dataesr/scientific-paragraphs-categorization huggingface dataset:
Indicator equal to 1 if the campaign ended on or after the cutoff date of 30 June 2016 and 0 otherwise. Pre-money valuation of the target company (in millions of euros) as displayed on the campaign page. Number of updates made to the campaign page during the campaign. The updates are organized under a separate company had positive "Updates" title. Indicator equal to 1 if the Platform database Platform database Platform database Platform database Platform database (continued on next page).
Investment-level variables Investment size Size of the investment (in Platform database euros). Regression models use the natural logarithm. Gift investment Indicator equal to 1 if the Platform database investor purchased the shares as a gift for another recipient and 0 if the investor made the investment for him/herself. Available Number of campaigns that Platform database campaigns were available on the focal platform on the day of the investment. Investment after Indicator equal to 1 if the Platform database cutoff investment was made on or after the cutoff date of 30 June 2016 and 0 otherwise. Investor-level variables Investor gender Indicator equal to 1 if the Platform database investor's probable gender Finnish Digital and based on his/her first name is Population Data Services female and 0 if it is male.
Agency U. S. Social Security Administration Investor age Investor's age (in years) at Platform database the time of the investment. Legal entity Indicator equal to 1 if the Platform database investor invests as a legal entity and 0 if the investor invests as a private person. Key investor Indicator equal to 1 if the Platform database investor has been assigned a special status by the equity crowdfunding platform and 0 otherwise. We refrain from using the platform's own term "lead investor" to avoid confusion with the general meaning of this term. Typically, investors whose cumulative investments exceed 10,000 euros are assigned this status. They may receive information about investment.
For this purpose, our study builds upon the proximity approach to analyse the determinants of scientific collaboration between African countries, using co-publications data from Thomson Reuters' Web of Science database as a proxy of such collaboration. Our results suggest that the majority of African regional economic communities (RECs)  have not yet had a significant effect on scientific co-publication. Nevertheless, some important region-specific factors do seem to be at play, such as a shared ethnical language, membership in the African and Malagasy Council for Higher Education (CAMES), and the presence of a common European partner as a third partner in co-publication. Existing policies aimed at the development of an Africa-wide research area should aim to leverage existing and emerging regional excellence networks and novel coordination models to accelerate the process of scientific integration in Africa.
We attempt to shed light on the importance of institutional proximity in participation in science collaboration as captured through common membership in a REC and in other historical regional institutions. The remainder of this paper is organised as follows. Section 2 briefly describes the policy background of continental and regional scientific integration in Africa. It also highlights the determinants of African scientific collaboration put forward by the literature. Finally, the international co-publications involving African regional economic communities are described. Section 3 briefly lays out the rationale and relevance of the proximity framework for determining the factors behind the existing scientific collaborations. It also describes the econometric approach taken in the present study as well as the dataset and variables.
Under our framework, these differences should translate into different magnitudes and levels of significance of the RECs or in their relative importance in shaping the patterns of African regional scientific collaboration. The following graphs illustrate the patterns of scientific collaborations for each REC. These were elaborated from the Web of Science's publications dataset and cover the international copublications recorded for each country member in the RECs. Fig. 1  shows the time trends for the share of co-publications over the total publications from the selected REC's country members. Fig. 2  shows the patterns for the regional and international publications of the RECs. The top half shows the countries that are not members of the REC, while the bottom half includes all the member states of the selected REC.
Indeed, it allows dealing with zero overdispersion as shown in a most recent article (Santos  Silva and Tenreyro, 2011). Our dataset displays a rate of 60 % zero values in the period 2002-2019. This average value decreases over time, from 81 % in 2002 down to 30 % in 2019. We performed two sets of regressions: the first one included all the years from 2002 to 2019, and the second set broke down the sample into three sub-periods to grasp changes in the determinants. In all the regressions, we controlled for the fixed effects at both the country and time levels  (Baldwin and Taglioni, 2006). Moreover, robust standard errors were clustered to control for error correlation in the panel, since the observations in pairs of countries are likely to be dependent across years.
Data and variables The main source of data for our study is the HCERES-OST scientific publications dataset, an enriched 7 version of Thomson Reuters' Web of Science (WoS) database -science citation index expanded (henceforth WoS). Despite the use of an enriched version of the WoS, the data suffers from the same drawbacks as the original data in terms of coverage. 8 First, in terms of scientific fields: the WoS has a bias towards hard sciences, while the social sciences and humanities are highly underrepresented. Second, de facto mainly scientific publications in English are reported and many national journals are not considered, and this is especially true for developing countries  (Rafols et al. , 2016). However, the WoS and Scopus information about international collaboration seems to correlate well with the actual international scientific publications of African researchers, according to  Boshoff and Akanmu (2017).
Nevertheless, the latter authors' domain-specific analysis of a Nigerian university revealed the existence of a bias related to international collaborations that involve exclusively African countries. These latter ones are underrepresented when compared with those collaborations including at least one non-African partner. On the one hand, this bias implies that international comparisons between African and non-African scientific activities can be misleading; but, on the other hand, it also implies that comparing African countries among themselves using this kind of source should be less problematic  (Boshoff and Akanmu, 2017). Finally, worldwide datasets, such as the WoS, have at least the advantage of quality filters, whereas other datasets cannot guarantee the same (Google Scholar for instance).
The sample covers a subset of works published in 11 scientific fields 9 between 2002 and 2019, reporting among other items the authors' affiliations, which were located in 53 African countries. This means that the initial sample concerned all publications involving at least one author affiliated in one of the 53 African countries and that we have information on the collaborations with authors located outside of the continent. Note, Gambia and South Sudan were excluded, essentially because the country code of Gambia was not available at the time of the download while only five years of date were available for South Sudan (the country had only obtained its independence (from the North) in 2011). A complementary source of data is the Cepii Gravity Dataset 7 HCERES-OST: Haut Conseil de l'évaluation de la recherche et de l'enseignement supérieur.
For this latter variable, we opt for the second-most important language of the country (that is the ethnic language dummy in the Cepii dataset), because the official one may be redundant or may reflect some information already captured by the common colonial heritage dummy. 12 Additionally, a dummy variable is included to signal if the two countries belong to the CAMES. For each dyad, we calculated the variables that can vary over time; for each of these, we had 24,804 observations. The co-publication was the count of the publications co-authored by two countries at time t (i. e. copub ijt ) and past co-publications was the amount of publications coauthored by the two countries in the previous three years (i. e. pastcopub ijt and its logarithm: log pastcopub ijt ). This variable could proxy the social proximity of the two countries, since it measures the existing collaborations.
Data The exchange rate data employed for SETAR modeling consist of daily closing of RMB/USD exchange rates (US price of one unit of RMB) from August 11th, 2015 to November 30th, 2022, with the start date of the sample corresponding to the date of the exchange regime reform of that year. A positive (negative) percentage change of exchange rate is interpreted as RMB appreciation (depreciation). The natural logarithms of the exchange rate series and their first differences were plotted against time in order (See Fig. 2 ). This study also uses monthly foreign currency reserves data of China for the same sample period. All data are extracted from DataStream. Following the conventional practice in the literature, a stationarity check will be made before specifying the nonlinear characteristic of the series.
Trust and the likelihood of receiving dividends We next provide evidence that investors are more likely to receive dividends when they believe themselves to be less trusting. We use data from CentER Savings Surveys that ask individuals how trusting they are and whether they hold investments that pay dividends. 9 For the regressions used in this analysis, our outcome variable is Received Dividends, which is an indicator that equals 1 if the individual says they received dividends in the past year. 10 Our variable of interest is a self-reported measure of trust where the individuals rate themselves on a scale of 1-7, with 1 representing "trusting, credulous" and 7 representing "suspicious. " We label this self-reported trust variable Level of Suspicion because higher values indicate greater suspicion.
13  To determine total assets, we sum the self-reported valuations for the main asset categories that appear in every survey from 1997 to 2002. This includes the self-reported valuations of the individual's automobiles, savings account, checking account, etc. Specifically, we sum items 1-4, 6, 7, 8, 11-18,  19Og, 19Hy, and 20-25. For survey years 1997-1999, we also include item 5 to better capture total savings. To mitigate concerns about outliers and to normalize total assets across years, we form total asset deciles for each year. 14 Number of Stocks is the natural logarithm of one plus the number of stocks directly owned by the individual. 15  After 2003, there is no trust data available. Prior to 1997, the question pertaining to dividends received is different, and in 1996 the question measuring one's trust level is missing.
16  We also consider a stronger sample restriction by limiting the dataset to individuals that report positive stock holdings. These results are also reported in Table  2. 1 3 Dividends, trust, and firm value is 0. 619. This last measure is a logarithm; when not in log form, the average number of stocks owned by individuals in the sample is 1. 759, with a standard deviation of 4. 216. Figure  1  shows the percentage of sample individuals that receive dividends at each value of Level of Suspicion. The graph shows a monotonic increasing relationship between suspicion and dividends. Among the most trusting individuals (Level of Suspicion = 1), only about 25% hold investments that pay dividends. The percentage holding dividends increases with each increase in the Level of Suspicion, up to about 70% for the least trusting individuals (Level of Suspicion = 7).
We find that the mutual funds that own the fraudulent firm-and therefore have a larger negative shock to trust-tilt their portfolios more towards dividend-paying stocks after the fraud is discovered. This provides evidence that a reduction in investor trust increases their demand for dividend payers. Our data on accounting fraud discoveries comes from the Journal of Accounting Research's website (Call et al. 2018). 19  The data contains over 1,000 frauds revealed since 1978. After matching the companies in the dataset with Compustat, we are left with 773 frauds revealed from 1978 to 2011. 20  We assume that frauds are discovered and revealed to the public in the year that the SEC starts investigating the fraud. 21  We use these fraud discoveries as our shocks to trust. We measure a mutual fund's exposure to these shocks in a given year with the indicator variable Fraud Investment, which takes a value of 1 if a fraud was discovered for one of the stocks in the mutual fund's portfolio that year.
Our mutual fund data runs from 1984 to 2011. Our data on mutual fund characteristics comes from the Center for Research in Securities Prices (CRSP) Survivor Bias-Free US Mutual Fund database. Our data on funds' quarterly holdings come from the Thomson Reuters mutual fund holdings database. 22  Our sample consists of US equity mutual funds that are open-ended, diversified, and actively managed. 23  We also exclude very small and very young funds from the sample. 24  CRSP provides information on multiple share classes issued by the same fund. To avoid multiple counting, we aggregate share-class-level data to the portfolio level by taking the value-weighted average of a fund's characteristics across share classes. 25  After requiring non-missing observations for the main fund-level variables, 26 our final sample includes 21,722 mutual fund-year observations.
27 In addition, we obtain data on the stocks held by the mutual fund sample from CRSP. We only consider stocks with share codes 10, 11, 12, and 18 and exchange codes 1, 2, and 3. 28  To determine the prices of each stock, we use the end-of-month prices from CRSP. 22  We merge these databases using MFLINKS tables, which are available through Wharton Research Data Services (WRDS) and provide a reliable way to merge the Thomson and CRSP databases. 23  We focus on actively managed diversified equity funds: funds with CRSP objective codes EDYG (Growth), EDYB (Blend), EDYI (Value), EDCM (Mid-Cap), EDCS (Small-Cap), and EDCI (Micro-Cap). We eliminate funds with the CRSP objective code EDCL (S&P 500 Index Objective Funds) to avoid passive funds. We also eliminate index funds by using the CRSP-defined index fund flags and if their names include the words "index," "S&P," "idx," or "passive.
25  We aggregate monthly returns, turnover, and expenses, weighting each share class by its net assets. Fund age is computed as of the month-end relative to the fund's first offer date. We calculate gross returns before expenses by adding one-twelfth of the fund expense ratio to the net monthly return. 26  The sample includes mutual funds whether or not they ever experienced a fraud (i. e. , we keep mutual funds that always have Fraud Investment = 0). 27  For each fund each year, we use data from the last calendar quarter within the year. We then look at funds' portfolio changes from the last calendar quarter in year t-1 to the last calendar quarter in year t. We look at changes from year to year, rather than from quarter to quarter, to mitigate noise in our estimate of the fraud discovery date.
We also include fund style-by-year fixed effects to control for any changes in the propensity to pay dividends among the firms in the fund's investable universe. 31  In addition, all specifications include controls related to the fund's performance. 32  In some specifications, we also include controls for the change in the riskiness of the Y i,t = s,t + Fraud Investment i,t + Controls i,t + i,t. 29 For constructing both measures of average dividend yield, we calculate the average dividend yield at the end of years t and t-1 and take the difference. Each company's dividend yield at the end of a given year is its annual dividend payout from Compustat that year, divided by its equity market value from CRSP at the end of the year. For the value-weighted average, we weight each company's dividend yield by the value the fund has invested in the company, divided by the total value the fund has invested across all companies for which we have dividend yield data (including firms that do not pay dividends and thus have a yield of 0).
For the equal-weighted average, we include all companies with dividend yield data (again including those with a yield of 0) for which the fund owns any stock, and take the equal-weighted average of their dividend yields. 30  All three left-hand-side variables are winsorized at the 1st and 99th percentiles each year. 31  When both versions of Δ Dividend Yield i,t are on the left-hand side, these fixed effects also control for average changes in the valuation of dividend payers versus non-payers for all funds with the same investment style. This helps remove changes that were not initiated by active investment decisions. 32  These controls are for the fund's total net assets, expense ratio, turnover ratio, age, net quarterly return, and quarterly fund flows. They are measured in the last calendar quarter of year t.
Fraud events and firm valuation In our main tests on the valuation implications of trust and dividends, we again use accounting frauds as negative shocks to trust. We use two sets of analyses to study this question. For the first analysis, we aggregate the mutual fund data to the firmyear level to measure the fraction of a firm's investors experiencing negative shocks to trust because of exposure to fraud. We use this aggregated measure to study how having more investors exposed to fraud affects the stock market values of dividendpaying firms versus non-paying firms. For the second analysis, we examine how accounting fraud in a given US state impacts the stock market values of the state's dividend-and non-dividend-paying firms. Turning to the first analysis, we expect firms that are held by more funds experiencing shocks to trust will have higher values if they pay dividends and lower values if they do not.
For this test, we use the same accounting fraud and mutual fund data as in our previous tests to determine which mutual funds are investing in a given firm and which mutual funds are holding a stock that is committing fraud in a given year. For firm-level data, we use Compustat. Our sample for this test runs from 1985 to 2011, based on the availability of our mutual fund data and our fraud data. For the first analysis, we conduct a regression of the following model: The observations are at the firm-year level. The dependent variable is the natural logarithm of the market-to-book ratio, which is calculated by dividing market equity by common equity. Dividend Payer is an indicator equal to one if the firm pays dividends in that year. Fraud Fund Investment i,t-1 is the natural logarithm of one plus the number of funds that both invested in firm i in the previous year and experienced fraud shocks that year.
In the second column, we include controls for yearly Return on Equity deciles and yearly Leverage deciles. All regressions include firm and year fixed effects. We cluster standard errors by firm and by year. if a firm commits fraud, other firms from the same state will likely experience a drop in investor trust that is greater than the drop experienced by firms from other states. In turn, firms in the same state as the fraud will likely be more affected by an increase in demand for dividend payers, relative to non-payers. In line with this story, we find that dividend payers become more valuable (in terms of market-to-book ratio) relative to non-payers in the same state when another firm in the state commits fraud. 38  We again use the same accounting fraud data as our test showing that shocks to trust influence demand for dividends.
For firm-level data, we again use Compustat. Since our sample of fraud events is from 1978 to 2011, we restrict our firm financial database to the years 1979 to 2012. In doing so, we make it possible for there to have been a fraud in the firm's state in the previous year. Table  6 , Panel A shows that about half of the firm-year observations in our sample have a fraud revealed in their state in the previous year. Additionally, our fraud intensity measure, which equals the total number of frauds in the state in the previous year divided by the number of firms in that state, has an average of about 0. 4%. We consider the following regression, with firm-year observations, for firm i in state j in year t: The dependent variable is the logarithm of the market-to-book ratio, calculated by dividing market equity by common equity.
44  To generate a trust measure for the investor base within regions in the United States, we use data from the General Social Survey, which has interviewed Americans since 1972. The survey breaks the United States into nine different regions. We define a firm's investor base as individuals from the region where the firm is headquartered. The survey was administered annually from 1972 to 1994 (with some years missing due to funding issues) and biannually thereafter. The target sample size was 1,500 respondents up until 1993 and almost twice that starting in 1994; it grew to reach 4,500 in 2006. The most recent year we consider is 2016. We assign respondents to a region based on the location of the interview. The trust measure equals the fraction of survey respondents in the corresponding region-year that answer "most people can be trusted" to the question "Generally speaking, would you say that most people can be trusted or that you can't be too careful in dealing with people?" (We ignore answers of "I don't know.
Acknowledgements We received  IRB  approval for our survey from  Notre Dame Research Compliance  (Protocol  ID 21-09-6810 ). This project was supported by  Initiative d'Excellence Paris Seine. We thank conference and seminar participants at the  Review of Accounting Studies 2022 Conference , the  University of Mannheim , and the  University of Turku. For excellent advice, we thank  Zachary Kaplan , our discussant at the  RAST Conference. We also thank  Ingolf Kloppenburg ,  Stephen Penman ,  Matthew Shaffer , and  Paul Zarowin  for detailed feedback. All errors and omissions are our own.
This data can be found at the following website: https:// resea rch. chica goboo th. edu/ arc/ journ al-ofaccou nting-resea rch/ online-suppl ements/ volume-56. It is composed of enforcement action data that Gerald Martin collected. The dataset is based on the database developed inKarpoff et al. (2008a, b)  and explained in Karpoff et al. (2017).
The military aggression of Russia against Ukraine will also make some corrections in the interpretation of country risk from the war risk perspective. Country risk is usually assessed by use of composite that is average weighted indicator, with political risks assigned 50%, economic and financial risks -25% each. Political risks are assessed by expert method that allows for transforming a qualitative description into its quantitative measure. Economic and financial risks are the result of an aggregated quantitative estimate derived from data of macroeconomic, banking and financial statistics. When it is necessary to account for other components, such as environmental or epidemiological ones, the above groups of indicators can be supplemented by respective data. Another option is creating separate supplementary groups, which will require a restructuring of weights in the final indicator.