone monetary policy and increasing supranational fiscal authority of the EU. The analysis is further substantiated by a supervised machine learning analysis using a random forest and XGBoost algorithm.
Mamma mia! Revealing hidden heterogeneity by PCA-biplot : MPC puzzle for Italy's elderly poor
(2023)
I investigate consumption patterns in Italy and use a PCA-biplot to discover a consumption puzzle for the elderly poor. Data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS) indicate that Italian poor old-aged households boast lower levels of the marginal propensity to consume (MPC) than suggested by the dominant consumption models. A customized regression analysis exhibits group differences with richer peers to be only half as large as prescribed by a traditional linear regression model. This analysis has benefited from a visualization technique for high-dimensional matrices related to the unsupervised machine learning literature. I demonstrate that PCA-biplots are a useful tool to reveal hidden relations and to help researchers to formulate simple research questions. The method is presented in detail and suggestions on incorporating it in the econometric modeling pipeline are given.
Fund companies regularly send shareholder letters to their investors. We use textual analysis to investigate whether these letters’ writing style influences fund flows and whether it predicts performance and investment styles. Fund investors react to the tone and content of shareholder letters: A less negative tone leads to higher net flows. Thus, fund companies can use shareholder letters as a tactical instrument to influence flows. However, at the same time, a dishonest communication that is not consistent with the fund’s actual performance decreases flows. A positive writing style predicts higher idiosyncratic risk as well as more style bets, while there is no consistent predictive power for future performance.
Optimal monetary policy studies typically rely on a single structural model and identification of model-specific rules that minimize the unconditional volatilities of inflation and real activity. In their proposed approach, the authors take a large set of structural models and look for the model-robust rules that minimize the volatilities at those frequencies that policymakers are most interested in stabilizing. Compared to the status quo approach, their results suggest that policymakers should be more restrained in their inflation responses when their aim is to stabilize inflation and output growth at specific frequencies. Additional caution is called for due to model uncertainty.
Who should hold bail-inable debt and how can regulators police holding restrictions effectively?
(2023)
This paper analyses the demand-side prerequisites for the efficient application of the bail-in tool in bank resolution, scrutinises whether the European bank crisis management and deposit insurance (CMDI) framework is apt to establish them, and proposes amendments to remedy identified shortcomings.
The first applications of the new European CMDI framework, particularly in Italy, have shown that a bail-in of debt holders is especially problematic if they are households or other types of retail investors. Such debt holders may be unable to bear losses, and the social implications of bailing them in may create incentives for decision makers to refrain from involving them in bank resolution. In turn, however, if investors can expect resolution authorities (RAs) to behave inconsistently over time and bail-out bank capital and debt holders despite earlier vows to involve them in bank rescues, the pricing and monitoring incentives that the crisis management framework seeks to invigorate would vanish. As a result, market discipline would be suboptimal and moral hazard would persist. Therefore, the policy objectives of the CMDI framework will only be achieved if critical bail-in capital is not held by retail investors without sufficient loss-bearing capacity. Currently, neither the CMDI framework nor capital market regulation suffice to assure that this precondition is met. Therefore, some amendments are necessary. In particular, debt instruments that are most likely to absorb losses in resolution should have a high minimum denomination and banks should not be allowed to self-place such securities.
The loan impairment rules recently introduced by IFRS 9 require banks to estimate their future credit losses by using forward-looking information. We use supervisory loan-level data from Germany to investigate how banks apply their reporting discretion and adjust their lending upon the announcement of the new rules. Our identification strategy exploits a cut-off for the level of provisions at the investment grade threshold based on banks’ internal rating of a borrower. We find that banks required to adopt the new rules assign better internal ratings to exactly the same borrowers compared to banks that do not apply IFRS 9 around this cut-off. This pattern is consistent with a strategic use of the increased reporting discretion that is inherent to rules requiring forward-looking loss estimation. At the same time, banks also reduce their lending exposure to exactly those borrowers at the highest risk of experiencing a rating downgrade below the cutoff. These loans would be associated with additional provisions in future periods, both in the intensive and extensive margin. The lending change thus mitigates some of the negative effects of increased reporting opportunism on banks’ crisis resilience. However, when these firms with internal ratings around the investment grade cut-off obtain less external funding through banks, the introduction of IFRS 9 will likely also be associated with real economic effects
Using German and US brokerage data we find that investors are more likely to sell speculative stocks trading at a gain. Investors’ gain realizations are monotonically increasing in a stock’s speculativeness. This translates into a high disposition effect for speculative and a much lower disposition effect for non-speculative stocks. Our findings hold across asset classes (stocks, passive, and active funds) and explain cross-sectional differences in investor selling behavior which previous literature attributed primarily to investor demographics. Our results are robust to rank or attention effects and can be linked to realization utility and rolling mental account.