# Air Pollution and Mobility, What Carries COVID-19?

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. The Mexico City Metropolitan Area

#### 2.1. Data

#### 2.2. Restrictions Due to COVID-19

## 3. Econometric Methodology

## 4. Testing for Stationarity and Cross-Sectional Dependence

## 5. Main Estimation Results

## 6. Future Extensions and Limitations

#### 6.1. Future Extensions

#### 6.2. Limitations

- Our data only include the first wave of the pandemic in the MCMA. However, a year after the first wave, as many other countries in the world, Mexico has faced second and third waves. Unfortunately, the public RAMA database has been moved to another repository, and it could not be consulted by the time this paper was written.
- Models (1) and (2) do not cover the case where the panel includes a lagged dependent variable limiting a deeper dynamic analysis. In this respect, possible improvements can be considering models proposed by Chudik and Pesaran (2015), and Moon and Weidner (2017).

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**Plot of the annual average of public transport mobility on weekdays (

**top**) and on weekend (

**bottom**) in the MCMA in 2019. Subways and BRT lines are indicated in blue and green colors, respectively. The wider the color lines, the more public mobility. Source: Compiled by authors using data from INEGI.

## Notes

1 | The link is tomtom.com/engb/traffic-index/ranking/ (accessed on 1 November 2020). |

2 | Available online at datos.cdmx.gob.mx (accessed on 20 September 2020). |

3 | Mulcollinearity might easily occur in a model in which cross-sectional dependence is approximated by cross-sectional averages such as in the Pesaran’s approach. Some software codes check automatically for collinearity in data and chose an efficient algorithm to invert the data matrices. An usual method is by the generalized inverse as in Pesaran (2006). |

4 | Another difference between both approaches is the assumption regarding slope parameters. The MGE method assumes heterogeneous parameters, while IFE works with homogeneous slopes, i.e., ${\alpha}_{i}=\alpha $, ${\beta}_{1,i}={\beta}_{1}$, ${\beta}_{2,i}={\beta}_{2}$, and ${\beta}_{3,i}={\beta}_{3}$ for all i. |

5 | The analysis reports that all variables involved are stationary. Results are not reported here for the sake of brevity, but available upon request. |

6 | Pesaran’s CIPS test performances well in small-sample in the presence of a single unobserved common factor, however, if the number of common factors is higher, the test exhibits size distortions. In this respect, Pesaran et al. (2013) propose an extension to cover a multi-factor error structure. We employ the CIPS and CSB tests proposed for $r=1,2,3$ number of factors and we find the same conclusions. Tables are available upon request. |

7 | |

8 | |

9 | The plots for actual and fitted series, as well as that of the residual are available upon request |

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**Figure 1.**MCMA’s population (in thousands) in 2019. Source: Compiled by the authors using official data from state, municipal, and locality boundary files from the Mexican National Institute of Statistics, Geography and Informatics (INEGI).

**Figure 2.**Plots of annual average of contaminants PM10 (panel

**a**), and PM2.5 (panel

**b**) in ppb in the MCMA in 2019. In each panel, the darker the region, the more polluted it is. Source: Compiled by authors using data by Mexico City’s Automatic Air Quality Monitoring Network (RAMA).

**Figure 4.**Air pollution indexes in the MCMA. The figure shows the actual levels (solid), and the fitted values to a test for change in level (dashed). JNSD is shown in the shaded area.

**Figure 5.**Common factor and Google mobility indexes (scaled) for the specification considering the number of deaths due to COVID-19 and an incubation period of 14 days. JNSD is shown in the shaded area.

**Table 1.**Fisher combination (MW) and Cross-sectionally augmented IPS (CIPS) tests for panel unit roots.

MW. Maddala and Wu (1999) | CIPS. Pesaran (2007) | |||||||
---|---|---|---|---|---|---|---|---|

Incubation Period | ||||||||

15D | 7D | 15D | 7D | 15D | 7D | 15D | 7D | |

Without Trend | With Trend | Without Trend | With Trend | |||||

Death toll | 50.778 *** | 46.362 *** | 41.466 *** | 40.858 *** | −5.027 *** | −5.461 *** | −5.597 *** | −5.979 *** |

Cases | 64.173 *** | 48.814 *** | 130.05 *** | 127.100 *** | −3.533 *** | −3.744 *** | −4.511 *** | −4.681 *** |

PM10 | 44.345 *** | 47.272 *** | 57.327 *** | 69.405 *** | −6.164 *** | −6.929 *** | −5.173 *** | −5.918 *** |

PM2.5 | 41.298 *** | 44.364 *** | 36.233 *** | 41.658 *** | −6.295 *** | −6.878 *** | −5.503 *** | −6.100 *** |

$\widehat{\mathit{\alpha}}$ | Std. Err. | [95% Conf. Interval] | ||
---|---|---|---|---|

Inc. Period. 15 D | ||||

Death toll | 0.999 | 0.067 | 0.867 | 1.131 |

Cases | 1.001 | 0.038 | 0.925 | 1.078 |

PM10 | 1.001 | 0.041 | 0.919 | 1.083 |

PM2.5 | 1.001 | 0.049 | 0.904 | 1.098 |

Inc. Period. 7 D | ||||

Death toll | 1.001 | 0.071 | 0.860 | 1.140 |

Cases | 1.001 | 0.046 | 0.910 | 1.093 |

PM10 | 1.002 | 0.047 | 0.909 | 1.094 |

PM2.5 | 1.002 | 0.076 | 0.851 | 1.151 |

Dep. Variable | Daily Death Toll | |||||
---|---|---|---|---|---|---|

Incubation | 14 Days | 7 Days | ||||

Model | MG | CCEMG | IFE | MG | CCEMG | IFE |

PM10 | −0.149 *** | 0.017 | −0.044 | −0.190 *** | −0.001 | −0.021 |

(0.031) | (0.030) | (0.024) | (0.045) | (0.035) | (0.022) | |

PM2.5 | 0.240 *** | −0.016 | 0.063 | 0.0261 *** | 0.054 | 0.031 |

(0.051) | (0.029) | (0.039) | (0.071) | (0.034) | (0.024) | |

constant | 4.916 *** | −0.090 | 4.840 *** | 5.745 *** | 0.152 | 3.600 *** |

(1.316) | (0.811) | (0.070) | (1.493) | (0.555) | (0.051) | |

${R}^{2}$ | 0.547 | 0.803 | 0.847 | 0.538 | 0.800 | 0.925 |

$|\overline{\rho}|$ | 0.239 | 0.083 | 0.064 | 0.636 | 0.079 | −0.061 |

CIPS | −4.802 *** | −5.341 *** | −5.451 *** | −3.503 *** | −4.154 *** | −4.554 *** |

${J}_{KSS}$ | 55.36 *** | 62.29 *** | ||||

Dep. Variable | Daily number of cases | |||||

Incubation | 14 days | 7 days | ||||

Model | MG | CCEMG | IFE | MG | CCEMG | IFE |

PM10 | −1.139 *** | −0.034 | −0.019 | −1.287 *** | 0.027 | −0.034 |

(0.216) | (0.198) | (0.107) | (0.295) | (0.101) | (0.103) | |

PM2.5 | 1.318 *** | 0.042 | −0.103 | 1.350 *** | 0.082 | −0.045 |

(0.231) | (0.242) | (0.169) | (2.212) | (0.216) | (0.163) | |

constant | 51.359 *** | −0.041 | 40.100 *** | 53.137 *** | 1.402 | 37.300 *** |

(11.604) | (1.330) | (0.309) | (11.767) | (1.539) | (0.291) | |

${R}^{2}$ | 0.583 | 0.931 | 0.940 | 0.554 | 0.933 | 0.943 |

$|\overline{\rho}|$ | 0.250 | 0.066 | 0.066 | 0.649 | 0.061 | 0.060 |

CIPS | −4.829 *** | −5.492 *** | −5.674 *** | −3.664 *** | −3.859 *** | −4.149 *** |

${J}_{KSS}$ | 114.11 *** | 139.81 *** |

**Table 4.**Common factor correlations with mobility indexes considering additional incubation periods.

Mobility Index | Daily Death Toll | Daily Number of Cases | ||
---|---|---|---|---|

Incubation Period | 14 Days | 7 Days | 14 Days | 7 Days |

Recreation | 0.730 | 0.658 | 0.447 | 0.408 |

Groceries | 0.691 | 0.662 | 0.488 | 0.355 |

Parks | 0.751 | 0.663 | 0.426 | 0.296 |

Transport | 0.675 | 0.588 | 0.490 | 0.558 |

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**MDPI and ACS Style**

Rodríguez-Caballero, C.V.; Vera-Valdés, J.E. Air Pollution and Mobility, What Carries COVID-19? *Econometrics* **2021**, *9*, 37.
https://doi.org/10.3390/econometrics9040037

**AMA Style**

Rodríguez-Caballero CV, Vera-Valdés JE. Air Pollution and Mobility, What Carries COVID-19? *Econometrics*. 2021; 9(4):37.
https://doi.org/10.3390/econometrics9040037

**Chicago/Turabian Style**

Rodríguez-Caballero, C. Vladimir, and J. Eduardo Vera-Valdés. 2021. "Air Pollution and Mobility, What Carries COVID-19?" *Econometrics* 9, no. 4: 37.
https://doi.org/10.3390/econometrics9040037