dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods We use a ranked percentile comparison of the 33 nations of Latin America and the Caribbean. Rank percentile is a way of scoring the individual nations to ascertain who the frontrunners are in each component of the theoretical framework that we refer to as the DeePeR method. This refers to an energy equity evaluation of Distributional, Procedural and Restorative justice. Our study is a quantitative analysis of these emerging themes from the associated literature covered above. In line with FAIR data principles (ILO, 2016), we offer a global perspective by using open access global data on the national performance of LAC nations. We focus on the previous decade of 2010-2020. A detailed list of indicators used in this study can be found in Appendix 1 (Table  A1 ). We detail below our approach to data collection and analysis.
We focus instead on the insights provided by aggregated observations across the pre-pandemic period, indicating where trends in national performance may endure. Complete datasets were selected to ensure comparability across the region. The third aspect to raise regarding data collection is the sources used throughout our analysis. We began with the principle that the data used should be open access in line with FAIR data initiatives. We therefore did not include commercially sensitive data or data behind pay walls. To provide the highest level of sophistication, we combined existing datasets rather than relying on one source. We started with the most reliable international data source in energy, namely the International Energy Agency (IEA, 2021b), to establish a core dataset on energy sources for the region.
This was a necessary limitation to have the space to explore the key indicators as theoretically derived from the just transition framework  (McCauley and Heffron et al. , 2018). It constrained a wider interpretation of just transition. Future work could take alternative frameworks in this regard. Our study does not, thirdly, explore national data as it is explicitly a global data analysis. The lack of absolute comparability across different national datasets precluded their use in this analysis, as we wished to perform a fair and direct comparison of countries based on consistent criteria. This constrained our ability to delve into national statistics that related to our original theory-driven approach of the just transition framework. Lastly, we find that Latin American, and particularly Caribbean, countries are relatively underrepresented in core global data indicators.
In the fallout of the 2008 crisis, macroprudential policy has been installed as the policy remedy against future financial instability, a primary focus being developments in the real estate sector. With house prices consistently rising in the EU since 2014, causing alarm among macroprudential supervisory bodies, a core question of EU regulatory governance is how far macroprudential bodies have been capable of bringing about countercyclical actions against the build-up of such vulnerabilities. This paper investigates this question using a novel dataset of macroprudential intensity coded for the 17 EU countries that experienced real estate vulnerabilities post-euro crisis. Specifically, it asks which configuration of conditions account for the (in)capacity of countries to impose stringent countercyclical regulations against housing booms? Using fuzzy set qualitative comparative analysis technics coupled with qualitative analysis of country cases using expert interviews, we find that the absence of political salience of homeownership and the political independence of macroprudential authorities to be crucial conditions that jointly explain countercyclical macroprudential activity.
This reaction function is of primary interest to political economists, who have predicted these macroprudential actions likely to be weak, hindered by the political economy of house price appreciation  (Baker, 2018 , Fuller et al. , 2019) , but to date have not undertaken empirical studies to engage in more fine-grained empirical analyses of the factors impacting policy implementation. For this reason, we know little about how governance structures and political economy factors affected such decisionmaking, influencing the form and the extent these measures have taken in response to the cyclical upswing. This is what this paper proposes to analyze. We compile a novel dataset explicitly coded for the purpose of capturing the intensity of the macroprudential response from 2010 to 2020 (pre-COVID-19), that is, if macroprudential policies were indeed introduced, what combination of policies was used and how strictly were they calibrated.
In contrast, countries with high political salience of access to homeownership and limited institutional anchoring of macroprudential policies see little action. The rest of this paper proceeds as follows: Section 2 reviews the literature on the comparative political economy literature on countercyclical macroprudential action. Section 3 briefly documents the different real estate booms in Europe, which justifies our country selection. Section 4 presents our macroprudential policy dataset and documents actions in the EU post-crisis, as well as describes the conditions we deem important to explain action. Section 5 presents our fsQCA empirical analysis and results. Section 6 discusses and Section 7 concludes.
While from 2014 onward, the ECB has in theory the right to request a top-up of these measures in a given country, in case it deems them insufficient, the ECB has to date not exercised this right, instead seeking the dialogue with its national counterparts (interview former ECB manager, September 18, 2019, interview German Central Banker, June 18, 2021). In the following years, these national macroprudential authorities and the ESRB/ECB hence engaged in an attempt to generate a common framework for the supervision of the build-up of such risks both domestically but also on the European level, with a particular focus on the housing sector. Collaborating in working groups at the ESRB, regulators agreed on a common framework for such an analysis as well as the datasets and measures that should be used, providing legitimacy to the analytical framework thus developed  (Thiemann et al.
Coding the activity and stringency of macroprudential policy The index is constructed in several steps (further detailed in Appendix A): First, we compile a dataset of all macroprudential policy actions in the EU from the post-GFC to COVID-19 (January 2010 to January 2020). The bulk of our policy data comes from the MaPPED dataset developed by the ECB to provide comprehensive information on macroprudential policy in the banking sectors of the 28 EU member states. It covers 11 categories of regulatory instruments, and almost 1700 policy actions, that is, events of introduction, recalibration, or cancelation of these instruments from 1995 to 2016. The data was collected through questionnaires sent to experts from national central banks and supervisory authorities of all EU member states enabling rigorous and comprehensive data collection  (Budnik & Kleib, 2018).
We cross-checked, harmonized, and complemented these data by the ESRB Macroprudential Measures Database, which provides an exhaustive coverage of policy actions since 2014. Indeed, the Capital Requirements Directive IV and Capital Requirements Regulation (CRD/CRR) introduces requirements for macroprudential authorities of EU countries to notify the ESRB of macroprudential measures, which then enables and feeds into the ESRB dataset. Once the list of all policy actions from 2010 to 2020 is collected, we extracted the specific macroprudential tools that may be used to lean against housing booms in a countercyclical fashion. Specifically, we decide to focus on the three types of policy tools addressing these risks: • Borrower-based measures addressed at mortgage loans: LTV limits, debt service to income (DSTI) limits, as well as debt to income (DTI) and loan to income (LTI) limits and amortization requirements.
We finally sum up the weighted actions to create an overall macroprudential policy index designed to capture policy reaction to the housing boom and focus on the change in macroprudential intensity to capture policy reaction over the period of interest. 4. 1. 2. Patterns of macroprudential policy in the EU in the post-crisis period Our dataset reveals interesting stylized facts on the use of macroprudential policy in the EU. Figure  3  plots the resulting average macroprudential intensity index for our 17 EU countries, on a monthly basis as well as on a cumulative basis in the past decade. We note that discretionary macroprudential policy action unfolded as a pattern, which is reminiscent of a step-function, with crucial years being 2015 and 2018. Before 2014, very little discretionary macroprudential action can be observed, further justifying our focus on the period 2014 onwards.
Condition 2: Real estate crisis experience Widely used datasets of banking crises (e. g. ,  Laeven & Valencia, 2012)  are not suitable for our aim, as we are specifically interested here in the housing-related crises. We indeed argue that real estate macroprudential policy would depend on experiences specific to the housing market. We thus prefer to use the real estate crises data of  Bengtsson et al. (2020)  which provides a quantitative rather than expert-defined crisis dataset. The final list of EU countries deemed to experience a real estate crisis is 13 countries: DK, EE, ES, GR, HU, IE, LT, LV, NL, PT, SI, SK, UK, of which only LV, ES, GR, and HU are not in our sample. While, as discussed in Section 2, we can expect crisis experience to impact the likelihood of macroprudential action, there are no strong priors as to the direction of the impact.
Condition 3: Political salience To proxy the political salience of homeownership and the access to housing finance, we use a measure based on the mentions and support for homeownership in party manifestos. Specifically, for each party, we code as 1 those that mention and support homeownership in their manifesto in a given election leveraging on the dataset of  Kohl (2018a)  which collected data on housing content of political manifestos for 19 countries over several decades. We expanded Kohl's dataset, which only included 11 out of our 17 countries and which stopped in 2013. We completed the coding, adding the manifestos of the additional 6 countries and added all additional elections from 2013 to 2019 for the 17 countries. We then computed for each election the share of parties that promoted homeownership and took the average of this score across all executive elections since 2010.
Concluding remarks This paper set out to investigate the reaction of EU member states to the housing boom which occurred in the EU from 2014 onwards. Specifically, it focuses on the intensity of the response in the 17 countries which have been experiencing a worrying build-up of housing vulnerabilities as proxied by ESRB warnings. Leveraging on a newly compiled dataset of macroprudential intensity, it seeks to explain which configurations of conditions explain the variance in the macroprudential response in these countries. Our analysis confirms that macroprudential action cannot be explained by a single explanatory condition in isolation and instead highlights two different pathways to action: On the one hand, when independent authorities are in charge, we see countercyclical action independent of the salience of housing in that country.
Acknowledgments The views expressed in this paper are those of the authors and do not reflect those of any institutional affiliation. We would like to thank  Emiliano Grossman ,  Patrick Le Gales ,  Stefano Pagliari ,  Bart Stellinga ,  Eva Thomann ,  Kevin Young ,  Claudius Wagemann , and  Kent Weaver  for their valuable comments on the draft, as well as participants at the 11th  ECPR Biennial Conference of the SGEU and at seminars at City, University of London and Sciences Po Paris , and three reviewers. We would also like to thank  Suramya Shukla ,  Purti Sadhwani , and  Angelica Gil Cifuentes  for excellent research assistance. Matthias Thiemann acknowledges the support of a SAB Grant from  Sciences Po , titled "Managing expectations in financialized capitalism: The comparative political economy of macroprudential regulation in residential real estate markets in Germany, France, the  Netherlands and UK " (2020)(2021)(2022).
Data description The sample used in our study for portfolio formation consists of the daily prices of the First Trust NASDAQ Clean Edge Green Energy Index Fund (QCLN), 3 gold, crude oil, the US dollar, and a three-month Treasury bill. Following the literature, we use QCLN as a proxy for a green asset  (Rizvi et al. , 2021). We also use the daily prices of the S&P 500 index against which to test the portfolio's performance. The data are collected from Refinitiv Datastream. The sample starts on 1 December 2006, coinciding with the introduction of the clean energy index, and ends on 8 March 2022. Following  Sahamkhadam et al. (2018)  and  Karmakar and Paul (2019) , we use a rolling window for the prediction. Table  1  presents descriptive statistics. Crude oil appears to have the highest mean return, while the T-bill has the lowest mean return during the sample period.
As the findings of  Stoyanov et al. (2013) , we show that CVaR is less responsive to small differences in the tail index. Further, the results corroborate the scope of improvement over the minimum-variance portfolio. The results show that there is space to improve the CVaR of the minimum-variance portfolio as the minimumvariance portfolio CVaR may not be the least, as witnessed in the GMV portfolio CVaR values. As shown by  Karmakar and Paul (2019) , we find that the GARCH-EVT-Student's t copula model performs better than other models in predicting downside risk measures. Backtesting ensures the model's robustness when the data are heavytailed. When portfolio managers closely monitor prices and frequently rebalance their portfolios, non-Gaussian data becomes problematic. Hence, it requires sophisticated techniques to handle non-Gaussian data to yield an efficient portfolio.
Education was captured based on the national categories and then transformed into key categories of primary, secondary and higher education. Similarly, income was captured in categories that reflect the local income levels and then transformed into four categories: no income, low, medium and high income. Average wage statistics were obtained from the relevant OECD database. Wages were used instead of income because the wages database is more up to date and does not equivalize (adjustments allowing for post-tax figures and household size). Once the average wage was calculated per country, thresholds were derived to accommodate participants' responses: 0. 6 below the average (low), 0. 6 below-1. 4 above average (medium), 1. 4 and above the average (high). In Sighisoara Tarnava Mare wage statistics were obtained from the Romanian government's statistics institute as they were not available at the OECD database) (https://insse.
Structural equation modelling A structural equation modelling (SEM) approach (Bollen 1989) was followed in order to test the influence of the different factors on the level of support (figure  4 ). We hypothesize that all three factors (governance, social attributes and perceived social impacts) influence the level of support (H1). We also hypothesize that social profile, will influence gov- We also test whether social characteristics influence perceived social impacts (H3). In addition, we also include a random effects term as a covariate in order to account for unobserved heterogeneity in the response variable of public support as a source of regional heterogeneity (e. g. Lefcheck 2016). This is due to the fact that the typical SEM model assumes the data to be independent, which potentially might not hold in the current dataset since there are likely PA-specific characteristics.
To this end, we follow relevant research (e. g. Wooldridge 2002, Bollen and Brand 2010) and treat regional PA effects as a random effect variable, in the sense that the random term is assumed to be independent of all other model covariates. A small portion of the final selected dataset for the analysis included missing data in few of the variables. In overall, the percentage of missing values did not exceed the 5% of overall data in a single variable. Missing data in at least one of the variables were included in all PAs. Multiple imputation (MI) methodology was applied to impute missing data in the sample, which is considered one of the most reliable processes for handling missing data in multivariate analysis (see Rubin 1996, Graham and Schafer 1999). To perform MI, the SPSS MI module was utilized (IBM Corp.
Released 2020). The procedure for replacing missing values was conducted within each PA in order to ensure that the missing values will be replaced based on similar socioeconomic and regional profiles of the respondents. An exploratory factor analysis on the latent factors subsequently utilized for SEM was initially conducted (using SPSS 27. 0) in order to measure the percentage of variance explained by each latent factor. In addition, to test for the factors' reliability and validity, the Cronbach's alpha was calculated  (Bollen 1989). The SEM models were fitted by the method of weighted least squares (Jöreskog 1994), which is most suitable for non-normal data types, such Likert scale measurements. In order to examine the validity of indirect associations, the bootstrap approach introduced by Preacher and Hayes (  2004 ) is utilized.
Acknowledgments We would like to thank management authorities in all research areas for their valuable feedback during the design of the research and interpretation of results. These include park authorities of  Black Forest National Park ,  Calanques National Park ,  Snowdonia National Park ,  Peak District National Park ,  Pieniny National Park ,  Alonissos National Park ,  National Park of Eastern Macedonia  and Thrace,  Matsalu National Park  and  Fundatia ADEPT.
1. 7 (Fischbacher, 2007) and participants were recruited using ORSEE (Greiner, 2004). We analyze our data using R (R Core Team, 2020), reading the data in using package ztree (Kirchkamp,  2019), and derive clustered standard errors with the function coeftest from the package lmtest (Zeileis and Hothorn, 2002), which in turn uses the function vcovCL from the package sandwich (Zeileis et al. , 2020). For zero-inflated models, we use the function zeroinfl (Zeileis et al. , 2008) from the package pscl (Jackman, 2020). We generate regression tables using texreg (Leifeld, 2013)  and figures using ggplot2 (Wickham, 2016). Following the best practices laid out in Freeman et al. (2018), we report that at the beginning of each session, every participant receives the same instructions: information on both the trader and observer roles.
Market participation and trading activity Result 1. Market participation rates are significantly higher in noreg markets because informed traders are more active in the absence of regulation. Uninformed traders' participation rates do not vary consistently with the regulatory regime. Support. The top panel of Fig. 1  reports the participation rates per phase (left, middle, and right panel), trader type (bars on the left and behind for informed, and bars on the right and in front for uninformed traders), and regime (noreg -light; reg -dark), as well as -values from -tests contrasting values of a specific trader type between regimes. Table  WA1  in the Online Appendix, Section 2, is a tabular representation of these values also displaying pooled data for all phases. For these analyses, we classify traders as participating in a market when they have either posted at least one limit order or accepted (parts of) a limit order outstanding in the market.
Based on this classification, we then determine the share of participating traders among the respective trader types in a market. When considering pooled data from both trader types (not reported in Fig. 1  or Table  WA1 ), we find that 89. 82% of traders participated in noreg markets and 88. 35% in reg markets. The difference between these values is small and borderlines conventional significance levels (unpaired (574) = 1. 87,  =. 0626). Breaking down the data by trader type reveals that the treatment differences mainly originate from Phase 2 and are driven by both informed and uninformed traders. Considering informed trader data for all phases, we observe significantly higher participation rates in noreg markets (94. 38%) compared to reg markets (89. 36%, unpaired (2302) = 4. 26,  =. 0000).
Background Gender-based disparities in health-care are common and can affect access to care. We aimed to investigate the impact of gender and socio-environmental indicators on health-care access in oncology in France. Methods Using the national health insurance system database in France, we identified patients (aged ≥18 years) who were diagnosed with solid invasive cancers between the 1st of January 2018 and the 31st of December 2019. We ensured that only incident cases were identified by excluding patients with an existing cancer diagnosis in 2016 and 2017; skin cancers other than melanoma were also excluded. We extracted 71 socio-environmental variables related to patients' living environment and divided these into eight categories: inaccessibility to public transport, economic deprivation, unemployment, gender-related wage disparities, social isolation, educational barriers, familial hardship, and insecurity.
Research in context Evidence before this study Sex, while typically characterised by biological distinctions between males and females, and gender, related to sociocultural differences between men and women, influence health-care experiences, access to care, and interactions within health-care systems. Gender-based disparities in health-care access have long been recognised as a crucial issue, particularly in oncology. We conducted a systematic search of PubMed, Google Scholar and Scopus, between database inception and June 1, 2022, using the terms ("healthcare" or "health care") and ("sociodemographic" or "socio-demographic" or "socio-environmental") and ("gender" or "sex") and ("oncology" or "cancer care" or "cancerology" or "cancer"). No language restrictions were applied to the search.
Added value of this study Our study addresses three main gaps in the existing literature on the impact of gender and socio-environmental indicators on health-care access in oncology. First, by using a comprehensive and exhaustive nationwide database from the French national health insurance system, our research provides a large-scale and robust analysis of patients diagnosed with solid invasive cancers. Second, we investigate the influence of a diverse range of socio-environmental factors, including inaccessibility to public transport, economic deprivation, unemployment, gender-related wage disparities, social isolation, educational barriers, familial hardship and insecurity, offering a multidimensional approach to understanding health-care access disparities. Third, our study considers the interaction between gender and these socioenvironmental factors, shedding light on the unique challenges faced by women in accessing oncological care.