association between the z-score of online purchases over population and log expenditure growth across both rural and urban areas and all three regions in most cases. The magnitude of the coefficient is larger, above 0.4 for all households, and the coefficients of some categories can be as high as 2 or more for rural households and households in the west. The correlation between log automobile purchases consumption growth and the z-score of online purchases over population is 0.474 for the whole sample, 0.394 among urban households, 1.406 among rural households, 0.285 in the east, 0.615 in the central region, and 1.329 in the west. Similarly, a one standard deviation increase above the mean of online purchases over population is associated with 0.444 of the log durable goods consumption growth all over the country. The coefficient is 0.357 among urban households and 1.029 among rural households. Across regions, the coefficient is 0.371 in the east, 0.420 in the central region, but not statistically significant in the west.

Third, for personal services, such as expenditure on vehicles other than purchasing automobiles,<sup>28</sup> household repair, and education, the role of e-commerce intensity, measured by online purchases over census population, is not significant.<sup>29</sup> However, for health and fitness, there is a positive association between the z-score of online purchases over population and across urban and rural, as well as the three regions, since health and fitness consumption could be a combination of both personal service and purchased goods such as equipment and supplements. The magnitude is also larger in rural areas in the west. As for medical expenses, which is also a combination of personal services and purchased goods, with the former more important, we see a positive association among the population, urban households and in the central region, but not among rural households, neither in the east nor the west.

Table 3 presents the results of the basic model at the national level by household expenditure levels.<sup>30</sup> For the growth in total household consumption per capita, e-commerce matters the most for the poorest quartile with a coefficient of 0.266, followed by the 25 percent– 75 percent quartile with a coefficient of 0.198, compared to a coefficient of 0.126 for the richest quartile. E-commerce purchase intensity has a positive effect in 12 of 16 household consumption categories and the effect is the strongest among the poorest quartile for half of them (cosmetics and beauty, food, utilities, travel, medical, health and fitness). For three household consumption

> 28 Consumption on vehicles other than automobiles might also fall into this category, while the coefficient is smaller than 0.5. It is a combination of buying goods and personal services, as it includes repairing vehicles and communication devices.  As for consumption on vehicles other than automobile purchase, the coefficient is not significantly different from zero for the whole sample, across the three regions and among urban households, 0.841 among rural households, as shown in Table 2.

> 29 We do not have information on whether the slower growth in education expenditure is related to the availability of free or lower priced online information and books, or other emerging patterns on education expenditure. 30 Extreme value is dropped at 1 percent.

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