# Research on the Efficiency Measurement and Spatial Spillover Effect of China’s Regional E-Commerce Poverty Alleviation from the Perspective of Sustainable Development

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. Research on E-Commerce Development in Urban and Rural Areas

#### 2.2. Research on E-Commerce Poverty Alleviation

#### 2.3. Research on the Spatial Effect of E-Commerce Poverty Alleviation

## 3. Methods

#### 3.1. Super-Efficiency DEA Evaluation Method

- (1)
- Model construction

_{1}, x

_{2},…, x

_{i},…, x

_{m}) in an economic activity, where xi is the i-th input, the output vector is Y = (y

_{1}, y

_{2},…, y

_{r},..., y

_{s}), where y

_{r}represents the r-th output and (x

_{j}, y

_{j}) represents the input vector and output vector of the j-th decision unit, and (X

_{0}, Y

_{0}) is the corresponding indicator of the decision unit. Therefore, (X, Y) is the economic activity of this decision-making unit. n decision-making units are derived from an input matrix of an order of n × m, and the corresponding output set can constitute an n × s order production out of the matrix. Chaens et al. [30] proposed a CCR model for data envelopment analysis to evaluate the overall effectiveness of the technology.

- (2)
- Selection of input and output indicators

#### 3.2. Spatial Measurement

#### 3.2.1. Spatial Correlation Analysis

#### 3.2.2. Panel Spatial Econometric Model

- (1)
- Spatial lag model (SLM)

- (2)
- Spatial Error Model

- (3)
- Spatial Durbin model (SDM)

_{ij}(N × N) is a geospatial weight matrix, ij represents a spatial unit, t represents the year, Y

_{it}represents the N × 1 vector, X

_{it}represents the N × K matrix, N represents the number of spatial observation points, and K is the explanation. ${\eta}_{i}$ and ${\alpha}_{t}$ are the spatially fixed and time-fixed variables, respectively, ε

_{it}is a random disturbance term, δ represents the degree of change in the surrounding area caused by the change of the explained variable in a certain area, β

_{1}represents the degree of change of the explained variable in the same area caused by the change of the explanatory variable in a certain area, and β

_{2}represents the degree of change of the explanatory variable in a certain area to the explained variable in the surrounding area. If β

_{2}= 0, the SLM model was selected. If β

_{2}+ δβ

_{1}= 0, the SEM model was selected. In this study, the parameters of the maximum likelihood estimation model were used, and the Wald test and LR test were used to verify whether the spatial panel Durbin model can be divided into a spatial lag model and a spatial error model. Direct and indirect effects were used to explain the results of the spatial econometric model estimates. Direct effects refer to the average variation in the province’s dependent variables caused by changes in the province’s independent variables. Indirect effects refer to the average changes in the neighborhood dependent variables caused by changes in the independent variables in the region.

#### 3.2.3. Spatial Spillover Effect Decomposition

_{r}(W)

_{ij}evaluates the impact of the r-th explanatory variable in the region j on the explained variable in the region i, while S

_{r}(W)

_{ii}evaluates the impact of the rth explanatory variable in region i on the local explained variable impact. If j and r are not equal, then Y

_{i}to X

_{jr}is usually not 0, and the i-th and j-th elements contained in the S

_{r}(W) matrix will affect it. At the same time, the partial derivative will usually not be βr. In addition to the obvious correlation between the regional explanatory and explained variables in this region, there is also an internal correlation with the explained variables in the surrounding regions. The theory proposed by Pace and Lesage considers that these variables are the direct effect and the indirect effect, and the sum of the two is the total effect. In short, direct effects represent the intra-regional spillover effects, while indirect effects represent the inter-regional spillover effects.

## 4. Results

#### 4.1. Results of Poverty Alleviation Efficiency of E-Commerce in Various Regions of China

#### 4.2. Spatial Correlation Analysis Results

#### 4.3. Results of Spatial Spillover Effect Decomposition of China’s E-Commerce Poverty Alleviation Efficiency

#### 4.3.1. Model Checking

- (1)
- Model selection test

- (2)
- Model Form Test

#### 4.3.2. Spatial Durbin Model Estimation Results

- (1)
- Indicator selection

- (2)
- Model construction

_{i,t}is the disturbance term, and ρ represents the spatial lag coefficient. The model examines the “spatial effect” of each influencing factor on the regional e-commerce poverty alleviation efficiency by introducing the spatial lag term $W{\mu}_{-i,t}$ of all explanatory variables. If the coefficients of all spatial lag variables are not significant, then there is no “spatial effect”. Conversely, if the coefficients of one or more spatial lag variables are significant, then the “spatial effect” is not negligible. The spatial lag variable coefficient and significance level reflect the specific direction and effect of the “spatial effect” of each influencing factor.

- (3)
- Estimation results

^{2}= 0.9877 shows that the explanatory variables explain the vast majority of the explained variables. The spatial autoregressive coefficient in Table 5 is 0.1765 and passed the significance test, which means that the e-commerce poverty alleviation efficiency has a positive spillover effect on its neighborhood, that is, for every 1% increase in a region’s e-commerce poverty alleviation efficiency, there will be a resultant 0.1765% increase in poverty alleviation efficiency, meaning that there is a significant spatial effect of the efficiency of China’s e-commerce poverty alleviation. It also can be seen that at least four of the six variables, after the inclusion of spatial effects, are significant, indicating that the model fits more scientifically and accurately after the inclusion of spatial effects.

- (4)
- Spatial spillover effect decomposition results of e-commerce poverty alleviation efficiency

## 5. Conclusions and Applications

#### 5.1. Conclusions

- (1)
- From the perspective of space, the efficiency of e-commerce poverty alleviation varies greatly among regions, with Tianjin, Beijing, and Shanghai being the most efficient regions. In general, the efficiency of e-commerce poverty alleviation is the highest in the eastern region of China and the lowest in the western region of China.
- (2)
- There is a significant spatial autocorrelation effect in e-commerce poverty alleviation efficiency. The Moran’s I index values are all greater than 0.5; that is, e-commerce poverty alleviation efficiency among neighboring regions is high. Mutual influences have positive spatial correlations, while economic factors contribute to this spatial correlation.
- (3)
- From the regression results of influencing factors, the influence of the level of communication facilities is the most significant, and the influence coefficient reaches 0.331, which is the basic premise for the rapid development of e-commerce. At the same time, the level of transportation infrastructure is also at the core of promoting the rapid development of e-commerce. The elasticity coefficient factor had values up to 0.112. If the other factors remain fixed, the impact of the financial environment on the efficiency of poverty alleviation is minimal, but it is worth noting that it has a positive impact. Only by starting research in the medium and long terms can it be clear that these factors have a positive effect on poverty alleviation efficiency.
- (4)
- From the regression results of the influencing factors, the influence of the level of communication facilities is the most significant, and the influence coefficient reaches 0.331, which is the basic premise for the rapid development of e-commerce. At the same time, the level of transportation infrastructure is also at the core of promoting the rapid development of e-commerce. The elasticity coefficient factor has values up to 0.112. If the other factors remain fixed, the impact of the financial environment on the efficiency of poverty alleviation is minimal, but it is worth noting that it has a positive impact.
- (5)
- From the decomposition of the spillover effects, the three most significant factors from the perspective of direct effects are the level of communication facilities, transportation infrastructure, and the level of human capital. It can be seen that the levels of communication facilities, transportation infrastructure, and human capital are major factors affecting the future development of e-commerce and are the basic premise for its rapid development. It is worth noting that the direct effect of financial conditions is the least significant factor.

#### 5.2. Suggestions

#### 5.3. Limitations and Prospects

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Region | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Mean | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

East | Beijing | 2.012 | 2.015 | 2.018 | 2.112 | 2.132 | 2.146 | 2.157 | 2.188 | 2.167 | 2.233 | 2.256 | 2.269 | 2.142 |

Tianjin | 1.793 | 1.799 | 1.813 | 1.823 | 1.848 | 1.986 | 1.995 | 2.018 | 2.117 | 2.165 | 2.167 | 2.178 | 1.975 | |

Hebei | 0.489 | 0.493 | 0.499 | 0.503 | 0.501 | 0.511 | 0.516 | 0.522 | 0.518 | 0.533 | 0.536 | 0.522 | 0.512 | |

Liaoning | 0.401 | 0.413 | 0.424 | 0.436 | 0.441 | 0.447 | 0.449 | 0.458 | 0.467 | 0.472 | 0.478 | 0.385 | 0.439 | |

Shanghai | 1.986 | 1.994 | 1.999 | 2.012 | 2.018 | 2.119 | 2.134 | 2.144 | 2.156 | 2.167 | 2.189 | 2.199 | 2.093 | |

Jiangsu | 0.811 | 0.815 | 0.819 | 0.826 | 0.838 | 0.849 | 0.864 | 0.877 | 0.889 | 0.895 | 0.915 | 0.933 | 0.861 | |

Zhejiang | 0.798 | 0.803 | 0.816 | 0.828 | 0.837 | 0.854 | 0.869 | 0.889 | 0.898 | 0.901 | 0.905 | 0.916 | 0.860 | |

Fujian | 0.765 | 0.789 | 0.797 | 0.805 | 0.811 | 0.818 | 0.829 | 0.833 | 0.852 | 0.866 | 0.883 | 0.898 | 0.829 | |

Sahndong | 0.465 | 0.469 | 0.471 | 0.477 | 0.487 | 0.495 | 0.501 | 0.513 | 0.522 | 0.534 | 0.544 | 0.556 | 0.503 | |

Guangdong | 0.891 | 0.899 | 0.911 | 0.927 | 0.936 | 0.949 | 0.959 | 0.969 | 0.989 | 0.995 | 0.999 | 1.101 | 0.960 | |

Hainan | 0.613 | 0.615 | 0.618 | 0.624 | 0.627 | 0.635 | 0.647 | 0.688 | 0.701 | 0.733 | 0.762 | 0.782 | 0.670 | |

Central | Shanxi | 0.412 | 0.415 | 0.425 | 0.433 | 0.437 | 0.453 | 0.476 | 0.488 | 0.498 | 0.517 | 0.542 | 0.573 | 0.472 |

Jilin | 0.501 | 0.506 | 0.511 | 0.514 | 0.516 | 0.515 | 0.517 | 0.521 | 0.524 | 0.528 | 0.533 | 0.542 | 0.519 | |

Heilong jiang | 0.572 | 0.573 | 0.582 | 0.587 | 0.586 | 0.590 | 0.592 | 0.598 | 0.603 | 0.608 | 0.612 | 0.619 | 0.594 | |

Anhui | 0.611 | 0.617 | 0.623 | 0.632 | 0.639 | 0.648 | 0.655 | 0.667 | 0.678 | 0.682 | 0.687 | 0.693 | 0.653 | |

Jinagxi | 0.711 | 0.719 | 0.714 | 0.721 | 0.722 | 0.719 | 0.725 | 0.729 | 0.737 | 0.753 | 0.765 | 0.768 | 0.732 | |

Henan | 0.501 | 0.513 | 0.515 | 0.517 | 0.519 | 0.527 | 0.524 | 0.529 | 0.535 | 0.538 | 0.544 | 0.553 | 0.526 | |

Hubei | 0.476 | 0.481 | 0.488 | 0.491 | 0.494 | 0.498 | 0.503 | 0.508 | 0.514 | 0.519 | 0.517 | 0.525 | 0.501 | |

Hunan | 0.651 | 0.655 | 0.664 | 0.674 | 0.672 | 0.683 | 0.689 | 0.691 | 0.693 | 0.698 | 0.707 | 0.718 | 0.683 | |

West | Neimenggu | 0.578 | 0.581 | 0.589 | 0.584 | 0.588 | 0.593 | 0.596 | 0.603 | 0.608 | 0.609 | 0.612 | 0.616 | 0.596 |

Guangxi | 0.603 | 0.609 | 0.611 | 0.614 | 0.619 | 0.617 | 0.618 | 0.619 | 0.624 | 0.627 | 0.626 | 0.635 | 0.619 | |

Chonhqing | 0.503 | 0.505 | 0.511 | 0.513 | 0.519 | 0.518 | 0.522 | 0.529 | 0.527 | 0.535 | 0.538 | 0.544 | 0.522 | |

Sichaun | 0.515 | 0.518 | 0.523 | 0.528 | 0.531 | 0.536 | 0.534 | 0.541 | 0.547 | 0.552 | 0.558 | 0.566 | 0.537 | |

Guizhou | 0.316 | 0.319 | 0.326 | 0.336 | 0.341 | 0.348 | 0.364 | 0.373 | 0.383 | 0.389 | 0.394 | 0.399 | 0.357 | |

Yunnan | 0.502 | 0.505 | 0.509 | 0.513 | 0.519 | 0.526 | 0.527 | 0.534 | 0.536 | 0.535 | 0.538 | 0.547 | 0.524 | |

Shanxi | 0.442 | 0.445 | 0.451 | 0.456 | 0.464 | 0.476 | 0.481 | 0.487 | 0.495 | 0.498 | 0.501 | 0.507 | 0.475 | |

Gansu | 0.209 | 0.213 | 0.215 | 0.216 | 0.223 | 0.226 | 0.231 | 0.237 | 0.239 | 0.241 | 0.352 | 0.254 | 0.238 | |

Qinghai | 0.363 | 0.365 | 0.369 | 0.373 | 0.379 | 0.386 | 0.388 | 0.389 | 0.391 | 0.393 | 0.392 | 0.398 | 0.382 | |

Ningxia | 0.423 | 0.427 | 0.435 | 0.436 | 0.438 | 0.445 | 0.463 | 0.477 | 0.489 | 0.498 | 0.534 | 0.565 | 0.469 | |

Xinjiang | 0.341 | 0.345 | 0.349 | 0.354 | 0.361 | 0.369 | 0.375 | 0.381 | 0.388 | 0.387 | 0.394 | 0.397 | 0.370 |

Year | Poverty Alleviation Efficiency | Year | Poverty Alleviation Efficiency | ||
---|---|---|---|---|---|

Moran | p-Value | Moran | p-Value | ||

2010 | 0.5034 | 0.0003 | 2016 | 0.5233 | 0.0004 |

2011 | 0.5512 | 0.0011 | 2017 | 0.5245 | 0.0013 |

2012 | 0.5034 | 0.0009 | 2018 | 0.5278 | 0.0004 |

2013 | 0.5316 | 0.0012 | 2019 | 0.5363 | 0.0004 |

2014 | 0.5019 | 0.0014 | 2020 | 0.5378 | 0.0009 |

2015 | 0.5176 | 0.0011 | 2021 | 0.5344 | 0.0016 |

Test Method | Statistical Value | p-Value |
---|---|---|

Wald spatial lag | 77.091 | 0.001 |

Wald spatial error | 75.223 | 0.001 |

LR spatial lag | 66.129 | 0.002 |

LR spatial error | 63.339 | 0.001 |

Hausman Test | Chi-Sq. Statistic | Chi-Sq. d.f. | Prob. |
---|---|---|---|

Cross-section random | 21.31 | 10 | 0.022 |

Variable | Regression Coefficient | Variable | Regression Coefficient |
---|---|---|---|

LnJT | 0.112 *** | W × LnJT | 0.145 *** |

LnTX | 0.331 *** | W × LnTX | 0.361 *** |

LnCZ | 0.034 *** | W × LnCZ | 0.112 *** |

LnJJ | 0.219 * | W × LnJJ | 0.204 ** |

LnRL | 0.003 *** | W × LnRL | 0.012 *** |

LnJR | 0.009 | W × LnJR | 0.013 * |

W*dep.var | 0.1765 *** | ||

R-squared | 0.9877 | ||

log-likelihood | 461.092 |

Variable | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|

LnJT | 0.2312 *** | 0.1113 *** | 0.3425 *** |

lnTX | 0.2933 *** | 0.0528 *** | 0.3461 *** |

lnCZ | 0.0431 *** | 0.0127 | 0.0558 *** |

lnJJ | 0.0899 *** | 0.2134 *** | 0.3033 * |

lnRL | 0.1198 *** | −0.0132 | 0.1066 *** |

LnJR | 0.2312 | 0.0146 | 0.2458 * |

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**MDPI and ACS Style**

Xu, G.; Zhao, T.; Wang, R.
Research on the Efficiency Measurement and Spatial Spillover Effect of China’s Regional E-Commerce Poverty Alleviation from the Perspective of Sustainable Development. *Sustainability* **2022**, *14*, 8456.
https://doi.org/10.3390/su14148456

**AMA Style**

Xu G, Zhao T, Wang R.
Research on the Efficiency Measurement and Spatial Spillover Effect of China’s Regional E-Commerce Poverty Alleviation from the Perspective of Sustainable Development. *Sustainability*. 2022; 14(14):8456.
https://doi.org/10.3390/su14148456

**Chicago/Turabian Style**

Xu, Guoyin, Tong Zhao, and Rong Wang.
2022. "Research on the Efficiency Measurement and Spatial Spillover Effect of China’s Regional E-Commerce Poverty Alleviation from the Perspective of Sustainable Development" *Sustainability* 14, no. 14: 8456.
https://doi.org/10.3390/su14148456