**<u>Table 5 Credit Constraint and Fraud Victimization: 2SLS estimates</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Panel A: 2<sup>nd</sup>stage|<br>d|<br>ummy of bein|<br>g a fraud vic|<br>tim||<br>lnVicti|<br>m_loss||
||All|HHs|Approa|ched HHs|All|HHs|Approa|ched HHs|
|Creditcons|0.099<sup>*</sup>|0.191<sup>**</sup>|0.225<sup>***</sup>|0.363<sup>**</sup>|0.479|1.177<sup>*</sup>|1.277<sup>**</sup>|2.299<sup>**</sup>|
|_|(0.057)|(0.090)|(0.077)|(0.142)|(0.464)|(0.713)|(0.627)|(1.101)|
|Lincome||<br>-0.000||<br>-0.003||<br>0.005||<br>-0.006|
|||(0.001)||(0.002)||(0.009)||(0.016)|
|Head age||<br>-0.002<sup>***</sup><br>(0.001)||<br>-0.004<sup>***</sup><br>(0.001)||<br>-0.015<sup>**</sup><br>(0.006)||<br>-0.029<sup>***</sup><br>(0.009)|
|Head age sq||<br>2.7*10<sup>-5***</sup>||<br>5.1*10<sup>-5***</sup>||<br>1.8*10<sup>-4**</sup>||<br>3.5*10<sup>-4***</sup>|
|||(8*10<sup>-6</sup>)||(1.3*10<sup>-5</sup>)||(6.5*10<sup>-5</sup>)||(1*10<sup>-4</sup>)|
|Illness||<br>0.000||<br>-0.005||<br>0.022||<br>-0.004|
|||(0.004)||(0.006)||(0.030)||(0.043)|
|Alone||<br>0.003||<br>0.008||<br>0.022||<br>0.058|
|||(0.008)||(0.013)||(0.059)||(0.095)|
|Male||<br>-0.009<sup>*</sup>||<br>-0.013<sup>**</sup>||<br>-0.062<sup>*</sup>||<br>-0.094<sup>**</sup>|
|||(0.004)||(0.006)||(0.033)||(0.048)|
|Schooling||<br>0.001<sup>*</sup><br>(0.001)||<br>0.001<br>(0.001)||<br>0.010<br>(0.006)||<br>0.009<br>(0.011)|
|Panel B: 1<sup>st</sup>stage|||End|ogenous variabl|e: credit cons|traint|||
|<br>Branch|-0.298<sup>***</sup>|-0.199<sup>***</sup>|<br>-0.274<sup>***</sup>|<br>-0.154<sup>**</sup>|<br>-0.298<sup>***</sup>|<br>-0.199<sup>***</sup>|-0.274<sup>***</sup>|-0.154<sup>**</sup>|
|(within 5km)<br>PPR|(0.082)<br>-0.056<sup>***</sup><br>(0.006)|(0.073)<br>-0.036<sup>***</sup><br>(0.006)|(0.083)<br>-0.052<sup>***</sup><br>(0.007)|(0.076)<br>-0.034<sup>***</sup><br>(0.007)|(0.082)<br>-0.056<sup>***</sup><br>(0.006)|(0.073)<br>-0.036<sup>***</sup><br>(0.006)|(0.083)<br>-0.052<sup>***</sup><br>(0.007)|(0.076)<br>-0.034<sup>***</sup><br>(0.007)|
|County dummy|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|<br>Observations|24701|23587|16439|15818|24701|23587|16439|15818|
|Weak IV (F Stat.)|70.37|26.55|46.33|15.24|70.37|26.55|46.33|15.24|
|<br>Over Ident. (Hansen J)|0.166|0.00001|0.061|0.043|0.076|0.023|0.006|0.177|
|<br>Over Ident. (P-Value)|0.684|0.997|0.805|0.835|0.783|0.880|0.939|0.674|

Note: This table reports the 2SLS estimates of the effect of credit constraints on fraud victimization, and the sample of urban households is used. The first instrumental variable is property privatization reform experience dummy (PPR) which equals to 1 if household members working in various level of state owned enterprises or government before the reform, the second instrument variable is bank density, which is measured by the total number of bank institutions (in 1,000) within 5 kilometers of households’ current residence (Branch). Panel A shows the 2<sup>nd</sup> stage while Panel B presents the 1<sup>st</sup> stage results. The dependent variable is a dummy variable indicating whether the household is a fraud victim in columns (1) – (4), while for columns (5) – (8), it is the total amount of economic losses subject to fraud scheme. Full sample including all households is used in columns (1), (2), (5), and (6), and restricted sample compromised of approached households is used in columns (3), (4), (7), and (8). 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. County fixed effects are controlled for in all specifications. Robust standard errors are clustered at city level and reported in parenthesis.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

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