1932

Abstract

The growth of the availability and use of detailed household financial transaction micro data has dramatically expanded the ability of researchers to understand both household decision making and aggregate economic fluctuations across a wide range of fields. This class of transaction data is derived from a myriad of sources, including financial institutions, FinTech apps, and payment intermediaries. We review how these detailed data have been utilized in finance and economics research and analyze both their benefits and limitations as compared to more traditional measures of income, spending, and wealth. Finally, we discuss the future potential of this flexible class of data in firm-focused research, real-time policy analysis, and macro statistics.

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2022-08-12
2024-06-24
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Literature Cited

  1. Agarwal S, Bubna A, Lipscomb M. 2021a. Timing to the statement: understanding fluctuations in consumer credit use. Manag. Sci. 67:84643–5300
    [Google Scholar]
  2. Agarwal S, Liu C, Souleles NS. 2007. The reaction of consumer spending and debt to tax rebates—evidence from consumer credit data. J. Political Econ. 115:6986–1019
    [Google Scholar]
  3. Agarwal S, Qian W. 2014. Consumption and debt response to unanticipated income shocks: evidence from a natural experiment in Singapore. Am. Econ. Rev. 104:124205–30
    [Google Scholar]
  4. Agarwal S, Qian W, Zou X. 2021b. Thy neighbor's misfortune: peer effects on consumption. Am. Econ. J. Econ. Policy 13:21–25
    [Google Scholar]
  5. Agarwal S, Qian W, Zou X. 2021c. Disaggregated sales and stock returns. Manag. Sci. 67:116629–7289
    [Google Scholar]
  6. Aladangady A, Aron-Dine S, Dunn W, Feiveson L, Lengermann P, Sahm C. 2019. From transactions data to economic statistics: constructing real time, high frequency, geographic measures of consumer spending Finance Econ. Discuss. Ser. 2019-057 Board Gov. Fed. Reserve Syst. Washington, DC:
    [Google Scholar]
  7. Andersen AL, Hansen ET, Johannesen N, Sheridan A. 2020a. Pandemic, shutdown and consumer spending: lessons from Scandinavian policy responses to COVID-19. arXiv:2005.04630 [econ.GN]
  8. Andersen AL, Hansen ET, Johannesen N, Sheridan A. 2020b. Social distancing laws cause only small losses of economic activity during the Covid-19 pandemic in Scandinavia. PNAS 117:3420468–73
    [Google Scholar]
  9. Andersen AL, Jensen AS, Johannesen N, Kreiner CT, Leth-Petersen S, Sheridan A 2020c. How do households respond to job loss? Lessons from multiple high-frequency data sets CEBI Work. Pap. 12/20 Cent. Econ. Behav. Inequal., Univ. Copenhagen Copenhagen, Den:.
    [Google Scholar]
  10. Andersen AL, Johannesen N, Sheridan A. 2020d. Bailing out the kids: new evidence on informal insurance from one billion bank transfers CEBI Work. Pap. 20/19, Cent. Econ. Behav. Inequal., Univ. Copenhagen Copenhagen, Den:.
    [Google Scholar]
  11. Aydin D. 2022. Consumption response to credit expansions: evidence from experimental assignment of 45307 credit lines. Am. Econ. Rev. 112:11–40
    [Google Scholar]
  12. Bachas P, Gertler P, Higgins S, Seira E. 2020. How debit cards enable the poor to save more. Work. Pap., World Bank Res. Washington, DC:
    [Google Scholar]
  13. Baker SR. 2018. Debt and the response to household income shocks: validation and application of linked financial account data. J. Political Econ. 126:41504–57
    [Google Scholar]
  14. Baker SR, Baugh B, Kueng L 2020a. Income fluctuations and firm choice. J. Financ. Quant. Anal 56:6220836
    [Google Scholar]
  15. Baker SR, Baugh B, Sammon M. 2020b. Measuring customer churn and interconnectedness NBER Work. Pap 27707
    [Google Scholar]
  16. Baker SR, Farrokhnia RA, Meyer S, Pagel M, Yannelis C. 2022a. Income, liquidity and the consumption response to the 2020 economic stimulus payments. Rev. Finance. In press
    [Google Scholar]
  17. Baker SR, Kueng L, Meyer S, Pagel M. 2022b. Consumption imputation errors in administrative data. Rev. Financ. Stud. 35:6302159
    [Google Scholar]
  18. Baker SR, Yannelis C. 2017. Income changes and consumption: evidence from the 2013 federal government shutdown. Rev. Econ. Dyn. 23:99–124
    [Google Scholar]
  19. Barber BM, Odean T. 2000. Trading is hazardous to your wealth: the common stock investment performance of individual investors. J. Finance 55:2773–806
    [Google Scholar]
  20. Barber BM, Odean T. 2001. Boys will be boys: gender, overconfidence, and common stock investment. Q. J. Econ. 116:1261–92
    [Google Scholar]
  21. Batch A, Chen J, Driessen A, Dunn A, Kyle K. 2019. Off to the races: a comparison of machine learning and alternative data for predicting economic indicators. Work. Pap., US Bur. Econ. Anal. Washington, DC:
    [Google Scholar]
  22. Baugh B. 2015. Payday borrowing and household outcomes: evidence from a natural experiment Work. Pap., Ohio State Univ. Columbus:
    [Google Scholar]
  23. Baugh B, Ben-David I, Park H. 2018. Can taxes shape an industry? Evidence from the implementation of the “Amazon tax. .” J. Finance 73:41819–55
    [Google Scholar]
  24. Baugh B, Correia F. 2022. Does paycheck frequency matter? Evidence from micro data. J. Financ. Econ. 143:31026–42
    [Google Scholar]
  25. Becker G. 2017. Does FinTech affect household saving behavior? Findings from a natural field experiment. Work. Pap., Goethe Univ. Frankfurt:
    [Google Scholar]
  26. Bernstein A. 2021. Negative home equity and household labor supply. J. Finance 76:62963–95
    [Google Scholar]
  27. Bounie D, Camara Y, Galbraith JW. 2020. Consumers' mobility, expenditure and online offline substitution response to Covid-19: evidence from French transaction data. Work. Pap., Inst. Polytech. Paris Paris:
    [Google Scholar]
  28. Brauer K, Hackethal A, Hanspal T. 2019. Consuming dividends SAFE Work. Pap. Ser. 280 Leibniz Inst. Financ. Res. Frankfurt:
    [Google Scholar]
  29. Carlin B, Olafsson A, Pagel M. 2017. FinTech adoption across generations: financial fitness in the information age NBER Work. Pap. 23798
    [Google Scholar]
  30. Carvalho L, Olafsson A, Silverman D. 2019. Misfortune and mistake: the financial conditions and decision making ability of high cost loan borrowers NBER Work. Pap 26328
    [Google Scholar]
  31. Carvalho VM, Garcia JR, Hansen S, Mora SR, Ortiz A et al. 2021. Tracking the COVID-19 crisis with high resolution transaction data. R. Soc. Open Sci. 8:8 https://doi.org/10.1098/rsos.210218
    [Crossref] [Google Scholar]
  32. Chen H, Qian W, Wen Q. 2021. The impact of the COVID-19 pandemic on consumption: learning from high frequency transaction data. AEA Pap. Proc. 111:307–11
    [Google Scholar]
  33. Chetty R, Friedman JN, Hendren N, Stepner M, Team TOI. 2020. The economic impacts of COVID-19: evidence from a new public database built using private sector data NBER Work. Pap 27431
    [Google Scholar]
  34. Choi JJ, Laibson D, Metrick A. 2001. How does the Internet affect trading? Evidence from investor behavior in 401(k) plans. J. Financ. Econ. 64:3397–421
    [Google Scholar]
  35. Chronopoulos DK, Lukas M, Wilson JOS. 2020. Consumer spending responses to the COVID-19 pandemic: an assessment of Great Britain Work. Pap., Univ. St. Andrews, St. Andrews, UK:
    [Google Scholar]
  36. Cookson JA. 2018. When saving is gambling. J. Financ. Econ. 129:124–45
    [Google Scholar]
  37. Cox N, Farrell D, Gannong P, Greig F, Noel P et al. 2020. Initial impacts of the pandemic on consumer behavior: evidence from linked income, spending and savings data. Brook. Pap. Econ. Act. Summer:Part 135–69
    [Google Scholar]
  38. D'Acunto F, Rauter T, Scheuch CK, Weber M 2020. Perceived precautionary savings motives: evidence from FinTech NBER Work. Pap 26817
    [Google Scholar]
  39. D'Acunto F, Rossi AG, Weber M. 2019. Crowdsourcing financial information to change spending behavior CESifo Work. Pap. 7533, CESifo Munich, Ger:.
    [Google Scholar]
  40. De Giorgi G, Frederiksen A, Pistaferri L. 2020. Consumption network effects. Rev. Econ. Stud. 87:1130–63
    [Google Scholar]
  41. Einav L, Klenow P. 2021. Customers and retail growth Work. Pap., Stanford Univ. Stanford, CA:
    [Google Scholar]
  42. Elizondo A, Seira E. 2017. Are information disclosures effective? Evidence from the credit card market. Am. Econ. J. Econ. Policy 9:1277–307
    [Google Scholar]
  43. Fagereng A, Halvorsen E. 2017. Imputing consumption from Norwegian income and wealth registry data. J. Econ. Soc. Meas. 42:167–100
    [Google Scholar]
  44. Fulford S, Schuh S. 2020. Revolving versus convenience use of credit cards: evidence from U.S. Credit Bureau data. Econ. Fac. Work. Pap. Ser. 53 W. Va. Univ. Morgantown:
    [Google Scholar]
  45. Ganong P, Jones D, Noel PJ, Greig FE, Farrell D, Wheat C. 2020. Wealth, race and consumption smoothing of typical income shocks NBER Work. Pap 27552
    [Google Scholar]
  46. Ganong P, Noel P. 2019. Consumer spending during unemployment: positive and normative implications. Am. Econ. Rev. 109:72383–424
    [Google Scholar]
  47. Ganong P, Noel P. 2020. Why do borrowers default on mortgages? A new method for causal attribution NBER Work. Pap 27585
    [Google Scholar]
  48. Garcia I, Medina P, Pagel M. 2021. Does saving cause borrowing? NBER Work. Pap 28956
    [Google Scholar]
  49. Garmaise M, Levi Y, Lustig H. 2020. Spending less after (seemingly) bad news NBER Work. Pap 27010
    [Google Scholar]
  50. Gathergood J, Loewenstein G, Quispe-Torreblanca EG, Stewart N 2019. The red, the black and the plastic: paying down credit card debt for hotels, not sofas. Manag. Sci. 65:114951–5448
    [Google Scholar]
  51. Gathergood J, Olafsson A. 2020. The co-holding puzzle: new evidence from transaction level data. Work. Pap., Univ. Nottingham Nottingham, UK:
    [Google Scholar]
  52. Gathergood J, Sakaguehi H, Stewart N, Weber J. 2021. How do consumers avoid penalty fees? Evidence from credit cards. Manag. Sci. 67:41993–2656
    [Google Scholar]
  53. Gelman M. 2021a. The self-constrained hand to mouth. Rev. Econ. Stat. In press. https://doi.org/10.1162/rest_a_01026
    [Crossref] [Google Scholar]
  54. Gelman M. 2021b. What drives heterogeneity in the marginal propensity to consume? Temporary shocks versus persistent characteristics. J. Monet. Econ. 117:521–42
    [Google Scholar]
  55. Gelman M, Gorodnichenko Y, Kariv S, Koustas D, Shapiro MD et al. 2017. The response of consumer spending to changes in gasoline prices. NBER Work. Pap. 22969
    [Google Scholar]
  56. Gelman M, Kariv S, Shapiro MD, Silverman D. 2019. Rational illiquidity and consumption: theory and evidence from income tax withholding and refunds NBER Work. Pap 25757
    [Google Scholar]
  57. Gelman M, Kariv S, Shapiro MD, Silverman D, Tadelis S. 2020. How individuals respond to a liquidity shock: evidence from the 2013 government shutdown. J. Public Econ. 189:103917
    [Google Scholar]
  58. Giglio S, Maggiori M, Stroebel J, Utkus S. 2021. Five facts about beliefs and portfolios. Am. Econ. Rev. 111:51481–522
    [Google Scholar]
  59. Gu Q, He J, Qian W. 2018. Housing booms and shirking. Work. Pap., Natl. Univ. Singap. Singapore:
    [Google Scholar]
  60. Hacioglu S, Kanzig D, Surico P. 2020. Consumption in the time of COVID-19: evidence from UK transaction data. CEPR Discuss. Pap. 14646 Cent. Econ. Policy Res. London:
    [Google Scholar]
  61. Jorring AT. 2019. Financial sophistication and consumer spending. Work. Pap., Univ. Chicago Chicago:
    [Google Scholar]
  62. Karger E, Rajan A. 2020. Heterogeneity in the marginal propensity to consume: evidence from Covid-19 stimulus payments. Work. Pap. 2020-15 Fed. Reserve Bank Chicago Chicago:
    [Google Scholar]
  63. Kim O, Parker J, Schoar A. 2020. Revenue collapses and the consumption of small business owners in the early stages of the COVID-19 pandemic NBER Work. Pap 28151
    [Google Scholar]
  64. Koijen R, Van Nieuwerburgh S, Vestman R 2014. Judging the quality of survey data by comparison with truth as measured by administrative records: evidence from Sweden. Improving the Measurement of Consumer Expenditures R Koijen, S Van Nieuwerburgh, R Vestman 308–46 Chicago: Univ. Chicago Press
    [Google Scholar]
  65. Kuchler T, Pagel M. 2018. Sticking to your plan: the role of present bias for credit card paydown. NBER Work. Pap 24881
    [Google Scholar]
  66. Kueng L. 2018. Excess sensitivity of high income consumers. Q. J. Econ. 133:41693–751
    [Google Scholar]
  67. Kukk M, Raaij WFV. 2021. Joint and individual savings within families: evidence from bank accounts. J. Fam. Econ. Issues. https://doi.org/10.1007/s10834-021-09783-3
    [Crossref] [Google Scholar]
  68. Levi Y, Benartzi S. 2020. Mind the app: mobile access to financial information and consumer behavior. Work. Pap., Univ. South. Calif. Los Angeles:
    [Google Scholar]
  69. Loos B, Meyer S, Pagel M. 2020. The consumption effects of the disposition to sell winners and hold losers NBER Work. Pap 26668
    [Google Scholar]
  70. Medina PC. 2021. Side effects of nudging: evidence from a randomized intervention in the credit card market. Rev. Financ. Stud. 34:52580–607
    [Google Scholar]
  71. Meeuwis M, Parker JA, Schoar A. 2021. Belief disagreement and portfolio choice NBER Work. Pap 25108
    [Google Scholar]
  72. Meyer S, Pagel M. 2019. Fully closed: individual responses to realized gains and losses NBER Work. Pap 25542
    [Google Scholar]
  73. Odean T. 1998. Are investors reluctant to realize their losses?. J. Finance 53:51775–98
    [Google Scholar]
  74. Olafsson A, Pagel M. 2017. Family finances: intra-household bargaining spending, and capital structure. Work. Pap., Copenhagen Bus. Sch. Copenhagen, Den:.
    [Google Scholar]
  75. Olafsson A, Pagel M. 2018. The liquid hand-to-mouth: evidence from personal finance management software. Rev. Financ. Stud. 31:114398–446
    [Google Scholar]
  76. Olafsson A, Pagel M. 2019. Borrowing in response to windfalls. Work. Pap., Copenhagen Bus. Sch. Copenhagen, Den:.
    [Google Scholar]
  77. Park H. 2016. Financial fragility and mortgage delinquency. Work. Pap., Ohio State Univ. Columbus:
    [Google Scholar]
  78. Ponce A, Seira E, Zamarripa G. 2017. Borrowing on the wrong credit card? Evidence from Mexico. Am. Econ. Rev. 107:41335–61
    [Google Scholar]
  79. Schuh S. 2018. Measuring consumer expenditures with payment diary surveys. Econ. Inq. 56:113–49
    [Google Scholar]
  80. Weber A. 2019. Financial management skills and entrepreneurial success: evidence from transaction level data. Work. Pap., New York Univ. New York:
    [Google Scholar]
  81. Xing J, Zou E, Yin Z, Wang Y, Li Z. 2021. Quick response economic stimulus: the effect of small-value digital coupons on spending. NBER Work. Pap. 27596
    [Google Scholar]
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