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Open Access 06.05.2024

Effects of electronic cash registers on reported revenue

verfasst von: Per Engström, Johannes Hagen, Alireza Khoshghadam, Andrea Schneider

Erschienen in: International Tax and Public Finance

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Abstract

We assess the impact of a Swedish regulatory change, which required businesses with any business-to-consumer transactions, whether by cash or card, to use a certified electronic cash register (ECR), on reported revenue. To do this, we use administrative data on the monthly reported revenue of all affected firms and a staggered difference-in-differences approach. Our findings indicate that there was an immediate increase of about 2.7–4.3% in reported revenue following the implementation of a certified ECR. However, the effect was temporary and diminished to zero after just a few months, which indicates that firms found innovative methods to underreport their revenue.

1 Introduction

Tax administrations around the world embrace digital transformation to enhance tax compliance. A notable example is the introduction of mandatory electronic cash registers (ECR)—certified electronic devices designed to register and calculate transactions at a point of sale. Motivated by its potential to process and monitor transactions in cash-intensive businesses, 16 OECD countries have already made certified ECRs mandatory by 2020.1 Mandatory ECRs have also been raised by the European Commission as a way to curb under-reporting of value-added tax (VAT).2 This has led to a recent initiative on “VAT in the digital age”, including recommendations on mandatory ECRs (European Commission, 2022b).
The introduction of ECRs reflects a broader shift towards digitized business transactions, with Electronic Invoicing (E-invoicing) being its closest counterpart.3 E-invoicing involves the electronic creation, transmission, reception, and management of invoices, typically using standardized digital formats. This method streamlines the exchange of invoice data between suppliers and buyers in electronic form, eliminating the need for traditional paper documentation. While E-invoicing primarily targets business-to-business transactions, ECRs are relevant for business-to-consumer transactions; however, both provide tax agencies with enhanced capabilities to detect and prevent VAT fraud.
Nevertheless, technological advancements in consumption taxation are a double-edged sword. On one side, these advancements can improve the monitoring and enforcement of tax compliance among firms, particularly in countries with limited state capacity (Fan et al., 2018; Bellon et al., 2022; Juniult, 2023). Conversely, such changes frequently provoke behavioral adaptations in the impacted businesses that may diminish the anticipated benefits (Agrawal & Fox, 2021). In addition, the efficacy of such measures in reducing VAT fraud largely hinges on their influence on audit effectiveness and the monitoring capacity of tax agencies. The overall impact of the implementation of mandatory ECRs is therefore an empirical question.
Despite the widespread adoption of mandatory ECRs, there is surprisingly limited availability of reliable firm-level data regarding their impact. Recent evidence from various developing countries suggests that the impact of ECRs can vary significantly depending on factors such as the implementation process and local context (Ali et al., 2015; Awasthi & Engelschalk, 2018; Fjeldstadt et al., 2020). An exception to the lack of evidence on the impact of ECRs in developed countries is a policy report by Lovics et al. (2019), which focuses on Hungary. The report shows that the introduction of ECRs led to a significant increase in reported revenue of 23–35% in the retail, accommodation, and food service sectors.
We contribute to this literature by studying the effects on tax filing behavior of a reform in Sweden that enforced businesses that handle cash payments to use a certified ECR. Under the legislation, all transactions made at the point of sale, whether by cash, debit, or credit card, were required to be recorded on a certified ECR. Before the reform, businesses were not required to use a specific type of ECR to record their revenue, nor were they obligated to issue a receipt unless specifically requested by the customer (Skatteverket, 2013). The Swedish reform not only facilitated the electronic storage of pertinent tax information, enhancing auditors’ ability to identify instances of tax evasion, but it was also accompanied by an increase in on-site tax inspections.
Our study benefits from access to unique administrative data containing information on high-frequency (monthly) output VAT for all Swedish firms that reported the use of ECRs to the Swedish Tax Agency. Our identification strategy exploits the fact that we can track firms both before and after acquiring ECRs at different points in time, providing robust insights into the causal effect of ECRs on reported revenue. Sweden is a country that shows high trust in the Tax Authority (Statskontoret, 2024) and a high level of tax compliance by small and medium-sized firms (DeBacker et al., 2015).4 On one hand, high tax morale and trust in the government imply that businesses in Sweden may be more willing to comply with tax regulations, including the use of certified ECRs. On the other hand, low initial tax evasion may also contribute to a modest effect on reported revenue.
We find that the implementation of an ECR by firms results in an immediate increase in reported revenue of 2.7–4.3%. Nevertheless, the impact appears to be temporary. Within a few months, reported revenue exhibited no substantial deviation from the levels witnessed before the implementation of the ECR. The results are robust to two distinct difference-in-differences (DiD) estimators that are typically applied in settings with staggered treatment timing: a standard two-way fixed effects (TWFE) estimator and a multi-period DiD estimator following Callaway and Sant’Anna (2021). The observed temporary increase in reported revenue is evident across virtually all sectors impacted by the legislation, with hairdressing, restaurants, and retail businesses being the most prevalent.
To establish a causal interpretation of our results, we demonstrate that the immediate increase in reported revenue after the acquisition of the ECR is preceded by a period of similar trends in reported revenue, covering a duration of up to one year. Moreover, we rule out the possibility that changes in the underlying economic conditions might have influenced the observed shift in reported revenue. To be specific, we demonstrate that there is no discernible alteration in the total wage cost of the firms, suggesting that they did not encounter significant changes in their economic environment around the time of the ECR implementation.
Our interpretation of the temporary nature of the effect is that firms have discovered novel methods of under-reporting their tax base. In a survey conducted by the Swedish Tax Agency, which targeted business owners affected by the ECR legislation, the majority of respondents viewed the reform as a favorable development that made it harder to evade taxes. However, some respondents pointed out that firms quickly adapted to the new regulations and found alternative ways to evade taxes (Skatteverket, 2013). This perspective is shared by employees at the tax authorities who work with auditing firms’ compliance with the ECR legislation. For instance, instead of canceling recorded revenues on their previous ECRs, firms now refrained completely from registering certain revenues on the new ECR.
Our main finding of a small and significant, but short-lived, impact on reported revenue underscore the mixed evidence presented in previous studies concerning the effectiveness of improved information exchange in mitigating tax evasion, which have mainly focused on third-party reporting.5 For instance, Slemrod et al. (2017), Adhikari et al. (2020, 2021) demonstrate that the effect of increased third-party reporting of credit card transactions on small business tax compliance is relatively modest on average, but more significant in business-to-consumer industries and among firms with initially weak tax compliance. Slemrod et al. (2017) recommend that information reporting be targeted specifically at groups suspected of significant tax evasion and those with large shares of income subject to such reporting. Adhikari et al. (2022) discovered that the increase in reported revenue following third-party reporting of credit card transactions in the United States was partially offset by increases in reported expenses. Carrillo et al. (2017) observe that most firms in Ecuador failed to respond to a threat of third-party reporting and conclude that governments face constraints in enforcement policies, even with access to information.
Our results also align with insights from an expanding body of literature that investigates the impact of audits on tax compliance. While audits exert an immediate mechanical effect by curbing tax evasion, their long-term impact is theoretically ambiguous. Audited firms may positively adjust their expectations of future audits (through Bayesian updating), fostering lasting compliance. Conversely, taxpayers might perceive audits as decreasing their likelihood of future scrutiny (referred to as the “Bomb crater effect” by Mittone (2006). Empirical evidence on the behavioral outcomes of audits varies. Some studies, such as those by DeBacker et al. (2018), Advani et al. (2023), suggest lasting positive behavioral effects (i.e., increased compliance), while Best et al. (2021) find no discernible impact in a large-scale randomized audit study in Pakistan. Furthermore, Gemmel and Ratto (2012) provide evidence indicating that compliant taxpayers subject to audits may subsequently decrease their compliance, in line with the bomb crater effect, while they find that non-compliant taxpayers increased their compliance.
The remainder of the paper is structured as follows: Section 2 presents the institutional background regarding the Swedish cash-register reform. Section 3 describes the data utilized in the analysis, while Sect. 4 outlines the empirical approach employed. The empirical results are presented in Sects. 5 and 6 concludes.

2 The cash register reform

The cash register reform that we study in this paper was implemented in 2010. Prior to the reform, businesses could use any type of cash register to keep track of their revenues and were not obliged to provide a receipt unless requested to do so by the customer. This made it relatively easy for businesses to manipulate registered revenue or not report revenue at all (Prop. 2006/07:105). As in many countries, tax evasion among Swedish businesses in industries that handle large volumes of cash, such as retail, hospitality, and hair care, was widespread and considered a threat to the legitimacy of the tax system (SOU 2005:35).
The new legislation required businesses handling either cash or credit/debit card payments to use a certified ECR. A certified ECR should have a manufacturer declaration and a special control unit, a black box, accessible only by the staff at the Swedish Tax Agency. As part of the legislation, businesses were mandated to report their acquisition of a certified ECR to the tax authorities. However, small businesses that handle low volumes of cash were exempt from this requirement. At the time of the reform, the threshold for low cash balances was set at SEK 170,000 per year (1 USD \(\approx\) 8 SEK). Certain industries were also exempt, including the taxicab industry. The vast majority of firms purchased an ECR (as opposed to leasing one) with an average investment cost of approximately SEK 23,000 (Skatteverket, 2013).
Within two years of the reform, there were around 75,000 active firms with a certified ECR.6 Overall, the firms that acquired an ECR due to the reform correspond quite well to the specified target industries, i.e., small firms that rely heavily on cash transactions and whose customers are individual consumers with low interest in getting a receipt (Skatteverket, 2013). In the industries with the highest ECR density, between 60 and 70% of all firms have a certified ECR, including restaurants, hairdressers, and small-scale retail stores.
There were two additional elements of the cash register reform with potential implications for tax compliance. First, firms always have to print and offer the customer a receipt, independent of whether the customer asked for a receipt or not. Second, the reform gave the tax authorities enhanced audit rights, including unannounced inspections.
Within three years of the implementation of the new law, the tax authorities conducted more than 100,000 on-site inspections. The purpose of the majority of these inspections was to ensure that businesses complied with the new ECR requirements. Businesses not complying with the law were fined by SEK 10,000, and if the company once again failed to comply with the law within a year, a fine of SEK 20,000 was charged.7 These inspections were proportionally distributed across sectors depending on the sector’s share of the total number of ECRs. A recent study by Swedish Tax Agency does not show any signs that these inspections lead to improved compliance with regulations (Skatteverket, 2022). We therefore do not anticipate that the potential effects of ECRs will be confounded by the impact of these audits.
Due to the high demand for ECRs at the time of the reform, delivery times were often long and unpredictable for firms. For this reason, inspected firms without an ECR were not fined if they could verify that they had ordered an ECR. From July 2010, the authorities fined all non-complying firms. In 2010, 500 fines were implemented compared to 2,900 fines in 2012. Initially, the Tax Agency mainly fined firms for not having a certified ECR. Later on, fines tilted more towards reporting errors, such as not registering revenues or providing receipts (Skatteverket, 2013).

3 Data

3.1 Data sources and sample restrictions

We use administrative data from the Swedish Tax Agency, which encompasses all firms operating in Sweden that reported an ECR between September 2009 and March 2013. Periodic VAT data for these firms are available between January 2008 and March 2013. Moreover, we supplement the analysis with firm-level information on total monthly wage payments, which we obtain solely for those firms with at least one employee. Such firms account for about half of the total number of firms that have an ECR and report VAT.
We observe in total 75,530 firms that both reported an ECR during this period and reported VAT. In order to arrive at our analysis sample, we apply three sample restrictions. First, we limit the sample to firms that report VAT on a monthly basis during the event window to ensure high data frequency for our event study-based empirical approach, resulting in a reduction to 49,813 firms. Second, we narrow our focus to firms that acquired an ECR between October 2009 and December 2010. This period covers the vast majority of ECRs in the data; after that, the number of registered ECRs at the monthly level become very small. This second restriction reduces the sample size to 35,169 firms. Third, we further narrow down our sample by selecting firms that consistently report positive VAT 12 months before and after the acquisition of the ECR. This criterion helps us avoiding the risk of attributing changes in reported revenue solely to the timing of business re-starts or expansions, rather than the actual impact of the cash register reform. After applying these additional sample restrictions, we end up with a final analysis sample of 22,026 firms and 550,650 firm-month observations.
Table 6 in the Appendix reports the sample size as well as summary statistics for reported revenue after imposing each sample restriction. We perform several robustness tests to verify the sensitivity of our findings to variations in the sample specification. Subsection 5.3 presents the results of these tests, which generally support the qualitative and quantitative implications of our main results.

3.2 Key variables and descriptive statistics

The primary dependent variable under examination is the revenue reported by firms, which refers to the total value of products (goods and services) sold within a specified time frame. Monthly VAT records are used to calculate reported revenue. Sweden implements three different VAT tax rates: 25%, 12%, and 6%. To compute revenue (excluding VAT) for a firm \(i\) in period \(t\), we employ the formula \(y_{i,t}=\frac{\text {VAT25}_{i,t}}{0.25}+\frac{\text {VAT12}_{i,t}}{0.12}+\frac{\text {VAT6}_{i,t}}{0.06}\).8 Additionally, in Subsects. 5.4 and 5.5, respectively, we use the total sum of wages and reported input VAT of a company as dependent variables.
The independent variable of interest is the event when a business registers and begins using a certified ECR. Our identification approach, which we discuss in more detail in Sect. 4, leverages the fact that companies adopted ECRs at various points in time, and we have access to reported revenue data both before and after this event.
Figure 1 depicts the distribution of months during which the Swedish Tax Agency received ECR reports in 2010.9 We have included firms that reported an ECR in late 2009, a few months before the new legislation came into effect. Anticipating the new legislation that had already been announced, these firms likely procured an ECR and registered it accordingly. The peak in the number of registrations occurred in June 2010, which coincided with the tax authorities’ announcement of adopting a stricter stance during their control visits.
Table 1 presents the descriptive statistics of our analysis sample. On average, the monthly output VAT and corresponding revenue are approximately SEK 300,000 and SEK 1.4 million, respectively. The input VAT averages around SEK 277,000. Revenues vary widely across firms, and to address the significant left-skew of the revenue distribution (see Fig. 4 in the Appendix), we use a log transformation of the revenue variable. For the subset of firms with at least one employee, the average monthly wage bill is around SEK 700,000.
Table 1
Descriptive statistics
Dependent variables
Mean
SD
Reported revenue
1,405,678
20,223,339
Output VAT
299,582
4,354,916
Input VAT
276,587
4,200,698
Total wage costs
705,056
16,328,892
Number of firms
22,026
 
Notes The dependent variables are measured on a monthly basis and reported in Swedish Krona (SEK) at the 2010 price level (1 USD \(\approx\) SEK 8)
The diversity in firm size is reflective of the various sectors we examine in this study. In our analysis of heterogeneity (see Subsect. 5.2), we examine four sub-groups of industries. These industries are distributed as follows: restaurants comprise 22% of all firms, hairdressers 17%, food 8%, and retail 33%. The remaining firms fall into the "Others" category. Table 7 in the Appendix provides a detailed description of the industry classification.10 It is worth noting that hairdressers and restaurants make up close to 40% of the sample. These types of businesses are often small in scale, with average monthly revenue of less than SEK 100,000 and SEK 400,000, respectively.

4 Empirical framework

We use a staggered DiD approach to estimate the causal impact of ECR adoption on reported revenue among Swedish firms. The staggered treatment timing comes from the time variation in the adoption of ECRs across firms, following the new legislation in 2010 that enforced firms that handle cash payments to use a certified ECR. Our models are estimated using monthly VAT data at the firm level, with a symmetric window of 25 months around the treatment month.
Two-way Fixed Effects: We first evaluate the impact of ECR adoption using a standard TWFE model. We estimate both a static model and an event-study model that allows for dynamic treatment effects. In the static TWFE model, we estimate a single indicator measuring the average effect of implementing an ECR. The model specification takes the following form:
$$\begin{aligned} y_{i,t}=\alpha +\nu _{i}+\delta _{t}+\lambda D_{i,t,l} + \epsilon _{i,t} \end{aligned}$$
(1)
The dependent variable, \(y_{i,t}\), is the log of reported revenue of firm i at month t. Firm-fixed effects are captured by \(\nu _i\) and month-fixed-effects by \(\delta _{t}\). The indicator \(D_{i,t,l}\in \{0,1\}\) captures the treatment status of firm i at month t being l month away from the first treatment. If a firm i has an ECR in month t it is treated and \(D_{i,t,l}=1\); otherwise it is \(D_{i,t,l}=0\). The first month of treatment is captured by \(l=0\). All firms are treated at some point between September 2009 and December 2010 and treatment is absorbing, i.e., \(D_{i,t,l}=1\) for all \(l\ge 0\). Our main interest lies in the parameter \(\lambda\) which captures the change in reported revenue after the introduction of the ECR. The parameter \(\lambda\) captures, in general, a weighted average of treatment effects.
The event-study version of the TWFE estimator allows for dynamic treatment effects. The purpose of this model is to examine whether the potential changes in reported revenue that follow from the adoption of an ECR are permanent or temporary. We estimate the following equation:
$$\begin{aligned} y_{i,t}=\nu _{i}+\delta _{t} +\sum _{\begin{array}{c} l=-12 \\ l\ne -1 \end{array}}^{12}\lambda _l D_{i,t,l}+ \epsilon _{i,t} \end{aligned}$$
(2)
Again, \(y_{i,t}\) denotes log reported revenue of firm i at month t, the indicator \(D_{i,t,l}\in \{0,1\}\) captures the treatment status of firm i at month t being l month away from the first treatment, and \(\nu _i\) and \(\delta _{t}\) denote firm- and month-fixed effects, respectively. The coefficients of interest are the \(\lambda _{l}\) that capture monthly reported revenue between 12 months before and 12 months after the adoption of an ECR. The month prior to the ECR, \(l=-1\), is excluded so that the estimate for each month is relative to the revenue level in that month.
Multi-period difference-in-differences: Recent econometric work raises concerns about the causal interpretation of two-way fixed effects parameters when there is treatment effect heterogeneity. Such concerns apply both to static TWFE estimators and dynamic event study models (Sun & Abraham, 2021). We therefore estimate the average treatment effect on the treated using the alternative estimator proposed by Callaway and Sant’Anna (2021). We measure group-time ATTs by comparing the expected change in the log of reported monthly revenue of firms that receive the ECR in a specific month with the respective change for firms that are not yet treated, i.e., have not acquired an ECR until then.
The group-time ATTs are then aggregated into a single parameter measuring the average treatment effect on the treated. More precisely, we implement the doubly robust DiD estimator based on inverse probability weighting and OLS (Callaway & Sant’Anna, 2021). This parameter is comparable with \(\lambda\) in the TWFE estimation in Eq. (1) but accounts for the heterogeneity of treatment effects across different groups (e.g., Goodman-Bacon, 2021; Baker et al., 2022). Similar to the TWFE estimation, we are interested in understanding the dynamic group-time ATT, which examines the heterogeneity of treatment effects in relation to the duration of exposure to treatment.
Given our sample restrictions, we cannot estimate treatment effects for the 11th and 12th months after the implementation of the ECR when we use the multi-period DiD model because there are too few observations in the control group of firms that have not implemented an ECR yet. Therefore, we will estimate the effects for the 10 months following the introduction of the ECR. In addition to estimating the effects for the 10 months following the introduction of the ECR, we also estimate the pre-treatment effects for the 10 months preceding the treatment.

5 Empirical results

5.1 Effect on reported revenue

Table 2
Effect of ECR acquisition on reported revenue
 
(1)
(2)
(3)
(4)
 
TWFE
TWFE
DiD
DiD
Static TWFE
0.0430***
   
 
(11.89)
   
Group-time ATT
  
0.0274***
 
   
(3.21)
 
t=\(-\)10
 
0.0067
 
\(-\)0.0104*
  
(0.87)
 
(\(-\)1.80)
t=\(-\)9
 
\(-\)0.0017
 
\(-\)0.0121***
  
(\(-\)0.23)
 
(\(-\)2.69)
t=\(-\)8
 
\(-\)0.0081
 
\(-\)0.0080*
  
(\(-\)1.23)
 
(\(-\)1.92)
t=\(-\)7
 
\(-\)0.0079
 
0.0015
  
(\(-\)1.32)
 
(0.37)
t=\(-\)6
 
\(-\)0.0137**
 
\(-\)0.0056
  
(\(-\)2.36)
 
(\(-\)1.32)
t=\(-\)5
 
\(-\)0.0108**
 
\(-\)0.0012
  
(\(-\)2.01)
 
(\(-\)0.28)
t=\(-\)4
 
\(-\)0.0093*
 
0.0028
  
(\(-\)1.93)
 
(0.69)
t=\(-\)3
 
\(-\)0.0056
 
0.0110***
  
(\(-\)1.31)
 
(2.71)
t=\(-\)2
 
0.0012
 
0.0080**
  
(0.35)
 
(2.01)
t=\(-\)1
   
\(-\)0.0001
    
(\(-\)0.02)
t=0
 
0.0281***
 
0.0276***
  
(8.68)
 
(6.67)
t=1
 
0.0411***
 
0.0382***
  
(10.69)
 
(6.58)
t=2
 
0.0369***
 
0.0362***
  
(9.17)
 
(5.67)
t=3
 
0.0270***
 
0.0351***
  
(6.52)
 
(4.82)
t=4
 
0.0118***
 
0.0199**
  
(2.73)
 
(2.04)
t=5
 
0.0063
 
0.0161
  
(1.43)
 
(1.28)
t=6
 
\(-\)0.0020
 
0.0111
  
(\(-\)0.43)
 
(0.89)
t=7
 
\(-\)0.0039
 
0.0275**
  
(\(-\)0.84)
 
(2.03)
t=8
 
\(-\)0.0058
 
0.0389**
  
(\(-\)1.29)
 
(2.44)
t=9
 
\(-\)0.0036
 
0.0476***
  
(\(-\)0.83)
 
(2.73)
t=10
 
\(-\)0.0091**
 
0.0030
  
(\(-\)2.19)
 
(0.15)
\(N\)
550 650
550 650
419 307
419 307
Notes Dependent variable: log reported revenue; t/z-statistics in parentheses
* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\)
Table 2 shows our main findings. Columns (1) and (2) present the outcomes of our analysis using the static and dynamic versions of the TWFE model, respectively. Columns (3) and (4) illustrate static and dynamic group-time effects using the multi-period DiD model. In the dynamic models, we have reported ten coefficients on each side of the treatment month.11
The static TWFE estimates indicate a significant increase of approximately 4.3% in reported revenue after the introduction of a certified ECR. In Column (3), the group-time ATT from the multi-period DiD model shows a slightly smaller increase of 2.7%.
When considering the dynamic effects, both models demonstrate a positive effect on reported revenue in the four months after the ECR acquisition. However, after five months, the effect becomes insignificant and approaches zero. Towards the end of the observation period, the models produce slightly different outcomes. While the TWFE estimates remain consistently close to zero and are estimated with good precision, the point estimates of the multi-period DiD show an increase towards the end of the post-treatment period. The estimates for the multi-period DiD are, however, imprecise in later periods as there are increasingly fewer not-yet-treated firms that can act as a control. To ensure that differences between the two models are not driven by differences in the sample composition, we also estimate the TWFE model only accounting for observations also used in the multi-period DiD approach. The findings, shown in Table 8 in the Appendix, indicate that variations in results can be attributed to differences in estimation methodologies rather than disparities in sample composition.
To examine pre-trends, Fig. 2 displays the event studies for the TWFE and the multi-period DiD 10 months before and after the first ECR acquisition. The outcomes from both models indicate parallel pre-trends, where most pre-treatment estimates are insignificant and close to zero.
To understand the mechanisms behind these findings, we examine potential variations across industries and the effects on input VAT and wages in subsequent sections. We will expand upon these insights in the concluding discussion found in Sect. 6.

5.2 Industry heterogeneity

To examine whether the effects that we find in the previous section mask heterogeneous effects across firms in different sectors, we repeat the analysis for firms in various industries. We focus on the multi-period DiD estimates and relegate TWFE estimates to Table 9 in the Appendix.
Table 3 reports the aggregated group-time ATT separately for restaurants, hairdressers, wholesale of food, retail sale, and all other firms. The estimates for the three first categories are insignificant and close to zero. Retailers and firms in the category "Others" show a positive and significant effect of around 3.4% and 7.4%, respectively. For the complete dynamic group-time ATTs and the event studies, we refer to Table 9 and Fig. 5 in the Appendix. We do not find any evidence of a particular trend in reported revenue prior to the adoption of ECRs in any of the industries. Additionally, we observe a discontinuous increase in reported revenue after the treatment. The temporary positive effect on reported revenue that we previously observed is primarily driven by restaurants and hairdressers. While the positive effects on reported revenue for retailers seem to persist, the initial increase observed for the other industries diminishes after a few months.
Cautious interpretation is necessary for the result that retailers and firms classified under the residual category "Others" exhibit a more persistent reaction to cash register acquisitions. Contrary to the findings from the multi-period DiD analysis, the dynamic TWFE analysis, as shown in Table 9, reveals that the effects are indeed temporary, similar to those observed in other sectors. In addition, the average effect is positive and significant for all industries, not just for retailers and others.
Table 3
Sector-specific effects of ECR acquisition on reported revenue
 
Restaurants
Hairdressers
Food
Retailers
Others
Group-time ATT
0.0099
0.0087
0.0153
0.0337***
0.0744***
 
(0.50)
(0.39)
(0.82)
(2.73)
(3.02)
\(N\)
89 046
74 516
31 804
139 433
84 321
Notes Dependent variable: log reported revenue; t/z-statistics in parentheses
* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\)

5.3 Robustness checks

We present a set of results in which we assess the sensitivity of our main findings to the sample selection. We report the average treatment effects in Table 4 while the dynamic estimates are shown in Table 10 in the Appendix.
First, we relax the assumption on our analysis sample that cash registers had to be registered before the end of year 2010. We instead include firms that reported a cash register at any point in time between up until our data ends in March 2013. The results from this analysis is shown in the first column of Table 4.
Second, we restrict the sample to firms that have reported only one cash register to the tax authorities. For firms operating multiple facilities, each with its point-of-sale system, the timing of the intervention is not as straightforward as it is for firms deploying a single cash register. Consequently, the post-treatment periods for firms with multiple cash register acquisitions may be influenced by these additional registers. Column (2) of Table 4 presents the findings for firms that have implemented just one ECR, representing approximately half of the sample.
Third, despite utilizing the logarithm of reported revenue as the output variable in our primary estimation, the possibility remains that outliers may be influencing our results. Therefore, we conduct a robustness check by removing the top and bottom percentiles of firms, based on their average reported revenue over the observed 25-month period (cf. Column (3) of Table 4).
All three robustness checks confirm, in general, our main result. Depending on the sample restriction we find an increase in reported revenue of 1.6\(-\)3.4% compared to the 2.7% increase we find for the main sample. While the effect for the sample including late ECR adopters shows rather short-lived effects (as in the main estimation), effects on reported revenue are more persistent for the firms that only have implemented one ECR and when excluding outliers from the sample.
Fourth, as previously pointed out, if firms expand their business in conjunction with acquiring an ECR, we may attribute pure timing effects to causal effects of ECR acquisition on reported revenue. As firms have the autonomy to decide when to purchase the ECR, such decisions may coincide with other crucial events for the firm, such as business expansion, annual bookkeeping efforts, or high revenue peaks. Such patterns could also explain the temporary nature of the effect that we observe. Although our model incorporates general time effects and seasonal patterns, it is possible that we cannot fully account for such firm-specific timing effects.
To strengthen the causal interpretation and rule out this mechanism, we utilize the fact that ECR adoption should be more exogenous to the firm’s operations or expansions around the time when the tax authorities initiated a “strict approach” on inspections, and the delivery times for ECRs were long and uncertain. Between May and July 2010, the adoption of ECRs is more likely driven by suppliers catching up with excess demand and the tax authorities conducting stricter audits, as opposed to a demand-driven expansion on behalf of the firms.
Column (4) of Table 4 displays the average treatment effect derived from the TWFE model when the sample is limited to firms that acquired an ECR between May and July 2010. The dynamic estimates can be found in the fourth column of Table 10 in the Appendix. Due to the constrained treatment period, a multi-period model is not feasible. The average treatment effect is found to be significant, amounting to 2.2%, although slightly lower than the main TWFE estimate of 4.3%. Similarly, the dynamic estimates also indicate a comparable pattern, with a brief increase in reported revenue, followed by a gradual decrease towards a null effect.
Lastly, we report findings for firms that file VAT on a quarterly basis. As mentioned in Sect. 3, about 25,000 firms from our initial dataset report VAT quarterly. These firms typically exhibit lower turnover and total wage bills in comparison to their counterparts that file VAT monthly, as illustrated in Table 6. It raises the possibility that their response to cash register legislation might differ from that of firms reporting VAT monthly. Column (5) of Table 4 reports the results of the TWFE analysis on the subset of firms that submit VAT returns on a quarterly basis. Due to the constrained treatment period, a multi-period model is again not feasible. Similar to our main estimation, we find a positive significant effect of acquiring an ECR on reported revenue. With 6.8% this effect is even larger than the increase of 4.3% that we find in the main analysis.
Table 4
Robustness checks: Effects of ECR acquisition on reported revenue
 
(1)
(2)
(3)
(4)
(5)
 
Including late
At most
Excluding
ECR during
Quarterly VAT
 
ECR adopters
one ECR
outliers
intensive period
reporting
Group-time ATT
0.0156**
0.0337***
0.0276***
  
 
(2.25)
(2.76)
(3.19)
  
Static TWFE
   
0.0220***
0.0676***
    
(3.21)
(10.72)
\(N\)
555 397
213 129
412 591
234 700
108 737
Notes (1) Sample also includes firms that acquired ECR from 2011 to 2013. (2) Only firms with at most one reported ECR considered. (3) Outliers (top and bottom 1% in average reported revenue) excluded. (4) Only firms acquiring an ECR between May and July 2010 included. (5) Analysis of firms reporting VAT quarterly. Group-time ATT findings in Columns (1)-(3), with static TWFE analyses in Columns (4) and (5). t/z-statistics in parentheses
* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\)

5.4 Effect on total wage costs

We have found that the implementation of an ECR by firms results in a temporary increase in reported revenue. A natural follow-up question arises: Can this increase be attributed to companies increasing their tax compliance? Or is it possible that the implementation of ECRs coincides with larger shifts in the overall economic climate that impact firms’ revenue? Firstly, since firms adopt ECRs at different points in time, it is improbable that the effect is caused by a broad-based change in the overall economic environment. Secondly, the econometric model takes into account general time effects, indicating that any observed effect is not simply due to changes in the time period studied.
To further address this concern, we investigate the impact of ECRs on the total wage costs of firms. In contrast to changes in the economic environment, increases in detection probabilities or fines should not affect firms’ optimal production decisions. If the implementation of an ECR is combined with an additional change in the economic environment, we should expect changes in firms’ output choices and input decisions. In contrast, if the reform increases tax compliance among firms, we should not expect any real changes in business activities. We therefore investigate the impact of ECRs on firms’ total wage costs.
We re-estimate the static and dynamic group-time ATTs with the log of the total wage costs as the dependent variable. Since not all firms in our analysis sample have employees or report wages monthly, this restricts our sample size to approximately half of the firms.12
The results reveal that the adoption of ECRs has no significant effect on firms’ total wage cost, with the estimated coefficient being close to zero and insignificant (see the group-time ATT in Column (1) of Table 5).
Additionally, our dynamic event study estimates (as shown in panel (a) of Fig. 3) indicate insignificant effects in all post-treatment periods. Table 11 in the Appendix provides complete dynamic effects for the TWFE and the multi-period DiD. Given that changes in the economic environment are unlikely to be the primary driver behind the temporary increase in reported revenue, the adjustment needs to occur at the firm level.
Table 5
Effect of ECR acquisition on total wage costs and reported input VAT
 
(1)
(2)
 
Total sum of wages
Input VAT
Group-time ATT
0.0021
\(-\)0.0077
 
(0.24)
(\(-\)0.79)
\(N\)
240 164
418 150
Notes Dependent variable in (1) log total wage costs; in (2) log input VAT; z-statistics in parentheses
* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\)

5.5 Effect on input VAT

We proceed to estimate the impact of acquiring an ECR on input VAT. Unlike reported revenue (output VAT), the projections for input VAT are not as straightforward. This is because ECRs are not directly employed by firms in the procurement of input factors.
An alteration in VAT during the ECR acquisition could be interpreted in several ways. Firstly, it could suggest the existence of timing effects, i.e., that firms may acquire an ECR in conjunction with a general expansion of their business. Secondly, it could signify that firms reporting a larger proportion of their actual revenue after obtaining an ECR may compensate by reporting additional input VAT. Small businesses can engage in tax evasion on either side of the revenue function. To reduce their tax liability, firms may either under-report output VAT, i.e., revenue, or over-report input VAT, as observed in prior research (see, e.g., Waseem, 2023). Additionally, firms may have a vested interest in maintaining a consistent ratio between input and output VAT, as any discrepancies could trigger suspicion from the tax authorities (see, e.g., Matthews & Lloyd-Wiliams, 2001). Thirdly, it could encompass the direct cost of buying the ECR.
We report estimates from the static and dynamic multi-period model after replacing the dependent variable with the log of input VAT.13 The group-time ATT is shown in Table 5 while the dynamic estimates are shown in panel (b) in Fig. 3. The group-time ATT shows no significant impact on input VAT. The dynamic estimates reveal an initial increase of approximately 3.4% in the treatment month, followed by an immediate decline in the effect, which is statistically insignificant in most post-treatment months (see Table 11 in the Appendix).
Given the average input VAT of SEK 277,000 (as reported in Table 1), the estimated 3.4% effect translates to an increase of around SEK 9,250 in input VAT. Assuming a VAT rate of 25%, this suggests an additional expense of SEK 37,000, which is only slightly higher than the typical cost of an ECR during the period of our study. Although we cannot determine which of the three aforementioned interpretations holds the most weight, we lean towards the view that the observed pattern is primarily driven by the additional VAT expense incurred due to the ECR acquisition.

6 Discussion and conclusions

We have shown that the introduction of mandatory ECRs in Sweden resulted in a short-term increase in reported revenue. We ascribe this response to companies anticipating greater costs associated with tax evasion. In our case, the expected costs can depend on multiple factors, such as the likelihood of being caught for tax evasion, the severity of the penalties imposed, or the efficacy of the monitoring mechanisms in place. According to survey data, most business owners believe that the tax authorities were able to monitor firms more efficiently due to the new legislation, which is a view that the tax authorities themselves endorse. Additional evidence supporting the claim that the reform enhanced tax compliance can be observed in an evaluation of the extent to which firms had to amend their revenues following a review by an auditor. The evaluation indicated that the audits conducted after the reform resulted in relatively minor increments in the reported amounts in comparison to those recorded before the reform (Skatteverket, 2013).
The introduction of ECRs made it risky to engage in fraudulent activities when customers paid with credit/debit cards, as all such transactions were now registered. However, it remained possible to circumvent the cash register entirely by paying with cash and not recording the purchase. Furthermore, in 2012, shortly after the reform, Sweden introduced Swish, an electronic payment system enabling instantaneous money transfers between subscribers. Consequently, customers could now conduct transactions outside the purview of cash registers, even without using paper money. In line with this, survey responses from business owners indicated that firms quickly adapted and identified alternative methods to evade taxes (Skatteverket, 2013). This illustrates the dual nature of technological advancements in combating tax evasion. Although facilitating easier information exchange and enhanced monitoring capabilities, such advancements also provide new avenues for swift and covert transactions.
While our findings may offer valuable insights for policymakers in other developed countries, it is crucial to contextualize the short-lived effects we observed. The reform under examination took place in 2010, a time when cash transactions still held considerable sway in Sweden, constituting 39% of all point-of-sale transactions (Riksbanken, 2022).14 This implies that our study captures a period where cash remained widely used, predating the significant decline observed in more recent years.15
In international comparison, Sweden belongs to a group of countries known for their higher tax morale, trust in the government, and relatively low incidents of tax evasion. Indeed, countries that mandate ECRs typically grapple with more substantial VAT tax discrepancies compared to Sweden (see Table 12). Among the countries that have implemented mandatory ECRs, Hungary presents a stark contrast to Sweden, having one of the largest tax gaps. Variations in tax gaps and underlying institutional features could account for the pronounced differences between our findings and those documented in the policy report by Lovics et al. (2019) on Hungary’s adoption of ECRs. It is, however, noteworthy that the reduction in the VAT tax gap over the last decade has not been markedly more pronounced in countries that have adopted mandatory ECRs compared to those that have not, as indicated in Table 12. To gain a more comprehensive insight into the influence of ECRs and other point-of-sale systems on tax compliance in developed nations, evidence from a broader range of countries is needed.
To ensure effective tax compliance, policymakers should adopt a comprehensive approach that goes beyond the introduction of ECRs. Many new and sometimes innovative approaches developed to support the formalization of point-of-sale transactions will have little impact on the shadow economy if applied without accompanied enforcement (Casey & Castro, 2015). Such complementary measures could include enduring tax inspections, third-party reporting, and improved information sharing between government agencies. In addition, by requiring businesses to use certified ECRs, tax authorities can better monitor all transactions, regardless of the payment method used, and detect instances of non-compliance.

Acknowledgement

We are grateful to Daniela Andrén, Vidar Christiansen, Oliver Falck, Johan Klaesson, José Mata, Andreas Stephan, Robert Ullmann, and Lennard Zyska for providing constructive feedback on our paper. Furthermore, we would like to acknowledge seminar participants at Jönköping International Business School, the CIHR Summer Program in Aging (SPA), and at Leibniz University Hannover for useful comments and suggestions. We would also like to express our gratitude to two anonymous reviewers and the editor, David R. Agrawal, whose insightful comments contributed to further enhancing the quality of our paper.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​.
Anhänge

Appendix

See Tables 6, 7, 8, 9, 10, 11 and 12, Figs. 4 and 5.
Table 6
Sample size and reported revenue by sample restriction
   
Reported revenue
 
Number of
 
(in million SEK)
 
firms
Observations
Mean
SD
Min
Max
All firms with ECR and VAT records
75,530
1,141,851
1.24
21.02
\(-\)275
2989
Monthly VAT reporting
49,813
962,264
1.28
22.89
\(-\)275
2989
Acquired the ECR in 2009 or 2010
35,169
761,434
1.13
17.73
-275
2112
Consecutive and positive revenue in event window
22,026
550,650
1.41
20.52
0.00
2112
Report wages
12,275
326,931
1.98
24.73
0.00
2112
Table 7
Industry classification
Industry
NACE code
Description
Restaurants
56
Food and beverage service activities
55101
Hotels with restaurant except conference centres
Hairdressers
96,012
Washing and (dry-)cleaning for households
9602
Hairdressing and other beauty treatment
9604
Physical well-being activities
9609
Other personal service activities n.e.c
96021
Hairdressing
Food
463
Wholesale of food, beverages and tobacco
471
Retail sale in non-specialised stores
472
Retail sale of food, beverages and tobacco in specialised stores
Retail
45 except 451
Wholesale and retail trade and repair of motor vehicles and motorcycles
 
but not sale of motor vehicles
46 except 463
Wholesale trade, except of motor vehicles and motorcycles
 
but not wholesale of food, beverages and tobacco
47 except 471, 472, 478, and 479
Retail trade, except of motor vehicles and motorcycles
 
but not retail sale in non-specialised stores
 
and not retail sale of food, beverages and tobacco in specialised stores
 
and not retail sale via stalls and markets
 
and not retail trade not in stores, stalls or markets
Others
Top five codes
All firms not belonging to restaurants, hairdressers, food or retail
43
Specialised construction activities
93
Sports activities and amusement and recreation activities
10
Manufacture of food products
55
Accommodation
85
Education
Table 8
Effect of ECR acquisition on reported revenue; TWFE results for DiD sample
Static TWFE
0.0407***
 
(10.64)
t=0
0.0277***
 
(8.21)
t=1
0.0379***
 
(9.38)
t=2
0.0368***
 
(8.55)
t=3
0.0264***
 
(5.79)
t=4
0.0099**
 
(2.01)
t=5
0.0070
 
(1.31)
t=6
\(-\)0.0095
 
(\(-\)1.45)
t=7
\(-\)0.0068
 
(\(-\)0.97)
t=8
0.0022
 
(0.32)
t=9
0.0092
 
(1.42)
\(N\)
419 307
Notes Dependent variable: log reported revenue; t/z-statistics in parentheses
* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\)Period t=10 has been omitted in the estimation
Table 9
Sector-specific dynamic TWFE and group-time ATTs of ECR acquisition on reported revenue
 
Restaurants
Hairdressers
Food
Retailers
Others
 
TWFE
DiD
TWFE
DiD
TWFE
DiD
TWFE
DiD
TWFE
DiD
Static
0.0468***
 
0.0332***
 
0.0261**
 
0.0223***
 
0.0690***
 
TWFE
(6.18)
 
(6.82)
 
(2.46)
 
(3.69)
 
(6.22)
 
Group-time
 
0.0099
 
0.0087
 
0.0153
 
0.0337***
 
0.0744***
ATT
 
(0.50)
 
(0.39)
 
(0.82)
 
(2.73)
 
(3.02)
t=0
0.0333***
0.0333***
0.0346***
0.0283***
0.0237***
0.0183
0.0114**
0.0135**
0.0350***
0.0402***
 
(5.13)
(3.65)
(6.40)
(4.16)
(2.67)
(1.42)
(2.25)
(2.11)
(3.44)
(3.20)
t=1
0.0458***
0.0462***
0.0327***
0.0252***
0.0251**
0.0151
0.0322***
0.0279***
0.0439***
0.0661***
 
(5.67)
(3.40)
(5.75)
(3.08)
(2.46)
(0.78)
(5.05)
(3.03)
(3.77)
(3.83)
t=2
0.0306***
0.0273*
0.0384***
0.0250***
0.0155
0.0107
0.0282***
0.0278***
0.0691***
0.0821***
 
(3.64)
(1.87)
(6.48)
(2.67)
(1.46)
(0.63)
(4.18)
(2.70)
(5.63)
(4.18)
t=3
0.0247***
0.0287*
0.0291***
0.0157
0.0066
0.0078
0.0189***
0.0252**
0.0691***
0.0838***
 
(3.01)
(1.74)
(4.58)
(1.44)
(0.63)
(0.47)
(2.62)
(2.14)
(5.55)
(3.64)
t=4
0.0069
\(-\)0.0043
0.0246***
0.0117
0.0038
\(-\)0.0329
0.0108
0.0283*
0.0382***
0.0618**
 
(0.79)
(\(-\)0.19)
(3.55)
(0.62)
(0.36)
(\(-\)1.05)
(1.41)
(1.94)
(2.96)
(2.07)
t=5
0.0106
\(-\)0.0142
0.0156**
0.0011
\(-\)0.0097
\(-\)0.0067
0.0116
0.0321*
0.0218*
0.0614
 
(1.26)
(\(-\)0.56)
(2.26)
(0.05)
(\(-\)0.87)
(\(-\)0.29)
(1.45)
(1.75)
(1.68)
(1.44)
t=6
\(-\)0.0064
\(-\)0.0177
0.0297***
\(-\)0.0173
\(-\)0.0196
0.0242
0.0054
0.0440**
0.0090
0.0689*
 
(\(-\)0.72)
(\(-\)0.63)
(4.30)
(\(-\)0.71)
(\(-\)1.64)
(0.66)
(0.68)
(2.56)
(0.66)
(1.75)
t=7
\(-\)0.0011
0.0013
0.0153**
\(-\)0.0227
\(-\)0.0260*
0.0062
0.0083
0.0752***
0.0033
0.0874**
 
(\(-\)0.12)
(0.04)
(2.23)
(\(-\)0.82)
(\(-\)1.97)
(0.14)
(1.03)
(3.89)
(0.24)
(2.08)
t=8
\(-\)0.0082
\(-\)0.0067
0.0169**
\(-\)0.0044
\(-\)0.0020
0.0379
\(-\)0.0057
0.0621***
0.0058
0.1007**
 
(\(-\)0.86)
(\(-\)0.19)
(2.53)
(\(-\)0.12)
(\(-\)0.15)
(0.79)
(\(-\)0.72)
(2.67)
(0.44)
(2.15)
t=9
\(-\)0.0005
\(-\)0.0069
0.0050
\(-\)0.0247
0.0014
0.0959
0.0048
0.0627**
\(-\)0.0064
0.1124**
 
(\(-\)0.05)
(\(-\)0.17)
(0.76)
(\(-\)0.52)
(0.11)
(2.35)
(0.66)
(2.49)
(\(-\)0.51)
(2.33)
t=10
\(-\)0.0128
0.0224
0.0017
0.0574
\(-\)0.0050
\(-\)0.0087**
0.0081
\(-\)0.0282
\(-\)0.0299**
0.0538
 
(\(-\)1.43)
(0.52)
(0.28)
(0.78)
(\(-\)0.43)
(\(-\)0.22)
(1.17)
(\(-\)0.99)
(\(-\)2.38)
(0.95)
\(N\)
121 375
89 046
93 675
74 516
42 650
31 804
182 800
139 433
109 900
84 321
Notes Dependent variable: log reported revenue; t-statistics/z-statistics in parentheses
* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\); post-treatment effects for 10 months after acquisition of the first ECR
Table 10
Robustness checks: Effect of ECR acquisition on reported revenue
 
(1)
(2)
(3)
(4)
 
Including late
At most
Excluding
ECR during
 
ECR adopters
one ECR
outliers
intensive period
Group-time ATT
0.0156**
0.0337***
0.0276***
 
 
(2.25)
(2.76)
(3.19)
 
Static TWFE
   
0.0220***
    
(3.21)
t=0
0.0288***
0.0265***
0.0274***
0.0245***
 
(7.58)
(4.44)
(6.55)
(4.33)
t=1
0.0389***
0.0338***
0.0381***
0.0320***
 
(7.64)
(4.32)
(6.48)
(3.02)
t=2
0.0377***
0.0359***
0.0368***
0.0224
 
(6.79)
(4.17)
(5.70)
(1.43)
t=3
0.0307***
0.0406***
0.0348***
0.0180
 
(5.01)
(4.09)
(4.72)
(1.04)
t=4
0.0188**
0.0258**
0.0197**
0.0150
 
(2.54)
(1.97)
(1.98)
(0.88)
t=5
0.0115
0.0281*
0.0161
0.0259
 
(1.36)
(1.78)
(1.26)
(1.61)
t=6
0.0057
0.0190
0.0105
0.0275*
 
(0.59)
(1.15)
(0.84)
(1.79)
t=7
0.0040
0.0382**
0.0282**
0.0165
 
(0.37)
(2.05)
(2.04)
(1.15)
t=8
0.0046
0.0406*
0.0383**
0.0060
 
(0.40)
(1.75)
(2.36)
(0.48)
t=9
0.0088
0.0613**
0.0485***
0.0103
 
(0.72)
(2.33)
(2.74)
(0.90)
t=10
\(-\)0.0179
0.0206
0.0054
0.0131
 
(\(-\)1.29)
(0.69)
(0.26)
(1.36)
\(N\)
555 397
213 129
412 591
234 700
Notes (1) Sample also includes firms that acquired ECR from 2011 to 2013. (2) Only firms with at most one reported ECR considered. (3) Outliers (top and bottom 1% in average reported revenue) excluded. (4) Only firms acquiring an ECR between May and July 2010 included. Group-time ATT findings in Columns (1)-(3), with static TWFE analysis in Column (4). t/z-statistics in parentheses
* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\)
Table 11
Effect of ECR acquisition on total wage costs and reported input VAT
 
Total sum of wages
Input VAT
 
TWFE
DiD
TWFE
DiD
 
(1)
(2)
(3)
(4)
Static TWFE
0.0006
 
0.0453***
 
 
(0.14)
 
(12.44)
 
Group-time ATT
 
0.0021
 
\(-\)0.0077
  
(0.24)
 
(\(-\)0.79)
t=0
0.0005
\(-\)0.0013
0.0454***
0.0344***
 
(0.15)
(\(-\)0.31)
(9.82)
(6.09)
t=1
\(-\)0.0030
\(-\)0.0050
0.0358***
0.0190***
 
(\(-\)0.71)
(\(-\)0.85)
(7.47)
(2.94)
t=2
\(-\)0.0041
\(-\)0.0079
0.0129***
\(-\)0.0052
 
(\(-\)0.92)
(\(-\)1.12)
(2.71)
(\(-\)0.72)
t=3
\(-\)0.0070
\(-\)0.0113
0.0104**
0.0002
 
(\(-\)1.50)
(\(-\)1.40)
(2.18)
(0.03)
t=4
\(-\)0.0016
\(-\)0.0013
0.0038
\(-\)0.0028
 
(\(-\)0.34)
(\(-\)0.13)
(0.80)
(\(-\)0.26)
t=5
0.0032
0.0084
\(-\)0.0040
\(-\)0.0133
 
(0.64)
(0.73)
(\(-\)0.84)
(\(-\)0.93)
t=6
\(-\)0.0057
0.0043
\(-\)0.0023
\(-\)0.0259**
 
(\(-\)1.15)
(0.36)
(\(-\)0.48)
(\(-\)2.00)
t=7
\(-\)0.0011
0.0059
\(-\)0.0133***
\(-\)0.0347**
 
(\(-\)0.21)
(0.43)
(\(-\)2.60)
(\(-\)2.38)
t=8
0.0023
\(-\)0.0072
\(-\)0.0043
\(-\)0.0152
 
(0.48)
(\(-\)0.44)
(\(-\)0.85)
(\(-\)0.83)
t=9
0.0000
0.0217
0.0000
0.0044
 
(0.01)
(1.15)
(0.00)
(0.21)
t=10
0.0063
0.0173
0.0015
\(-\)0.0460*
 
(1.45)
(0.69)
(0.31)
(\(-\)1.72)
\(N\)
326 709
240 164
549 446
418 150
Notes Dependent variable in (1) and (2) log total wage costs, in (3) and (4) log input VAT; (1) and (3) TWFE estimations, t-statistics in parentheses; (2) and (4) multi-period DiD estimations, z-statistics in parentheses
* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\)
Table 12
VAT rate and VAT gap in 2011 and 2020 for countries in the EU-27
Country
Standard
VAT gap
VAT gap
Change in
VAT rate
2011
2020
VAT gap
2011/2020
(in %)
(in %)
2011–2020
Panel A: Countries with mandatory ECRs
Austria
20
13
8.6
\(-\)4.4
Belgium
21
12
14
2.0
Bulgaria
20
24
6.3
\(-\)17.7
Czech Republic
21
17
11.9
\(-\)5.1
Denmark
25
8
5
\(-\)3.0
France
20
14
8
\(-\)6.0
Greece
24
38
19.7
\(-\)18.3
Hungary
27
24
5.1
\(-\)18.9
Italy
22
32
20.8
\(-\)11.2
Latvia
21
37
3.6
\(-\)33.4
Poland
23
19
11.3
\(-\)7.7
Slovakia
20
33
13.9
\(-\)19.1
Slovenia
22
9
5.5
\(-\)3.5
Sweden
25
4
2
\(-\)2.0
Average
22.2
20.3
9.7
− 10.6
Panel B: Countries without mandatory ECRs
Estonia
20
14
1.8
\(-\)12.2
Finland
24
5
1.3
\(-\)3.7
Germany
19
10
4.8
\(-\)5.2
Ireland
23
12
12.5
0.5
Lithuania
21
36
19.3
\(-\)16.7
Luxembourg
17
5
6
1.0
Portugal
23
11
8
\(-\)3.0
Spain
21
19
4.7
\(-\)14.3
The Netherlands
21
4
2.8
\(-\)1.2
Average
21
12.9
6.8
− 6.1
Sources Standard VAT rate: European Commission (2014, 2022a); VAT gap 2011 (European Commission, 2014); VAT gap 2020 (European Commission, 2022a); Change in VAT gap (own calculations); information whether a countries has implemented mandatory ECRs until 2020 (OECD, 2022)
Notes The table lists information on the standard VAT tax rate and VAT gap for the EU-27 countries in 2011 and 2020. Cyprus and Croatia have been excluded as data for the VAT gap in 2011 has not been available. Malta and Romania have been excluded because they are not part of the OECD so that information on the ECRs is missing. VAT tax rates have not changed between 2011 and 2020 for the listed countries
Fußnoten
1
These countries are Austria, Belgium, Czech Republic, France, Greece, Hungary, Israel, Italy, Korea, Latvia, Lithuania, Norway, Poland, Slovak Republic, Slovenia, and Sweden (OECD, 2020).
 
2
The EU-wide VAT compliance gap as a percent of the VAT total tax liability was estimated to 9.1% in 2020 (European Commission, 2022a).
 
3
Another example of this shift is the general trend towards E-commerce. For an early evaluation of its fiscal consequences (see Bruce & Fox, 2000).
 
4
As presented in Table 12, Sweden had in 2011 the lowest VAT gap among all OECD countries. Estimates of income underreporting among the self-employed in Sweden, derived using the Pissarides and Weber (1989) method, reveal that between 20 and 30% of their true income is underreported, as documented by Engström and Hagen (2017), Engström et al. (2023).
 
5
By mandating specific storage conditions and requiring the issuance of receipts and reports, ECRs enhance the efficient transformation of transaction data to tax authorities.
 
6
The legislators expected that around 110,000–130,000 firms would be affected by the ECR requirement. Around 90,000 firms had reported an ECR by 2012 and 5,000 firms had been granted an exemption (Skatteverket, 2013). The remaining firms had either failed to unsubscribe their ECR upon closure or transfer of their business, or failed to report an ECR at all.
 
7
The penalty currently amounts to SEK 12,500 and 25,000, respectively.
 
8
The VAT tax rates remained unaltered during the study’s time period.
 
9
The ECR distribution of our analysis sample is similar to that of the unregulated sample comprising 75,530 firms.
 
10
The most numerous industries in the category "Others" include specialised construction activities, sports activities and amusement and recreation activities, manufacture of food products, accommodation and education.
 
11
For comparison reasons, we also only report coefficients for t=-10 to t=10 for the TWFE estimation.
 
12
It is worth noting that our primary results for reported revenue remain unchanged even when we restrict the analysis to the sample of firms that report wages. Results are available on request.
 
13
We have access to the monthly input VAT records for each firm. However, unlike output VAT, we do not possess this data by VAT rate, and thus cannot convert input VAT into real expenditures for input goods.
 
14
Cash usage in European countries has been declining similar to the trend observed in Sweden, with an increasing number of transactions being conducted electronically. However, cash remained the most frequently used method for payments at the point of sale in the Euro Area (ECB, 2022).
 
15
In 2022, cash transactions accounted for only 8% of all point-of-sales transactions in Sweden (Riksbanken, 2022).
 
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Metadaten
Titel
Effects of electronic cash registers on reported revenue
verfasst von
Per Engström
Johannes Hagen
Alireza Khoshghadam
Andrea Schneider
Publikationsdatum
06.05.2024
Verlag
Springer US
Erschienen in
International Tax and Public Finance
Print ISSN: 0927-5940
Elektronische ISSN: 1573-6970
DOI
https://doi.org/10.1007/s10797-024-09844-x