Causal Inference and Data-Fusion in Econometrics

Learning about cause and effect is arguably the main goal in applied econometrics. In practice, the validity of these causal inferences is contingent on a number of critical assumptions regarding the type of data that has been collected and the substantive knowledge that is available. For instance, unobserved confounding factors threaten the internal validity of estimates, data availability is often limited to non-random, selection-biased samples, causal effects need to be learned from surrogate experiments with imperfect compliance, and causal knowledge has to be extrapolated across structurally heterogeneous populations. A powerful causal inference framework is required to tackle these challenges, which plague most data analysis to varying degrees. Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal learning that allow to leverage information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016). In this paper, we discuss recent advances in this literature that have the potential to contribute to econometric methodology along three dimensions. First, they provide a unified and comprehensive framework for causal inference, in which the aforementioned problems can be addressed in full generality. Second, due to their origin in AI, they come together with sound, efficient, and complete algorithmic criteria for automatization of the corresponding identification task. And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer a perfect middle ground between these two competing literature streams.Comment: Abstract change

Schwerpunktbericht zur Innovationserhebung 2012

Dieser Bericht präsentiert Ergebnisse zu einer Schwerpunktfrage in der Innovationserhebung 2012 im Rahmen des Mannheimer Innovationspanels (MIP), die sich dem Thema Innovationspartnerschaften entlang von Wertschöpfungsketten und den Gründen für einen Verzicht auf eine Zusammenarbeit mit anderen Unternehmen und Einrichtungen widmete. Außerdem werden die Eckdaten der Innovationserhebung 2012 im Hinblick auf Stichprobenumfang, Rücklauf, Erhebungsinstrument und Datenaufbereitung dargestellt. Innovationspartnerschaften bezeichnen dabei die Zusammenarbeit eines Unternehmens mit anderen Unternehmen und Einrichtungen mit dem Ziel, Innovationen zu entwickeln oder einzuführen. Die Partner können Kunden, Lieferanten, Wettbewerber oder Wissenschaftseinrichtungen sein. Die Zusammenarbeit in der Partnerschaft kann von formalen Kooperationen bis zum informellen Informationsaustausch reichen. Nicht alle an einer Innovationspartnerschaft beteiligten Akteure müssen dabei selbst Innovationen einführen, eine Beteiligung an einer Innovationspartnerschaft kann auch unterstützende Leistungen für die Innovationen der Partner umfassen

Staging innovation projects : (when) does it pay off?

Building on real options literature, this study shows that the use of a staged approach for the management of innovation projects affects the innovation output of firms differently depending on firm characteristics and ambitions. In particular, while staged project management increases the effect of inno- vation expenditures on new product sales for firms envisaging incremental or continuous innovations, this moderating effect is absent for firms aspiring radical innovations. In addition, while staged project management has a pos- itive moderating effect in firms with resource slack, this is not the case when firms are resource-constrained. We further investigate the underlying mecha- nisms to this latter finding by demonstrating that in resource-abundant firms staged project organization is associated with delaying projects until more information becomes available. Thereby these firms reap the waiting value inherent to real options reasoning. By contrast, resource-constrained firms using staged project management are shown to abandon a larger share of their innovation projects and to concentrate resources on fewer projects. It appears, however, that, due to budgetary pressure, they make the decision to abandon at a too early stage where uncertainty is insufficiently resolved. This can explain why there is no effect of staged project management on the sales of resource-constrained firms from new products. The paper contributes to theory development on when and why the staging of innovation projects affects the innovation output of firms and to the literature on real options reasoning in general

On the Nuisance of Control Variables in Regression Analysis

Control variables are included in regression analyses to estimate the causal effect of a treatment on an outcome. In this note we argue that the estimated effect sizes of control variables are unlikely to have a causal interpretation themselves though. We therefore recommend to refrain from reporting marginal effects of controls in regression tables and instead to focus exclusively on the variables of interest in the results sections of empirical research papers.Comment: 17 pages, 1 figure, 1 tabl

Public procurement as policy instrument for innovation

The use of public procurement to promote private innovation activities has attracted increasing attention recently. Germany implemented a legal change in its procurement framework in 2009, which allowed government agencies to specify innovative aspects of procured products as selection criteria in tender calls. We analyze a representative sample of German firms to investigate whether this reform stimulated innovation in the business sector. Across a wide set of specifications—OLS, nearest-neighbor matching, IV regressions and difference-in-differences—we find a robust and significant effect of innovationdirected public procurement on turnover from new products and services. However, our results show that the effect is largely attributable to innovations of more incremental nature rather than market novelties

Entry and shakeout in dynamic oligopoly

In many industries, the number of firms evolves non-monotonically over time. A phase of rapid entry is followed by an industry shakeout: a large number of firms exit within a short period. We present a simple timing game of entry and exit with an exogenous technological process governing firm effi- ciency. We calibrate our model to data from the post World War II penicillin industry. The equilibrium dynamics of the calibrated model closely match the patterns observed in many industries. In particular, our model gener- ates richer and more realistic dynamics than competitive models previously analyzed. The entry phase is characterized by preemption motives while the shakeout phase mimics a war of attrition. We show that dynamic strategic incentives accelerate early entry and trigger the shakeout by comparing a Markov Perfect Equilibrium to an Open-loop Equilibrium

Estimating the local average treatment effect of R&D subsidies in a pan-European program

We investigate the effect of Europe's largest multilateral subsidy program for R&D-performing, small and medium-sized enterprises on firm growth. The program was organized under a specific budget allocation rule, referred to as Virtual Common Pot (VCP), which is designed to avoid cross-subsidization between participating countries. This rule creates exogenous variation in treatment status and allows us to identify the local average treatment effect of public R&D grants. In addition, we compare the program's effect under the VCP rule with the standard situation of a Real Common Pot (RCP), where program authorities allocate a single budget according to uniform project evaluation criteria. Our estimates suggest no average effect of grants on firm growth but treatment effects are heterogeneous and increase with project quality. A Real Common Pot would have reduced the cost of policyinduced job creation by 27%. We discuss the implications of our findings for the coordination of national policy programs within the European Research Area

A global decline in research productivity? Evidence from China and Germany

In a recent paper, Bloom et al. (2020) find evidence for a substantial decline in research productivity in the U.S. economy during the last 40 years. In this paper, we replicate their findings for China and Germany, using detailed firm-level data spanning three decades. Our results indicate that diminishing returns in idea production are a global phenomenon, not just confined to the U.S

More R&D, less growth? China’s decreasing research productivity in international comparison

Innovation is widely considered the primary driver of growth in high-income economies. The efficiency by which an economy is able to transform research & development (R&D) inputs into output growth is captured by the measure of research productivity. In a recent study we were able to show that research productivity is declining over time, not just in the U.S., which has been shown before, but also in China and Germany. This implies that new ideas and innovations are universally harder to find. In Germany, business R&D spending has increased by an average of approximately 3.3% per year during the last three decades. At the same time, research productivity has fallen on average by 5.2% per year, which is very similar to the estimates obtained for the U.S. In China, we observe a substantial expansion of research activities during the first and second decade of this century, indicated by a growth rate of 21.9% in research spending. The resulting output growth, however, is not proportional to such inputs, which is reflected by a 23.8% decrease in estimated research productivity, or a reduction by half in only three years. We argue that China’s substantial decrease in research productivity is related to diminishing returns to technological catching-up as well as mission-driven policy targeting technological self-sufficiency and national security

Innovation activities of firms in Germany - results of the German CIS 2012 and 2014 : background report on the surveys of the Mannheim Innovation Panel conducted in the years 2013 to 2016

Innovation is regarded as a key driver of productivity and market growth and thus has a great potential for increasing wealth. Surveying innovation activities of firms is an important contribution to a better understanding of the process of innovation and how policy may intervene to maximise the social returns of private investment into innovation. Over the past three decades, research has developed a detailed methodology to collect and analyse innovation activities at the firm level. The Oslo Manual, published by OECD and Eurostat (2005) is one important outcome of these efforts. In 1993 both organisations have started a joint initiative, known as the Community Innovation Survey (CIS), to collect firm level data on innovation across countries in concord (with each other). The German contribution to this activity is the so-called Mannheim Innovation Panel (MIP), an annual survey implemented with the first CIS wave in 1993. The MIP fully applies the methodological recommendations laid down in the Oslo Manual. It is designed as a panel survey, i.e. the same gross sample of firms is surveyed each year, with a biannual refreshment of the sample. The MIP is commissioned by the German Federal Ministry of Education and Research (BMBF) and conducted by the Centre for European Economic Research (ZEW) in cooperation with the Fraunhofer Institute for Systems and Innovation Research (ISI) and the Institute for Applied Social Science (infas). This publication reports main results of the MIP surveys conducted in the years 2013, 2014, 2015 and 2016. The surveys of the years 2013 and 2015 were the German contribution to the CIS for the reference years 2012 and 2014. The purpose of this report is to present descriptive results on various innovation indicators for the German enterprise sector