1,721,003 research outputs found
Replication data for: Who Pays Sin Taxes? Understanding the Overlapping Burdens of Corrective Taxes
No full textWe find that sin good purchases are highly concentrated with 10% of households paying more than 80% of taxes on alcohol and cigarettes. Total sin tax burdens are poorly explained by demographics (including income), but are well explained by eight household clusters defined by purchasing patterns. The two most taxed clusters com- prise 8% of households, pay 63% of sin taxes, are older, less educated, and lower income. Taxes on sugary beverages broaden the tax base but add to the burdens of heavily taxed households. Efforts to increase sin taxes should consider the heavy burdens borne by few households. (2022-07-01)
Replication files are also available at https://github.com/chrisconlon/sin_taxe
Common Ownership Data: Scraped SEC form 13F filings for 1999-2017
No full textIntroduction
In the course of researching the common ownership hypothesis, we found a number of issues with the Thomson Reuters (TR) "S34" dataset used by many researchers and frequently accessed via Wharton Research Data Services (WRDS). WRDS has done extensive work to improve the database, working with other researchers that have uncovered problems, specifically fixing a lack of records of BlackRock holdings. However, even with the updated dataset posted in the summer of 2018, we discovered a number of discrepancies when accessing data for constituent firms of the S&P 500 Index. We therefore set out to separately create a dataset of 13(f) holdings from the source documents, which are all public and available electronically from the Securities and Exchange Commission (SEC) website. Coverage is good starting in 1999, when electronic filing became mandatory. However, the SEC's Inspector General issued a critical report in 2010 about the information contained in 13(f) filings.
The process:
We gathered all 13(f) filings from 1999-2017 here. The corpus is over 318,000 filings and occupies ~25GB of space if unzipped. (We do not include the raw filings here as they can be downloaded from EDGAR).
We wrote code to parse the filings to extract holding information using regular expressions in Perl. Our target list of holdings was all public firms with a market capitalization of at least $10M. From the header of the file, we first extract the filing date, reporting date, and reporting entity (Central Index Key, or CIK, and CIKNAME).
Beginning with the September 30 2013 filing date, all filings were in XML format, which made parsing fairly straightforward, as all values are contained in tags.
Prior to that date, the filings are remarkable for the heterogeneity in formatting.
Several examples are linked to below. Our approach was to look for any lines containing a CUSIP code that we were interested in, and then attempting to determine the "number of shares" field and the "value" field. To help validate the values we extracted, we downloaded stock price data from CRSP for the filing date, as that allows for a logic check of (price * shares) = value. We do not claim that this will exhaustively extract all holding information. We can provide examples of filings that are formatted in such a way that we are not able to extract the relevant information.
In both XML and non-XML filings, we attempt to remove any derivative holdings by looking for phrases such as OPT, CALL, PUT, WARR, etc.
We then perform some final data cleaning: in the case of amended filings, we keep an amended level of holdings if the amended report a) occurred within 90 days of the reporting date and b) the initial filing fails our logic check described above.
The resulting dataset has around 48M reported holdings (CIK-CUSIP) for all 76 quarters and between 4,000 and 7,000 CUSIPs and between 1,000 and 4,000 investors per quarter. We do not claim that our dataset is perfect; there are undoubtedly errors. As documented elsewhere, there are often errors in the actual source documents as well. However, our method seemed to produce more reliable data in several cases than the TR dataset, as shown in Online Appendix B of the related paper linked above.
Included Files
Perl Parsing Code (find_holdings_snp.pl). For reference, only needed if you wish to re-parse original filings.
Investor holdings for 1999-2017: lightly cleaned. Each CIK-CUSIP-rdate is unique. Over 47M records. The fields are
CIK: the central index key assigned by the SEC for this investor. Mapping to names is available below.
CUSIP: the identity of the holdings. Consult the SEC's 13(f) listings to identify your CUSIPs of interest.
shares: the number of shares reportedly held. Merging in CRSP data on shares outstanding at the CUSIP-Month level allows one to construct \beta. We make no distinction for the sole/shared/none voting discretion fields. If a researcher is interested, we did collect that starting in mid-2013, when filings are in XML format.
rdate: reporting date (end of quarter). 8 digit, YYYYMMDD.
fdate: filing date. 8 digit, YYYYMMDD.
ftype: the form name.
Notes: we did not consolidate separate BlackRock entities (or any other possibly related entities). If one wants to do so, use the CIK-CIKname mapping file below. We drop any CUSIP-rdate observation where any investor in that CUSIP reports owning greater than 50% of shares outstanding (even though legitimate cases exist - see, for example, Diamond Offshore and Loews Corporation). We also drop any CUSIP-rdate observation where greater than 120% of shares outstanding are reported to be held by 13(f) investors. Cases where the shares held are listed as zero likely mean the investor filing lists a holding for the firm but that our code could not find the number of shares due to the formatting of the file. We leave these in the data so that any researchers that find a zero know to go back to that source filing to manually gather the holdings for the securities they are interested in.
Processed 13f holdings (airlines.parquet, cereal.parquet, out_scrape.parquet). These are used in our related AEJ:Microeconomics paper. The files contain all firms within the airline industry, RTE cereal industry, and all large cap firms (a superset of the S&P 500) respectively.
These are a merged version of the scrape_parsed.csv file described above, that include the shares outstanding and percent ownership used to calculate measures of common ownership. These are distributed as brotli compressed Apache Parquet (binary) files. This preserves date information correctly.
mgrno: manager number (which is actually CIK in the scraped data)
rdate: reporting date
ncusip: cusip
rrdate: reportaing date in stata format
mgrname: manager name
shares: shares
sole: shares with sole authority
shared: shares with shared authority
none: shares with no authority
isbr/isfi/iss/isba/isvg: is this blackrock, statestreet, vanguard, barclay, fidelity
numowners: how many owners
prc: price at reporting date
shares_out: shares outstanding at reporting date
value: reported value in 13(f)
beta: shares/shares_out
permno: permno
Profit weight values (i.e. \kappa) for all firms in the sample. (public_scrape_kappas_XXXX.parquet). Each file represents one year of data and is around 200MB and distributed as a compressed (brotli) parquet file. Fields are simply CUSIP_FROM, CUSIP_TO, KAPPA, QUARTER.
Note that these have not been adjusted for multi-class share firms, insider holdings, etc. If looking at a particular market, some additional data cleaning on the investor holdings (above) followed by recomputing profit weights is recommended.
For this, we did merge the separate BlackRock entities prior to computing \kappa.
CIKmap.csv (~250K observations)
Mapping is from CIK-rdate to CIKname. Use this if you want to consolidate holdings across reporting entities or explore the identities of reporting firms.
In the case of amended filings that use different names than original ones, we keep the earliest name.
Example of Parsing Challenge
Prior to the XML era, filings were far from uniform, which creates a notable challenge for parsing them for holdings. In the examples directory we include several example text files of raw 13f filings.
Example 1 is a "well behaved" filing, with CUSIP, followed by value, followed by number of shares, as recommended by the SEC.
Example 2 shows a case where the ordering is changed: CUSIP, then shares, then value. The column headers show "item 5" coming before "item 4".
Example 3 shows a case of a fixed width table, which in principle could be parsed very easily using the tags at the top, although not all filings consistently use these tags.
Example 4 shows a case with a fixed width table, with no tag for the CUSIP column. Also, notice that if the firm holds more than 10M shares of a firm, that number occupies the entire width of the column and there is no longer a column separator (i.e. Cisco Systems on line 374).
Example 5 shows a comma-separated table format.
Example 6 shows a case of changing the column ordering, but also adding an (unrequired) column for share price.
Example 7 shows a case where the table is split across subsequent pages, and so the CUSIP appears on a different line than the number of shares.
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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