Traded corporations are required by law to have a majority of outside directors on their board. This requirement allows the existence of directors who sit on the board of two or more corporations at the same time, generating what is commonly known as interlocking directorates. While research has shown that networks of interlocking directorates facilitate the transmission of information between corporations, little is known about the extent to which such interlocking networks can explain the fluctuations of stock price returns. Yet, this is a special concern since the risk of amplifying stock fluctuations is latent. To answer this question, here we analyze the board composition, traders’ perception, and stock performance of more than 1,500 US traded corporations from 2007-2011. First, we find that the fewer degrees of separation between two corporations in the interlocking network, the stronger the temporal correlation between their stock price returns. Second, we find that the centrality of traded corporations in the interlocking network correlates with the frequency at which financial traders talk about such corporations, and this frequency is in turn proportional to the corresponding traded volume. Third, we show that the centrality of corporations was negatively associated with their stock performance in 2008, the year of the big financial crash. These results suggest that the strategic decisions made by interlocking directorates are strongly followed by stock analysts and have the potential to correlate and amplify the movement of stock prices during financial crashes. These results may have relevant implications for scholars, investors, and regulators.
The stability analysis of socioeconomic systems has been centred on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. Therefore, a key stability question that has been under-investigated is how strongly the conditions of a system itself can change before the system moves to a qualitatively different behaviour, i.e. how structurally stable the systems is. Here, we introduce a framework to investigate the structural stability of socioeconomic systems formed by a network of interactions among agents competing for resources. We measure the structural stability of the system as the range of conditions in the distribution and availability of resources compatible with the qualitative behaviour in which all the constituent agents can be self-sustained across time. To illustrate our framework, we study an empirical representation of the global socioeconomic system formed by countries sharing and competing for multinational companies used as proxy for resources. We demonstrate that the structural stability of the system is inversely associated with the level of competition and the level of heterogeneity in the distribution of resources. Importantly, we show that the qualitative behaviour of the observed global socioeconomic system is highly sensitive to changes in the distribution of resources. We believe that this work provides a methodological basis to develop sustainable strategies for socioeconomic systems subject to constantly changing conditions.
This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation design, which is based on large scale labor market data from Germany and varies w.r.t. treatment selectivity, effect heterogeneity, the share of treated, and the sample size. The results suggest that in general, the bootstrap procedures dominate the asymptotic ones in terms of size and power for both matching and weighting estimators. Furthermore, the results are qualitatively quite robust across the various simulation features.
Based on empirical data from selected public universities in Khabarovsk, Russia, this paper compares first and fifth year students regarding their attitudes towards corruption in general and university corruption in particular. Even after making both groups of students comparable with respect to a range of socio-economic characteristics by a matching approach, the results suggest that fifth year students are more open to a range of informal and corrupt practices than first years. Our analysis therefore points to the possibility that the Russian higher education system might ‘favor’ compliance with corruption and informal practices, with potentially detrimental consequences for the Russian society as a whole.
This paper proposes a method to detect bid-rigging by applying mutually reinforcing screens to a road construction procurement data set from Switzerland in which no prior information about collusion was available. The screening method is particularly suited to deal with the problem of partial collusion, i.e. collusion which does not involve all firms and/or all contracts in a specific data set, implying that many of the classical markers discussed in the corresponding literature will fail to identify bid-rigging. In addition to presenting a new screen for collusion, it is shown how benchmarks and the combination of different screens may be used to identify subsets of suspicious contracts and firms in a data set. The discussed screening method succeeds in isolating a group of “suspicious” firms exhibiting the characteristics of a local bid-rigging cartel operating with cover bids and a – more or less pronounced – bid rotation scheme. Based on these findings the Swiss Competition Commission (ComCo) decided to open an investigation.
This paper investigates the effects of an information campaign about a governmental rural development program (RDP) in the Former Yugoslav Republic of Macedonia on the farmers’ intention to participate in the RDP. In the course of a survey among farmers, the treatment group received an information brochure with relevant details on selected RDP measures, while the control group received no information. Even though the intervention had been planned as experiment, randomization was not properly conducted, requiring sample adjustments and controlling for observed covariates in the estimation process. The results suggest that while the intervention succeeded in informing farmers, it had a negative, albeit marginally statically significant, effect on farmers’ reported possibility and intention to use RDP support in the near future. Evidence from further outcome variables suggests that this may be due to the information about administrative burden associated with RDP participation provided in the brochure. We also find that the negative effect is driven by the subsample of unprofitable farmers.
We introduce a wild bootstrap algorithm for the approximation of the sampling distribution of pair or one-to-many propensity score matching estimators. Unlike the conventional iid bootstrap, the proposed wild bootstrap approach does not construct bootstrap samples by randomly resampling from the observations with uniform weights. Instead, it fixes the covariates and constructs the bootstrap approximation by perturbing the martingale representation for matching estimators. We also conduct a simulation study in which the suggested wild bootstrap performs well even when the sample size is relatively small. Finally, we provide an empirical illustration by analyzing an information intervention in rural development programs.
This study empirically evaluates the impact of the war in eastern Ukraine on the political attitudes aThis paper proposes a difference-in-differences approach for disentangling a total treatment effect on some outcome into a direct impact as well as an indirect effect operating through a binary intermediate variable – or mediator – within strata defined upon how the mediator reacts to the treatment. We show under which assumptions the direct effects on the always and never takers, whose mediator is not affected by the treatment, as well as the direct and indirect effects on the compliers, whose mediator reacts to the treatment, are identified. We provide an empirical application based on the Vietnam draft lottery. The results suggest that a high draft risk due to the lottery leads to a relative increase in the support for the Republican Party and that this increase is mostly driven by those complying with the lottery outcome.
