effects, the selection on unobservables would have to be about on average 3.5 times greater than selection on observables.

Assessing the likelihood that estimates are biased because of unobservable characteristics allow us to conclude that it is less likely that unobservable characteristics fully drive the estimated <u>Table 4: Robustness</u> - <u>Selection on observables vs. unobservables</u>

|Controls in the restricted<br>model|Control in the unrestricted<br>model|Trust|in public in|stitutions|Trus|t in the neighbourhood|
|---|---|---|---|---|---|---|
||||**Panel **|**A - OLS fo**|**r the m**|**ain equation**|
|No individual level control variables|Full set of controls from equation (12)||-10.03|||-2.73|
|Age, Sex, Wage earner|Full set of controls from equation (12)||-2.88|||-2.57|
||||**Panel B **|**- OLS for **|**the frs**|**t step equation**|
|||(1)|(2)|(3)|(4)|(5)<br>(6)|
|No individual level control variables|Full set of controls from equation (12)|-1.86|-3.28|8.14|3.54|- 96.47<br>2.89|
|Age, Sex, Wage earner|Full set of controls from equation (12)|1.93|-3.64|8.42|4.61|-271<br>3.41|
|No individual level control variables<br>Age, Sex, Wage earner<br>ˆ<br>|Full set of controls from equation (12)<br>and instruments included individually<br>|-1.11<br>-1.12|9.66<br>9.66|25.82<br>22.69|7.73<br>9.10|1.68<br>24.90<br>1.92<br>29.23|
|Notes. The table reports the ratios<br>_β_<br>ˆ<br>_β_<sup>_r_</sup>_−_<br>the equation of interest while Panel B re<br>observables. The coefcient <sup>ˆ</sup>_β_<sup>_r_ </sup>is from <br>model, which includes a full set of contr|<sup>ˆ</sup><br>_β_<sup>_f_ of the coefcients for trust in public i</sup><br>ports the ratios for the frst step equations<br>the restricted model, which include a limi<br>ol variables.|<sup>nstituti</sup><br> as addi<br>ted set|<sup>ons and the</sup><br>tional sensi<br>of control v|<sup>neighborho</sup><br>tivity check<br>ariables; <sup>ˆ</sup>_β_<sup>_f_ </sup>|<sup>od. Pan</sup><br>s on sele<br> is obtai|<sup>el A presents of ratios for</sup><br>ction on unobservables vs.<br>ned from the unrestricted|

# **4.2 Alternative estimation method**

Although the main findings employ linear estimation model following the literature on tax morale, such a model might not be appropriate as the dependent variable is ranking information between 1 and 10. In this section, I conduct a sensitivity analysis employing a non-linear model which might be more relevant because the ten-category dependent variable is neither continuous nor normally distributed. Hence, the first set of robustness checks consists in using the two stages conditional maximum likelihood (2SCML) procedure, which has the advantage of testing and controlling for potential endogeneity issues.

The procedure involving a two-step estimation was initially developed by Alvarez (1994) and Rivers and Vuong (1988). The first step implies using the ordinary least squares to estimate the reduced form equation of the endogenous variables and predicts the residuals. Further, the 2SCML methodology consists in including the residuals from the first step estimation in the ordered probit regression as additional control variables. The underlying intuition of the 27