Cash-Management in Times of Covid-19

Fernando Alvarez; David Argente

doi:10.1515/bejm-2020-0269

Published by De Gruyter June 23, 2021

Cash-Management in Times of Covid-19

Fernando Alvarez and David Argente

From the journal The B.E. Journal of Macroeconomics

https://doi.org/10.1515/bejm-2020-0269

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Abstract

The incidence of COVID-19 has systematically decreased households’ use of cash as means of payment as well as the average size and frequency of cash withdrawals. We argue that the structure of Baumol–Tobin type inventory theoretical models and their extensions can be used to separate the confounding factors, such as the desired level of consumption and the choice of the fraction of consumption paid in cash, from the cash management behavior, i.e. the size and frequency of cash withdrawals. Using this insight we argue that the observed cash management is consistent with COVID-19 increasing the fixed cost of withdrawing cash. We use detailed data on ATM cash disbursements in Argentina, Chile, and the US to estimate how much the pandemic has changed the transaction cost of using cash. This estimation shows that if the intensity of the virus doubles in a county, cash transaction cost increases by approximately 2%. The results from Argentina, Chile, and the US are remarkably similar and robust to several forms of measurement error and endogeneity.

Keywords: COVID-19; cash; means of payments

JEL Classification: E4; E5

Corresponding author: David Argente, Pennsylvania State University, 403 Kern Building, University Park, PA 16801, USA, E-mail: dargente@psu.edu

Acknowledgements

We want to thank Andy Neumeyer for helpful comments and suggestions. We also thank Banco Bilbao Vizcaya Argentaria, S.A© - “BBVA” - for providing us with the data for Argentina, particularly Marcos Dal Bianco and Federico Forte. We thank SafeGraph for making their US data freely available to the research community. We also thank Ignacia Cuevas and Agustin Gutierrez for excellent research assistance, both conceptual and empirical. First draft: October 2020.

Appendix A: United States

Figure A1:

Share of cash expenditures by County.

The figure shows the share of cash expenditures (ATM disbursements) over the total expenditures, at the county level. The data include information of 3199 counties.

Figure A2:

Total expenditures and Income.

The figure shows the relationship between total spending and total income in a county. Total spending is computed averaging across 2017–2019. Income is measured using individual income tax returns (Forms 1040) filed with the Internal Revenue Service (IRS) between January 1, 2017 and December 31, 2017. The size of the marker indicates the size of the population in each county obtained from the US Census.

Table A1:

Summary statistics – County level (US).

	(1)	(2)	(3)	(4)	(5)
	Mean	Std. Dev.	Pct. 25	Median	Pct. 75
ATM transactions	8.64	10.75	1.51	4.73	11.94
ATM disbursements	1252.66	1485.06	232.92	713.08	1747.41
ATM disbursements per transaction	149.97	26.30	136.50	147.08	159.53
Share of cash expenditures (expenditures)	0.13	0.04	0.11	0.13	0.15
Share of cash expenditures (transactions)	0.03	0.01	0.03	0.03	0.04
Total expenditures	9311.48	10,460.00	1859.56	5732.00	13,260.34
Total transactions	249.28	278.01	50.46	155.48	352.65
Total expenditures per transaction	37.78	6.40	34.58	36.84	39.41
New COVID-19 cases (bi-weekly)	64.25	300.27	1.71	6.69	27.72
New COVID-19 deaths (bi-weekly)	2.57	13.92	0.00	0.08	0.69

The table shows descriptive statistics of the variables of interest at the county level (mean, standard deviation, 25th percentile, median, and 75th percentile) in the year 2020. The share of cash expenditures (Expenditures) is the total cash expenditures over the total expenditures, including ATM disbursements and card transactions. The share of cash expenditures (Transactions) indicates the total ATM transactions over the total transactions. The variables presented are daily averages, except those that relate to the COVID-19 pandemic. “New COVID-19 Cases” indicates the changes in the confirmed cases in a 14-day period at the county level. “New COVID-19 Deaths” indicates the changes in the confirmed deaths in a 14-day period at the county level. The average of these variables is taken after the first case was confirmed on January 21st, 2020.

Table A2:

COVID-19 and the use of cash: withdrawals (W).

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Log COVID(t)	−0.006^***	0.008^***	0.013^**	0.017^***	0.004^**	0.003^*	0.010^**
Log COVID(t)	(0.002)	(0.001)	(0.005)	(0.004)	(0.001)	(0.002)	(0.004)
Log C(t)		0.328^***	0.326^***	0.331^***	0.244^***	0.218^***	0.248^***
Log C(t)		(0.010)	(0.012)	(0.013)	(0.019)	(0.026)	(0.021)
Observations	21,009	21,009	17,698	20,914	20,863	20,863	20,856
Within R-squared	0.094	0.454	–	–	–	–	–
County	Y	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6). The dependent variable is the daily average size of withdrawals. The independent variable is the logarithm of the COVID index i t = ( C a s e s i t ) 1 / 2 ( D e a t h s i t ) 1 / 2 , where Cases_it are the total confirmed cases in the county over the last 14 days and Deaths_it are the total confirmed deaths over the last 14 days in county i and period t. In column (2) we control for the logarithm of total expenditures paid in cash. In column (3) we instrument the logarithm of the COVID index_it with its one-period lagged value. In column (4) we instrument the logarithm of the COVID index_it with a leave-out instrument as described in the main text. In column (5) we instrument the logarithm of total cash expenditures with the logarithm of total expenditures and its lagged value. In column (6) we instrument the logarithm of total cash expenditures with a leave-out instrument of the logarithm of total expenditures and its lagged value. In column (7) we instrument both the COVID index_it and the logarithm of total cash expenditures using the leave out instrument and the logarithm of total expenditures and its lagged value. We consider county-two-week pairs with at least 5 ATM transactions and use Driscoll and Kraay standard errors with four lags. All the specifications include county and time effects. The ^***, ^**, and ^*, represent statistical significance at 1%, 5%, and 10% levels, respectively.

Table A3:

COVID-19 and the use of cash: transactions (N).

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Log COVID(t)	−0.036^***	−0.008^***	−0.013^**	−0.017^***	−0.004^**	−0.003^*	−0.010^**
Log COVID(t)	(0.005)	(0.001)	(0.005)	(0.004)	(0.001)	(0.002)	(0.004)
Log C(t)		0.672^***	0.674^***	0.669^***	0.756^***	0.782^***	0.752^***
Log C(t)		(0.010)	(0.012)	(0.013)	(0.019)	(0.026)	(0.021)
Observations	21,009	21,009	17,698	20,914	20,863	20,863	20,856
Within R-squared	0.365	0.831	–	–	–	–	–
County	Y	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6). The dependent variable is the daily average of the total ATM transactions for each county at the bi-weekly level. The independent variable is the logarithm of the COVID index i t = ( C a s e s i t ) 1 / 2 ( D e a t h s i t ) 1 / 2 , where Cases_it are the total confirmed cases in the county over the last 14 days and Deaths_it are the total confirmed deaths over the last 14 days in county i and period t. In column (2) we control for the logarithm of total expenditures paid in cash. In column (3) we instrument the logarithm of the COVID index_it with its one-period lagged value. In column (4) we instrument the logarithm of the COVID index_it with a leave-out instrument as described in the main text. In column (5) we instrument the logarithm of total expenditures paid in cash with the logarithm of total expenditures and its lagged value. In column (6) we instrument the logarithm of total expenditures paid in cash with a leave-out instrument of the logarithm of total expenditures and its lagged value. In column (7) we instrument both the COVID index_it and the logarithm of total expenditures paid in cash using the leave out instrument and the logarithm of total expenditures and its lagged value. We consider county-two-week pairs with at least 5 ATM transactions and use Driscoll and Kraay standard errors with four lags. All the specifications include county and time effects. The ^***, ^**, and ^*, represent statistical significance at 1%, 5%, and 10% levels, respectively.

Table A4:

COVID-19 and the use of cash: transactions (N) – Poisson.

	(1)	(2)	(3)	(4)	(5)	(6)
Log COVID(t)	−0.033^***	−0.007^***
Log COVID(t)	(0.003)	(0.001)
Log Cases(t)			−0.025^***	−0.004^***
Log Cases(t)			(0.002)	(0.001)
Log Deaths(t)					−0.017^***	−0.006^***
Log Deaths(t)					(0.003)	(0.001)
Log C(t)		0.694^***		0.695^***		0.696^***
Log C(t)		(0.005)		(0.005)		(0.006)
Observations	20,921	20,921	20,921	20,921	20,921	20,921
County	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6) using a Poisson model. The dependent variable is the daily average of the total ATM transactions for each county at the bi-weekly level. The independent variable in columns (1) and (2) is the logarithm of the COVID index i t = ( C a s e s i t ) 1 / 2 ( D e a t h s i t ) 1 / 2 , where Cases_it are the total confirmed cases in the county over the last 14 days and Deaths_it are the total confirmed deaths over the last 14 days in county i and period t. The independent variable in columns (3) and (4) is the total confirmed cases in the county over the last 14 days and in columns (5) and (6) is the total confirmed deaths over the last 14 days in county i and period t. In columns (2), (4), and (6) we control for the logarithm of total expenditures paid in cash. We consider county-biweek pairs with at least 5 ATM transactions and use bootstrap standard errors. All the specifications include county and time effects. The ^***, represent statistical significance at 1% level.

Table A5:

Cases, deaths, and the use of cash: transaction cost (W/N).

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Log Cases(t)	0.018^***	0.008^***	0.013^**	0.015^*
Log Cases(t)	(0.003)	(0.002)	(0.005)	(0.007)
Log Deaths(t)					0.021^***	0.014^***	0.032^***	0.044^***
Log Deaths(t)					(0.003)	(0.003)	(0.009)	(0.009)
Log C(t)		−0.345^***	−0.350^***	−0.341^***		−0.346^***	−0.352^***	−0.343^***
Log C(t)		(0.020)	(0.025)	(0.027)		(0.021)	(0.025)	(0.026)
Observations	21,009	21,009	17,698	20,914	21,009	21,009	17,698	20,916
Within R-squared	0.321	0.425	–	–	0.32	0.426	–	–
County	Y	Y	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6). The dependent variable is the transaction cost of adjusting the stock of cash, which is approximated using the ratio of the daily average size of withdrawals and the daily average of the total ATM transactions for each county at the bi-weekly level. The independent variable in columns (1) to (4) is the total confirmed cases in the county over the last 14 days and in columns (5) to (8) is the total confirmed deaths over the last 14 days in county i and period t. In columns (2) and (6) we control for the logarithm of total expenditures paid in cash. In columns (3) and (7) we instrument the logarithm of the total confirmed cases and the total deaths with their respective lag variables. In columns (4) and (8) we instrument the logarithm of the total confirmed cases and the total deaths with a leave-out instrument as described in the main text. We consider county-biweek pairs with at least 5 ATM transactions and use Driscoll and Kraay standard errors with four lags. All the specifications include county and time effects. The ^***, ^**, and ^*, represent statistical significance at 1%, 5%, and 10% levels, respectively.

Table A6:

COVID-19 and the use of cash: transaction cost (W/N) – all cash transactions.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Log COVID(t)	0.027^***	0.016^***	0.022^*	0.031^***	0.008^**	0.005	0.017^*
Log COVID(t)	(0.004)	(0.003)	(0.010)	(0.009)	(0.003)	(0.004)	(0.009)
Log C(t)		−0.263^***	−0.266^***	−0.258^***	−0.460^***	−0.539^***	−0.454^***
Log C(t)		(0.032)	(0.040)	(0.039)	(0.058)	(0.075)	(0.060)
Observations	21,165	21,165	17,820	21,067	21,016	21,016	21,009
Within R-squared	0.385	0.434	–	–	–	–	–
County	Y	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6). The estimates include MCC 6010 (“Manual Cash Disbursements”), which includes face-to-face cash disbursements at financial institutions. The dependent variable is the transaction cost of adjusting the stock of cash, which is approximated using the ratio of the daily average of withdrawals and the daily average of the total ATM transactions for each county at the bi-weekly level. The independent variable is the logarithm of the COVID index i t = ( C a s e s i t ) 1 / 2 ( D e a t h s i t ) 1 / 2 , where Cases_it are the total confirmed cases in the county over the last 14 days and Deaths_it are the total confirmed deaths over the last 14 days in county i and period t. In column (2) we control for the logarithm of total expenditures paid in cash. In column (3) we instrument the logarithm of the COVID index_it with its one-period-lagged value. In column (4) we instrument the logarithm of the COVID index_it with a leave-out instrument as described in the main text. In column (5) we instrument the logarithm of total expenditures paid in cash with the logarithm of total expenditures and its lagged value. In column (6) we instrument the logarithm of total expenditures paid in cash with a leave-out instrument of the logarithm of total expenditures and its lagged value. In column (7) we instrument both the COVID index_it and the logarithm of total expenditures paid in cash using the leave out instrument and the logarithm of total expenditures and its lagged value. We consider county-two-week pairs with at least 5 ATM transactions and use Driscoll and Kraay standard errors with four lags. All the specifications include county and time effects. The ^***, ^**, and ^*, represent statistical significance at 1%, 5%, and 10% levels, respectively.

Table A7:

COVID-19 and the use of cash: transaction cost (W/N) – alternative standard errors.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Log COVID(t)	0.030^***	0.015^***	0.026^***	0.033^***	0.008^***	0.006^**	0.021^***
Log COVID(t)	(0.003)	(0.002)	(0.006)	(0.005)	(0.003)	(0.003)	(0.005)
Log C(t)		−0.344^***	−0.349^***	−0.339^***	−0.511^***	−0.564^***	−0.504^***
Log C(t)		(0.010)	(0.010)	(0.010)	(0.014)	(0.019)	(0.014)
Observations	20,921	20,921	17,698	20,914	20,863	20,863	20,856
Within R-squared	0.007	0.159	–	–	–	–	–
County	Y	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6). The dependent variable is the transaction cost of adjusting the stock of cash, which is approximated using the ratio of the daily average size of withdrawals and the daily average of the total ATM transactions for each county at the bi-weekly level. The independent variable is the logarithm of the COVID index i t = ( C a s e s i t ) 1 / 2 ( D e a t h s i t ) 1 / 2 , where Cases_it are the total confirmed cases in the county over the last 14 days and Deaths_it are the total confirmed deaths over the last 14 days in county i and period t. In column (2) we control for the logarithm of total expenditures paid in cash. In column (3) we instrument the logarithm of the COVID index_it with its one-period lagged value. In column (4) we instrument the logarithm of the COVID index_it with a leave-out instrument as described in the main text. In column (5) we instrument the logarithm of total expenditures paid in cash with the logarithm of total expenditures and its lagged value. In column (6) we instrument the logarithm of total expenditures paid in cash with a leave-out instrument of the logarithm of total expenditures and its lagged value. In column (7) we instrument both the COVID index_it and the logarithm of total expenditures paid in cash using the leave out instrument and the logarithm of total expenditures and its lagged value. We consider county-two-week pairs with at least 5 ATM transactions and cluster the standard errors at the county level. All the specifications include county and time effects. The ^*** and ^**, represent statistical significance at 1% and 5% levels, respectively.

Table A8:

COVID-19 and the use of cash: transaction cost (W/N) – monthly.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Log COVID(t)	0.025^***	0.013^***	0.088^***	0.013^**	0.009^***	0.008^***	0.008
Log COVID(t)	(0.003)	(0.003)	(0.018)	(0.005)	(0.003)	(0.003)	(0.005)
Log C(t)		−0.435^***	−0.419^***	−0.435^***	−0.584^***	−0.636^***	−0.584^***
Log C(t)		(0.013)	(0.015)	(0.013)	(0.019)	(0.024)	(0.020)
Observations	11,860	11,860	8976	11,860	11,853	11,853	11,853
Within R-squared	0.008	0.228	–	–	–	–	–
County	Y	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6). The dependent variable is the transaction cost of adjusting the stock of cash, which is approximated using the ratio of the daily average size of withdrawals and the daily average of the total ATM transactions for each county at the monthly level. The independent variable is the logarithm of the COVID index i t = ( C a s e s i t ) 1 / 2 ( D e a t h s i t ) 1 / 2 , where Cases_it are the total confirmed cases in the county over the last month and Deaths_it are the total confirmed deaths over the last month in county i and period t. In column (2) we control for the logarithm of total expenditures paid in cash. In column (3) we instrument the logarithm of the COVID index_it with its one-period lagged value. In column (4) we instrument the logarithm of the COVID index_it with a leave-out instrument as described in the main text. In column (5) we instrument the logarithm of total expenditures paid in cash with the logarithm of total expenditures and its lagged value. In column (6) we instrument the logarithm of total expenditures paid in cash with a leave-out instrument of the logarithm of total expenditures and its lagged value. In column (7) we instrument both the COVID index_it and the logarithm of total expenditures paid in cash using the leave out instrument and the logarithm of total expenditures and its lagged value. We consider county-monthly pairs with at least 5 ATM transactions and cluster the standard errors at the county level. All the specifications include county and time effects. The ^*** and ^**, represent statistical significance at 1% and 5% levels, respectively.

Table A9:

COVID-19 and the use of cash: transaction cost (W/N) – first stage.

	(1)	(2)	(3)	(4)	(5)	(6)
	Log COVID(t)		Log C(t)		Log COVID(t)	Log C(t)
Log COVID(t − 1)	0.474^***
Log COVID(t − 1)	(0.039)
Log COVID(t − 1) − IV		0.734^***			0.718^***	−0.006
Log COVID(t − 1) − IV		(0.006)			(0.009)	(0.005)
Log C(t)	−0.214^***	−0.117^**
Log C(t)	(0.036)	(0.040)
Log E(t)			1.117^***		−0.230^***	1.087^***
Log E(t)			(0.014)		(0.022)	(0.031)
Log E(t − 1)			−0.104^***		−0.236^**	−0.072
Log E(t − 1)			(0.024)		(0.090)	(0.043)
Log E(t) − IV				0.968^***
Log E(t) − IV				(0.035)
Log E(t − 1) − IV				0.013
Log E(t − 1) − IV				(0.026)
Log COVID(t)			−0.009^***	−0.019^***
Log COVID(t)			(0.002)	(0.004)
Observations	17,819	21,002	20,951	20,951	20,944	28,068

Table A9:

(continued)

	(1)	(2)	(3)	(4)	(5)	(6)
	Log COVID(t)		Log C(t)		Log COVID(t)	Log C(t)
F-statistic	105.5	7422.7	2257.8	333.6	1577.6	1413.4
County	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y

The tables shows the first-stage regressions of the instrumented specifications in Table 2. In columns (1) and (2) the instrumented variable is the logarithm of the COVID index i t = ( C a s e s i t ) 1 / 2 ( D e a t h s i t ) 1 / 2 , where Cases_it are the total confirmed cases in the county over the last 14 days and Deaths_it are the total confirmed deaths over the last 14 days in county i and period t. In column (1) the instrument is the lagged value of COVID index. In column (2) the instrument is a leave-out instrument of the mean of COVID index at the county level where we use the commuting flows as weights. In both columns we control for total cash expenditures. In columns (3) and (4) the instrumented variable is total cash expenditures. In column (3) the instruments are the logarithm of total expenditures and its lagged value. In column (4) the instrument is a leave-out instrument of the logarithm of total expenditures and its lagged value. In both columns we control for COVID index. The estimates in columns (5) and (6) correspond to those presented in column (8) of Table 2, where we instrument both Log COVID index and Log C(t). In column (5) we instrument COVID index with a leave-out instrument of the mean of COVID index at the county level. In column (6) we instrument Log C(t) with the logarithm of total expenditures and its lagged value. We consider county-two-week pairs with at least 5 ATM transactions and use Driscoll and Kraay standard errors with four lags. All the specifications include county and time effects. The ^*** and ^**, represent statistical significance at 1% and 5% levels, respectively.

Appendix B: Argentina

Table B1:

Summary statistics – locality level (Argentina).

	(1)	(2)	(3)	(4)	(5)
	Mean	Std. Dev.	Pct. 25	Median	Pct. 75
ATM transactions	823.33	2769.90	319.70	461.47	685.32
ATM disbursements	80,982.04	258,954.65	34,888.18	51,003.71	70,525.66
ATM disbursements per transaction	103.44	17.87	91.71	102.19	114.49
New COVID-19 cases (bi-weekly)	641.08	1255.20	168.81	331.98	685.23
New COVID-19 deaths (bi-weekly)	20.12	43.11	2.87	8.68	24.14

The table shows descriptive statistics of the variables of interest at the locality level (mean, standard deviation, percentile 25th, median, and percentile 75th) in the year 2020. The exchange rate used is the one that prevailed on January 1, 2020 (i.e. 1 Argentine Peso equals 0.01671 United States Dollar); all amounts are expressed in real dollars. The variables presented are daily averages, except those that relate to the COVID-19 pandemic. “New COVID-19 Cases” indicates the changes in the confirmed cases in a 14-day period at the locality level. “New COVID-19 Deaths” indicates the changes in the confirmed deaths in a 14-day period at the locality level. The average of these variables is taken after the first case was confirmed on March 3rd, 2020.

Table B2:

Cases and the use of cash: Argentina.

	(1)	(2)	(3)	(4)	(5)	(6)
	Log W N	Log W	Log N	Log W N	Log W	Log N
Log Cases(t)	0.019^***	0.006^***	−0.012^***	0.014^***	0.007^***	−0.007^***
Log Cases(t)	(0.005)	(0.002)	(0.004)	(0.003)	(0.002)	(0.002)
Log C(t)				−0.808^***	0.096^***	0.904^***
Log C(t)				(0.014)	(0.007)	(0.007)
Observations	2532	2532	2532	2532	2532	2532
Within R-squared	0.514	0.889	0.531	0.742	0.894	0.914
Locality	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6) for Argentina. The dependent variable in columns (1) and (4) is the transaction cost of adjusting the stock of cash, which is approximated using the ratio of the daily average size of withdrawals and the daily average of the total ATM transactions for each locality at the bi-weekly level. The dependent variable in columns (2) and (5) is the average size of withdrawals and in columns (3) and (6) the dependent variable is the total ATM transactions. The independent variable is the logarithm of the total confirmed cases over the last 14 days in locality i and period t. In columns (4)–(6) we control for the logarithm of total expenditures paid in cash. We use Driscoll and Kraay standard errors with four lags. All the specifications include locality and time effects. The ^***, represent statistical significance at 1% level.

Table B3:

Deaths and the use of cash: Argentina.

	(1)	(2)	(3)	(4)	(5)	(6)
	Log W N	Log W	Log N	Log W N	Log W	Log N
Log Deaths(t)	0.016^***	0.006^***	−0.010^**	0.013^***	0.007^***	−0.007^***
Log Deaths(t)	(0.004)	(0.002)	(0.003)	(0.003)	(0.002)	(0.002)
Log C(t)				−0.814^***	0.093^***	0.907^***
Log C(t)				(0.015)	(0.008)	(0.008)
Observations	2532	2532	2532	2532	2532	2532
Within R-squared	0.507	0.888	0.526	0.739	0.893	0.913
Locality	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6) for Argentina. The dependent variable in columns (1) and (4) is the transaction cost of adjusting the stock of cash, which is approximated using the ratio of the daily average size of withdrawals and the daily average of the total ATM transactions for each locality at the bi-weekly level. The dependent variable in columns (2) and (5) is the average size of withdrawals and in columns (3) and (6) the dependent variable is the total ATM transactions. The independent variable is the logarithm of the total confirmed deaths over the last 14 days in locality i and period t. In columns (4)–(6) we control for the logarithm of total cash expenditures. We use Driscoll and Kraay standard errors with four lags. All the specifications include locality and time effects. The ^*** and ^**, represent statistical significance at 1% and 5% levels, respectively.

Appendix C: Chile

Table C1:

Summary statistics – Comuna level (Chile).

	(1)	(2)	(3)	(4)	(5)
	Mean	Std. Dev.	Pct. 25	Median	Pct. 75
ATM transactions	22,940.02	83,756.93	7831.40	11,276.47	17,964.81
ATM disbursements	1,886,936	5,715,463	720,431	1,051,416	1,693,311
ATM disbursements per transaction	90.69	16.60	79.84	88.42	98.85
New COVID-19 cases (monthly)	2182.56	4127.08	205.14	467.57	1783.43
New COVID-19 deaths (monthly)	82.13	170.13	4.25	13.00	58.75

The table shows descriptive statistics of the variables of interest at the commune level (mean, standard deviation, percentile 25th, median, and percentile 75th) in the year 2020. The exchange rate used is the one that prevailed on January 1, 2020 (i.e. 1 Chilean Peso equals 0.0014 United States Dollar); all amounts are expressed in real dollars. The variables presented are daily averages, except those that relate to the COVID-19 pandemic. “New COVID-19 Cases” indicates the changes in the confirmed cases in a month at the commune level. “New COVID-19 Deaths” indicates the changes in the confirmed deaths in a 1 month at the commune level. The average of these variables is taken after the first case was confirmed on March 3rd, 2020.

Table C2:

Cases and the use of cash: Chile.

	(1)	(2)	(3)	(4)	(5)	(6)
	Log W N	Log W	Log N	Log W N	Log W	Log N
Log Cases(t)	0.049^**	0.010^***	−0.039^**	0.024^***	0.012^***	−0.012^***
Log Cases(t)	(0.015)	(0.002)	(0.012)	(0.005)	(0.002)	(0.002)
Log C(t)				−0.834^***	0.083^*	0.917^***
Log C(t)				(0.072)	(0.036)	(0.036)
Observations	1873	1873	1914	1873	1873	1873
Within R-squared	0.254	0.703	0.499	0.65	0.716	0.922
Commune	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6) for Chile. The dependent variable in columns (1) and (4) is the transaction cost of adjusting the stock of cash, which is approximated using the ratio of the daily average size of withdrawals and the daily average of the total ATM transactions for each commune at the monthly level. The dependent variable in columns (2) and (5) is the average size of withdrawals and in columns (3) and (6) the dependent variable is the total ATM transactions. The independent variable is the logarithm of the total confirmed cases over the last month in commune i and period t. In columns (4)–(6) we control for the logarithm of total expenditures paid in cash. We use Driscoll and Kraay standard errors with four lags. All the specifications include commune and time effects. The ^***, ^**, and ^*, represent statistical significance at 1% and 10% levels, respectively.

Table C3:

Deaths and the use of cash: Chile.

	(1)	(2)	(3)	(4)	(5)	(6)
	Log W N	Log W	Log N	Log W N	Log W	Log N
Log Deaths(t)	0.005	0.001	−0.005	0.002	0.001	−0.001
Log Deaths(t)	(0.004)	(0.001)	(0.003)	(0.002)	(0.001)	(0.001)
Log C(t)				−0.851^***	0.074^*	0.926^***
Log C(t)				(0.075)	(0.037)	(0.037)
Observations	1873	1873	1914	1873	1873	1873
Within R-squared	0.22	0.699	0.481	0.642	0.709	0.921
Commune	Y	Y	Y	Y	Y	Y
Time	Y	Y	Y	Y	Y	Y

The table reports the estimates of Eq. (6) for Chile. The dependent variable in columns (1) and (4) is the transaction cost of adjusting the stock of cash, which is approximated using the ratio of the daily average size of withdrawals and the daily average of the total ATM transactions for each commune at the monthly level. The dependent variable in columns (2) and (5) is the average size of withdrawals and in columns (3) and (6) the dependent variable is the total ATM transactions. The independent variable is the logarithm of the total confirmed deaths over the last month in commune i and period t. In columns (4)–(6) we control for the logarithm of total cash expenditures. We use Driscoll and Kraay standard errors with four lags. All the specifications include commune and time effects. The ^*** and ^*, represent statistical significance at 1% and 10% levels, respectively.

Appendix D: Mexico

Table D1:

Summary statistics – municipality level (Mexico).

	(1)	(2)	(3)	(4)	(5)
	Mean	Std. Dev.	Pct. 25	Median	Pct. 75
ATM transactions	65,386.14	25,5850.70	0.00	2635.67	21,929.00
Banks	4.00	4.35	1.00	2.00	5.00
ATMs	26.28	117.95	0.00	1.00	7.00
Branches	5.07	19.49	0.00	0.00	2.00
Branches closed	0.75	3.63	0.00	0.00	0.00
New COVID-19 cases (monthly)	63.99	211.57	3.50	9.17	30.05
New COVID-19 deaths (monthly)	7.68	26.08	0.50	1.25	3.76

The table shows descriptive statistics of the variables of interest at the municipality level (mean, standard deviation, 25th percentile, median, and 75th percentile) in the year 2020. The variables presented are daily averages, except those that relate to the COVID-19 pandemic. “New COVID-19 Cases” indicates the changes in the confirmed cases in a month at the municipality level. “New COVID-19 Deaths” indicates the changes in the confirmed deaths in a month at the municipality level. The average of these variables is taken after the first case was confirmed on February 28th, 2020.

Table D2:

COVID-19 and the use of cash: transactions (N) – Mexico.

	(1)	(2)	(3)	(4)
Log Cases(t)	−0.013^*
Log Cases(t)	(0.005)
Log Deaths(t)		−0.008^*
Log Deaths(t)		(0.004)
Log COVID(t)			−0.012^*
Log COVID(t)			(0.005)
Log Branches Closed(t)				−0.097^*
Log Branches Closed(t)				(0.042)
Observations	32,167	32,167	32,167	41,300
Within R-squared	0.002	0.002	0.002	0.013
Bank-municipality	Y	Y	Y	Y
Time	Y	Y	Y	Y

The table reports the estimates of Eq. (6). The dependent variable is the logarithm of the total ATM transactions for each bank-municipality at the monthly level. The independent variable in column (1) is the logarithm of the total confirmed cases over the last month in a given municipality and period. In column (2) the independent variable is the total confirmed deaths over the last month. In column (3) the independent variable is COVID index = (Cases)^1/2(Deaths)^1/2, where Cases are the total confirmed cases in the municipality over the last month and Deaths are the total confirmed deaths over the last month in a given municipality and period. In column (4) the independent variable is the total branches closed due to COVID-19 for a given bank-municipality and period. We use Driscoll and Kraay standard errors. All the specifications include bank-municipality and time effects. The ^*, represent statistical significance at 10% level.

Table D3:

Branches closed and the use of cash: transactions (N) – Mexico.

	(1)	(2)	(3)	(4)
Log Branches Closed(t)	−0.097^***	−0.086^***	−0.099^***	−0.133^***
Log Branches Closed(t)	(0.015)	(0.015)	(0.016)	(0.019)
Observations	40,935	39,110	40,830	39,005
Within R-squared	0.001	0.001	0.001	0.002
Bank-county	Y	Y	Y	Y
Time	Y	N	N	N
County-time	N	Y	N	Y
Bank-time	N	N	Y	Y

The table reports the estimates of Eq. (6). The dependent variable is the logarithm of the total ATM transactions for each bank-municipality at the monthly level. The independent variable is the total branches closed due to COVID-19 for a given bank-municipality and period. The data is monthly at the bank-municipality level and comes from the National Banking and Securities Commission (CNBV). The standard errors are clustered at the municipality-time level. The ^***, represent statistical significance at 1% level.

Figure D1:

COVID-19 and the use of cash: Mexico.

The figure shows the evolution of ATM transactions normalized to 1 on January 2020 (black line) and the logarithm of COVID index (i.e. COVID index = (Cases)^1/2(Deaths)^1/2) in Mexico (purple line).

We use the Financial Inclusion Database (BDIF) from the National Banking and Securities Commission (CNBV). The data consist of monthly data gathered from commercial banks and other financial entities related to financial inclusion. The databases include variables such as bank branches, ATMs, ATM transactions, and debit contracts.^[24] Data set is disaggregated at the bank and municipality level and contains information on the number of bank branches that have closed due to the pandemic each time period. The data gathered for this paper corresponds to the period 2011–2020. Since we study the pandemic period, we focus on data from January to August 2020.

The average municipality in our data has 65,386 (std. 255,850) ATM transactions per month. It also has 4 banks, 5 bank branches, 26 ATMs. The table also reports the average changes in the confirmed cases and deaths in a month. Over our sample period, the average municipality suffered an increase of approximately 64 new confirmed cases per month.

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Received: 2020-12-15

Revised: 2021-04-06

Accepted: 2021-05-30

Published Online: 2021-06-23

Cash-Management in Times of Covid-19

Abstract

Acknowledgements

Appendix A: United States

Appendix B: Argentina

Appendix C: Chile

Appendix D: Mexico

References

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