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
4.2. The connectedness at different tail risk levels
4.3. Business linkages and tail risk spillovers between closely linked industries
4.4. Business linkages and tail risk spillovers between nonfinancial industries
Robustness Checks. In this subsection, I run a series of Fama-Macbeth (1973) regressions using different weight- ing schemes, samples and measures of asymmetric dependence at other exceedance levels to check the robustness of the findings in Table 2.8. I use Xxxxxxx (1997) four factor adjusted excess return as the dependent variable with the full set of controls in these regressions. The results are reported in Table 2.9. Regression (1) and (2) report the value-weighted regression results with full set of con- trols. The weighting variable is firm’s market capitalization at the beginning of each month. The regression coefficients now reflect the impacts for each dollar invested. Similar as the findings in the univariate portfolio sorts, the signs and statistical significance of LQP, UQP and DownAsy remain intact, but the economic magnitude of the impacts are reduced for LQP and DownAsy. Regression (3) and (4) report the regression results when the sample is restricted to NYSE stocks only. Since stocks listed on NYSE tend to be larger size stocks, the findings are similar to value-weighted results. Regression (5) and (6) report the regression results using non-overlapping yearly obser- vations. Using non-overlapping periods are less efficient statistically, but do not cause the returns to be autocorrelated, so the standard t-statistics are reported. The findings are almost the same as the results using overlapping periods, with only small changes to some coefficients.
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. Thus far, we have relied on bidder announcement returns to measure the effect of CEO home bias on the value of the firm. In this section, we discuss some potential econometric concerns with such an approach and provide a series of robustness checks which, by and large, confirm our main conclusions. Endogeneity is often a major concern in corporate finance studies. In our setting, causal interpretations of the coefficients of interest are only valid if, conditional on our other explanatory variables, the “CEO home bias’’ is randomly assigned. To illustrate this omitted variable problem, suppose that birth rates are higher in exactly the same target states that, for whatever reason, are associated with value destroying acquisitions. In this case, our results could be driven by this omitted variable. In order to address this problem, we try to control for the joint distribution of acquirers and targets using simulations.14 To illustrate this approach, consider first the subsample of cross-state acquisitions. For each cross-state acquisition in which the CEO birth state was the same as the target state, we randomly choose another acquisition with the same bidder and target state but with different CEO birth state. This produces a sample in which the likelihood of a CEO home bias is fifty percent. Next, we run a regression of bidder announcement returns on the CEO home bias dummy and the controls described in Table 3. To prevent our results from being driven by this particular choice of control acquisitions, we repeat this process 1,000 times and use the distribution of coefficients to draw our statistical inferences. Table 9 presents the results using both the states (Panel A) and distances (Panel B) as our measure of birth region proximity. For brevity, we only report the empirical distributions and empirical p-values for the Home Bias coefficients. Consistent with our previous results, we find a negative and significant impact of home bias, but only for distant mergers. For example, in Panel A, the home bias coefficients for in state mergers are not statistically significant, and the economic magnitude is roughly 1/7th of the cross state mergers. The results for distance-based home bias mergers in Panel B are similar. Another potential problem with the interpretation of the coefficients in Table 3 is that our approach relies on bidder announcement returns, whereas it is possible that the market incorrectly assesses the relative merits of home bias mergers. I...
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. 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. The previous analysis showed that the implementation of the NMW led to relative health improvements for low-paid workers. This section provides several robustness checks for the main findings. First, I estimate IV models by using an indicator for the policy change as an instrument for reported monthly income. Table 2.10 presents the treatment effects obtained from this specification, whereas the estimates correspond to changes following an increase of income by $1000. It is noticeable that the results are fairly consistent with the previous DD results. The findings provide additional evidence supporting the claim that income improves health and financial well-being of treated workers. In order to test for additional robustness of the main findings, I construct a second control (control group 2) which consists of workers earning a fixed salary who were financially unaffected by the policy change.24 This allows testing for whether the observed health declines were solely experienced by workers who are paid hourly wages. The estimates in Table 2.11 confirm the presence of significant health improvements when comparing outcomes of treated individuals with the new control group. The fact that the magnitude of the ordered logit coefficient and the marginal effects remain analogous to the baseline results indicates that the estimates are robust to the selection of the control group and provide additional evidence for the presence of a downward trend in health in the UK shortly after the reform. Similar finding are found when looking at changes in health conditions between the two groups. Next, I conduct a falsification test by comparing changes in health between the two control groups. Since neither group was financially affected by the reform, no health differences are expected to be found. The results in Table 2.12 confirm this expectation. Financially unaffected workers who are paid hourly are 0.04 and 0.01 percentage points more likely to report excellent health and very good health, respectively. Furthermore, I find no differential impacts on the likelihood of reporting several health conditions as a result of the policy. These findings strengthen the claim that the observed health improvements shown in section (6) are a result of increases in wages that followed the introduction of the NMW.
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
