**<u>Table 8 credit constraint and fraud victimization: alternative explanations</u>**

|approached HHs|(1)|(2)|(3)|(4)<br>(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|
|Panel A: OLS|dum|my ofbein|gfraud vic|tim|lnVictim_|loss||
|Credit_cons|0.023<sup>***</sup>|0.022<sup>***</sup>|0.023<sup>***</sup>|0.023<sup>***</sup><br>0.186<sup>***</sup>|0.182<sup>***</sup>|0.185<sup>***</sup>|0.185<sup>***</sup>|
||(0.006)|(0.006)|(0.006)|(0.006)<br>(0.045)|(0.047)|(0.045)|(0.045)|
|Inf. Acq=2|-0.005<br>|||-0.069<br>||||
||(0.012)|||(0.091)||||
|Inf. Acq=3|-0.007|||-0.081||||
||(0.010)|||(0.083)||||
|Inf. Acq=4|-0.017<sup>*</sup>|||-0.137<sup>*</sup>||||
||(0.010)|||(0.077)||||
|Inf. Acq=5|<br>-0.013<br>(0.011)|||<br>-0.127<br>(0.085)||||
|Log(internet exp)||-0.000|||-0.001|||
|||(0.001)|||(0.010)|||
|Finan. Literacy|||0.014<sup>**</sup>|||0.073||
|(correct d)|||(0.007)|||(0.050)||
|Finan. Literacy||||0.007<sup>**</sup>|||0.033|
|(correct #)||||(0.003)|||(0.023)|
|county dummy|Yes|Yes|Yes|Yes<br>Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes<br>Yes|Yes|Yes|Yes|
||||Panel B:|2SLS (2<sup>nd</sup>stage)||||
|Credit_cons|0.318<sup>**</sup>|0.320<sup>**</sup>|0.326<sup>**</sup>|0.327<sup>**</sup><br>2.017<sup>*</sup>|2.034<sup>*</sup>|2.064<sup>*</sup>|2.070<sup>*</sup>|
||(0.137)|(0.137)|(0.139)|(0.139)<br>(1.063)|(1.049)|(1.079)|(1.082)|
|Inf. Acq=2|-0.003|||-0.064||||
||(0.013)|||(0.093)||||
|Inf. Acq=3|0.002|||-0.027||||
||(0.011)|||(0.082)||||
|Inf. Acq=4|-0.004|||-0.058||||
||(0.011)|||(0.084)||||
|Inf. Acq=5|-0.002|||-0.062||||
||(0.012)|||(0.093)||||
|Log(internet exp)||0.000<br>(0.002)|||0.001<br>(0.011)|||
|Finan. Literacy|||0.014<sup>*</sup>|||0.074||
|(correct D.)|||(0.008)|||(0.057)||
|Finan. Literacy||||0.006|||0.025|
|(correct #)||||(0.003)|||(0.027)|
|county dummy|Yes|Yes|Yes|Yes<br>Yes|Yes|Yes|Yes|
|<br>Ivs. Mills|Yes|Yes|Yes|Yes<br>Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes<br>Yes|Yes|Yes|Yes|
|Observations|15288|14744|15298|15298<br>15288|14744|15298|15298|
|Weak IV F. stat|14.24|13.91|14.16|14.15<br>14.24|13.91|14.16|14.15|
|Over Ident.|0.0002|0.131|8*10<sup>-5</sup>|2*10<sup>-4</sup><br>0.090|0.311|0.077|0.072|
|(Hansen J)||||||||
|-p-value|0.990|0.718|0.998|0.997<br>0.765|0.577|0.781|0.789|

Note: This table reports the results of credit constraint and fraud victimization after ruling out alternative explanations, we employ urban households that are approached by fraudsters as our sample, the models are estimated using OLS in Panel A, 2SLS in panel B. The explained variable for the first four columns is fraud victim dummy, while the explained variable for last four columns is economic loss of fraud. _Credit_cons_ is a dummy variable indicating whether household is credit constrained. _Lincome_ is the logarithm of the total sum of household income. _Schooling_ is the years of schooling for the household head. _Illness_ is a dummy variable to indicate if the household head suffers from at least one chronic disease. _Alone_ is a dummy variable to indicate whether the household head lives alone. And _Male_ equals to 1 if respondent is male. In column (1) and (5), we controlled household information acquisition dummy variables to the model, in column (2) and (6), we alternatively uses total amount of (in log) expenditure on internet, magazine, newspapers as proxy for information acquisition. In column (3) and (7), we add financial literacy dummy variable which equals to one if respondent answer questions correctly regarding financial calculation, while in column (4) and (8), we proxy financial literacy by using total number of correct answer. In Panel B, we also included inverse mills ratio in all specifications to rule out potential selection issue, which is discussed in selection bias section, and we also reported weak IV test statistics (F statistics) and over identification test statistics (Hansen J statistics and corresponding P-value). County fixed effects are controlled for in all specifications. Robust standard errors are clustered at city level and reported in parenthesis. _* p < 0.1, ** p < 0.05, *** p < 0.01._ 39