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The determinants of unit non-response in the Ifo Business Survey

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Abstract

It is well-known that non-response affects the results of surveys and can even cause biases due to selectivities if it cannot be regarded as missing at random. In contrast to household surveys, response behaviour in business surveys is rarely examined in literature. This paper analyses a large business survey at a microdata level for unit non-response. The data base is the Ifo Business Survey, which was established in 1949 and is completed by more than 5000 firms every month. The panel structure makes it possible to use statistical modelling with the inclusion of different types of time dimensions, as well as firm-specific effects. The results show that there are strong time-dependent effects on the response rate and that non-response is more frequent in economic good times.

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Notes

  1. More information to the definitions of unit and sectors in the official statistics can be found in the German Classification of Economic Activities (edition 2008): https://www.destatis.de/DE/Methoden/Klassifikationen/GueterWirtschaftklassifikationen/klassifikationwz2008_erl.pdf?__blob=publicationFile.

  2. Official statistics in Germany does weight their results in the same way, e.g. for the production statistics in manufacturing.

  3. We are aware of the fact that this kind of informative drop-out might have some effect on the results. However, the number of firms which declared to be no longer interested in survey participation is small (16) in contrast to the number of firms which were taken over or went bankrupt (1904) or for which no information was recorded (1781).

  4. Notice that for the construction and manufacturing firms only the number of employees is available whereas for the trade firms only the annual sales volume is recorded.

  5. For an discussion of the different panel data approaches see Gardiner et al. (2009).

  6. Notice that these results are based on the evaluation of large firms.

References

  • Abberger K, Wohlrabe K (2006) Einige Prognoseeigenschaften des ifo Geschäftsklimas—Ein Überblick über die neuere wissenschaftliche Literatur. ifo Schnelldienst 59(22):19–26

    Google Scholar 

  • Abberger K, Birnbrich M, Seiler C (2009) Der 'Test des Tests` im Handel—Eine Metaumfrage zum ifo Konjunkturtest. ifo Schnelldienst 62(21):34–41

    Google Scholar 

  • Bachmann R, Elstner S, Sims E (2013) Uncertainty and economic activity: evidence from business survey data. Am Econ J—Macroecon 5(2):217–249

    Article  Google Scholar 

  • Brehm J (1994) Stubbing out toes for a foot in the door? Prior contacts, incentives and survey response. Int J Pub Opin Res 6(1):45–63

    Article  Google Scholar 

  • Drechsel K, Scheufele R (2012) The performance of short-term forecasts of the German economy before and during the 2008/2009 recession. Int J Forecasting 28:428–445

    Article  Google Scholar 

  • European Union (2006) Joint harmonised EU programme of business and consumer surveys. Off J Eur Union 49(245):5–8

  • Gardiner JC, Luo Z, Roman LA (2009) Fixed effects, random effects and GEE: what are the differences? Statistics Med 28:221–239

    Article  MathSciNet  Google Scholar 

  • Goldrian G (ed) (2007) Handbook of survey-based business cycle analysis. Edward Elgar Publishing, Cheltenham.

  • Groves RM, Fowler JF, Couper MP, Lepkowski JM, Singer E, Tourangeau R (2004) Survey methodology. Wiley, New Jersey

    MATH  Google Scholar 

  • Harris-Kojetin B, Tucker C (1999) Exploring the relation of economical and political conditions with refusal rates to a government survey. J Off Statistics 15(2):167–184

    Google Scholar 

  • Heagerty P, Zeger S (1996) Marginal regression models for clustered ordinal measurements. J Am Statistical Assoc 91:1024–1036

    Article  MATH  Google Scholar 

  • Janik F, Kohaut S (2012) Why don’t they answer? - Unit non-response in the IAB establishment panel. Qual Quant 46(3):917–934

    Article  Google Scholar 

  • Kholodilin KA, Siliverstovs S (2006) On the forecasting properties of the alternative leading indicators for the German GDP: recent evidence. J Econ Statistics 226(3):234–259

    Google Scholar 

  • de Leeuw E, de Heer W (2002) Trends in household survey nonresponse: a longitudinal and international comparison. In: Groves RM, Dillman DA, Eltinge JL, Little RJ (eds) Survey Nonresponse (Chapt. 3). Wiley, New York, pp 41–54

  • Liang K-Y, Zeger SL (1986) Longitudinal data analysis using generalized linear models. Biometrika 73:13–22

    Article  MathSciNet  MATH  Google Scholar 

  • Pesaran MH, Timmermann A (2009) Testing dependence among serially correlated multicategory variables. J Am Statistical Assoc 104(485):325–337

    Article  MathSciNet  Google Scholar 

  • Robinzonov N, Wohlrabe K (2010) Freedom of choice in macroeconomic forecasting. CESifo Econ Stud 56(2):192–220

    Article  Google Scholar 

  • Rottmann H, Wollmershäuser T (2013) A Micro data approach to the identification of credit crunches. Appl Econ 45:2423–2441

    Article  Google Scholar 

  • Seiler C (2012) The data sets of the LMU-ifo economics & business data center—a guide for researchers. J Appl Soc Sci Studies 132(4):609–618

    Google Scholar 

  • Tomaskovic-Devey D, Leiter J, Thompson S (1994) Organizational survey nonresponse. Adm Sci Q 39:439–457

    Article  Google Scholar 

  • Willimack DK, Nichols E (2010) A hybrid response process model for business surveys. J Off Statistics 26(1):3–24

    Google Scholar 

  • Willimack DK, Nichols E, Sudman S (2002) Understanding unit and item nonresponse in business surveys. In: Groves RM, Dillman DA, Eltinge JL, Little RJ (eds) Survey nonresponse (Chapt. 14) Wiley, New York

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Seiler, C. The determinants of unit non-response in the Ifo Business Survey. AStA Wirtsch Sozialstat Arch 8, 161–177 (2014). https://doi.org/10.1007/s11943-014-0142-9

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