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This paper describes and defends the counter-hegemonic methods applied to the investigation of a high-profile national policy for Early Education and Care (ECCE) in Ireland. The policy, the Access and Inclusion Model (AIM) seeks to ensure the full inclusion and meaningful participation of children with disabilities in mainstream, state funded ECCE. It makes a significant contribution to data and debate on how research about inclusion can become inclusion in the context of policy evaluation. The design of the policy evaluation included surveys, in depth interviews and qualitative case studies of pre-schools and children supported by AIM which were deliberately designed to be counter-hegemonic through the recruitment of practitioners as co-researchers (as expert representatives within a feminised workforce), and the use of a participative method of elicitation that sough the perspectives and lived experiences of inclusion among fourteen children supported by AIM. This method was multi-modal mapping. With a focus on these counter-hegemonic elements, the paper poses questions about how the approach was counterhegemonic in terms of its theoretical underpinning, practical approach, and outcomes. Thematic analysis of the data collected by practitioner researchers for the child case studies showed that the approach did achieve counter-hegemony through the achievement of redistribution, representation, and recognition in both the enactment of the research, and in the reporting of children's lived experience in the study as a whole. However, the extent of counter-hegemony achieved was limited when practitioner researchers were unable to deploy the multi-modal mapping method because of limited time, or because the child was not a speaker of English or was as yet, non-speaking. In a context where policy makers have a preference for positivist and rationalist approaches to evaluating the impact of policies, we assert that research about policies for inclusion, should be enacted as inclusion and social justice through the deliberate deployment of participatory and counter-hegemonic methods. We also assert that multi-modal mapping holds particular promise for researching the lived experience of inclusion and participation from the perspective of children and argue that more work needs to be done on developing these methods so that they are effective with all children, including those who are non-speaking. Finally, we posit that Fraser's triune model of social justice can be applied as a benchmark for designing and evaluating counter-hegemonic modii and outcomes. [ABSTRACT FROM AUTHOR]

This paper studies the performance of machine learning predictions for the counterfactual analysis of air transport. It is motivated by the dynamic and universally regulated international air transport market, where ex post policy evaluations usually lack counterfactual control scenarios. As an empirical example, this paper studies the impact of the COVID-19 pandemic on airfares in 2020 as the difference between predicted and actual airfares. Airfares are important from a policy makers' perspective, as air transport is crucial for mobility. From a methodological point of view, airfares are also of particular interest given their dynamic character, which makes them challenging for prediction. This paper adopts a novel multi-step prediction technique with walk-forward validation to increase the transparency of the model's predictive quality. For the analysis, the universe of worldwide airline bookings is combined with detailed airline information. The results show that machine learning with walk-forward validation is powerful for the counterfactual analysis of airfares. [ABSTRACT FROM AUTHOR]

The marine economic development demonstration zone (MEDDZ) is the regional marine functional policy that assumes the significant tasks of innovation in the system and mechanism of the marine economy, optimization of the marine industrial structures, and the construction of marine ecological civilization. This paper constructs a panel database of 11 coastal provinces in China from 2006 to 2019, takes the Zhejiang Marine Economy Development Demonstration Zone Plan officially approved by the State Council of China in 2011 as a quasi-natural experiment, applies the synthetic control method (SCM) to evaluate the effect of implementation of the MEDDZ policy on marine industrial structure optimization in Zhejiang. The results show that: (1) The MEDDZ policy significantly promotes the structure optimization of the marine industry in Zhejiang and the existence of the policy lag phenomenon. (2) Based on the results of the robustness tests, we find that the fitted path of policy effects is better when the time of MEDDZ implementation is adjusted to 2012. (3) Government financial support and marine cargo capacity play a role in implementing the MEDDZ policy, positively affecting and optimizing the coastal marine industry's structure. Therefore, we make some suggestions from the perspectives of strengthening the policy implementation process, focusing on the policy lag phenomenon, and broadening funding sources, thus enriching theoretical research on policies in the field of the marine industry and providing practical references for the development of other coastal demonstration zones. [ABSTRACT FROM AUTHOR]

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With the recent emphasis on carbon neutrality following COP27, many countries prioritise shifting to electric mobility as a crucial strategy to combat carbon emissions. A significant portion of this comes from transportation. Notably, countries such as Norway, China, and the United States have taken the lead in the electric vehicle (EV) industry, driven by their effective e-mobility policies. In contrast, the 10 member nations of the Association of Southeast Asian Nations (ASEAN) are still in the early stages of adopting this emerging trend. This paper conducts a comprehensive evaluation of the current e-mobility policies within the ASEAN region, employing the STEELUP (Sociocultural and demographic, Technological, Economic, Environmental, Legislative, Urban design, and Political) framework to provide a holistic perspective on the e-mobility landscape in the ASEAN countries. By critically assessing the implementation of e-mobility policies in each country, this paper aims to identify key gaps and challenges that hinder the acceleration of EV adoption in the region. The findings from the STEELUP framework, coupled with a thorough analysis of the current conditions in ASEAN, provide crucial insights for governments and policymakers to act upon. Through evidence-based recommendations, this study concludes by suggesting effective strategies to expedite the uptake of EVs in the ASEAN region. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals, adopted by all United Nations Member States, emphasize sustainable cities and communities, aiming to strengthen development planning and foster positive links between urban and rural areas by 2030. As the newly leading platform for urbanization development, characteristic towns ('Tese Xiaozhen' in Chinese) contribute to the target of sustainable urbanisation, wherein town policies are especially important for leading the sound development of characteristic towns. However, the effect of these characteristic towns policies remains uncertain. Additionally, most related studies have utilized qualitative methodologies in policy evaluation, which may be inadequate to guide actual practice. Thus, in order to accurately acknowledge the effect of characteristic towns policies, a policy evaluation framework is established in this paper, utilising the multiple streams theory, text mining and the Policy Modelling Consistency Index model (PMC-Index model). Then, taking 225 policy documents promulgated in China from 2015 to 2022 as the research objects, policy evaluation indicators were selected by combining the multiple streams theory to improve the PMC-Index model and a text mining method. Thereafter, an empirical analysis was conducted to evaluate the consistency of 10 characteristic towns policies chosen. The results indicate that the mean value of the PMC indexes of the 10 examined policies is 7.13 in the total of 9 points, which means that the general performance of the characteristic town policies is satisfactory, yet the low scores in terms of effectiveness, content, and guarantee. In the end, some recommendations and suggestions are proposed for clarifying administrative attributes, optimizing the integration of urban and rural resources, and adjusting the policy hierarchy to a bottom-up approach. This study not only provides a policy evaluation framework to comprehensively understand the consistency of global urbanization policies but also offers a beneficial reference for promote urban–rural sustainable development. [ABSTRACT FROM AUTHOR]

After nearly forty years of development, China's land consolidation policies (CLCP) have become an important tool for promoting rural revitalization and sustainable development. However, as a major land management policy, there is still a lack of quantitative evaluation research on its text. This paper establishes an evaluation system for CLCP using text analysis and the PMC-Index model. Based on a reasonable definition of the connotation of land consolidation, this paper collects 313 related policies issued by China's central government from 1982 to 2022, using text analysis to sort out the characteristics of issuance time, policy types, issuing institutions, and cooperation networks. By combining the outcomes of text mining with the previous research results to set evaluation indicators for the PMC-Index model, it distinguishes between comprehensive policies and specialized policies and separately evaluates them. Then, the PMC-Surface is established to clearly display the calculation results. The results show that the evaluation scores of comprehensive policies and specialized policies showed an upward trend over time. This indicates that the content of CLCP is constantly being enriched and expanded. The evaluation scores for different dimensions of comprehensive policies are relatively balanced, whereas there are significant differences in the evaluation scores of various dimensions of specialized policies. Both comprehensive policies and specialized policies have weaknesses in policy functions, incentives and constraints, and implementation guarantees, so improvements may be needed in these areas in the future. This study provides valuable insights into the advantages and disadvantages of a single land consolidation policy in China. [ABSTRACT FROM AUTHOR]

The article introduces the special section on "Geographical Clustering and the Evaluation of Cluster Policies". We first provide a motivation of cluster policies and briefly review the state of the art in cluster evaluation research. We proceed with summaries of the papers comprising the special section, which take different perspectives and cover distinct facets of cluster policy evaluation. The papers identify challenges for evaluation by highlighting the variety of approaches towards cluster policies addressed at different policy levels. Additional challenges for an identification of policy impact result from the complexity as well as the timing of policy effects. Thus, proper evaluations should employ and combine a broad set of evaluation methods. Based on the main lessons learned, we finally propose several avenues for future research to cope with these challenges. [ABSTRACT FROM AUTHOR]

To promote the precise governance of China's new-energy vehicle (NEV) industry, this paper quantitatively analyzes 204 policy texts on the NEV industry in China since 2007 and constructs an evaluation system of policy effectiveness from three dimensions of policy attributes, policy objectives, and policy measures to reveal the effectiveness and evolutionary trends of China's NEV industry policies. In addition, this paper explores the two-way effects of different types of policy measures on the NEV supply and demand markets through an econometric model to reveal the differential impact effectiveness of various policy instruments in China's NEV industry. The results indicate that China's NEV industry has changed from "government-driven" to "government-driven + market-driven", and the multi-sectoral policy coordination needs to be further improved; the dynamic evolution over the years reveals a similar pattern of change in the total effectiveness of policy issuance as influenced by the number of policies; the quantity of patents in China is large but the quality of patents is insufficient, and an overall problem of low level of core technology is being faced; taxation and subsidy measure, technical innovation measures, social guidance measures, and environmental support measures all effectively promote the development of the supply-side market, while taxation and subsidy measures, social guidance measures and legal regulation measures can better promote the development of the demand-side market; the impact of financial support measures on both supply and demand-side markets is not significant. [ABSTRACT FROM AUTHOR]

Despite the promising outcomes achieved over time in Asset Management, data accessibility, correlation, analysis, and visualization still represent challenges. The integration, readability, and interpretation of heterogeneous information by different stakeholders is a further concern, especially at the urban scale, where spatial data integration is required to correlate virtual information with the real world. The Geographic Information System (GIS) allows these connections, representing and digitizing extensive areas with significant benefits for asset analysis, management, and decision-making processes. Such benefits are central for managing large and widespread university campuses as they are comparable to small cities, covering a wide urban region and including resources highly integrated into the urban context. The paper presents how GIS integrated into Business Intelligence (BI) tools can support university Asset Management System (AMS) creation for the optimal use of resources, illustrating the University of Turin case study. The results discussion considers the relationship between the different elements of the assets and their synergy with the city. It focuses on four themes, dealing with the asset identification of buildings and resources, especially the educational ones, asset spatiotemporal evolution, and buildings' distances for proximity analysis. The benefits achievable through the AMS, related challenges, and possible future developments are highlighted. [ABSTRACT FROM AUTHOR]

This paper studies a central issue in modern reinforcement learning, the sample efficiency, and makes progress toward solving an idealistic scenario that assumes access to a generative model or a simulator. Despite a large number of prior works tackling this problem, a complete picture of the trade-offs between sample complexity and statistical accuracy has yet to be determined. In particular, all prior results suffer from a severe sample size barrier in the sense that their claimed statistical guarantees hold only when the sample size exceeds some enormous threshold. The current paper overcomes this barrier and fully settles this problem; more specifically, we establish the minimax optimality of the model-based approach for any given target accuracy level. To the best of our knowledge, this work delivers the first minimax-optimal guarantees that accommodate the entire range of sample sizes (beyond which finding a meaningful policy is information theoretically infeasible). This paper is concerned with the sample efficiency of reinforcement learning, assuming access to a generative model (or simulator). We first consider γ-discounted infinite-horizon Markov decision processes (MDPs) with state space S and action space A. Despite a number of prior works tackling this problem, a complete picture of the trade-offs between sample complexity and statistical accuracy has yet to be determined. In particular, all prior results suffer from a severe sample size barrier in the sense that their claimed statistical guarantees hold only when the sample size exceeds at least | S ‖ A | (1 − γ) 2 . The current paper overcomes this barrier by certifying the minimax optimality of two algorithms—a perturbed model-based algorithm and a conservative model-based algorithm—as soon as the sample size exceeds the order of | S ‖ A | 1 − γ (modulo some log factor). Moving beyond infinite-horizon MDPs, we further study time-inhomogeneous finite-horizon MDPs and prove that a plain model-based planning algorithm suffices to achieve minimax-optimal sample complexity given any target accuracy level. To the best of our knowledge, this work delivers the first minimax-optimal guarantees that accommodate the entire range of sample sizes (beyond which finding a meaningful policy is information theoretically infeasible). Funding: Y. Wei is supported in part by the Google Research Scholar Award and the National Science Foundation [Grants CCF-2106778, DMS-2147546, and DMS-2143215]. Y. Chi is supported in part by the Office of Naval Research [Grants N00014-18-1-2142 and N00014-19-1-2404] and the National Science Foundation [Grants CCF-1806154, CCF-2007911, and CCF-2106778]. Y. Chen is supported in part by the Alfred P. Sloan Foundation [research fellowship], Google [research scholar award], the Air Force Office of Scientific Research [Grants FA9550-19-1-0030 and FA9550-22-1-0198], the Office of Naval Research [Grant N00014-22-1-2354], and the National Science Foundation [Grants CCF-2221009, CCF-1907661, DMS-2014279, IIS-2218713, and IIS-2218773]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2451. [ABSTRACT FROM AUTHOR]

Climate change is one of the largest challenges facing mankind, and the question of how to reduce carbon emissions has raised extensive concern all over the world. However, due to the lack of mechanisms to explain the impact of political factors on environmental regulatory tools, the evaluation of carbon emissions reduction is insufficient in the majority of previous studies. How to better explore the path of carbon emissions reduction has become the key for China to achieve carbon neutralization as soon as possible. Based on a quasi-natural experiment regarding China's carbon emission trading policy, this paper adopts a difference-in-differences model to address the impact of political mobility on China's carbon emissions trading policy, and the selected pilot and non-pilot provinces of this policy in China enabled the model to be matched. Using a panel database with 30 provincial administrative units as the observation objects, the results show that China's carbon emissions trading policy and the horizontal mobility experience of the provincial governors exert a significant positive effect on carbon emission reduction. Additionally, this study identifies a latent factor previously ignored by the existing literature: the correlation between political factors and carbon emissions. This verifies our theoretical hypothesis that officials transferred from the provincial level tend to have higher performance regarding carbon emission reduction. This paper also provides suggestions for the central government to further plan and implement carbon emission reduction policies and mobilize the incentives of local officials in environmental governance. [ABSTRACT FROM AUTHOR]

This paper develops an offline reinforcement learning framework that identifies and evaluate the ad sequencing policy that optimizes user engagement. In this paper, we propose a unified dynamic framework for adaptive ad sequencing that optimizes user engagement with ads. Our framework comprises three components: (1) a Markov decision process that incorporates intertemporal tradeoffs in ad interventions, (2) an empirical framework that combines machine learning methods with insights from causal inference to achieve personalization, counterfactual validity, and scalability, and (3) a robust policy evaluation method. We apply our framework to large-scale data from the leading in-app ad network of an Asian country. We find that the dynamic policy generated by our framework improves the current practice in the industry by 5.76%. This improvement almost entirely comes from the increased average ad response to each impression instead of the increased usage by each user. We further document a U-shaped pattern in improvements across the length of the user's history, with high values when the user is new or when enough data are available for the user. Next, we show that ad diversity is higher under our policy and explore the reason behind it. We conclude by discussing the implications and broad applicability of our framework to settings where a platform wants to sequence content to optimize user engagement. History: Olivier Toubia served as the senior editor for this article. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mksc.2022.1423. [ABSTRACT FROM AUTHOR]

Along with the deep comprehension and accumulated practice of corporate social responsibility (CSR), people are increasingly aware of the positive role of the government in the development of CSR. Chinese governments at all levels have issued many policies to guide and regulate CSR behavior in Chinese enterprises. However, there has been little research on the evaluation of CSR policy's effectiveness. In this paper, we conducted a text analysis of 76 corporate social responsibility policies (CSRPs) and statistics of high-frequency words. Based on the existing policy evaluation index system, combined with the characteristics of CSRPs, we constructed a CSRPs content evaluation index system based on the policy modeling consistency index (PMC-index) model. Additionally, we conducted content analysis and quantitative evaluation of six CSRPs selected from different levels and regions of government agencies. The results show that the evaluation levels of the six policies were all good, which could play a positive role in the CSR development of their policy objectives. However, policies in different regions show obvious differences in the design of implementation suggestions and incentive and constraint measures, and there is a large space for further optimization. This study not only provides specific policy optimization suggestions for the government and enterprises based on case studies but also provides methods for evaluating the content of CSRPs, filling the research gap in this field. [ABSTRACT FROM AUTHOR]

Strict carbon emission regulations are set with respect to countries' territorial seas or shipping activities in exclusive economic zones to meet their climate change commitment under the Paris Agreement. However, no shipping policies on carbon mitigation are proposed for the world's high seas regions, which results in carbon intensive shipping activities. In this paper, we propose a Geographic-based Emission Estimation Model (GEEM) to estimate shipping GHG emission patterns on high seas regions. The results indicate that annual emissions of carbon dioxide equivalent (CO2-e) in shipping on the high seas reached 211.60 million metric tonnes in 2019, accounting for about one-third of all shipping emissions globally and exceeding annual GHG emissions of countries such as Spain. The average emission from shipping activities on the high seas is growing at approximately 7.26% per year, which far surpasses the growth rate of global shipping emission at 2.23%. We propose implementation of policies on each high seas region with respect to the main emission driver identified from our results. Our policy evaluation results show that carbon mitigation policies could reduce emissons by 25.46 and 54.36 million tonnes CO2-e in the primary intervention stage and overall intervention stage, respectively, with 12.09% and 25.81% reduction rates in comparison to the 2019 annual GHG emissions in high seas shipping. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the heterogeneous impacts of the media sentiment about policies with different themes on the real estate market in China. Based on the policy texts collected from both official and unofficial sources, we construct sentiment indices to capture the sentiment about policies with different themes, including real estate policies, fiscal policies, monetary policies, land policies, healthcare policies, household registration policies, and education policies, using text mining methods. Mediation models and GARCH models are then established to examine the impact of these sentiment indices on the real estate market. The E-GARCH model is established to examine the asymmetric effect of positive and negative sentiment on real estate market. The results show the following: (1) The real estate market in China is more affected by the policy sentiment on official media compared with the unofficial ones. (2) Policy sentiment affects the real estate price through the mediating variables of interest rate, real estate construction area, and real estate sales. (3) The impacts of sentiment with different themes on the volatility of the real estate market are heterogeneous. (4) The impacts of policy sentiment on official media are more pronounced in a tight government-policy environment than those in a loose one. (5) The effect of negative unofficial media policies sentiment on real estate price is bigger than the positive unofficial media policies sentiment. [ABSTRACT FROM AUTHOR]

As European governments adopt new circular built environment policies to cope with the socio-ecological crisis, the need for evaluating such policies gains in urgency. Ex post evaluation is, however, difficult as these policies have not been in place long enough to have had significant effects. Nonetheless, ex ante policy evaluation may be possible by assessing policy coherence or the alignment and synergies of policy goals, instruments and implementation practices. This paper proposes a framework to analyse circular built environment policies. This framework is based on a combination of two existing analytical frameworks: circular city development and policy coherence analysis. The framework is tested for the case of a circular built environment in campus development at Delft University of Technology in the Netherlands, which is regarded as an urban development proxy. Policy documents and semi-structured interviews were analysed and coded. Results confirm previous findings about a prevailing focus on looping actions and indicates limited policy instrumentalisation across governance levels. Identified multilevel (in)coherence in circular city policy is pointed out as consequence of siloed-led and supply chain-based thinking and underdeveloped circular policy frameworks. Finally, the analytical benefits of circular city development and policy coherence frameworks are discussed. [ABSTRACT FROM AUTHOR]

This paper examines agricultural policy interventions in Newfoundland and Labrador. We focus specifically on efforts geared towards building the capacity of agriculture through the attraction and retention of people into the industry. We found that the agricultural policy environment is a complex structure of private and public actors, who intervene both collaboratively and distinctively to make farming attractive to the general public and to keep farmers in the industry. These efforts manifest through financial incentivizing, informational and resources support, skill development and capacity building, land reforms, institutional support, and changes in program requirements and operational mechanisms. However, despite the multiactor driven diverse agricultural policy actions that target various industry capacity sectors, structural limitations in the policy environment, including unclear policy language, red tape, stringent program requirements, a one-sided focus of policy actions, and lack of partnership and silo approach to farm programs undermine how beneficiaries perceive, accept, and participate in policy interventions. More importantly, these challenges reveal the (in)effectiveness of policy actions by highlighting the lack of room for experimentation, inflexibility, and lack of even playing grounds for beneficiaries. Through these findings, this paper contributes to the literature on agricultural policy and policy evaluations by providing empirical case evidence of the dynamics in policy environments and how that could be improved. [ABSTRACT FROM AUTHOR]

The importance of small- and medium-sized enterprises (SMEs) from economic, social, and environmental point of views and the crucial role of energy efficiency are widely recognized. However, the development of effective policies and their analysis are still challenging topics, for which research is relatively scarce. The main reasons for this are the high heterogeneity of SMEs, their low energy impact compared with energy-intensive enterprises, the lack of quantitative in-formation on the energy consumption of SMEs, and the low awareness of SMEs on energy topics. The structural paradigm change in the energy markets has underlined the importance of EE in SMEs. Several studies have been devoted to analyzing EE policies, using different methods and targets, not necessarily with a focus on SMEs or quantitative figures. This study presents a scoping review aimed at mapping the scientific literature on this topic, assessing its volume, nature, characteristics, type of evidence available, key concepts, and possible gaps. The existing contributions were systematized on three assessment levels regarding the EE policies for SMEs: general framework for their classification; comprehensive analysis of contributions providing qualitative information; in-depth analysis of evaluation studies including quantitative information. This study highlights the need for a good balance between economic and supportive mechanisms and the crucial role of energy audits. The analyzed contributions show that despite SMEs' efforts to develop EE policies, there are still important barriers to be addressed. Moreover, there is an important lack of quantitative analyses, which are necessary for the development a harmonized policy evaluation approach. Based on a synthesis of the review findings, key learnings for the better design, implementation, and evaluation of EE policies for SMEs are provided. [ABSTRACT FROM AUTHOR]

As was true for many others, my professional life was turned upside down in the early days of the pandemic. The crisis touched almost every field in economics: international supply chains broke down, economic activity was heavily constrained either by non-pharmaceutical measures to fight the pandemic or by voluntary action, and the labour market experienced unprecedented levels of short-time work and huge (temporary) lay-offs. Governments struggled to provide cash and find ways to compensate affected people and businesses. Financial markets tumbled and monetary policy faced new challenges on top of an already tense situation. [ABSTRACT FROM AUTHOR]

This paper sets out to make a contribution to the extensive literature that seeks to develop methods that allow rigorous and robust analysis of the spatial and temporal impacts of public policy interventions on property (real-estate) values. It argues that the hybrid repeat-sales/hedonic method developed in real-estate studies over the last 30 years has considerable, but as yet under-developed, potential as a policy analysis tool. Using data from Glasgow, UK, the empirical analysis illustrates how the technique can be used to understand the spatial spillovers and the dynamic temporal effects of a historic £100 million state-led, area-based, urban-renewal programme, New Life for Urban Scotland. The paper concludes by arguing that, with the rise in the availability of rich geocoded, micro-datasets, this framework is sufficiently flexible to be used to evaluate the real-estate market impacts of a wide range of public policy interventions. Significantly, as the case study demonstrates, the framework overcomes some of the sustained criticisms of the more commonly used hedonic modelling approach. There is, however, still much to do to enhance the technical qualities of the models through further application. [ABSTRACT FROM PUBLISHER]

Background: A classic methodology used in evaluating the impact of health policy interventions is interrupted time-series (ITS) analysis, applying a quasi-experimental design that uses both pre- and post-policy data without randomization. In this paper, we took a simulation-based approach to estimating intervention effects under different assumptions.Methods: Each of the simulated mortality rates contained a linear time trend, seasonality, autoregressive, and moving-average terms. The simulations of the policy effects involved three scenarios: 1) immediate-level change only, 2) immediate-level and slope change, and 3) lagged-level and slope change. The estimated effects and biases of these effects were examined via three matched generalized additive mixed models, each of which used two different approaches: 1) effects based on estimated coefficients (estimated approach), and 2) effects based on predictions from models (predicted approach). The robustness of these two approaches was further investigated assuming misspecification of the models.Results: When one simulated dataset was analyzed with the matched model, the two analytical approaches produced similar estimates. However, when the models were misspecified, the number of deaths prevented, estimated using the predicted vs. estimated approaches, were very different, with the predicted approach yielding estimates closer to the real effect. The discrepancy was larger when the policy was applied early in the time-series.Conclusion: Even when the sample size appears to be large enough, one should still be cautious when conducting ITS analyses, since the power also depends on when in the series the intervention occurs. In addition, the intervention lagged effect needs to be fully considered at the study design stage (i.e., when developing the models). [ABSTRACT FROM AUTHOR]

Housing has become pivotal in attracting and retaining talent in first-tier cities. Although numerous cities are actively promoting the provision of talent housing in China, little is known about the talent's evaluations of talent housing policies or the effect on their urban settlement intention. This paper aims to investigate whether talent housing alleviates the housing difficulties of talent and its role in retaining talent. A questionnaire was conducted face-to-face in talent housing in Shanghai. Binary logistic regression was employed to analyse the factors significantly contributing to the settlement intentions of the talent. Talent housing was confirmed to alleviate the talent's housing pressures and further increase their urban settlement intention. The local hukou was determined to be crucial in accelerating the willingness of talent to settle in Shanghai. However, housing affordability (including school district housing) may jeopardise such positive effects. It is crucial to provide more choices of talent housing and increase the coverage of good-quality educational resources. In the long run, more talent can be attracted and retained in the locality under a broader coverage of the talent housing scheme. [ABSTRACT FROM AUTHOR]

Policy-makers at regional, national and European Union (EU) levels of governance use a variety of subsidy programmes to stimulate firm-level innovation. Against this backdrop, this paper investigates three important issues that have not received sufficient attention in the literature: (1) whether evaluating the impact of subsidies from each individual source is biased by ignoring firms that receive a mix of subsidies from different sources at the same point in time; (2) whether receiving a mix of subsidies from regional, national and EU sources crowds out firm-level innovation; and (3) if effective, whether subsidy mix stimulates forms of innovation with higher private or social returns. The findings demonstrate that ignoring subsidy mix significantly biases evaluations of subsidies from individual sources. Moreover, subsidy mix can be a highly effective means of stimulating forms of firm-level innovation with the highest social returns, precisely where market and systemic failures are most acute. [ABSTRACT FROM AUTHOR]