Adjustment Costs Research Articles

The Impact of Digital Economy on Firm Performance: Evidence from China

ABSTRACT This paper employs a multidimensional fixed effects model and examines the impact of the digital economy on the performance of firms in traditional industries. We have identified a pattern similar to the “IT productivity paradox” observed in the United States during the 1980s. Using data from A-share listed Chinese firms from 2011–2019, we find that the digital economy significantly reduces the short-term performance of firms by intensifying market competition and increasing adjustment costs. On the contrary, the digital economy improves firms’ long-term performance through accumulation of human capital and greater innovation capability. Competitive strategies strengthen the effect of the digital economy on the long-term performance of firms. Additionally, our research demonstrates that technology-intensive firms and small firms experience more pronounced negative effects in the short term but firms with high-tech titles suffer smaller shocks than other technology-intensive firms. These findings presents novel perspectives on the influence of the digital economy on firm performance, offering valuable insights for managers, investors, and policymakers involved in economic decision-making.

Re-estimation of the savings retention coefficient in OECD countries: a new measure of home country bias

The recent home bias is primarily related to the equity bias as measured by the international capital asset pricing model (CAPM); this measure has declined over the past two decades amid financial globalization, but remains high in most developed countries. The key to understanding this puzzling phenomenon lies in how accurately this indicator can be predicted. In this paper, we propose to use the savings retention coefficient estimated by the Feldstein–Horioka (F–H) regression as a cross-country measure of home bias. We re-estimate the savings retention coefficient based on an estimation model in which the FH regression is embedded in the model derived from Tobin's q-theory of saddle-road dynamics of investment under convex adjustment costs. We then use dynamic panel estimation to estimate the new measure in OECD countries. The main empirical results are as follows. The new measure of home country bias, such as the equity bias measure, declined steadily until 2008, but recovered to the level of the 60 s and 70 s after the 2008 financial crisis. Interestingly, people expected home country bias to be very high after the financial crisis, but in fact it simply returned to its previous level.

Does executives’ overseas experience improve firms’ labor investment efficiency?

Based on upper echelons theory and using a sample of private Chinese A-share listed firms from 2008 to 2020, this paper studies the impact of executives’ overseas experience on firms’ labor investment efficiency. The results show that executives with overseas experience significantly enhance firms’ labor investment efficiency. After distinguishing between different types of overseas experience, the results show that executives with only study experience or with both study and work experience abroad have a significant positive effect on firms’ labor investment efficiency, while executives with only overseas work experience have no significant effect. A cross-sectional analysis based on labor adjustment costs shows that the positive impact of executives’ overseas experience is more pronounced in labor-intensive firms, firms with a greater proportion of R&D investment and firms with a higher percentage of highly educated employees. The mechanism tests show that executives with overseas experience can optimize labor investment efficiency by lowering agency costs, attracting analyst attention and alleviating financing constraints under conditions of underemployment. This study enriches the literature on the characteristics of executive teams and the efficiency of labor investment, providing reference and inspiration for firms to attract more high-quality returnees and optimize their labor resource allocation.

A hybrid metaheuristic algorithm for resource-constrained proactive project scheduling with uncertainty-handling effort

This paper addresses a resource-constrained proactive project scheduling problem, where activity durations are stochastic variables and uncertainty-handling effort is devoted to decreasing the standard deviation of activity duration. The task is to determine the optimal level of uncertainty-handling effort and the corresponding baseline schedule under the constraints of renewable resources and project deadline, minimizing the total cost which consists of the uncertainty-handling cost and the robustness and adjustment costs of the baseline schedule. Based on the problem formulation, an optimization model is constructed, in which there exist two kinds of trade-off relationships among related costs. Then, for the NP-hardness of the problem, a hybrid metaheuristic algorithm is designed by combining a variable neighborhood search with a tabu search. In light of the characteristics of the problem, four measures are proposed to improve the searching efficiency of the algorithm. Finally, an extensive computational experiment is conducted on a randomly generated dataset. Based on the obtained results, the algorithm and improvement measures are evaluated, and a sensitivity analysis of the effect of key parameters on the objective function value is also carried out. The research conclusions are as follows: the algorithm equipped with all the improvement measures is the most promising algorithm for the studied problem, and the key parameters may generate an effect on the costs relevant to uncertainty.

Attitude- and cost-driven consistency optimization model for decision-making with probabilistic linguistic preference relation

Probabilistic linguistic term set (PLTS) conveys the preference for a linguistic term by assigning a probability distribution, which can more correctly and flexibly reflect the decision-maker (DM)’s evaluation information and make the decision outcome more scientific and rational. Probabilistic linguistic preference relation (PLPR) allows the DM to represent decision information for pairwise comparison of alternatives with PLTS, allowing a more comprehensive representation of the DM’s valid preferences. Consistency is essential in the preference decision-making process and directly influences the dependability of the final results. Therefore, this paper develops a minimum cost consistency adjustment mechanism based on attitudes and emotions for solving the decision-making problem with PLPR. Firstly, a new PLTS normalization model is proposed to handle the unknown information, considering the DM’s preferred attitude and emotional tone. Secondly, the expected parametric multiplicative consistency index for PLPR is defined, and parameter settings improve the flexibility and relevance of the consistency. Then, the concepts of linguistic hesitancy, probabilistic uncertainty, and probabilistic incompleteness are defined for the PLTS that can reflect the psychological characteristics of DMs’ hesitancy; they are then used to measure hesitation and unit consistency adjustment costs. Next, a new distance measure is established between the two PLTSs to measure the difference between the preference information. Subsequently, a direction-guided minimum cost consistency optimization model with unacceptable consistency is constructed for the PLPR. Finally, the usefulness of the suggested decision-making method with PLPR is tested using a numerical example of talent selection, and parametric sensitivity analysis and comparative analysis are utilized to show the benefits of the suggested methodology.

A Lost Game of Bank Bargains: West Germany and International Banking Regulation between Bretton Woods and Basel I (1972–1988)

Abstract Current theories of financial regulation suggest expanding rules-based formal state intervention to promote international banking stability. Such policy solutions should then be global in scope. This article instead argues that principles-based informal co- and self-regulation through domestic (gentlemen’s) agreements underpinned West German bank internationalisation until the 1980s. The analytic narrative approach allows the tracing of the social dynamics of the German politics of regulation, drawing on unused primary sources: Coalitions between the German regulating and regulated actors were bolstered by a liberal domestic framework and bespoke national policies. In contrast, regulatory harmonisation through international prudential standards came with high domestic adjustment costs for the German banking sector. Thus, domestic makeshift solutions to deal with and internalise cross-border financial risks proved most workable.

A minimum cost-maximum consensus jointly driven feedback mechanism under harmonious structure in social network group decision making

This article investigates a minimum cost-maximum consensus jointly driven feedback mechanism under a harmonious power structure by twofold group and individual attention recommendations for building social network consensus. Harmonious power structure is first constructed with subgroup-centrality-IOWA operator by (i) extracting subgroup importance rankings through social network analysis, and (ii) minimising group structure conflict to search the harmony weight allocation. Subsequently, a twofold attention recommendation approach that balances group attention and individual attention is proposed to generate feedback recommendations for the feedback recipients. Based on this, optimisation models that minimise individual adjustment cost and maximise group consensus are constructed, jointly driving the feedback mechanism. Finally, a demonstration example is provided to illustrate the efficacy of the proposed model.

Three-way group consensus method based on probabilistic linguistic preference relations with acceptable inconsistency

In the realm of addressing consensus challenges within group decision-making (GDM) that encompass individual preferences, this article presents an innovative approach. This article introduces a three-way group consensus methodology grounded in probabilistic linguistic preference relations (PLPRs) that ensures an acceptable level of inconsistency by applying the three-way decision (TWD) principle. This methodology is referred to as the TWD-GC-AIC approach. The TWD-GC-AIC approach consists of two core components, each dedicated to distinct aspects: ensuring the internal consistency of individual expert preference relations and achieving consensus within the expert group. The first component is dedicated to enhancing the consistency of individual expert preferences. This process commences with the determination of expert weights and the development of a novel aggregation formula. Subsequently, the consistency metric is refined through the incorporation of a newly devised distance formula. Once the predefined consistency threshold is reached, this study seamlessly integrates a planning model with an iterative algorithm. This integration serves a dual purpose: minimizing adjustment costs while retaining as much of the original information provided by the experts as possible. In the consensus-reaching phase, known as the consensus reaching process (CRP) of the expert group, this study employs iterative algorithms that incorporate TWD within the feedback mechanism. This strategic approach aims to comprehensively consider the emotional attitudes of the experts when adjusting the evaluation information, ultimately leading to a more informed and rational final decision outcome. Finally, this paper empirically validates the effectiveness and efficiency of the TWD-GC-AIC method, particularly in scenarios involving probabilistic linguistic term sets (PLTSs). The method is applied to a practical example and rigorously benchmarked against various other consensus methodologies to assess its performance.

Winning and losing the resource lottery: Governance after uncertain oil discoveries

Natural resource discoveries are often followed by delays and uncertain production outcomes, creating challenges for governments that anticipate resource revenues. I leverage exogenous subnational variation in offshore oil discoveries in Brazil to identify dynamic effects of discovery news and revenue shocks on local public finances, public goods provision, and politics. Municipalities where discoveries are realized enjoy significant growth in revenues and spending, but fail to improve public goods provision or stimulate economic activity. Municipalities that experience discovery announcements but never receive windfalls suffer long-term declines in revenues, investment, and public goods provision relative to never-treated controls. I show that electoral responses underlie these dynamics: discovery announcements draw less-educated candidates into local politics, and shortfalls between anticipated and realized oil revenues increase political turnover. Findings highlight discovery uncertainty as a fundamental resource governance challenge, and reveal mismanagement of windfalls and adjustment costs after disappointment as two faces of the Resource Curse.

Aggregate demand uncertainty outbreaks and employment hysteresis in G7 countries

The slow recovery following the recent financial crisis in many developed countries, and the predictable long lasting economic effects of the Covid-19 pandemic have raised a new interest on the topic of employment hysteresis. In the presence of hysteresis there is no predetermined long-run equilibrium level of aggregate employment. As the economic system is not self-adjusting toward a unique equilibrium, timely, and sustained expansionary macroeconomic policies should be applied to mitigate the impact of negative shocks. The purpose of this paper is to uncover hysteresis effects in the macrodynamics of employment along with variations in its intensity that may result from outbreaks in aggregate demand uncertainty. We estimate a switching employment equation based on the play model of hysteresis, which describes a dynamic process whereby non-convex adjustment costs and uncertainty create intervals of weak reaction of employment to small changes in forcing variables, but spurts in the reaction to large demand shocks. As a novel feature, the estimation allows the presence of structural breaks in the value of the switching parameter of the employment equation due to aggregate demand uncertainty outbreaks. We have concluded that hysteresis effects increased in general in crisis periods associated to outbreaks of uncertainty in aggregate demand.

An Emergency Coordinated Control Strategy to Improve the Transient Stability of a Single-Ended Distribution Network with Flexible Interconnection Channel Blocking.

Based on the scenario of high-penetration distributed photovoltaic connected to an AC/DC distribution network, this paper analyzes the dynamic characteristics of frequency and voltage in a distribution network after the blocking failure of the flexible interconnection channel. In order to enhance the transient stability of the system after the fault, this paper comprehensively considers the active regulation ability of photovoltaic units, and puts forward an emergency coordinated control strategy for a single-ended distribution network with flexible interconnection channel blocking. Firstly, the non-fault channel is overloaded for a short time, then the comprehensive influence of factors such as electrical distance, response time and adjustment cost on the frequency modulation effect of the system is quantitatively evaluated; according to the evaluation results, the photovoltaic and synchronous units are controlled by "control instead of tripping", and finally, the high-frequency tripping is carried out, based on the principle of "photovoltaics first". After the frequency control is completed, the reactive power optimization model of the system is established, and the improved tabu-particle swarm optimization algorithm is used to solve it, so as to optimize the voltage of the distribution network nodes. Finally, an equivalent simulation model is established to verify the coordinated control strategy.

Multi-attribute decision-making based on data mining under a dynamic hybrid trust network

The issues arising from the evolution of big data and network environment have rendered the relationship among experts to no longer be an independent multi-attribute group decision-making process. In response, a PROMETHEE multi-attribute decision-making method based on data mining under dynamic hybrid trust network is proposed. This method incorporates evaluation similarity based on degree centrality and expert trust values based on K-hop centrality in the social network to determine the experts’ weights. Furthermore, because the expert weight is somewhat affected by the attribute weight, the public-level attribute weight is obtained through data crawling and TF-IDF technology. The comprehensive attribute weight is acquired through a combination of expert-level attribute weights. Additionally, establishing a minimum adjustment cost model enables experts to follow a dynamic consensus reaching process in a composed hybrid trust network. Finally, to express the evaluation information more accurately in a complex linguistic environment, the probabilistic linguistic PROMETHEE method is introduced and applied to the site selection evaluation of charging and swapping stations. This highlights the feasibility and effectiveness of the proposed method through the comparison of decision-making methods and parameter sensitivity analysis.

Weight Penalty Mechanism for Noncooperative Behavior in Large-Scale Group Decision Making With Unbalanced Linguistic Term Sets

This paper proposes a novel framework to manage subgroups' non-cooperative behavior by weight penalty in largescale group decision making (LSGDM). To do that, a trustconsensus index (TCI) is defined by combining trust score and consensus degree among experts expressed by unbalanced linguistic term sets. A Louvain algorithm clustering process based on undirected graph composed of TCI is introduced to detect the subgroups in large network. Hence, a weight penalty feedback model is established to manage the subgroups detected as discordant and non-cooperative. The proposed method novelty resides in that the minimum adjustment cost can be obtained with respect to the penalty parameter. A detail analysis regarding the computation of the optimal penalty parameter to prevent excessive penalization is reported. Finally, a detailed numerical and comparative analyses are provided to verify the validity of the proposed method.

Are all economic fluctuations bad for consumers?

Are business cycles always costly? This paper sheds new light on this question in the context of a two-sector neoclassical business cycle model by focusing on the roles of the origin of shocks and the degree of real frictions that restrict factor reallocation both inter-temporally (investment adjustment cost) and intra-temporally (inter-sectoral factor immobilities). We find that under the benchmark parameterization, investment-specific technology shocks are welfare-improving while consumption-specific technology shocks are welfare-detrimental, regardless of the degree of real frictions. While aggregate TFP shocks can be both depending on the degree of real frictions, welfare-improving business cycles are not supported by empirical evidence.

The labor effects of judicial bias in bankruptcy

We study the effect of judicial bias favoring firm continuation in bankruptcy on the labor market outcomes of employees by exploiting the random assignment of cases across courts in the State of São Paulo in Brazil. Employees of firms assigned to courts that favor firm continuation are more likely to stay with their employer, but they earn, on average, lower wages three to five years after bankruptcy. We discuss several potential mechanisms that can rationalize this result, and provide evidence that imperfect information about outside options in the local labor market and adjustment costs associated with job change play an important role.

The Impact of Exchange Rate Volatility on Export Earnings: Evidence from Sri Lanka

In Sri Lanka, the relationship between exchange volatility and exports is a persistent issue in economic growth and development. Through uncertainty and adjustment costs, exchange rate volatility can have a direct impact on trade. The objective of this study is to analyze the exchange rate volatility on exports in Sri Lanka from 2001 to 2019. The motivation of this study is to investigate the relationship between exchange rate volatility on real export earnings in SriLanka after the implementing floating exchange rate policy. The study found a positive relationship between exchange rate volatility and export earnings in Sri Lanka.

Does supply chain network centrality affect firm cost stickiness?

This study constructs a supply chain network for Chinese listed firms from 2009 to 2021 and investigates the influence of the firm’s supply chain network position on cost stickiness. Our findings reveal that the supply chain network centrality exacerbates cost stickiness by increasing adjustment costs and agency problems, this effect applies to firms in both industrial and services sectors, and remains valid whether before and after the COVID-19 pandemic. Furthermore, high-quality internal controls and external audits can mitigate the effect of supply chain network centrality on cost stickiness. This study enriches the determinants of cost stickiness literature by providing evidence at the supply chain network level.

Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading

In the context of China's dual‑carbon goals and electricity market reforms, the high penetration of renewable energy sources (RES) in a single microgrid (MG) imposes challenges to its flexible operation. This paper addresses the cooperation within a virtual power plant (VPP) aggregated by multiple MGs. The VPP, managed by the VPP operator, acts as an intermediate entity to facilitate MGs' economic and low-carbon operation. To ensure the interests of various stakeholders in the VPP, this work proposes a low carbon oriented tri-stage collaborative energy management framework. In the 1st stage, the day-ahead optimal scheduling of individual MGs and the bidding strategy of the VPP operator are modeled, where an internal pricing method is incorporated. The 2nd stage addresses the real-time redispatch of the VPP operator using a receding horizon-based model predictive control (MPC) solution. This aims to satisfy the real-time operational requirements of MGs and reduce adjustment costs. Furthermore, in the 3rd stage, a penalty allocation mechanism is established for MGs based on their forecasting errors, to protect the VPP operator's interests. The effectiveness of the proposed solution is evaluated through comparative simulations in the context of the Guangdong electricity market.

Multinational corporations’ capital allocation decisions across asymmetric risk locations: intertemporal equilibrium and optimal transitional adjustment paths

Abstract Multinational corporations operate across locations with different risk profiles. We examine how multinational corporations address the optimal allocation of capital across multiple locations and analyse the transition path to the intertemporal equilibrium. Our model considers returns, risks and adjustment costs to reflect the dynamics of allocating capital assets across locations over time, as well as the mix of assets across locations in equilibrium. Variational calculus is employed to show that the model confirms standard expectations that where a location’s rates of return on assets increase, or adjustment costs decrease, equilibrium capital allocation and transitional capital flows to that location will increase. Symmetrically, rising (falling) risk increases (decreases) the proportion of the capital asset holdings of a location. The crucial insight is that for the transitional dynamics to intertemporal equilibrium, the optimal relative capital flow response to changes in risk can generate relative portfolio allocations that may initially move in the opposite direction to that implied by the stock equilibrium. Specifically, an increase in risk for the high-risk location may initially result in an increase in the relative capital asset flow to the high-risk location relative to the low-risk location. Empirical research must account for the possibility of non-monotonicity in asset allocation flows to avoid misspecification. Moreover, policy makers will have to anticipate possible pressure for reversal resulting from short-term worsening capital flows. These reflections are mirrored in recent research calls for separating structural and transition effects of institutional change on the investment decisions by multinational corporations.

Optimal diagnosis interval for online quality control methods

Online quality control methods emphasize manufacturing processes to attain maximum conformance with respect to the specifications of the concerned quality characteristics of a product. One key factor that affects the effectiveness of these methods is the diagnosis interval. In this paper, the existing cost model along with its cost components for online quality control methods has been revisited and modified by incorporating new variables like the rate of production, the loss due to false alarm, the loss due to non-detection of process abnormalities, and considering a workable break-up of diagnosis cost for finding the optimal diagnosis interval from the perspective of present-day manufacturing engineering. As already mentioned, the proposed cost model has not ignored the loss due to the generation of defective items as well as the adjustment cost available in the pertinent literature. The modified cost function thus proposed has been appropriately minimized to obtain the corresponding optimal diagnosis interval. The proposed methodology has been compared numerically with other methodologies to establish its effectiveness. The cornerstone of the proposed methodology lies in reinforcing its effectiveness through a real-life case example in manufacturing. Sensitivity analysis has also been carried out for the real-life case example to fortify the proposed methodology.