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Field
Abstract
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Before
Since 2000, more than half of China's population have been connected to and by the internet. Over this period, the number of people buying and/or selling products online in China has grown from practically zero to more than 400 million in 2015, surpassing the US as the largest online retail market. In this context, the Chinese government has recently made the expansion of e-commerce to the countryside a policy priority to foster rural economic development, and entered a collaboration with the internet firm Alibaba as part of the so called 100 Thousand Villages Project (HTVP). This project combines a new collection of microdata with a randomized control trial (RCT) to study the impact of this program on household economic welfare. Alibaba owns the dominant Chinese e-commerce platform Taobao through which consumers and producers can buy and sell products of all kinds. The HTVP aims to provide the necessary transport logistics to ship products to and sell products from 100,000 villages that were largely unconnected to e-commerce. As part of this operation, Alibaba installs a Taobao terminal at a central village location where households can buy and sell products through the terminal manager's Taobao account. We were given Alibaba's authorization to randomly select control villages in several Chinese counties from a list of candidates that had been extended for the purpose of this research. We collect household and store price survey data for treatment and control villages, and complement these data with online transaction records from Taobao. We plan to use this empirical setting to answer three central questions: i) what are the household economic gains from e-commerce integration?, ii) what are the channels underlying these effects?, and iii) what is the distribution of the gains from e-commerce across different groups of households?
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After
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.
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Field
Experimental Design (Public)
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Before
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 Taobao 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 Taobao 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 Taobao 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.
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After
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.
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