dataset.<sup>86</sup> We categorize companies as unionized or non-unionized. We define a unionized company as one where 30% or more of its workforce is unionized or covered by a collective bargaining agreement.<sup>87</sup> We are unable find unionization rates for all the firms in our sample. Of the 269 firms in our sample, we are able to find unionization rates for 163 firms. Of the 163 firms, we classify 69 as unionized.

Conceivably, there could be significant differences between unionized and nonunionized companies that might impact our results. Hence, we want to be sure that our results do not reflect differences between the two sets of firms, particularly regarding differences in credit risk. We compute summary statistics for various default measures and financial characteristics of unionized and non-unionized companies. These are the same measures used in Anginer and Yildizhan (2010) and discussed in detail in that paper. These results are summarized in Table 1. Aggregate volatility, _totvol_ , is measured using daily returns over the previous 12 months. o _-score_ is the Ohlson (1985) default score.<sup>88</sup> Idiosyncratic volatility, _idiovol_ , is calculated relative to the Fama-French 3- factor model.<sup>89</sup> _bm_ is the book-to-market ratio. _lme_ is the log of market capitalization. _nimtavg_ measures profitability and is calculated as a geometrically declining average of past values of the ratio of net income to the market value of total assets. Leverage is measured by _tlmta,_ which is the ratio of total liabilities to the market value of total assets.

86 As a robustness check, we also examined industry-level unionization rates, which are obtained from the Union Membership and Coverage Database (Unionstats). This database provides annual industry estimates at the 4-digit level. Results, however, do not substantially differ from the reported results employing firm-level unionization rates. Moreover, there is a 58% correlation between firm- and industrylevel unionization rates.

87 As a robustness check, we also employed alternative methodologies: (i) we used different percentages (20% and 50%) to define a unionized company; and (ii) we sorted companies into, alternatively, three buckets and five buckets, based upon unionization rates. Results, however, do not substantially differ from the reported results employing the 30% unionization definition. 88 O-score is defined as: _o_ -score = - 1.32 - 0.407 log( _SIZE_ ) + 6.03 _T LT A_ - 1.43 _W CT A_ + 0.076 _CLCA_ - 1.72 _OENEG_ - 2.37 _NIT A_ - 1.83 _FUT L_ + 0.285 _INT W O_ - 0.521 _CHIN_ where _SIZE_ is total assets divided by the consumer price index, _TLTA_ is the ratio of total liabilities to total assets, _CLCA_ is the ratio of current liabilities to current assets, _OENEG_ is a dummy variable set equal to one if total liabilities exceeds total assets and zero otherwise, _NITA_ is the ratio of net income to total assets, _FUTL_ is the ratio of funds from operations to total liabilities, _INTWO_ is a dummy variable equal to one if net income was negative for the past two years and zero otherwise, and _CHIN_ is change in net income over the last quarter: ( _NIt-NIt−1_ )/(| _NIt_ | + | _NIt−1_ |).

89 We regress daily stock returns from the previous calendar year on the Fama-French 3 factors: r _ti_ = _a i_ + _b MKTi MKT t_ + _bSMBi SMB t_ + _bHMLi HMLt_ + _eti_ . Idiosyncratic volatility is then calculated as the standard deviation of the residuals: var(<sup>_e_</sup> _ti_ )<sup>.</sup> 24