IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/42440.html
   My bibliography  Save this paper

Re-engineering the ISAE manufacturing survey

Author

Listed:
  • Malgarini, Marco
  • Margani, Patrizia
  • Martelli, Bianca Maria

Abstract

The Joint harmonized Manufacturing survey for Italy, carried out by the Institute of Studies and Economic Analysis (ISAE, formerly ISCO), has a long history: it began on a quarterly basis in 1959, becoming monthly in 1962. The survey was then broadly modified in several occasions; in particular, in 1986 it was re-designed in order to provide data also at the regional level, adopting a new stratified random sample, the strata represented by the sector, region and size of the firm. In 1998, the sample was upgraded further, using an optimal allocation of the reporting units to the sample strata (Cochran, 1977). These changes satisfied the demand for more detailed and, at the same time, better harmonized data. However, at this stage, the processing of the results was still based on a very detailed industry grid based on the old NACE1970 classification, re-codified to obtain harmonized data for the Main Industrial Groups and total manufacturing. Size weights were used in the processing of the results, but there were still some differences in the elaboration of the data at the national and regional level, resulting in a not fully-fledged comparability between local and national data. For these reasons, in 2003 ISAE started a re-thinking of the manufacturing survey processing phase. The resulting re-engineering process recently implemented by ISAE is described in this paper. It has reached two main relevant goals: i. The underlying industrial structure for the aggregation of survey results is now based on the NACERev1.1 classification, at the 3-digit level, adapted to take into consideration the structure of Italian economy. ii. The weighting scheme is now based on a coherent system of size weights, based on a four-stage method in which, firstly, the balance Ba,j for question a, firm j, is aggregated in each strata, using the j-firm employees as weights; in the following stages, the result for each strata is progressively aggregated to calculate the Industry total, using value added weights, provided by an external source (i.e., the National Institute for Statistics, ISTAT). The main consequence is that now results at the regional and dimensional level are fully comparable to the ones for the entire industry. Historical data up to 1991 have been recalculated accordingly to the new aggregation scheme and are presented here as a conclusion of the paper.

Suggested Citation

  • Malgarini, Marco & Margani, Patrizia & Martelli, Bianca Maria, 2005. "Re-engineering the ISAE manufacturing survey," MPRA Paper 42440, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:42440
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/42440/1/MPRA_paper_42440.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bruno Giancarlo & Lupi Claudio, 2003. "Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data," ISAE Working Papers 33, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    2. Giancarlo Bruno & Claudio Lupi, 2004. "Forecasting industrial production and the early detection of turning points," Empirical Economics, Springer, vol. 29(3), pages 647-671, September.
    3. Giuseppe Parigi & Paolo Carnazza, 2003. "Tentative business confidence indicators for the Italian economy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(8), pages 587-602.
    4. Filippo Altissimo & Domenico J. Marchetti & Gian Paolo Oneto, 2000. "The Italian Business Cycle: Coincident and Leading Indicators and Some Stylized Facts," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 59(2), pages 147-220, September.
    5. Bruno, Giancarlo & Malgarini, Marco, 2002. "An Indicator of Economic Sentiment for the Italian Economy," MPRA Paper 42331, University Library of Munich, Germany.
    6. Christian M. Dahl & Lin Xia, 2004. "Quantification of Qualitative Survey Data and Test of Consistent Expectations: A New Likelihood Approach," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(1), pages 71-92.
    7. Martelli, Bianca Maria, 1998. "Le Inchieste Congiunturali dell'ISCO: aspetti metodologici; Chapter 1 of: Le inchieste dell'ISCO come strumento di analisi della congiuntura economica [The ISCO short term surveys: methodological a," MPRA Paper 16331, University Library of Munich, Germany.
    8. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1, National Bureau of Economic Research, Inc.
    9. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, July.
    10. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-138, May.
    11. D'Elia, Enrico, 1991. "La quantificazione dei risultati dei sondaggi congiunturali: un confronto tra procedure [Quantifying the results of tendency surveys: a comparison among different procedures]," MPRA Paper 16434, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sergio de Nardis & Carmine Pappalardo, 2009. "Export, Productivity and Product Switching: The Case of Italian Manufacturing Firms," ISAE Working Papers 110, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    2. Emma De Angelis & Carmine Pappalardo, 2009. "(String Matching Algorithms,An Applicatione ti ISAE and ISTAT Firms's Registers)," ISAE Working Papers 115, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    3. Bianca Maria Martelli & Gaia Rocchetti, 2006. "The ISAE Market Services Survey: Methodological Upgrading, Survey Reliability, First Empirical Results," ISAE Working Papers 71, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    4. Tatiana Cesaroni & Marco Malgarini & Gaia Rocchetti, 2005. "L'inchiesta ISAE sugli investimenti delle imprese manifatturiere ed estrattive: aspetti metodologici e risultati," ISAE Working Papers 50, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. G. Bruno & L. Crosilla & P. Margani, 2019. "Inspecting the Relationship Between Business Confidence and Industrial Production: Evidence on Italian Survey Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(1), pages 1-24, April.
    2. Bruno, Giancarlo & Otranto, Edoardo, 2008. "Models to date the business cycle: The Italian case," Economic Modelling, Elsevier, vol. 25(5), pages 899-911, September.
    3. Giancarlo Bruno & Edoardo Otranto, 2003. "Dating the Italian Business Cycle: A Comparison of Procedures," Econometrics 0312003, University Library of Munich, Germany.
    4. Giancarlo Bruno & Marco Malgarini, 2002. "An Indicator of Economic Sentiment for the Italian Economy," ISAE Working Papers 28, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    5. Soh, Ann-Ni, 2020. "A Review on the Leading Indicator Approach towards Economic Forecasting," MPRA Paper 103854, University Library of Munich, Germany.
    6. Ataman Ozyildirim & Brian Schaitkin & Victor Zarnowitz, 2010. "Business cycles in the euro area defined with coincident economic indicators and predicted with leading economic indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 6-28.
    7. Antonio Bassanetti & Michele Caivano & Alberto Locarno, 2010. "Modelling Italian potential output and the output gap," Temi di discussione (Economic working papers) 771, Bank of Italy, Economic Research and International Relations Area.
    8. Tatiana Cesaroni & Stefano Iezzi, 2017. "The Predictive Content of Business Survey Indicators: Evidence from SIGE," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 75-104, May.
    9. Edoardo Otranto, 2005. "Extraction of Common Signal from Series with Different Frequency," Econometrics 0502011, University Library of Munich, Germany.
    10. Maria Rita Ippoliti & Luigi Martone & Fabiana Sartor & Graziella Spera, 2023. "Surveys on trade sector: a comparison between qualitative and quantitative indicators," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 77(1), pages 4-12, January-M.
    11. Gagea Mariana, 2012. "The Contribution Of Business Confidence Indicators In Short-Term Forecasting Of Economic Development," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 617-623, July.
    12. Luciana Crosilla, 2006. "The seasonality of ISAE business and consumer surveys: methodological aspects and empirical evidence," ISAE Working Papers 68, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    13. J. Cuñado & L. Gil-Alana & F. Gracia, 2009. "US stock market volatility persistence: evidence before and after the burst of the IT bubble," Review of Quantitative Finance and Accounting, Springer, vol. 33(3), pages 233-252, October.
    14. Harding, Don & Pagan, Adrian, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 86-95.
    15. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
    16. Chun-Chang Lee & Chih-Min Liang & Hsing-Jung Chou, 2013. "Identifying Taiwan real estate cycle turning points- An application of the multivariate Markov-switching autoregressive Model," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 3(2), pages 1-1.
    17. Agnello, Luca & Nerlich, Carolin, 2012. "On the severity of economic downturns: Lessons from cross-country evidence," Economics Letters, Elsevier, vol. 117(1), pages 149-155.
    18. Mr. Thomas Helbling & Mr. Tamim Bayoumi, 2003. "Are they All in the Same Boat? the 2000-2001 Growth Slowdown and the G-7 Business Cycle Linkages," IMF Working Papers 2003/046, International Monetary Fund.
    19. Ghoshray, Atanu, 2021. "Are coffee farmers worse off in the long run?," 95th Annual Conference, March 29-30, 2021, Warwick, UK (Hybrid) 311084, Agricultural Economics Society - AES.
    20. Theobald, Thomas, 2013. "Markov Switching with Endogenous Number of Regimes and Leading Indicators in a Real-Time Business Cycle Forecast," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79911, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    Survey methods; aggregation; weights;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:42440. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.