JBNST - Vol. 236/5 - 2016

This paper employs a novel firm-level dataset that combines financial accounts of German firms with data from a business survey to shed new light on the demand for capital. The empirical analysis employs firm-specific indicators in order to explore the effects of sales, the cost of capital and indicators of the business climate, which are used by the ifo Institute to provide a leading indicator for the German economy. The empirical results support a robust significant effect of a firm’s cost of capital on the stock of capital with an elasticity not significantly different from –1. Controlling for sales, a good rather than normal business situation is found to be associated with about 8?% higher investment.

The p-value is often considered as the gold standard in inferential statistics. The standard approach for evaluating empirical evidence is to equate low p-values with a high degree of credibility and to refer to findings with p-values below certain thresholds (e.g., 0.05) as statistically significant. The p-value is also referred to as error probability. Both terms are problematic as they invite serious misconceptions. In addition, researchers’ fixation on obtaining statistically significant results may introduce biases and increase the rate of false discoveries. Misinterpretations of the p-value as well as the introduction of bias through arbitrary analytical choices (p-hacking) have been critically discussed in the literature for decades. Nonetheless, they seem to persist in empirical research and criticisms of inappropriate approaches have increased in the recent past – mainly due to the non-replicability of many studies. Unfortunately, the critical concerns that have been raised in the literature are not only scattered over many academic disciplines but often also linguistically confusing and differing in their main reasons for criticisms. Against this background, our methodological comment systematizes the most serious flaws and discusses suggestions of how best to prevent future misuses.

With the Programme for the International Assessment of Adult Competencies (PIAAC) researchers can shed light on how competencies are acquired, how its use helps us maintain and further develop skills, and whether adults are prepared for the challenges of modern knowledge societies (OECD 2013a). The Organisation for Economic Co-operation and Development (OECD) initiated PIAAC in more than 30 countries to assess competencies of the adult population. Similar to the Programme for International Student Assessment (PISA), PIAAC is planned to be repeated in regular intervals. Therefore, the next cycle of PIAAC is planned for 2022. The OECD published the PIAAC international public use file of the first cycle of PIAAC (OECD 2015) in 2013. Due to German confidentiality rules, GESIS published a scientific use file (Rammstedt et?al. 2015) that includes information that could not be released in the public use file. Further national data and para data for PIAAC can shed light on further research questions as well as methodological aspects of PIAAC. This data is currently and will be made available in the Research Data Center PIAAC (RDC PIAAC) at GESIS. In addition to this, various add-on studies were and are currently being conducted in Germany, such as Competencies in Later Life (CiLL) and the longitudinal study PIAAC-L. However, the PIAAC data presents challenges due to imputed competency scores (plausible values) and country-specific complex sample techniques. The RDC PIAAC provides information on analytic methods and the available analysis tools. It also offers workshops to familiarize users with the data and to teach them how to analyze the PIAAC data. Given the brevity of the PIAAC data release, an impressive number of research papers were published that use PIAAC data. Research with PIAAC focuses, for example, on the returns to skills (e.?g., Hanushek et?al. 2015), skill and wage inequality (Paccagnella 2015), skill mismatch (Allen et?al. 2013a; Perry et?al. 2014), non-monetary outcomes, such as trust (Borgonovi/Burns 2015), and also methodological aspects, such as incentives in large-scale assessments (Martin et?al. 2014). This paper aims to present central aspects of PIAAC, analytical procedures for the competence measures and the complex sample design, as well as data, information and services provided through the RDC PIAAC at GESIS.