Table 8.

Controlling endogeneity using instrumental variables approach

VariablesFirst stageSecond stage
LP_QUALITYRP_QUALITY(1)(2)(3)(4)
NCSKEWDUVOLCOUNTCRASH
AVE_ LP_QUALITY1.312*** (0.437)
AVE_ RP_QUALITY0.127*** (0.041)
PRED_LP_QUALITY0.886*** (0.306)0.783** (0.356)1.892*** (0.610)4.389*** (1.463)
PRED_RP_QUALITY0.678** (0.295)0.599** (0.285)1.447** (0.629)3.356*** (1.119)
C and control variablesYesYesYesYesYesYes
Year FEYesYesYesYesYesYes
Industry FENoNoNoNoNoNo
N2,8982,8982,8982,8982,8982,898
R20.2340.2410.2180.0350.080
Adj_R20.2190.2260.2020.0150.062
Pseudo R20.084
F15.460***16.110***14.130***1.829***4.423***
LR Chi2274.260***
Note(s):

This table presents the results of the two-stage least squares (2SLS) analysis using industry-average partner quality (AVE_LP_QUALITY and AVE_RP_QUALITY) as instruments. The first-stage estimates confirm strong instrument relevance, while second-stage coefficients for PRED_LP_QUALITY and PRED_RP_QUALITY remain negative and significant, indicating robustness to endogeneity. All models include control variables and year fixed effects. Robust standard errors are in parentheses. Statistically significant coefficients are shown in italics for ease of reference. *, ** and *** denote statistical significance at the 10, 5 and 1% levels, respectively

Source(s): Authors’ own work

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