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We enquire whether short-time work (STW) avoids firings as intended by policymakers and is associated with unintended side effects by subsidising some establishments and locking in some employees. Additionally, where it was feasible, establishments used working from home (WFH) to continue working without risking an increase in COVID-19 infections and allowing employed parents to care for children attending closed schools.

Using 21 waves of German high-frequency establishment panel data collected during the COVID-19 crisis, we investigate how STW and WFH are associated with hirings, firings, resignations and excess labour turnover (or churning).

Our results show the important influences of STW and working from home on employment dynamics during the pandemic. By means of STW, establishments are able to avoid an increase in involuntary layoffs and hiring decreases significantly. In contrast, WFH is associated with a rise in resignations, as can be expected from a theoretical perspective.

While most of the literature on STW and WFH is unrelated and remains descriptive, we consider them in conjunction and conduct panel data analyses. We apply data and methods that allow for the dynamic pattern of STW and working from home during the pandemic. Furthermore, our data include relevant establishment-level variables, such as the existence of a works council, employee qualifications, establishment size, the degree to which the establishment was affected by the COVID-19 crisis, industry affiliation and a wave indicator for the period the survey was conducted.

Using data from the 2013 European Company Survey, this chapter operationalizes the representation gap as the desire for greater employee involvement in decision-making expressed by the representative of the leading employee representative body at the workplace. According to this measure, there is evidence of a substantial shortfall in employee involvement in the European Union, not dissimilar to that reported for the United States. The chapter proceeds to investigate how the size of this representation gap varies by type of representative structure, information provided by management, the resource base available to the representatives, and the status of trust between the parties. Perceived deficits are found to be smaller where workplace representation is via works councils rather than union bodies. Furthermore, the desire for greater involvement is reduced where information provided the employee representative on a range of establishment issues is judged satisfactory. A higher frequency of meetings with management also appears to mitigate the expressed desire for greater involvement. Each of these results is robust to estimation over different country clusters. However, unlike the other arguments, the conclusion that shortfalls in employee involvement representation are smaller under works councils than union bodies is nullified where trust in management is lacking.

A framework for reliable modeling of high-frequency structures by nested kriging with an improved sampling procedure is developed and extensively validated. A comprehensive benchmarking including conventional kriging and previously reported design of experiments technique is provided. The proposed technique is also demonstrated in solving parameter optimization task.

The keystone of the proposed approach is to focus the modeling process on a small region of the parameter space (constrained domain containing high-quality designs with respect to the selected performance figures) instead of adopting traditional, hyper-cube-like domain defined by the lower and upper parameter bounds. A specific geometry of the domain is explored to improve a uniformity of the training data set. In consequence, the predictive power of the model is improved.

Building the model in a constrained domain allows for a considerable reduction of a training data set size without a necessity to either narrow down the parameter ranges or to reduce the parameter space dimensionality. Improving uniformity of training data set allocation permits further reduction of the computational cost of setting up the model. The proposed technique can be used to expedite the parameter optimization and enables locating good initial designs in a straightforward manner.

The developed framework opens new possibilities inaccurate surrogate modeling of high-frequency structures described by a large number of geometry and/or material parameters. Further extensions can be investigated such as the inclusion of the sensitivity data into the model or exploration of the particular geometry of the model domain to further reduce the computational overhead of training data acquisition.

The efficiency of the proposed method has been demonstrated for modeling and parameter optimization of high-frequency structures. It has also been shown to outperform conventional kriging and previous constrained modeling approaches. To the authors’ knowledge, this approach to formulate and handle the modeling process is novel and permits the establishment of accurate surrogates in highly dimensional spaces and covering wide ranges of parameters.

We analyze and assess new evidence on employment dynamics from a new data source – the National Establishment Time Series (NETS). The NETS offers advantages over existing data sources for studying employment dynamics, including tracking business establishment relocations that can contribute to job creation or destruction on a regional level. Our primary purpose in this paper is to assess the reliability of the NETS data along a number of dimensions, and we conclude that it is a reliable data source although not without limitations. We also illustrate the usefulness of the NETS data by reporting, for California, a full decomposition of employment change into its six constituent processes, including job creation and destruction stemming from business relocation, which has figured prominently in policy debates but on which there has been no systematic evidence.

The purpose of this paper is to identify the critical success factors of emblematic hotels from the perspective of the guest, by analysing the direct activities that make up the value chain of these types of establishments.

The authors use the case study methodology to derive conclusions that contribute to the development of a theory about the success factors of emblematic hotels. The case selected is the Spanish Tourist Parador chain. The authors carried out over a period of two years a data mining analysis of the online comments posted by its guests.

The results indicate that the attributes of location and facilities are critical success factors expected a priori given the nature of the business of such establishments, based on the singular nature of the buildings. Another critical success factor is personnel, which seems to indicate that the Paradors support their business model by employing highly qualified staff, but give less attention to restaurant services or the room, according to guest perceptions.

The paper provides required evidence on the critical success factors of emblematic hotels adapting Porter’s value chain, for the tourism accommodation sector, through the analysis of direct value chain activities. In addition, the existing literature is broadened by taking a perspective scarcely studied, the guest perception of hotel establishments, online content posted by the user on the establishment’s website, rather than simply considering the traditional views of the experts/managers, through structures questionnaires. Besides, the results provide practical and useful implications for the managements of the emblematic hotels under study.

The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain.

The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain.

Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system's behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity.

The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are short-period factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1-AGO acting, the information mining and extrapolation prediction will have a good effect.

The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.

The purpose of this study is to forecast the development performance of important economies in a smart city using mixed-frequency data.

This study introduces reverse unrestricted mixed-data sampling (RUMIDAS) to support vector regression (SVR) to develop a novel RUMIDAS-SVR model. The RUMIDAS-SVR model was estimated using a quadratic programming problem. The authors then use the novel RUMIDAS-SVR model to forecast the development performance of all high-tech listed companies, an important sector of the economy reflecting the potential and dynamism of urban economic development in Shanghai using the mixed-frequency consumer price index (CPI) producer price index (PPI), and consumer confidence index (CCI) as predictors.

The empirical results show that the established RUMIDAS-SVR is superior to the competing models with regard to mean absolute error (MAE) and root-mean-squared error (RMSE) and multi-source macroeconomic predictors contribute to the development performance forecast of important economies.

Smart city policy makers should create a favourable macroeconomic environment, such as controlling inflation or stabilising prices for companies within the city, and companies within the important city economic sectors should take initiative to shoulder their responsibility to support the construction of the smart city.

This study contributes to smart city monitoring by proposing and developing a new model, RUMIDAS-SVR, to help the construction of smart cities. It also empirically provides strategic insights for smart city stakeholders.

This study aims to investigate the external effect of the economic growth target pressure of local governments on establishment-level SO₂ emissions.

Based on manually collected panel data of 74,058 China's industrial establishments and more than 330 thousand observations from CIED and ESR, the authors use a firm-fixed effect model, instrumental variables estimation and heterogeneity tests to identify the environmental externality of economic growth target pressure.

The establishments in cities that meet or slightly exceed the economic growth target experience greater negative externality measured by SO₂ emission intensity. This external effect is more pronounced in regions: with a strict and overweighted target setting; with stronger officials' promotion incentives; with a low degree of marketization; and in firms with great economic importance. The authors identify the underlying mechanisms of dependence on dirty industry and the relaxation of environmental enforcement. And the environmental protection constraints in 2007 mitigate the negative externality.

The paper sheds light on to what extent economic growth target pressure has a negative externality of pollution in China and how this pressure may conflict with environmental protection.

This paper complements prior research on the economic effects of economic growth targets, expands the knowledge on the determinants of establishment-level pollution emission from the perspective of target pressure and provides insight into the environmental externality that results from political factors.

Using Spanish establishment-level data on temporary and permanent job and worker flows, we examine firms’ relative usage of fixed-term contracts in response to changes in their prior net employment expectations for the short-run and the long-run – viewed as proxies of how a wide variety of future shocks are ultimately perceived by establishments. The employment response of establishments to changing net employment expectations for the short-run is, primarily, suggestive of their reliance on fixed-term contracts as a buffer to cushion short-run changes in demand as well as to shield permanent workers from downward workforce adjustments. In contrast, their response to changes in net employment expectations for the long-run mostly hints on the use of fixed-term contracts as a screening device. Therefore, policies providing financial incentives to convert fixed-term into permanent contracts – thus targeting firms’ using fixed-term contracts as a screening device, are likely to only have limited effectiveness.

In this paper, a high-frequency radar test system was used to collect the data of clean ballast bed and fouled ballast bed of ballasted tracks, respectively, for a quantitative evaluation of the condition of railway ballast bed.

Based on original radar signals, the time–frequency characteristics of radar signals were analyzed, five ballast bed condition characteristic indexes were proposed, including the frequency domain integral area, scanning area, number of intersections with the time axis, number of time-domain inflection points and amplitude envelope obtained by Hilbert transform, and the effectiveness and sensitivity of the indexes were analyzed.

The thickness of ballast bed tested at the sleep bottom by high-frequency radar is up to 55 cm, which meets the requirements of ballast bed detection. Compared with clean ballast bed, the values of the five indexes of fouled ballast bed are larger, and the five indexes could effectively show the condition of the ballast bed. The computational efficiency of amplitude envelope obtained by Hilbert transform is 140 s·km⁻¹, and the computational efficiency of other indexes is 5 s·km⁻¹. The amplitude envelopes obtained by Hilbert transform in the subgrade sections and tunnel sections are the most sensitive, followed by scanning area. The number of intersections with the time axis in the bridge sections was the most sensitive, followed by the scanning area. The scanning area can adapt to different substructures such as subgrade, bridges and tunnels, with high comprehensive sensitivity.

The research can provide appropriate characteristic indexes from the high-frequency radar original signal to quantitatively evaluate ballast bed condition under different substructures.