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Table 4 Impact of online food shopping on dietary diversity: ESC model estimations

From: Does online food shopping boost dietary diversity? Application of an endogenous switching model with a count outcome variable

Variables

Selection (coefficients)

Dietary diversity

Online food shoppers (coefficients)

Non-shoppers (coefficients)

Age

− 0.039 (0.007)***

− 0.003 (0.002)*

− 0.004 (0.001)***

Gender

− 0.181 (0.132)

− 0.006 (0.033)

− 0.010 (0.024)

Education

0.057 (0.020)***

0.011 (0.004)***

0.007 (0.003)**

Household size

− 0.028 (0.043)

− 0.026 (0.011)**

0.006 (0.007)

Land size

− 0.000 (0.001)

0.000 (0.000)*

− 0.001 (0.001)

Oven ownership

0.477 (0.129)***

0.094 (0.036)***

0.040 (0.025)

Heath knowledge

0.569 (0.158)***

0.071 (0.033)**

0.046 (0.030)

Motor ownership

− 0.046 (0.148)

− 0.013 (0.039)

− 0.002 (0.026)

Distance to credit

− 0.021 (0.013)

− 0.023 (0.008)***

− 0.002 (0.002)

Distance to market

− 0.009 (0.026)

0.037 (0.008)***

− 0.002 (0.004)

Shandong

− 0.068 (0.188)

− 0.075 (0.056)

− 0.047 (0.030)

Guangxi

0.005 (0.188)

− 0.236 (0.054)***

− 0.256 (0.034)***

Henan

0.180 (0.179)

− 0.329 (0.055)***

− 0.402 (0.037)***

IV

1.328 (0.258)***

  

Constant

− 0.644 (0.526)

2.178 (0.131)***

2.163 (0.093)***

\({\text{Ln}}\sigma _{1}\)

 

− 5.806 (1.942)***

 

\(\rho_{1}\)

 

− 0.898 (0.064)***

 

\({\text{Ln}}\sigma_{0}\)

  

− 6.559 (3.084)**

\(\rho_{0}\)

  

− 0.907 (0.104)**

Observations

947

171

776

  1. *p < 0.10, **p < 0.05, and ***p < 0.01. The reference province is Sichuan. Robust standard errors are presented in parentheses