metadata: authors: 0: author: Luo,Xubei
1: author: Wang,Yue-000541442
2: author: Zhang,Xiaobo
count: China
docna: 0: docna: E-Commerce Development and Household
            Consumption Growth in China
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Educational Sciences,Transport Services,Gender and Development,International Trade and Trade Rules
teratopic: Industry,Education,Transport,Gender,International Economics and Trade
abstracts: cdata!: China has quickly become the largest
            e-commerce market in the world. By matching a nationally
            representative China Family Panel Studies survey with
            county-level e-commerce information obtained from Alibaba,
            this paper examines how e-commerce development has shaped
            household consumption growth in China. The paper presents
            three major findings. First, e-commerce development is
            associated with higher consumption growth. Second, the
            relationship is stronger for the rural sample, inland
            regions, and poor households, suggesting that e-commerce
            development helps reduce spatial inequality in consumption.
            Third, the consumption of durable goods and in-style goods
            has grown faster than the consumption of local services.
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repnme: repnme: E-Commerce Development and Household
            Consumption Growth in China
display_title: E-Commerce Development and Household
            Consumption Growth in China
listing_relative_url: /research/2019/04/30973642/e-commerce-development-household-consumption-growth-china
topicv3: Transport m1326661 135,Trade m1326924 698,Education m1327048 470,Gender m1327621 615,Health m1504661 3480
publishtoextweb_dt: 2019-04-10T00:00:00Z
historic_topic: Industry,Education,Transport,Gender,International Economics and Trade
no_of_pages: 48
author_keys: 0: author_key: 677436
1: author_key: 1683066
2: author_key: 166740
model_extractions: Policy Research Working Paper 8810 E-Commerce Development and Household Consumption Growth in China _Xubei Luo Yue Wang Xiaobo Zhang_ Poverty and Equity Global Practice April 2019
Policy Research Working Paper 8810

# **Abstract**

China has quickly become the largest e-commerce market in the world. By matching a nationally representative China Family Panel Studies survey with county-level e-commerce information obtained from Alibaba, this paper examines how e-commerce development has shaped household consumption growth in China. The paper presents three major findings. First, e-commerce development is associated with higher consumption growth. Second, the relationship is stronger for the rural sample, inland regions, and poor households, suggesting that e-commerce development helps reduce spatial inequality in consumption. Third, the consumption of durable goods and in-style goods has grown faster than the consumption of local services.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at xluo@worldbank.org, yw548@cornell.edu, and x.zhang@nsd.pku.edu.cn.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **E-Commerce Development and Household Consumption Growth in China**

Xubei Luo, Yue Wang, Xiaobo Zhang<sup></sup> JEL Codes: L81, E21, D23, R12 Key Words: E-commerce, consumption growth, transaction costs, spatial inequality

>  Xubei Luo is a senior economist of the World Bank; Yue Wang is a Ph.D. candidate at the Cornell University; and Xiaobo Zhang is a distinguished chair professor of economics at the National School of Development, Peking University in China, and senior research fellow of International Food Policy Research Institute. The authors thank Shaohua Chen, Samuel Freije-Rodriguez, John Giles, Bert Hofman, Deepak Mishra, Ambar Narayan, Hoon Sahib Soh, and Salman Zaidi for valuable comments and discussions; Zhengwei Jiang, Zhenzhong Sheng, Ruidong Zhang, and researchers from AliResearch for suggestions and for sharing the Alibaba online data for the research; and Xuejiao Xu for research support. The views expressed here are those of the authors and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.
# **I Introduction**

The first big move in online sales came with the founding of Amazon.com in 1994. At the time, the company sold only one product, books, but founder Jeff Bezos had bigger plans to sell “Anything with a capital A.” By 2016, the company had succeeded in that plan and sold $136 billion in goods.<sup>1</sup> Meanwhile, in 1999, not long after Bezos launched Amazon, Jack Ma established Alibaba.com, a business-to-business marketplace based in China. In 2018, Alibaba set a milestone for online sales with its Singles’ Day sale, which netted $30.8 billion in transactions, doubling the combined sales for the two biggest sales days in the United States, Black Friday and Cyber Monday.<sup>2</sup> E-commerce is a new technology and business mode that allows buyers to make online transactions and receive local package delivery or pickup from sellers. It has reshaped consumption patterns in the years since it was introduced. The welfare implications of e- commerce have been discussed in the literature, mostly focusing on developed countries (Brown and Goolsbee 2000, Hortaçsu et al. 2009, Gorodnichenko and Talavera 2017). On the one hand, e-commerce offers consumers lower search cost and more product variety than traditional retail stores, improving their welfare. Consumers may also gain from accessing merchants online who do not have local brick-and-mortar stores (Dolfen et al. 2019). On the other hand, the intense competition of e-commerce can result in efficiency and welfare gains for the entire society through effects on prices. However, the intense competition may also squeeze out brick-andmortar stores and smaller retailers, which in turn depresses offline shopping. For example, facing online competition, many physical bookstores have closed (Goldmanis et al. 2010). However, there is a net market expansion effect for consumer electronics, although e-commerce diverts sales from offline, and consumers benefit more than firms from the introduction of e-commerce (Duch-Brown et al. 2017). The net impact of e-commerce depends upon the relative importance of the above factors.

In developing countries, a large proportion of people live in remote areas with limited access to offline retail stores. In these countries, e-commerce may have greater potential to reach a wider range of consumers who are otherwise constrained by limited access to markets than in developed countries. Building a multiregional general equilibrium model and using city-level data, Fan et al. (2016) show that e-commerce development in China disproportionately improves consumer welfare in remote cities. Drawing data from eight counties in three provinces where

> 1 <u>http://www.latimes.com/business/la-fi-amazon-history-20170618-htmlstory.html#</u>

> 2 https://www.businessinsider.com/black-friday-cyber-monday-vs-singles-day-sales-2018-12.

·· 2
Alibaba’s Rural Taobao program was present, Couture et al. (2017) finds that e-commerce expansion reduces the cost of living for certain groups of the rural population who are induced to use it, though the average effect is muted.

Following the same spirit of Fan et al. (2016) and Couture et al. (2017), our paper examines the relationship between e-commerce development and consumption in China. We extend the model of Startz (2018) to investigate the differential impact of e-commerce on consumption growth by various goods and services. Compared with Fan et al. (2016) and Couture et al. (2017), our paper has a few new features. First, by matching a nationally representative China Family Panel Studies (CFPS) survey with county-level e-commerce information obtained from Alibaba, we can directly examine the impact of e-commerce development on consumption growth at the household level rather than at the aggregate city level as in Fan et al. (2016). Second, our findings are likely more representative because CFPS covers many more counties than the sample used in Couture et al. (2017). Third, using the rich consumption information of the CFPS survey, we can study the heterogenous associations between e-commerce development and various categories of consumption, which were not discussed in Fan et al. (2016) and Couture et al. (2017).

Our paper offers three major findings. First, e-commerce development is associated with higher consumption growth. Lower search cost is a key feature of e-commerce (Bajari and Hortaçsu 2003, Hong and Shum 2006, Brynjolfsson, Dick and Smith 2010, Lieber and Syverson 2011, Levin 2011). Lower search cost makes price discovery easier, bringing the law of one price closer to reality (Gorodnichenko and Talavera 2017). Lower transaction costs increase the level of specialization in the society and create more trade. More competitive prices tend to reduce the cost of living for residents. Couture et al. (2017) show that the expansion of e-commerce to the Chinese countryside is associated with lower cost of living, and for the goods that are available at both the Rural Taobao online terminal and in the village, the median price from the online terminal is cheaper by 15 percent. According to a McKinsey report (McKinsey, 2013), e-tailing may have lowered China’s average retail price by 0.2 to 0.4 percent in 2011 and 0.3 to 0.6 percent in 2012. Holding disposable income constant, lower cost of living means more discretionary spending power, which implies higher consumption.

Second, the impact on consumption is conspicuously larger for the rural residents, inland regions, and the poor households, suggesting that e-commerce development helps reduce spatial inequality in consumption. For people in remote areas with limited access to markets, the saving in search costs and the increase in variety of products accessible online compared with in ·· 3
traditional brick-and-mortar stores can be particularly large. E-tailing is not just a replacement of purchases that would otherwise take place but could spur incremental consumption particularly in small cities and towns where there is pent-up demand for goods that local physical stores cannot deliver (McKinsey, 2013). Couture et al. (2017) show that 62 percent of goods bought through Alibaba’s Rural Taobao online terminal were not available in the village, which rises to 84 percent for durable goods. Therefore, they likely benefit more from e-commerce development than their counterparts in more populous and developed regions.

Third, the consumption of durable goods and in-style goods exhibits stronger growth than the consumption of local services. Startz (2018) examines the search and contracting frictions in trade and their role in goods for which sourcing costs and style evolution are of different importance in traditional international trade. Under this framework, by enabling traders to locate nearby producers and sell products directly to consumers, e-commerce can lower travel costs and contracting costs associated with remote ordering. Thanks to the lower costs of travel and contracting, sellers can source more frequently and provide more _a la mode_ products and greater variety to consumers. If the cost saving is passed through, consumers will also get lower prices. As the importance of stylishness and degree of cost savings from search and contracting frictions vary by goods and services, the impact of e-commerce on consumer welfare is likely to differ by types of goods and services. Our finding is accordance with the prediction of Startz (2018). Following this introduction, the paper consists of the following parts: Section II describes e- commerce development in China; Section III discusses the theoretical motivation; Section IV presents the empirical model and data; Section V discusses the empirical results; Section VI conducts robustness analysis; and Section VII concludes.

# **II E-commerce development in China**

China has quickly become the largest e-commerce market in the world. The number of Internet users in China reached 772 million in 2017,<sup>3</sup> of which 533 million made purchases online.<sup>4</sup> The annual total e-commerce trade volume in China increased thirtyfold from RMB 930

> 3 China Internet Network Information Center (CNNIC) (2018). 41st Statistical Report on the Internet Development in China.  http://www.cac.gov.cn/files/pdf/cnnic/CNNIC41.pdf

> 4 Ministry of Commerce (2017). E-commerce in China 2017.

> http://cif.mofcom.gov.cn/cif/html/upload/20181101112235744_2017%E5%B9%B4%E4%B8%AD%E5%9B%BD%E7 %94%B5%E5%AD%90%E5%95%86%E5%8A%A1%E6%8A%A5%E5%91%8A.pdf

·· 4
billion in 2004 to RMB 29,160 billion in 2017, a compound annual growth rate of 30 percent.<sup>5</sup> Express mail service exceeded 30 billion pieces in 2016,<sup>6</sup> of which some 60 percent is related to e-commerce.<sup>7</sup> According to a 2017 McKinsey report, China’s worldwide e-commerce transaction value grew from less than 1 percent a decade ago to over 40 percent now, exceeding that of France, Germany, Japan, the United Kingdom, and the United States combined (Woetzel et al. 2017). Online retail sales have grown even faster, from RMB 125.7 billion in 2008 to RMB 5,155.6 billion in 2016 – only 1 percent of total retail sales of consumer goods was purchased online in China in 2008, compared with 16 percent in 2016. The number of packages sent through online sales increased tenfold from one billion in 2006 to 10 billion in 2014 (Goldman Sachs 2018). According to the State Post Office, Chinese express firms delivered 40 billion packages in 2017, the majority related to e-commerce.<sup>8</sup> The development of online retailing, however, remained uneven across Chinese provinces. In 2015, in Beijing, 45 percent of the total retail sales of consumer goods was purchased online, followed by nearly 40 percent in Shanghai, 35 percent in Zhejiang, and 28 percent in Guangdong. However, this share was much lower (less than 2 percent) in nine inland provinces (Figure 1), as measured by the Online Business Index (OBI) and the Online Shopping Index (OSI) developed by AliResearch (map 1 and map 2).<sup>9</sup>

> 5 Ministry of Commerce of China. E-Commerce in China (2015, 2016, and 2017).

> 6 Source: National Bureau of Statistics of the People’s Republic of China, <u>http://www.stats.gov.cn/tjsj/sjjd/201706/t20170626_1506952.html</u> 7 Source: State Post Bureau of the People’s Republic of China, <u>http://www.spb.gov.cn/zy/xxgg/201706/t20170624_1196398.html</u>

> 8http://www.spb.gov.cn/xw/dtxx_15079/201806/t20180604_1581131.html

> 9 The OBI and OSI data are obtained from the AliResearch team. OBI is a constructed index measuring the density of online stores and the percent of online stores with annual online sales above RMB 240,000. The value of the index ranges from 0 to 100. The higher the value, the more developed the online sales. OSI is a constructed index measuring the density of online buyers and the percent of online buyers with annual online consumption above RMB 10,000. The value of the index ranges from 0 to 100. The higher the value, the more developed the online purchases. The online transaction numbers are from the Alibaba platform, which accounts for the majority of online transactions in China. The values of the OBI and OSI are not comparable over time due to changes in the methodologies.

·· 5
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There is also a gap in the development of online retail between urban and rural areas **.** Up to December 2016, the rural Internet users in China accounted for 27.4 percent of the national total (731 million), while urban Internet users took up 72.6 percent of the total, much higher than the share of urban population (57 percent). The Internet penetration in urban areas was 69.1 percent, while in rural areas it was only 33.1 percent. Nearly three-quarters of online stores and Internet users were concentrated in urban areas.<sup>10</sup> From a positive perspective, the wide gaps across regions and between rural and urban areas imply large growth potential in the less developed areas. In fact, total online retail transactions have grown faster in rural areas than in urban areas in the past several years. Rural online retail transactions increased from RMB 353 billion in 2015 to RMB 895 billion in 2016, representing 17 percent of the total online retail transactions, an increase from 9 percent in 2015.<sup>11</sup>

**Figure 1. Share of online retail sales to total retail sales of consumer goods in provinces (2015)**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br> to Total<br>Sales<br>Share of Online Retail Retail Sales of Consumer Goods (2015) Beijing Shanghai Zhejiang Guangdong Fujian Jiangsu Shaanxi Tianjin Sichuan Anhui Hainan Hubei Chongqing Hebei Jiangxi Hunan Shandong Henan Yunnan Guangxi Liaoning Guizhou Gansu Xinjiang Shanxi Ningxia Heilongjiang Jilin Qinghai Inner Mongolia Tibet<br><!-- End of picture text --> Source: Staff calculations based on _China Statistical Yearbook 2016_

# **III Theoretical motivation: Extension of Starts (2018)**

In the model developed in Startz (2018), consumers care about both quantities and style of the goods they consume. Traders choose whether to order remotely from suppliers or travel to the suppliers. They also need to decide the optimal stocking period (frequency) to maximize average profit over a period _T_ , taking travel cost and the cost of search and contracting associated

> 10 Ministry of Commerce of the People’s Republic of China 2016. “E-commerce in China, 2016” (中国电子商务报告). 11 Calculated based on data from the National Bureau of Statistics of China and the Ministry of Commerce of the People’s Republic of China, “E-Commerce in China.” <u>Total rural online retail transactions refers to the sum of online retail</u> transactions from e-commerce enterprises (including individuals) operated in the administrative regions at the county level or below (excluding city districts) (http://images.mofcom.gov.cn/dzsws/201706/2017061110205702.pdf).

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with remote ordering into account. The optimal stocking period is shorter for varieties evolving more frequently and in greater demand. This holds true also for traders with lower travel cost as the style frontier can only be observed when traders travel to the production location. In a world without e-commerce, traders will choose to travel to the production site if the loss due to search friction and contracting premium overtakes the travel cost, and order less up-to-date varieties remotely otherwise.

E-commerce provides a third option for traders, that is, to locate in the same location as suppliers and sell remotely online to consumers. In this way, they can observe the frontier style without the travel costs, facing lower search friction and contracting costs. In addition, there is a saving in fixed storage cost as they can directly source from suppliers to meet the online order. Therefore, they can keep a limited inventory with minimum cost. In summary, e-commerce can improve consumer welfare in three ways. First, they provide more up-to-date goods. Second, in the face of lower entry cost, more traders will enter the market and sell more varieties. Third, the intense competition may force traders to pass part of the cost saving to consumers, leading to lower prices, which directly improve consumer welfare.

Under this framework, associated with e-commerce development, consumers will purchase more goods that evolve faster in style, such as cosmetics and clothes, as well as durable goods that have high storage costs, particularly in remote areas with higher travel costs. Moreover, e-commerce may yield an indirect income effect thanks to the lower price. Lower prices for goods purchased online mean that consumers have more disposable income. The extra income is likely to be spent on goods and services with high income elasticity not available online, such as travel and dining out.

However, some personal services, such as kindergarten and health care, require frequent interactions between suppliers and consumers. In this case, local service providers enjoy a greater advantage than remote ones. As a result, e-commerce development likely will have little effect on such locally provided personal services.<sup>12</sup>

# **IV Empirical method and data description**

> 12 Our discussion here refers to personal services where the product cannot be separated from the supplier, such as teaching, medical service, and personal care. Services such as typing, photo processing, and consulting are not included. The latter kinds of services, where the product can be separated from the labor provider, share the same features as goods.

·· 8
In this paper, we examine the relationship between e-commerce development and household consumption growth in China using three data sources: Alibaba e-commerce data, household survey data, and official statistics. First, we construct a series of indicators to measure the development of e-commerce at the county level using online sales and purchase information provided by the Alibaba Group, gross domestic product (GDP) from the China Statistical Yearbook, and population drawn from the Population Census 2010. Second, we merge the county-level e-commerce development measures with household consumption data obtained from the CFPS survey administered by Peking University.

# _<u>Indicator construction</u>_

We measure county-level e-commerce development on three dimensions: penetration, intensity, and market size (as a share of the national online market). For each of the three dimensions, we construct two indicators measuring, respectively, the online sales and purchase using the amount of online sales and online purchases (Gross Merchandise Value, or GMV), numbers of online sellers and online purchasers, and numbers of packages sent and received associated with online transactions. The three indicators are comparable over time. Therefore, compared with Alibaba’s three official e-commerce indicators, Alibaba e-commerce development indicators (aEDI), OBI, and OSI, which use different weights across years, our three indicators are comparable over years.

We constructed the indicators in three steps. First, we extracted the original indicators for each index. Second, we normalized the indicators and transformed them into z-scores for easy comparison.<sup>13</sup> The number of buyers, annual online purchase GMV, national online purchase GMV, and total number of national online buyers are from Alibaba. GDP is obtained from China Statistical Yearbooks. Residential population is from the Population Census 2010.<sup>14</sup> The three sets of indicators are defined as follows:

# _E-commerce penetration indexes:_

> 13 See Appendix 1 for a detailed description of the construction of the indicators.

> 14 Measures using different sources of population yield consistent results. We use census population information in our main analysis, rather than the population reported in the statistics year books. In addition, we apply the 2013 residential population at the county level collected from local statistical bureaus as a robust check.

·· 9
<!-- Start of picture text --> ( annual purchase GMV )<br>number of online buyers 2013<br>( annual purchase GMV )<br>residential population 2013<br>(rev annual purchase GMV —_) x 100<br>national online purchase GMV 2013<br>( number of online buyers ) x 100<br>total number of national online buyers /5913<br>z — score of measure x = ———————_(Xct—mean of x)<br>standard error ofx<br><!-- End of picture text -->
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ic,t __<sup><u>numberofadultsthatarenotworking+numberofchildren</u></sup> ict ~~ family size ic,t ic,t ic icts _= Bo + fi ¢,2013 t+B2 ict-2Ss + B3 ic,t +B, ict/10 + Bs 2 + Be ic,t + By ic,t + Be ic,t +o +6 + Eic,t ic,t ic,t
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**Table 1. E-commerce development and total household expenditure per capita**

|VARIABLES|(1)<br>All|(2)<br>Urban|(3)<br>Rural|(4)<br>East|(5)<br>Central|(6)<br>West||
|---|---|---|---|---|---|---|---|
|Z-score of purchase/<br>|0.303***|0.247***|0.718***|0.267***|0.313**|0.538***||
|population||||||||
||(0.067)|(0.061)|(0.098)|(0.083)|(0.119)|(0.157)||
|Lag log household<br>|-0.596***|-0.548***|-0.664***|-0.581***|-0.582***|-0.641***||
|expenditure per capita||||||||
||(0.015)|(0.019)|(0.016)|(0.026)|(0.024)|(0.025)||
|Average age of household<br>|-0.057***|-0.049***|-0.099***|-0.064***|-0.056***|-0.068***||
|members/10||||||||
||(0.009)|(0.014)|(0.014)|(0.015)|(0.017)|(0.019)||
|Square of average age of|0.001***|0.000***|0.002***|0.001***|0.001**|0.001***||
|household members/100||||||||
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)||
|Household dependent ratio|-0.131***|-0.045|-0.244***|-0.103**|-0.133***|-0.185**||
||(0.033)|(0.039)|(0.044)|(0.049)|(0.043)|(0.090)||
|Urban/rural area dummy<br>(Urban=1)|0.242***|||0.254***|0.204***|0.302***||
||(0.028)|||(0.055)|(0.043)|(0.050)||
|West|0.033|0.047|0.035|||||
||(0.051)|(0.061)|(0.059)|||||
|Central|0.063|0.035|0.111**|||||
||(0.046)|(0.055)|(0.048)|||||
|Constant|6.092***|5.795***|7.152***|5.963***|6.038***|6.720***||
||(0.169)|(0.227)|(0.187)|(0.258)|(0.292)|(0.297)||
|Observations|11,107|5,183|5,924|4,457|3,486|3,163||
|Adjusted R-squared|0.319|0.285|0.319<br>0.368|<br>0.285<br>0.321|<br>0.368<br>0.301|0.321<br>0.338|0.301|

Note: Robust standard errors clustered at the county level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 We examine the pattern of association between e-commerce development and each of the 18 categories of subcomponents of household consumption in Table 2. For most of the 18 categories of consumption, the association with e-commerce development is positive, stronger in the less developed regions and in rural areas. Three patterns are apparent from the table.

**Table 2. E-commerce development and various categories of household expenditure per capita by region**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Urban|Rural|East|Central|West|
|Cosmetics and beauty|0.490***|0.449***|0.983***|0.510***|0.415***|1.391***|
||(0.121)|(0.125)|(0.235)|(0.162)|(0.152)|(0.476)|
|Food|0.355***|0.288***|0.842***|0.336***|0.352***|0.673**|
||(0.066)|(0.061)|(0.118)|(0.081)|(0.111)|(0.298)|
|Of which: Food at home|0.389***|0.348***|0.805***|0.357***|0.412***|0.606**|
||(0.073)|(0.066)|(0.144)|(0.094)|(0.111)|(0.263)|

·· 14
|Of which: Dining out|0.580***<br>(0.164)|0.501***<br>(0.165)|1.260***<br>(0.267)|0.423**<br>(0.211)|0.777***<br>(0.231)|1.528***<br>(0.495)|
|---|---|---|---|---|---|---|
|Clothes|0.306***|0.269***|0.720***|0.257*|0.376***|0.590**|
||(0.102)|(0.103)|(0.163)|(0.131)|(0.125)|(0.287)|
|Utilities|0.181***|0.180***|0.275**|0.183***|0.154***|0.469|
||(0.033)|(0.032)|(0.138)|(0.040)|(0.047)|(0.314)|
|Communications|0.242***|0.210***|0.565***|0.184***|0.309***|0.545***|
||(0.051)|(0.050)|(0.078)|(0.058)|(0.069)|(0.198)|
|Local transport|0.435***|0.384***|0.732***|0.328***|0.502***|0.518**|
||(0.092)|(0.093)|(0.138)|(0.115)|(0.096)|(0.253)|
|Travel|0.872***|0.770***|1.595***|0.751**|0.899***|2.131***|
||(0.211)|(0.214)|(0.250)|(0.283)|(0.157)|(0.583)|
|Entertainment|0.655***|0.547***|1.171***|0.546**|0.852***|0.441|
||(0.169)|(0.174)|(0.325)|(0.235)|(0.163)|(0.404)|
|Automobiles|0.474***|0.394***|1.406***|0.285*|0.615***|1.329***|
||(0.127)|(0.115)|(0.291)|(0.148)|(0.202)|(0.323)|
|Other vehicles|0.182|0.116|0.841**|0.071|0.380|0.162|
||(0.136)|(0.126)|(0.377)|(0.137)|(0.371)|(0.566)|
|Durable goods|0.444***|0.357***|1.029**|0.371**|0.420**|0.621|
||(0.118)|(0.104)|(0.397)|(0.158)|(0.181)|(0.958)|
|Medical|0.166**|0.154*|0.309|0.071|0.287**|0.672|
||(0.075)|(0.085)|(0.201)|(0.086)|(0.127)|(0.432)|
|Health and fitness|0.395***|0.327***|0.534***|0.351*|0.381***|0.636**|
||(0.126)|(0.123)|(0.195)|(0.188)|(0.138)|(0.299)|
|Education|-0.102|-0.091|0.008|-0.157*|0.100|-0.165|
||(0.118)|(0.125)|(0.274)|(0.091)|(0.204)|(0.402)|
|Home repair|-0.043|-0.072|0.116|-0.001|-0.109|-0.280|
||(0.083)|(0.086)|(0.314)|(0.131)|(0.149)|(0.707)|
|Gifts|0.061|0.073|0.454|0.013|0.210*|0.281|
||(0.107)|(0.099)|(0.276)|(0.140)|(0.120)|(0.545)|

Note: The model of estimation is the same as Table 1, the dependent variable is the logarithmic form of household per capita consumption growth, and the variables of control include lagged log value of household expenditure per capita, average age/10, age squared/100, dependency ratio, urban-rural dummies, and regional dummies. For readability, we present only the results of interest - the coefficients of the z-score of online purchase / population in 2013. Fully results available upon request. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

First, for goods and services regularly consumed by most households,<sup>22</sup> including expenditures on cosmetics and beauty, food, clothes, utilities, communication, local transportation, and entertainment, we see a positive association between the z-score of online purchases over

> 22 From the CFPS data, over 50 percent of the households reported such consumption expenditures.

·· 15
population and log expenditure growth across both rural and urban areas and all three regions in most cases.<sup>23</sup> The coefficients are larger among rural households and in the interior region than in urban areas and in the east systematically.<sup>24</sup> A one standard deviation increase of online purchases over population is associated with a 0.490 increase in growth of log annual household expenditure per capita on cosmetics and beauty products.<sup>25</sup> The magnitude is larger among rural households (0.983) than that among urban households (0.449). And the magnitude is the largest in the west, which is as high as 1.391. The relationship between the z-score and food consumption follows a similar pattern. When online purchases over population increases by one standard deviation above the mean, log household food consumption per capita growth increases by 0.355. The increase is 0.288 among urban households and almost three times that among rural households (0.842). In the east, it is 0.336 while in the central, it is similar to that in the east (0.352) and in the west, it is twice as large as that in the east (0.673). When purchases over population increases by one standard deviation above the mean, log household dining out consumption per capita growth increases by 0.580. In rural areas, it is as high as 1.260 and, in the west, it is as high as 1.528. A one standard deviation increase of online purchases over population is associated with 0.306 increase in growth of log annual household expenditure on clothes. The magnitude among rural households is almost threefold as large as that among urban households (0.729 and 0.269 respectively for rural and urban). And across the three regions, it is the largest in the west, which is 0.590, followed by the central region, which is 0.376 and is the smallest in the east, which is 0.257. When the z-score increases by one, log household communication consumption per capita growth increases by 0.242 for the whole sample, 0.210 for urban households, 0.565 for rural households, 0.184 in the east, 0.309 in the central region, and 0.545 in the west.<sup>26</sup> When the z-score increases by one, log household entertainment consumption per capita growth increases by 0.655 for the whole sample, 0.547 for urban households, 1.171 for rural households, 0.546 in the east, 0.852 in the central region, and 0.441 in the west.

Second, for goods and services that require a larger payment at one time and are less frequently purchased,<sup>27</sup> i.e., travel, automobiles, and durable goods, we also see a positive

> 23 The coefficient of health and fitness expenditure is about 0.3 for all households. As only roughly 10 percent of households reported health and fitness expenditure, we do not discuss this in the paper.

> 24 The coefficient of utility expenditure is about 0.18 for all households, smaller than those of other variables. This might be related to the nature of utility consumption.

> 25 Cosmetic and beauty products, including a wide range in prices from the very low end in the spectrum, are becoming a popular purchase online, in particular, among young female online buyers.

> 26 We do not have information on whether dining out refers to sit-down dining only or includes fast food consumed outside or home delivery. One possible explanation for the large coefficient of dining out is the rapid increase of the availability of online food delivery.

> 27 From the CFPS data, less than 50 percent of the households reported such consumption expenditures.

·· 16
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.

·· 17
categories, including cosmetics and beauty, food, , and travel, e-commerce purchase intensity is associated with positive consumption growth for households of all income groups, and for two household consumption categories – medical, and health and fitness consumption – the effect is positive only for the poorest quartile. The effect of e-commerce on the consumption of durable goods and vehicles other than automobiles is not significant in any income group. For two household consumption categories – communication and entertainment , the magnitude of the correlation is the largest for the middle-income group. For local transport and automobile purchases, it is the largest for the top income group. Similar to the results of Table 2, the effect of online purchases over population is not significant for most consumption groups in three categories: education, home repair, and gifts.

**Table 3. E-commerce development and various categories of household expenditure per capita by household consumption level**

||(1)<br>All|(2)<br>Bottom 25%|(3)<br>25%-75%|(4)<br>Top 25%|
|---|---|---|---|---|
|Total household expenditure|0.303***|0.325***|0.157***|0.112***|
||(0.067)|(0.091)|(0.049)|(0.037)|
|Cosmetics and beauty|0.490***|0.568**|0.239*|0.179**|
||(0.121)|(0.227)|(0.133)|(0.086)|
|Food|0.355***|0.489***|0.253***|0.129***|
||(0.066)|(0.120)|(0.063)|(0.042)|
|Of which: Food at home|0.389***|0.516***|0.293***|0.195***|
||(0.073)|(0.148)|(0.077)|(0.056)|
|Of which: Dining out|0.580***|0.274|0.202|0.208*|
||(0.164)|(0.220)|(0.159)|(0.125)|
|Clothes|0.306***|0.199|0.068|0.013|
||(0.102)|(0.223)|(0.090)|(0.082)|
|Utilities|0.181***|0.294**|0.128***|0.041|
||(0.033)|(0.124)|(0.036)|(0.028)|
|Communications|0.242***|0.028|0.138**|0.111***|
||(0.051)|(0.116)|(0.056)|(0.031)|
|Local transport|0.435***|0.175|0.178**|0.257**|
||(0.092)|(0.252)|(0.080)|(0.121)|
|Travel|0.872***|0.695**|0.443**|0.535***|
||(0.211)|(0.297)|(0.174)|(0.181)|
|Entertainment|0.655***|0.133|0.386*|0.367***|
||(0.169)|(0.144)|(0.206)|(0.115)|
|Automobiles|0.474***|0.135|0.171|0.326**|
||(0.127)|(0.173)|(0.136)|(0.145)|
|Other vehicles|0.182|-0.088|0.029|··18<br>-0.017|
||(0.136)|(0.273)|(0.193)|(0.113)|
|---|---|---|---|---|
|Durable goods|0.444***|0.016|0.061|0.092|
||(0.118)|(0.389)|(0.115)|(0.121)|
|Medical|0.166**|0.552**|-0.028|0.166|
||(0.075)|(0.230)|(0.116)|(0.154)|
|Health and fitness|0.395***|0.445**|-0.006|0.160|
||(0.126)|(0.210)|(0.108)|(0.117)|
|Education|-0.102|-0.392|-0.105|0.080|
||(0.118)|(0.324)|(0.115)|(0.224)|
|Home repair|-0.043|-0.218|-0.091|-0.372***|
||(0.083)|(0.281)|(0.120)|(0.122)|
|Gifts|0.061|0.501|-0.002|-0.272*|
||(0.107)|(0.411)|(0.116)|(0.144)|

Note: The model of estimation is the same as Table 1, the dependent variable is the logarithmic form of household per capita consumption growth, and the variables of control include lagged log value of household expenditure per capita, average age/10, age squared/100, dependency ratio, urban-rural dummies, and regional dummies. For readability, we present only the results of interest - the coefficients of the z-score of online purchase / population in 2013. Fully results available upon request. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Similar to the findings in Table 2, the z-score of online purchases over population is associated with an increase of consumption for food, cosmetics and beauty, utilities, clothes, travel, and health and fitness, with the largest increase for the poorest segment of the population. The sizes of the coefficients of the z-scores are moderate. When the z-score of online purchases over population increases by one, log household food consumption per capita growth increases by 0.489 for the poorest quartile, by 0.253 for the middle two quartiles, and by 0.179 for the richest quartile; log household cosmetics and beauty consumption per capita growth increases by 0.568 for the poorest quartile, by 0.239 for the middle two quartiles, and 0.179 for the richest quartile; log household utilities consumption per capita growth increases by 0.294 for the poorest quartile, by 0.128 for the middle two quartiles, but has no significant change for the richest quartile;. When the z-score of online purchases over population increases by one, log household medical expenditure growth increases by 0.552 for the poorest quartile, and log household health and fitness expenditure growth increases by 0.445 for the poorest quartile, while the association among the middle two quartiles and the top quartile for these two categories is not significant and smaller in size. The marginal effects tend to decline as the income quartiles move up. This suggests that online access to a larger selection of products (including potentially products at different quality and price levels) can have a stronger stimulation effect in household ·· 19
consumption for the poor by unlocking the potential demand for basic goods that are not available (or available at a higher price) at the traditional local markets. When the z-score of online purchases over population increases by one, log household travel expenditure growth is associated with 0.695 increase for the poorest quartile, 0.443 for the middle two quartiles and 0.535 for the richest quartile. For communication and entertainment consumption expenditure, the coefficient for the e-commerce indicator is the highest for the middle two quartiles (0.138 and 0.386 respectively).

When online purchases over population increases by one standard deviation for the poorest quartile, log household consumption growth on cosmetics and beauty products increases by 0.568, log household consumption growth on travel increases by 0.695.<sup>31</sup> The large magnitudes of the coefficients suggest that online purchases over population has strong effects on the consumption growth of the goods and services of higher income elasticity / higher in style consumption nature (such as travel and cosmetics and beauty products), particularly for the poorer households, whose demand might have been disproportionately limited by their poor access to markets through traditional markets.

Consistent with the prediction of the model in Startz (2018), the results show that e- commerce development is associated with stronger growth in consumption of goods with rapidly evolving styles (such as cosmetics and beauty products, clothes, and entertainment), and goods with higher storage costs (such as durable goods that require more resources for stocking for local traders). We also see higher consumption growth of goods and services with higher income elasticities, such as travel and dining out, even though these are not online products or services. In the meantime, there is some indication that e-commerce development benefits consumers in areas with high travel costs (such as in the interior regions).

Across the different categories of consumption of goods and services, the association between e-commerce development and food consumption growth is particularly strong. From 2012 to 2016, China's e-grocery sales saw a compound annual growth rate of 52.9 percent, according to a recent report by Agriculture and Agri-Food Canada.<sup>32</sup> The extremely strong growth of online food consumption in China seems to defy the Engel curve. The puzzle is likely

> 31 Health and fitness consumption is different from medical treatment consumption. The former does not include medical necessities but only healthy supplements and fitness while the latter is associated with treatment in hospital or medicines bought from a pharmacy. The results are consistent with expectations as health and fitness expenditure might have a stronger income elasticity while medical expenditure is more need-based.

> 32 Source: http://www.agr.gc.ca/eng/industry-markets-and-trade/international-agri-food-marketintelligence/asia/market-intelligence/e-grocery-market-in-china/?id=1504037238257.

·· 20
due to the large increase in online consumption of baby food. Baby food and formula are the top two package foods sold online in China.<sup>33</sup> Using the CFPS data, we find that breast feeding is negatively correlated with e-commerce development; the coefficient between food consumption and e-commerce z-score is larger for households with young children under three years old.<sup>34</sup> This is consistent with the Chinese culture and the strong willingness of parents to spend on children, while the judge is still out for the impact on nutrition.

The fast growth in food consumption may also stem from the online purchases of food varieties by higher-end consumers. In more developed areas, particularly tier one and tier two cities, people can purchase online specialty fresh produce, meat, or seafood (which are not available in traditional markets) with guaranteed fast delivery. End-to-end value chain digitalization, such as the Alibaba’s Hema store, which sells online high-end groceries and readymade food with guaranteed fast delivery, is one example that might be changing the pattern of online-offline retails in the future.<sup>35</sup> However, the contribution of such high-end online-offline grocery purchases to food consumption is likely to be limited for the period of our analysis given the limited coverage and the high requirement of logistics, although this might increase in the future, particularly in more developed areas.

As to personal services, such as education, medical, fitness, and health services for which personal interaction is more important, consumption growth is not associated with e-commerce development.<sup>36</sup> These services involve strong personal interactions. For example, high quality of service in education often requires instructor customization of teaching strategy, content, and speed according to students’ needs and feedback. As labor mobility is lower than the mobility of goods, it is costly to send service providers to distant customers.

# **VI Robustness**

> 33 See https://www.newyorker.com/magazine/2018/07/23/how-e-commerce-is-transforming-rural-china 34 Additional results on the relations between food consumption and whether the household has young children and those on the breastfeeding and e-commerce z-scores are available upon request. Forthcoming in another research paper. 35 https://www.pwccn.com/en/retail-and-consumer/publications/global-consumer-insights-survey-2018-china-report.pdf

> 36 Due to data limitation, we cannot examine the use of online education or health services, such as interactive online learning program for children and health monitoring apps, which may have a spillover effect over time for consumers to consume more of not just online consumption goods but also get exposed to the kind of goods and services that can potentially enhance human capital.

·· 21
To check robustness, we conducted a series of alternative specifications to examine the possible effects of other variables on the growth rate of household per capita total consumption as well as that of each of the 18 categories of consumption.

We first tested the various indicators measuring the penetration, intensity, and market size of e-commerce from both the purchase and selling aspects. Table A1 in the Appendix presents the empirical results of the baseline model using other constructed indicators measuring e-commerce penetration, intensity, and market size. The results are similar to those presented in Table 1 – e-commerce development, measured in different ways, plays a positive role in shaping household consumption, with a stronger correlation in inland regions and rural areas, and in households with lower income levels.

We introduced several additional variables of control to test whether the relation between e-commerce development and household consumption growth is a result of variables missing in the basic model.

Tables A.2 and A.3 present the results of an extended model, with additional household control variables, including lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age, age squared/100, dependency ratio, urban-rural dummies, and regional dummies.

Tables A.4 and A.5 present the results of a further extended model, with additional variables of control, including the lagged log household per capita income growth, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age, age squared/100, dependency ratio, urban-rural dummies, and regional dummies.

Tables A.6 and A.7 present the results of an alternative extended model, with additional variables of control, including the lagged log household per capita income per capita (2010 value), lagged log value of household expenditure per capita in the consumption category in question, household average years of schooling, household gender ratio, average age, age squared/100, dependency ratio, urban-rural dummies, and regional dummies.

Tables A.8 and A.9 present the results of another alternative extended model, with additional variables of control, including consumer price index (CPI) by category, lagged log value of household expenditure per capita in the consumption category in question, household ·· 22
average years of schooling, household gender ratio, average age, age squared/100, dependency ratio, urban-rural dummies, and regional dummies.

The results of the alternative models in the robustness tests are largely consistent with those in Tables 1 and 2, which suggests that after controlling for household demographic characteristics, as well as income growth, initial income level, or inflation, the relation between e- commerce development and household consumption growth remains largely the same: e- commerce development is associated with strong household consumption growth.

# **VII Conclusion**

Our results show that, in China, household consumption growth is positively associated with initial local e-commerce development. The relationship is stronger for households in the less developed inland regions and in rural areas than in the coastal regions and urban areas, in particular for the poorer households than for the richer ones. E-commerce development reduces consumption inequality across regions and income groups. Thanks to the lower search and transaction costs, more products become tradable online at lower price, if the cost saving is passed on to consumers due to competition. The potential decline in price, increase in variety and smaller travel cost are likely the main channels that contribute to an increase in consumer welfare, as argued by Couture et. al. (2018). Income increase associated with e-commerce business development in an area is also a potential channel of the consumption growth. We tested this in Table 4. For the poorest quartile, the coefficient of the z-core of online purchases over population on log household income per capita growth is not significantly different from zero. This implies that the impact of e-commerce on consumption mainly stems from price and variety channels for the poor. We cannot rule out the income channel for other groups.

**Table 4. E-commerce development and log household income per capita growth**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|All|Urban|Rural|East|Central|West|bottom 25%|25%-75%|top 25%|
|Z-score of purchase/<br>population|0.353***<br>(0.0701)|0.288***<br>(0.062)|0.824***<br>(0.099)|0.344***<br>(0.087)|0.315***<br>(0.104)|0.710***<br>(0.144)|-0.146<br>(0.128)|0.147***<br>(0.025)|0.081**<br>(0.035)|
|Lag log household<br>|-0.735***|-0.722***|-0.764***|-0.706***|-0.740***|-0.780***|-0.931***|-0.919***|-0.952***|
|expenditure per capita|(0.0172)|(0.024)|(0.019)|(0.033)|(0.023)|(0.033)|(0.018)|(0.008)|(0.021)|

·· 23
|Average age of|-0.006|0.057***|-0.090***|-0.022|-0.003|-0.204|0.000|0.002|-0.003|
|---|---|---|---|---|---|---|---|---|---|
|household members/10|(0.013)|(0.018)|(0.021)|(0.017)|(0.025)|(0.126)|(0.022)|(0.007)|(0.013)|
|Square of average age<br>|0.000|-0.001***|0.001***|0.000|-0.000|0.021|0.000|-0.000|0.000|
|of household<br>members/100|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.014)|(0.000)|(0.000)|(0.000)|
|Household dependent<br>|-0.269***|-0.250***|-0.381***|-0.209***|-0.285***|-0.312***|-0.226***|-0.067***|-0.126***|
|ratio|(0.0348)|(0.050)|(0.050)|(0.051)|(0.056)|(0.077)|(0.064)|(0.022)|(0.039)|
|Average years of<br>|0.059***|0.068***|0.047***|0.064***|0.050***|0.067***|0.014**|0.016***|0.010***|
|schooling|(0.004)|(0.005)|(0.005)|(0.006)|(0.005)|(0.009)|(0.006)|(0.002)|(0.004)|
|Urban/rural area|0.185***|||0.158***|0.190***|0.216***|-0.020|0.020|0.010|
|dummy (Urban=1)|(0.0312)|||(0.055)|(0.038)|(0.069)|(0.058)|(0.014)|(0.031)|
|West|-0.081|-0.065|-0.103||||-0.002|-0.003|0.015|
||(0.058)|(0.077)|(0.062)||||(0.069)|(0.025)|(0.047)|
|Central|0.011|-0.011|0.056||||0.125*|0.029|-0.021|
||(0.052)|(0.061)|(0.058)||||(0.067)|(0.018)|(0.038)|
|Constant|6.808***|6.484***|7.827***|6.572***|6.916***|7.711***|7.290***|8.595***|10.036***|
||(0.194)|(0.262)|(0.245)|(0.350)|(0.266)|(0.288)|(0.244)|(0.094)|(0.221)|
|Observations|10,138|4,709|5,429|4,055|3,224|2,858|2,062|5,180|2,896|
|Adjusted R-squared|0.501|0.472|0.540|0.469|0.520|0.530|0.756|0.887|0.760|
|F test|215.7|126|219.5|85.36|168.8|157.4|436|1751|330.9|

Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

The positive association between e-commerce and consumption growth is significant not only for total household consumption, but also for most consumption categories, ranging from basic consumption, such as food, clothes, and local transportation, to consumption of durable goods, travel, and entertainment. The magnitude of the association of e-commerce intensity is larger for the second consumption group than for the first. For most of the consumption categories, the association between e-commerce development and consumption growth is largest for the poorest quartile and declines as household income level moves up. While we have tried to control for as many factors as possible, some variables correlated with both the outcome variable and e-commerce indicator may have been omitted, which may bias the estimates of the e- commerce variable. Our findings imply only associations and not causality. More studies are needed to tease out the causal relationship between e-commerce development and consumption growth.

Developing e-commerce requires not only Internet, but also many other factors, such as infrastructure and logistics services, skills and entrepreneurship, and an overarching enabling business environment facilitating online and offline business. China has been on a fast track of e- commerce development in recent years. With the right policies, further development of e·· 24
commerce can help accelerate consumption growth and narrow the regional gaps in e-commerce development, which in turn can lead to a more inclusive spatial development pattern.

Due to data limitations, our study cannot separate total household consumption into online consumption and offline consumption, and therefore cannot examine the effects of e- commerce development on these two types of consumption separately. Studies on whether e- commerce results in additional offline consumption or online consumption substitutes for offline consumption will be interesting future research topics. Of equal if not more importance, studies on how e-commerce contributes to entrepreneurship and employment, and how this varies across sectors, regions, and individuals of different characteristics, will be crucial to understanding the role of e-commerce in the production and income aspects. In what areas and to what extent e- commerce adds to or substitutes for traditional offline business, and as a result, the net effect on the economy remains an important question for more empirical studies.

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·· 26
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·· 27
<!-- Start of picture text --> Eroeor) numberpopulationof online sellers x 100<br>(Temper numberpopulation ofommnemuyers) of online buyers x 100<br>annual online sales GMV<br>eee GDP x 100<br>annual online purchase GMV<br>eer) GDP x 100<br><!-- End of picture text --> <!-- Start of picture text --> annual sales GMV<br>number of online sellers<br>annual purchase GV<br>number of online buyers<br>annual sales GMV<br>population<br>annual purchase GMV<br>population<br>— national annual online salh e s sales GMVhe GMV x 100<br>—_total number number of o fnational f online online sellers online se t ers sellers __ — 100<br>error annual purchase GMV oe x 100<br>national online purchase GMV<br>—__humber number of of online online buyers buyers __ x 100<br>total number of national online buyers<br><!-- End of picture text -->
# **Step 2 Normalize the original indicators to z-score**

We normalized each indicator to a z-score. The z-score is the difference between the raw value and the population mean divided by the population standard deviation. It is calculated using the formula below.

The z-score is the signed number of standard deviations by which the value of an observation is above or below the population mean value. It is an abstract value that is only used to represent the position in the population distribution of a data point. The mean of the z-score is 0 and the standard deviation of it is 1. The z-score is 0 for observed values at the mean level, positive above the mean and negative below the mean. The absolute value of the z-score measures the distance between a value and the mean value. The advantages of using the z-score are that it makes different indicators more comparable and that we could not infer the original value of each indicator.

The constructed z-score indicators serve as proxy of e-commerce development level derived from the original raw data, including the gross merchandise values of online sales and online purchases, numbers of online sellers and online purchasers, and numbers of packages sent and received due to online sales and online purchases. Meanwhile, the constructed indicators preserve the confidentiality of the original data since the z-score reflects only the position of a data point in a distribution but not the value. If two observed values are from two identical distributions at the same relative position in each distribution, their z-score should be the same regardless of the difference in the raw value of the observation. Hence, the z-scores of the indicators cannot be used to infer the raw purchase/sales GMV, number of buyers/sellers, number of packages.

·· 29
# **Appendix 2. Robustness Check**

## **Table A.1. E-commerce development and total household expenditure per capita using alternative measures**

|VARIABLES|(1)<br>All|(2)<br>Urban|(3)<br>Rural|(4)<br>East|(5)<br>Central|(6)<br>West|
|---|---|---|---|---|---|---|
|Z-score of purchase/census pop in|0.303***|0.247***|0.718***|0.267***|0.313**|0.538***|
|2013|(0.0671)|(0.061)|(0.098)|(0.083)|(0.119)|(0.157)|
|Z-score of buyers/census|0.189***|0.150***|0.455***|0.127**|0.344***|0.422***|
|population in 2013|(0.064)|(0.057)|(0.145)|(0.057)|(0.104)|(0.117)|
|Z-score of buyers share in the|0.208***|0.175***|0.734***|0.179***|0.660***|0.664***|
|national market in 2013|(0.064)|(0.051)|(0.143)|(0.040)|(0.206)|(0.194)|
|Z-score of purchase share in|0.136***|0.114***|0.545***|0.118***|0.485**|0.649***|
|national market in 2013|(0.042)|(0.033)|(0.117)|(0.026)|(0.185)|(0.199)|
|Z-score of purchase over GDP in|0.275***|0.269***|0.266***|0.431***|0.171**|0.252**|
|2013|(0.067)|(0.079)|(0.088)|(0.097)|(0.084)|(0.105)|
|Z-score of per consumer purchase|0.126***|0.149***|0.093***|0.148***|0.112***|0.022|
|in 2013|(0.021)|(0.026)|(0.029)|(0.028)|(0.036)|(0.049)|
|Observations|11,107|5,183|5,945|4,458|3,486|3,163|

Note: The model of estimation is the same as Table 1, the dependent variable is the logarithmic form of household per capita consumption growth, and the variables of control include lagged log value of household expenditure per capita, average age/10, age squared/100, dependency ratio, urban-rural dummies, regional dummies, and year fixed effect except for gift and house repair and upgrading. For readability, we present only the results of interest - the coefficients of the z-score each measure in 2013. Fully results available upon request. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 30
**Table A.2. E-commerce development and various categories of household expenditure per capita by region (Extended Model 1)**

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Urban|Rural|East|Central|West|
|Total household expenditure|0.257***|0.194***|0.695***|0.248***|0.247*|0.538***|
||(0.067)|(0.058)|(0.103)|(0.080)|(0.126)|(0.157)|
|Observations|10,872|5,072|5,800|4,359|3,428|3,163|
|Adjusted R square|0.343|0.320|0.379|0.346|0.320|0.338|
|Cosmetics and beauty|0.404***|0.339***|0.945***|0.462***|0.289*|0.996***|
||(0.115)|(0.112)|(0.235)|(0.151)|(0.154)|(0.334)|
|Observations|10,764|5,023|5,741|4,312|3,399|3,053|
|Adjusted R square|0.344|0.367|0.326|0.330|0.361|0.361|
|Food|0.311***|0.238***|0.816***|0.308***|0.283**|0.523**|
||(0.065)|(0.056)|(0.121)|(0.079)|(0.116)|(0.229)|
|Observations|10,720|4,993|5,727|4,279|3,396|3,045|
|Adjusted R square|0.345|0.343|0.360|0.359|0.309|0.386|
|Of which: Food at home|0.345***|0.288***|0.773***|0.322***|0.331***|0.458**|
||(0.071)|(0.060)|(0.160)|(0.092)|(0.116)|(0.224)|
|Observations|10,717|4,991|5,726|4,277|3,395|3,045|
|Adjusted R square|0.389|0.400|0.388|0.333|0.379|0.475|
|Of which: Dining out|0.424***|0.286**|1.275***|0.302*|0.561**|1.168**|
||(0.142)|(0.136)|(0.272)|(0.168)|(0.240)|(0.453)|
|Observations|10,878|5,075|5,803|4,363|3,430|3,085|
|Adjusted R square|0.332|0.323|0.364|0.322|0.328|0.357|
|Clothes|0.217**|0.155*|0.671***|0.196|0.243*|0.489*|
||(0.095)|(0.089)|(0.171)|(0.129)|(0.128)|(0.262)|
|Observations|10,606|4,922|5,684|4,225|3,342|3,039|
|Adjusted R square|0.325|0.342|0.319|0.327|0.350|0.292|
|Utilities|0.156***|0.153***|0.251*|0.188***|0.094*|0.417|
||(0.035)|(0.035)|(0.148)|(0.045)|(0.049)|(0.309)|
|Observations|10,658|4,945|5,713|4,282|3,363|3,013|
|Adjusted R square|0.305|0.294|0.317|0.312|0.297|0.325|
|Communications|0.189***|0.150***|0.523***|0.149***|0.223***|0.402**|
||(0.044)|(0.038)|(0.078)|(0.052)|(0.069)|(0.190)|
|Observations|10,546|4,902|5,644|4,212|3,344|2,990|
|Adjusted R square|0.337|0.353|0.335|0.308|0.387|0.313|
|Local transport|0.353***|0.295***|0.659***|0.277**|0.402***|0.307|
||(0.083)|(0.078)|(0.153)|(0.114)|(0.088)|(0.291)|
|Observations|10,490|4,865|5,625|4,188|3,323|2,979|
|Adjusted R square|0.306|0.318|0.299|0.290|0.321|0.318|
|Travel|0.713***|0.549***|1.495***|0.668**|0.643***|1.768***|
||(0.192)|(0.177)|(0.260)|(0.284)|(0.162)|(0.508)|
|||||||··31|
|Observations|10,667|4,950|5,717|4,287|3,369|3,011|
|---|---|---|---|---|---|---|
|Adjusted R square|0.297|0.289|0.340|0.288|0.276|0.355|
|Entertainment|0.550***|0.409***|1.159***|0.461**|0.650***|0.040|
||(0.145)|(0.145)|(0.339)|(0.210)|(0.164)|(0.284)|
|Observations|10,641|4,940|5,701|4,270|3,363|3,008|
|Adjusted R square|0.283|0.285|0.305|0.272|0.281|0.315|
|Automobiles|0.388***|0.265**|1.434***|0.302**|0.516**|0.929***|
||(0.124)|(0.109)|(0.289)|(0.148)|(0.218)|(0.332)|
|Observations|10,534|4,885|5,649|4,218|3,325|2,991|
|Adjusted R square|0.234|0.243|0.230|0.232|0.238|0.238|
|Other vehicles|0.154|0.119|0.869**|0.074|0.366|-0.001|
||(0.141)|(0.129)|(0.359)|(0.129)|(0.372)|(0.586)|
|Observations|10,626|4,936|5,690|4,263|3,357|3,006|
|Adjusted R square|0.396|0.400|0.395|0.399|0.395|0.397|
|Durable goods|0.320***|0.184*|1.004**|0.317**|0.254|0.281|
||(0.120)|(0.105)|(0.396)|(0.135)|(0.193)|(0.916)|
|Observations|10,841|5,049|5,792|4,336|3,423|3,082|
|Adjusted R square|0.429|0.424|0.442|0.420|0.446|0.425|
|Medical|0.170**|0.130|0.442**|0.093|0.321**|0.471|
||(0.082)|(0.094)|(0.179)|(0.117)|(0.136)|(0.410)|
|Observations|10,733|5,004|5,729|4,279|3,395|3,059|
|Adjusted R square|0.377|0.366|0.396|0.388|0.382|0.358|
|Health and fitness|0.273**|0.143|0.544***|0.211|0.257|0.529|
||(0.110)|(0.102)|(0.185)|(0.142)|(0.153)|(0.325)|
|Observations|10,844|5,055|5,789|4,349|3,422|3,073|
|Adjusted R square|0.303|0.286|0.367|0.302|0.320|0.290|
|Education|-0.143|-0.116|0.093|-0.215**|0.079|-0.305|
||(0.114)|(0.126)|(0.268)|(0.085)|(0.203)|(0.373)|
|Observations|10,775|5,026|5,749|4,314|3,406|3,055|
|Adjusted R square|0.222|0.232|0.224|0.227|0.220|0.236|
|Home repair|-0.089|-0.126|0.055|-0.068|-0.172|-0.359|
||(0.080)|(0.085)|(0.310)|(0.089)|(0.143)|(0.629)|
|Observations|10,666|4,961|5,705|4,282|3,372|3,012|
|Adjusted R square|0.450|0.433|0.470|0.476|0.456|0.408|
|Gifts|-0.004|-0.005|0.390|-0.045|0.126|0.106|
||(0.101)|(0.096)|(0.273)|(0.132)|(0.127)|(0.577)|
|Observations|8,839|4,143|4,696|3,452|2,817|2,570|
|Adjusted R square|0.111|0.113|0.114|0.078|0.118|0.165|

Note: Extended model with additional variables of control, including, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age/10, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 32
**Table A.3. E-commerce development and various categories of household expenditure per capita by region (Extended Model 1)**

||(1)<br>All|(2)<br>bottom 25%|(3)<br>25%-75%|(4)<br>top 25%|
|---|---|---|---|---|
|Total household expenditure|0.257***|0.305***|0.143***|0.053|
||(0.067)|(0.102)|(0.049)|(0.048)|
|Observations|10,872|2,984|5,627|2,232|
|Adjusted R square|0.343|0.416|0.440|0.446|
|Cosmetics and beauty|0.404***|0.643**|0.197|0.164*|
||(0.115)|(0.246)|(0.133)|(0.084)|
|Observations|10,764|2,956|5,578|2,201|
|Adjusted R square|0.344|0.330|0.370|0.423|
|Food|0.311***|0.502***|0.243***|0.108**|
||(0.065)|(0.128)|(0.058)|(0.045)|
|Observations|10,720|2,929|5,558|2,204|
|Adjusted R square|0.345|0.428|0.353|0.445|
|Of which: Food at home|0.345***|0.538***|0.279***|0.177***|
||(0.071)|(0.159)|(0.071)|(0.057)|
|Observations|10,717|2,929|5,555|2,204|
|Adjusted R square|0.389|0.463|0.401|0.391|
|Of which: Dining out|0.424***|0.169|0.144|0.111|
||(0.142)|(0.214)|(0.147)|(0.118)|
|Observations|10,878|2,987|5,630|2,232|
|Adjusted R square|0.332|0.455|0.341|0.380|
|Clothes|0.217**|0.190|0.036|-0.043|
||(0.095)|(0.247)|(0.085)|(0.085)|
|Observations|10,606|2,920|5,475|2,182|
|Adjusted R square|0.325|0.328|0.384|0.348|
|Utilities|0.156***|0.305**|0.118***|0.038|
||(0.035)|(0.131)|(0.039)|(0.031)|
|Observations|10,658|2,956|5,532|2,141|
|Adjusted R square|0.305|0.341|0.311|0.328|
|Communications|0.189***|0.023|0.118**|0.087***|
||(0.044)|(0.137)|(0.049)|(0.032)|
|Observations|10,546|2,913|5,478|2,128|
|Adjusted R square|0.337|0.302|0.382|0.431|
|Local transport|0.353***|0.048|0.148*|0.235*|
||(0.083)|(0.244)|(0.080)|(0.119)|
|Observations|10,490|2,911|5,437|2,113|
|Adjusted R square|0.306|0.338|0.327|0.310|
|Travel|0.713***|0.811**|0.371**|0.411**|
||(0.192)|(0.322)|(0.147)|(0.190)|

·· 33
|Observations|10,667|2,957|5,536|2,145|
|---|---|---|---|---|
|Adjusted R square|0.297|0.437|0.346|0.283|
|Entertainment|0.550***|0.159|0.335*|0.341***|
||(0.145)|(0.146)|(0.179)|(0.107)|
|Observations|10,641|2,952|5,516|2,144|
|Adjusted R square|0.283|0.315|0.303|0.326|
|Automobiles|0.388***|0.229|0.132|0.330**|
||(0.124)|(0.177)|(0.154)|(0.144)|
|Observations|10,534|2,935|5,465|2,106|
|Adjusted R square|0.234|0.333|0.233|0.243|
|Other vehicles|0.154|-0.048|0.025|0.022|
||(0.141)|(0.295)|(0.196)|(0.119)|
|Observations|10,626|2,947|5,507|2,144|
|Adjusted R square|0.396|0.412|0.403|0.396|
|Durable goods|0.320***|-0.225|0.024|0.062|
||(0.120)|(0.358)|(0.122)|(0.128)|
|Observations|10,841|2,984|5,610|2,218|
|Adjusted R square|0.429|0.488|0.445|0.409|
|Medical|0.170**|0.548**|-0.041|0.192|
||(0.082)|(0.247)|(0.120)|(0.158)|
|Observations|10,733|2,947|5,550|2,207|
|Adjusted R square|0.377|0.379|0.384|0.382|
|Health and fitness|0.273**|0.322*|-0.044|0.057|
||(0.110)|(0.164)|(0.104)|(0.107)|
|Observations|10,844|2,980|5,615|2,220|
|Adjusted R square|0.303|0.434|0.335|0.287|
|Education|-0.143|-0.552**|-0.124|0.040|
||(0.114)|(0.228)|(0.113)|(0.223)|
|Observations|10,775|2,954|5,573|2,219|
|Adjusted R square|0.222|0.230|0.219|0.279|
|Home repair|-0.089|-0.144|-0.112|-0.400***|
||(0.080)|(0.328)|(0.120)|(0.131)|
|Observations|10,666|2,959|5,531|2,147|
|Adjusted R square|0.450|0.435|0.462|0.453|
|Gifts|-0.004|0.437|0.002|-0.310**|
||(0.101)|(0.413)|(0.115)|(0.145)|
|Observations|8,839|2,258|4,701|1,857|
|Adjusted R square|0.111|0.140|0.117|0.160|

Note: Extended model with additional variables of control, including, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age/10, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 34
**Table A.4. E-commerce development and various categories of household expenditure per capita by region (Extended Model 2)**

|VARIABLES|(1)<br>All|(2)<br>Urban|(3)<br>Rural|(4)<br>East|(5)<br>Central|(6)<br>West|
|---|---|---|---|---|---|---|
|Total household expenditure|0.245***|0.176***|0.722***|0.231***|0.234**|0.538***|
||(0.062)|(0.052)|(0.096)|(0.078)|(0.107)|(0.157)|
|Observations|9,502|4,402|5,100|3,808|3,018|3,163|
|Adjusted R square|0.341|0.314|0.383|0.336|0.325|0.338|
|Cosmetics and beauty|0.406***|0.327***|1.011***|0.419***|0.311*|1.259***|
||(0.125)|(0.118)|(0.248)|(0.155)|(0.181)|(0.302)|
|Observations|9,422|4,366|5,056|3,772|2,997|2,653|
|Adjusted R square|0.342|0.367|0.323|0.317|0.368|0.362|
|Food|0.289***|0.219***|0.764***|0.302***|0.233**|0.479*|
||(0.063)|(0.053)|(0.122)|(0.079)|(0.099)|(0.243)|
|Observations|9,398|4,346|5,052|3,751|2,998|2,649|
|Adjusted R square|0.326|0.321|0.341|0.333|0.302|0.360|
|Of which: Food at home|0.342***|0.279***|0.814***|0.338***|0.291**|0.453*|
||(0.067)|(0.059)|(0.126)|(0.086)|(0.108)|(0.257)|
|Observations|9,395|4,344|5,051|3,749|2,997|2,649|
|Adjusted R square|0.375|0.385|0.375|0.316|0.372|0.459|
|Of which: Dining out|0.349**|0.214|1.202***|0.234|0.464*|1.165**|
||(0.146)|(0.132)|(0.265)|(0.173)|(0.240)|(0.496)|
|Observations|9,503|4,402|5,101|3,808|3,019|2,676|
|Adjusted R square|0.329|0.321|0.359|0.308|0.331|0.359|
|Clothes|0.211**|0.144|0.738***|0.195|0.233|0.502*|
||(0.103)|(0.096)|(0.178)|(0.137)|(0.146)|(0.289)|
|Observations|9,297|4,285|5,012|3,704|2,951|2,642|
|Adjusted R square|0.321|0.341|0.312|0.312|0.349|0.304|
|Utilities|0.149***|0.141***|0.321**|0.190***|0.076*|0.486|
||(0.034)|(0.034)|(0.141)|(0.044)|(0.041)|(0.307)|
|Observations|9,469|4,376|5,093|3,787|3,012|2,670|
|Adjusted R square|0.298|0.284|0.313|0.311|0.292|0.309|
|Communications|0.190***|0.154***|0.547***|0.148**|0.233***|0.375**|
||(0.045)|(0.040)|(0.084)|(0.057)|(0.061)|(0.177)|
|Observations|9,382|4,338|5,044|3,734|2,996|2,652|
|Adjusted R square|0.341|0.364|0.334|0.303|0.397|0.324|
|Local transport|0.364***|0.297***|0.807***|0.300**|0.410***|0.281|
||(0.083)|(0.077)|(0.165)|(0.117)|(0.083)|(0.299)|
|Observations|9,335|4,308|5,027|3,708|2,979|2,648|
|Adjusted R square|0.299|0.307|0.296|0.285|0.315|0.303|
|Travel|0.704***|0.534***|1.609***|0.638**|0.657***|1.888***|
||(0.198)|(0.184)|(0.257)|(0.285)|(0.189)|(0.567)|
|Observations|9,478|4,382|5,096|3,794|3,014|2,670|
|Adjusted R square|<br>0.296|<br>0.291|<br>0.333|<br>0.286|<br>0.279|<br>0.350|
|Entertainment|0.528***|0.379**|1.158***|0.398*|0.687***|0.106|
||(0.151)|(0.147)|(0.348)|(0.212)|(0.186)|(0.287)|
|Observations|9,455|4,371|5,084|3,778|3,011|2,666|
|Adjusted R square|0.289|0.291|0.314|0.270|0.292|0.325|
|Automobiles|0.384***|0.247**|1.600***|0.291*|0.505**|0.979***|

·· 35
||(0.133)|(0.119)|(0.339)|(0.169)|(0.222)|(0.329)|
|---|---|---|---|---|---|---|
|Observations|9,370|4,328|5,042|3,733|2,981|2,656|
|Adjusted R square|0.233|0.237|0.238|0.229|0.234|0.248|
|Other vehicles|0.179|0.123|0.994**|0.101|0.352|0.097|
||(0.137)|(0.125)|(0.387)|(0.130)|(0.348)|(0.677)|
|Observations|9,448|4,371|5,077|3,773|3,008|2,667|
|Adjusted R square|0.393|0.394|0.396|0.391|0.397|0.392|
|Durable goods|0.348***|0.201*|1.039**|0.315**|0.314|0.298|
||(0.130)|(0.120)|(0.400)|(0.145)|(0.224)|(0.871)|
|Observations|9,476|4,381|5,095|3,787|3,015|2,674|
|Adjusted R square|0.429|0.422|0.444|0.420|0.446|0.422|
|Medical|0.146*|0.095|0.459***|0.069|0.281*|0.458|
||(0.082)|(0.096)|(0.171)|(0.110)|(0.143)|(0.410)|
|Observations|9,395|4,350|5,045|3,748|2,989|2,658|
|Adjusted R square|0.362|0.350|0.385|0.375|0.370|0.339|
|Health and fitness|0.222**|0.095|0.514**|0.100|0.322*|0.610*|
||(0.107)|(0.099)|(0.215)|(0.132)|(0.165)|(0.353)|
|Observations|9,478|4,384|5,094|3,798|3,014|2,666|
|Adjusted R square|0.299|0.278|0.371|0.294|0.313|0.301|
|Education|-0.142|-0.127|0.167|-0.232***|0.087|0.082|
||(0.116)|(0.130)|(0.269)|(0.083)|(0.201)|(0.396)|
|Observations|9,417|4,359|5,058|3,767|2,997|2,653|
|Adjusted R square|0.217|0.228|0.219|0.221|0.210|0.245|
|Home repair|-0.065|-0.153*|0.393|-0.082|-0.186|0.088|
||(0.082)|(0.084)|(0.349)|(0.103)|(0.134)|(0.648)|
|Observations|9,470|4,386|5,084|3,785|3,017|2,668|
|Adjusted R square|0.452|0.437|0.469|0.483|0.445|0.420|
|Gifts|-0.013|-0.031|0.545**|-0.050|0.115|0.170|
||(0.101)|(0.098)|(0.243)|(0.137)|(0.113)|(0.495)|
|Observations|7,802|3,626|4,176|3,039|2,525|2,238|
|Adjusted R square|0.111|0.113|0.116|0.079|0.120|0.167|

Note: Extended model with additional variables of control, including the lagged household per capita income growth, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling household gender ratio, average age/10, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 36
# **Table A.5. E-commerce development and various categories of household expenditure per capita by household consumption level (Extended model 2)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||All|bottom 25%|25%-75%|top25%|
|Total household expenditure|0.245***|0.371***|0.142***|0.039|
||(0.062)|(0.078)|(0.047)|(0.050)|
|Observations|9,502|2,590|4,945|1,946|
|Adjusted R square|0.341|0.407|0.431|0.456|
|Cosmetics and beauty|0.406***|0.540**|0.214|0.177*|
||(0.125)|(0.272)|(0.146)|(0.098)|
|Observations|9,422|2,569|4,905|1,927|
|Adjusted R square|0.342|0.322|0.370|0.425|
|Food|0.289***|0.447***|0.226***|0.104**|
||(0.063)|(0.134)|(0.055)|(0.043)|
|Observations|9,398|2,551|4,895|1,931|
|Adjusted R square|0.326|0.395|0.331|0.457|
|Of which: Food at home|0.342***|0.502***|0.267***|0.203***|
||(0.067)|(0.170)|(0.071)|(0.056)|
|Observations|9,395|2,551|4,892|1,931|
|Adjusted R square|0.375|0.447|0.384|0.382|
|Of which: Dining out|0.349**|0.155|0.097|0.034|
||(0.146)|(0.245)|(0.155)|(0.118)|
|Observations|9,503|2,590|4,946|1,946|
|Adjusted R square|0.329|0.458|0.335|0.364|
|Clothes|0.211**|0.187|0.062|-0.054|
||(0.103)|(0.267)|(0.096)|(0.093)|
|Observations|9,297|2,536|4,833|1,907|
|Adjusted R square|0.321|0.325|0.374|0.353|
|Utilities|0.149***|0.337**|0.105***|0.051|
||(0.034)|(0.129)|(0.036)|(0.032)|
|Observations|9,469|2,584|4,935|1,929|
|Adjusted R square|0.298|0.321|0.303|0.349|
|Communications|0.190***|0.029|0.123**|0.094***|
||(0.045)|(0.135)|(0.052)|(0.034)|
|Observations|9,382|2,557|4,888|1,918|
|Adjusted R square|0.341|0.312|0.378|0.434|
|Local transport|0.364***|-0.053|0.181**|0.261**|
||(0.083)|(0.263)|(0.086)|(0.120)|
|Observations|9,335|2,555|4,855|1,904|
|Adjusted R square|0.299|0.339|0.315|0.302|
|Travel|0.704***|0.966**|0.376**|0.393**|
||(0.198)|(0.380)|(0.162)|(0.186)|
|Observations|9,478|2,586|4,936|1,935|
|Adjusted R square|0.296|0.420|0.350|0.277|
|Entertainment|0.528***|0.244|0.290|0.338***|
||(0.151)|(0.175)|(0.180)|(0.115)|
|Observations|9,455|2,580|4,921|1,933|
|Adjusted R square|0.289|0.332|0.308|0.321|
|Automobiles|0.384***|0.297|0.170|0.298*|

·· 37
||(0.133)|(0.203)|(0.164)|(0.162)|
|---|---|---|---|---|
|Observations|9,370|2,567|4,881|1,902|
|Adjusted R square|0.233|0.329|0.233|0.238|
|Other vehicles|0.179|0.279|0.002|0.051|
||(0.137)|(0.309)|(0.205)|(0.117)|
|Observations|9,448|2,578|4,917|1,933|
|Adjusted R square|0.393|0.406|0.405|0.388|
|Durable goods|0.348***|-0.113|0.075|0.057|
||(0.130)|(0.428)|(0.149)|(0.134)|
|Observations|9,476|2,588|4,930|1,937|
|Adjusted R square|0.429|0.486|0.444|0.403|
|Medical|0.146*|0.584**|-0.049|0.169|
||(0.082)|(0.290)|(0.113)|(0.145)|
|Observations|9,395|2,559|4,887|1,928|
|Adjusted R square|0.362|0.381|0.363|0.367|
|Health and fitness|0.222**|0.151|-0.047|-0.012|
||(0.107)|(0.144)|(0.107)|(0.114)|
|Observations|9,478|2,587|4,934|1,936|
|Adjusted R square|0.299|0.419|0.334|0.284|
|Education|-0.142|-0.414*|-0.158|0.086|
||(0.116)|(0.241)|(0.119)|(0.203)|
|Observations|9,417|2,563|4,899|1,934|
|Adjusted R square|0.217|0.226|0.215|0.271|
|Home repair|-0.065|-0.064|-0.010|-0.447***|
||(0.082)|(0.383)|(0.130)|(0.131)|
|Observations|9,470|2,586|4,931|1,932|
|Adjusted R square|0.452|0.434|0.463|0.460|
|Gifts|-0.013|0.562|-0.009|-0.306**|
||(0.101)|(0.387)|(0.119)|(0.154)|
|Observations|7,802|1,983|4,172|1,631|
|Adjusted R square|0.111|0.142|0.117|0.162|

Note: Extended model with additional variables of control, including the lagged log household per capita income growth, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age squared/100, dependency ratio, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 38
**Table A.6. E-commerce development and various categories of household expenditure per capita by region (Extended Model 3)**

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Urban|Rural|East|Central|West|
|Total household expenditure|0.210***|0.151***|0.623***|0.207***|0.189|0.538***|
||(0.061)|(0.054)|(0.103)|(0.067)|(0.124)|(0.157)|
|Observations|10,171|4,740|5,431|4,049|3,185|3,163|
|Adjusted R square|0.366|0.345|0.395|0.374|0.347|0.338|
|Cosmetics and beauty|0.328***|0.256**|0.845***|0.387***|0.221|0.879***|
||(0.110)|(0.107)|(0.226)|(0.128)|(0.165)|(0.320)|
|Observations|10,069|4,695|5,374|4,006|3,157|2,906|
|Adjusted R square|0.349|0.376|0.326|0.338|0.362|0.367|
|Food|0.268***|0.205***|0.718***|0.280***|0.218*|0.444**|
||(0.059)|(0.052)|(0.124)|(0.070)|(0.113)|(0.218)|
|Observations|10,037|4,673|5,364|3,982|3,155|2,900|
|Adjusted R square|0.358|0.360|0.367|0.373|0.337|0.384|
|Of which: Food at home|0.289***|0.245***|0.639***|0.289***|0.240**|0.362*|
||(0.063)|(0.055)|(0.165)|(0.086)|(0.107)|(0.214)|
|Observations|10,034|4,671|5,363|3,980|3,154|2,900|
|Adjusted R square|0.399|0.416|0.390|0.340|0.410|0.470|
|Of which: Dining out|0.330**|0.171|1.192***|0.207|0.444*|1.092**|
||(0.134)|(0.127)|(0.266)|(0.151)|(0.241)|(0.493)|
|Observations|10,176|4,742|5,434|4,052|3,187|2,937|
|Adjusted R square|0.337|0.328|0.370|0.326|0.333|0.362|
|Clothes|0.137|0.079|0.513***|0.128|0.155|0.304|
||(0.087)|(0.082)|(0.169)|(0.115)|(0.131)|(0.254)|
|Observations|9,924|4,603|5,321|3,926|3,104|2,894|
|Adjusted R square|0.338|0.359|0.327|0.346|0.359|0.301|
|Utilities|0.130***|0.130***|0.191|0.174***|0.037|0.408|
||(0.034)|(0.037)|(0.141)|(0.041)|(0.042)|(0.307)|
|Observations|9,972|4,623|5,349|3,979|3,125|2,868|
|Adjusted R square|0.305|0.302|0.311|0.313|0.303|0.319|
|Communications|0.156***|0.117***|0.481***|0.126**|0.175**|0.334|
||(0.041)|(0.034)|(0.080)|(0.049)|(0.066)|(0.200)|
|Observations|9,868|4,582|5,286|3,915|3,108|2,845|
|Adjusted R square|0.348|0.373|0.338|0.322|0.407|0.311|
|Local transport|0.292***|0.235***|0.527***|0.223**|0.319***|0.137|
||(0.075)|(0.068)|(0.160)|(0.102)|(0.090)|(0.289)|
|Observations|9,812|4,546|5,266|3,886|3,087|2,839|
|Adjusted R square|0.319|0.335|0.309|0.305|0.335|0.327|
|Travel|0.579***|0.395***|1.270***|0.518**|0.489***|1.697***|
||(0.164)|(0.146)|(0.200)|(0.239)|(0.153)|(0.484)|

·· 39
|Observations|9,978|4,626|5,352|3,982|3,131|2,865|
|---|---|---|---|---|---|---|
|Adjusted R square|0.304|0.300|0.342|0.301|0.280|0.352|
|Entertainment|0.519***|0.394***|1.020***|0.417**|0.618***|0.036|
||(0.139)|(0.141)|(0.310)|(0.194)|(0.181)|(0.300)|
|Observations|9,955|4,616|5,339|3,967|3,126|2,862|
|Adjusted R square|0.284|0.288|0.299|0.271|0.281|0.319|
|Automobiles|0.311***|0.204*|1.234***|0.210|0.423*|0.846***|
||(0.118)|(0.106)|(0.273)|(0.135)|(0.219)|(0.295)|
|Observations|9,850|4,565|5,285|3,912|3,088|2,850|
|Adjusted R square|0.241|0.250|0.238|0.240|0.239|0.252|
|Other vehicles|0.085|0.062|0.728**|0.007|0.288|-0.061|
||(0.146)|(0.139)|(0.364)|(0.132)|(0.395)|(0.561)|
|Observations|9,940|4,614|5,326|3,960|3,119|2,861|
|Adjusted R square|0.397|0.403|0.395|0.402|0.399|0.392|
|Durable goods|0.187|0.052|0.853**|0.176|0.121|0.181|
||(0.118)|(0.110)|(0.389)|(0.121)|(0.188)|(0.929)|
|Observations|10,141|4,718|5,423|4,027|3,180|2,934|
|Adjusted R square|0.435|0.432|0.443|0.423|0.455|0.429|
|Medical|0.136|0.097|0.442**|0.040|0.291**|0.515|
||(0.089)|(0.101)|(0.195)|(0.134)|(0.142)|(0.388)|
|Observations|10,045|4,679|5,366|3,978|3,154|2,913|
|Adjusted R square|0.376|0.369|0.389|0.383|0.383|0.359|
|Health and fitness|0.183*|0.055|0.345*|0.118|0.179|0.383|
||(0.098)|(0.093)|(0.195)|(0.116)|(0.163)|(0.307)|
|Observations|10,144|4,722|5,422|4,040|3,179|2,925|
|Adjusted R square|0.309|0.293|0.372|0.304|0.330|0.300|
|Education|-0.142|-0.131|0.174|-0.231***|0.035|-0.089|
||(0.114)|(0.126)|(0.274)|(0.086)|(0.196)|(0.358)|
|Observations|10,081|4,695|5,386|4,007|3,165|2,909|
|Adjusted R square|0.221|0.231|0.220|0.226|0.217|0.236|
|Home repair|-0.124|-0.156*|0.033|-0.114|-0.263|-0.213|
||(0.087)|(0.091)|(0.342)|(0.081)|(0.162)|(0.660)|
|Observations|9,975|4,633|5,342|3,975|3,134|2,866|
|Adjusted R square|0.452|0.434|0.471|0.483|0.455|0.407|
|Gifts|-0.062|-0.054|0.227|-0.115|0.053|0.011|
||(0.100)|(0.098)|(0.275)|(0.134)|(0.133)|(0.608)|
|Observations|8,279|3,870|4,409|3,214|2,623|2,442|
|Adjusted R square|0.111|0.108|0.118|0.078|0.122|0.165|

Note: Alternative extended model, with additional variables of control, including the lagged household per capita income, legged log of household expenditure per capita of the consumption category in question, household average years of schooling household gender ratio, average age, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 40
**Table A.7. E-commerce development and various categories of household expenditure per capita by household consumption level (Extended model 3)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||All|bottom 25%|25%-75%|top 25%|
|Total household expenditure|0.210***|0.289***|0.114**|0.047|
||(0.061)|(0.105)|(0.046)|(0.043)|
||10,171|2,770|5,287|2,088|
||0.366|0.427|0.449|0.458|
|Cosmetics and beauty|0.328***|0.555**|0.176|0.128*|
||(0.110)|(0.251)|(0.137)|(0.071)|
|Observations|10,069|2,743|5,239|2,061|
|Adjusted R square|0.349|0.332|0.365|0.436|
|Food|0.268***|0.466***|0.227***|0.094**|
||(0.059)|(0.131)|(0.055)|(0.044)|
|Observations|10,034|2,720|5,225|2,063|
|Adjusted R square|0.399|0.464|0.406|0.400|
|Of which: Food at home|0.289***|0.492***|0.264***|0.144**|
||(0.063)|(0.167)|(0.069)|(0.055)|
|Observations|10,034|2,018|5,116|2,877|
|Adjusted R square|0.399|0.464|0.366|0.410|
|Of which: Dining out|0.330**|0.141|0.081|0.061|
||(0.134)|(0.220)|(0.135)|(0.123)|
|Observations|9,924|2,712|5,144|2,042|
|Adjusted R square|0.338|0.329|0.393|0.376|
|Clothes|0.137|0.165|-0.002|-0.066|
||(0.087)|(0.248)|(0.090)|(0.075)|
|Observations|9,924|2,061|5,178|2,911|
|Adjusted R square|0.338|0.479|0.362|0.343|
|Utilities|0.130***|0.279**|0.111***|0.023|
||(0.034)|(0.126)|(0.040)|(0.030)|
|Observations|9,972|2,743|5,198|2,005|
|Adjusted R square|0.305|0.330|0.313|0.328|
|Communications|0.156***|0.033|0.106**|0.081***|
||(0.041)|(0.131)|(0.049)|(0.029)|
|Observations|9,868|2,704|5,148|1,991|
|Adjusted R square|0.348|0.294|0.398|0.436|
|Local transport|0.292***|0.030|0.121|0.220**|
||(0.075)|(0.243)|(0.082)|(0.110)|
|Observations|9,812|2,703|5,105|1,978|
|Adjusted R square|0.319|0.344|0.335|0.325|
|Travel|0.579***|0.771**|0.299**|0.322*|
||(0.164)|(0.327)|(0.126)|(0.170)|

·· 41
|Observations|9,978|2,745|5,199|2,008|
|---|---|---|---|---|
|Adjusted R square|0.304|0.450|0.345|0.288|
|Entertainment|0.519***|0.190|0.298*|0.374***|
||(0.139)|(0.144)|(0.178)|(0.098)|
|Observations|9,850|2,722|5,132|1,971|
|Adjusted R square<br>|0.241|0.336|0.240|0.248|
|Automobiles|0.311***|0.199|0.094|0.307**|
||(0.118)|(0.172)|(0.150)|(0.125)|
|Observations|9,850|2,722|5,132|1,971|
|Adjusted R square|0.241|0.336|0.240|0.248|
|Other vehicles|0.085|-0.129|0.046|-0.030|
||(0.146)|(0.288)|(0.206)|(0.131)|
|Observations|9,940|2,735|5,173|2,007|
|Adjusted R square|0.397|0.416|0.401|0.399|
|Durable goods|0.187|-0.275|-0.019|0.010|
||(0.118)|(0.380)|(0.120)|(0.132)|
|Observations|10,141|2,770|5,270|2,075|
|Adjusted R square|0.435|0.496|0.446|0.412|
|Medical|0.136|0.498*|-0.051|0.181|
||(0.089)|(0.257)|(0.118)|(0.172)|
|Observations|10,045|2,737|5,216|2,066|
|Adjusted R square|0.376|0.367|0.386|0.385|
|Health and fitness|0.183*|0.229*|-0.059|-0.007|
||(0.098)|(0.135)|(0.107)|(0.111)|
|Observations|10,144|2,766|5,276|2,076|
|Adjusted R square|0.309|0.424|0.338|0.294|
|Education|-0.142|-0.410*|-0.160|0.024|
||(0.114)|(0.213)|(0.123)|(0.202)|
|Observations|10,081|2,744|5,235|2,076|
|Adjusted R square|0.221|0.229|0.217|0.275|
|Home repair|-0.124|-0.090|-0.169|-0.325**|
||(0.087)|(0.341)|(0.128)|(0.136)|
|Observations|9,975|2,746|5,196|2,007|
|Adjusted R square|0.452|0.435|0.465|0.451|
|Gifts|-0.062|0.505|-0.014|-0.376**|
||(0.100)|(0.379)|(0.115)|(0.152)|
|Observations|8,279|2,094|4,425|1,738|
|Adjusted R square|0.111|0.133|0.119|0.150|

Note: Alternative extended model, with additional variables of control, including the lagged log household per capita, income per capita lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age squared/100, dependency ratio, and urbanrural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** ·· 42
**Table A.8. E-commerce development and various categories of household expenditure per capita by region (Extended Model 4)**

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Urban|Rural|East|Central|West|
|Total household expenditure|0.226***|0.155***|0.692***|0.170**|0.238*|0.538***|
||(0.064)|(0.056)|(0.105)|(0.069)|(0.125)|(0.157)|
|Observations|10,701|5,072|5,629|4,253|3,428|3,163|
|Adjusted R square|0.344|0.324|0.378|0.351|0.320|0.338|
|Cosmetics and beauty|0.353***|0.280***|0.914***|0.396**|0.258*|1.004***|
||(0.113)|(0.106)|(0.251)|(0.150)|(0.150)|(0.328)|
|Observations|10,597|5,023|5,574|4,210|3,399|2,988|
|Adjusted R square|0.343|0.368|0.322|0.332|0.361|0.353|
|Food|0.283***|0.229***|0.737***|0.238***|0.269**|0.479**|
||(0.063)|(0.056)|(0.121)|(0.070)|(0.117)|(0.217)|
|Observations|10,550|4,993|5,557|4,174|3,396|2,980|
|Adjusted R square|0.347|0.343|0.365|0.366|0.310|0.390|
|Of which: Food at home|0.304***|0.266***|0.682***|0.240***|0.309**|0.425*|
||(0.067)|(0.058)|(0.164)|(0.084)|(0.115)|(0.224)|
|Observations|10,547|4,991|5,556|4,172|3,395|2,980|
|Adjusted R square|0.390|0.400|0.390|0.339|0.380|0.476|
|Of which: Dining out|0.424***|0.267*|1.258***|0.210|0.589**|1.137**|
||(0.148)|(0.147)|(0.273)|(0.161)|(0.245)|(0.446)|
|Observations|10,707|5,075|5,632|4,257|3,430|3,020|
|Adjusted R square|0.331|0.322|0.361|0.324|0.329|0.353|
|Clothes|0.192**|0.125|0.634***|0.145|0.232*|0.433*|
||(0.094)|(0.091)|(0.186)|(0.131)|(0.129)|(0.251)|
|Observations|10,437|4,922|5,515|4,121|3,342|2,974|
|Adjusted R square|0.326|0.342|0.319|0.327|0.350|0.295|
|Utilities|0.171***|0.138***|0.321**|0.169***|0.122**|0.432|
||(0.041)|(0.036)|(0.153)|(0.048)|(0.057)|(0.281)|
|Observations|10,493|4,945|5,548|4,178|3,363|2,952|
|Adjusted R square|0.308|0.295|0.329|0.313|0.300|0.337|
|Communications|0.177***|0.132***|0.525***|0.119**|0.210***|0.396**|
||(0.044)|(0.038)|(0.086)|(0.052)|(0.066)|(0.178)|
|Observations|10,383|4,902|5,481|4,110|3,344|2,929|
|Adjusted R square|0.337|0.354|0.334|0.308|0.387|0.316|
|Local transport|0.332***|0.272***|0.601***|0.217*|0.407***|0.215|
||(0.081)|(0.077)|(0.160)|(0.111)|(0.090)|(0.289)|
|Observations|10,327|4,865|5,462|4,086|3,323|2,918|
|Adjusted R square|0.306|0.318|0.298|0.290|0.321|0.321|
|Travel|0.650***|0.465***|1.423***|0.496*|0.626***|1.743***|
||(0.187)|(0.169)|(0.273)|(0.272)|(0.156)|(0.515)|

·· 43
|Observations|10,502|4,950|5,552|4,183|3,369|2,950|
|---|---|---|---|---|---|---|
|Adjusted R square|0.297|0.291|0.337|0.291|0.276|0.355|
|Entertainment|0.495***|0.342**|1.065***|0.383*|0.600***|0.016|
||(0.143)|(0.142)|(0.356)|(0.215)|(0.156)|(0.291)|
|Observations|10,477|4,940|5,537|4,167|3,363|2,947|
|Adjusted R square|0.284|0.287|0.305|0.275|0.282|0.315|
|Automobiles|0.310**|0.158|1.465***|0.213|0.458**|0.905***|
||(0.120)|(0.105)|(0.305)|(0.160)|(0.196)|(0.318)|
|Observations|10,372|4,885|5,487|4,116|3,325|2,931|
|Adjusted R square|0.235|0.245|0.230|0.231|0.239|0.245|
|Other vehicles|0.057|-0.023|0.818**|-0.103|0.304|-0.022|
||(0.143)|(0.128)|(0.387)|(0.123)|(0.372)|(0.591)|
|Observations|10,461|4,936|5,525|4,159|3,357|2,945|
|Adjusted R square|0.397|0.403|0.396|0.402|0.396|0.398|
|Durable goods|0.178|0.025|0.929**|0.125|0.172|0.220|
||(0.114)|(0.101)|(0.426)|(0.112)|(0.195)|(0.917)|
|Observations|10,670|5,049|5,621|4,230|3,423|3,017|
|Adjusted R square|0.430|0.427|0.439|0.422|0.447|0.422|
|Medical|0.163**|0.124|0.407**|0.085|0.327**|0.429|
||(0.082)|(0.097)|(0.192)|(0.135)|(0.123)|(0.391)|
|Observations|10,562|5,004|5,558|4,173|3,395|2,994|
|Adjusted R square|0.378|0.366|0.397|0.387|0.382|0.359|
|Health and fitness|0.221**|0.059|0.563***|0.148|0.217|0.519|
||(0.106)|(0.093)|(0.200)|(0.138)|(0.144)|(0.327)|
|Observations|10,673|5,055|5,618|4,243|3,422|3,008|
|Adjusted R square|0.303|0.288|0.366|0.301|0.321|0.289|
|Education|-0.151|-0.156|0.095|-0.185**|0.010|-0.248|
||(0.117)|(0.130)|(0.274)|(0.085)|(0.201)|(0.369)|
|Observations|10,604|5,026|5,578|4,208|3,406|2,990|
|Adjusted R square|0.221|0.232|0.222|0.227|0.221|0.232|
|Home repair|-0.121|-0.202**|0.077|-0.121|-0.209|-0.458|
||(0.091)|(0.095)|(0.319)|(0.095)|(0.158)|(0.616)|
|Observations|10,505|4,961|5,544|4,182|3,372|2,951|
|Adjusted R square|0.449|0.433|0.468|0.473|0.456|0.409|
|Gifts|-0.028|-0.023|0.353|-0.031|0.074|0.121|
||(0.107)|(0.107)|(0.286)|(0.156)|(0.128)|(0.565)|
|Observations|8,716|4,143|4,573|3,377|2,817|2,522|
|Adjusted R square|0.112|0.113|0.116|0.077|0.121|0.171|

Note: Alternative extended model, with additional variables of control, including provincial CPI of the corresponding consumption category, lagged value of log household per capita in consumption category in question, household average years of schooling household gender ratio, average age, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 44
**Table A.9. E-commerce development and various categories of household expenditure per capita by household consumption level (Extended model 4)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||All|bottom 25%|25%-75%|top 25%|
|Total household|||||
|expenditure|0.226***|0.287***|0.131***|0.043|
||(0.064)|(0.100)|(0.048)|(0.051)|
|Observations|10,701|2,959|5,547|2,166|
|Adjusted R square|0.344|0.417|0.439|0.442|
|Cosmetics and beauty|0.353***|0.570**|0.194|0.141|
||(0.113)|(0.248)|(0.135)|(0.089)|
|Observations|10,597|2,931|5,500|2,137|
|Adjusted R square|0.343|0.330|0.367|0.425|
|Food|0.283***|0.442***|0.236***|0.098**|
||(0.063)|(0.129)|(0.058)|(0.048)|
|Observations|10,550|2,904|5,478|2,139|
|Adjusted R square|0.347|0.428|0.356|0.447|
|Of which: Food at home|0.304***|0.456***|0.264***|0.149**|
||(0.067)|(0.154)|(0.070)|(0.060)|
|Observations|10,547|2,904|5,475|2,139|
|Adjusted R square|0.390|0.465|0.401|0.392|
|Of which: Dining out|0.424***|0.191|0.172|0.143|
||(0.148)|(0.218)|(0.147)|(0.137)|
|Observations|10,707|2,962|5,550|2,166|
|Adjusted R square|0.331|0.453|0.341|0.379|
|Clothes|0.192**|0.204|0.036|-0.068|
||(0.094)|(0.254)|(0.086)|(0.089)|
|Observations|10,437|2,895|5,396|2,117|
|Adjusted R square|0.326|0.330|0.386|0.348|
|Utilities|0.171***|0.347**|0.145***|0.027|
||(0.041)|(0.134)|(0.047)|(0.033)|
|Observations|10,493|2,931|5,457|2,076|
|Adjusted R square|0.308|0.345|0.313|0.333|
|Communications|0.177***|0.049|0.116**|0.071**|
||(0.044)|(0.138)|(0.049)|(0.035)|
|Observations|10,383|2,888|5,404|2,064|
|Adjusted R square|0.337|0.302|0.384|0.432|
|Local transport|0.332***|0.045|0.139*|0.233*|
||(0.081)|(0.247)|(0.079)|(0.128)|
|Observations|10,327|2,886|5,362|2,050|
|Adjusted R square|0.306|0.340|0.327|0.309|
|Travel|0.650***|0.841**|0.358**|0.305*|
||(0.187)|(0.325)|(0.146)|(0.182)|

·· 45
|Observations|10,502|2,932|5,461|2,080|
|---|---|---|---|---|
|Adjusted R square|0.297|0.437|0.347|0.282|
|Entertainment|0.495***|0.168|0.279|0.328***|
||(0.143)|(0.147)|(0.174)|(0.109)|
|Observations|10,477|2,927|5,441|2,080|
|Adjusted R square|0.284|0.313|0.304|0.327|
|Automobiles|0.310**|0.199|0.063|0.308**|
||(0.120)|(0.175)|(0.151)|(0.155)|
|Observations|10,372|2,910|5,391|2,043|
|Adjusted R square|0.235|0.333|0.234|0.243|
|Other vehicles|0.057|-0.102|-0.050|-0.073|
||(0.143)|(0.302)|(0.199)|(0.111)|
|Observations|10,461|2,922|5,432|2,079|
|Adjusted R square|0.397|0.413|0.404|0.400|
|Durable goods|0.178|-0.256|-0.072|-0.063|
||(0.114)|(0.373)|(0.128)|(0.127)|
|Observations|10,670|2,959|5,530|2,152|
|Adjusted R square|0.430|0.489|0.445|0.409|
|Medical|0.163**|0.538**|-0.083|0.274|
||(0.082)|(0.237)|(0.116)|(0.173)|
|Observations|10,562|2,922|5,470|2,141|
|Adjusted R square|0.378|0.378|0.385|0.383|
|Health and fitness|0.221**|0.328**|-0.070|0.024|
||(0.106)|(0.163)|(0.108)|(0.106)|
|Observations|10,673|2,955|5,535|2,154|
|Adjusted R square|0.303|0.432|0.337|0.284|
|Education|-0.151|-0.578**|-0.137|0.028|
||(0.117)|(0.233)|(0.112)|(0.243)|
|Observations|10,604|2,929|5,493|2,153|
|Adjusted R square|0.221|0.230|0.217|0.278|
|Home repair|-0.121|-0.128|-0.140|-<br>0.436***|
||(0.091)|(0.336)|(0.128)|(0.135)|
|Observations|10,505|2,934|5,456|2,086|
|Adjusted R square|0.449|0.434|0.463|0.446|
|Gifts|-0.028|0.357|-0.005|-0.299*|
||(0.107)|(0.419)|(0.118)|(0.159)|
|Observations|8,716|2,240|4,643|1,810|
|Adjusted R square|0.112|0.142|0.117|0.160|

Note: Alternative extended model with additional variables of control, including CPI of the corresponding consumption category, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age squared/100, dependency ratio, , and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 46