E-Commerce Integration and Economic Development: Evidence from China
Last registered on June 01, 2017

Pre-Trial

Trial Information
General Information
Title
E-Commerce Integration and Economic Development: Evidence from China
RCT ID
AEARCTR-0001582
Initial registration date
October 06, 2016
Last updated
June 01, 2017 12:01 PM EDT
Location(s)

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Primary Investigator
Affiliation
Department of Economics, UC Berkeley
Other Primary Investigator(s)
PI Affiliation
Department of Political Science, Stanford University
PI Affiliation
Institute of Economic and Social Research, Jinan University
PI Affiliation
Haas School of Business, UC Berkeley
Additional Trial Information
Status
In development
Start date
2015-12-21
End date
2017-12-31
Secondary IDs
Abstract
The number of people buying and selling products online in China has grown from practically zero in 2000 to more than 400 million by 2015. Most of this growth has occurred in cities. In this context, the Chinese government recently announced the expansion of e-commerce to the countryside as a policy priority with the aim to close the rural-urban economic divide. As part of this agenda, the government entered a collaboration with one of the largest Chinese e-commerce platforms through which consumers and producers can buy and sell products of all kinds. The program aims to provide the necessary transport logistics to ship products to and sell products from tens of thousands of villages that were largely unconnected to e-commerce. As part of this operation, the firm installs an e-commerce terminal at a central village location where households can buy and sell products through the terminal manager's account. This paper combines a new collection of survey and administrative microdata with a randomized control trial (RCT) that we implement in collaboration with the Chinese e-commerce firm. We use this empirical setting to provide evidence on the potential of e-commerce integration to foster economic development in the countryside, the underlying channels and the distribution of the gains across households and villages.
External Link(s)
Registration Citation
Citation
Couture, Victor et al. 2017. "E-Commerce Integration and Economic Development: Evidence from China." AEA RCT Registry. June 01. https://www.socialscienceregistry.org/trials/1582/history/18205
Experimental Details
Interventions
Intervention(s)
The program aims to build the necessary logistics to ship products to and sell products from villages that were previously largely unconnected to e-commerce. The objective is to bring the transport costs of e-commerce-related shipments to and from the participating villages to the same level as for the urban parts of the county in question. As part of this operation, the e-commerce firm with support from the government builds warehouses, subsidizes e-commerce-related transport fares to and from the villages, and installs an e-commerce terminal at a central village location where households can buy and sell products through the store manager's account. Households can pay or be paid in cash at the store without the need for online payments.
Intervention Start Date
2016-01-14
Intervention End Date
2017-12-31
Outcomes
Outcomes (end points)
The objective is to evaluate the effect on household real incomes, the underlying channels, and their heterogeneity across pre-existing household and village characteristics. The main outcomes of interest are household use and expenditure shares on e-commerce across 10 retail product groups, local retail prices in pre-existing establishments and household incomes broken up by source. We also plan to investigate the heterogeneity of these effects across pre-existing household and village characteristics (income, education, age, distance to the terminal, commercial delivery status of the village, characteristics of the terminal operator).
Outcomes (explanation)
We plan to investigate heterogeneity using the following definition of bins: For continuous interaction variables (e.g. initial income per capita), we plan to report interactions using medians or quartiles as bins. For ages, we plan to use below 35, 35-60 and above 60 as the main thresholds of interest. For education, we plan to differentiate between below-Junior High, Junior High and above-Junior High as the main thresholds. For distance to the terminal we plan to differentiate between inner and outer zones (see methodology) as well as the continuous log distance to the terminal.
Experimental Design
Experimental Design
The study is based on 8 counties in the three provinces of Anhui, Henan and Guizhou. These counties are: Huoqiu (Anhui), Linying (Henan), Linzhou (Henan), Minquan (Henan), Suixi (Anhui), Tianchang (Anhui), Xifeng (Guizhou) and Zhenning (Guizhou).

The unit of randomization is the village. For each county, we obtain a list of candidates that had been extended by 5 promising village candidates that would have not been part of the list in absence of our research. Upon receipt of this extended list of village candidates for each county, we randomly select 5 control villages and 7-8 treatment villages from the list of candidates for each county. The remaining villages on the list also receive e-commerce terminals as planned. The full sample thus includes 40 control villages and 60 treatment villages across the 8 counties, which we selected from a total number of candidates of 432 villages (on average 54 villages per county). We restrict the list of villages entering the stratification and randomization to villages with at least 2.5 km distance to the nearest village on the county list. We then stratify treatment and control villages along four dimensions: existence of commercial delivery services, the local store applicants’ test score, the village population, and the ratio of non-agricultural employment over the local population.

After obtaining the candidate list for each county, we have about 2-3 weeks to run the randomization and send in the survey teams for data collection in 5 control villages and 7-8 treatment villages before the terminal installations take place and e-commerce begins in the treatment villages.

During the first round of data collection (December 2015 and January, April and May 2016), we collect data from 28 households per village. 14 of those households are randomly sampled within a 300 m radius (distance) of the planned terminal location, and 14 households are randomly sampled from other parts of the village. Household respondents are members with the most knowledge of household consumption expenditures and incomes. Households are offered a gift to thank them for their participation in the survey (e.g. box of premium sweets, soaps, hand towels, etc). The value of the gift is about 4.5 USD. In case the most knowledgeable respondent is not present at the time of the visit, a follow-up visit to the household is scheduled by the surveyor.

In the second round of data collection (same period but one year after), we collect data from the same households, and in addition add 10 randomly sampled households within the inner ring around the planned terminal location. This expansion of our sample served the objective to increase the statistical power in our estimations (and was possible due to remaining funds on the project). If either the survey respondent or the primary earner of the initially surveyed household no longer resides at the same address, we record this in our data and replace the household with another randomly sampled household within the same sampling zone (inner circle or outer). The 10 additional households were added by randomly sampling within the inner zone as in the first round of data collection.

For store prices, we aim to collect data on 115 price quotes for each village. 100 of these prices are from 9 household consumption categories for retail products (food and beverages, tobacco and alcohol, medicine and health, clothing and accessories, other every-day products, fuel and gas, furniture and appliances, electronics, transport equipment), and 15 price quotes are for local production/business inputs. The sampling of products across consumption categories is based on budget shares observed among rural households in Anhui and Henan that we observe in the microdata of the China Family Panel Study (CFPS) for the year 2012. The sampling across stores is aimed to provide a representative sample of local retail outlets (stores and market stalls). The sampling of products within stores is aimed at capturing a representative selection of locally purchased items within that outlet and product group. Each price quote is at the barcode-equivalent level where possible (recording brand, product name, packaging type, size, flavor if applicable).

In the second round of data collection (one year after the first round), we aim to collect the price quotes of the identical products in the identical retail outlet where this is possible. Where this is not possible (due to either store closure or absence of product in the store), we record the reason for the absence and then include a new price quote within the same product category that is sampled in the same way as in the first round.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by computer.
Randomization Unit
The unit of randomization is the village.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
100 villages
Sample size: planned number of observations
2800 households in first round, 3800 households in second round, 11,500 local price quotes per round.
Sample size (or number of clusters) by treatment arms
40 control villages, 60 treatment villages.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Office for the Protection of Human Subjects, UC Berkeley
IRB Approval Date
2016-04-15
IRB Approval Number
Protocol No: 2015-09-7944