nep-law New Economics Papers
on Law and Economics
Issue of 2023‒08‒14
six papers chosen by
Yves Oytana, Université de Franche-Comté

  1. Financial Crime and Punishment: A Meta-Analysis By Laure de Batz; Evžen Kočenda; Evžen Kocenda
  2. Organized Crime, Corruption and Economic Growth By Tamara Fioroni; Andrea Mario Lavezzi; Giovanni Trovato
  3. The Digital Markets Act and the Whack-A-Mole Challenge By Jens-Uwe Franck; Martin Peitz
  4. Weberian Civil Service and Labor Enforcement By Dewey, Matías; Ronconi, Lucas
  5. Algorithms, Incentives, and Democracy By Elizabeth Maggie Penn; John W. Patty
  6. Law-Abiding Immigrants: The Incarceration Gap Between Immigrants and the US-born, 1850–2020 By Ran Abramitzky; Leah Platt Boustan; Elisa Jácome; Santiago Pérez; Juan David Torres

  1. By: Laure de Batz; Evžen Kočenda; Evžen Kocenda
    Abstract: We provide the first quantitative synthesis of the literature on how financial markets react to the disclosure of financial crimes committed by listed firms. While consensus expects negative stock price returns, the exact size of the effect is far from clear. We survey 111 studies published over three decades, from which we collect 480 estimates from event studies. Then, we perform a thorough meta-analysis based on the most recent available techniques. We show that the negative abnormal returns found in the literature seem to be exaggerated by more than three times. Hence, the “punishment” effect, including a reputational penalty, suffers from a serious publication bias. After controlling for this bias, negative abnormal returns suggest the existence of an informational effect. We also document that accounting frauds, crimes committed in common-law countries such as the United States, and allegations are particularly severely sanctioned by financial markets, while the information channels and types of procedures do not influence market reactions.
    Keywords: meta-analysis, event study, financial misconduct, trust, information and market efficiency, listed companies, crime
    JEL: C83 G14 G18 K42 N24
    Date: 2023
  2. By: Tamara Fioroni; Andrea Mario Lavezzi; Giovanni Trovato
    Abstract: In this paper we study the relationship between organized crime, corruption and economic growth. To shed light on this nexus, we propose a growth model in which organized crime can embezzle public spending by corrupting and threatening public officers. Then we bring the empirical implications of the model to data from Italian regions, as stylized facts show that less developed regions are characterized by the highest levels of corruption and of presence of criminal organizations of Mafia-type. Our main findings are: i) the per capita GDP dynamics of Italian regions in the period considered is characterized by multiple regimes identified by the initial level of organized crime, a finding consistent with a multiple steady state growth dynamics (e.g. Durlauf and Johnson, 1995); ii) in the regions with the higher levels of organized crime the estimated share of embezzled public expenditure is higher and, moreover, public expenditure has a negative effect on per capita GDP. Differently, in the regions with lower levels of organized crime the estimated share of embezzled public expenditure is lower and the effect of public expenditure on per capita income is positive.
    Keywords: Corruption, Organized crime, Economic growth, Public expenditure
    JEL: K42 O17 R11 O23
    Date: 2023–07–01
  3. By: Jens-Uwe Franck; Martin Peitz
    Abstract: The article addresses the implementation of the Digital Market Act’s rules on ‘anticircumvention’. We present an effects-based approach and propose a three-step procedure to identify whether a certain practice should be conceptualized as circumventing an obligation. We apply this approach to several practices suspected of circumventing the ban on parity clauses and analyse how our results fit into the Digital Market Act’s concept and instruments for avoiding circumvention. Moreover, we elaborate on the role that the anticircumvention rules may play in safeguarding the effectiveness of the restrictions on bundling and self-preferencing in ranking, thus illustrating how they may operate to future-proof the Digital Markets Act but also where their limitations lie.
    Keywords: Digital Markets Act, anti-circumvention, antitrust law, price parity clauses, bundling, self-preferencing
    JEL: K21
    Date: 2023–07
  4. By: Dewey, Matías (University of St. Gallen); Ronconi, Lucas (University of Buenos Aires)
    Abstract: Most workers in the developing world do not receive the benefits they are legally entitled to. Why, then, is there so little public enforcement? This paper argues that this is partly because of a lack of an autonomous and professional bureaucracy. Using a novel dataset with objective measures of labor inspections and fines across countries, we show that Weberian bureaucracies are more likely to enforce labor standards. We provide OLS and 2SLS estimates that address endogeneity concerns and use ethnographic evidence collected in Latin America to understand the mechanisms better. The case study suggests that politicized bureaucracies underinvest in labor inspection because elected officials have short-term horizons and do not internalize the social benefits of enforcement (such as formal job creation and enhancement of the rule of law) because they take time to materialize.
    Keywords: enforcement, autonomy, compliance, state-capture, labor
    JEL: J88 K42 O17 P50
    Date: 2023–07
  5. By: Elizabeth Maggie Penn; John W. Patty
    Abstract: Classification algorithms are increasingly used in areas such as housing, credit, and law enforcement in order to make decisions affecting peoples' lives. These algorithms can change individual behavior deliberately (a fraud prediction algorithm deterring fraud) or inadvertently (content sorting algorithms spreading misinformation), and they are increasingly facing public scrutiny and regulation. Some of these regulations, like the elimination of cash bail in some states, have focused on \textit{lowering the stakes of certain classifications}. In this paper we characterize how optimal classification by an algorithm designer can affect the distribution of behavior in a population -- sometimes in surprising ways. We then look at the effect of democratizing the rewards and punishments, or stakes, to algorithmic classification to consider how a society can potentially stem (or facilitate!) predatory classification. Our results speak to questions of algorithmic fairness in settings where behavior and algorithms are interdependent, and where typical measures of fairness focusing on statistical accuracy across groups may not be appropriate.
    Date: 2023–07
  6. By: Ran Abramitzky; Leah Platt Boustan; Elisa Jácome; Santiago Pérez; Juan David Torres
    Abstract: We combine full-count Census data (1850–1940) with Census/ACS samples (1950–2020) to provide the first nationally representative long-run series (1850–2020) of incarceration rates for immigrants and the US-born. As a group, immigrants had higher incarceration rates than US-born white men before 1870, similar rates between 1880-1950, and lower rates since 1960. Although there are substantial differences in incarceration by origin country, the relative decline in incarceration since 1960 occurred among immigrants from all sending regions. This decline cannot be explained by changes in immigrants’ observable characteristics or immigration policy, but may reflect immigrants’ resilience to economic shocks.
    JEL: K4 N31
    Date: 2023–07

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