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Scarcity and consumers’ credit choices


We study the effect of scarcity on decision making by low income Swedes. We exploit the random assignment of welfare payments to study their borrowing decisions within the pawn and mainstream credit market. We document that higher educated borrowers borrow less frequently and choose lower loan to value ratios when their budget constraints are exogenously tighter. In contrast, low-educated borrowers do not respond to temporary elevated levels of scarcity. This lack of response translates into a significantly higher probability to default and an 11.6% increase in borrowing cost. We show that a difference in access to liquidity and/or buffer stocks cannot explain our results. Instead a framework, where the awareness of self-control problems is positively correlated with education can explain that high-educated consumers choose a lower LTV as a commitment device to increase their likelihood to repay. Analogously, low-educated with less awareness of their future self-control problems, do not tie themselves to the mast and thus ignore the consequences of their credit decisions when focusing on solving acute liquidity problems. Our findings highlight that increased levels of scarcity risk reinforcing the conditions of poverty through overborrowing.

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  1. 1.

    From the album: ’I Am Just a Rebel’.

  2. 2.

    See for example Morse (2011), Bhutta et al., (2015), Agarwal & Bos (2014).

  3. 3.

    The most relevant behavioral biases studied in this context include, but are not limited to: (i) inconsistent time preferences (Laibson et al., 2003; Meier & Sprenger, 2010), (ii) Biased price perceptions (Gabaix & Laibson, 2006; Bertrand & Morse, 2011), (iii) tendency towards optimism (Brunnermeier & Parker, 2005), (iv) reliance on crude heuristics (Stango & Zinman, 2014). See Bos et al. (2015) and Schilbach et al. (2016) for an overview of this topic.

  4. 4.

    Other recent papers come to a similar conclusion with alternative mechanisms, for example: Gabaix and Laibson (2017) present a model, where agents who are unable to think carefully about an intertemporal tradeoff, for example due to a high cognitive load, will exhibit more discounting, even though agents are patient. Bernheim et al. (2015) show theoretically that low levels of assets undermine the individuals’ capacity for self-control by diminishing the effectiveness of self-imposed punishments.

  5. 5.

    Borrowers reveal this preference to repay and ultimately retrieve their collateral when they decide to pawn their gold, instead of selling it at the gold-to-cash vendor. The lion’s share of pawn loans (more than 80%) in the full sample are secured by gold (see Bos et al. (2012)), and we limit our sample to pawn loans collateralized by gold, to calculate the loan to value ratio. A pawn loan contract is typically 3–4 months long and thus the pawn broker is exposed to the risk that the price of gold will fall during this time. Furthermore, the pawnbroker has to bear the cost of administering the loan and storing the gold. The gold-to-cash service can, in theory, resell the gold immediately with a lower administrative burden.

  6. 6.

    An individual’s probability of taking a pawn loan increases, on average, by 2% per day since payday.

  7. 7.

    Stephens (2003, 2006), Shapiro (2005), Mastrobuoni and Weinberg (2009), Huffman and Barenstein (2005).

  8. 8.

    As education correlates with income, wealth and preferences, we discuss alternative interpretations below.

  9. 9.

    Note that Allcott et al. (2020) find that three quartiles of the studied payday loan population are sophisticated borrowers. If the lion share of our studied customers would be sophisticated, we would expect a large share of borrowers using a commitment device (i.e., a lower loan to value). This is not what we find. We believe that the main difference between their paper and ours lies in the sample selection. Our identification strategy of exogenous exposure to scarcity only works for the unemployed pawnborrowers receiving welfare benefits every month, that is, the poor people. This selection might explain the smaller share of sophisticated borrowers in our sample compared to Allcott et al. (2020) that sample randomly from a pool of all payday borrowers. In addition, we consider education level as a proxy for sophistication, while Allcott et al. (2020) define the sophistication level as a forecast error (the difference between perceived and actual probability of borrowing in the next 8 weeks). The best proxy for sophistication is ultimately an empirical question on which the literature is still debating.

  10. 10.

    We observe, among other things, the borrowers’ mainstream credit applications, balances and limits of their credit cards and installment loans, and arrears.

  11. 11.

    Online Appendix A.1 presents further details.

  12. 12.

    We use a 7-day cutoff for three reasons. Firstly, expenditure needs may differ depending on the day of the week, so we ensure that all weekdays are in the post-period. This is especially relevant as the pawnbroker is typically not open on Sunday, which constrains participation for either the early or late born when their payday is moved. Secondly, the trends until 7 days before payday are parallel, after which divergence occurs (see Fig. A1). Finally, we follow Carvalho et al. (2016), who also define the last week before payday as the scarce period. We demonstrate robustness to this choice of cutoff in Online Appendix A, Table A6.

  13. 13.

    See Bos et al. (2012) for a comparison of the Swedish and US pawn industries and their customers.

  14. 14.

    In addition, trust does not play a role, as the asset is physically handed over to the pawnbroker, avoiding costly liquidation or bankruptcy procedures.

  15. 15.

    We calculate the LTV ratio using the gold price at the time of the loan origination and the grams of gold we observe in the dataset.

  16. 16.

    The probabilities of default are estimated by the credit bureau, with a model based on data from the entire Swedish adult population. The model specifications are proprietary.

  17. 17.

    As we use borrower fixed effects in our regression, adding all social transfer recipients that do not take pawn loans to our estimation sample does not affect the quantitative results.

  18. 18.

    An extensive theoretical literature has emerged studying the consequences of hyperbolic discounting for consumption choices. See, amongst others, Laibson (1997), Harris and Laibson (2001), Fehr (2002), DellaVigna and Malmendier (2004)

  19. 19.

    Skiba and Tobacman (2008) find that a model with hyperbolic discounting and different degrees of sophistication matches the data on payday borrowing, rollover and default. The distinction between sophisticates and naifs is from the seminal article of O’Donoghue and Rabin (1999).

  20. 20.

    Our main result is robust to defining 3 groups of education (primary, secondary and post-secondary).

  21. 21.

    See for example Heidhues and Kőszegi (2010), Skiba and Tobacman (2008), Kuchler and Pagel (2018)

  22. 22.

    Tables A1 and A2 show coefficients for all included variables.

  23. 23.

    The pre-period mean reported in column 4 is taken over all borrowers, as opposed to the non-scarce mean for low-educated borrowers, given in columns 1–3.

  24. 24.

    A different way of studying the intensity of treatment would be to consider two (or more) consecutive long payday cycles. However, this pattern hardly occurs in our panel: Only 3 (1) months out of 48 were long for the early- (late-) born after the previous month being long as well.

  25. 25.

    We also limit our sample to pawn shops, where at least 5% of all loans in the full sample had a maturity of 2 months, which we define to be the shops offering the menu of contracts. The results are very similar to using different cut-offs.

  26. 26.

    The average loan size is around 4,000 SEK. The fees and interest costs per kronor borrowed amount to 0.25 SEK for the 77% of loans that end up being repaid, giving costs of \(4,000\times 0.25\times 0.77=770\) SEK. For the 23% of loans that end up in default, and given the average LTV of 76%, the costs amount to \(4,000\times 0.23/0.76=1211\) SEK. During periods of increased scarcity, we estimate a 10.7% increased probability to borrow (Sect. 3.2) as well as a 4% points higher likelihood of defaulting. The repayment costs and default costs due to scarcity amount to \(0.107\times 4000\times 0.25\times 0.73=78\) SEK and \(0.107\times 4000\times 0.27/0.76=152\) SEK, respectively. Hence, scarcity increases pawn credit costs by \(100\times (78+152)/(770+1211)=11.6\%\).

  27. 27.

    The results using continuous variables for income and age in the regression are representative for an extensive specification search, using for instance dummies for income deciles or retirees. These results are not reported for brevity.

  28. 28.

    Again, we have used a battery of other specifications for these variables (continuous, dummies etc.) which all gave similar results.

  29. 29.

    Using the full payday cycle, or the last 4 weeks, instead of the last 3 weeks, does not affect the results of our main regression (see Panel b of Table A6). The Wald test for parallel trends using the full payday cycle instead of the last 3 weeks does not reject the null hypothesis of parallel trends in the non-scarce period, for neither the probability to take a pawn loan (p = 0.48) nor the LTV ratio (p = 0.30).

  30. 30.

    We thank the referee for this suggestion.


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Correspondence to Chloé Le Coq.

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We thank Renee Adams, Bob Hunt, Leandro Carvalho, Cristina Cella, Ronel Elul, Daria Finocchiaro, Andrew Hertzberg, Alexander Ljungqvist, Leonard Nakamura, Farzad Saidi, Erik von Schedvin, Giancarlo Spagnolo and Per Strömberg, Ferdinand Vieider (editor), two anonymous referees and numerous seminar and conference participants for helpful comments. Jesper Böjeryd provided excellent research assistance. All errors are our own. Funding from VINNOVA is gratefully acknowledged.

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Bos, M., Le Coq, C. & van Santen, P. Scarcity and consumers’ credit choices. Theory Decis (2021).

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  • Poverty
  • Decision making
  • Consumer credit
  • Time preferences