**<u>Table 7 Credit Constraint and Fraud Victimization: Check on Selection Bias</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Panel A:|Hec<br>|kman (twostep<br>|): outcome|model||2SLS:<br>|2<sup>nd</sup>stage||
||Dummy o<br>Vi|f being fraud<br>ctim|lnVic|tim_loss|Dummy o<br>Vi|f being fraud<br>ctim|lnVic|tim_loss|
|||All|HHS||All HHs|Approached<br>HHs|All HHs|Approached<br>HHs|
|Credit_cons|0.021<sup>***</sup><br>(0.005)|0.023<sup>***</sup><br>(0.005)|0.168<sup>***</sup><br>(0.041)|0.181<sup>***</sup><br>(0.040)|0.191<sup>**</sup><br>(0.090)|0.323<sup>**</sup><br>(0.140)|1.179<sup>*</sup><br>(0.711)|2.050<sup>*</sup><br>(1.086)|
|Lincome||0.006<sup>**</sup>||0.040<sup>**</sup>|0.001|0.006<sup>*</sup>|0.013|0.044<sup>*</sup>|
|||(0.003)||(0.019)|(0.002)|(0.003)|(0.015)|(0.025)|
|Head age||-0.002<sup>*</sup><br>||-0.014<sup>**</sup><br>|-0.002<sup>**</sup><br>|-0.003<sup>**</sup><br>|-0.014<sup>**</sup><br>|-0.019<sup>**</sup><br>|
|||(0.001)||(0.007)|(0.001)|(0.001)|(0.007)|(0.009)|
|Head age sq||1.7*10<sup>-5***</sup><br>(9.3*10<sup>-6</sup>)||1.4*10<sup>-4 ***</sup><br>(6.7*10<sup>-5</sup>)|2.4*10<sup>-5***</sup><br>(1*10<sup>-5</sup>)|3.2*10<sup>-5***</sup><br>(1.4*10<sup>-5</sup>)|1.7*10<sup>-4 **</sup><br>(7.2*10<sup>-5</sup>)|2.4*10<sup>-4 ***</sup><br>(1 *10<sup>-4</sup>)|
|Illness||0.021<sup>***</sup>||0.147<sup>***</sup>|0.002|0.010|0.029|0.078|
|||(0.006)||(0.043)|(0.005)|(0.007)|(0.035)|(0.050)|
|Alone||0.000||0.010|0.001|-0.003|0.013|-0.004|
|||(0.011)||(0.078)|(0.008)|(0.013)|(0.063)|(0.100)|
|Male||-0.007||-0.055|-0.008<sup>*</sup>|-0.015<sup>**</sup>|-0.059<sup>*</sup>|-0.104<sup>**</sup>|
|||(0.005)||(0.035)|(0.004)|(0.006)|(0.033)|(0.047)|
|Schooling||0.004<sup>***</sup>||0.022<sup>**</sup>|0.002<sup>**</sup>|0.005<sup>***</sup>|0.014<sup>*</sup>|0.033<sup>**</sup>|
|||(0.001)||(0.008)|(0.001)|(0.002)|(0.008)|(0.013)|
|Inverse Mills|0.125<sup>***</sup>|0.266<sup>***</sup>|0.707<sup>***</sup>|1.546<sup>***</sup>|0.041|0.246<sup>***</sup>|0.176|1.400<sup>***</sup>|
||(0.019)|(0.055)|(0.146)|(0.413)|(0.036)|(0.071)|(0.265)|(0.493)|
|Panel B|Hec|kman (twostep|): selection|model||2SLS:|1<sup>st</sup>stage||
||<br>|<br>Dummy of Bei|<br>ng approach|<br>ed|Endo|genous variable:|<br>credit constrain|t dummy|
|E-Commerce|0.050<sup>***</sup>|0.050<sup>***</sup>|0.050<sup>***</sup>|0.050<sup>***</sup>|||||
|Coverage|(0.010)|(0.010)|(0.010)|(0.010)|||||
|<br>Branch|||||-0.178<sup>**</sup>|-0.140<sup>*</sup>|-0.178<sup>**</sup>|-0.140<sup>*</sup>|
|(within 5km)|||||(0.072)|(0.077)|(0.072)|(0.077)|
|<br>PPR|||||<br>-0.035<sup>***</sup>|<br>-0.033<sup>***</sup>|<br>-0.035<sup>***</sup>|<br>-0.033<sup>***</sup>|
||||||(0.006)|(0.007)|(0.006)|(0.007)|
|Other controls|Yes|Yes|Yes|Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|County dummy|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Observations|23737|23737|23737|23737|22877|15298|22877|15298|
|Weak IV (F|||||24.31|14.13|24.31|14.13|
|<br>Stat.)|||||||||
|Hansen J|||||0.005|0.002|0.019|0.059|
|-P-value|||||0.945|0.964|0.891|0.808|

Note: This table report the result of selection bias correction for our benchmark results, all urban residents are in the sample. We employ Heckman selection strategy to deal with selection bias issue, in column (1) – (4), we reports the results of Heckman selection model, panel A lists the results of outcome model, in panel B, the selection model is presented, the explained variable for column (1) and (2) is fraud victimization dummy, which equals to 1 if household have been a victim of fraud, while explained variable for column (3) and (4) is amount of economic loss subject to fraud scheme. In column (5) –(8), we employ 2SLS to consider the endogeneity of credit constraint and sample selection bias jointly, in panel A, we present the 2nd stage results of 2SLS, and reports 1st stage of 2SLS in panel B, the explained variable for column (5) and (6) is fraud victimization dummy, while explained variable for column (7) and (8) is amount of economic loss subject to fraud scheme, in column (5) and (7), all households are used in our analysis, and we restricted our analysis in column (6) and (8) to households that have been approached by fraudsters, we also report weak IV test statistics (F statistics) and over identification test statistics (Hansen J statistics and corresponding P-value). _Credit_cons_ is a dummy variable indicating whether household is credit constrained. Being credit constrained is defined as unable to borrow money for any of the following reasons: (a) need loan, but rejected by bank or other credit institutions; (b) unable to obtain sufficient loan from bank or other credit institutions; (c) didn’t apply for loan because no collateral/complex paperwork/outstanding loan/maybe rejected. _Lincome_ is the logarithm of the total sum of household income, which consists of labor income, business income, agriculture income, investment income, transfer income and other 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, such as hypertension, hyperlipidemia, diabetes, stroke, heart disease, mental issue, Alzheimer's disease, and Parkinson. _Alone_ is a dummy variable to indicate whether the household head lives alone. And _Male_ equals to 1 if respondent is male. E-commerce coverage is our exogenous variation to predict the probability of being approached by fraudsters, it is measured by the average amount of online shopping expenditure last month at community level. 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._ 38