Heard it through the Grapevine: The Direct and Network Effects

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of a Tax Enforcement Field Experiment on FirmsI

William C. Boninga,∗, John Guytonb,, Ronald Hodgeb,, Joel Slemroda,

University of Michigan, 611 Tappan Ave., Ann Arbor, MI 48109, United States

Internal Revenue Service, 77 K St. NE, Washington, D.C. 20002, United States

Abstract

Tax enforcement may have deterrent effects that extend beyond directly treated taxpayers,

but evidence of such deterrent effects for major sources of revenue is limited. This paper

studies the effects of a large-scale field experiment on employer deposits that make up most

U.S. tax collections. In-person visits by Revenue Officers have a large direct effect on visited

firms’ tax deposits. The other clients of visited firms’ tax preparers also deposit more

tax, a network effect that suggests preparers disseminate information. Aggregating over all

links, this network effect accounts for 1.2 times as much revenue as the direct effect. Letters

conveying the same message are found to have much smaller direct effects and no measurable

network effects.

Keywords: Tax Enforcement, Randomized Experiments, Networks

JEL: H26, L14, C93

We have benefited from conversations with John Friedman, Zachary Liscow, Michael Lovenheim, Christian Traxler, and Danny Yagan, and, regarding the data and IRS enforcement processes, with Brian Best,

Jeff Butler, Patrick Langetieg, Juan Mendez, Stacy Orlett, and Alex Turk. We also thank Barry Johnson,

Michael Weber, and Alicia Miller for facilitating this project through the Joint Statistical Research Program

of the Statistics of Income Division of the United States Internal Revenue Service. Boning acknowledges

support by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE

1256260. Slemrod is an IRS employee without pay under an agreement made possible by the Intragovernmental Personnel Act of 1970 (5 U.S.C. 3371-3376). Any opinions and conclusions expressed herein are

those of the author(s) and do not necessarily represent the views of the Internal Revenue Service, the U.S.

Department of the Treasury, or the National Science Foundation. All results have been reviewed to ensure

that no confidential information is disclosed. All data work for this project involving confidential taxpayer

information was done at IRS facilities, on IRS computers, by IRS employees, and at no time was confidential

taxpayer data ever outside of the IRS computing environment.

∗

Corresponding author: University of Michigan, 611 Tappan Ave., Ann Arbor, MI 48109, USA

Email addresses: wcboning@umich.edu (William C. Boning), john.guyton@irs.gov (John Guyton),

ronald.h.hodgeii@irs.gov (Ronald Hodge), jslemrod@umich.edu (Joel Slemrod)

1. Introduction

The effects of tax enforcement directed at one taxpayer are not limited to that taxpayer’s

behavior. Increased enforcement can deter evasion in the canonical Allingham and Sandmo

(1972) model of tax evasion by changing other taxpayers’ perceptions of the probability

that evasion will be detected and punished. Deterrence may be general to all taxpayers or

limited to those who receive information about the level of enforcement from the treated

taxpayer through a shared network connection. Beginning with the audit threat letters

discussed in Blumenthal et al. (2001) and Slemrod et al. (2001) and continuing with a large

recent literature surveyed Hallsworth (2014) and Slemrod (forthcoming), field experiments in

cooperation with tax authorities have provided substantial insights into the effects of feasible

tax enforcement initiatives. The literature on tax enforcement still contains understudied

issues and important gaps, which this paper begins to fill using results from a large-scale

field experiment done in partnership with the Internal Revenue Service (henceforth the IRS).

The first gap this paper addresses is a lack of attention to compliance and enforcement for

collecting what we call employment taxes, which include payroll taxes and employee income

taxes withheld and remitted by employers. The lack of attention to collecting employment

taxes is surprising given that they bring in a large amount of revenue. In fiscal year 2017,

US FICA1 payroll tax revenue was $1.05 trillion and individual income tax withheld by

employers was $1.33 trillion, comprising 31.6 percent and 38.9 percent of total collections,

respectively, or together over 70 percent of taxes collected by the IRS2 .

This paper also provides evidence about how the effects of tax enforcement spill over to

firms connected to treated taxpayers through networks. We examine the response of firms

connected to treated taxpayers through several distinct, sometimes-overlapping networks.

The network effects capture responses driven by financial ties or by information about enforcement spread by word-of-mouth. The spillover effects contribute to the total revenue

impact of the enforcement initiatives. Understanding network effects could improve the

cost-effectiveness of enforcement policy; for example, treating the most-connected taxpayers increases voluntary compliance in the agent-based model of Andrei et al. (2014), and

degl’Innocenti and Rablen (2019) find large simulated revenue gains from using some network information to target enforcement. Taking a broader perspective, the network effects

are a crucial link between the specific deterrence effects (i.e., on the treated taxpayers) of an

enforcement initiative and the general deterrent effect of changing all actual and prospective

evaders’ perceptions of the likelihood that evasion will be detected and punished. While

we are not the first to note the potential network effects of tax enforcement, existing field

experiments focus primarily on geographic connections between households. One example is

the study of the spillover effects on nearby households of in-person visits by Austrian TV tax

inspectors in Rincke and Traxler (2011) and Drago et al. (2015), as opposed to the inter-firm

links studied in this paper; neither the professional preparer nor parent-subsidiary links for

FICA is the Federal Insurance Contributions Act, which covers contributions toward Social Security

and Medicare. This number does not include other employment taxes: SECA, unemployment insurance, or

railroad retirement.

From Table 1 of the IRS Data Book 2017.

which we find evidence have previously been examined. In contrast, Meiselman (2018) finds

no evidence that sending letters to Detroit city income tax non-filers leads their neighbors

to file. Pomeranz (2015) is a notable exception to the focus on household ties, in which

an experiment shows that an audit threat increases the VAT declarations of the treated

firms’ suppliers, but not treated firms’ clients. This pattern is consistent with the incentives

greater VAT enforcement provides for treated firms to insist that transactions with suppliers

are reported, and for treated firms’ suppliers to match reports with the treated firm, and

is not informative about word-of-mouth diffusion of information in a payroll tax or income

tax setting like the one we study3 .

Finally, this study contributes to the literature that examines to what extent the delivery

mechanism of an enforcement intervention matters. Ortega and Scartascini (2018) show

that in the context of Colombian taxes visits are more effective than emails, which are more

effective than letters, and Ortega and Scartascini (2015) show that phone calls are more

effective than letters, but this pattern has not been demonstrated for taxes in advanced

economies.

We study both direct and network effects in a large-scale field experiment conducted in

partnership with the IRS, in which 12,172 firms suspected of failure to remit all of the tax

they owe, but not subject to any compliance intervention by the IRS, were assigned either

to one of two treatment arms or to a control group. One treatment was an informational

letter, while the other was a much more dramatic intervention, an in-person visit to the

place of business by an IRS Revenue Officer.

We find that in-person visits have large, persistent direct effects on tax payments, while

letters have small, fleeting direct effects. A visit from a Revenue Officer causes firms to

remit an average of $3,686 in additional tax one quarter after the visit. This effect slowly

diminishes to $1,652 four quarters after the visit. The visit also raises the probability of

remitting any tax by 12.9 percentage points and log (tax remitted) by 13.2 log points one

quarter after treatment. Receiving a letter does not cause firms to remit more tax on

average, but it does increase the probability they remit any tax by three percentage points

one quarter after treatment.

We also find evidence of network effects. Firms whose tax preparers’ other clients receive

an in-person visit eventually remit more tax, a network effect that lags the direct effect by

three quarters. On average, firms in the experimental group share a tax preparer with 23

other firms. These 23 other firms each remit an average of an additional $243 four quarters

after the visit, an effect that is highly statistically significant. This effect takes time to

develop. Point effects on tax remitted in the first two quarters after treatment are $86 and

$52 and statistically insignificant, while the point effect three quarters after treatment is $156

and is statistically significant at only the ten percent level. This phasing-in is consistent with

an informational story, in which tax preparers pass information to their clients only during

infrequent contacts. Taking into account the large number of linked firms, the aggregate tax

preparer network effect summing over the four quarters following the visit is 1.2 times the

Alstadster et al. (2018) study how information about a legal tax avoidance scheme diffuses. Perez-Truglia

and Troiano (2018) study how the visibility of shaming affects the rate of payment of tax delinquencies.

direct effect.

The paper proceeds as follows. In Section 2 we describe the experimental setting and

treatments. In Section 3 we present the direct effects of our two tax enforcement interventions, the in-person visit and the letter. In Section 4 we describe the network effects. In

Section 5 we discuss the economic significance of the estimates. Section 6 presents a conceptual framework to think about the welfare effects of the interventions and the consequences

for policy design, and Section 7 concludes.

2. Setting and Treatments

More than 6.5 million U.S. firms deposited federal income tax withheld from wages and

salaries, federal unemployment insurance taxes, and FICA taxes between the fourth quarter

of 2013 and the fourth quarter of 2014. Firms report these tax remittances using Form

941, ”Employer’s Quarterly Federal Tax Return”. Most employers are required to make

semi-weekly or monthly Federal Tax Deposits (FTDs) of these employment taxes.

The IRS uses an algorithm to identify and prioritize firms at risk of falling behind on their

required deposits in each quarter. The IRS assigns at-risk firms into categories called FTD

Alerts. For firms with high priority alerts (Alert A or B status), the IRS assigns a Revenue

Officer to contact the firm within fifteen days of the alert’s issuance. The experiment we

study was carried out on a third group of firms, designated as having Alert C status. These

are firms for which the algorithm indicates a higher risk of falling behind on their deposits

than the general population, but not as high a risk as firms designated Alert A or B. In

some quarters prior to the experiment, Alert C firms may have received a letter about their

deposits. Some, but by no means all, firms receive the same FTD Alert designation for

more than one consecutive quarter4 . It is especially relevant from a tax enforcement policy

standpoint to understand the behavior of Alert C firms, because these firms are at the

margin of enforcement action from the IRS, and are therefore the most relevant population

when considering whether to expand or contract the set of firms the IRS contacts.

This paper uses a randomized experiment to study the effects of sending letters to and

visiting at-risk firms at the margin of enforcement action. There were 12,172 such firms

assigned Alert C status by algorithm based on payments before and during the fourth quarter

of 2014. These firms were randomly assigned to one of three groups. A control group received

no FTD Alert-related contact. A second group received an informational letter5 early in the

first quarter of 2015. The letter notes that the firm’s deposits have decreased, discusses the

firm’s deposit responsibility and potential penalties, and provides information and resources

about federal tax deposits and their payment. The third group of firms received an initial

Due to high turnover from quarter to quarter (e.g., only 28 percent of control group firms continue to

have the Alert C designation after one quarter), we expect that a few of the firms randomly assigned in

the experiment we study would have received an enforcement action prior to the experiment because of an

earlier Alert status. Random assignment makes this fact unlikely to bias our results, although it is relevant

when considering how our results generalize to other contexts.

A copy of the letter is included in the online appendix. If a taxpayer has filed a form giving a representative power of attorney, the representative also receives a copy of any written correspondence.

in-person contact at the place of business from an IRS Revenue Officer6 . Initial contact

procedures emphasize providing the taxpayer with information about the collection process,

discussing the taxpayer’s deposit compliance status, and gathering basic information. In

some cases, a Revenue Officer may use information from an initial contact to determine that

further investigation or contact is warranted, following collection procedures.

Alert C firms show signs of noncompliance before treatment. As Table 1 shows, compared

to the average firm filing a quarterly employment tax return, firms with Alert C status as of

the fourth quarter of 2014 had more employees but remitted less tax7 and were less likely to

have remitted any tax. As expected due to randomization, the treatment groups are similar

before treatment.

Table 1: Descriptive Statistics One Quarter Before Treatment

Form 941

21,604

[57,279]

Any Tax Remitted

0.686

[0.464]

Employees

15.0

[32.6]

Median Tax Remitted 2,650

Median Employees

Number of Firms

6,489,930

Tax Remitted

Alert C

10,683

[20,554]

0.570

[0.495]

27.6

[39.1]

2,846

12,172

Control

10,499

[20,022]

0.570

[0.495]

27.5

[39.3]

2,841

3,894

Letter

11,024

[21,265]

0.573

[0.495]

27.4

[38.4]

2,793

4,069

Visit

10,523

[20,342]

0.567

[0.496]

27.9

[39.5]

2,899

4,209

Notes: Means reported except where otherwise indicated. Sample standard

deviations in brackets. Form 941 statistics are from a ten percent random

sample of all firms filing Form 941 at any point in the prior year. Employees

is the number of Forms W-2 filed in the calendar year before treatment. Tax

remitted and employees are winsorized at the 98th percentile.

All three groups’ tax remittances, probability of remitting any tax, and log (tax remitted), depicted in Figure 1 fall sharply over the four quarters prior to treatment. Control

firms’ remittances also rebound to an extent one quarter after treatment, a pattern analogous to the ”Ashenfelter dip” discussed by Heckman and Smith (1999) in the context

of labor market interventions, wherein those who qualify for job training often have temporarily depressed earnings that tend to revert upward toward their longer-term mean even

absent treatment. Without an experimental control group, it would be difficult to construct

a control group from observational data that would not underestimate the control group’s

rebound in compliance and thus tend to overestimate the effect of treatment.

IRS records indicate that Revenue Officers dedicated time to contacting nearly all assigned firms.

Winsorized at the 98th percentile.

Figure 1: Outcome Means by Treatment Group

(b) Any Tax Remitted

10000

Tax Remitted

15000

20000

Probability of Remitting Any Tax

25000

(a) Tax Remitted

-4

-3

-2

-1

Quarters After Treatment

Control

Letter

-4

-3

-2

Visit

-1

Quarters After Treatment

Control

Letter

9.2

9.4

Log(Tax Remitted)

9.6

9.8

-4

-3

-2

-1

Quarters After Treatment

Control

Letter

Visit

Notes: Tax remitted winsorized at the 98th percentile. Log(tax remitted) excludes firms remitting no tax.

Visit

2.1. Follow-up Treatment

Recent work by Bhargava and Manoli (2015) and Guyton et al. (2017) has shown that

many enforcement initiatives have short-lived effects on taxpayer behavior and that reminders, essentially follow-up rounds of treatment, can boost the persistence of the policy’s

effect. This inspired a novel (in the context of tax administration research) feature of the

design of this experiment, drawing on practice in medicinewhere patients who are initially

unresponsive to treatment may receive continued treatment8 .

At the end of the quarter during which treatment took place, the algorithm that determines whether firms are designated high risk (Alert C) ran again, and some of the 12,172

firms in the experiment were again designated high risk. Firms that were again designated

high risk received a second dose of their assigned treatment in the following quarter. Thus,

each firm assigned, for example, to the visit group received one visit early in Q1 2015 and,

if the firm remained at high risk based on its payments through week twelve of Q1 2015,

it received a second visit in the second quarter of 2015. The same procedure was followed

with the letter treatment. After the second quarter, no firm received further experimental

treatment, although some businesses in the experiment might have been assigned to very

high risk (Alert A or B) status and thereby been subject to routine enforcement action.

Table 2 presents a treatment timeline.

Table 2: Treatment Timeline

By December 31, 2014

January 1-15, 2015

By March 31, 2015

April 1-15, 2015

Q4 2014 Alert C status determined by algorithm,

treatment groups randomly assigned.

Treatment carried out.

Q1 2015 Alert C status determined by algorithm.

Firms receive a follow-up round of their assigned treatment

if they have both Q4 2014 and Q1 2015 Alert C status.

Turnover in high risk status, detailed in Table 3, is large only 28 percent of control group

firms remained in this category one quarter after random assignment. Among firms assigned

to receive a letter, 28 percent continued to have high risk status in the following quarter

and received a second letter. Among firms assigned to receive a visit, just 19 percent-about

one-third less–continued to have high risk status in the next quarter and therefore received

a second visit. The lower fraction of firms assigned to receive a visit continuing in high risk

status is consistent with the result, detailed below, that the visit increased remittances.

This follow-up treatment allows us to assess the effects of a realistic treatment protocol

in which recalcitrant cases receive a follow-up intervention. If the treatment interventions

we study were to become standard practice, follow-up treatment of unresponsive firms might

well become tax administration procedure. We include firms regardless of follow-up treatment status, but the proper interpretation of our results includes the follow-up treatment

See, for example, Zonder et al. (2003) on leukemia and Diehl et al. (2003) on treatment of refractory

Hodgkin’s lymphoma with a second course of high-dose chemotherapy.

Table 3: Status One Quarter After Treatment

Alert A or B

Visit (percent)

Letter (percent)

No Treatment (percent)

Alert C

No Status

Notes: Alert A or B status reflects higher risk than firms in

with Alert C status, and Alert C status reflects higher risk than

the general population of Form 941 filers. All firms with Alert

A or B status receive field contact as part of routine procedure.

Firms that continued to have Alert C status one quarter after

treatment received a follow-up dose of their initially assigned

treatment. Firms with no status one quarter after treatment

did not receive a follow-up dose of treatment. Source: Author

calculations.

administered to firms whose remittance behavior continued to indicate high risk. Beginning

two quarters after treatment, the estimated impacts capture both the persistent component

of the initial treatment administered to all firms in the treatment group and the effect of the

follow-up treatment administered one quarter later to a subset of treatment group firms.

3. Direct Effects

3.1. Event Study Regression Design

Our preferred specification uses an event-study regression design that reduces residual

variance and allows for a flexible time path of the treatment response. This design rests

on the assumptions that there are no contemporaneous changes that affect the treatment

and control groups differentially, and that absent treatment the time paths of the outcome

variables in the treated and control groups would evolve in a parallel fashion. In fact,

there were no contemporaneous IRS policy changes that might affect the treatment groups

differentially. Figure 1 illustrates that the trends in the outcome variables we study are

similar across treatment groups for several quarters prior to treatment, which supports the

assumption that these trends would continue to be parallel absent the experiment. We

estimate models of the form

Yit =

βjq 1(Ti = j)1(t = q) + ηt + eit ,

(1)

where Yit denotes the outcome of interest, e.g. the log amount of employment tax that firm

i remitted with Form 941 in quarter t, βjq is the coefficient that indicates the direct effect

of treatment j on the outcome q quarters after treatment, 1(Ti = j) is an indicator variable

equal to one if firm i received treatment j, 1(t = q) is an indicator equal to one if t is q

quarters after treatment, ηt is a fixed effect for quarter t, and eit is the regression error term.

Standard errors are clustered at the firm level to account for possible serial correlation in

the error term. We study how letters and visits affect tax remitted (in dollars, winsorized

at the 98th percentile), the probability of remitting any employment tax, for which we use

a linear probability model, and log (tax remitted), which omits firms that do not remit any

tax.

3.2. Direct Effects Results

We find that in-person visits have large, lasting direct effects on tax payments. Figure

2 illustrates that the effect on tax remitted overall and on the probability of remitting any

tax last four quarters after treatment. Table 4 shows the effect estimates. One quarter

after a visit firms remit an additional $3,686. Visited firms are 12.9 percentage points

more likely to remit any tax one quarter after treatment; this effect is large relative to

the 58 percent of control group firms that remitted any tax one quarter after treatment.

This effect shrinks to 6.9 percentage points by four quarters after treatment. The effect

on log (tax remitted) lasts only a single quarter. Although control firms’ compliance does

improve after treatment, which is consistent with mean reversion and the Ashenfelter dip,

visited firms’ compliance rebounds much more. Control firms rebounding suggests that

observational studies comparing firms receiving a visit or letter to firms selected from the

general population would likely overstate the effects of the compliance treatments, and

further indicates the value of conducting randomized experiments.

Letters have much smaller, and fleeting, direct effects. Letters do not lead to substantially

higher average tax payments or increases in log (tax remitted), as shown in Figure 2. Letters

have an effect only on the probability that firms remit any tax one quarter after treatment,

which rises by three percentage points. This effect is highly statistically significant, but does

not persist beyond one quarter after initial treatment, suggesting that follow-up letters have

little or no effect.

The causal effect of the initial visit beyond one quarter cannot be separated from the

combined effect of the follow-up procedure in which continually non-compliant firms receive a

second visit, but effects are largest one quarter after treatment, and a second letter appears

to have no effect. Section 5 compares the estimated impact of these treatments to their

cost to evaluate their impact on net revenue and assess them from a welfare economics

perspective.

3.3. Direct Effects and Firm Size

We next explore whether larger firms respond more to treatment using a triple-difference

regression specification that compares the direct effect for the largest ten percent of firms

to the direct effect for the smallest ten percent of firms. We define size to be the number

of employees in the calendar year before treatment, as measured by Forms W-2 filed with

the IRS. The largest ten percent of firms have at least 67 employees, while the smallest ten

percent of firms have at most two employees.

Figure 2: Direct Effects

(b) Letter: Tax Remitted

-1000

-4

-3

-2

-1

Quarters After Treatment

-2000

Tax Remitted

2000

1000

4000

2000

(a) Visit: Tax Remitted

-4

-2

-1

Quarters After Treatment

(d) Letter: Any Tax Remitted

-.04

-.05

Probability of Remitting Any Tax

.05

Probability of Remitting Any Tax

-.02

.02

.04

.15

.06

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

(f) Letter: Log(Tax Remitted)

-.1

-.05

Log(Tax Remitted)

.05

Log(Tax Remitted)

.15

(e) Visit: Log(Tax Remitted)

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-3

-2

-1

Quarters After Treatment

Notes: figures plot estimates and 95 percent confidence intervals. Tax remitted winsorized at the 98th percentile. Log(tax

remitted) excludes firms remitting no tax.

Table 4: Direct Effects

Tax Remitted

Letter * One Quarter Post

94.4

(382)

Letter * Two Quarters Post

-112

(438)

Letter * Three Quarters Post

163

(459)

Letter * Four Quarters Post

177

(459)

Visit * One Quarter Post

3,686***

(399)

Visit * Two Quarters Post

2,726***

(438)

Visit * Three Quarters Post

2,169***

(451)

Visit * Four Quarters Post

1,652***

(448)

Quarter Fixed Effects

Yes

Number of Firm-Quarters

109,548

R-Squared

0.0281

Any Tax Remitted

0.0302***

(0.0110)

0.0112

(0.0118)

0.0158

(0.0122)

0.0136

(0.0125)

0.129***

(0.0113)

0.104***

(0.0120)

0.0803***

(0.0122)

0.0694***

(0.0126)

Yes

109,548

0.0513

Log(Tax Remitted)

0.00476

(0.0358)

-0.0171

(0.0359)

-0.0160

(0.0376)

-0.00353

(0.0384)

0.132***

(0.0348)

0.0344

(0.0349)

0.0309

(0.0362)

0.0197

(0.0364)

Yes

77,051

0.0220

Notes: Standard errors (in parentheses) clustered by firm. * p < 0.1 ** p < 0.05 ***

p < 0.01. Probability results from linear probability model.

In dollar terms, the direct effect of a visit is much larger for firms in the top ten percent

by pre-treatment size than for firms in the bottom ten percent. The direct effect of the

visit on tax remitted one quarter after treatment is $6,595 dollars larger for the largest firms

than for the smallest firms, as depicted in Figure 3, a difference that is highly statistically

significant. The responses of the largest and smallest firms, summarized in Table 5 are

otherwise similar, including for the letter. Holding the cost of contacting a firm constant,

visiting larger firms uses the same resources to collect more additional revenue than visiting

smaller firms.

Figure 3: Direct Effects: Top Ten Percent vs. Bottom Ten Percent by Size

(b) Letter: Tax Remitted

-5000

-4

-3

-2

-1

Quarters After Treatment

-10000

-5000

Tax Remitted

5000

Tax Remitted

5000

10000

15000

(a) Visit: Tax Remitted

-4

-3

-2

-1

Quarters After Treatment

Notes: figures plot estimates and 95 percent confidence intervals. Tax remitted is winsorized at the 98th percentile. Log(tax

remitted) excludes firms remitting no tax. Size is the number of W-2 employees in the year before treatment. The largest ten

percent of firms have at least 67 employees, while the smallest ten percent of firms have at most two employees.

4. Network Effects

The administrative data we use enable us to study the network deterrent effects of

enforcement interventions, which operate through connections between untreated firms and

firms directly receiving the enforcement intervention9 . As discussed earlier, this analysis

could provide insight about how information regarding enforcement actions diffuses to alter

the generally perceived probability that tax evasion will be detected. Even if the per-linkedfirm network effect is small, many linked firms per treated firm can still result in a substantial

aggregate effect of network connections on total remittance behavior. Understanding the

information network structure could also inform the design of information campaigns, as

Network effects through connections between treated and untreated firms in the randomly assigned group

violate the usual assumption in a randomized experiment that the untreated firms receive no treatment.

This violation would tend to bias our estimates of the direct effects towards zero, but our direct effects

estimates are unchanged when we control for network links.

Table 5: Direct Effects: Top vs. Bottom Ten Percent by Size

Letter * Top vs Bottom Ten Percent

* One Quarter Post

Letter * Top vs Bottom Ten Percent

* Two Quarters Post

Letter * Top vs Bottom Ten Percent

* Three Quarters Post

Letter * Top vs Bottom Ten Percent

* Four Quarters Post

Visit * Top vs Bottom Ten Percent

* One Quarter Post

Visit * Top vs Bottom Ten Percent

* Two Quarters Post

Visit * Top vs Bottom Ten Percent

* Three Quarters Post

Visit * Top vs Bottom Ten Percent

* Four Quarters Post

Quarter Fixed Effects

Number of Firm-Quarters

R-Squared

Tax Remitted

-889

(2,425)

2,063

(2,829)

-786

(2,961)

-1,465

(3,016)

6,595***

(2,404)

4,826*

(2,712)

2,668

(2,799)

1,582

(2,881)

Yes

24,687

0.222

Any Tax Remitted Log(Tax Remitted)

-0.0300

-0.0297

(0.0483)

(0.175)

0.00604

0.102

(0.0513)

(0.178)

-0.0486

-0.0462

(0.0526)

(0.181)

-0.0819

0.0386

(0.0526)

(0.187)

-0.0311

0.147

(0.0491)

(0.169)

-0.0127

0.191

(0.0507)

(0.174)

-0.0577

0.0624

(0.0519)

(0.172)

-0.0586

0.190

(0.0527)

(0.173)

Yes

24,687

16,593

0.0852

0.221

Notes: Standard errors (in parentheses) clustered by firm. * p < 0.1 ** p < 0.05 *** p < 0.01.

Any tax remitted results from linear probability model. Firms in the bottom 10 percent by size

have at most two W-2 employees in the year before treatment, while firms in the top ten percent

by size have at least 67 such employees.

models show higher voluntary compliance results from providing information to or about

taxpayers with the most links (Andrei et al. (2014), degl’Innocenti and Rablen (2019)). We

investigate several types of network, some of which have been examined before (although

usually with respect to households rather than firms), and some that have not been heretofore

studied. Our rich data set allows us to examine certain links between firms that have not

been rigorously studied before.

One connection between the firms in our sample and others is geographic. Geography

ties together firms with addresses in the same ZIP Code or, at a more fine-grained level,

a shared ZIP+4. The 42,000 five-digit ZIP Codes in the United States indicate a shared

postal facility and are assigned to either geographic areas or post office boxes, while a ZIP+4

is a nine-digit designation for a small group of blocks or segment of a postal route (USPS,

2016). Firms in our experimental sample share a ZIP Code with an average of 65910 other

employers filing quarterly employment tax returns, and share a ZIP+4 with an average of

just 3 other employers.

Firms also share tax preparers or tax preparation firms. Each individual tax preparer has

a unique Preparer Tax Identification Number (PTIN), which that preparer includes on each

return he or she prepares. If the preparer is part of a tax preparation firm, the firm’s unique

Employer Identification Number (EIN) is also included on each prepared return. These

identifiers allow us to identify when two firms’ returns are prepared by the same individual

preparer or by preparers working at the same tax preparation firm. We consider two firms

linked to a tax preparer or tax preparation firm if that tax preparer or tax preparation

firm prepared at least one Form 941 for that firm in the four quarters prior to treatment;

it is plausible that firms might have contact with a tax preparer or tax preparation firm

they have used in the past year even if they are no longer using that preparer, especially if

they are concerned about IRS enforcement action related to past filings. Each firm in our

experimental sample shares a tax preparer with an average of 23 other employers and a tax

preparation firm with an average of 98 other employers.

Network effects through shared tax preparers are of interest for two reasons beyond their

implications for correctly estimating the revenue impact of enforcement initiatives. First,

preparers may be an effective target for expanded information reporting or other enforcement

treatments. Second, and related, the fact that the treatment spills over to other firms with

the same tax preparer suggest that preparers play a role in firms’ decision-making, an issue

addressed by Klepper et al. (1991) and recently by Klassen et al. (2015), who analyze

confidential data from the IRS to examine whether the party primarily responsible for a

firm’s tax compliance function-an external auditor or the internal tax department-is related

to the firm’s tax aggressiveness.

Finally, we investigate links between parent corporations and their subsidiaries. Parent/subsidiary relationships meet one of two sets of criteria in the year prior to treatment

assignment. In the first case, the parent corporation files IRS Form 851, ”Affiliations Sched10

As some firms are linked to more than one Alert C firm, the sample of firms linked by ZIP code to Alert

C firms is somewhat smaller than the number of links per firm times the size of the Alert C sample (536

linked firms instead of 659), and similarly for the other network channels we study.

ule,” with a consolidated group annual tax return indicating that the parent owns stock

with 80 percent or more of both the total value and voting power of the subsidiary directly

or indirectly through other corporations in the consolidated group. In the second case, the

parent corporation is a subchapter S corporation and has filed Form 8869, electing to treat

a domestic corporation whose stock it wholly owns as a qualified subchapter S subsidiary

which is deemed liquidated. This definition implies that firms have at most one parent and

that parent firms cannot themselves have a parent, as parents in our sample are either the

ultimate parent of a consolidated group or S corporations whose owners are required by law

to be individual people. The business operations of the parent and subsidiary are presumably tightly linked, given the degree of ownership and filing of a consolidated annual tax

return.

These three sets of networks capture a diverse range of relationships between firms. For

example, the network effect per link to a firm visited by a Revenue Officer may be large

for one channel but not others, and the network effect per link to a letter firm need not

be large for that channel. One might expect that letters have network effects through ZIP

and ZIP+4, as these links capture both geographic proximity and shared postal delivery,

while visits might have especially strong effects through shared preparers or tax preparation

firms, as the preparer or firm may interact directly with the Revenue Officer. Additionally,

links to visited firms through a given channel, for example a shared preparer, may affect tax

payments overall, only on the extensive margin captured by the indicator for remitting any

tax, or only on the intensive margin captured by log (tax remitted).

4.1. Identifying Network Effects with Non-Random Selection into Network Linkages

We aim to identify the causal network deterrence effects of the letter and visit treatments. This causal effect captures the difference between a firm’s compliance behavior if its

network “neighbors” happen to receive a letter or visit and that firm’s behavior if its network

neighbors happen to receive no treatment. When estimating these effects, it is important

to keep in mind that simply comparing the post-treatment behavior of firms with network

neighbors that received a letter or visit to the post-treatment behavior of all firms without

treated network neighbors would provide a biased estimate of the network effect. This is

because having treated network neighbors requires having network neighbors with high-risk

(Alert C) status, so that network links may very well not be random.

Firms with Alert C status are less likely than other employers to have remitted any Form

941-related tax, as Table 1 shows, and so it is natural to suppose that the network neighbors of firms with Alert C status might have systematically different remittance behavior

compared to other firms’ network neighbors. For example, if adverse local economic shocks

make firms in a neighborhood less likely to remit tax payments, firms in that neighborhood

are both more likely to have Alert C status themselves and more likely to be linked to

firms with Alert C status. The resulting correlation between connections to treated firms

and lower tax payments would bias network effects estimates downward. The same concern

arises for links through preparer networks; some preparers may be more experienced, or

more sympathetic, or condoning, towards at-risk businesses and thus develop clienteles of

such businesses. Parents and their subsidiaries are also likely to share similar compliance

behavior.

To address the selection bias concern, we compare firms with the same number of Alert

C neighbors. Consider the example of two firms, each sharing its own unique ZIP Code with

exactly one Alert C firm in the experimental sample. Prior to random assignment, the likelihood of each firm sharing its ZIP Code with a firm that receives a visit is 1/3. Conditional

on the number of links to Alert C firms, network treatment is randomly assigned and thus

independent of firms’ characteristics and potential compliance outcomes. Comparing firms

with the same number of links to Alert C firms allows us to identify an unbiased causal effect

of being linked to a treated firm, because before treatment the network treated and control

groups are equally likely to have low tax payments on the basis of their similar connections

to Alert C firms. The regression approach we implement is a generalized version of the

event-study approach used above to study direct effects, where we pool firms with different

numbers of links to Alert C firms to produce a single treatment estimate, but control for

differential patterns of compliance over time between firms based on their total links to

Alert C firms. This approach relies on the assumption that, conditional on the number of

total links to Alert C firms, the trends in compliance would be parallel across firms linked

to different treatment groups absent treatment. Specifically, separately for each network

channel c we run regressions of the form:

θclt + eit ,

(2)

ρcjq Lcij 1(t = q) +

Yit =

where Yit is the outcome for firm i in quarter t, ρcjq is the network effect through channel

c of treatment j, q quarters after treatment, Lcij is the number of links through network

channel c that firm i has to firms that received treatment j, 1(t = q) is an indicator equal

to one if t is q quarters after treatment, θclt is a fixed effect common to all firms connected

through network channel c to a total of l treated and control firms in quarter t, and eit is

the regression error term. Note that, conditioning on a fixed value of the total number of

links to Alert C firms, this specification is a standard event-study specification with quarter

fixed effects. The specification pools the event-study specifications across different numbers

of total links to Alert C firms and constrains the estimated network effect to be linear in the

number of links to treated firms. We do this in separate specifications for firms sharing a

preparer, preparer firm, ZIP Code, or ZIP+4 with an Alert C firm and for the subsidiaries

and parents of Alert C firms. We cluster the standard errors at the level of the channel used

in that specification, e.g. ZIP Code, preparer, or parent, which addresses correlation in the

error term between firms sharing, e.g., a preparer or parent as well as serial correlation in

the error term.

4.2. Tax Preparer Network Effects Results

We find evidence that tax enforcement interventions have network effects transmitted

through a shared tax preparer several quarters after treatment, but not immediately. In

person visits increase the tax remitted by visited firms’ tax preparers’ other clients by an

average of $156 three quarters after treatment and by $243 four quarters after treatment, as

shown in Figure 4 and Table 6. The effect three quarters after treatment is statistically significant at the ten percent level, while the effect four quarters after treatment is statistically

significant at the one percent level. We find that letters increase log (tax remitted) by letter

recipients’ tax preparers’ other clients four quarters after treatment by 1.09 log points. This

effect is statistically significant at the five percent level. The time delay between treatment

and these tax preparer network effects is consistent with the low frequency with which most

firms exchange information with their tax preparers.

Table 6: Preparer Network Effects

Tax Remitted

Preparer Links to Letter Firms 35.3

* One Quarter Post

(72.2)

Preparer Links to Letter Firms 27.0

* Two Quarters Post

(88.6)

Preparer Links to Letter Firms 73.0

* Three Quarters Post

(92.6)

Preparer Links to Letter Firms 146

* Four Quarters Post

(103)

Preparer Links to Visit Firms

85.5

* One Quarter Post

(61.3)

Preparer Links to Visit Firms

52.3

* Two Quarters Post

(81.9)

Preparer Links to Visit Firms

156*

* Three Quarters Post

(88.3)

Preparer Links to Visit Firms

243***

* Four Quarters Post

(94.1)

Quarter Fixed Effects

Yes

Quarter * Total Preparer Links Yes

to Alert C Fixed Effects

Number of Preparer Clusters

10,219

Number of Firm-Quarters

1,796,994

R-Squared

0.00361

Any Tax Remitted

-0.00183

(0.00152)

-0.000140

(0.00170)

-0.00201

(0.00216)

-0.00111

(0.00238)

0.00188

(0.00122)

0.000315

(0.00149)

0.00123

(0.00189)

0.00162

(0.00224)

Yes

Log(Tax Remitted)

-0.00437

(0.00624)

-0.0000449

(0.00428)

0.00264

(0.00434)

0.0109**

(0.00455)

-0.00236

(0.00505)

-0.00328

(0.00353)

0.00112

(0.00368)

0.000830

(0.00357)

Yes

10,219

1,796,994

0.00500

9,357

1,193,501

0.0120

Notes: Standard errors (in parentheses) clustered by Preparer. * p < 0.1 ** p < 0.05 ***

p < 0.01. Any tax remitted results from linear probability model.

4.3. Tax Preparation Firm Network Effects Results

In contrast to the network effects of shared individual tax preparers, we do not find

evidence of network effects through shared tax preparation firms. The results shown in

Figure 4: Tax Preparer Network Effects

Tax Remitted

200

-200

Tax Remitted

200

400

600

(b) Letter: Tax Remitted

600

(a) Visit: Tax Remitted

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

(d) Letter: Any Tax Remitted

Probability of Remitting Any Tax

-.005

Probability of Remitting Any Tax

.005

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

Log(Tax Remitted)

.01

-.01

-.02

-.01

Log(Tax Remitted)

.01

.02

(f) Letter: Log(Tax Remitted)

.02

(e) Visit: Log(Tax Remitted)

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-3

-2

-1

Quarters After Treatment

Notes: figures plot estimates and 95 percent confidence intervals. Standard errors clustered by preparer. Tax remitted is

winsorized at the 98th percentile. Log(tax remitted) excludes firms remitting no tax.

Figure 5 and in Table 7, are precisely estimated, ruling out large per-firm spillovers, and are

not statistically significant at the five percent level. While Alert C firms on average share a

tax preparer with 23 other firms, they share a tax preparation firm with an average of 659

other businesses, limiting the influence of a single contact with one of the preparation firms’

clients on the firm’s other clients.

Table 7: Preparation Firm Network Effects

Tax Remitted

-5.67

(103)

-116

(102)

-67.5

(91.8)

-132*

(80.2)

2.47

(110)

-52.7

(89.7)

65.9

(79.9)

22.9

(79.8)

Yes

Preparation Firm Links to Letter

Firms * One Quarter Post

Preparation Firm Links to Letter

Firms * Two Quarters Post

Preparation Firm Links to Letter

Firms * Three Quarters Post

Preparation Firm Links to Letter

Firms * Four Quarters Post

Preparation Firm Links to Visit

Firms * One Quarter Post

Preparation Firm Links to Visit

Firms * Two Quarters Post

Preparation Firm Links to Visit

Firms * Three Quarters Post

Preparation Firm Links to Visit

Firms * Four Quarters Post

Quarter Fixed Effects

Quarter * Total Preparation Firm

Links to Alert C Fixed Effects

Number of Preparation Firm Clusters 9,759

Number of Firm-Quarters

3,563,361

R-Squared

0.00502

Any Tax Remitted

-0.00109

(0.00107)

-0.000442

(0.00119)

-0.00115

(0.00141)

-0.00149

(0.00168)

-0.000349

(0.000943)

-0.000465

(0.00117)

0.000129

(0.00148)

0.000206

(0.00185)

Yes

Log(Tax Remitted)

-0.000987

(0.00380)

-0.00361

(0.00317)

0.000720

(0.00331)

0.000841

(0.00300)

-0.00154

(0.00309)

-0.00331

(0.00248)

0.00189

(0.00225)

0.00246

(0.00222)

Yes

9,759

3,563,361

0.00620

9,053

2,468,149

0.0131

Notes: Standard errors (in parentheses) clustered by Preparation Firm. * p < 0.1 ** p < 0.05 ***

p < 0.01. Any tax remitted results from linear probability model.

4.4. Narrow Geographic (ZIP+4) Network Effects Results

We find mixed evidence of narrow geographic network effects between firms on the same

postal route, presented in Figure 6 and Table 8, and no evidence of such network effects on

tax remitted overall. One quarter after treatment, log (tax remitted), which excludes firms

remitting nothing, rises by 3.26 log points for firms sharing a postal route with a visited

firm, and three quarters after treatment the probability of remitting any tax falls by 1.39

Figure 5: Tax Preparation Firm Network Effects

(b) Letter: Tax Remitted

-400

-200

Tax Remitted

200

Tax Remitted

200

400

600

(a) Visit: Tax Remitted

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

(d) Letter: Any Tax Remitted

-.005

-.004

Probability of Remitting Any Tax

.005

Probability of Remitting Any Tax

-.002

.002

.01

.004

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

(f) Letter: Log(Tax Remitted)

-.01

-.005

Log(Tax Remitted)

.005

Log(Tax Remitted)

.005

.01

.015

(e) Visit: Log(Tax Remitted)

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-3

-2

-1

Quarters After Treatment

Notes: figures plot estimates and 95 percent confidence intervals. Standard errors clustered by tax preparation firm. Tax

remitted is winsorized at the 98th percentile. Log(tax remitted) excludes firms remitting no tax.

percentage points for firms sharing a postal route with a letter recipient. These effects are

statistically significant at the five percent level, and do not accompany substantial changes

in tax remitted overall.

Table 8: ZIP+4 Network Effects

Tax Remitted

ZIP+4 Links to Letter Firms 59.5

* One Quarter Post

(527)

ZIP+4 Links to Letter Firms -117

* Two Quarters Post

(495)

ZIP+4 Links to Letter Firms -545

* Three Quarters Post

(526)

ZIP+4 Links to Letter Firms -255

* Four Quarters Post

(481)

ZIP+4 Links to Visit Firms

847

* One Quarter Post

(784)

ZIP+4 Links to Visit Firms

319

* Two Quarters Post

(563)

ZIP+4 Links to Visit Firms

-41.5

* Three Quarters Post

(568)

ZIP+4 Links to Visit Firms

84.9

* Four Quarters Post

(660)

Quarter Fixed Effects

Yes

Quarter * Total ZIP+4 Links Yes

to Alert C Fixed Effects

Number of ZIP+4 Clusters

5,916

Number of Firm-Quarters

290,745

R-Squared

0.00326

Any Tax Remitted Log(Tax Remitted)

-0.00469

0.0217

(0.00619)

(0.0156)

-0.00748

0.00782

(0.00536)

(0.0120)

-0.0139**

-0.00767

(0.00628)

(0.0126)

-0.0123

0.0129

(0.00770)

(0.0131)

0.00158

0.0326**

(0.00540)

(0.0162)

0.00225

0.0131

(0.00554)

(0.0131)

-0.00302

0.00650

(0.00631)

(0.0134)

-0.000572

0.0128

(0.00734)

(0.0127)

Yes

5,916

290,745

0.0104

5,476

201,828

0.00891

Notes: Standard errors (in parentheses) clustered by ZIP+4. * p < 0.1 ** p < 0.05 ***

p < 0.01. Any tax remitted results from linear probability model.

4.5. Geographic (ZIP Code) Network Effects Results

We do not find evidence of spillovers at the ZIP code level on tax remitted, although

visits do have small, positive spillovers conditional on remitting any tax. As Figure 7 and

Table 9 show, two quarters after treatment, firms in the same ZIP code as a visited firm

have log(tax remitted), which is conditional on remitting any tax, that is 0.412 log points

higher (with a p-value less than 0.05), and four quarters after treatment the effect on this

outcome is 0.666 log points (with a p-value less than 0.01). These effects do not translate

to higher tax payments overall.

Figure 6: Narrow Geographic (ZIP+4) Network Effects

(b) Letter: Tax Remitted

-1000

-2000

Tax Remitted

1000

Tax Remitted

-1000

2000

1000

3000

(a) Visit: Tax Remitted

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

Probability of Remitting Any Tax

-.02

-.01

-.03

-.02

Probability of Remitting Any Tax

-.01

.01

(d) Letter: Any Tax Remitted

.02

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

(f) Letter: Log(Tax Remitted)

-.04

-.02

Log(Tax Remitted)

.02

Log(Tax Remitted)

.02

.04

.06

(e) Visit: Log(Tax Remitted)

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-3

-2

-1

Quarters After Treatment

Notes: figures plot estimates and 95 percent confidence intervals. Standard errors clustered by ZIP+4. Tax remitted is

winsorized at the 98th percentile. Log(tax remitted) excludes firms remitting no tax.

Figure 7: Geographic (ZIP Code) Network Effects

100

Tax Remitted

-100

-200

-100

Tax Remitted

100

200

(b) Letter: Tax Remitted

200

(a) Visit: Tax Remitted

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

Probability of Remitting Any Tax

.002

-.004

-.002

Probability of Remitting Any Tax

-.002

.002

.004

(d) Letter: Any Tax Remitted

.004

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

(f) Letter: Log(Tax Remitted)

-.005

Log(Tax Remitted)

.005

Log(Tax Remitted)

.005

.01

(e) Visit: Log(Tax Remitted)

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-3

-2

-1

Quarters After Treatment

Notes: figures plot estimates and 95 percent confidence intervals. Standard errors clustered by ZIP Code. Tax remitted is

winsorized at the 98th percentile. Log(tax remitted) excludes firms remitting no tax.

Table 9: ZIP Code Network Effects

ZIP Code Links to Letter Firms

* One Quarter Post

ZIP Code Links to Letter Firms

* Two Quarters Post

ZIP Code Links to Letter Firms

* Three Quarters Post

ZIP Code Links to Letter Firms

* Four Quarters Post

ZIP Code Links to Visit Firms

* One Quarter Post

ZIP Code Links to Visit Firms

* Two Quarters Post

ZIP Code Links to Visit Firms

* Three Quarters Post

ZIP Code Links to Visit Firms

* Four Quarters Post

Quarter Fixed Effects

Quarter * Total ZIP Code Links

to Alert C Fixed Effects

Number of ZIP Code Clusters

Number of Firm-Quarters

R-Squared

Tax Remitted

21.1

(66.0)

-7.65

(61.2)

48.4

(69.2)

2.79

(67.3)

-14.9

(68.9)

-31.4

(66.7)

-0.644

(77.9)

-35.3

(67.1)

Yes

Any Tax Remitted Log(Tax Remitted)

-0.0000667

0.00299

(0.000702)

(0.00219)

0.000433

0.00342*

(0.000772)

(0.00201)

0.000872

0.00297

(0.000883)

(0.00223)

0.00175*

0.00357

(0.000967)

(0.00225)

0.000339

0.00368*

(0.000740)

(0.00219)

-0.000198

0.00412**

(0.000798)

(0.00203)

-0.00000113

0.00355

(0.000914)

(0.00225)

0.000122

0.00666***

(0.000984)

(0.00226)

Yes

7,046

3,181,959

0.00170

7,046

3,181,959

0.00624

7,008

2,159,992

0.00949

Notes: Standard errors (in parentheses) clustered by ZIP Code. * p < 0.1 ** p < 0.05 ***

p < 0.01. Any tax remitted results from linear probability model.

4.6. Parent and Subsidiary Network Effects Results

There is limited evidence that letters and visits have effects on the parents of contacted

firms. Few treated firms have parents, and therefore the estimates, presented in Figure 8

and Table 10 are imprecise. Three quarters after treatment, parents of visited firms remit

an additional $4.15 million, but this effect is statistically significant only at the ten percent

level, and as such is weak evidence. Across other quarters, outcomes, and both treatments,

there is little evidence of an effect on treated firms’ parents.

Table 10: Effects on Parents of Treated Firms

Subsidiary Letter *

One Quarter Post

Subsidiary Letter *

Two Quarters Post

Subsidiary Letter *

Three Quarters Post

Subsidiary Letter *

Four Quarters Post

Subsidiary Visit *

One Quarter Post

Subsidiary Visit *

Two Quarters Post

Subsidiary Visit *

Three Quarters Post

Subsidiary Visit *

Four Quarters Post

Quarter Fixed Effects

Parent Clusters

Observations

R-Squared

Tax Remitted

-2,862,994

(1,774,428)

-2,431,920

(1,535,133)

928,559

(944,069)

758,309

(1,143,241)

-1,647,927

(2,035,611)

-2,313,524

(1,534,115)

2,282,318*

(1,243,672)

150,468

(1,199,528)

Yes

684

0.0201

Any Tax Remitted

0.0455

(0.0456)

0.0455

(0.0456)

0.0455

(0.0456)

3.68e-13

(0.000000566)

0.0455

(0.0456)

0.00198

(0.0631)

0.00198

(0.0631)

-0.0435

(0.0437)

Yes

684

0.0365

Log(Tax Remitted)

-0.955

(0.682)

-1.00

(0.648)

-0.793

(0.659)

-0.132

(0.293)

-0.779

(0.668)

-0.614

(0.751)

-0.502

(0.784)

-0.361

(0.444)

Yes

253

0.0512

Notes: Standard errors (in parentheses) clustered by parent. * p < 0.1 ** p < 0.05

*** p < 0.01. Any tax remitted results from linear probability model.

Contacting a parent firm has similarly ambiguous effects on its subsidiaries. Figure 9 and

Table 11 show that the in-person visit raises tax remitted by the visited firm’s subsidiaries in

the quarter after treatment by $915,000, an effect that is highly statistically significant, yet

at the same time decreases the probability the subsidiary remits any tax by 0.917 percentage

points. The letter has no effects on subsidiaries that are statistically significant at the five

percent level. Only 49 treated firms are parents, and there is evidence, shown in Table A.14,

of a pre-treatment trend in subsidiaries’ tax payments, so these results should be interpreted

with caution.

Figure 8: Effects on Parents of Treated Firms

-4

-3

-2

-1

Quarters After Treatment

-5000000

Tax Remitted

5000000

Tax Remitted

5000000

1.00e+07

(b) Letter: Tax Remitted

1.00e+07

(a) Visit: Tax Remitted

-4

-2

-1

Quarters After Treatment

(d) Letter: Any Tax Remitted

Probability of Remitting Any Tax

-.1

Probability of Remitting Any Tax

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

(f) Letter: Log(Tax Remitted)

-3

-2

Log(Tax Remitted)

-1

Log(Tax Remitted)

-1

(e) Visit: Log(Tax Remitted)

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-3

-2

-1

Quarters After Treatment

Notes: figures plot estimates and 95 percent confidence intervals. Standard errors clustered by parent firm. Tax remitted is

winsorized at the 98th percentile. Log(tax remitted) excludes firms remitting no tax.

Figure 9: Effects on Subsidiaries of Treated Firms

(b) Letter: Tax Remitted

-4

-3

-2

-1

Quarters After Treatment

-200000

-2000000

-1000000

Tax Remitted

1000000

Tax Remitted

200000 400000 600000 800000

2000000

(a) Visit: Tax Remitted

-4

-2

-1

Quarters After Treatment

(d) Letter: Any Tax Remitted

Probability of Remitting Any Tax

-.05

.05

-.2

-.1

Probability of Remitting Any Tax

-.1

.15

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-2

-1

Quarters After Treatment

(f) Letter: Log(Tax Remitted)

Log(Tax Remitted)

-1

Log(Tax Remitted)

(e) Visit: Log(Tax Remitted)

-3

-4

-3

-2

-1

Quarters After Treatment

-4

-3

-2

-1

Quarters After Treatment

Notes: figures plot estimates and 95 percent confidence intervals. Standard errors clustered by parent firm. Tax remitted is

winsorized at the 98th percentile. Log(tax remitted) excludes firms remitting no tax.

Table 11: Effects on Subsidiaries of Treated Firms

Tax Remitted

Parent Letter *

293,983

One Quarter Post

(216,372)

Parent Letter *

259,715*

Two Quarters Post

(153,028)

Parent Letter *

112,997

Three Quarters Post

(95,483)

Parent Letter *

103,595

Four Quarters Post

(80,073)

Parent Visit *

1,065,494***

One Quarter Post

(314,180)

Parent Visit *

83,654

Two Quarters Post

(658,621)

Parent Visit *

402,655

Three Quarters Post

(784,156)

Parent Visit *

-110,721

Four Quarters Post

(644,678)

Quarter Fixed Effects

Yes

Parent Clusters

Number of Firm-Quarters 3,573

R-Squared

0.114

Any Tax Remitted

0.0259

(0.0236)

0.00521

(0.0357)

-0.0165

(0.0414)

-0.0124

(0.0412)

-0.00504

(0.00556)

-0.0532

(0.0358)

-0.0624*

(0.0341)

-0.0674

(0.0404)

Yes

3,573

0.0720

Log(Tax Remitted)

0.140

(0.300)

1.04

(0.935)

0.999

(0.860)

0.885

(0.858)

0.205

(0.206)

1.24

(0.953)

1.37

(0.882)

1.16

(0.859)

Yes

768

0.263

Notes: Standard errors (in parentheses) clustered at the parent level. * p < 0.1 **

p < 0.05 *** p < 0.01. Any tax remitted results from linear probability model.

To summarize the network effects results, focusing on overall tax remitted, visits have

delayed spillover effects on tax remitted through shared individual tax preparers, while there

is not strong evidence of effects through the other networks we study.

5. Comparison of Aggregate Network Effects to Direct Effects

Even if the network effect is small per linked firm, a large number of network links

between firms can imply substantial network effects in the aggregate. To compare the

aggregate network effects to the direct effects, we define the network multiplier, equal to

the ratio of the aggregate network effect of a treatment to the treatment’s direct effect. We

multiply the coefficient or sum of coefficients on tax remitted by the average number of

links per firm to obtain a per-letter or per-visit effect, which we divide by the direct effect

to obtain a network multiplier. Limiting our focus to estimates with 95 percent confidence

intervals that exclude zero, we find that the tax preparer network multiplier over the year

following the visit is 1.2, and the subsidiary network multiplier one quarter after the visit is

8.1 (although violations of the parallel trends assumption before treatment for subsidiaries

make us skeptical of this subsidiary estimate). Multipliers for the quarter after treatment

are reported in Table 12 and for the four quarters after treatment in Table 13. Over the

following year, each visit leads the visited firm to remit an additional $10,233, and also

generates an additional $12,258 from firms sharing a tax preparer with the visited firm.

Table 12: Dollar Values and Network Multipliers: First Quarter After Treatment

Links

Letter Effect per Link

(se)

Dollars per Letter

(se)

Letter Network Multiplier

Visit Effect per Link

(se)

Dollars per Visit

(se)

Visit Network Multiplier

(1)

(2)

Direct

Preparer

22.8

94.4

35.3

382

72.2

94.4

806

382

1,649

8.53

3,686***

85.5

399

61.3

3,686***

1,952

399

1,399

0.530

Notes: * p < 0.1 ** p < 0.05 *** p < 0.01.

(3)

(4)

Prep Firm ZIP+4

98.1

2.84

-5.67

59.5

103

527

-556

169

10,116

1,497

-5.89

1.79

2.47

847

110

784

242

2,404

10,837

2,224

0.0658

0.652

(5)

ZIP Code

659

21.1

66.0

13,938

43,493

148

-14.9

68.9

-9,800

45,440

-2.66

(6)

(7)

Parent

Subsidiary

0.00657

0.0326

-2,862,994

293,983

1,774,428

216,372

-18,817

9,589

11,662

7,057

-199

102

-1,647,927 1,065,494***

2,035,611

314,180

-10,831

34,752***

13,379

10,247

-2.94

9.43

Table 13: Dollar Values and Network Multipliers: Four-Quarter Totals

Links

Letter Effect per Link

(se)

Dollars per Letter

(se)

Letter Network Multiplier

Visit Effect per Link

(se)

Dollars per Visit

(se)

Visit Network Multiplier

(1)

(2)

Direct

Preparer

22.8

322

281

1,483

273

322

6,417

1,483

6,231

19.9

10,233***

537**

1,495

250

10,233*** 12,258**

1,495

5,702

1.20

Notes: * p < 0.1 ** p < 0.05 *** p < 0.01.

(3)

(4)

Prep Firm ZIP+4

98.1

2.84

-322

-857

296

1,759

-31,580

-2,434

29,062

4,993

-98.0

-7.55

38.6

1,209

273

2,046

3,789

3,433

26,820

5,807

0.370

0.335

(5)

(6)

ZIP Code

Parent

659

0.00657

64.7

-3,608,046

215

2,745,525

42,647

-23,714

141,806

18,045

132

-73.6

-82.2

-1,528,665

233

3,013,540

-54,207

-10,047

153,582

19,806

-5.30

-0.982

(7)

Subsidiary

0.0326

770,290

502,352

25,124

16,385

78.0

1,441,081

2,356,680

47,002

76,865

4.59

These estimates depend on several simplifying assumptions. Although we multiply the

mean effect per link by the mean number of links, both the effect and the number of links

are unlikely to be distributed evenly across the population. These calculations also do

not account for heterogeneous direct effects by firm size or for non-linear dose response to

multiple links to treated firms. The networks we discuss above intersect, as firms for example

may share both a neighborhood and a tax preparer, though in unreported results we find

that including all of the networks in a single specification does not substantially change the

tax preparer network effect of the visit. Despite these assumptions, the network multiplier

calculations demonstrate that network effects may be economically substantial.

6. Implications for Policy

How do these findings inform resource allocation decisions? Should each treatment be

expanded or cut back? To answer these questions, we need to consider all the costs and

benefits of each treatment. Before proceeding, we note that all the estimated effects pertain

to revenue remittance and not, as is true in most similar studies, reported tax liability (that

may not be remitted in a timely way, or at all).

6.1. Would Net Revenue Rise?

A treatment boosts net revenue if the marginal revenue it raises exceeds its marginal

administrative costs. There are three components to the revenue raised: the direct effect on

the treated group, the network effect, and the general deterrent effect in the population at

large, denoted as rDt , rN t , and rGt , respectively, where subscript t indicates the treatment,

either V for visit or L for letter. In this paper, we have estimated the direct effect and the

network effect, but not the general deterrent effect. The revenue raised should be compared

to the marginal administrative cost, denoted at . The calculation for each treatment is simply

whether rDt + rN t + rGt ≡ rt > at .

To address these questions, we begin by referring to the dollar values for the year following

treatment calculated in Table 13, where we show that rDV = $10, 233 and rDL = $322. Based

on IRS data, aV = $220 and aL = $4. Both treatments clearly increase net revenue without

taking network or general deterrent effects into account. Assuming the general deterrent

effect, which we cannot observe, were negligible, incorporating the statistically significant tax

preparer network effect of the visit yields rN V = $12, 258. Then rV = $10, 233 + $12, 258 =

$22, 491 $220. Similarly, we can calculate that rL = $322 > $4. Even absent general

deterrent effects, both treatments easily pass this simple net-revenue-increasing test.

There are, though, other issues to consider. These calculations ignore compliance costs

incurred by treated taxpayers, which are likely higher for the visit. In addition, we have

ignored any difference between the average effect we have estimated and the marginal effect,

although this difference may not be large given that the population of firms we study are

not the highest-risk firms routinely subject to treatment, but instead a group of firms that

typically are not treated. These calculations should be done on a discounted present-value

basis. Given that current interest rates are near zero, discounting itself is not a substantively

important issue over the course of a single year. What is not known is whether the estimated

effects would reverse sign if carried out past the year we examine. In other words, we will

be overstating the net revenue gain to the extent that the treatments cause payments to

accelerate but not increase in total; we see no sign of this over the course of a year but

cannot be sure it is not an issue in the longer term.

6.2. Would Re-Allocating Resources Raise More Revenue?

Given a fixed resource budget, would more re-allocating resources between visits and

letters raise more revenue? If the objective of the tax authority is to maximize revenue

net of cost, then the answer depends on whether the following inequality holds: if it does,

resources should be shifted from letters to visits11 :

rDV + rN V + rGV >

(rDL + rN L + rGL ).

(3)

In Expression 3, (aV /aL ) represents the trade-off in the extent of alternative treatments:

visiting one fewer firm enables the tax authority to send (aV /aL ) more letters while staying

within the given budget. Now the relative general deterrence effects of the two treatments

can matter. If we are willing to assume that the general deterrence effects are proportional

to the sum of the direct and network effects, rGV /(rDV + rN V ) = rGL /(rDL + rN L ), then

Expression 3 simplifies to:

(rDL + rN L ).

(4)

rDV + rN V >

Using our values from above, the left-hand side of Expression 4 is $10, 233 + $12, 258 =

$22, 491, while the right-hand side is (220/4) ∗ $322 = $17, 710. Because letters deliver

about 1/70 of the visit’s return for 1/55 of the cost, the average per-dollar-spent return

is slightly higher for the visit and thus a fiscally-constrained tax agency would increase

revenue by shifting resources from letters to visits at the margin12 . Given the degree of

uncertainty surrounding our estimates of both the direct and network effects, however, we

cannot confidently rule out that the per-dollar returns to the two interventions are the same.

6.3. Would Policy Changes Increase Welfare?

The evaluation of whether welfare would rise when a given policy changes is more complicated. For one thing, such an evaluation should account for marginal compliance costs

(resource costs borne directly by private citizens in the form of time and expenditure), which

are social costs that do not show up in government budgets. Second, the appropriate criterion is not whether revenue net of cost increases, because that ignores the fact that any

additional tax remittance is a transfer from private hands, which has social value, to the

All the point estimates have associated confidence bands, and thus the cost-benefit analyses are themselves subject to error.

If the average return in our sample equals the marginal return, and in equilibrium the deterrent effects

of the two treatments are not related, as they would be if for example firms that do not respond to a letter

are later visited as a result. This possibility is not addressed by the experiments we conduct, because we do

not vary the operational procedure in which populations judged to be higher-risk than our sample receive

visits.

government that provides services that are of value to the population. As shown in Keen

and Slemrod (2017), which draws on Slemrod and Yitzhaki (1987) and Mayshar (1991), the

welfare impact of the intervention can be approximated by

∆W ≡ (v − 1)∆R − v ∆a − ∆c.

(5)

In Expression 5, v is the marginal social value of an additional dollar of revenue. If the

question is whether to increase administrative effort, ceteris paribus, then v represents the

marginal social value of raising a dollar of net revenue for public spending. If the question

is whether to increase administrative effort while reducing, say, the tax rate in a revenue0

neutral way, then v represents the social cost saved by reducing the tax collected via the tax

rate by one dollar, sometimes referred to as the marginal efficiency cost of raising funds. In

either case, the first term on the right-hand-side of Expression 5 is the marginal social value

of the additional net revenue collected when an administrative policy instrument increases

by one unit. Because raising revenue is costly, the value of v will exceed one. The other

two terms on the right-hand-side of Expression 4 are the marginal social cost of increasing

government spending and the marginal compliance cost; the former is multiplied by v to

reflect that government spending must be funded by raising distortionary, and therefore

socially costly, taxation. To see the implications of Expression 5, following Mayshar (1991)

we set v = 1.17 and assume that the marginal compliance cost is twice the marginal

administrative cost. In addition, we assume that the general deterrent effect is zero. Then

Expression 5 becomes the following for letters and visits respectively:

∆WL = (1.17 − 1) ∗ 322 − 1.17 ∗ 4 − 8 = 42.1 0,

∆WV = (1.17 − 1) ∗ 22, 491 − 1.17 ∗ 220 − 440 = 3, 126 0.

(6)

(7)

In these calculations, additional letters and visits each enhance welfare. To be sure, these

illustrative calculations depend on arbitrary assumptions about the social value of marginal

revenue, the marginal compliance cost, and the general deterrent effect of expanding enforcement instruments. The calculations do, though, illustrate the difference between subjecting

enforcement initiatives to a net-revenue-maximizing criterion and subjecting enforcement

initiatives to a welfare-maximizing criterion.

7. Conclusion

This paper uses a randomized experiment conducted in partnership with the IRS to

estimate both the change in employment payroll and withholding taxes remitted caused

by receiving a letter noting that the firm’s deposits have decreased, discussing the firm’s

deposit responsibility and potential penalties, and providing general information, or caused

by an in-person visit from an IRS Revenue Officer. In addition, we estimate the network, or

spillover, effects on taxes remitted by firms linked to letter and visit recipients by geography,

tax preparers, and parent-subsidiary relationships. To our knowledge, no previous research

has investigated the effects of tax enforcement on firms sharing a tax preparer with the

treated firm or on the treated firm’s parent or subsidiaries.

We find large, immediate effects of in-person visits on tax remitted that persist for at

least four quarters and are transmitted through tax-preparer networks. Although the perfirm-link tax-preparer network effects of the visit are much smaller than the direct effects,

their aggregate effect is 1.2 times the size of the direct effect. We find that letters increase

the likelihood that firms remit any tax by three percentage points, but this effect lasts only

one quarter, and the effect of the letter on tax remitted overall is not statistically significant.

There is no evidence of network effects of the letter. Given the empirical results, both visits

and letters pass a net-revenue-increasing criterion. With a fixed tax authority budget, net

revenue from one additional dollar of resources spent on in-person visits is slightly higher

than net revenue from an additional dollar spent to send letters. With some additional

assumptions, both treatments also easily pass a welfare-increasing criterion.

Allingham, M. G., Sandmo, A., 1972. Income tax evasion: A theoretical analysis. Journal of Public Economics 1 (3–4), 323–338.

Alstadster, A., Kopczuk, W., Telle, K., October 2018. Social networks and tax avoidance: Evidence from a

well-defined norwegian tax shelter. Working Paper 25191, National Bureau of Economic Research.

URL http://www.nber.org/papers/w25191

Andrei, A., Comer, K., Koehler, M., 02 2014. An agent-based model of network effects on tax compliance

and evasion. Journal of Economic Psychology 40, 119–133.

Bhargava, S., Manoli, D., 2015. Psychological frictions and the incomplete take-up of social benefits: Evidence from an irs field experiment. American Economic Review 105 (11), 3489–3529.

Blumenthal, M., Christian, C., Slemrod, J., 2001. Do normative appeals affect tax compliance? evidence

from a controlled experiment in minnesota. National Tax Journal, 125–138.

degl’Innocenti, D. G., Rablen, M. D., 2019. Tax evasion on a social network. Tech. rep., University of

Sheffield Economic Research Paper.

Diehl, V., Stein, H., Hummel, M., Zollinger, R., Connors, J. M., 2003. Hodgkin’s lymphoma: Biology and

treatment strategies for primary, refractory, and relapsed disease. ASH Education Program Book 2003 (1),

225–247.

Drago, F., Mengel, F., Traxler, C., 2015. Compliance behavior in networks: Evidence from a field experiment.

Tech. rep., IZA Discussion Paper.

Guyton, J., Langetieg, P., Manoli, D., Payne, M., Schafer, B., Sebastiani, M., 2017. Reminders and recidivism: Using administrative data to characterize nonfilers and conduct eitc outreach. American Economic

Review 107 (5), 471–75.

Hallsworth, M., 2014. The use of field experiments to increase tax compliance. Oxford Review of Economic

Policy 30 (4), 658–679.

Heckman, J. J., Smith, J. A., 1999. The pre-programme earnings dip and the determinants of participation in

a social programme. implications for simple programme evaluation strategies. Economic Journal 109 (457),

313–348.

Keen, M., Slemrod, J., 2017. Optimal tax administration. Journal of Public Economics 152, 133–142.

Klassen, K. J., Lisowsky, P., Mescall, D., 2015. The role of auditors, non-auditors, and internal tax departments in corporate tax aggressiveness. Accounting Review 91 (1), 179–205.

Klepper, S., Mazur, M., Nagin, D., 1991. Expert intermediaries and legal compliance: The case of tax

preparers. Journal of Law and Economics 34 (1), 205–229.

Mayshar, J., 1991. Taxation with costly administration. Scandinavian Journal of Economics 93, 75–88.

Meiselman, B. S., 2018. Ghostbusting in detroit: Evidence on nonfilers from a controlled field experiment.

Journal of Public Economics 158, 180–193.

URL http://www.sciencedirect.com/science/article/pii/S0047272718300057

Ortega, D., Scartascini, C., 2015. Who’s calling: The effect of phone calls as a deterrence mechanism. Tech.

rep., Inter-American Development Bank.

Ortega, D., Scartascini, C., 2018. Don’t blame the messenger: A field experiment on delivery methods for

increasing tax compliance. Tech. rep., Inter-American Development Bank.

Perez-Truglia, R., Troiano, U., 2018. Shaming tax delinquents. Journal of Public Economics 167, 120–137.

URL http://www.sciencedirect.com/science/article/pii/S0047272718301762

Pomeranz, D., 2015. No taxation without information: Deterrence and self-enforcement in the value added

tax. American Economic Review 105 (8), 2539–2569.

Rincke, J., Traxler, C., 2011. Enforcement spillovers. Review of Economics and Statistics 93, 1224–1234.

Slemrod, J., forthcoming. Tax compliance and enforcement. Journal of Economic Literature.

Slemrod, J., Blumenthal, M., Christian, C., 2001. Taxpayer response to an increased probability of audit:

Evidence from a controlled experiment in minnesota. Journal of Public Economics 79 (3), 455–483.

URL http://www.sciencedirect.com/science/article/pii/S0047272799001073

Slemrod, J., Yitzhaki, S., 1987. The optimal size of a tax collection agency. Scandinavian Journal of Economics 89, 183–192.

USPS, 2016. Faq. https://faq.usps.com/, accessed October 30, 2016.

Zonder, J. A., Pemberton, P., Brandt, H., Mohamed, A. N., Schiffer, C. A., 2003. The effect of dose increase

of imatinib mesylate in patients with chronic or accelerated phase chronic myelogenous leukemia with

inadequate hematologic or cytogenetic response to initial treatment. Clinical Cancer Research 9 (6),

2092–2097.

Table A.1: Direct Effect of Letter: All Quarters

Tax Remitted

Letter * Four Quarters Pre

273 (571)

Letter * Three Quarters Pre

69.7 (570)

Letter * Two Quarters Pre

-194 (554)

Letter * One Quarter Pre

-337 (471)

Letter * One Quarter Post

94.4 (382)

Letter * Two Quarters Post

-112 (438)

Letter * Three Quarters Post

163 (459)

Letter * Four Quarters Post

177 (459)

Quarter Fixed Effects

Yes

Number of Firm-Quarters

109,548

R-Squared

0.0281

Any Tax Remitted

0.0210* (0.0120)

0.00317 (0.0119)

0.000663 (0.0116)

-0.0172 (0.0112)

0.0302*** (0.0110)

0.0112 (0.0118)

0.0158 (0.0122)

0.0136 (0.0125)

Yes

109,548

0.0513

Log(Tax Remitted)

-0.00170 (0.0368)

0.0189 (0.0369)

0.0142 (0.0361)

0.0562 (0.0350)

0.00476 (0.0358)

-0.0171 (0.0359)

-0.0160 (0.0376)

-0.00353 (0.0384)

Yes

77,051

0.0220

* p < 0.1, ** p < 0.05, *** p < 0.01

Table A.2: Direct Effect of Visit: All Quarters

Tax Remitted Any Tax Remitted

Visit * Four Quarters Pre

52.7 (585)

0.00234 (0.0122)

Visit * Three Quarters Pre

92.1 (572)

-0.00707 (0.0119)

Visit * Two Quarters Pre

-393 (556)

-0.00559 (0.0117)

Visit * One Quarter Pre

-299 (465)

-0.0149 (0.0112)

Visit * One Quarter Post

3,686*** (399) 0.129*** (0.0113)

Visit * Two Quarters Post

2,726*** (438) 0.104*** (0.0120)

Visit * Three Quarters Post 2,169*** (451) 0.0803*** (0.0122)

Visit * Four Quarters Post

1,652*** (448) 0.0694*** (0.0126)

Quarter Fixed Effects

Yes

Number of Firm-Quarters

109,548

R-Squared

0.0281

0.0513

* p < 0.1, ** p < 0.05, *** p < 0.01

Log(Tax Remitted)

-0.00842 (0.0368)

0.0174 (0.0366)

-0.0145 (0.0359)

-0.00144 (0.0353)

0.132*** (0.0348)

0.0344 (0.0349)

0.0309 (0.0362)

0.0197 (0.0364)

Yes

77,051

0.0220

Table A.3: Preparer Letter Network Effects with Pre-Treatment Quarters as Placebo Test

Tax Remitted

304**

(132)

71.9

(95.2)

-15.0

(109)

13.8

(102)

35.3

(72.2)

27.0

(88.6)

73.0

(92.6)

146

(103)

Yes

Preparer Links to Letter Firms *

Four Quarters Pre

Preparer Links to Letter Firms *

Three Quarters Pre

Preparer Links to Letter Firms *

Two Quarters Pre

Preparer Links to Letter Firms *

One Quarter Pre

Preparer Links to Letter Firms *

One Quarter Post

Preparer Links to Letter Firms *

Two Quarters Post

Preparer Links to Letter Firms *

Three Quarters Post

Preparer Links to Letter Firms *

Four Quarters Post

Firm Fixed Effects

Quarter Fixed Effects

Quarter * Total Preparer Links

to Alert C Fixed Effects

Preparer Clusters

10,219

Number of Firm-Quarters

1,796,994

R-Squared

0.00361

Any Tax Remitted

-0.0000760

(0.00313)

-0.000335

(0.00260)

0.0000893

(0.00185)

-0.000667

(0.00145)

-0.00183

(0.00152)

-0.000140

(0.00170)

-0.00201

(0.00216)

-0.00111

(0.00238)

Yes

Log(Tax Remitted)

0.00676

(0.00521)

-0.00548

(0.00663)

-0.000276

(0.00469)

-0.000396

(0.00464)

-0.00437

(0.00624)

-0.0000449

(0.00428)

0.00264

(0.00434)

0.0109**

(0.00455)

Yes

10,219

1,796,994

0.00500

9,357

1,193,501

0.0120

Notes: Standard errors (in parentheses) clustered by Preparer. * p < 0.1 ** p < 0.05 ***

p < 0.01. Probability results from linear probability model.

Table A.4: Preparer Visit Network Effects with Pre-Treatment Quarters as Placebo Test

Tax Remitted

197

(132)

-9.41

(78.7)

-101

(89.4)

-40.3

(89.0)

85.5

(61.3)

52.3

(81.9)

156*

(88.3)

243***

(94.1)

Yes

Preparer Links to Visit Firms *

Four Quarters Pre

Preparer Links to Visit Firms *

Three Quarters Pre

Preparer Links to Visit Firms *

Two Quarters Pre

Preparer Links to Visit Firms *

One Quarter Pre

Preparer Links to Visit Firms *

One Quarter Post

Preparer Links to Visit Firms *

Two Quarters Post

Preparer Links to Visit Firms *

Three Quarters Post

Preparer Links to Visit Firms *

Four Quarters Post

Firm Fixed Effects

Quarter Fixed Effects

Quarter * Total Preparer Links

to Alert C Fixed Effects

Preparer Clusters

10,219

Number of Firm-Quarters

1,796,994

R-Squared

0.00361

Any Tax Remitted Log(Tax Remitted)

0.000657

0.00683

(0.00238)

(0.00428)

-0.000457

-0.00378

(0.00216)

(0.00554)

-0.00160

-0.00101

(0.00176)

(0.00393)

-0.000983

0.00523

(0.00121)

(0.00375)

0.00188

-0.00236

(0.00122)

(0.00505)

0.000315

-0.00328

(0.00149)

(0.00353)

0.00123

0.00112

(0.00189)

(0.00368)

0.00162

0.000830

(0.00224)

(0.00357)

Yes

10,219

1,796,994

0.00500

9,357

1,193,501

0.0120

Notes: Standard errors (in parentheses) clustered by Preparer. * p < 0.1 ** p < 0.05 ***

p < 0.01. Probability results from linear probability model.

Table A.5: Preparation Firm Letter Network Effects with Pre-Treatment Quarters as Placebo Test

Tax Remitted

Preparation Firm Links to Letter 117

Firms * Four Quarters Pre

(173)

Preparation Firm Links to Letter 107

Firms * Three Quarters Pre

(94.7)

Preparation Firm Links to Letter 24.2

Firms * Two Quarters Pre

(99.3)

Preparation Firm Links to Letter 65.6

Firms * One Quarter Pre

(95.5)

Preparation Firm Links to Letter -5.67

Firms * One Quarter Post

(103)

Preparation Firm Links to Letter -116

Firms * Two Quarters Post

(102)

Preparation Firm Links to Letter -67.5

Firms * Three Quarters Post

(91.8)

Preparation Firm Links to Letter -132*

Firms * Four Quarters Post

(80.2)

Firm Fixed Effects

Yes

Quarter Fixed Effects

Yes

Quarter * Total Preparation Firm Yes

Links to Alert C Fixed Effects

Preparation Firm Clusters

9,759

Number of Firm-Quarters

3,563,361

R-Squared

0.00502

Any Tax Remitted

-0.000384

(0.00206)

-0.000913

(0.00186)

0.000699

(0.00141)

0.0000650

(0.001000)

-0.00109

(0.00107)

-0.000442

(0.00119)

-0.00115

(0.00141)

-0.00149

(0.00168)

Yes

Log(Tax Remitted)

0.00364

(0.00436)

0.00269

(0.00403)

0.000498

(0.00346)

0.00239

(0.00286)

-0.000987

(0.00380)

-0.00361

(0.00317)

0.000720

(0.00331)

0.000841

(0.00300)

Yes

9,759

3,563,361

0.00620

9,053

2,468,149

0.0131

Notes: Standard errors (in parentheses) clustered by Preparation Firm. * p < 0.1 ** p < 0.05

*** p < 0.01. Probability results from linear probability model.

Table A.6: Preparation Firm Visit Network Effects with Pre-Treatment Quarters as Placebo Test

Tax Remitted

Preparation Firm Links to Visit

128

Firms * Four Quarters Pre

(169)

Preparation Firm Links to Visit

22.9

Firms * Three Quarters Pre

(92.7)

Preparation Firm Links to Visit

-32.4

Firms * Two Quarters Pre

(86.2)

Preparation Firm Links to Visit

16.9

Firms * One Quarter Pre

(82.2)

Preparation Firm Links to Visit

2.47

Firms * One Quarter Post

(110)

Preparation Firm Links to Visit

-52.7

Firms * Two Quarters Post

(89.7)

Preparation Firm Links to Visit

65.9

Firms * Three Quarters Post

(79.9)

Preparation Firm Links to Visit

22.9

Firms * Four Quarters Post

(79.8)

Firm Fixed Effects

Yes

Quarter Fixed Effects

Yes

Quarter * Total Preparation Firm Yes

Links to Alert C Fixed Effects

Preparation Firm Clusters

9,759

Number of Firm-Quarters

3,563,361

R-Squared

0.00502

Any Tax Remitted

0.00311

(0.00241)

-0.000201

(0.00167)

-0.000834

(0.000974)

-0.00135

(0.00102)

-0.000349

(0.000943)

-0.000465

(0.00117)

0.000129

(0.00148)

0.000206

(0.00185)

Yes

Log(Tax Remitted)

0.000758

(0.00377)

-0.00203

(0.00351)

-0.00342

(0.00308)

0.00256

(0.00210)

-0.00154

(0.00309)

-0.00331

(0.00248)

0.00189

(0.00225)

0.00246

(0.00222)

Yes

9,759

3,563,361

0.00620

9,053

2,468,149

0.0131

Notes: Standard errors (in parentheses) clustered by Preparation Firm. * p < 0.1 ** p < 0.05

*** p < 0.01. Probability results from linear probability model.

Table A.7: ZIP+4 Letter Network Effects with Pre-Treatment Quarters as Placebo Test

ZIP+4 Links to Letter Firms

* Four Quarters Pre

ZIP+4 Links to Letter Firms

* Three Quarters Pre

ZIP+4 Links to Letter Firms

* Two Quarters Pre

ZIP+4 Links to Letter Firms

* One Quarter Pre

ZIP+4 Links to Letter Firms

* One Quarter Post

ZIP+4 Links to Letter Firms

* Two Quarters Post

ZIP+4 Links to Letter Firms

* Three Quarters Post

ZIP+4 Links to Letter Firms

* Four Quarters Post

Firm Fixed Effects

Quarter Fixed Effects

Quarter * Total ZIP+4 Links

to Alert C Fixed Effects

ZIP+4 Clusters

Number of Firm-Quarters

R-Squared

Tax Remitted

-806

(700)

-436

(570)

-422

(459)

-1,044**

(417)

59.5

(527)

-117

(495)

-545

(526)

-255

(481)

Yes

Any Tax Remitted

-0.0132*

(0.00705)

-0.0159***

(0.00585)

-0.00867*

(0.00453)

-0.00929**

(0.00384)

-0.00469

(0.00619)

-0.00748

(0.00536)

-0.0139**

(0.00628)

-0.0123

(0.00770)

Yes

Log(Tax Remitted)

-0.000138

(0.0132)

0.00427

(0.0164)

0.0147

(0.0122)

-0.0167

(0.0113)

0.0217

(0.0156)

0.00782

(0.0120)

-0.00767

(0.0126)

0.0129

(0.0131)

Yes

5,916

290,745

0.00326

5,916

290,745

0.0104

5,476

201,828

0.00891

Notes: Standard errors (in parentheses) clustered by ZIP+4. * p < 0.1 ** p < 0.05 ***

p < 0.01. Probability results from linear probability model.

Table A.8: ZIP+4 Visit Network Effects with Pre-Treatment Quarters as Placebo Test

ZIP+4 Links to Visit Firms

* Four Quarters Pre

ZIP+4 Links to Visit Firms

* Three Quarters Pre

ZIP+4 Links to Visit Firms

* Two Quarters Pre

ZIP+4 Links to Visit Firms

* One Quarter Pre

ZIP+4 Links to Visit Firms

* One Quarter Post

ZIP+4 Links to Visit Firms

* Two Quarters Post

ZIP+4 Links to Visit Firms

* Three Quarters Post

ZIP+4 Links to Visit Firms

* Four Quarters Post

Firm Fixed Effects

Quarter Fixed Effects

Quarter * Total ZIP+4 Links

to Alert C Fixed Effects

ZIP+4 Clusters

Number of Firm-Quarters

R-Squared

Tax Remitted

681

(568)

962

(904)

435

(540)

-10.4

(493)

847

(784)

319

(563)

-41.5

(568)

84.9

(660)

Yes

Any Tax Remitted

0.00438

(0.00689)

-0.00148

(0.00621)

0.00511

(0.00500)

-0.00614*

(0.00328)

0.00158

(0.00540)

0.00225

(0.00554)

-0.00302

(0.00631)

-0.000572

(0.00734)

Yes

Log(Tax Remitted)

0.000777

(0.0126)

0.0248

(0.0184)

0.0254*

(0.0133)

0.0155

(0.0127)

0.0326**

(0.0162)

0.0131

(0.0131)

0.00650

(0.0134)

0.0128

(0.0127)

Yes

5,916

290,745

0.00326

5,916

290,745

0.0104

5,476

201,828

0.00891

Notes: Standard errors (in parentheses) clustered by ZIP+4. * p < 0.1 ** p < 0.05 ***

p < 0.01. Probability results from linear probability model.

Table A.9: ZIP Code Letter Network Effects with Pre-Treatment Quarters as Placebo Test

Tax Remitted

ZIP Code Links to Letter Firms -48.4

* Four Quarters Pre

(74.4)

ZIP Code Links to Letter Firms 47.6

* Three Quarters Pre

(75.3)

ZIP Code Links to Letter Firms -27.4

* Two Quarters Pre

(61.8)

ZIP Code Links to Letter Firms 17.5

* One Quarter Pre

(55.5)

ZIP Code Links to Letter Firms 21.1

* One Quarter Post

(66.0)

ZIP Code Links to Letter Firms -7.65

* Two Quarters Post

(61.2)

ZIP Code Links to Letter Firms 48.4

* Three Quarters Post

(69.2)

ZIP Code Links to Letter Firms 2.79

* Four Quarters Post

(67.3)

Firm Fixed Effects

Yes

Quarter Fixed Effects

Yes

Quarter * Total ZIP Code Links Yes

to Alert C Fixed Effects

ZIP Code Clusters

7,046

Number of Firm-Quarters

3,181,959

R-Squared

0.00170

Any Tax Remitted

0.0000352

(0.00104)

0.000554

(0.000946)

-0.000641

(0.000825)

0.000703

(0.000625)

-0.0000667

(0.000702)

0.000433

(0.000772)

0.000872

(0.000883)

0.00175*

(0.000967)

Yes

Log(Tax Remitted)

-0.00130

(0.00225)

-0.000593

(0.00256)

-0.000523

(0.00193)

-0.00151

(0.00186)

0.00299

(0.00219)

0.00342*

(0.00201)

0.00297

(0.00223)

0.00357

(0.00225)

Yes

7,046

3,181,959

0.00624

7,008

2,159,992

0.00949

Notes: Standard errors (in parentheses) clustered by ZIP Code. * p < 0.1 ** p < 0.05 ***

p < 0.01. Probability results from linear probability model.

Table A.10: ZIP Code Visit Network Effects with Pre-Treatment Quarters as Placebo Test

Tax Remitted

ZIP Code Links to Visit Firms

25.3

* Four Quarters Pre

(73.3)

ZIP Code Links to Visit Firms

53.7

* Three Quarters Pre

(73.5)

ZIP Code Links to Visit Firms

7.88

* Two Quarters Pre

(60.7)

ZIP Code Links to Visit Firms

59.7

* One Quarter Pre

(56.5)

ZIP Code Links to Visit Firms

-14.9

* One Quarter Post

(68.9)

ZIP Code Links to Visit Firms

-31.4

* Two Quarters Post

(66.7)

ZIP Code Links to Visit Firms

-0.644

* Three Quarters Post

(77.9)

ZIP Code Links to Visit Firms

-35.3

* Four Quarters Post

(67.1)

Firm Fixed Effects

Yes

Quarter Fixed Effects

Yes

Quarter * Total ZIP Code Links Yes

to Alert C Fixed Effects

ZIP Code Clusters

7,046

Number of Firm-Quarters

3,181,959

R-Squared

0.00170

Any Tax Remitted

-0.000526

(0.00113)

0.000587

(0.00100)

-0.000323

(0.000849)

-0.000222

(0.000686)

0.000339

(0.000740)

-0.000198

(0.000798)

-0.00000113

(0.000914)

0.000122

(0.000984)

Yes

Log(Tax Remitted)

-0.00101

(0.00226)

0.000942

(0.00258)

0.00171

(0.00200)

0.00210

(0.00194)

0.00368*

(0.00219)

0.00412**

(0.00203)

0.00355

(0.00225)

0.00666***

(0.00226)

Yes

7,046

3,181,959

0.00624

7,008

2,159,992

0.00949

Notes: Standard errors (in parentheses) clustered by ZIP Code. * p < 0.1 ** p < 0.05 ***

p < 0.01. Probability results from linear probability model.

Table A.11: Effect of Letter on Parent: All Quarters

Subsidiary Letter *

Four Quarters Pre

Subsidiary Letter *

Three Quarters Pre

Subsidiary Letter *

Two Quarters Pre

Subsidiary Letter *

One Quarter Pre

Subsidiary Letter *

One Quarter Post

Subsidiary Letter *

Two Quarters Post

Subsidiary Letter *

Three Quarters Post

Subsidiary Letter *

Four Quarters Post

Subsidiary Visit *

Quarter Fixed Effects

Parent Clusters

Observations

R-Squared

Tax Remitted

1,669,376

(1,226,347)

1,047,398

(2,112,822)

1,420,764

(1,207,557)

429,299

(702,842)

-2,862,994

(1,774,428)

-2,431,920

(1,535,133)

928,559

(944,069)

758,309

(1,143,241)

-1,647,927

Yes

684

0.0201

Any Tax Remitted

0.123*

(0.0709)

0.123*

(0.0709)

0.0777

(0.0561)

0.0777

(0.0561)

0.0455

(0.0456)

0.0455

(0.0456)

0.0455

(0.0456)

3.68e-13

(0.000000566)

0.0455

Yes

684

0.0365

Log(Tax Remitted)

-0.947

(0.721)

-1.17

(0.899)

-1.09

(0.734)

-1.30

(0.916)

-0.955

(0.682)

-1.00

(0.648)

-0.793

(0.659)

-0.132

(0.293)

-0.779

Yes

253

0.0512

Notes: Standard errors (in parentheses) clustered by parent. * p < 0.1 ** p < 0.05

*** p < 0.01. Any tax remitted results from linear probability model.

Table A.12: Effect of Visit on Parent: All Quarters

Subsidiary Visit *

Four Quarters Pre

Subsidiary Visit *

Three Quarters Pre

Subsidiary Visit *

Two Quarters Pre

Subsidiary Visit *

One Quarter Pre

Subsidiary Visit *

One Quarter Post

Subsidiary Visit *

Two Quarters Post

Subsidiary Visit *

Three Quarters Post

Subsidiary Visit *

Four Quarters Post

Quarter Fixed Effects

Parent Clusters

Observations

R-Squared

Tax Remitted

1,496,364

(1,239,504)

100,974

(1,659,865)

1,565,585

(1,226,307)

1,509,764

(1,134,349)

-1,647,927

(2,035,611)

-2,313,524

(1,534,115)

2,282,318*

(1,243,672)

150,468

(1,199,528)

Yes

684

0.0201

Any Tax Remitted

0.0909

(0.0629)

0.0909

(0.0629)

0.0455

(0.0456)

0.0455

(0.0456)

0.0455

(0.0456)

0.00198

(0.0631)

0.00198

(0.0631)

-0.0435

(0.0437)

Yes

684

0.0365

Log(Tax Remitted)

-0.299

(0.690)

-0.674

(0.740)

-0.301

(0.705)

-0.190

(0.627)

-0.779

(0.668)

-0.614

(0.751)

-0.502

(0.784)

-0.361

(0.444)

Yes

253

0.0512

Notes: Standard errors (in parentheses) clustered by parent. * p < 0.1 ** p < 0.05

*** p < 0.01. Any tax remitted results from linear probability model.

Table A.13: Effect of Parent Letter on Subsidiary: All Quarters

Tax Remitted

Parent Letter *

21,784

Four Quarters Pre

(22,310)

Parent Letter *

234,560

Three Quarters Pre

(171,417)

Parent Letter *

91,092**

Two Quarters Pre

(36,695)

Parent Letter *

40,538

One Quarter Pre

(39,979)

Parent Letter *

293,983

One Quarter Post

(216,372)

Parent Letter *

259,715*

Two Quarters Post

(153,028)

Parent Letter *

112,997

Three Quarters Post

(95,483)

Parent Letter *

103,595

Four Quarters Post

(80,073)

Quarter Fixed Effects

Yes

Parent Clusters

Number of Firm-Quarters 3,573

R-Squared

0.114

Any Tax Remitted

0.0641

(0.0554)

0.0765

(0.0529)

0.0931*

(0.0508)

0.0176

(0.0236)

0.0259

(0.0236)

0.00521

(0.0357)

-0.0165

(0.0414)

-0.0124

(0.0412)

Yes

3,573

0.0720

Log(Tax Remitted)

1.18

(0.982)

1.03

(1.04)

0.0251

(0.247)

0.234

(0.334)

0.140

(0.300)

1.04

(0.935)

0.999

(0.860)

0.885

(0.858)

Yes

768

0.263

Notes: Standard errors (in parentheses) clustered at the parent level. * p < 0.1 **

p < 0.05 *** p < 0.01. Any tax remitted results from linear probability model.

Table A.14: Effect of Parent Visit on Subsidiary: All Quarters

4 Quarters Pre * Parent Visit

3 Quarters Pre * Parent Visit

2 Quarters Pre * Parent Visit

1 Quarters Pre * Parent Visit

Parent Visit *

One Quarter Post

Parent Visit *

Two Quarters Post

Parent Visit *

Three Quarters Post

Parent Visit *

Four Quarters Post

Quarter Fixed Effects

Parent Clusters

Number of Firm-Quarters

R-Squared

Tax Remitted

-455,328***

(94,688)

354,676***

(64,243)

-247,099**

(105,832)

-289,645*

(172,415)

1,065,494***

(314,180)

83,654

(658,621)

402,655

(784,156)

-110,721

(644,678)

Yes

3,573

0.114

Any Tax Remitted Log(Tax Remitted)

0.0390

0.949

(0.0498)

(0.973)

0.0514

0.665

(0.0471)

(1.01)

0.0679

-0.292

(0.0447)

(0.220)

-0.00413

0.573**

(0.00464)

(0.250)

-0.00504

0.205

(0.00556)

(0.206)

-0.0532

1.24

(0.0358)

(0.953)

-0.0624*

1.37

(0.0341)

(0.882)

-0.0674

1.16

(0.0404)

(0.859)

Yes

3,573

768

0.0720

0.263

Notes: Standard errors (in parentheses) clustered at the parent level. * p < 0.1 ** p < 0.05

*** p < 0.01. Any tax remitted results from linear probability model.

Figure A.1: Letter

Department of the Treasury

Internal Revenue Service

Date:

Dear

Your federal tax deposits

We understand federal tax deposit requirements may be confusing and the resulting penalties can be significant.

With this in mind, we reviewed your federal tax deposit history and your deposits appear to have decreased.

This may be due to a change in your payroll, because you are a new business owner and are not familiar with

deposit requirements, or it may be due to other factors.

Your responsibility as an employer

You, as the employer, have the responsibility of withholding trust fund taxes from employees' paychecks.

Trust fund tax is money withheld, by an employer, from employees' wages for FICA (social security and

Medicare tax) and income tax held in trust until paid to the Department of Treasury. This money must be paid

periodically to the Treasury by making federal tax deposits.

What you need to do

Please tell us about the decrease in your deposits so that your account can be updated. You may do one of the

following:

• Call the IRS at 1-866-897-4289 Monday through Friday, 8 AM to 8 PM eastern time, or

• Complete and return the enclosed Form 14143, Reason for Decrease to Federal Tax Deposit.

Penalty for failing to pay

Individuals who are required to account for and pay these taxes for the business may be personally liable for a

penalty if the business fails to pay trust fund taxes. The penalty is equal to the amount of the unpaid trust fund

taxes that the business owes the Treasury. For additional information, see the enclosed Notice 784, Could You

be Personally Liable for Certain Unpaid Federal Taxes?

Penalty for failing to pay timely

If you do not pay these taxes on time or you do not include the required payment with your Form 941,

Employer's Quarterly Federal Tax Return, interest and penalties will be assessed on any unpaid balance.

Additionally, penalties of up to 15% of the amount not deposited may also be assessed, depending on the

number of days the federal tax deposits are late.

Letter 4594 (Rev. 10-2013)

Catalog Number 54939M

Penalty for failing to file your return timely

In the event you are unable to pay your taxes timely, it is imperative to file your Form 941 Employer's

Quarterly Federal Tax Return timely. If the return is filed after the due date, the law provides penalties for

filing late unless there is a reasonable cause for the delay.

Additional information

For further information, please see Publication 15, Circular E, Employer's Tax Guide, or the Internal Revenue

Service's small business employment tax section. Both are available at www.irs.gov. The employment tax

section of the small business web page can be accessed by selecting "Businesses" at the home page, then

selecting "Employment Taxes" under Business Topics.

Thank you for taking the time to keep up with your employment tax obligations.

Program Manager

Centralized Processing Operation

Philadelphia Compliance Services

Enclosures:

Form 14143

Notice 784

Letter 4594 (Rev. 10-2013)

Catalog Number 54939M

This is a copy of a public record, reproduced as it was published. It is not legal advice, and it may not be the version a court would rely on. Check the official source before you cite it.