dataesr/scientific-paragraphs-categorization huggingface dataset:
We searched the databases of Scopus, Web of Science (Core Collection, KCI, MEDLINE, RSCI, SciELO), ScienceDirect, EBSCO Host, Cairn International (for English), Cairn (for French), and Google Scholar (for Italian and German). Given the number of languages involved and the presence of different terms used for homestay, 2 we designed a 2 Texts frequently use the word "hospitality" (hospitalité in French and ospitalità in Italian), but we also find "hosting" in English and similar phrases in French (hébergement or accueil) and in Italian (accoglienza). Many other variations are used, such as "homestay," "family hosting," "private accommodation," "home accommodation," "private housing research strategy testing a large set of keywords (see the Supplemental Online Appendix). The research identified 2,339 documents.
We deleted duplicates, using Zotero reference management software, reducing the corpus to 1,557 items (919 in English, 327 in French, 202 in Spanish, 245 in German, and 75 in Italian). These records were screened in two phases: First based upon titles and upon abstracts, and second, based upon full text. In the first phase, we excluded 1,508 records because they covered various topics such as touristic hospitality, other types of accommodation arrangements for refugees, foster care, or migration and integration policies. The 49 remaining records were eligible for the second phase. Exploiting an existing database managed by Refugees Welcome International and our prior knowledge, we added 43 additional documents (10 in English, 13 in French, 4 in German, and 14 in Italian). Then we read these 90 documents to assess whether they (a) discussed homestay accommodation for refugees as a main or secondary topic, and/or (b) described the practice of homestay accommodation for refugees.
See: https://www. prio. org/publications/12877 2 For example, in 2013, 80 (70) percent of respondents in the UK (France) are concerned about illegal immigration, compared to 40 (32) percent about legal migration (see Hatton, 2017). So, reducing irregular migration is clearly a priority for electorates and the governments. The Eurobarometer (May 2015) indicates that, on average, 87% of respondents in Europe support additional measures against illegal immigration, with a minimum support of 72% in Romania and maximum support of 94% in Cyprus 3 They make up 1 percent of the European workforce and 5 percent of the U. S. workforce, see: Martin, Philip. 2019. "Irregular migrant workers in the EU and the US". Migration data portal -blog. June 19. https://www. migrationdataportal. org/blog/irregular-migrant-workers-eu-and-us A key element in the design of workable temporary work permits requires taking into account migrants' incentives to comply with the visa rules.
which greatly improved the paper. All remaining errors are ours. Emmanuelle Auriol  acknowledges TSE-IAST funding from the  French National Research Agency (ANR)  under the  Investments for the Future (Investissements d'Avenir) program , grant  ANR-17-EURE-0010  and financial support from the  French Development Agency. Alice Mesnard  thanks the  Centre for Global Development  for initial discussion of the project and financial support.
a b s t r a c t This article evaluates the impact of cumulative ICT and management changes on long-term sickness absences. We use a unique dataset matching a company-level survey on computerisation and organisational change with an administrative file allowing us to track health issues amongst the working population. We implement a difference-in-difference approach using two time windows: a three-year period after changes have occurred and another period corresponding to the period of implementation of changes. We identify three treatments according to the sets of tools implemented by firms and reflecting different types and degrees of organisational changes: Information and Communication technology (ICT) changes only, management changes only, and cumulative ICT and management changes. We find the following core result: cumulative changes in ICT and management tools increase occupational risks and detrimentally affect em ployees' health, while management changes only reduce long-term sickness absences.
We use a French nationally representative survey at the employer level on computerisation and organisational changes (the COI-TIC survey) linked with an administrative health insurance panel (the Hygie database). Employers provide detailed information about their adoption of a large set of ICT and management tools over a three-year time period (20 03-20 05). In the Hygie panel, which features a large and representative sample of employees attached to the surveyed sample of employers, we follow up in time over nine years (20 0 0-20 08)-three years before and three years after the change period. Our difference-in-difference approach combined with coarsened exact matching compares employees who faced organisational changes in their workplace (treatment group) with employees in inert firms (control group) using three subperiods: before the occurrence of changes (20 0 0 to 2002), during their implementation  (2003 to 2005)  and after they took place  (2006 to 2008).
Data The objective of this study is to evaluate the impact of changes in uses of ICT and management tools, and in particular of cumulative ICT and management changes on long-term sickness absences. We use a French representative survey at the employer level on computerisation and organisational changes (the COI-TIC survey) linked with an administrative health insurance panel (the Hygie database). The COI-TIC survey provides company-level measures on the use of a large set of ICT and management tools at the date of the survey (January 2006) and three years earlier. Hygie followed individuals over 20 0 0-20 08 with precise measures of health issues: sick leave, work accidents, occupational diseases, and medical consumption.
Organisational change and computerisation (COI-TIC survey) The COI-TIC survey was carried out by the National Institute of Statistics and Economic Studies (INSEE), while the scientific guidance for its development took place at the Centre d'Etudes de l'Emploi (CEE). A random sample of private firms in the non-agricultural market sector with 10 employees or more was selected. The data were stratified by industry and firm size with a comprehensive layer beyond 500 employees. The survey received a high response rate of 85%, leading to a sample of 13,687 firms  ( Greenan et al. , 2010 ). The questionnaire includes retrospective questions where the respondent describes the situation of the organisation at the date of the survey (January 2006) and three years earlier. This structure allows to measure changes that took place in 20 03, 20 04 or 20 05.
Hygie administrative database The Hygie database describes career and sick leave episodes for individuals insured under the French general social security system  ( Ben Halima et al. , 2018 ). It relies on a random sample of 538,870 nonfarm employees working in the private sector and aged 22 to 70 years in 2005 who have contributed at least once to the general pension scheme during their lifetime. It combines two administrative data files. The first one, the French National Pension Fund administrative file ( Caisse nationale d'assurance vieillesse , CNAV, covering the private sector), contains a wide range of variables on workers' status in the labour market and on the characteristics of their employing firms from 2005 to 2008. Individuals are followed along their career path from the time when they entered the labour market.
It is nevertheless unfortunate that short-term sick leaves before 2003 are not also recorded in our database, as  Böckerman et al. (2012)  find opposite relations between the use of HPWSs and short and long absence spells. 4 ) to (  7 ), we compute the frequency difference tests between entering, exiting or mobile employees and employees with a continuous presence: * * * significant at 1%, * * significant at 5%, * significant at 10%. We do not know the precise duration of the sickness absence because of the nature of our administrative database. Employees validate a quarterly period as a qualifying period on sick leave when they receive sickness benefits over sixty consecutive days within a maximum spell of 4 quarters per calendar year. We thus have annual information about long sickness absences and injury leaves before, during and after the implementation of organisational changes by the firms responding to the COI-TIC survey.
Sample and descriptive statistics Our goal is to assess the consequences of organisational changes for long-term sickness absences amongst employees who experienced the period when changes were implemented in the company. Our study sample is constructed in two steps. First, we select in Hygie the individuals with working information over the period 20 0 0-20 08. This selection leads to a sample of 477,250 individuals employed in the whole private sector. Second, matching of the COI-TIC survey with the Hygie database allows us to describe the career path of 26,321 individuals affiliated with a firm from the COI-TIC sample for at least a year from 2003 to 2005. These workers were employed in 12,366 COI-TIC firms, corresponding to 90% of the total number of firms in the COI-TIC sample. This sample is representative of non-farm employees from private sector firms with 10 employees or more.
Note that the decrease in the significance of the coefficients may be due to a problem of precision linked to the reduction in the number of employees in the treated groups reported in Table  A10  of statistical Appendix 6. The logic is somewhat similar for the effects measured during the changes : the protective effects of changes in ICT and managerial systems alone become insignificant as the intensity of change is increased. We find no significant modification in the effect estimated for the combined changes. In our baseline estimates, we assessed the effects of the changes over the widest window that we could use in our database, i. e. , over the three years before and after the implementation of the organisational changes. However, as shown in Fig. 1  above and in Table  A4  in the appendix, workers in firms that implemented managerial changes saw a significant increase in the frequency of long-term absences in the last year before implementation.
First, our use of an administrative database on only active employees makes it impossible to monitor the long-term health consequences for employees who become inactive. Therefore, we cannot identify whether the changes led to health impairments for some workers that caused them to withdraw from the labour force. Second, the information available in our database does not allow us to measure the presence of any new organisational changes in firms between 2006 and 2008. Here again, our data do not allow us to precisely quantify the extent of this bias. The fact that we measure significant changes that are likely not to recur over a short time horizon already mitigates this issue. Indeed, since the implementation of many new tools is necessary to cross the threshold of significant change, such a degree of change is less likely to be repeated in subsequent years.
9 and 2. 6 pp, respectively, for workers leaving firms that changed their ICT system or both systems simultaneously). Unfortunately, our database does not record the reasons for departure, and it is therefore impossible to distinguish voluntary departures from dismissals. Indeed, the administrative database used to track the situation of workers is organised in such a way as to record accumulated social rights in terms of health protection and retirement. It is therefore not feasible to provide practical evidence of the pathways by which workers left the firms. However, we are able to highlight how the changes specifically influenced future movers and stayers during their implementation phase (20 03-20 05). In the first column of Table  12  , we report these impacts and thus the evolution of the health of movers before they left and of that of stayers.
Conclusion This article makes an innovative contribution to the evaluation of organisational changes by analysing their impact on long-term sickness absences. Rather than focusing on the efficiency gains generated by organisational changes, the analysis targets their possible associated health impairments for the workforce. To this end, we use an original database resulting from matching the Hygie panel administrative database and a large and representative company survey on organisational change and computerisation (COI-TIC). The first describes both career and sick leave episodes of a sample of people insured under the general social security system, while the second describes the changes implemented in firms over three years in their uses of ICT and management tools. Using a difference-in-difference estimator with coarsened exact matching, we compare the long-term sickness absences of employees before, during and after significant changes were implemented in their firms with those of a control group of employees in inert firms.
Again, younger employees do not behave significantly differently in terms of absence in inert companies and those that have innovated in managerial terms. The influence of women's maternity choices could therefore only explain the specific influence of managerial changes only for a very specific subgroup of female employees, in the form of decisions to become less pregnant when such innovations are implemented. However, it is difficult to explain such a behaviour both in relation to the existing literature on pregnancy choices and given the information available in our database.
65 8. 72 1. 57 9. 95 24. 88 Teams or autonomous work groups ICTs Requirement for suppliers to tight deadline 0. 40 0. 53 9. 21 8. 24 2. 00 0. 90 10. 83 13. 28 25. 07 20. 65 Website Call or contact centres 0. 84 61. 2 6. 19 73. 3 1. 38 0. 065 10. 12 13. 62 Local Area Network Methods of problem solving (FMECA) 0. 15 61. 3 8. 11 66. 7 0. 11 0. 071 16. 41 18. 53 Software or firmware for HRM Environmental or ethical certification 0. 01 63. 4 8. 17 65. 3 0. 06 0. 064 19. 18 15. 87 Intranet Just in time production 0. 56 47. 9 3. 96 57. 8 0. 66 0. 084 6. 09 9. 69 Software or firmware for R&D Tools for data analysis Data source: COI-TIC survey, 2006. Electronic data interchange system (EDI) Databases for HRM Extranet ERP Databases for R&D Tools for interfacing databases Tools for automated data archiving or research Tools for collaborative work (groupware) Coverage: Appendix 2: 47.
95 2. 59 25. 02 Tools for interfacing databases 1. 10 18. 48 18. 60 3. 93 34. 20 Extranet 0. 84 16. 05 18. 74 3. 40 23. 43 Tools for automated data archiving or research 1. 36 13. 83 15. 48 1. 73 23. 13 Tools for collaborative work (groupware) 0. 21 13. 80 17. 58 0. 15 19. 60 ERP 1. 18 8. 77 9. 25 2. 49 14. 50 Databases for HRM 0. 42 10. 89 11. 70 1. 72 19. 04 Databases for R&D 0. 60 9. 07 9. 36 1. 37 16. 85 Software or firmware for R&D 0. 50 8. 43 8. 55 1. 02 16. 28 Tools for process modelling (workflow) 0. 03 9. 34 10. 41 0. 06 16. 95 Software or firmware for HRM 0. 40 8. 00 8. 57 1. 04 14. 28 ( continued on next page ).
| DATA The primary source of data containing information on patients' hospitalization and diagnoses in France is PMSI dataset, which served as the main source of data in this study. It is an administrative dataset covering all French hospitals starting from 2006. Actual PMSI data were split into three different datasets, which separately contained information about: It covers all individuals living in both metropolitan and overseas French departments. Full PMSI data access was given for the years 2010-2013. The resulting data contains a rich set of individual level characteristics (including age, region and community of residence, length of hospital stay, month and year of hospital leave, etc. ) and hospital characteristics, which include a hospital unique identifier, ownership information (i.
Different tariff schedules are set for for-profit and non-profit hospitals. To obtain information about tariffs on the basis of which hospitals are compensated, we use publicly available data provided by ATIH. 4 On average, for-profit and non-profit institutions were affected differently by the reform. As shown in Table  1 , the mean value of Δtariff was positive (negative) for for-profit (non-profit) hospitals, indicating that C-sections became relatively more (less) profitable to perform in these types of facilities. Income data were collected by Institut National de la Statistique et des Etudes Economiques (INSEE) and are available on the level of PMSI pseudo postal codes. To control for medical obstetric care availability, we use a gynecologist availability index calculated for the year 2011 at the municipal level.
It takes into account gynecologists' volume of activity, service use rates differentiated by population age structure, supply and demand factors in neighboring municipalities and other parameters. 5  Despite the fact that PMSI is the most complete administrative dataset on hospital admissions available to researchers in France, it does not directly record several characteristics important in our context. For instance, the unipara and multipara status was only observable for women who delivered normally. This creates an obstacle for predicting normal delivery tariffs in the post-reform period for women who delivered via a C-section, since those tariffs depended on the multiparty status. To recover the unipara/multipara status at individual level, we used variables containing DRG roots from previous obstetrics PMSI datasets available from 2005.
568. Overall, the share of all multipara cases that were retrieved at individual level is   ( M2010-2013 )  ( 2010-2013 ) = 0. 947. The missing 5. 3% of cases only cover C-section cases, since the multiparity status is completely observed for women who had a normal delivery. In order to minimize the potential effect of this missing data on the internal validity of our results, in main models we restrict our analysis to multipara mothers, whose status is derived directly from PMSI or retrieved following the above described procedure. In addition, after encoding all hospital stays into both pre-and post-reform DRG classification versions, we find that around 1% of cases are misclassified. We exclude these cases from the sample.
| EMPIRICAL STRATEGY The individual-level dataset constructed for this study offers a unique opportunity to investigate healthcare providers' reaction to changes in hospital-level financial stimuli, following the March 2012 reform. In this study, we leverage the fact that after the DRG refinement, clinically identical cases were reimbursed differently, relative to pre-reform periods. As evidenced by the example presented in Table  2 , uncomplicated C-sections and normal deliveries with the gestational age between 37 and 44 weeks in the pre-reform period were reimbursed based on a single tariff in each child delivery method, regardless of the single/multiple birth and mother's multiparity status. However, after the 2012 DRG refinement, the single tariff for normal deliveries was split into four new tariff groups based on both of these characteristics, while the single C-section tariff was differentiated only based on the multiparity status, resulting in two DRG tariffs.
e. , ante-partum). We implement the following five-step empirical strategy to estimate the effect of financial incentives: 1. We estimate the probabilities of each delivery outcome (i. e. , scheduled, urgent, unscheduled C-sections, and normal delivery) based on patient characteristics observed in ante-partum: (a) The estimation is based on the pre-reform (training) dataset using nested logistic models with the tree structure provided in Figure  1 ;  are estimated probabilities to observe a given severity level s for outcome w in pre-and post-reform periods (0 and 1, respectively). For example, in Figure  1 , the estimated probability  d0 1 to observe severity level 1 if mother X delivers her child via a scheduled C-section in the pre-reform period is 0. 65. 4. We calculate an incentive measure reflecting the attractiveness of scheduled C-sections: (a) Compute the expected tariff as a probability-weighted average of tariffs over the set of possible severity levels s in preand post-reform periods (0 and 1, respectively): tarif f 0 = ∑ ̂ 0 , ⋅ tarif f 0 , and tarif f 1 = ∑ ̂ 1 , ⋅ tarif f 1 ,.
In CMD each diagnosis is associated with a medical procedure. The former are provided in the "RSA" dataset. In "RSA" each hospital stay has a unique patient identifier which allows us to track the cases of hospitalization across different years for a given patient. In addition, it allows us to find a corresponding medical activities contained in the previous dataset. It is worthwhile to note that since in France each version of DRG comes into effect on the 1st of March of each year, "RSA" dataset classifies diagnoses based on two versions of DRG, that were operational before and after the 1st of March. Apart from diagnoses, "RSA" dataset contains a rich set of individual level characteristics (including age, region and community of residence, length of hospital stay, month of exit, etc. ) and hospital characteristics, which include hospital unique identifiers, ownership information (i.
e. , public or private), legal status (i. e. , profit or non-profit), geographic location, etc. The final "DIAG" dataset lists complications (or secondary diagnoses) that were observed during each hospital stay. These complications describe medical conditions that arise both before and after hospital admission. The data are presented in a way similar to "Actes" dataset. In DRG, these complications have different severity levels, the number of which may vary from 1 to 3 depending on year and diagnosis roots. It is worth noting that with the introduction of DRG version 11d in 2012, not only diagnosis roots underwent a major revision, but also severity levels.
Type 3 maternity wards, which typically are large university hospitals, are meant to provide obstetric and neonatal care in the most medically complicated childbirth cases. Thus, the share of mothers with severe co-morbidities is the lowest in Type 1 and the highest in Type 3 facilities. While the DiD analysis may shed light on the differences in the DRG reform impact across maternity ward types, there are a number of practical difficulties that make this analysis challenging. In particular, PMSI has two identification codes for care facilites-a legal entity code ("finess juridique") and a geographic entity code ("finess geographique"). While one legal entity code typically corresponds to several geographic entity codes, the maternity ward type is determined by the geographic entity code. However, due to PMSI data limitations, until 2012 only legal entity codes were provided, which makes it impossible to assign the maternity ward type to legal entities with multiple geographic entities.
As a result, the merging of datasets results in a considerable and non-random loss of observations. In particular, the majority of Paris region public hospitals were dropped after the dataset merger. Similarly, most private non-profit maternity wards could not be included in the sample due to the impossibility to establish a correspondence between legal entity codes and maternity ward types. The estimation results for models by hospital size and hospital status are provided in Tables  C2-C4. Due to data missingness discussed above, 19. 7%, 93. 9%, and 1% of observations in public, private non-profit, and private for-profit hospitals, were dropped, respectively. The estimates of interest are generally statistically insignificant in both Table  C2  testing a single effect for the post-intervention period and in Table  C4  presenting the results of the DRG reform impact on two treatment groups with a change in tariff incentives superior/inferior to 100/-100.