dataesr/scientific-paragraphs-categorization huggingface dataset:
Indirect effects of leadership on FIB for the clustered data set (Model 2b) Model 2b showed an acceptable fit to the data (χ 2 = 8,412. 10 [p <. 001], df = 4,356, and RMSEA =. 03, CFI =. 96, SRMR =. 06). In line with Model 2a, social identification had a significant and positive unique effect on FIB across all culture clusters, beyond personal identification with the leader and the four positive forms of leadership. Personal identification with the leader had a significant and negative unique effect on FIB across cultural clusters when controlling for social identification and the four positive forms of leadership. Social identification only mediated the effect of transformational leadership on FIB in the Germanic Europe cluster, but this effect was negative (β = À. 08, p <. 05, 95% CI = [À.
DISCUSSION The present research sought to extend our understanding of the relationship between leadership and FIB by focusing on underlying mechanisms and moderators. To this end, we conducted a series of secondary analyses of the first GILD project data set combined with data from one of the GLOBE studies (GLOBE, 2020), using SEM models and multilevel moderated mediation analyses. Our findings show that all forms of leadership-with the exception of transformational leadership-had unique positive main effects on FIB beyond the other three forms of leadership. Similarly, social identification had a unique positive effect whereas personal identification with the leader had a unique negative effect on FIB. In terms of indirect effects, both social identification and personal identification with the leader acted as parallel mediating mechanisms, but with opposite effects.
, 2008) , transformational leadership was not measured using its mainstream instrument (the MLQ;  Bass & Avolio, 1995). At first glance, this might seem trivial, but by using a measure of transformational leadership that was based on a conceptualization of transformational leadership other than the full-range leadership model  (Carless et al. , 2000) , we aimed to be better able to distinguish between the unique contribution of followers' attributions of charisma to their leader and their attributions of authenticity  (Banks et al. , 2016). Here, then, our data suggest that, when prized apart from other aspects of transformational leadership, leader charisma may stifle engaged followership.
Limitations and future research The most obvious limitation of the present research is that our data were cross-sectional and hence preclude causal inference. Nevertheless, as our modeling aligns with extant theorizing on leadership and innovative behavior, we relied on existing theory to make causal predictions. Importantly, some of this research has been supported by experimental studies in which it has been possible to isolate causal relationships pertinent to the present analysis  (Hughes et al. , 2018). We also endeavored to address concerns about common-method variance by employing the ULMC technique  (Richardson et al. , 2009). At the same time, the simultaneous testing of different identification targets was a strength of our study. Here too, though, the shared variance between personal identification with the leader and social identification might be masking the true direction of the effect of personal (or social) identification as a mediating mechanism.
ACKNOWLEDGMENTS We are grateful to  Kim von der Wehl ,  Christopher Lark ,  Charlotte Rabener ,  Leon Hoche , and  Berrit Cordes  for their help in setting up and monitoring the online surveys and  Julia Heimrich  for her support with data checks. Roberto Gonz alez was supported by the  Interdisciplinary Center for Social Conflict and Cohesion Studies (COES)  ( ANID/FONDAP #15130009 ) and the  Center for Intercultural and Indigenous Research (CIIR)  ( ANID/FONDAP #15110006 ) and Fondecyt (1161371). Open Access funding enabled and organized by Projekt DEAL.
Insurance has been based on the principle of risk pooling since the end of the 18th century  (Daston, 1986). Although this principle is in part normatively motivated  (Ewald, 1986) , it is largely motivated by technical considerations. 1  The purpose of insurance is to protect against risk, but when an insurer is deciding to accept or refuse a potential client, they are not in a position to know whether this particular client will actually face the risk against which they wish to be covered. The insurer can, however, know whether, on average and within a given population, one individual in 100 or in 10,000 will see the risk materialise. This form of prediction became possible in the second half of the 18th century, with the development of probability calculations and the first databases. The pooling of risks helps insurers to make more informed decisions: What risk do they face providing this service to a given individual? At what price? One might be tempted to assume that the emergence of so-called "Big Data" would disrupt this economic model.
Data and methods This article is based on an analysis of the trajectory of four telematics-based car insurance offers in France between 2010 and 2019. They were developed by two insurance companies (whose names will be kept anonymous, at the request of the interviewees), CapitalInsur and MutualInsur, that have been part of the French insurance landscape for at least three decades. In 2019, these companies were among the ten largest in the French insurance market, based on sales figures. The French auto insurance market in France is dominated by ten companies, which account for between 85% and 90% of sales. Both companies are part of this group; neither is specialised in auto insurance, and neither occupies a dominant position within it. Both are established companies. They may be distinguished from each other by multiple features.
Experimental approaches Insurance companies' attitudes toward the arrival of Big Data in their sector, as Pavitt describes (2006), were proactive and focused on developing experimental strategies to contend with the innovation. The approaches they implemented were all explicitly experimental; in no case did they seek to create a systematic offer available to all potential clients. The person in charge of the CI2 offer at CapitalInsur explicitly described the process in experimental terms: We experimented with this with a desire to learn and therefore to test the waters. With our private partner we intentionally chose to make it limited in order to gain experience. We didn't go full throttle, so to speak.
[Manager in charge of CI2] The experimental slant also clarifies the objective, which is not to make money, but to learn. The offers implemented were not primarily aimed at verifying profitability, but at gathering information on service provider reliability, client reactions, sales staff motivation, the nature of the data, device precision, data-processing quality, etc. The manager of one of MutualInsur's regional offers (MI2) explained: We knew from the start that we would lose money on our experiment. We knew that. But on the other hand, we wanted to test whether there was any potential. [Manager 2 in charge of MI2] Every step in the production chain of the telematics offer must be tested. Upstream, various means of collecting and processing data must be investigated. At MutualInsur, for example, data collection was initially carried out using an application on customers' mobile phones.
The system had its drawbacks: the application had to be activated, and quickly drained the phone's battery when it was. After a few months, it was decided that subscribers would be asked to install a box in their vehicle to record the data, instead. Once collected, the data itself had to be scrutinised: was it reliable, or not? Below, the manager of MutualInsur's MI2 offer explains how customers expressed how consistent they felt their scores were with their own perceptions, leading managers to decide to test the system themselves: We sometimes had clients who said "well, I don't understand, because my score is not very good. Whereas …" We had a few specific cases, so we also tested it with colleagues' children, which made communication easy. They were our beta testers. Testing it ourselves, we found it was mainly we who felt that our scores did not always correlate with the reality on the ground.
[Manager 1 in charge of MI2] The second question to be explored was of whether the data, once produced, actually helped improve pricing. For those predicting the Big Data revolution in the insurance sector, the answer is yes. Insurers wished to put this hypothesis to test. To do so, they first needed to know the specific nature of the information being integrated into their pricing tools. In most cases, the raw data recorded by devices (acceleration and deceleration speeds, abrupt braking or cornering, etc. ) was not used directly. Instead, this data was processed to create an overall score, which was then used by insurers to try to improve rate calculations. These indicators were integrated into the generalised linear models used by actuaries, following a classic methodology; in other words, the experiments carried out within insurance companies aimed to improve existing actuarial models, following stabilised methods.
This meant that experimentation occurred within a framework that possessed a degree of cognitive inertia (M1. 2). The experimental nature of the approach, in other words, did not imply that all cognitive frameworks were to be called into question. Experimentation also took place at an organisational level, both externally and internally. Externally, insurers had to form partnerships to collect telematic data, and sometimes to process it, since they did not possess the technical expertise needed to develop the boxes or applications that collected the data. They therefore had to seek out service providers and test their reliability, as well: would they really be willing to share the data they collected? CapitalInsur's CI2 offer, for example, involved a cascade of service providers: the broker selling CapitalInsur's policies, the subsidiary of a major equipment manufacturer, and the independent company to which it subcontracted some of its work.
CapitalInsur's access to the raw data ultimately proved so complex that the manager chose to end the collaboration: Everyone knows that data is the raw material … So it has value and the owner absolutely wants to monetise it or at least keep it. We, moreover, were at the end of the chain with a delegated broker, another service provider, and then the subsidiary of [the equipment manufacturer], which meant that we were pretty much the last in line, we were driving a bit blind. In fact, we had difficulty getting access. We got it at the very end, after three years, by going to great lengths  […]. Because also, afterwards, these are things that have to be stipulated contractually from the start, like everything else, so that the rules are applied. And if they are not applied, there are exit mechanisms, so that we leave as good friends and find someone else who is more transparent.
So that's why we encountered logistical problems, problems with calculating scores, problems that are really linked to the technological aspect, on the data platform on the box, which MutualOnline didn't have. [Manager 2 in charge of MI2] Organisational inertia seemed likely to hinder the adoption of the innovation, but those involved were aware of it. In order to test the innovation, they either deliberately choose to deploy it in a context where obstacles were smaller (at CapitalInsur), or to take these obstacles into account when interpreting results (at MutualInsur). Companies were not passive, in other words (M3): they identified and anticipated the challenges they were likely to face, and attempted to deal with them proactively. Continuing down the production chain, we see that the innovation also had to be tested commercially.
CapitalInsur and MutualInsur thus had to identify populations to whom they could propose their telematic policy offers. They targeted small populations: young drivers (in three of the four offers we studied), and corporate fleets. Young drivers were selected because they have trouble finding insurers willing to take them on: some simply exclude them from policy offers, while others charge rates as high as the regulations allow. Company fleets were targeted for reasons of convenience and scale: with just a dozen corporate contracts, CapitalInsur hoped to collect data on several hundred vehicles. One facet of the experimentation sought to explore the reactions of the targeted populations: young drivers, in particular, are perceived as more willing to share data and less hostile to new technologies.
At MutualInsur, this reluctance was intensified by a lack of financial incentive: We have salespeople who, from the start, found this new offer really top-notch and signed up straightaway. They systematically suggested it. We have others who thought that it was extra work, on top of everything else they had to do. And that, ultimately, they could continue to insure young drivers without it. Meaning that we have sales representatives who proposed it, and others who did not. And that's the game, that's how it is. They did not receive a specific commission for selling this policy. [Manager 2 in charge of MI2] Telematics systems are also technically cumbersome, both for customers and for sales teams. Where the data is recorded by a smartphone application, the driver has to remember to launch the application when driving.
While this interpretation does not contradict the one proposed by the manager, it should be noted that he justified his reluctance in moral, rather than utilitarian termsand did so spontaneously. MutualInsur's reluctance to exclude 'bad' risks is one of the few cases where we can specifically identify forms of normative reluctance (M2. 2). This relative scarcity of normative reluctance could be linked to the state of the legislative framework when these offers were put in place. Most were defined in 2015 and 2016, when the European framework (GDPR) was being finalised. The provision of personal data was governed at the national level before the definition of this framework (in France by the CNIL), but the adoption of the GDPR in 2016 and its passage into French law in 2018 has raised the profile of these issues among actors in the industry  (Ewick and Silbey, 1998) , particularly the actuaries and sales professionals who were in charge of creating these experimental offers.
Lawyers, who were better informed about normative issues, remained marginal in their creation. In any case, both in MutualInsur and CapitalInsur, the use of scores did not actually make it possible to distinguish between good and bad risks. Nor could scores be used in another practice that was supposed to be a cornerstone of their economic efficiency: personalised rates. Scores as a tool for individualisation. Whereas, as we saw in the previous section, telematics can be used to sort and select, their use in individual pricing follows a different logic, that of hyper-flexibility: telematics, theoretically, makes it possible for each person to pay for their own risk. Telematics tools promise a cognitive revolution: because the data they provide is rich and precise, the behavioural variables they produce make it possible to measure the risk attributable to an individual's driving.
The challenge, then, consists of using this more granular understanding to target pricing more effectively and offer individualised rates. This objective has only been poorly achieved. The link that insurers have managed to build between risk measurement and claims experience is very tenuous, as this CapitalInsur manager explains: You said that there was a gamble and that you had not found the correlation between driving and accidents. Yes, exactly. In other words, your scores were not correlated with the loss? No. Afterwards, I'm not privy to the secrets of insurers, but the specialists I've met with on the subject are unanimous in saying that no one has yet found the answer. It is all the more difficult to improve existing models because the data collected by telematic devices has so far proven to be rather poor.
At the beginning of the 2010s, every insurer operating in the car insurance market knew that telematics devices existed, and had been installed by car manufacturers since the beginning of the 2000s. All of them could imagine the potential uses of these data. No one knew, however, when these new pricing techniques would become the new norm. If (or when) the market was to change, they simply wanted to be ready, to have data on telematics that would allow them to offer their own rates, and to have spotted pitfalls in the new value chains. Without this information, they feared, they would be left to insure 'bad' risks only. This is what a telematics offer manager at MutualInsur explains: If the whole market was going to change, we thought that those who had the telematics data would be better able to segment the risks.
Sample To construct the sample, we use ExecuComp to obtain inside debt and CEO-related data, Standard and Poor's Compustat Industrial Annual to obtain accounting data and the Center for Research in Security Prices (CRSP) to obtain stock returns. The initial sample consists of the intersection of firms included in the above-mentioned databases and covers the period 2006-2019. The sample period is driven by the availability of inside debt data, as the year 2006 was the first year that firms were required by the SEC to disclose and describe their top executives' deferred compensation plans, pension benefits and other post-employment payments. From this sample, we eliminate firm-year observations for which net assets are negative, or the market value of equity is negative, or dividends are negative.
We then identify overconfident CEOs as those that fail to exercise vested options that are at least 67% in-the-money at least twice (starting from the first time) during their tenure. Accordingly, this measure captures a 'stable' overconfidence effect because it targets CEOs who 'habitually' exercise options late. 7 The advantage of this measure is that it allows us to include more firmyear observations from the ExecuComp database. The disadvantage is that the measure is less precise because it uses aggregate data on CEOs' option holdings. Nevertheless,  Campbell et al. (2011)  find that this measure of overconfidence generates 6 Prior literature uses various measures to operationalize overconfidence. Broadly, the measures are categorized as follows: (i) behavioural measures which are based on revealed beliefs -captured by CEOs' preference not to exercise timely deep in-the-money stock options or by net purchases on the firm's own stock  (Malmendier and Tate, 2005) ; (ii) media-based measures  (Hirshleifer, Low and Teoh 2012; Malmendier and Tate, 2008) ; (iii) formative measures based on prior success proxies  (Billett and Qian, 2008; Doukas and Petmezas, 2007; Hayward and Hambrick, 1997) ; and (iv) survey-based measures  (Graham, Harvey and Puri, 2013).
Matteo Fiorini, Bernard Hoekman, Dennis Quinn  The regressor of interest, , is constructed as , where is the level of services trade restrictiveness for country and service sector , and is a weighting coefficient that reflects the use of service by manufacturing sector in country. To minimize the potential endogeneity of input-output weights (policy restrictions might affect the use of services as inputs in manufacturing sectors), technical coefficients for the mid-1990s from the input-output (IO) table for the US (obtained from the OECD STAN database) are used. 5 The baseline measure of productivity ( ) is the output per worker in 2008, constructed using the United Nations Industrial Development Organization (UNIDO) industrial statistics database. The estimated coefficients on and the interaction term can be used to assess the impact of services trade policy restrictions on downstream industry performance, conditional on institutional quality, as follows: 6 In the quantification exercise that follows we focus on a hypothetical removal of all restrictions to Mode 3 services trade.
Estimates computed using results in Panel A of Table  A1 , with governance measured by control of corruption. The estimates in each cell are identified by the country implementing the reform, the services sector that is liberalized and the potentially impacted manufacturing sector. The statistical significance of the estimates meets the 10% threshold for the following country-services pairs: BWA-finance; BWA-telecom; MAR-finance; MAR-telecom; MUS-finance; MUS-telecom; TZA-finance; TZAtelecom; ZAF-finance; ZAF-telecom. The four shades of grey -from lightest to darkest -identify observations in the 25 th percentile of the distribution, respectively. Manufacturing sectors reflect ISIC 2-digit Rev. 3. ISIC codes in parentheses following sectoral labels in the first row. Sources: World Bank Governance Indicators; World Bank Services Trade Restrictiveness Database; Labour productivity (output per worker) from the UNIDO industrial statistics database.
Given the large number of missing values in the capital series reported in the UNIDO database, when we construct an estimation sample that includes data on the log of the capital labour ratio the number of countries falls to seven: Cameroon, Egypt, Ethiopia, Morocco, Madagascar, Tunisia, and Tanzania. Summary statistics for this smaller sample of countries are reported in Table  4. Estimation results using data for the seven African countries for which data on capital stocks are available are reported in the last column of Table  5. The results illustrate that policies affecting payments for invisibles may have significant effects on sectoral productivity performance. The negative and statistical relationship between the composite indicator of payment restrictiveness and downstream levels of labour productivity is confirmed, with the magnitude of the coefficient estimate increasing relative to the sample of twelve African countries.
Notes: 'Impact' refers to the percentage change in sectoral labour productivity of removing all barriers to Mode 3 services trade in financial, transport, communication, and professional services. Columns (2) and (5) report the estimated impact, given the prevailing quality of governance in the corresponding country (rule of law, regulatory quality, and control of corruption in panels A, B, and C, respectively). When not statistically significant, the point estimate is reported in parentheses next to an impact value of 0. Columns (3) and (6) report impact values assuming the highest measure of the respective governance variable. Sectors based on ISIC 2-digit World Bank Governance Indicators and Services Trade Restrictiveness databases. Output per worker from UNIDO industrial statistics database.
All regressions include country and sector fixed effects as well as the input tariff regressor x. Robust (country-clustered) standard errors in parenthesis. Statistical significance: * p<0. 1; ** p<0. 05; *** p<0. 01. Governance variables are from the World Bank Governance Indicators. Services trade policies from the World Bank Services Trade Restrictiveness Database. Labour productivity (output per worker) from the UNIDO industrial statistics database.
Output per worker from UNIDO industrial statistics database. Sector Impact, current inst. Impact, counterfac-inst. tual high Sector Impact, inst. current Impact, inst. counterfactual high Openness Institutions (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Control of corruption (Highest in sample: Botswana) Botswana furniture 34. 32*** 34. 32 food/bev 38. 21*** 38. 21 6 Burundi food/bev 0 (-1. 59) 27. 80 metals 0 (-. 57) 10 2 Ethiopia food/bev 0 (20. 08) 98. 05 minerals 0 (32. 94) 160. 83 8 Malawi food/bev 0 (6. 38) 26. 63 chemicals 0 (6. 13) 25. 60 4 Mauritius textiles/app 14. 24** 18. 48 food/bev 23. 48** 30. 47 1 Morocco food/bev 0 (10. 51) 29. 99 textiles 0 (6. 7) 19. 13 3 South Africa food/bev 34. 72** 55. 19 coke/oil 10. 64** 16. 91 7 Tanzania food/bev 0 (14. 14) 41. 56 minerals 0 (17. 97) 52.