Demonetisation: An Estimation of Losses due to ATM Queuing

We highlight the inconvenience and the consequent losses that the general public has faced due to one factor: difficulty in withdrawing cash from ATMs. Our methodology is based on a widely-used technique—“willingness to pay.” The monetary value of losses thus obtained could be viewed as "cash transaction tax"—an implicit tax that the government has imposed on cash transactions due to the lack of liquidity. This tax can therefore be thought of as the government’s way of incentivising cashless transactions in the country.

The central government’s decision to render 500 and 1,000 denomination notes as invalid legal tender has been a part of various social, political and economic discussions since the announcement on 8 November 2016. The government’s claims of a “surgical strike” on black money mellowed down as the days passed. The sudden shift from “surgical strike” to “cashless India” was arguably a desperate damage control measure by the government. Economists from across the spectrum have cautioned against the fallout of the resultant liquidity crunch (Basu 2016; Venugopal 2016; Gopinath 2016; Patnaik 2016; Rai 2016; Rogoff 2016). The long-term effects are more debatable and uncertain, but things do not look very rosy there either (Mishra 2017). To make matters worse, the government’s haste and lack of preparedness (reflected in the long queues, lack of cash in banks and ATMs, poor management by the RBI and banks, non-calibrated ATMs, and of course the printing of notes and their distribution) made daily life of ordinary citizens difficult.

In the following sections, we present our methodologies, effects of demonetisation and the main results from our survey. Finally, we end with some comments on “Cashless India” which is being projected as a “magic bullet” for various problems.

1 Methodology

We undertook fieldwork in four areas of New Delhi (IIT Delhi, Nehru Place, Connaught Place, and SDA Market). The places selected have a high ATM density and there were many queues in all four areas. This ensured the possibility of interacting with people of different occupations (from daily wagers to housewives as well as salaried employees). Our informal survey consisted of identifying an ATM queue (comprising more than than 30 persons) to interview roughly every fifth person in the queue. We interviewed 150 respondents from 3–12 December 2016. Most of our respondents were male (81%) and two-thirds were in the age group of 20–40 years. Only 10% were below 20 years, and 23% were between 40 and 60 years. Salaried persons (44%) and self-employed persons (21%) comprised two-thirds of our sample. Nearly one-fifth (17%) were students. Casual labourers and housewives were a small minority in our sample.

2 Results

We present our results in two parts—the effect of demonetisation and monetary value of time lost by standing in ATM queues.

2.1 Effects of Demonetisation

2.1.1 Experience of Cash Withdrawal: ATM queues across the country have been huge since the 8 November 2016 announcement. This has not only led to chaos at the ATMs, but also meant that the proportion of successful trips to an otherwise reliable “All Time Money” has reduced. In our survey, we found that only 40% of all ATM visits were successful. A low success rate combined with the reduced limit on cash withdrawals at ATMs meant that 58% of respondents had to visit ATMs more than five times in the month following demonetisation as against 24% of respondents before demonetisation. Further, more than half of the respondents had to stand in queues for 1–3 hours, irrespective of the success of the visit. One-tenth of respondents claimed to have queued for more than 3 hours. The average hours lost in queuing per respondent since demonetisation (between 8 November and 12 December) was a whopping 12.8 hours.

Finally, a successful ATM transaction was not necessarily the end of the story because almost 49% of successful withdrawals consisted of 2,000 notes. Daily wagers and housewives, especially, complained about the problems in using a 2,000 note to carry out their day to day transactions such as buying vegetables. 

2.1.2 Impact of Lack of Liquidity on Daily Life of the Respondents: As expected, a large proportion of respondents claimed to have problems in managing their daily expenses like transport and buying fruits. As many as 95% of respondents reported wastage of time in getting cash while other responses included disruption in educational and wedding plans (31% and 15% respectively) and health emergencies (7%). Approximately, one-fourth of the respondents (35 out of 150) reported a reduction in earnings. Amongst those who reported such a reduction, earnings had fallen, on an average, by 46%. The highest reduction was reported by self-employed persons (53%).

Respondents also reported cutting back on expenses in order to cope with the lack of liquidity. Overall, about three-quarters of respondents said that they had cut back on expenditures, and on an average, they reported spending about a third less than before. Somewhat curiously, the highest cut-back was reported by salaried respondents (who had experienced no reduction in earnings). This reduction in expenses across occupational groups is alarming if it is true on a wider scale because it would imply a reduction in aggregate demand (Nag 2016).

2.2 Monetary Value of Standing in the ATM Queues

In this section, we attempt to quantify the losses borne by people while standing in ATM queues. We have devised two methods to estimate this loss in monetary terms. 

2.2.1 Method 1: As mentioned earlier, this method is based on “willingness to pay” (WTP). In this case, we first asked about the length of the ATM queue they would generally join or stand in to withdraw money from ATM. Let that length be “L.” Once we have this value, we present a hypothetical situation in which they were asked, “If given an option to withdraw money without standing in the line, how much of 2,000 (standard withdrawal limit) are you willing to let go?” Let this price that they are willing to pay be “P,” in rupees.

These two questions allow us to get an estimate of the value of queuing time. The respondent first tells the time they would be willing to stand in queue and then they tell us the amount which they are willing to pay. To calculate the time spent in the queue, we assume that each ATM transaction takes an average time of 2 minutes. From the time thus obtained we can calculate the per hour loss due to standing in ATM queues.[1] 

Using this, we have bypassed accounting for other parameters such as travel time, productive–unproductive time since the respondent is valuing his/her time on their own. From the survey, we have data on queuing time of the respondent in their last visit. Let this queuing time be “Q,” in minutes. Then the willingness to pay for (being spared) the queuing time, in per hour, is

WTP = P/ [(L X 2)/60] = [(P X 30)/L] 

The monetary loss per transaction can then be calculated as

WTP * Queuing time (in hours) = [(P X 30)/L] *(Q/60) = P X (Q/2L)

To illustrate, suppose a person is willing to join a queue that has 30 people (L=30), and is willing to pay 60 to avoid queuing (P=60). This means that the person is willing to pay 1/minute to avoid queuing. For this person, the per hour WTP is 60. Further, one successful visit to ATM will yield 2,000, and if the respondent had paid 60 to avoid that hour of queuing, in percentage terms they would have lost 3% of the cash withdrawn. Further, assume that the queuing time from the last visit to ATM is 90 minutes (that is 1.5 hours). So, the total monetary loss in this transaction will be WTP per hour multiplied by the queuing time, which is 90.

2.2.2 Method 2: As a check on the first method, we asked our respondents to estimate the per hour monetary value of their queuing time directly. Let this be M. This method gives us a direct estimate as the respondents are themselves putting a value on their time. For queuing time, we use Q as in method 1. Thus: 

Monetary Loss ( per hour) = M

Queuing time in last transaction (hours) = Q/60 

Monetary Loss per transaction = M x (Q/60)

For our estimation, we average over four variables: L, P, M and Q. In Table 1, we report results for the 89 respondents (out of 150) who answered questions for both method 1 and method 2. For some questions, the respondents were unable to give a clear or satisfactory answer. In Table 2, we report losses obtained by applying each method to the subset of respondents for whom relevant data was available (102 respondents for method 1 and 130 respondents for method 2).


In the calculation of total transaction loss, queuing time is assumed to be (L X 2) minutes. This is an under-estimate: “L x 2” is less than the actual time spent per transaction because in general, people could not withdraw money in a single ATM visit and queued multiple times for one transaction. Also, our estimate excludes time spent on travelling. For instance, we met people who had travelled from Faridabad to Connaught Place (a journey of about 30 kilometre) to withdraw money. It is therefore safe to say that all estimates presented in Tables 1 and 2 are essentially lower than the actual figures. 

3 Main results

Using method 1, we find that on an average, respondents’ WTP is 76 per hour. This corresponds to an implicit (cash transaction) tax of 120, or 6%, on a standard transaction of 2,000. The chaos and helplessness of citizens is reflected in the fact that the average reported value of L was more than 50. To add to this, the average P value was 135—respondents were willing to let go of about 7% of the money (2,000) consciously just to avoid the hassle of standing in ATM queues.

The per hour wage loss was lowest at 30 for casual labourers. Even though the WTP was lowest in absolute terms for casual labourers, demonetisation affected them the most in relative terms as the total loss (average loss per transaction multiplied by the number of visits) is equivalent to losing roughly one day’s worth of income (assuming that the average daily wage is 250). If we combine the drastic reduction in job opportunities and these losses for the labourers, the results give us an idea of the magnitude of losses this section of the society had to suffer due to demonetisation. 

Interestingly, students and housewives recorded the highest per hour WTP at 103 and 119 respectively. The explanation for the high WTP and comparatively lower L values for students and housewives is that many respondents standing in queues claimed to be preparing for competitive exams and valued their time much more than others. Students also pointed out that an hour in an ATM queue effectively meant the loss of 4–5 hours because they not only got tired but also lost the flow in their studies due to a disrupted routine.

Interactions with housewives painted an even more dismal picture. The burden of standing in ATM queues and managing the household was unbearable for them. A housewife we interviewed in Nehru place said “I have money in my account but the LPG cylinder delivery person will not take money via PayTM. I have been cooking food on electricity. I am unable to cook good food for my children. My problems have increased manifold.” 

The per hour WTP for self-employed and salaried persons stood at 92 and 50 respectively. While salaried persons were not very concerned about the money (as they got fixed salaries), they did complain about frequent spats with their superiors for turning up late at work because they had to queue at ATMs. The self-employed complained of huge losses in their business and claimed that demonetisation had an irreparable effect on their business. A construction contractor said, "Two of my contracts have been delayed and there will obviously be much lesser opportunities next year. I don’t see myself recovering from these losses in the near future."
When directly asked to estimate per hour monetary value of their queuing time (method 2), self-employed businessmen claimed huge business losses. For them, hourly monetary loss (M) was 440. The average percentage loss per ATM transaction for the self-employed respondents was 38%. Students, housewives and salaried employees also put a much larger per hour value than in Method 1: 377, 256 and 221 respectively (see Table 1). A student at IIT Delhi estimated his per hour monetary loss at 500. He justified it by saying, "I have worked very hard to get into IIT and put double the efforts ever since I entered the institute. I am sure I will get a good salary package. My price [worth] would be even higher and my time even more precious!" Even casual labourers put a higher value by this method (51 as against 30 from method 1).[2]  
Overall, we recorded a staggering average per hour loss of 283 (23% per transaction). A 23% loss per transaction essentially means that 460 out of the transacted 2,000 was the implicit tax by this method. The implications of the second methodology are clear: people from all the occupational categories value their time a lot and are experiencing a huge inconvenience and monetary losses due to demonetisation.
The difference between percentage losses per transaction when calculated from method 1 and method 2 is significant. This could be because of two factors. First, in method 1, we are asking the respondents: "How much of 2,000 (standard withdrawal amount) are you willing to let go?,” while in method 2, we ask them to put a value to their time. It is a human tendency to try to pay less for a service while in the latter case of putting a value to their time, there will be a psychological bias in the opposite direction. In a sense, paying money is like a loss and monetary value of time is like a reward.
Secondly (and more importantly), the respondents are asked to shell out money from the 2,000 they will withdraw from the ATM visit in method 1. They are therefore bound by an implicit upper limit (around 20% of 2,000) of money that they can offer for the option. In the second case, there is no such restriction and a person can declare any value for their time. Therefore, there is more predictability of P values in method 1 (roughly less than 300–400) while the value of M (in method 2) is higher and more widely distributed.


The percentage losses in money per ATM transaction by methods 1 and 2 (6% and 23% respectively) clearly point to the huge inconvenience and wage losses the common public has suffered. The "black money" discourse died down as the "cashless" India pitch gained strength. While going cashless has many advantages (like convenience of transactions, reduced production cost of currency, increased surveillance leading to a wider tax net and reduced fraud, and positive impact of data drives decisions), we need to appreciate that it is not practical to dream of being cashless in a country like India where a large section of people is still not connected to the banking system. As pointed out by others, "less cash"[3]  would have been a better way to approach things (Shah 2017). We also need to realise that cash in the economy is not the enemy as such and going cashless will not solve our problems magically. Being cashless would bring with it its own problems including digital transaction costs and privacy (especially in today's big-data driven world). In fact, the global cashless debate (for example, in early 2016 in Germany) brought forth some very interesting arguments against a completely cashless society. These were the exacerbation of inequality, increased powers to banks and financial technology (“fintech”) companies, and infringement of privacy among others (Frisby 2016). 

To sum up, the methodology used here for the estimation of wages has shortcomings, and there can be a better model for wage estimation. Our attempt was to bring out the costs borne by the general public. While the government made statements about having things under control, our fieldwork and estimates clearly show otherwise.


Image Courtesy: Modified. Representational Image. Wikimedia Commons/Kottakkalnet (Own work) [CC BY-SA 4.0]


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