Data and Code for: Financial Technology Adoption: Network Externalities of Cashless Payments in Mexico

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Project Citation:

Higgins, Sean. Data and Code for: Financial Technology Adoption: Network Externalities of Cashless Payments in Mexico. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-10-11. https://doi.org/10.3886/E202904V1

Project Description

Summary:

Do coordination failures constrain financial technology adoption? Exploiting the Mexican government's rollout of one million debit cards to poor households from 2009-2012, I examine responses on both sides of the market, and find important spillovers and distributional impacts. On the supply side, small retail firms adopted point-of-sale terminals to accept card payments. On the demand side, this led to a 21% increase in other consumers' card adoption. The supply-side technology adoption response had positive effects on both richer consumers and small retail firms: richer consumers shifted 13% of their supermarket consumption to small retailers, whose sales and profits increased.

Funding Sources: Banco de México Summer Research Program

Scope of Project

Subject Terms: financial technology; network externalities; technology adoption; cashless payments; retail firms

JEL Classification:
      G21 Banks; Depository Institutions; Micro Finance Institutions; Mortgages
      G51 Household Finance: Household Saving, Borrowing, Debt, and Wealth
      O16 Economic Development: Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
      O33 Technological Change: Choices and Consequences; Diffusion Processes

Geographic Coverage: Mexico

Time Period(s): 12/2006 – 12/2021 (CNBV data); 2010 – 2021 (DENUE data); 2006 – 2014 (ENIGH data); 2005 – 2016 (ENOE data); 3/2006 – 10/2018 (Banco de Mexico fees data); 2017 – 2017 (Global Findex data); 7/2017 – 7/2017 (INE regions data); 11/2017 – 11/2017 (INEGI auxiliary data); 2000 – 2020 (ITER data); 2017 – 2017 (MCC codes); 2012 – 2012 (Medios de Pago survey); 2009 – 2009 (Mexican Family Life Survey); 2018 – 2018 (SEPOMEX data); 2016 – 2016 (Shapefiles); 2022 – 2022 (Survey of corner store owners)

Collection Date(s): 12/2006 – 12/2021 (CNBV data); 2014 – 2014 (DENUE 2014 data); 2017 – 2017 (DENUE 2017 data); 2021 – 2021 (DENUE 2021 data); 2006 – 2014 (ENIGH data); 2005 – 2016 (ENOE data); 3/2006 – 10/2018 (Banco de Mexico fees data); 2017 – 2017 (Global Findex data); 7/2017 – 7/2017 (INE regions data); 11/2017 – 11/2017 (INEGI auxiliary data); 2000 – 2000 (ITER 2000 data); 2005 – 2005 (ITER 2005 data); 2010 – 2010 (ITER 2010 data); 2020 – 2020 (ITER 2020 data); 2017 – 2017 (MCC codes); 2012 – 2012 (Medios de Pago survey); 2009 – 2009 (Mexican Family Life Survey); 2018 – 2018 (SEPOMEX data); 2016 – 2016 (Shapefiles); 6/20/2022 – 8/12/2022 (Survey of corner store owners)

Universe:

Commercial and government banks in Mexico (CNBV data)

All firms in Mexico (DENUE data)

Sample of households in Mexico (ENIGH data)

Sample of workers in Mexico (ENOE data)

Commercial banks in Mexico (Banco de Mexico fees data)

Sample of people throughout world (Global Findex data)

States in Mexico (INE regions data)

Municipalities in Mexico (INEGI auxiliary data)

Localities in Mexico (ITER data)

Merchant categories (MCC codes)

Sample of Prospera beneficiaries in Mexico (Medios de Pago survey)

Sample of households in Mexico (Mexican Family Life Survey)

Postal codes in Mexico (SEPOMEX data)

Census blocks, localities, municipalities, and states (Shapefiles)

Sample of corner store owners in Mexico (Survey of corner store owners)

Data Type(s): administrative records data; aggregate data; census/enumeration data; geographic information system (GIS) data; program source code; survey data

Methodology

Response Rate:

For the survey of corner store owners, the survey team attempted surveys at 6,065 corner stores and successfully completed 1,760 surveys, for an overall response rate of 29%. The attempted surveys included corner stores that existed in the DENUE data but had since closed permanently, as well as stores that were closed at the time the surveyor visited and stores that were open but in which the owner or decision-maker was not present. The response rate conditional on the store being open and the owner being present was 70%.

Sampling:

For the survey of corner store owners:

Sampling of municipalities. First, a set of 29 urban municipalities in which surveys would be conducted was drawn from the set of 217 potential urban municipalities. The set of 217 potential municipalities are urban municipalities that were not included in the Prospera debit card rollout. Urban municipalities are defined as municipalities with at least one urban locality (which is the geographic level below municipality), where localities are defined as urban if they have at least 15,000 inhabitants. Municipalities were drawn using a weighted random sampling method, where the weight for each municipality was chosen to make the sample included in the survey match the bivariate distribution of POS terminal adoption and debit card adoption in the municipalities that were included in the Prospera debit card rollout. Priority was given to municipalities in states that are closer to Mexico City in order to reduce travel costs. This was accomplished by initially restricting the sampling to municipalities in eight states within driving distance of Mexico City: Guanajuato, Guerrero, Hidalgo, Estado de México, Morelos, Puebla, Querétaro, and Tlaxcala. However, for some bins from the bivariate distribution of POS terminal and debit card adoption there were not sufficient municipalities in these states to match the bivariate distribution in the municipalities included in the debit card rollout. Thus, after drawing from these states, the restriction was removed and an unrestricted weighted random sampling of municipalities was conducted to complete the sample of municipalities.

Sampling of corner stores. Within each sampled municipality, I first restricted to the urban locality within the municipality (as municipalities are one geographic level above locality; the CNBV data used for the sampling is at the municipality level but the debit card rollout was at the locality level). I then created a sampling frame of all corner stores in the municipality. The sampling frame was obtained from the 2021 National Statistical Directory of Economic Units (DENUE), compiled by the National Institute of Statistics and Geography (INEGI) in Mexico. This publicly available directory contains information of active businesses in the country and includes a business type identifier using the North American Industry Classification System (NAICS). The DENUE is publicly available and includes name of the business, business type (6-digit NAICS code), address, geographic coordinates, size in terms of number of employees, and telephone number. The 6-digit NAICS code was used to filter DENUE and only include corner stores (6-digit NAICS code 461110) in the sampling frame. The number of surveys to be conducted in each locality was also weighted to replicate the desired bivariate distribution of debit card and POS adoption faced by firms in localities included in the debit card rollout. Finally, to ensure a proportional geographic distribution of surveyed corner stores within each sampled locality, I set the number of successful surveys to complete within each of the 212 postal codes within the 29 sampled localities.

Unit(s) of Observation: Corner store owners (Survey of corner store owners)

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V1 [2024-10-11]

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