Robustness Checks. This paper focuses on explaining the degree of access that interest groups obtain to public officials. However, access can be conceptualized as a two-step process in which first interest groups gain access, and, subsequently, they can have repeated access. This section evaluates the robustness of the results while accounting for those organizations without access. More specifically, Table 4.3 below presents the results of hurdle negative binomial models, a two- step method that first assesses the probability of obtaining the binary outcome, in this case obtaining access or not, and subsequently calculates the effects of the same explanatory factors on the level of access (see Xxxxxx et al. 2019 for a similar approach). This model is appropriate in the case of a sequential decision-making process. Even though the two stages of granting access once and deciding to grant access multiple times are estimated separately, the second stage should be interpreted as conditional on the first stage. Prioritizing Professionals? The first step of the model (binary logit), shows that organizational capacity increases the likelihood of gaining access. In contrast, member involvement and functioning as a transmission belt is not related to the probability of gaining access.31 That is, the same organizational factors that explain the level of access seem to explain the likelihood of gaining access. Additionally, the second step of the model (zero-truncated negative bi- nomial) confirm the results presented in Table 4.2. The only differences are found in the significance levels of some control variables. More specifically, the second step of hurdle models show that only organizational scale and resources are significantly related to the degree of access, yet this result is not consistent across all model specifications in Table
Robustness Checks. 4.1. Tail risk connectedness in different market conditions In this section, we examine the tail risk connectedness network between US industries, and how the spillovers are affected by their business linkages in different market conditions. We include a crisis dummy variable as well as its interaction terms with all explanatory variables in the first stage LASSO quantile regression. The crisis dummy (𝐷) takes the value of 1 for weeks starting from 1 January 2007 to 31 December 2009, and 0 otherwise. The coefficients corresponding to the loss exceedance terms capture the tail risk spillover between industries in normal period. The coefficients of the interaction terms between the crisis dummy and loss exceedance show the change in tail risk spillovers between industries in crisis period. We obtain two tail risk connectedness matrices from the LASSO quantile regression: 𝑨 is the tail risk connectedness matrix in normal period constructed from the coefficients of the loss exceedance terms, and 𝑫𝑨 presents the change in 𝑨 due to crisis. The elements in 𝑫𝑨 are the coefficients of the interaction terms between the dummy crisis and loss exceedances. We also construct matrix 𝑨𝑫𝑨 as the sum of 𝑨 and 𝑫𝑨 matrices, which shows the value of the spillover coefficients in the crisis period. This framework can generate several scenarios of the difference of tail risk spillovers between normal time and distress time. First, for a non-zero entry in 𝑨, we say that the tail risk connectedness changes in crisis time if its corresponding entry in 𝑫𝑨 is different from zero, and there is no change in the crisis period if its corresponding entry in 𝑫𝑨 is zero. If the corresponding non-zero entry in 𝑫𝑨 has the opposite sign and almost similar magnitude with the entry in 𝑨, the tail risk spillover between the two industries almost disappears in crisis period. Second, for a zero entry in 𝑨, a corresponding non-zero entry in 𝑫𝑨 implies that an industry starts to affect its partner in crisis time. Due to the large scale of the 𝑨, 𝑫𝑨, and 𝑨𝑫𝑨 matrices, we do not report these tables in our paper. The tables are available from the authors upon request. The results of this investigation show that there are changes in the tail risk transmissions between industries in crisis period. We observe a 5.4% increase in the number of relevant spillovers, from 608 spillovers in normal time to 641 spillovers in crisis time. No tail risk connectedness disappears in crisis...
Robustness Checks. Finally, I check the robustness of the findings. Because crises are nonrandom events, the basic selection problem may arise if the crisis actors differ in significant unmeasured ways from the actors who were not involved in crises. There may be important factors not included in the equation for testing crisis outcomes that drive states into crises and try harder to win. Therefore, to account for the nonrandom selection into crises, I use a Xxxxxxx selection model and test the equation for crisis outcomes using the independent variables used in the equation for crisis initiation. A linear combination used to examine the predicted probabilities of the challenger’s victory at different levels of audience costs provides support for the hypothesis that nuclear weapons have a coercive effect on crisis outcomes only when the challenger has high ACC and incurs high audience costs.
Robustness Checks. Adding Population as a Control to Hy- potheses 1 and 2 224 A.2.10 Weighting the Dependent Variables for Hypotheses 1 and 2 000 X.0 Xxxxxxxx to Chapter 5 231 A.3.1 Different CAR Windows in OLS Regression 231 A.3.2 Reconceptualizing the Independent Variable: Any Private Prisons234
Robustness Checks. As reported in Tables 5 and 6, I re-estimated the models by including market orientation and cross-functional integration as control variables to ensure that CBM effect was still significant after controlling for those two related conditions. H1a and b and the results for the interactions are consistent with the results of the analysis without these control variables. Moreover, the incremental R2 explained by adding CBM to models that included market orientation and cross functional integration, respectively, is statistically significant at p<0.05 in the sales growth and employee engagement models indicating that a CBM measure that focuses on internal and community constituencies explains variance over and above these two established variables9. To examine persistence and proxy for omitted variables, I included the lag dependent variable in the sales growth model, in addition to the firm and country level controls. The pattern of results remained largely stable to this addition. In order to verify that the choice of the estimation approach did not bias the results, I also re-estimated the four recursive equations utilizing Roodman’s (2009) conditional mixed process 9 The effect of the CBM measure that includes market constituents however becomes weaker when market orientation is added to the model and in some cases becomes insignificant.
Robustness Checks. Earlier findings that link experience with risk taking could be driven by the fact that there is an inverse correlation between Tracking Error and market capitalization of a fund’s holdings. That is, all other things being equal, a large cap fund has lower Tracking Error than a small cap fund. Furthermore, given that junior managers manage smaller funds, on average, we would expect more Tracking Error for junior managers simply because they manage smaller funds. In order to alleviate these concerns, I divide all funds into two groups based on Morningstar’s categorization of fund size capitalization. Using Morn- ingstar Category grouping, I run the same regressions for risk taking by experience for large and mid/small cap funds separately. Results are reported in Table 11. In Table 11, I again find results consistent with my findings in Table 9 for all fund size groups. Funds managed by junior fund managers take significantly more risk when com- pared to their seasoned counterparts for the more recent period. The results for the earlier period are also consistent throughout the fund size group with earlier finding. With these results, I show that my findings of the relationship between risk taking and experience are robust to the negative correlation between Tracking Error and fund market capitalization. Next, I use other measures of risk taking to test whether my finding is robust to the choice of measuring risk in Table 12. The first alternative measure I use is Amihud and Goyenko (2013) R2 (AG Rsq). I regress the future twelve months of monthly fund excess return over the one month T-xxxx rate on Xxxx-Xxxxxx-Xxxxxxx 4 Factor return to get the R2. As I noted previously, since a high R2 implies lower risk taking/selectivity, I subtract the R2 measure from one to be in accordance with other measures that capture risk taking. The R2 measure has the benefit of not having to know or define the specific benchmark each mutual fund is using and thus is able to successfully detect funds that are truly active in picking stocks against funds that invest in multiple index funds and hide under the radar of other active management measures. The results of the first two specifications show the consistent result that junior fund managers take more risk in the more recent period compared to their seasoned counterparts when AG Rsq is used. The next two measures are based on the holdings of each mutual fund. In order to use holdings-based measures, it is necessary that I co...
Robustness Checks. In order to further test for the validity of the main results of the study, several robustness checks are presented in this section. In Table 1.7, I take advantage of the longitudinal nature of the PSID and estimate model with individuals fixed effects. In the main part of the paper, I use the data as cross sections given that respondents typically do not remain eligible for the EITC for many consecutive years which leads to a drastic reduction of the sample size. Testing whether fixed effect models provide consistent results as the baseline models can further remove concerns about the fact that changes in the composition of the sample are potentially driving the findings of improved health.
Robustness Checks. In this section we report the results of several robustness tests of our findings to alternative variable definitions and sample restrictions. In doing so, we build on the single-split specification (II) to 12Regression coefficients in probit models cannot be interpreted as simple slopes as in ordinary linear regressions, but have to be interpreted in terms of Z-scores (i.e. as changes in Z-score for one unit increase in the explanatory variable). consider all available observations and guarantee a sufficient number of observations for the different sample restrictions. First, Table 7 reports the results obtained from modifications of specification (II) aimed at ensuring a vertical-type connection between a firm’s imported inputs and its core export product. In particular, columns (1) and (2) report the results when we restrict the sample to import transactions that are classified as intermediates or capital goods according to the Broad Economic Categories classification; columns (3) and (4) show the results when we use our alternative dependent variable (d integr IFEXihjt), which conditions the classification of transactions as intra-firm also on the existence of a firm’s affiliate in the source country declaring intra-firm export activities. The results in Table 7 confirms those in Table 4: better IPR quality diminishes the propensity to integrate in relatively downstream stages for complements, while the impact for substitutes is not statistically significant. Moreover, the differences between complements and substitutes, in line with theoretical predictions, become more pronounced both with respect to inputs’ upstreamness and relative knowledge intensity along the value chain. Specifically, the impact of Upstr remains significantly negative for complements, while it becomes significantly positive for substitutes in column (2); the interaction between lnIPR and Upstr becomes significantly negative in column (2); and the impact of d knint downstr turns insignificant for complements in column (3), while remaining highly significant and positive for substitutes. Second, Table 8 presents the results obtained using two alternative indicators of sequential com- plements/substitutes described in Section 4.2. In particular, columns (1)-(4) use the indicator d complrho×alpha(ind.) based on the core product’s demand elasticity rho (as a proxy for ρ) and the industry average of the Xxxxxxxxxx index (as a proxy for (inverse) α); columns (5-8) use in- stead the dumm...
Robustness Checks. In this section, we perform a series of sensitivity analysis tests to check the robustness of our results. Notably, we employ alternative measures of the dependent variable and the variables of interest. We also use alternative statistical approaches as well as alternative sample compositions and an alternative model specification. Results are reported in Table 5.
Robustness Checks. Private protection rackets are famously xxxx in Russia and Ukraine, though they are less active in the other post-communist countries. In a survey of Russian shopkeepers by Xxxxxxxxxxx and Xxxx (1998), 33% reported that one of the roles of private protection organizations was to enforce agreements (though far more reported their role was to “protect” the shopkeepers from other criminals). According to anecdotes, though, the mafia plays a larger role with shops than with manufacturing firms of the sort we surveyed. Firms reporting disputes with trading partners were asked whether “an informal private agency specializing in such cases” aided in the resolution of the dispute. Only 5% of firms gave this response, though 48% of Russian firms and 26% of Ukrainian firms reporting disputes said they used such an agency. We create two variables that provide some control for the availability of private enforcement. When added to the basic regression reported in column 3, neither “other third party enforcement” (β=2.01, t=0.52) nor using “an informal private agency specializing in such cases” (β=-8.95, t=1.25) has a significant effect on credit. Their inclusion has almost no effect on the relational contracting variables or on the courts variable. The results shown on Table 3 are also robust to modifications in the sample criteria. All of the reported coefficients remain significant and of close to the same magnitude when relationships and/or firms started more than 10 years before the survey -- prior to the beginning of economic reforms -- are excluded from the sample. The results are similarly robust to limiting the sample to the three Eastern European countries. Including state-owned and export customers also has only modest effects. A regression with state enterprises and export customers is shown in Appendix B, along with separate regressions for each country. (Russia and Ukraine are combine because of the small sample sizes in these countries.) Courts are positively associated with credit in each of the country level regressions, though the effect is significant only in Slovakia.22 With the exception of social networks in Poland, the information network variables also have the right sign everywhere, and are significant most of the time.
