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Re-engineering the ISAE manufacturing survey

Author

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  • 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
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    References listed on IDEAS

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    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. 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.
    4. 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.
    5. 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).
    6. 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.
    7. repec:bla:econom:v:42:y:1975:i:166:p:123-38 is not listed on IDEAS
    8. 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.
    9. 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.
    10. 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.
    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.
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    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).

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    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

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