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ESMT Working Paper
ESMT Working Paper No. 21-02
Hans W. Friederiszick, Alexis Walckiers (2021)
An increasing number of countries have introduced some form of prohibition of abuses of economic dependence or broadened the scope of their existing legislation. Yet, very little has been written on the economics of economic dependence, that is on economic reasoning, tools or metrics that can be relied upon to identify whether a company is economically dependent on another company. The present paper aims to fill this gap, and argues that bargaining theory and the economics of relative market power can be helpful to characterise economic dependence. We summarise a number of takeaways from this literature, and describe empirical strategies that can be relied upon to try and quantify economic dependence in specific cases.
ISSN (Print)
1866–3494
Subject(s)
Economics, politics and business environment
Keyword(s)
Reverse privatization, solid waste collection, mixed oligopoly, state-owned enterprises, competition law enforcement, logit regression
JEL Code(s)
L33, L44, L88, H44, K21
After earlier waves of privatization, local governments have increasingly taken back control of local service provisions in some sectors and countries and instead started providing those services themselves (reverse privatization). Using a unique panel dataset on the mode of service provision for solid waste collection for German municipalities that cover the years 2003, 2009, and 2015, we investigate the motives for reverse privatization. Our results show that -- in deciding whether to insource or not -- municipalities react to the cost advantages of private suppliers as well as to the competitive environment and municipal activity: There is more switching to insourcing in concentrated markets and in markets with horizontally or vertically related public services. Local interest groups influence this decision as well.
Pages
43
ISSN (Print)
1866–3494
ESMT Working Paper
ESMT Working Paper No. 19-02 (R3)
Işık Biçer, Florian Lücker, Tamer Boyaci (2021)
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management
Keyword(s)
Product proliferation, lead-time reduction, process redesign, delayed differentiation
Product proliferation occurs in supply chains when manufacturers respond to diverse market needs by trying to produce a range of products from a limited variety of raw materials. In such a setting, manufacturers can establish market responsiveness and/or cost efficiency in alternative ways. Delaying the point of the proliferation helps manufacturers improve their responsiveness by postponing the ordering decisions of the final products until there is partial or full resolution of the demand uncertainty. This strategy can be implemented in two different ways: (1) redesigning the operations so that the point of proliferation is swapped with a downstream operation or (2) reducing the lead times. To establish cost efficiency, manufacturers can systematically reduce their operational costs or postpone the high-cost operations. We consider a multi-echelon and multi-product newsvendor problem with demand forecast evolution to analyze the value of each operational lever of the responsiveness and the efficiency. We use a generalized forecast-evolution model to characterize the demand-updating process, and develop a dynamic optimization model to determine the optimal order quantities at different echelons. Using anonymized data of Kordsa Inc., a global manufacturer of advanced composites and reinforcement materials, we show that our model outperforms a theoretical benchmark of the repetitive newsvendor model. We demonstrate that reducing the lead time of a downstream operation is more beneficial to manufacturers than reducing the lead time of an upstream operation by the same amount, whereas reducing the upstream operational costs is more favorable than reducing the downstream operational costs. We also indicate that delaying the proliferation may cause a loss of profit, even if it can be achieved with no additional costs. Finally, a decision typology is developed, which shows effective operational strategies depending on product/market characteristics and process flexibility.
Pages
52
ISSN (Print)
1866–3494
Subject(s)
Economics, politics and business environment; Information technology and systems; Technology, R&D management
Keyword(s)
Fiber optic technology, state aid, ex-post evaluation, efficiency, OECD countries
JEL Code(s)
C51, C54, H25, L52, O38
The deployment of new broadband networks (NBNs) based on fiber-optic transmission technologies promises high gains in terms of productivity and economic growth, and has attracted subsidies worth billions from governments around the world in the form of various state aid programs. Yet, the effectiveness and the efficiency of such programs remains largely unstudied. We employ panel data from 32 OECD countries during 2002-2019 to provide robust empirical evidence of both. We find that state aid significantly increases NBNs by facilitating the deployment of new connections to 22% of households in the short term and 39.2% in the long term. By comparing the actual amounts of state aid support to the estimated impact on GDP growth, we also find it to be highly cost efficient, as the programs break even after three years on average.
Pages
35
ISSN (Print)
1866–3494
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management; Technology, R&D management
Keyword(s)
Data, machine learning, data product, pricing, incentives, contracting
This paper explores how firms that lack expertise in machine learning (ML) can leverage the so-called AI Flywheel effect. This effect designates a virtuous cycle by which, as an ML product is adopted and new user data are fed back to the algorithm, the product improves, enabling further adoptions. However, managing this feedback loop is difficult, especially when the algorithm is contracted out. Indeed, the additional data that the AI Flywheel effect generates may change the provider's incentives to improve the algorithm over time. We formalize this problem in a simple two-period moral hazard framework that captures the main dynamics among ML, data acquisition, pricing, and contracting. We find that the firm's decisions crucially depend on how the amount of data on which the machine is trained interacts with the provider's effort. If this effort has a more (less) significant impact on accuracy for larger volumes of data, the firm underprices (overprices) the product. Interestingly, these distortions sometimes improve social welfare, which accounts for the customer surplus and profits of both the firm and provider. Further, the interaction between incentive issues and the positive externalities of the AI Flywheel effect has important implications for the firm's data collection strategy. In particular, the firm can boost its profit by increasing the product's capacity to acquire usage data only up to a certain level. If the product collects too much data per user, the firm's profit may actually decrease, i.e., more data is not necessarily better.
Pages
48
ISSN (Print)
1866–3494
Working Paper
CEPR Discussion Paper No. DP16243
Simon P. Anderson, Özlem Bedre-Defolie (2021)
Subject(s)
Economics, politics and business environment
Keyword(s)
Trade platform, hybrid business model, antitrust policy, tax policy
JEL Code(s)
D42, L12, L13, L40, H25
We provide a canonical and tractable model of a trade platform enabling buyers and sellers to transact. The platform charges a percentage fee on third-party product sales and decides whether to be "hybrid", like Amazon, by selling its own product. It thereby controls the number of differentiated products (variety) it hosts and their prices. Using the mixed market demand system, we capture interactions between monopolistically competitive sellers and a sizeable platform product. Using long-run aggregative games with free entry, we endogenize seller participation through an aggregate variable manipulated by the platform's fee. We show that a higher quality (or lower cost) of the platform's product increases its market share and the seller fee, and lowers consumer surplus. Banning hybrid mode benefits consumers. The hybrid platform might favor its product and debase third-party products if the own product advantage is sufficiently high. We also provide some tax policy implications.
With permission of CEPR
Working Paper
SSRN Working Paper
Daniel Grodzicki, Alexei Alexandrov, Özlem Bedre-Defolie, Sergei Koulayev (2021)
Subject(s)
Economics, politics and business environment; Finance, accounting and corporate governance
Keyword(s)
Credit card demand reactions to fees, late fee regulation, limited attention
JEL Code(s)
D12, D90, G50
We introduce a model of a rational credit card user's rather complex usage choices and develop an empirical framework to test its predictions. Employing a large national database of U.S. card accounts, we estimate how prices impact card usage and find that price effects are mostly well explained within our model. An exception is less borrowing in response to declining late-fees among low credit-score (subprime) users. Extension of our model based on "focusing theory" predicts this behavior. It also implies substantial indirect benefits of the CARD Act's late-fee cap due to subprime users re-focusing toward reducing their debt.
Pages
46
ESMT Working Paper
ESMT Working Paper No. 20-03 (R2)
Forthcoming in Management Science.
Francis de Véricourt, Huseyin Gurkan, Shouqiang Wang (2021)
Subject(s)
Health and environment; Information technology and systems
Keyword(s)
Public health, epidemic control, information design, strategic behavior
This paper explores how governments may efficiently inform the public about an epidemic to induce compliance with their confinement measures. Using an information design framework, we find the government has an incentive to either downplay or exaggerate the severity of the epidemic if it heavily prioritizes the economy over population health or vice versa. Importantly, we find that the level of economic inequality in the population has an effect on these distortions. The more unequal the disease's economic impact on the population is, the less the government exaggerates and the more it downplays the severity of the epidemic. When the government weighs the economy and population health sufficiently equally, however, the government should always be fully transparent about the severity of the epidemic.


Pages
41
ISSN (Print)
1866–3494
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management; Technology, R&D management
Keyword(s)
Machine-learning, rational inattention, human-machine collaboration, cognitive effort
The rapid adoption of AI technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhance the complementary strengths of humans. Indeed, because of their immense computing power, machines can perform specific tasks with incredible accuracy. In contrast, human decision-makers (DM) are flexible and adaptive but constrained by their limited cognitive capacity. This paper investigates how machine-based predictions may affect the decision process and outcomes of a human DM. We study the impact of these predictions on decision accuracy, the propensity and nature of decision errors as well as the DM's cognitive efforts. To account for both flexibility and limited cognitive capacity, we model the human decision-making process in a rational inattention framework. In this setup, the machine provides the DM with accurate but sometimes incomplete information at no cognitive cost. We fully characterize the impact of machine input on the human decision process in this framework. We show that machine input always improves the overall accuracy of human decisions, but may nonetheless increase the propensity of certain types of errors (such as false positives). The machine can also induce the human to exert more cognitive efforts, even though its input is highly accurate. Interestingly, this happens when the DM is most cognitively constrained, for instance, because of time pressure or multitasking. Synthesizing these results, we pinpoint the decision environments in which human-machine collaboration is likely to be most beneficial.
Pages
40
ISSN (Print)
1866–3494
Working Paper
SSRN Working Paper
Gishan Dissanaike, Wolfgang Drobetz, Paul P. Momtaz, Jörg Rocholl (2020)
Subject(s)
Economics, politics and business environment; Finance, accounting and corporate governance
Keyword(s)
mergers and acquisitions, acquirer returns, law and finance, takeover law, law enforcement
JEL Code(s)
G30, G34, G38, K20, K22
This paper examines the impact of takeover law enforcement on corporate acquisitions. We use the European Takeover Directive as a natural experiment, which harmonizes takeover law across countries, while leaving its enforcement to the discretion of individual countries. We exploit this heterogeneity in enforcement quality across countries in a difference-in-differences-in-differences model, while employing an overall inductive research approach, following Karpoff and Whittry’s (2018) recommendation. We find that acquirer returns increase in countries with improvements in takeover law, driven by better target selection and lower cost of financing. The increase in acquirer returns is lower in weak enforcement jurisdictions, which we identify by developing a novel Takeover Law Enforcement Index (TLEI). The findings show that takeover law can mitigate agency conflicts, but its true value depends on its enforcement. Our results are robust to a number of robustness tests.
Pages
56