Journal of Economic Interaction and Coordination最新文献

The mandatory shift to remote work during the COVID-19 pandemic has made employers and employees increasingly aware of the productivity benefits that may arise from the digital revolution. To explore the characteristics of these gains, we build a model that enables companies to choose from three types of relationship inputs: face-to-face, remote synchronous, and remote asynchronous. Once remote interactions are included, five factors influencing job satisfaction and therefore worker productivity can be identified: (i) reduced mobility, (ii) interaction frequency, (iii) optimal time/place, (iv) work-life balance, and (v) relationship decay effects. We compute the optimal distribution of the three relationship types that maximize corporate profits, conditioning on reasonable parametric assumptions on these five effects. Additionally, we evaluate the potential productivity growth for companies employing only face-to-face interactions when introducing remote interactions. We test our theoretical predictions with a Structural Equation Model, revealing that remote work enhances worker satisfaction and willingness to contribute additional effort at the same wage. Our empirical findings have relevant implications for industrial and environmental policies at both national and supranational levels.

The economic shocks that followed the COVID-19 pandemic have brought to light the difficulty, both for academics and policy makers, of describing and predicting the dynamics of inflation. This paper offers an alternative modelling approach. We study the 2020–2023 period within the well-studied Mark-0 Agent-Based Model, in which economic agents act and react according to plausible behavioural rules. We include a mechanism through which trust of economic agents in the Central Bank can de-anchor. We investigate the influence of regulatory policies on inflationary dynamics resulting from three exogenous shocks, calibrated on those that followed the COVID-19 pandemic: a production/consumption shock due to COVID-related lockdowns, a supply chain shock, and an energy price shock exacerbated by the Russian invasion of Ukraine. By exploring the impact of these shocks under different assumptions about monetary policy efficacy and transmission channels, we review various explanations for the resurgence of inflation in the USA, including demand-pull, cost-push, and profit-driven factors. Our main results are fourfold: (i) without appropriate fiscal policy, the shocked economy can take years to recover, or even tip over into a deep recession; (ii) the success of monetary policy in curbing inflation is primarily due to expectation anchoring, rather than to the direct economic impact of interest rate hikes; (iii) however, perhaps paradoxically, strong inflation anchoring is detrimental to consumption and unemployment, leading to a narrow window of “optimal” policy responses due to the trade-off between inflation and unemployment; (iv) the two most sensitive model parameters are those describing wage and price indexation. The results of our study have implications for Central Bank decision-making, and offer an easy-to-use tool that may help anticipate the consequences of different monetary and fiscal policies.

This paper presents conditions to guarantee the convergence of simulations to a stochastic steady state, characterized by an invariant probability distribution, in an endowment economy with a finite number of heterogeneous agents, 1 period real assets offered in zero net supply, aggregate uncertainty, incomplete markets and uncountable shocks. The results are robust to the presence of multiple discontinuous equilibria and are numerically implementable. We work on a Markov environment with an enlarged state space to characterize ergodic equilibria and differentiate them with respect to time-independent and stationary ones. We show, by imposing a mild restriction on the discontinuity set, that every measurable time-independent selection approximates the stochastic steady state of the model. The results in this paper are constructive and based on assumptions imposed on the primitives of the model. Thus, they can help to design calibration and estimation methods for heterogeneous agent models based on unconditional moments.

We propose a dynamic model where the real estate market generates wealth segregation within a town. The model develops on the Schelling’s checkerboard dynamics with one main difference: Agents are fully characterized by their wealth, which changes with the progress of the model dynamics. The driver of the segregation is a positive neighborhood externality such that households’ utility is increasing in the average wealth of the neighbors. The identification of a potential function enables prediction of the long-run limiting behavior of the dynamics: Wealth segregation is an endemic result, unless a perturbation at the individual level is introduced. Public policies that mimic the perturbation can reduce wealth segregation.

This paper examines the conditions under which a representative agent (RA) model can accurately approximate the output of a multi-agent model that assumes many interacting agents. The study compares the widely used Keynesian cross diagram, which employs a representative agent, to an extended model that explicitly considers multiple interacting households and firms. The extended model reduces to the original RA model when there is one agent of each type. The findings suggest that the RA Keynesian cross diagram model does not accurately approximate the extended multi-agent model when: (1) the network structure of the economy is asymmetric (e.g., firms have different sizes), or (2) the rationality of agents is too low. Additionally, when income inequality is considered by introducing capitalists, the RA model is no longer a good approximation, even if agents are rational. However, fiscal policies that redistribute income can improve the accuracy of the RA model’s predictions. In general, features that increase the overall rationality of the economy and decrease heterogeneity tend to improve the performance of the RA approximation.

In this paper, we argue that not all economic interactions can be simulated. Specific types of interactions, instantiated in and instantiating of institutional structures, are embodied in ways that do not admit entailing laws and cannot be expressed in a computational model. Our arguments have two implications: (i) zero intelligence is not merely a computational phenomenon but requires an “embodied” coupling with the environment (theoretical implication); and (ii) some interactions, on which collective phenomena are based, are unprestatable and generate emerging phenomena which cannot be entailed by computation (methodological implication).

A surge in banks’ liquidity needs increases settlement costs that could burden the functioning of the real economy through its impact on banks’ lending behavior. A liquidity saving mechanism (LSM) can help reduce banks’ liquidity needs, but it could also affect banks’ strategic behavior. To understand how an LSM affects banks’ behavior in a real-time gross settlement system, this study models settlements in a day as a timing game in which banks decide when to make payments, thereby trading off the cost of delaying payments against the cost of borrowing liquidity. An LSM provides a partial offsetting service, whose direct effect is to decrease the cost of liquidity associated with payments that are offset. The study’s stylized analyses reveal that an LSM indirectly affects banks’ strategic behavior in a network context. Without an LSM, a positive strategic spillover effect can arise through the network of payments, which an LSM can dismiss by cutting off the underlying payment network. To demonstrate its welfare impact along with the network structures, this study theoretically analyzes a class of core-periphery networks. The density of the network is shown to have implications on the welfare consequence of adding an LSM. From a policy perspective, the implications on a policy mix between an LSM and the fee setting for intraday lending are discussed.

Abstract

In the wake of the COVID-19 pandemic, the Government of India imposed production restrictions on various sectors of the economy. Prima facie there is reason to believe that the cost of the quantity constraints may be greater than their simple sum. This is because quantity constraints percolate through the production network forcing some sectors to reduce output because of the non-availability of inputs. This paper uses an input–output network model (IO-NET model) to study the impact of the lockdown on the Indian economy. We calibrate our IO-NET model to the Indian economy using data on sectoral linkages. We then examine the impact of the lockdown using sector-based computational experiments. Such experiments allow us to examine the out-of-equilibrium time dynamics that emerge in response to the lockdown. The transient dynamics reveal certain counterintuitive phenomena. The first of which is that the supply of output of some sectors increases during and immediately after the lockdown. Second, recovery after the relaxation of the lockdown entails the overshooting of GDP above its normal levels. And the size of the overshooting depends on the stickiness of prices. These counterintuitive phenomena are intimately related to the network interaction between firms as buyers and sellers of intermediate inputs. The paper also measures the network effect of the lockdown across different sectors. There is sizeable heterogeneity among sectors in how their network position amplifies the quantity constraints imposed on sectors distantly related to them as buyers–sellers of intermediate inputs. Ultimately, models like our own can serve as testbeds for policy experiments, especially when the model is calibrated to granular data on buyer–seller linkages in the economy.