Dynamic Modelling of Nonresponse in Business Surveys

It is well-known that nonresponse affects the results of surveys and can even causebias due to selectivities if it cannot be regarded as missing at random. In contrast tohousehold surveys, response behaviour in business surveys has been examined rarely inthe literature. This paper is one of the first which analyses a large business survey onmicro data level for unit nonresponse. The data base is the Ifo Business TendencySurvey, which was established in 1949 and has more than 5,000 responding firms eachmonth. The panel structure allows to use statistical modelling including time-varyingeffects to check for the existence of a panel fatigue. The results show that there are hugedifferences in business characteristics such as size or sub-sector and that nonresponse ismore frequent in economically good times.Business survey, logistic regression, nonresponse, panel survey, varyingcoefficient model.

Microdata Imputations and Macrodata Implications: Evidence from the Ifo Business Survey

A widespread method for now- and forecasting economic macro level parameters such as GDP growth rates are survey-based indicators which contain early information in contrast to official data. But surveys are commonly affected by nonresponding units which can produce biases if these missing values can not be regarded as missing at random. As many papers examined the effect of nonresponse in individual or household surveys, only less is known in the case of business surveys. So, literature leaves a gap on this issue. For this reason, we analyse and impute the missing observations in the Ifo Business Survey, a large business survey in Germany. The most prominent result of this survey is the Ifo Business Climate Index, a leading indicator for the German business cycle. To reflect the underlying latent data generating process, we compare different imputation approaches for longitudinal data. After this, the microdata are aggregated and the results are compared with the original indicators to evaluate their implications on the macro level. Finally, we show that the bias is minimal and ignorable

Dynamic modelling of Nonresponse in Business Surveys

It is well-known that nonresponse affects the results of surveys and can even cause bias due to selectivities if it cannot be regarded as missing at random. In contrast to household surveys, response behaviour in business surveys has been examined rarely in the literature. This paper is one of the first which analyses a large business survey on micro data level for unit nonresponse. The data base is the Ifo Business Tendency Survey, which was established in 1949 and has more than 5,000 responding firms each month. The panel structure allows to use statistical modelling including time-varying effects to check for the existence of a panel fatigue. The results show that there are huge differences in business characteristics such as size or subsector and that nonresponse is more frequent in economically good times

Dynamic modelling of Nonresponse in Business Surveys

It is well-known that nonresponse affects the results of surveys and can even cause bias due to selectivities if it cannot be regarded as missing at random. In contrast to household surveys, response behaviour in business surveys has been examined rarely in the literature. This paper is one of the first which analyses a large business survey on micro data level for unit nonresponse. The data base is the Ifo Business Tendency Survey, which was established in 1949 and has more than 5,000 responding firms each month. The panel structure allows to use statistical modelling including time-varying effects to check for the existence of a panel fatigue. The results show that there are huge differences in business characteristics such as size or subsector and that nonresponse is more frequent in economically good times

Ranking Economists and Economic Institutions Using RePEc: Some Remarks

In socio-economic sciences the RePEc network (Research Papers in Economics) has become an essential source both for the spread of existing and new economic research. Furthermore the calculation of rankings for authors and academic institutions play a central role. We provide some cautionary remarks on the ranking methodology employed by RePEc and show how the aggregated rankings maybe biased. Furthermore we offer anew ranking approach, based on standardization of scores, which allows interpersonal comparisons and is less sensitive to outliers. We illustrate our new approach with a large data set provided by RePEc based on 24,500 authors.Rankings, RePEc, ranking aggregation, standardization

RePEc – An Independent Platform for Measuring Output in Economics

Bewertung; Netzwerk; Ranking-Verfahren; Deutschland

On the Robustness of the Balance Statistics with respect to Nonresponse

Business cycle indicators based on the balance statistics are a widely used method tomonitor the actual economic situation. In contrast to official data, indicators frombusiness surveys are early available and typically not revised after their first publication.But as surveys can be in general affected by distortions through the response behaviour,these indicators can also be biased. In addition, time-dependent nonresponse patternscan produce even more complex forms of biased results. This paper examines aframework which kind of nonresponse patterns lead to biases and decreases in performance.We perform an extensive Monte Carlo study to analyse their effects on the indicators.Our analyses show that these indicators are extremely stable towards selection biases