An indebtedness atlas for Argentina

The indebtedness atlas

An indebtedness atlas for Argentina

The level of indebtedness of the general population has recently been signaled by national authorities [as one of the most pressing problems in Argentina1. While the country ranks relatively low in terms of financial inclusion, for example, with less than 10% of adults borrowing from a traditional bank in 2017 (Demirguc-Kunt, et. al, 2018), there is also an active debate on the role of high-interest rates loans, such as payday loans2.

While the literature has shown that neighborhoods matter for upward income mobility or in shaping children outcomes (Chetty et al. 2020), the relationship between neighborhoods and access to credit or indebtedness has remained relatively unexplored.

Which neighborhoods in Argentina are the most problematic in terms of financial distress of their population? Which neighborhoods exhibit signals to be credit constrained? Does the physical proximity of credit suppliers play a role? Are there neighborhoods which tend to finance at higher costs, for example, due to a higher participation of payday borrowing?

What has been the effects of national policies providing low cost to the population on their indebtedness and financial distress?

In this project we are building fine-grain maps of the indebtedness of the population in the country. We are also using maps to investigate the effects of targeted policies. Our aim is to contribute to the understanding of financial depth at the local level, and inform the generation of localized policies.

Methods

We are constructing fine-grained maps of population indebtedness and related statistics using individual-level data from the Argentina Credit Bureau. This registry is a monthly panel of data covering more than 8 million individuals which have took debt with banks or non-financial providers of credit.

Illustrative example: Average outstanding debt in the City of Buenos Aires, at the hex-level*.

*This is the average for all individuals resident in the spatial hexagon of the average outstanding debt incurred in the period 2018-5 to 2020-4. Hexagons are sized 1/10 of km2. Debt includes all debts with financial and non-financial institutions.

Stay tuned for more information on the project.

Team

• Ricardo A. Pasquini, FCE Universidad Austral and School of Government, Universidad Torcuato Di Tella.

• Hernan Garritano, FCE Universidad Austral. Ph.D. Candidate IAE Business School

References

Bertrand, M., & Morse, A. (2011). Information disclosure, cognitive biases, and payday borrowing. The Journal of Finance, 66(6), 1865-1893.

Campbell, J. Y., Jackson, H. E., Madrian, B. C., & Tufano, P. (2011). Consumer financial protection. Journal of Economic Perspectives, 25(1), 91-114.

Chetty, R., Friedman, J. N., Hendren, N., Jones, M. R., & Porter, S. R. (2020). The opportunity atlas: Mapping the childhood roots of social mobility (No. w25147). National Bureau of Economic Research.

Demirguc-Kunt, A., Klapper, L., Singer, D., Ansar, S., & Hess, J. (2018). The Global Findex Database 2017: Measuring financial inclusion and the fintech revolution. The World Bank.

Melzer, B. T. (2011). The real costs of credit access: Evidence from the payday lending market. The Quarterly Journal of Economics, 126(1), 517-555.

Morse, A. (2011). Payday lenders: Heroes or villains?. Journal of Financial Economics, 102(1), 28-44.

1 See for instance news coverage here.↩

2 See, for instance, Melzer (2011), Morse (2011), Bertrand and Morse (2011).↩