Empirical Relevance Research Articles

The spillover effect of global uncertainty through input–output linkage

AbstractThis study examines the cross‐country spillover effects of global uncertainty on domestic value‐added by introducing indices for input‐ and output‐linked uncertainty. Employing a panel fixed‐effect regression model, the analysis covers 70 countries from 1995 to 2019, using the World Uncertainty Index and the OECD Inter‐Country Input–Output Table. Our findings reveal that input‐linked uncertainty from procuring foreign intermediate goods has the most significant negative impact on domestic value‐added. The effects of domestic uncertainty and output‐linked uncertainty from foreign product sales follow this. Additionally, these negative effects exhibit a dynamic cumulative impact over time, rather than being confined to a specific period. Considering both the magnitude and duration of these impacts, the primary transmission pathway of global uncertainty on a country's domestic production activity is through its input linkage. Furthermore, the study identifies country‐specific variations in the ripple effects of global uncertainty. Analysing specific cases such as Korea and China affirms the empirical relevance of our findings to their economic contexts.

Least Squares Learning? Evidence from the Laboratory

We report on an experiment testing the empirical relevance of least squares (LS) learning, a common way of modelling how individuals learn a rational expectations equilibrium (REE). Subjects are endowed with the correct perceived law of motion (PLM) for a price level variable they are seeking to forecast, but do not know the true parameterization of that PLM. Instead, they must choose and can adjust the parameters of this PLM over 50 periods. Consistent with the E-stability of the REE in the model studied, 97.8% of subjects achieve weak convergence to the REE in terms of their price level predictions. However, the number of participants that can be characterized as least squares learners via the adjustments they make to the parameterization of the PLM over time depends on properties of the data generating process of the dependent and independent variables. Participants learn the REE faster, and behave more like least squares learners when there is greater variance in the independent variable of the model. We consider several alternatives to least squares learning and find evidence that many subjects employ a simple satisficing approach.

NEC violation in [formula omitted] gravity in the context of a non-canonical theory via modified Raychaudhuri equation

In this work, we develop the Raychaudhuri equation in f(R̄,T̄) gravity in the setting of a non-canonical theory, namely K-essence theory. We solve the modified Raychaudhuri equation for the additive form of f(R̄,T̄), which is f1(R̄)+f2(T̄). For this solution, we employ two different scale factors to give two types of f(R̄,T̄) solutions. The ongoing debate between Fisher et al. and Harko et al. in 2020 regarding the additive form of f(R̄,T̄) may provide a resolution within the modified f(R̄,T̄) gravity theory. By conducting a viability test and analyzing energy conditions, we have determined that in the first scenario, the null energy condition (NEC) is violated between two regions where the NEC is satisfied. Additionally, we have observed that this violation of the NEC exhibits a symmetric property during the phase transition. These observations indicate that bouncing events may occur as a result of the symmetrical violation of the NEC during the expansion of the universe. Moreover, this model indicates that resonant-type quantum tunneling may take place during the period when the NEC is violated. The findings of NEC violation through the power law of scale factor may have empirical relevance in contemporary observations. In the second scenario, our model indicates that the strong energy condition is violated, but the NEC and weak energy conditions are satisfied. The effective energy density decreases and is positive, while the effective pressure and equation of state parameters are negative. This suggests that the universe is expanding with acceleration and is dominated by dark energy.

Optimal fisheries management and the response to price changes

The literature on how price changes affect optimally managed fisheries is mostly concerned with how fish stocks and harvest rates are affected in steady state. There is little published on how prices affect optimal harvest rates at stock levels outside of steady state. Here we show the effect of an unanticipated and permanent price increase. It is shown that in a model of a pure schooling fishery, if the stock is below the steady state, it is optimal to harvest less if the price goes up and vice versa. It is also shown that in a model with stock dependent harvest costs, the optimal response to a price increase is to reduce the harvest rate for low stock levels even if the optimal harvest rate increases close to the steady state. Empirical relevance is demonstrated by illustrating the theoretical results in an estimated model.

Carrot or stick? Environmental and welfare implications of sustainable aviation fuel policies

Sustainable aviation fuel (SAF) serves as a critical short-term measure for reducing aviation's carbon footprint. Two main policy tools, subsidy and quota, have been developed to support its usage. We build an economic model to compare the environmental and welfare impacts of these two policies. First, we find that if an airline uses the same portion of SAF under both policies, the subsidy approach results in reduced SAF costs, augmented airline output, and heightened emissions. Second, the subsidy policy is better than the quota policy in terms of consumer surplus, airline profits, SAF blender profit, and social welfare, if the traditional aviation fuel is sufficiently inexpensive and the emission regulation under the subsidy policy is adequately stringent. Finally, to demonstrate the empirical relevance of our theoretical framework, we have employed empirically-validated parameters for model calibration. Sensitivity analysis indicates that SAF production costs and market potential are pivotal factors influencing governmental policy formulation. With the reduction in SAF production expenses and the growth of the aviation sector, the government is positioned to adopt a more aggressive policy stance, characterized by higher SAF quotas and increased subsidies, to stimulate the uptake of SAF by airlines.

Optimal Early-Bird Discounts and the Challenges of Diversion

Variable pricing and early-bird discounts can shift some demand or the time of purchase from higher-priced periods to lower-priced periods without increasing the total demand. This diversion effect influences optimal price levels, and the present paper presents a model for calculating optimal early-bird discounts when incorporating diversion. The model also reveals the differences in price elasticities and the feasible price interval for positive profit. The paper contributes to the literature on early-bird pricing strategies and offers practical insights for service providers seeking to optimize their pricing strategies. The empirical relevance is documented using survey and real-life experimental data among skiers at a major ski resort in Norway. The main insights of the paper are that the profit-maximizing discounts and the gap between the optimal price with and without diversion increase with the length of the pre-booking period.

GMM estimation for high-dimensional panel data models

In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where the factors are unobserved and these factor loadings are nonparametrically unknown smooth functions of individual characteristic variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with the sample size. This is a very general framework and is closely related to many existing linear and nonlinear panel data models. In order to estimate the unknown parameters, factors and factor loadings, we propose a sieve-based generalized method of moments estimation method and we show that under a set of simple identification conditions, all those unknown quantities can be consistently estimated. Further we establish asymptotic distributions of the proposed estimators. In addition, we propose tests for over-identification, specification of factor loading functions, and establish their large sample properties. Moreover, a number of simulation studies are conducted to examine the performance of the proposed estimators and test statistics in finite samples. An empirical example on stock return prediction is studied to demonstrate both the empirical relevance and the applicability of the proposed framework and corresponding estimation and testing methods.

Simulation and evaluation of ecosystem service value along the Yellow River in Henan Province, China

The unprecedented growth in population and swift industrial advancements exert considerable strains on the ecosystem, particularly within medium-sized and large urban landscapes. The critical investigation into the intricate links between current and prospective land utilization, as well as the ecosystem service value (ESV), holds considerable empirical relevance for the calibration of land usage frameworks, thereby contributing to the sustainable evolution of extensive urban zones. Utilizing GlobeLand 30 data, the present research probes into the pattern of land transformation and the spatial-temporal dispersal of ESV in Henan’s Yellow River vicinity over a span from 2000 to 2020. For the enhancement of land usage alignment, a Markov-PLUS fusion model was devised to gauge three disparate ESV transition scenarios slated for 2030, namely, natural development scenario (NDS), cropland protection scenario (CPS), and ecological protection scenario (EPS). The principal determinants of land transformation within the 2000–2020 period were recognized as elevation, populace concentration, and atmospheric temperature. Amid the rapid accretion of construction land engulfing substantial cropland and grassland areas, there was an ESV diminution to the tune of 1.432 billion RMB between 2000 and 2020. The ESV’s high-value regions were discerned within relatively undisturbed ecosystem zones, with the lower-value sections identified in cropland and constructed areas, where human interventions exerted pronounced effects on the ecosystem. In accordance with the 2030 land usage simulations and analyses, in contrast to alternative scenarios, the EPS exhibited the least fluctuation in land type alterations in 2030, demonstrated the most pronounced escalation in cold spot concentration, and reached a peak agglomeration level. This underscores that the EPS not only offers a refinement in land utilization configuration but also mediates the equilibrium between economic and ecological considerations. The insights derived from this investigation afford innovative evaluative methods for spatial planning, ecological recompense, and sustainable land exploitation within large- and medium-scale urban domains.

Productive explanation: A framework for evaluating explanations in psychological science.

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Influence of policymakers and civil society stakeholders on sewage sludge management strategies: Empirical results from European utilities

Sewage sludge management is crucial for water utilities to move towards a circular valorisation of resources. The current literature focuses mainly on the technological aspects of sludge management strategies. However, the current discussion of these strategies does not consider possible pressures arising from the utilities' civil society stakeholders and from policymakers. To fill this gap, this paper develops a conceptual framework, based on the current literature, that identifies the utility's key decisions on sludge management strategies (valorisation route, overperformance and vertical integration), and links them to possible pressures arising from civil society and existing regulations. Subsequently, the study validates the framework through a multiple explanatory case study, investigating the empirical relevance of such pressures in six water utilities across Europe. The influence of citizens and municipalities is found to be crucial in the choice of sludge valorisation routes. Economic instruments, command and control instruments and, new to the literature, regulatory uncertainty are found to be key policy features influencing utilities' decisions on sludge management. The paper provides a first-of-its-kind investigation that highlights the mechanisms through which policymakers and civil society stakeholders shape utilities' sewage sludge management strategies. The results complement and extend existing theoretical knowledge on the role of institutional pressures in the implementation of sustainable environmental systems.

Research programs and the progress of theory in sociology: the post-positivist conception of J. Berger, M. Zelditch Jr. and D. Wagner

The article presents an explanation of the post-positivist conception of the progress of sociological theories in research programs, which was developed by J. Berger, M. Zelditch Jr. and D. Wagner. Their conception specified related types of theoretical structure and dynamics of research programs, as well as their functions in sociological theorizing. Structural and dynamic differences of research programs represent different types of conceptual connections between theories and their corresponding basic modes of theoretical growth. In particular, the elaboration of theories of certain explanatory domains by increasing their scope, rigor, precision, or empirical relevance is a mode of progress of linear programs. The proliferation of theories into new explanatory domains and the expansion of the subject range of their applicability is the mode of development of branching programs. The competition of theories in a certain explained domain for the best conceptual representation of its problems is a mode of progress of competing programs. Special types of conceptual connections and modes of theoretical progress may also emerge between theories in linear, branching, and competing research programs: variation of theories as a result of deepening them into their established subject domains due to increased conceptual insight, sophistication and detail; integration of theories. The theoretical development of research programs is provided by their functions, which are analogous to the functions of paradigms in mature sciences: determination of theoretical significance and doability of sociological problems; evaluation and cognitive-based selection of competing theoretical formulations of their solutions; ensuring synergy of the consequences of performing the above-mentioned functions.

Fractional gaussian noise: Spectral density and estimation methods

The fractional Brownian motion (fBm) process, governed by a fractional parameter , is a continuous‐time Gaussian process with its increment being the fractional Gaussian noise (fGn). This article first provides a computationally feasible expression for the spectral density of fGn. This expression enables us to assess the accuracy of a range of approximation methods, including the truncation method, Paxson's approximation, and the Taylor series expansion at the near‐zero frequency. Next, we conduct an extensive Monte Carlo study comparing the finite sample performance and computational cost of alternative estimation methods for under the fGn specification. These methods include two semi‐parametric methods (based on the Taylor series expansion), two versions of the Whittle method (utilising either the computationally feasible expression or Paxson's approximation of the spectral density), a time‐domain maximum likelihood (ML) method (employing a recursive approach for its likelihood calculation), and a change‐of‐frequency method. Special attention is paid to highly anti‐persistent processes with close to zero, which are of empirical relevance to financial volatility modelling. Considering the trade‐off between statistical and computational efficiency, we recommend using either the Whittle ML method based on Paxson's approximation or the time‐domain ML method. We model the log realized volatility dynamics of 40 financial assets in the US market from 2012 to 2019 with fBm. Although all estimation methods suggest rough volatility, the implied degree of roughness varies substantially with the estimation methods, highlighting the importance of understanding the finite sample performance of various estimation methods.

Bibliometric Analysis of Spanish Doctoral theses on Public Relations (2006-2021)

Objective: The aim of this article is to conduct a comprehensive and detailed analysis of doctoral theses in the field of public relations carried out at public universities in Spain between 2006 and 2021. This analysis seeks to identify and understand the main dimensions researched, the thematic and methodological evolution, and the contribution of these studies to both the professional practice and theoretical framework of public relations. Theoretical Framework: The theoretical framework is built upon the historical evolution of public relations in both the academic and professional spheres. It acknowledges changes in social demands for greater effectiveness and rigor in research outcomes in this field. Previous studies documenting the transition in methods and approaches used in public relations will be reviewed, emphasizing those that have marked milestones in the integration of theory and practice. Additionally, prevailing theoretical models and how they have been challenged and expanded by recent research will be analyzed. Method: The study employs an exploratory and descriptive methodological design. Content analysis was used to dissect and categorize information systematically for analyzing the doctoral theses. Theses were selected through a purposive sampling of documents available in Spanish academic databases, ensuring that these studies encompass various dimensions and approaches within the field of public relations. Variables such as thematic focus, methodologies employed, and the theoretical and practical contributions of each thesis were assessed. Results and Discussion: This bibliometric study reveals a predominance of doctoral theses in public relations in Spain focused on practical applications, demonstrating a primarily instrumental view of the discipline as a marketing tool. There is a marked imbalance towards applied research, with limited interest in theoretical development, suggesting a lack of exploration of public relations as a strategic and executive function. The scarcity of works that integrate public relations into global marketing strategies and their operational focus highlight an opportunity to expand its strategic role. These findings indicate the need to strengthen theoretical and strategic research in the field, which could enrich both the practice and teaching of public relations, strengthening its position as an essential discipline in organizational management. Originality/Value: This article contributes originality to the field of public relations by providing one of the first exhaustive systematizations of doctoral theses in this area in Spain. The review covers a significant fifteen-year period, allowing for observation of trends and changes in academic research in public relations. The value of this study lies in its ability to identify how demands for rigor and empirical relevance have shaped academic research, thus contributing to a better understanding of the field's evolution and offering a solid foundation for future research aimed at effectively integrating theory and practice.

Applying spending review theory to developing countries: The case of South Africa

ABSTRACT The seminal spending review (SR) theoretical framework by Catalano & Erbacci [2018. A theoretical framework for spending review policies at a time of widespread recession. OECD Journal on Budgeting 17(2), 9–24] draws on the cutback management and related academic literature to articulate the first theory for explaining the ability of medium-term SRs conducted after the global financial crisis of 2008 to effect targeted savings. This literature is however dominated by advanced economies with sophisticated public financial management systems. Comparatively little attention, however, has been paid to theoretical perspectives on developing countries, like South Africa. This article applies this theoretical framework by Catalano and Erbacci to the South African SR initiative in order to interrogate its theoretical and empirical relevance. Proposals are made for enhancing the operationalisation of the organisational and process dimensions of C&E’s theoretical framework to lay a foundation for future empirical research and its policy implications for South Africans SRs are explored.

Social Vulnerability and COVID-19 Pandemic Outcomes: Evidence from Spatial Quantile Regression

This paper evaluates the empirical relevance of a prominent social vulnerability measure to local economic and public health outcomes at two distinct stages of the COVID-19 pandemic. In contrast to traditional least-squares regression, conditional quantile regression offers stronger evidence in support of social vulnerability for predicting the pandemic’s uneven economic devastation and death tolls across the United States. An extension of the spatial autoregressive geographically weighted regression model to conditional quantile regression reveals the varying role of social vulnerability within and across broad regions that were exposed to different levels of pandemic impacts.

The Empirical Relevance of the Okishio Theorem

The Empirical Relevance of the Okishio Theorem

Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects

Abstract This study proposes an econometric framework to interpret and empirically decompose the difference between instrumental variables (IV) and ordinary least squares (OLS) estimates given by a linear regression model when the true causal effects of the treatment are nonlinear in treatment levels and heterogeneous across covariates. I show that the IV-OLS coefficient gap consists of three estimable components: the difference in weights on the covariates, the difference in weights on the treatment levels, and the difference in identified marginal effects that arises from endogeneity bias. Applications of this framework to return-to-schooling estimates demonstrate the empirical relevance of this distinction in properly interpreting the IV-OLS gap.

Inflation measurement in the presence of stockpiling and consumption smoothing

A chained price index is said to suffer from chain drift bias if it indicates an overall price change, even though the prices and quantities in the current period have reverted back to their levels of the base period. The empirical relevance of this bias is well documented in studies that apply sub‐annual chaining to scanner data. There it is shown that stockpiling can lead to downward chain drift bias. The present paper draws attention to the fact that smoothing consumption causes substantial upward chain drift. In addition, this study introduces a stochastic simulation approach that is consistent with both, stockpiling and consumption smoothing. A “stress test” is conducted that examines whether rolling window variants of multilateral indices (GEKS, TPD, and GK) effectively curtail the chain drift problem.

Enhanced or hindered research benefits? A realist review of community engagement and participatory research practices for non-communicable disease prevention in low- and middle-income countries

IntroductionCommunity engagement and participatory research are widely used and considered important for ethical health research and interventions. Based on calls to unpack their complexity and observed biases in their favour,...

Optimal Cross-Sectional Regression

Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on addressing the EIV issue: instead of viewing EIV biases as estimation errors that potentially contaminate next stage risk premium estimates, we consider them to be return innovations that follow a particular correlation structure. We factor this structure into our test design, yielding a new regression model that generates the most accurate risk premium estimates. We demonstrate the theoretical appeal as well as the empirical relevance of our new estimator. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The supplemental appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4966 .