2015
DOI: 10.2139/ssrn.2660191
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How to Construct Nationally Representative Firm Level Data from the ORBIS Global Database

Abstract: Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in… Show more

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Cited by 151 publications
(163 citation statements)
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“…8 There are of course many other ways to model this idea, and our approach is certainly not meant to represent the wide range of Design approaches, rather we see it as a simple example to illustrate the implications of a G(M i ) function which attains an interior maximum.…”
Section: Management By Designmentioning
confidence: 99%
See 1 more Smart Citation
“…8 There are of course many other ways to model this idea, and our approach is certainly not meant to represent the wide range of Design approaches, rather we see it as a simple example to illustrate the implications of a G(M i ) function which attains an interior maximum.…”
Section: Management By Designmentioning
confidence: 99%
“…Notes: *** denotes significance at the 1% level, ** denotes 5% significance and * denotes 10% significance. All columns with standard errors in parentheses under coefficients (clustered by firm except (3), (4), (8) and (12) which are bootstrapped with 150 replications). All columns use OLS except columns (2), (9) and (10) that use the Blundell and Bond (2000) method; columns (4), (8) and (12) uses the Olley-Pakes ("OP") estimator (column (4) with the Ackerberg et al, "ACF" correction), column (3) the Levinsohn-Petrin ("Lev-Pet") approach.…”
Section: Foreign Multinationalsmentioning
confidence: 99%
“…Our dataset includes both listed and unlisted companies and contains information on financial data of about 920 thousand firms operating in five peripheral euro area countries (Italy, Spain, Portugal, Greece and Slovenia) for the 2005-2014 period. We pursue a standard cleaning procedure to account for data irregularities (Kalemli-Özcan et al, 2015b). In particular, we take care of double entries, firm-year observations with inconsistent balance sheet or income statement relations, including those with negative debt or asset holdings.…”
Section: Datamentioning
confidence: 99%
“…Therefore, we consider it an appropriate time period to check for the robustness of our model and at the same time avoiding abnormal distortions which the financial crisis might generate to our analysis. Balance sheet data are deflated to keep track of inflation and cleaned according to standard procedure described in (Kalemli-Ozcan, 2015). The initial dataset comprises 33,397 companies.…”
Section: Datamentioning
confidence: 99%