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The research trend of security and privacy in digital payment.

1. Introduction

2. materials and methods, 3. results and discussion, 3.1. leading countries, 3.2. leading journals, 3.3. most-cited studies, 3.4. co-citation analysis, 4. co-citation network, 4.1. cluster 1 (red), 4.2. cluster 2 (green), 4.3. cluster 3 (blue), 5. discussion and future research, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Sahi, A.M.; Khalid, H.; Abbas, A.F.; Zedan, K.; Khatib, S.F.A.; Al Amosh, H. The Research Trend of Security and Privacy in Digital Payment. Informatics 2022 , 9 , 32. https://doi.org/10.3390/informatics9020032

Sahi AM, Khalid H, Abbas AF, Zedan K, Khatib SFA, Al Amosh H. The Research Trend of Security and Privacy in Digital Payment. Informatics . 2022; 9(2):32. https://doi.org/10.3390/informatics9020032

Sahi, Alaa Mahdi, Haliyana Khalid, Alhamzah F. Abbas, Khaled Zedan, Saleh F. A. Khatib, and Hamzeh Al Amosh. 2022. "The Research Trend of Security and Privacy in Digital Payment" Informatics 9, no. 2: 32. https://doi.org/10.3390/informatics9020032

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64 Key Online Payments Statistics: 2021 Market Share & Data Analysis

A new version of this article, featuring the latest data and statistics is available. Check out our report on Key Online Payment Software Statistics for 2022 .

The sector for online payments is arguably one of the most dynamic markets in the financial industry. The industry has evolved so fast that the projection into its future is purely enthralling. Technology has driven change in the payment space, sweeping past cards, cheques, and cash, into a new era dominated by cryptocurrencies, e-wallets, and one-click online payments. And, because of the COVID situation, we have witnessed a great surge in the popularity of online payments worldwide.

Indeed, cashless payments are becoming an option, impelled by tech innovations from both newcomers and dominant FinTech firms. The digital disruptions in the payment industry have led to the advent of the best payment gateway software , which helps streamline online transactions.

Like most business processes, online payments are constantly changing, and companies must adjust to the latest development to remain competitive. To help you stay abreast of the industry, we have compiled the following online payment statistics and industry insights. Similarly, if you’re a vendor, having these online payment data at your fingertips will help you configure your system better to match customer needs.

Online Payments Statistics Table of Contents

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• Statistics on The Rise of Generation Z

Voice Commerce Statistics

P2p mobile payment statistics, omnichannel payment statistics, customer experience (ux) statistics, mobile wallet statistics, ai and machine learning statistics, covid-19 and online payment statistics.

The year 2020 was a wild one. The outbreak of the pandemic has challenged the resolution of economies and the way the world went about its business. Online payment has been popular during the pre-pandemic era. However, because of social distancing regulations, Digital Commerce and Mobile POS Payment became even more popular around the world.

US consumers deliberately ordered online because of the pandemic

• 31% of US customers have deliberately ordered restaurant delivery/takeout online because of the COVID-19 pandemic. (Statista COVID-19 Barometer, 2020)
• 27% of US customers deliberately ordered hygiene products online because of the pandemic. (Statista COVID-19 Barometer, 2020)
• 26% deliberately ordered clothing online because of the pandemic. The same percentage goes for household cleaning products. (Statista COVID-19 Barometer, 2020)
• 24% ordered food and drink delivery from the supermarket online because of the pandemic. (Statista COVID-19 Barometer, 2020)
• 21% ordered health products online because of the pandemic.(Statista COVID-19 Barometer, 2020)

US Online Payments by Type 2020

• 55% of US consumers used a credit card for online payments in the past year. (Statista Global Survey, 2020)
• 52% of US consumers used a debit card in the last 12 months. (Statista Global Survey, 2020)
• 27% of US consumers used online gateways like Amazon Pay and PayPal. (Statista Global Survey, 2020)
• Only 25% of US consumers used direct debit for their online transactions. (Statista Global Survey, 2020)

Transaction Values of Digital Payments Worldwide in 2020

• $5.2 trillion – the global transaction value of digital payments in 2020. (Statista Market Outlook, 2021)
• 63% of the total digital payment transaction value came from Digital Commerce. (Statista Market Outlook, 2021)
• Mobile POS payments amount to $2 trillion which is around 30% of digital payments value. (Statista Market Outlook, 2021)
• In 2021, the forecasts show that the total transaction value by the end of the year will be $6.68 trillion. $4.195 trillion will come from Digital Commerce and $2.489 trillion will come from Mobile POS Payments. (Statista Market Outlook, 2021)

Online Payment Transaction Value 2020 to 2025

• From 2020 through 2025, transaction value will likely grow 16.3% in Europe, 15.2% in the US, and $11.2 in China. (Statista Market Outlook, 2021)
• In 2020, the UK is the biggest Digital Payments market in Europe with $218.3 billion. (Statista Market Outlook, 2021)
• Most digital customers are in China with 926.1 million. There were 256 million in the US and 480.9 million in Europe. (Statista Market Outlook, 2021)
• The same goes with Mobile POS Systems. China had 555.4 million users in 2020; Europe had 480.9 million, and the US had 256 million. (Statista Market Outlook, 2021)
• By 2025, China is projected to have 1.2 billion Digital Commerce users; the US will have 291.2 million, and Europe will have 569.8 million users. (Statista Market Outlook, 2021)
• By 2025, Mobile POS Payment users in China are projected to be 618.6 million; in the US will be 85.6 million, and Europe will have 174.6 million. (Statista Market Outlook, 2021)
• The US, however, has the highest transaction value among these regions in Digital Commerce in 2020 with an average of $7,012 per user. Europe’s average is at $2,084 per user, while China’s average is at $2,060. (Statista Market Outlook, 2021)
• The same thing goes when it comes to Mobile POS Payments. The average transaction value per user in the US is $2,060 in 2020. China’s is $1,460, while Europe’s is $1,402. (Statista Market Outlook, 2021)
• By 2025, it is predicted that the average value per user in the US will be $11,755 for Digital Commerce. Europe’s will be at $4,736, while China’s average transaction value will be $3,261. (Statista Market Outlook, 2021)
• Also, by 2025, the average transaction value per user in Mobile POS Payments will be $3,261 for the US. Europe’s average will be $1,982, while China will be at $1,815. (Statista Market Outlook, 2021)

It isn’t just the pandemic that’s accelerating this trend. The rise of digital natives, the Gen Zers, has influenced the popularity of digital payments as well.

Source: Statista Digital Market Outlook, 2021

Statistics On The Rise Of Generation Z

The emergence of Generation Z represents a powerful spark of revolution in the online payment space. The oldest members of this cohort are just young adults or teens, and the group was projected to represent 40% of total US consumers by 2020 . Labeled as “screen addicts,” or “view now, buy now consumers,” this group has taken over from the much-hyped Millennials, and it’s showing every sign that it is the future customer base for the online payment market.

Natively digital, Gen Z-ers are mobile mavens that crave immediacy and are socially dependent on Facebook, Instagram, Snapchat, Apple, Amazon, and Google. The generation gravitates to digital tools like payment, bill, and expense management applications, and generally, demand a highly-relevant and personalized experience.

• A third of Gen Z-ers want to share their online payments on social media, compared to only 3% of baby boomers. (Accenture)
• 69% of Gen Z-ers use mobile banking apps daily or weekly, while only 17% of baby boomers are interested in using mobile banking apps. (Accenture)
• Online payment statistics intimate that 82% of Generation Z consumers who own a smartphone shop online. (PayPal)
• Also, studies have shown that while 33% of millennials use cash, but only 18% of Gen Z-ers use cash. (Digital Transactions)
• Moreover,  58% of Generation Z have made an impulsive mobile purchase. (Digital Transactions)
• Studies have also found that when it comes to online shopping, 80% of Generation Z are influenced by social media (Retail Dive)

It’s vivid that the financial needs of Gen Z-ers are getting complex by the day, and industry players must up the ante to meet their desires. Businesses must rise to the occasion and elevate online payments by envisioning and actualizing innovations that meet the needs of these trendsetters. For example, the wall that exists between payments and social media needs to be broken down to pave the way for more intriguing online payments. Also, businesses should follow a well-defined guide on implementing payment gateway services to ensure consistency across all channels.

When voice search first entered the commerce industry, it was met with a dizzying onslaught of skepticism. However, voice commerce has come of age, and the use of voice assistants in the industry is rapidly gaining popularity as more and more consumers buy smart devices such as Amazon Alexa, Siri, and Google Assistant to streamline online shopping.

• Only 3.6% of US consumers engaged in voice commerce for retail and grocery products in 2018. (PYMNTS, 2020)
• Voice commerce became more popular in 2019 with a share of 6.2%. In 2020, the figure rose to 6.7%. (PYMNTS, 2020)
• Smart speakers don’t necessarily serve as primary sales channels as eMarketer notes. Smart speakers owners use them for different purposes including play music (97%), weather and news (94%), general questions (90%), reminder (87%), creating shopping lists (71%), make a purchase (62%), enabling smart home devices (57%), and email/calls (55%). (eMarketer)
• A study by Transaction Network Services (TNS) found that 26% of consumers that own smart voice assistant devices have used them to make a payment. (TNS, 2019)
• Also, it’s noted that 34% of consumers in the United States have already purchased food using a voice assistant, while 35% say that given an opportunity, they would use voice assistants to buy food online. (Statista)
• Online payment research has revealed that voice assistant technology is changing the shopping landscape. While the technology is still in its infancy, it’s hard to ignore its growing user base. According to OC&C Strategy Consultants, 36% of US consumers make a purchase via voice assistants. (OC&C)
• Also, OC&CS Strategy Consultants reckons that Amazon dominates the voice shopping space accounting for approximately 90% of total spend. (OC&C)
• Online payment data has shown that in 2022, 31% of consumers in the United States will have used a voice assistant to make payments. (BBVA)
• One alarming statistic is that 74% of consumers state that they are not open to making payments through voice assistants because of security concerns. (TNS, 2019)

Source: PYMNTS, 2020

The number of consumers who use voice assistants to make payments is relatively small, but the user base is poised for speedy growth. The evolution of AI technology and the efforts being made by innovators to align voice shopping with the needs of modern consumers will make voice payment methods ubiquitous in a few years to come. You better be ready for the voice revolution an arm yourself with one of the best payment gateway solutions  for your online and offline store.

If you own a smartphone and have a bank account, then chances are you’ve most likely used or have heard about the peer-to-peer payment service. Mobile peer-to-peer (P2P) payment is one of the most intriguing and fastest-growing technologies in the financial space. This innovative technology is a force to reckon with when it comes to simplicity and convenience.

• Online payment statistics by Accenture intimates that 68% of Gen Z-ers are delighted by instant P2P payments, more than any other demographic groups. (Accenture)
• In the United States alone, the total transaction volume of Mobile peer-to-peer (P2P) payments will surpass $300 billion by 2021. (Nasdaq)
• By 2020, Zelle will have the biggest online payments market share at 56.1 million users, followed closely by Venmo at 38.7 million users. (eMarketer)
• Zelle report that 80% of US consumers have used a P2P payment service, with 50% of new users being people aged 45 years or older. (Zelle, 2019)
• By the end of 2020, 52.5% of US smartphone owners will have made more than one P2P transactions. (eMarketer)
• According to online payment market share data by eMarketer, the number of mobile payment users will reach 150 million by 2020, and the total in-store mobile payment volume will reach $503 billion in the same year. (Business Insider)
• Being an early adopter of P2P payment services, China’s total volume of mobile P2P transactions is predicted to reach a monstrous $6.3 trillion by 2020, dominating a lion share of the global payment gateway market share. (Business Insider)

The incentive to securely and quickly transfer money without having to face the inconvenience of the tedious and complicated procedures has enabled P2P mobile service to cement its spot as the payment gateway of choice for today’s consumers. There is no doubt; P2P mobile payments have made it easy for consumers to send money anytime, anywhere, to whomever they want without worrying about restrictions over transaction amounts.

With P2P payment service, the tide is changing, and social networks are now offering payment services. Facebook, Instagram, and Snapchat are now enabling consumers to close sales within the social media environment, and this is a trend to keep an eye on. Also, going by the statistics, without implementing professional payment gateway tips for online stores , small businesses will miss the opportunity to tap into the power of P2P payments.

Decades ago, payment services were location-bound and pretty straightforward, with payments providers solely dictating the terms of accepting payments. Fast forward to today, things have changed, and the need to enable consumers to transact anywhere, anytime with ease, security, and speed has become integral to a business’ success. Technology has changed where and how payments are accepted, and the rise of omnichannel payments has reinvigorated the entire payment space, seamlessly fusing shopping experience across online and offline stores.

• 85% – the global share of customers who shopped online in 2020. (IMI International, 2020)
• Asia (86%) and South America (86%) are the leading regions with the most share of customers that shopped online. Europe has 83% while Australia (79%) and North America (78%) trail the main leaders. (IMI International, 2020)
• Putting some specific payment gateway market statistics to the omnichannel trends, 91% of consumers have plans to shop in-store, while 84% plan to shop online. Besides, 75% of consumers plan to shop both in stores and online. (PwC)
• According to WorldPay, omnichannel shoppers spend up to 300% more than those shopping on a single channel. 
• In addition, consumers think of shopping as a single, seamless experience, whether instore, on a mobile device, or online, and so must businesses. 

Source: IMI International, 2020

Businesses can get more value by integrating all channels with online, social media, and mobile channels. It’s true that online, mobile, in-app, and in-store sales channels are powerful in their own right. However, many of the best payment solutions today can consolidate these channels to create an impeccable omnichannel pay platform that is at once flexible and secure.

Top 5 Payment Gateway Software

• Stripe is a cloud-based all-in-one payment platform that accepts major credit and debit cards as well as digital wallets.
• PayPal Payments Pro is a payment processing solution that allows merchants to accept credit card payments online and host their own checkout pages.
• 2Checkout offers global availability in over 200 markets, so you can enjoy localized options for selling in the customer’s language and currency.
• Amazon Payments make it faster and more secure for customers to shop at your Amazon store.
• PayU has a direct connection to local acquirers plus an in-house anti-fraud system aside from facilitating online payments.

The changing financial needs of modern consumers and the advent of bold new technologies have put the race to innovate into overdrive. With payment being the most frequent touchpoint between the consumer and the business, customer experience has become the heartbeat and the principal competitive differentiator in payments revolution. For many consumers, payment experience equals customer experience, and they expect the process should be seamless regardless of when and how they choose to make payments.

• 61 % of consumers are for the idea of openly accessing their finances so they can view credit card and bank account balances when making payments via a mobile app. (Accenture)
• Most consumers, including 70% of Gen Z-ers and Millennials, have shown interest in digital payments consultancy and expense management services that enable them to better understand and control their spending. (Accenture)
•  50% of Gen Z-ers and Millennials are willing to share their online bank account details with third-party service providers. (Accenture)

Today’s tech-savvy consumers demand a complete digital experience in their transactions. As a result, forward-thinking businesses must innovate to enhance customer experience and provide the flexibility and convenience demanded by modern consumers. With optimized customer experience, it will be easy for merchants to grow their online payment market size.

Let’s face it: mobile wallets are giving traditional payment providers, like banks, a run for their money as more and more consumers make their finances mobile. Mobile wallet is a growing trend, especially in the developed markets, where previously underbanked consumers are taking advantage of the estimable consumer-focused payment experience for its perks such as proactive balance alerts, immediate rewards, and streamlined payments and charges.

• In the US, Millennials (46%) lead the share of consumers making digital or mobile wallet payments by age. Gen Z and Gen X follow closely in a tie at second with 46% each. Only 22% of Boomers and 8% of those beyond Boomers use digital or mobile wallets. (Statista, 2020)
• 23% of consumers in the US are willing to give up their digital banking app for a mobile wallet to consolidate all payment information in a single location. (Accenture)
• 75% of consumers in the US use digital wallets because they’re more comfortable than carrying around credit cards.
• Mobile wallets will play a huge role in the growth of the payment gateway market size. As Accenture predicts, in 2020, 64% of consumers will be using a mobile wallet, up from 46% in 2017. (Accenture)
• It’s estimated that over 110 million adults in the US say they’ve swapped credit and cash for a mobile wallet at least once. (Finder)
• At 28%, bank mobile wallets have a lower penetration rate, compared to the merchant’s wallets (39%) and Pays (Android Pay, Apple Pay, and Samsung Pay) at 49%. (Accenture)
• The top reason for using a mobile wallet include convenience (74%), easy to track expenditure (25%), easy to carry (25%), and greater security (23%). (Finder)
• More men (48%) have used a mobile wallet, compared to 41% of women. (Finder)

Source: FIS, 2020

Interestingly, even with the focus being mainly on the US markets, it’s hard to ignore the revolution of the Chinese mobile wallet markets, which is dominated by Alipay and WeChat Pay. According to Frost & Sullivan forecast, 950 million consumers in China will be using a mobile wallet by 2023. Besides, eMarketer predicts that 79.3% of Chinese smartphone users will be scanning, swiping, and tapping at the point of sale (POS) by 2021. This means that even as global small businesses explore legit payment gateway providers in the USA , they should not play blind to the events taking place in other markets such as China.

In the new era of fast-paced technological innovation, Artificial Intelligence (AI) and machine learning outline advanced analytical technologies with an outsized potential to overhaul the entire payment ecosystem for payment processors, banks, consumers, and merchants. AI has the potential to cut fraud, improve customer service, and reduce transaction times. While on the other hand, machine learning has already spread its wings in fraud management.

• Machine learning can help payment providers increase revenue from their existing customer by up to 15% (McKinsey)
• In addition, machine learning can help businesses reduce bad debt provision by up to 40%. (McKinsey)
• According to a payment gateway research by Juniper Research, AI chatbots are predicted to help the financial sector achieve $8 billion in cost savings by 2022. (Juniper)

What Does The Future Hold for Online Payments?

Numbers don’t lie, and it’s clear that the future of online payments is moving towards technological adoption. Currently, the industry has experienced tremendous improvements in terms of security, thanks to the implementation of biometrics, and payment transactions are now fast and frictionless. Payment gateway providers have embraced technological changes to provide an array of benefits craved by today’s consumers.

That said; one trend is conspicuous: online payment innovations are primarily centered around smartphones, AI, and machine learning.  As a result, merchants should come up with an overwhelming blend of virtual assistants, IoT, and smartphone apps to remain competitive in the already flooded e-commerce markets. The need to give a customer-centric experience refined by choice, flexibility, speed and convenience should also be a priority for any business that wants to meet the needs of modern consumers satisfactorily.

Above all, it’s good to note that the payment industry is continually changing, and keeping your eyes firmly fixed on the future will be a feather in your cap. For example, businesses should focus on emerging trends such as social payments, tabletop payment systems, and wearable payment devices, like Apple Watch and others, which have recorded a staggering 177% growth rate in recent years.

It won’t hurt, too, to learn more configuration techniques beyond drag-and-drop and plugin tools, the better you can optimize a payment gateway system, many of which have an open API for the taking. For that, you can take a look at this comprehensive guide on  how to integrate a payment gateway in PHP, Java, and C# .

Key Insights

• COVID-19 Impact : The pandemic significantly boosted the popularity of online payments due to social distancing regulations, with a notable increase in online orders for various products.
• Digital Payment Growth : The global transaction value of digital payments reached $5.2 trillion in 2020 and is projected to continue growing, with digital commerce and mobile POS payments as major contributors.
• Generation Z Influence : Gen Z consumers, who prefer digital tools and personalized experiences, are driving the shift towards online payments, with a significant portion using mobile banking and avoiding cash.
• Voice Commerce : Although still emerging, voice commerce is gaining traction, with smart devices like Amazon Alexa and Google Assistant becoming integral to the shopping experience.
• P2P Mobile Payments : Peer-to-peer payment services are rapidly growing, offering convenience and security, with platforms like Zelle and Venmo leading the market.
• Omnichannel Payments : Consumers increasingly expect seamless payment experiences across multiple channels, with omnichannel shoppers spending significantly more than single-channel shoppers.
• Customer Experience (UX) : Modern consumers demand seamless and digital payment experiences, with Gen Z and Millennials particularly interested in financial management services via mobile apps.
• Mobile Wallets : Mobile wallets are becoming a preferred payment method due to their convenience, with a high adoption rate among younger generations and significant usage in markets like China.
• AI and Machine Learning : These technologies are transforming the payment industry by enhancing fraud detection, customer service, and transaction efficiency, offering substantial cost savings and revenue growth.
• What impact did COVID-19 have on online payments? The COVID-19 pandemic accelerated the adoption of online payments as social distancing regulations increased the demand for digital commerce and mobile POS payments. Many consumers began ordering products online to avoid physical stores.
• How are Generation Z consumers influencing the online payment industry? Generation Z, known for their preference for digital tools and personalized experiences, is driving the shift towards online payments. They heavily use mobile banking apps and prefer digital transactions over cash, influencing the payment market to innovate and cater to their needs.
• What is the current state of voice commerce in the online payment space? Voice commerce is an emerging trend where consumers use voice assistants like Amazon Alexa and Google Assistant to make purchases. Although still in its early stages, it is gaining popularity, and its user base is expected to grow as technology improves.
• Why are P2P mobile payments becoming popular? Peer-to-peer (P2P) mobile payments are popular due to their simplicity, convenience, and security. They allow users to transfer money quickly and easily, bypassing traditional banking procedures, which has led to widespread adoption, especially among younger consumers.
• What are omnichannel payments, and why are they important? Omnichannel payments enable consumers to transact seamlessly across multiple channels, such as online, in-store, and mobile. They are important because they enhance the shopping experience and increase consumer spending, with omnichannel shoppers spending up to 300% more than single-channel shoppers.
• How does customer experience impact the online payment industry? Customer experience is crucial in the online payment industry as consumers expect seamless, digital, and flexible payment options. Improving customer experience through innovative payment solutions can help businesses attract and retain customers, ultimately boosting their market share.
• What role do mobile wallets play in the future of online payments? Mobile wallets are becoming increasingly popular due to their convenience and the ability to consolidate payment information in one place. They are particularly favored by younger generations and are expected to see continued growth, especially in markets like China.
• How are AI and machine learning transforming the payment industry? AI and machine learning are revolutionizing the payment industry by improving fraud detection, enhancing customer service, and making transactions more efficient. These technologies offer significant cost savings and can increase revenue for payment providers by optimizing various processes.

References:

• 2016 holiday outlook: It’s the most digital time of the year. (2016). PwC .
• Accenture. (2017). 10 megatrends driving the future of payments. Accenture .
• Alameda, T. (2017, November 16). Voice, the preferred interface for the payments of the future. BBVA .
• Cakebread, C. (2018, December 5). How much will mobile peer-to-peer payment usage grow in 2019? Insider Intelligence – eMarketer .
• Chatbot conversations to deliver $8 billion in cost savings by 2022. (2017, July 24). Juniper Research .
• Chow, O. (2021, March 18). The rise of digital wallets. finder.com .
• Consumers confirm smart payments adoption. (2019, April). TNS .
• Conversational commerce: The rise of voice assistants. (2018, May). eMarketer .
• Coppola, D. (2020, March 16). U.S. consumers paying with mobile wallets by age 2020. Statista .
• ECommerce spotlight: Winning in 2020 & Beyond. (2020, December 3). IMI Content Portal .
• eMarketer Editors. (2018, June 13). Zelle will overtake Venmo in 2018. Insider Intelligence – eMarketer .
• How will we pay. (2020). PYMNTS.com .
• Make room millennials, Zelle® study finds Generation X and boomers driving broader adoption of person-to-person (P2P) payments. (2019, March 4). Zelle® .
• The mobile payments report: Market forecasts, consumer trends, and the barriers and benefits that will influence adoption. (2016, June 4). Business Insider .
• Mole, K. (2016, September 1). Beyond the buzz: Harnessing machine learning in payments. McKinsey & Company .
• Paypal. (2017). PayPal mcommerce index: Australia 2017. PayPal .
• Sabanoglu, T. (2018, February 15). Voice assistant usage for shopping by type U.S. 2018. Statista .
• Safehaven. (2018, June 27). This payment giant just teamed up with Mastercard. Nasdaq .
• Salpini, C. (2017, July 17). Study: 80% of Gen Z purchases influenced by social media. Retail Dive .
• Statista. (2020, June). COVID-19 barometer 2020. Statista .
• Statista. (2020). Mobile payment usage in the United States. Statista .
• Statista. (2021, January). Digital payments report 2021. Statista .
• The talking shop: The rise of voice commerce. (2018, March 5). OC&C Strategy Consultants .
• Woodward, K. (2018, May 30). Generation Z emerges with a new way of thinking about payments. Digital Transactions .

By Nestor Gilbert

Nestor Gilbert is a senior B2B and SaaS analyst and a core contributor at FinancesOnline for over 5 years. With his experience in software development and extensive knowledge of SaaS management, he writes mostly about emerging B2B technologies and their impact on the current business landscape. However, he also provides in-depth reviews on a wide range of software solutions to help businesses find suitable options for them. Through his work, he aims to help companies develop a more tech-forward approach to their operations and overcome their SaaS-related challenges.

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Digital payments and consumer experience in India: a survey based empirical study

• Original Article
• Published: 05 January 2021
• Volume 5 , pages 1–20, ( 2021 )

Cite this article

• Sudiksha Shree 1 ,
• Bhanu Pratap 2 ,
• Rajas Saroy 2 &
• Sarat Dhal 2  

51k Accesses

18 Citations

Explore all metrics

Propelled by recent policy initiatives and technological developments, India’s digital payment system is a promising success story in the making. At the same time, the data also points towards an increasing usage of cash. While aggregate country-level data can indicate overall preferences of citizens, we use a novel online survey-based dataset to understand how factors such as ‘perception’ and ‘trust’ in digital payments, and experience with online frauds, affect the payment behaviour of consumers. While demographic factors like age, gender and income are relevant factors which determine this choice, we find compelling evidence that a person’s usage of digital payment methods is influenced by her perception of these instruments, as well as her trust in the overall payments framework and banking system in general. We find that the degree to which past-experience with online fraud deters usage of digital payments varies with the purpose of the transaction.

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Avoid common mistakes on your manuscript.

1 Introduction

The consensus around the origin and the forms of ancient money has kept changing over the course of recorded history. But, what has not changed over the years is what money does; broadly, it facilitates trade in goods and services as medium of exchange and acts as a credible store of value. Modern day trade demands massive payments to be settled fast over long distances with minimum transaction cost. Evidently, to suit these needs the payment systems are being digitised globally. Cash, however, remains a crucial part of the trade. Therefore, the discourse on the current age payment system revolves around cash vs digital transactions.

While cash might seem convenient as it’s ingrained in our habits and is still readily accepted at more places, digital payments offer convenience by saving time and labour. There are further issues with cash use. While it provides a suitable alternative to aid the informal or parallel economy [ 3 , 21 ], digital payment offers itself as a desirable tool for institutions to fix this problem of traceability. In fact, governments around the world have taken drastic measures at huge costs to clear markets of ‘black money’. Research in the behavioural sciences conveys that people experience higher ‘pain of paying’ when paying in cash than digitally, and this contributes to deferred payments [ 17 , 19 , 20 ]. While cash may not seem to impose any direct transactional cost like digital money, it is still costly for both governments and end-users. A 2014 study found out that residents of Delhi spent around 6 million hours and ₹91 million to access cash, while the Reserve Bank of India (RBI) and commercial banks together spent about ₹210 billion towards currency related operating expenses in the same year. But on the other hand, there are also huge implicit costs to digitise the existing systems and nudge people to change [ 14 ].

In the last decade, India has rapidly digitised its payment systems and promises huge potential in the area. Digital payments recorded an increase of 46.5% in total volume in FY19 on top of an increase of 60.6% in FY18. The Unified Payments Interface (UPI), a payment system that was launched in 2016, has surpassed the milestone of a billion transactions per month. The progress in digitisation has been driven by a healthy mix of technological innovation, policy interventions, and expansion and strengthening of existing infrastructure on the supply side, coupled with an increasing proportion of the population adopting financial and digital instruments on the demand side. The government of India and the RBI have been working in synergy to push for policy and regulatory reforms. Enablers such as Jan Dhan accounts, Aadhaar and penetration of mobiles, and policies like Demonetisation and Goods and Services Tax have brought people closer to technology and banks. Recently, NEFT (National Electronic Funds Transfer) was made operational for 24 h on all days of the week, and RTGS (Real Time Gross Settlement) is expected to follow soon. The launch of UPI, along with already available digital payment modes like NEFT, IMPS, cards and Prepaid Payment Instruments (PPIs) has increased the options available to the consumer. The number of PoS (point of sale) terminals have also increased by about 40 lakhs in the last five years. PoS terminals and lightweight acceptance infrastructure such as QR codes have boosted Card/PPI based payments. Additional payment systems such as Bharat Bill Payment System (BBPS), National Electronic Toll Collection (NETC) system, RuPay cards and AePS have also boosted digital payments and the intent to incorporate modern-day technologies such as tokenisation and contactless payments will further the progress.

Despite this progress, cash use still seems to be on the uptick in India. Our paper seeks to highlight the important factors at the individual level, which influence the consumer’s decisions to use cash or digital payment. While it is critical to push for technological innovations and policy reforms, it is also imperative to understand the aspects that motivate or hinder the adoption of these technologies by the end-user. A recent survey [ 5 ], on the readiness of consumers towards adoption of newer payment technologies, ranked India second out of 27 economies on the FinTech adoption Index. Research conducted at the individual consumer level can provide an insight to understand how certain aspects are at play while making a payment decision. To this end, we use a comprehensive and multidimensional online survey which addresses many hitherto untouched dimensions of this topic, such as the difference in digital spending over various expenditure categories (groceries, e-commerce, utility bills, etc.), the choice of consumers to go purely digital or exercise a mix of cash and digital options, and the effect of psychological factors like perception and trust.

There is a dearth of studies and data covering the behavioural aspects at individual level that have an impact on choice of payment behaviour in the Indian economy. Given the massive heterogeneity of our population, different samples might produce disparate results. The High-Level Committee on Deepening Digital Payments [ 15 ] recommended that there should be periodic surveys to gauge user experience and attitude towards digital payments. The present study, is a small step towards filling the research gap in the context of such analysis.

Our key findings point towards a significant impact of perception of the payment system on how people choose to pay. Not only does a positive perception motivate people to go ‘digital’, but a relatively negative outlook on cash also has a similar impact. This finding is important in light of increasing cash use at the macroeconomic level in the country. Another significant factor is confidence in the payment system. Respondents who trust the service providers and regulators seem to have a greater likelihood of paying digitally. We find inconsistent behaviour when studying the impact of experience of digital payment fraud on choice of payment tool. The impact that experiencing such a fraud has on the choice to pay digitally differs according to the purpose of the transaction. The remainder of the study is presented in five sections pertaining to existing literature, data and methodology, sample summary statistics, empirical findings and conclusion and policy implications.

2 Related literature

The terms digital transaction, electronic transactions, paperless transaction or cashless transaction are almost used interchangeably in common parlance. The RBI Ombudsman Scheme for Digital Transactions (2019) defines digital transactions as “a payment transaction in a seamless system effected without the need for cash at least in one of the two legs, if not in both. This includes transactions made through digital/electronic modes wherein both the originator and the beneficiary use digital/electronic medium to send or receive money”. However, in our paper, a digital transaction is one where the payer and payee both use digital modes of payment.

Policies in many parts of the world are being designed in favour of non-cash payments because of the various problems that cash poses. Cash fuels the parallel or black economy, therefore, phasing it out might solve this problem, especially with large denomination notes [ 20 ]. The cost of printing, destroying and other cash related operational expenses in India are estimated at 1.7% of GDP [ 23 ]. Cash, however, remains a significant part of all the transactions in most countries [ 6 ].

While reading into data on the macro-level can give us a broad idea of people’s overall preferences, data at the individual level gives us an insight into how certain factors impact the choices/decisions consumers make regarding the mode of payment. Following this line of thought, several studies have analysed such issues at the level of the consumers. They reveal that the choice of payment method is impacted by a host of consumer-specific and technological factors. Transaction size has a significant impact on what mode of payment people choose. A cross- country comparison of payment diary survey data of seven countries showed that cash was the preferred mode of payment for smallest 50% and largest 25% of transactions [ 2 ]. In another study, social marginal costs were computed for various instruments for small and large transaction sizes and it was found that for larger transaction sizes, there were significant differences in cost for electronic vs non-electronic payments [ 8 ]. Studies show that demographic characteristics also play a significant role in how people choose to pay. It was found that better education and higher income lead to lower cash use compared to non-cash modes. Certain categories of age show a stronger preference for digital payments Bagnall et al. [ 2 ].

Consumer perceptions on safety/risk, convenience/ease of use, anonymity and costs have been shown to affect payment systems adoption significantly. Png and Tan [ 16 ] show that concerns about privacy emerged as one of the main psychological factors causing a bias towards cash for retail transactions. Kahn et al. [ 10 ] show that business in the unorganised economy was attributed to transactions that could be made in cash and did not reveal the agent’s identity. Bagnall et al. [ 2 ] analysed data from cross-country consumer diary surveys and found that consumers who rated cash high on ‘ease of use’ ended up using it more. In a study assessing payment perception of Dutch consumers, non-price parameters such as ‘acceptance’, ‘convenience’, ‘transaction speed’ and ‘safety’ were used to gauge the perception of payment instruments used at PoS terminals [ 9 ]. Several studies have used the Technology Acceptance Model (TAM) to show ‘perceived usefulness’ and ‘perceived ease of use’ have a significant impact on behavioural intention and thus, actual use of electronic payment systems [ 12 , 18 ].

Perceived trust in the payment system is shown to have a positive effect on the usage of digital modes of payment [ 13 ]. While the central bank and banks are traditional regulators and service providers of payments systems respectively, non-banks have also emerged as new players in the framework. A recent empirical study conducted by the Monetary Authority of Singapore [ 16 ] found that trust in banks impacts the nature of the transaction. A cross-country analysis shows that residents in countries that reported lower trust in banks preferred cash for making transactions. In some cases, while an increase in trust can lead to the opening of accounts, it might not translate to actual usage of those accounts [ 7 ]. Central banks also play a pivotal role in ensuring safety, integrity and stability of the payments system. Experience of online fraud can shape beliefs of perception and trust and can have a direct impact on payment behaviour. Media coverage of these incidents is shown to affect card payment [ 11 ]. The direction, strength and frequency of media coverage affected debit card use. Few studies show that people simply use digital modes of payment because they have exhausted their stock of cash in hand. It is called ‘cash first’ or ‘cash-burning’ and is perceived to be an optimal policy by the consumer [ 1 ]. Some studies also point that people still pay in cash simply because it is difficult to grow out of habits [ 9 ].

3 Survey data and empirical methodology

For the purpose of this study, primary data is collected using a structured questionnaire circulated online (Appendix 1). Following snowball sampling, the survey was shared on various social media platforms for better reach. The questionnaire was drafted in English and Hindi, to both expand and diversify the sample. It consists of 28 questions that are divided into seven sections viz. demographics, access to and usage of technology, awareness of different modes of digital payment, preference and perception on cash and digital payment systems, spending habits, experience related to fraud, and feedback on awareness campaigns.

Our study broadly aims to understand the impact of user perception, trust in payment systems, and experience of online fraud on the choice of mode of payment. For regression analysis, mode of payment is taken as the dependent variable and the independent variable is added to a baseline model according to the hypothesis being tested. Firstly, a baseline model is obtained for all five types of purchases—grocery, utilities, online shopping, durables, and gold. These transactions range from low to high value transactions. The responses recorded for different types of purchases have the following three alternatives:

Always pay in cash,

Always pay digitally, and

Sometimes pay in cash and sometimes digitally.

Since the dependent variable is categorical and has more than two categories, a multinomial logistic regression is best suited for regression analysis. A multinomial logit model is an extension of logit model, with more than two categories, in no particular order. Maximum likelihood estimation is used to obtain the parameters of the model.

Let the model have j  = 1, 2 …, J categories for the dependent variable y , and X be the matrix of independent variables. In a multinomial logit model, we estimate a set of coefficients β j  = ( β 1, β 2…, β J ) corresponding to each outcome j . Setting j  = 1 as the reference or base category ( i.e ., β 1  =  0) , we have:

The parameters of the model are reported in terms of odds or log odds. Given any two possible categories for the dependent variable:

where ( β m  −  β n ) is the  effect of X on log of odds of m versus n . To get parameters of other categories of the outcome, they are similarly compared to the common reference category. For our study, cash usage is taken as the reference category. We begin by creating a baseline logistic regression model by taking demographic characteristics such as gender, age, education, family income, occupation, and place of residence as categorical independent variables. The dependent variable is coded as:

y  = 0 for cash (reference)

y  = 1 for digital payments

y  = 2 for sometimes cash and sometimes digital payments

The following multinomial logistic model is estimated:

The parameter β kj is a vector of β 0j, β 1j … β kj where j ( j  = 0, 1, 2) is the category of dependent variable and there are K  +  1 ( k  = 0, 1, …, K ) independent variables. Since cash is the reference category, β k0 is set to 0. Therefore, β k1 and β k2 are respective log odds relative to the reference category.

Since, all the independent variables are categorical, they are coded as dummy variables. The reference categories for each of the independent variable in the baseline model are mentioned in the first column of Table 1 below.

Next, we add four additional independent variables of interest to the baseline model one by one, to observe the impact of perception (of both cash and digital payment modes separately), confidence in the payment system and fraud experience on the choice to pay digitally.

The perception of cash and digital modes of payment is recorded for four parameters- cost, convenience, safety and privacy/anonymity on a three-point Likert scale with the alternatives ‘bad’ (0), ‘okay’ (1) and ‘good’ (2). The mean score for perception is computed as the simple average of parameter-wise scores for cash and digital payments. Confidence in payment systems is measured on the parameters- trust in the RBI, trust in your payment service providers (e.g. FinTechs) and trust in stability and integrity of your bank. A five-point Likert scale is to measure responses, ranging from strongly agree (0) to strongly disagree (4). The mean score is computed as a simple average of the four parameters. Online fraud experience is quantified based on familiarity with such incidents. The respondents were asked to choose from following alternatives-

I have been a victim to digital payment frauds.

I have received such calls/mails/texts but carefully avoided them.

I have not received such calls/mail/texts but know someone personally who has been a victim.

I have not received such calls/mail/texts and do not know anyone personally who has been a victim.

4 Sample summary statistics

A snapshot of our sample of 640 respondents is given in Chart  1 . The respondents are mostly male and educated. Most of them are either salaried employees, working in the government or private sector. This may be due to the online nature of the survey, and circulation limited to the social circles of the authors, which occurred due to the enforcement of the COVID-19 induced nationwide lockdown in India during the survey period. Responses were received from twenty states of India. The corresponding districts were divided into three tiers according to the HRA (Housing Rent Allowance) classification by the Department of Expenditure, Government of India.

Demographic characteristic of the sample

The responses are summarised in Appendix 2. Awareness as well as usage regarding various digital payment instruments were high in the sample. It is important to keep this in mind while interpreting how payment behaviour is affected by other variables. Our respondents, being from the relatively well-off sections of society, were much more aware and comfortable with cards and UPI, rather than AEPS and USSD code-based payments. Digital mode was preferred for online shopping, paying utility bills, and purchasing durables (mostly medium to high value transactions). A combination of cash and digital modes was preferred for purchases of grocery and gold, which are starkly different in terms of transaction value. Being solely dependent on cash was relatively less preferable for all purposes.

The perception of cash and digital payments are recorded on four parameters viz., ‘convenience’, ‘cost of payment’, ‘safety’, and ‘privacy/anonymity’. It is observed that on an average, digital payments perform better than cash on all four fronts. Confidence in digital payment systems is assessed on four parameters, with regards to banks (preference for depositing money in a bank, as well as trust in one’s own bank), the central bank and in other participants like payment aggregators. Respondents seemed more confident in the RBI and banks, as compared to other service providers.

Technical issues, followed by low acceptance and lack of trust were identified as the major hindrances with digital payments. The experience of online fraud is divided into four categories based on their potential intensity of impact of the fraud. Out of 630 respondents that answered the question, 532 have had some experience of online fraud. Out of 411 respondents who had experienced the incident personally, a majority (279) reported no change in the nature of payments and only 26 mentioned that either they had completely switched to cash or had reduced the use of digital mode of transaction. Respondents were also asked if they reported the incident to the concerned authority after they experienced the fraud personally. Most of the respondents did not report the incident, especially if they had not faced any losses.

5 Multinomial regression model: results and analysis

The baseline model (Appendix 3) provides insights on the effect of demographics on the choice of mode of payment.

5.1 Effect of demographics on mode of payment

Males are more likely to use digital modes of transaction as compared to their female counterparts for both purely digital or a combination of cash and digital instruments. With respect to age, there is pressing evidence in the case of online shopping that older individuals are less likely to pay digitally. While the coefficients are not statistically significant for other kinds of purchases, their signs support this general observation. Education is also seen to have an enabling effect on people when it comes to going digital. The tendency to avoid paying solely with cash for groceries and utilities dwindles with an increase in the level of education of the respondent. Income levels have a statistically significant, positive impact when it comes to online shopping and gold purchases through the exclusively digital payment route. Lower income groups may prefer paying using cash on delivery. Occupation and place of residence have a significant impact on choice of mode of payment for mid and high-value transactions. Homemakers, unemployed and self-employed respondents are least likely to pay digitally. For place of residence, respondents living in Tier-1 cities are more likely to pay digitally.

In general, our results point out that more affluent and privileged groups are still more likely to go digital, compared to disadvantaged groups. Hence, while efforts to expand relevant infrastructure and nudge behavioural change are welcome, an upliftment of the general standard of living of the public, education and urbanisation may also be important ways to promote digitisation of payments.

5.2 Experience of online fraud

The experience of digital payment fraud is measured on a scale of four, with ‘0’ implying ‘I have been a victim of digital payment fraud, which is the highest possible impact of fraud on a person. At the other end, ‘3’ stands for ‘neither experienced digital payments fraud nor know anyone who has been a victim’. The baseline model is augmented with these additional categorical variables, and the results are presented in Table 2 . The reference category for the four fraud indicator variables is the response ‘3’, i.e., the respondent has neither been a victim of digital payment fraud, nor do they know of someone who has. Our paper highlights that frauds have differential impact based on the purpose of the transaction. For grocery payments, experiencing such frauds, first hand or otherwise, seems to demotivate people from using digital payment modes, but there is no such evidence for other types of transactions. In fact, respondents preferred using a mix of digital payments and cash for utilities and durables even if they had previously fallen prey to such frauds. It may be easier for consumers to switch to cash for grocery purchase, as compared to settling utility bills or buying durables.

5.3 Perception of cash vs digital payments

Perception of cash is scored on four parameters- cost of payment through cash, convenience of payment, privacy or anonymity concerns about the payment, and safety of payment. The scores range from 0 (bad) to 2 (good). The total score is computed by taking an average of all the four parameters. The total score is a continuous variable and is added to the baseline model. The resultant coefficient is reported in log odds. As is evident from Table 3 , perception of cash has a strong and significant impact on which mode of payment is chosen by the respondent. As the perception of cash improves, the likelihood of paying digitally decreases across all purchase categories. The reference alternative for payment is taken as payments made only/ always in cash, implying no (zero) relation with perception of cash. As perception improves the likelihood decreases most for grocery (low-value payment) and online payments and least for payments made for purchasing gold followed by durables, both high-value payments.

On the flip side, we also consider the total score for perception of digital payments, which is calculated similar to that for cash above. The coefficients (Table 4 ) are positive and statistically significant, implying that as perception improves, so does the likelihood of paying digitally. Here also, the reference alternative is using only cash. In terms of magnitude, the perception variables seem to affect grocery spends the most and gold spends the least. It can be inferred that a positive outlook on digital payment modes motivates the respondent to pay digitally. However, digital payments still have a long way to go if they are to prove themselves as good substitutes to the cheapness, convenience and privacy of cash use. Another observation from the above results is that high-value payments (gold and durables) are relatively less affected by perception of modes of payment, when compared to low- value payments (grocery).

5.4 Trust in payment system

Besides their perception of payment modes, respondents were also asked about their trust or confidence in the payment system as a whole, which was measured on four parameters. A five-point Likert scale is used, with ‘0’ or ‘strongly agree’ implying high confidence in the payment system and ‘4’, which stands for ‘strongly disagree’ implying extreme lack of confidence in the payment system. The total score is computed by taking an average of scores obtained on all the parameters. As expected, a deterioration in consumer confidence in digital payment systems (or an increase in the ‘lack of trust’ score) worsens the likelihood of paying digitally (Table 5 ).

At the end of the survey, respondents were also asked to give their feedback on digital payments. This gives us an indication of overall sentiments and main concerns of consumers towards digital payments. In Chart  2 , a ‘ wordcloud ’ based on 50 most frequently occurring words in the feedback highlights that consumers favour the ‘convenience’ offered by digital payment methods and have an overall positive sentiment towards such technology-based inventions.

Textual analysis on feedback

6 Conclusion

While governments, regulators and service-providers are working in synergy to enhance the electronic payments systems and related infrastructure, it makes sense to study how these options are perceived by the end-user. The key policy recommendation from our study is that incorporating feedback and gauging public perception can further catalyse digitisation. We observe through our study that perception of digital payment instruments affects the payment behaviour of an individual. Digital payments were not only driven by a positive outlook on digital payments but also a negative outlook on cash. Contrary to popular belief, customers were seen to be willing to discount online fraud experience in the face of higher convenience offered by digital payment modes. The impact of experiencing fraud on the choice to pay digitally differs according to the purpose of the transaction. Also, we cannot ignore the role played by demographic factors in better digital payment adoption. Digital payments adoption is expected to increase in line with the overall socioeconomic development of the population.

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Survey on consumer experience and perception about digital modes of payments: questionnaire

See Appendix Tables 6 , 7 , 8 , 9 , 10 , 11 , 12 .

See Appendix Fig. 3 . 

Data summary

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Shree, S., Pratap, B., Saroy, R. et al. Digital payments and consumer experience in India: a survey based empirical study. J BANK FINANC TECHNOL 5 , 1–20 (2021). https://doi.org/10.1007/s42786-020-00024-z

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What to know about online payment processing

PayPal Editorial Staff

April 30, 2024

With e-commerce poised to make up nearly a quarter of all global retail sales by 2027, 1 many businesses are setting up digital shops to accept payments online and capture more sales. But after a customer hits "check out," how does the money get to you?

Whether you’re expanding a brick-and-mortar business to accept payments online or starting a new venture from the ground up, it’s important to understand how online payment processing really works – who's involved, how you get paid, and what processing fees you might incur. That way, you’ll be prepared with a suitable plan for you and your business.

In this guide, we’ll cover the basics of payment processors from how they work to their benefits for websites and e-commerce operations.

What is online payment processing?

The reasons businesses should incorporate streamlined online payment solutions are straightforward: they help to boost customer satisfaction, bolster transaction security, and further bottom lines.

But first, what is online payment processing? In basic terms, it refers to how money moves from your customer to your business. Though this may sound simple, there are many moving parts involved in processing credit and debit card payments, whether online, via phone sales, or even in person.

You, the business owner or merchant account are one end of the transaction. Your customer is on the other end of the transaction. A sophisticated array of technology bridges the gap between the two, designed to facilitate this exchange efficiently and securely.

By understanding these mechanisms, you can make informed decisions that enhance transaction reliability and customer trust, which may ultimately lead to business growth.

The business

The main moving part of online payment processing is the merchant, who is considered you or your business. However, to accept credit and debit card payments from online customers, you will likely need to partner with one of the many available merchant account providers, including our PayPal merchant account .

This merchant account (sometimes called an acquirer) accepts payments on your behalf and deposits them into a business merchant account they provide. For instance, you can accept online payments with PayPal .

The online payment processor

Payment processors (or merchant account services) handle all the heavy lifting in online payment processing, from moving the transaction through the processing network to sending you a billing statement, and then working with your bank to ensure you get paid. In other words, everything it takes to process online payments.

Often, your merchant bank is also your online payment processor, which helps simplify things. For example, with PayPal, funds are settled directly into your PayPal account, where they become available for immediate use – no bank transfers and waiting periods needed.

The payment gateway

One of the technologies involved in online payment processing that enable you and your customer to transact is the payment gateway . For us, it’s called the PayPal payment gateway . This is software that links your site's shopping cart to the processing network.

The payment gateway plays a crucial role in the checkout process. It not only securely transmits the customer's payment information from your website to your payment network for processing but also performs essential security checks to prevent fraud, including verifying digital signatures and encrypting sensitive data like credit card numbers.

Given that more than 70% of online shopping carts are abandoned on average, 2 choosing a payment gateway that provides a frictionless, secure payment process can be a game-changer for driving conversions and repeat business.

The customer

Traditionally, for your customers to pay for your goods and services, they would need a credit or debit card. The bank that then approves the card (and lends them the cash to pay you) is called the issuing bank.

Online payment processing benefits

Online payment systems offer convenient, quick, and secure transactions, leading to improved customer experiences, efficient record-keeping, and helpful integration capabilities. Adopting these systems can optimize payment processes and contribute to the overall growth and success of a business in the digital age.

Convenience

Online card processing systems have long provided a convenient way for customers to make purchases from anywhere, anytime – eliminating the need for physical store visits or carrying cash.

While this convenience was groundbreaking, the digital shopping landscape now demands even greater flexibility and innovation in payment solutions. Beyond traditional credit and debit cards, modern consumers expect options like digital wallets, like PayPal, Google Pay or Apple Pay, along with newer methods such as Venmo and buy now, pay later options.

With 13% of customers abandoning their shopping carts because of insufficient payment options, 2 these alternatives provide much-needed convenience. They also address common issues like payment decline – 9% of shoppers abandon their carts if their credit card is declined. 2

Expanded customer base

By accepting online payments, businesses can reach a broader customer base beyond their local area. Online platforms enable businesses to cater to customers from different regions and even international markets.

Quick transactions

Online payments are typically processed swiftly, allowing for faster transaction completion compared to traditional payment methods.

Improved security

Online payment systems often employ advanced security measures to help protect customer information.

Enhanced customer experience

Online payment systems offer a seamless and user-friendly interface for customers to complete transactions with features like saved payment information, one-click purchases, and automated recurring billing options.

Integration with other business systems

Many online payment processing systems integrate with other business software and systems, such as e-commerce platforms, inventory management, and customer relationship management (CRM) systems.

For example, linking payment processing with an e-commerce platform can automatically update inventory levels as sales occur, while CRM integration ensures customer data is continually refreshed with each transaction.

These integrations not only save time by reducing manual data entry but also provide valuable insights into customer behaviors and preferences, aiding in more informed decision-making.

How does online payment processing work?

As a business owner, it’s helpful to understand exactly what it looks like to process online payments. Or, in other words, how money moves from your customer to your business.

There are two stages to online payment processing : authorization (approving the sale) and settlement (getting the money in your account).

Card authorization

When it comes to how credit card processing works, the authorization process goes roughly like this:

• Your customer buys an item on your site with a credit or debit card.
• That information goes through the payment gateway , which encrypts the data to keep it private and sends it to the payment processor.
• The payment processor sends a request to the customer’s issuing bank to check to see if they have enough credit to pay for the item.
• The issuer responds with a yes (an approval) or a no (a denial).
• The payment processor sends the answer back to you that the sale was approved and also tells your merchant bank to credit your account.

The full range of these steps happens quickly, all of the above takes place within one to two seconds in most cases.

The second piece of the online payment system process (where you get paid!) is the settlement:

• The card issuer sends the funds to your merchant bank, which deposits the money into your account.
• The funds are available.

Although the settlement process can sometimes take a few days, certain banks might allow you to access your funds even before they are officially transferred. They also may keep a portion in your account that you can’t touch – just in case the customer returns the item later (called a reserve in payments).

Want funds sooner to better manage business expenses and improve cash flow? PayPal's payment processing can help. Once funds settle into a merchant’s PayPal account, they are available for immediate use – there's no need to wait for a bank transfer. Check out our step-by-step guide to PayPal payment processing .

How to set up online payment processing with a third-party system?

We know you may have questions swirling in your head: What do I look for in a payment processor? How do I choose a payment gateway a payment gateway ? How can I set this all up without being overwhelmed?

Here’s a general breakdown of how to set up online payments for your business:

• Find the right partner.  Research and select a reputable payment processor that suits your business needs, considering factors such as transaction fees, security features, integration options, and customer support.
• Create an account with a payment processor.  Sign up for a merchant account with the chosen payment processor. Provide the required business and banking information.
• Integrate the steps into your operation. Determine how you want to integrate online payments into your website. Some potential options include using an e-commerce platform with built-in payment integration or integrating a payment gateway into your custom website.
• Ensure compliance with Payment Card Industry Data Security Standard (PCI DSS).  Your website and payment processing setup must comply with PCI DSS guidelines  to protect cardholder data.
• Get started with test transactions.  Perform test transactions to ensure the payment processing system works correctly. Experiment with different scenarios, such as successful payments, declined transactions, and refunds, before launching the system to your customers.

Once set up, it’s important to continuously monitor your online payment processing system, review transaction reports, and stay updated on any changes or enhancements offered by your payment processor.

How to set up an online payment system without a third-party system?

Wondering how to set up an online payment form without a third-party processor? It can be a complex task. Here are a few steps you can take:

• Research payment service providers and sign up. Explore different payment service providers (PSPs) and select one that aligns with your business.
• Explore developer resources.  Access the developer resources provided by the PSP, which can help you integrate their payment solution into your website or application.
• Establish a secure website.  Implement SSL encryption on your website to ensure secure communication between your customers and the payment service provider during transactions. Obtain an SSL certificate and configure it on your website.
• Integration options. Choose the integration method, such as API integration, hosted payment pages, or shopping cart plugins. Follow the PSP's documentation and guidelines for seamless integration.
• Maintain compliance. Ensure your integration adheres to relevant security standards and compliance regulations, such as PCI DSS.
• Go live. Once you have successfully tested the integration, switch to the live or production environment provided by the PSP. Update your website or application to enable real transactions and start accepting payments from customers.

How to add a payment method to a website checklist

Now you’re ready to create a website with payment options or add payment to your existing website. Once you’ve selected a payment processor, set up an account, and chosen the integration methods, you can:

• Add payment buttons or forms to your website.
• Implement security measures.
• Perform test transactions.
• Start accepting payments from customers.

Payment processing pricing and fees

We’ve learned about how payments come in, but what about the other side of the coin? What will it cost? As you may have guessed, everyone who touches the transaction wants to get paid, including the issuing bank, the credit card association (Visa, MasterCard, etc.), the merchant bank, and the payment processor.

At its most basic, every time you process a sales transaction, you pay four payment processing fees :

• A percent of the transaction amount. The issuer gets paid by taking a percentage of each sale, which is called the interchange. This fee varies depending on various factors, such as industry, sale amount, and type of card used.
• Another percent of the transaction amount. The credit card association (Visa, MasterCard, etc.) also charges a fee, also known as an assessment.
• Yet another percent of the transaction amount. Your merchant bank takes a cut by charging you a percentage fee. The amount here also varies by industry, number of sales, monthly processing volume, etc.
• A dollar amount for every transaction processed.  The payment processor (who might also be your merchant bank) makes money by charging an authorization fee every time you process a transaction (whether it’s a sale, a decline, or a return). Plus, it can charge fees for setup, monthly usage, and even account cancellation.

Usually, the first three fees (the percentages) are all added together and quoted as a single rate, while the transaction fee is quoted separately (e.g., 2.9% + $0.30).

Most pricing structures generally fall into one of three categories:

• Flat-rate pricing. You pay a fixed percentage for all transaction volume, no matter what the actual costs are. All the above fees are baked into this single rate. For example, you are charged a bundled rate of 2.9% of the transaction amount + $0.30 per transaction. On a $100 sale, the fee you pay works out to be $3.20.
• Interchange plus pricing. Your merchant service charges you a fixed fee on top of the interchange. For example, 2.0% + $0.10 on top of a 1.8% interchange fee. On a $100 sale, that works out to be a $3.90 fee. Learn more about IC++ pricing .
• Tiered pricing. The processor takes many different interchange rates and lumps them into three buckets (or pricing tiers): qualified, mid-qualified, and nonqualified. This makes it simpler for you (and them) to understand. However, because the processor defines the buckets any way it wants, it can be expensive. As an example, the fees you pay on a $100 sale could range from $2.50 to $3.50, depending on how it has been classified.

For more information, visit PayPal pricing .

Ready to start accepting and processing payments quickly and easily? Explore PayPal Complete Payments and learn more about new innovations revolutionizing commerce .

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• Kreyòl Ayisyen

Costs of Electronic Payments in K-12 Schools

1. executive summary.

As of Fall 2023, more than 52 million students were enrolled at public schools throughout the United States. 1 Over a school year, students incur a series of expenses for school meals, bus passes, after-school programs, and technology and materials needed for class, among other costs. As the broader payments ecosystem continues shifting towards more digital options in the wake of the COVID-19 pandemic, school districts are increasingly contracting with payment processing companies to provide an avenue for families to pay school-related expenses online. While convenient for both families and school districts, electronic payment options present new costs and challenges for the families using them.

For example, in many schools, families can electronically load funds into an account that students can draw from to pay for school meals. Although federal policy specifies that schools must provide a fee-free option for school lunch payment, many payment processors charge a transaction fee each time a user electronically adds money to a student’s school cafeteria account. 2 Payment processing companies have broad control over fee rates, though payment companies maintain that school districts have the opportunity to negotiate these rates during the contracting process. Some districts cover part or all of this fee, but it is frequently paid by the families who make electronic payments. Over the course of a school year, transaction fees for electronic payments in and outside of the lunchroom can significantly increase a family’s total spending on school-related costs and may disproportionately impact families with lower incomes. 3

To better understand the emergence of electronic payment processors in K-12 schools, the CFPB analyzed publicly available information from the 300 largest public school districts in the U.S. and held unstructured interviews with public school officials and companies offering these payment platforms. The sample of school districts covers more than 16.7 million students across more than 25,000 schools. This spotlight highlights average costs and potential risks for families using electronic payment platforms to add money to their child’s school lunch account and reviews the market size and landscape of companies offering them, building upon initial observations referenced in the Fall 2023 edition of Supervisory Highlights. 4

1.1 Key Findings

• School districts are increasingly shifting to cashless operations. This shift is partly to prioritize administrative savings and efficiency and partly due to a larger shift towards digital payments. Many schools, including 87 percent of sampled school districts, contract with payment processors to enable electronic payments for expenses like school lunch costs.
• School lunch payment processors typically charge fees to add money to a student’s school lunch account, which collectively can cost families upwards of $100 million each year. The CFPB observed that school lunch payment processors within the sample levy transaction fees of $2.37 or, separately, 4.4 percent of the total transaction, on average, each time money is added into a payment account. Families may also incur similar transaction fees when paying for other school-related expenses online. Previously, the CFPB’s Fall 2023 edition of Supervisory Highlights noted that payment processors have maintained payment platforms on which consumers may have paid fees that they would not have paid had the consumers understood that they were entitled to free options.
• Over the course of a school year, families with children eligible for means-tested reduced price lunch programs may send $0.60 to payment processors for each $1 they spend on school lunch. Families making online payments every other week, an industry-estimated frequency that CFPB interviews indicate may be conservative in some cases, can incur as much as $42 in transaction fees over the course of a school year. For families paying full price for lunch, every $1 they spend to pay for their child’s lunch incurs $0.08 in transaction fees. For those paying reduced price for lunch, this ratio grows to $0.60 for every $1 spent. Additional fees may further increase the cost of using electronic payment platforms.
• Fee-free options may not be meaningfully available to all families, leading to fees that can be burdensome and difficult to avoid, particularly for low-income families. These fees, which are most often a flat amount, may be disproportionately burdening lower-income families making smaller payments more frequently, compared to families who can afford to load a substantial amount into their child’s account at one time. Both school districts and processors frequently fail to post the availability of free payment methods, and further, free options may be more burdensome than electronic options.
• Three companies (MySchoolBucks, SchoolCafé, and LINQ Connect) dominate the school lunch payments market. While more than 20 unique companies offer these services to school districts nationwide, 67 percent of enrolled students in the sample are served by just three market leaders.
• Complex payment processor company structures and contracts may insulate companies from competition and make school districts less likely to negotiate fees for these services. For school districts considering contracts, payment platforms may be just one element of a larger contract for back-end school nutrition or information management services.
• Consumers cannot choose their payment platform. Because contracts are determined at a school-district level, families have no choice over which company they must use to add funds into online student lunch accounts. As a result, it may be especially difficult for them to avoid harmful practices, including those that may violate federal consumer protection law.

2. Background

As digital payments have become increasingly popular across sectors, more and more school districts around the country are offering parents and caregivers the ability to pay school-related expenses, including for field trips, athletics, and school lunches, online. 5

Families can typically access online payment portals through a link on their school district website, or through the company’s own webpage or app. Depending on the district, schools may partner with one payment processor for all electronic payments or may have one platform for school meal payments, for example, and another for other school-related payments.

School districts contract with third-party payment processing companies with the expectation that they will lower school district processing costs and increase administrative efficiency, accuracy, and security. 6 For example, digital payment information can automatically be integrated with student information, potentially minimizing errors from manually applying funds to a student’s account balance. Many payment processors also offer electronic solutions that purport to lessen administrative burden on school district staff, such as automated messaging features to parents and caregivers about unpaid academic fees or negative lunch account balances.

Despite these perceived benefits, there are also risks related to accepting electronic payments. For example, families typically have to pay fees to make electronic transactions or may have difficulty accessing timely refunds of unspent funds. Some school districts may also limit their acceptance of other payment methods like cash, even though the ability to make cash payments may remain preferable or necessary for some families. 7

Due to both the administrative efficiencies offered by online payment platforms and the high volume of daily transactions, school lunch programs present a clear opportunity to explore online payments in K-12 schools. This issue spotlight primarily focuses on the companies processing electronic payments for school lunches and the potential risks they pose to school districts and families.

2.1 School Lunch Payments

Most public schools participate in the National School Lunch Program (NSLP) and the School Breakfast Program (SBP), which are both federally assisted meal programs from the U.S. Department of Agriculture (USDA) that provide low-cost, or free meals to K-12 students. 8 Each day, on average, 19 million students participate in the free lunch program, 1.1 million in the reduced price lunch program, and 8.5 million in the full-price lunch program at public and private schools throughout the country. 9 Families with incomes at or below 130 percent of the federal poverty line are eligible for free lunch, and those with incomes between 130 and 185 percent are eligible for reduced price lunch. 10

Participation in the free and reduced price meal programs may not always fully reflect a student’s ability to afford food or cover the number of meals needed in a day. 11 As such, students who need lower-cost lunch options but do not participate in the free or reduced price meal programs as well as those who receive free or reduced price meals may still need to pay for food at school, potentially using a payment platform.

Nationwide, the average price of a middle school lunch is $3.00, or $0.40 for those participating in the reduced price lunch program. 12 A family with two children paying full price for lunch at school every day can expect to spend, on average, $1,080 on school lunches over the course of a school year. 13 Given these averages, and daily participation in the NSLP, the CFPB estimates that participating schools across the country are paid approximately $26 million every day and $4.68 billion every year by families purchasing their child’s first lunch. 14 Schools may be collecting more as students purchase additional meals or a la carte items.

The school district’s “school food authority” (SFA) manages its school nutrition program and determines what payment options are available to facilitate these transactions. 15 While there is no official market-wide estimate, one payment processor estimated that as many as a third of students at school districts with an online payment processor pay for lunch using funds electronically loaded to their account. 16 Interviews with school district administrators suggest that online payment options are popular among both families and school districts for their perceived security and convenience. 17

2.2 Relevant Federal Authorities

Nonbank covered persons, including online payment processors, are generally subject to the CFPB’s regulatory and enforcement authority and must comply with federal consumer financial protection laws. 18 Particularly relevant is the Consumer Financial Protection Act’s prohibition of unfair, deceptive, and abusive practices. 19 The CFPB’s Fall 2023 edition of Supervisory Highlights noted that certain covered persons maintained online school lunch payment platforms, and that certain practices related to the platforms may not comply with federal consumer financial protection laws. 20 Although local rules and state laws may govern types of school-related purchases, other aspects of federal law are also relevant to school lunch payments.

The U.S. Department of Agriculture (USDA) has long established that children participating in school nutrition programs “shall not be charged any additional fees” for the services provided in conjunction with the delivery of school lunch benefits. 21 In this policy, the USDA specified, “by charging fees in addition to the regular reduced price or paid meal charge, a school is limiting access to the program and imposing an additional criterion for participation.” 22 In 2014, the USDA published a policy memorandum that specifically addressed online fees in school meal programs, stating that school food authorities can charge a fee for online services, but only if they also offer a method for households to add money to the account that doesn’t incur any additional fees. In the policy, the USDA suggests that schools accept money at the school food service office or cash payments at the point of service as fee-free options. 23 In 2017, the USDA issued another policy reiterating this requirement and stating that school food authorities cannot exclusively use online payment systems. 24 The 2017 guidance also requires school food authorities to notify families of all available payment options, including any associated fees. In the last seven years since the USDA published this guidance, the popularity of digital payments has grown significantly across sectors. 25

3. Market Overview

The market of K-12 payment processors overlaps with two related industries: general payment processing and student information management software. Broadly, the top K-12 payment platforms are offered by subsidiaries of large financial institutions and by companies specializing in comprehensive student data management software. Among companies that specialize in school lunch payments, the same parent holding company may operate multiple payment platforms (see Figure 1). In recent years, many smaller companies offering the same services have been acquired by larger firms or have begun offering compatible products.

Many school payment processors, which may appear to occupy a niche industry and may lack broader name recognition, are owned by, or serve, significantly larger institutions with robust revenue streams and compliance capabilities. 26 Eight of the 20 K-12 payment processors identified by the CFPB are affiliated with larger companies that offer multiple school lunch payment products, creating a potentially misleading sense of product variety and market competition. 27 Generally, the leading K-12 payment platforms are well connected to large companies in the payments and financial services sectors. For example, five are operated by independent sales organizations 28 that provide payment processing services and generate revenue for Wells Fargo, a company that is also dominant in the higher education payments market. 29

FIGURE 1: The Market for School Lunch Payment Processors

3.1 School District Contracts

School districts enter into contracts with payment processors to help manage a number of financial and administrative responsibilities. In addition to providing user-facing payment portals, many payment processors also provide back-end services like point-of-sale software for school cafeteria systems, HR management systems, and student information systems for collecting applications for free or reduced price lunch. All these services are typically acquired under one contract, which determines what the school district pays for the services provided.

User-facing payment platforms are governed by these contracts, which also set the rates for fees charged to end users. Unlike other elements of these larger contracts, school districts typically do not have to pay to enable electronic transactions via an affiliated payment platform. Since payment processing companies have a fee-based revenue model, much of their revenue comes in the form of transaction fees.

Although school districts may experience cost savings or efficiencies of their own when contracting with processors, those financial benefits are not usually passed directly to families. Transaction fees are sometimes fully paid by a school district. 30 The USDA has a policy explicitly allowing school districts to cover transaction fees on families’ behalf using the funds in their nonprofit school food service account. 31 However, transaction fees are more commonly paid in part or full by families themselves. 32 The CFPB did not encounter any examples of school districts paying for payment processing services, except through these transaction fees, nor any examples of school districts receiving revenue from the fees that payment processors charge. 33

For many districts, the back-end software may be the main consideration when choosing a company to contract with. When this happens, a user-facing payment platform comes as part-and-parcel of a larger school nutrition program management system, insulating payment processors from competition based on transaction fees and negotiation that could lower fees assessed on end-users. Since payment platforms are typically provided without any up-front costs for school districts when included as part of a larger contract, school districts are not incentivized to prioritize low rates on fees that they will typically, in part or whole, pass on to end users. Families are only able to use the payment platform that their district has chosen, making it impossible to shop around for lower fees.

School districts that try to minimize fees charged to families may also run into challenges. Many districts may be limited in which payment options they can provide, due to cost or resource constraints that incentivize choosing providers who ultimately charge fees to families. Negotiating with payment companies may also be difficult for school districts. Although two school districts published information online about successfully negotiating with a payment company to offer a lower fee rate, 34 in interviews school officials at several districts across the country expressed that they were unaware that they could negotiate fee rates or otherwise felt that fee rates were non-negotiable. 35 Negotiating power may also vary by school district, as large districts may have additional leverage with payment processing companies or may benefit from fee discounts based on higher overall transaction volume. Smaller districts may not have the same advantages.

3.2 Payment Platform Features and Fees

3.2.1 product features.

Most companies that enable electronic school lunch payments advertise additional features for users, including scheduling automatic payments, sending low balance alerts, sharing account balance and meal purchase information, and processing payments. Some payment processors also provide a space in their user portals for schools to upload monthly lunch menus or post other announcements for caregivers to review. For many districts, families also use these online portals to submit applications for the free and reduced price lunch programs. Apart from making electronic payments, companies promote many of these features as free with the creation of an account. 36

3.2.2 Product Fees

Payment processors typically charge transaction fees each time an electronic payment is made. Companies that process school lunch payments may also charge other fees like convenience fees, which may include a fee for transferring funds between student accounts, or annual program fees that increase the cost of making online payments. 37

As previously discussed, fee rates are determined by each school district’s contract with the payment processor. Interviews with school district officials and information published on school district websites suggest that companies have broad control over fee rates. 38 Payment processors’ terms of service also establish the company’s unilateral control over fee levels and its ability to change them at any time. 39 School districts that cover all transaction fees on behalf of their users may pay more favorable fee rates compared to individuals. At least one school district entering into a contract with MySchoolBucks received certain fee discounts after indicating that the district planned to cover all transaction fees. 40

3.2.3 Costs of Electronic Transactions

Electronic transactions incur costs for payment processors. These costs differ depending on which payment mechanism is used. On their online portals, payment processors typically offer credit, debit, and prepaid cards, and, in fewer cases, Automated Clearing House (ACH) transactions. 41 Typically, processor’s payment processing costs fall around 1.53 percent of a total transaction for credit, debit, or prepaid cards, 42 and between $0.26 and $0.50 per transaction for ACH transfers. 43 Nonetheless, even the lowest transaction fees assessed by payment processors in school districts observed in the CFPB sample ($1.00 or 3.25%) 44 are significantly higher than the payment processors’ costs of processing electronic transactions.

4. Sample Findings

In a sample consisting of the 300 largest public school districts in the United States, 45 261 school district websites disclosed a partnership with a payment processor for school lunch payments. Once a partnership was identified, the CFPB recorded a number of variables including information about associated fees, fee types, and amounts (see Appendix A).

4.1 Sample Market Composition

Three providers, MySchoolBucks, SchoolCafé, and LINQ Connect, are the largest school payment processors in the sample according to the number of school district partnerships, school partners, and related total enrollment (see Table 1). In the sample, these three providers served more than 9.2 million students across more than 13,500 schools and 181 public school districts. MySchoolBucks is the largest across all three metrics, with almost 100 school district partnerships and more than 5 million enrolled students within the sample.

Table 1: Top 5 Payment Processors in CFPB Sample

4.1 Fee Rates

4.2.1 transaction fees.

While USDA guidance requires that families are notified about available payment methods and associated fees, many school districts do not publish information related to fees on their websites. 46 Across the 63 school districts in the CFPB sample that do publish fee specifics, average transaction fees were $2.37 for flat fees, and 4.4 percent for percentage fees. Median fees were $2.49 and 4.5 percent. Since these figures are from only the 21 percent of school districts in the CFPB sample that publicly report fee amounts, they may misestimate the true market average.

In the CFPB sample, payment processors at more than 70 percent of the districts that publish fee information charge flat transaction fees. At around 25 percent of these school districts, payment processors charge percentage fees, and a much smaller portion have a transaction fee model that incorporates both a flat per transaction fee and a percentage fee that varies based on deposit size. 47 Overall, fee levels vary widely between providers, and for the same provider across different school districts (see Table 2).

Table 2: Fee Range and Average Fees for Top 5 Payment Processors in CFPB Sample

NA appears where fee data was not observed.

Flat fees observed in the CFPB sample ranged from $1.00 to $3.25 per transaction. 48 The highest flat fees observed were from school districts partnering with MySchoolBucks ($3.25) 49 and EZSchoolPay ($3.00). 50 Percentage fees ranged from 3.25 percent to 5 percent of the total deposit. The highest percentage-based transaction fees were observed at school districts partnering with SchoolCafé (5 percent). 51

4.2.2 Other Fees

In addition to transaction fees, some school district websites also mention other fees that may increase the total cost for families using these services. It is unclear whether school districts are able to negotiate these fees in their contracts.

• Though many payment processors advertise free account membership, one leading payment processor is also starting to roll out a one-time program fee to be paid when a new account is opened. The CFPB has observed this fee type costing $2.50 per account. 52
• Instead of paying a fee for each transaction, some payment processors offer annual fees that cover certain electronic transactions for a full year. The CFPB has observed this fee type costing $12.95 per year for a single student or $26.95 per year for a family. 53
• Another payment processor charges a convenience fee for users to transfer funds between student accounts within the same family. The CFPB has observed this fee type costing $2.99 for a full year of transfer capabilities. 54
• Some payment processors also set a maximum deposit amount limiting how much a user can upload to their student’s lunch account in one transaction. For example, with a cap of $200 per transaction, a family would have to make at least three deposits a year per student to cover the average cost of school lunches, with each transaction incurring its own fee. 55

5. Costs and Difficulties for Consumers

Online payment platforms offer convenient solutions for school districts and families, but they also present potential negative implications for consumers. School lunch costs can be a challenge to families across the country, in part illustrated by the national average meal debt of $180.60 per child, per year. 56 Families, particularly those that are struggling to cover the cost of lunch itself, may find it difficult to avoid fees and may face other difficulties exacerbated by the use of payment platforms.

5.1 Inaccessible Fee-Free Options

Although payment platforms often perform a variety of services for school districts, including certain functions that help enable compliance, companies leave it to school districts alone to create and, in many cases advertise or disclose, any fee-free payment methods. Some school district websites note that families can add funds in person or by sending cash or check with a student. 57 Other school districts have policies that limit the use of cash, personal checks, or both, 58 which may raise questions regarding the districts’ conformity with USDA policy. 59 Even if families are aware of alternative options for paying school-related expenses, they may also potentially come with their own costs and limitations, in the form of transportation costs or difficulty accessing financial services. 60

Even where school districts allow fee-free payment options, free methods may not be meaningfully available to all families. Although school districts are required by USDA to provide fee-free methods and to inform families of their options to pay for school lunch, 61 not all school districts make the information readily available to families on their website. School districts are also not required to provide comparable online payment options that do not incur fees. As a result, other payment methods may be less well-known and less accessible than online payments. For non-meal-related expenses, the CFPB did not encounter any examples of similar requirements, so families may not have any fee-free options for paying these other expenses.

It may also be difficult for families to predict the total cost of using an electronic payment option. In many cases, the first time a caretaker will see how much they must pay to use an online payment platform is at the point of sale, which obscures the total cost until near the end of the transaction. Only 21 percent of sampled school districts explicitly disclose the fees associated with online transactions and no payment processors in the sample include specific information about potential fees on their website.

In some cases, families may be paying fees for electronic payments without knowing that they are entitled to fee-free options. The CFPB’s Fall 2023 edition of Supervisory Highlights noted that payment processors have maintained payment platforms on which consumers may have paid fees that they would not have paid had the consumers understood that they were entitled to free options. As a result, the CFPB observed that the payment processors’ practices may not have complied with consumer financial protection laws. 62

5.2 Difficulty Canceling Automatic Payments

Many payment processors allow users to turn on automatic payments at scheduled intervals or when school lunch account balances fall below a certain threshold. Conversations with school district officials described certain issues faced by families who set up autopay and then had difficulties canceling or otherwise forgot to cancel it when no longer needed. 63 Excess funds can quickly accrue in a student’s school lunch account if automatic payments are accidentally left on. 64 Each automatic transaction still incurs a per-transaction fee assessed by the payment processor, so families using automatic payments may be paying additional per-transaction fees to add unnecessary funds. Families are instructed to go directly to their child’s school for refunds, so any extra funds paid into a student’s school lunch account create additional administrative tasks for school district staff and may further delay when a refund is ultimately received.

5.3 Difficulty Accessing Timely Refunds

At the end of an academic year, funds in a student’s lunch account generally roll over for use when the school year resumes in the fall. There may be times, however, when families need to request a refund of the funds paid into their student’s lunch account. Terms and conditions of payment platforms generally note that if a caregiver is seeking a refund from a student’s account, they must contact their child’s school directly. 65 Payment processors do not hold on to student lunch account funds, as funds are directly transferred to school district bank accounts once a payment is made. Each school has its own process for distributing refunds. 66

At some school districts, the refund process can be complicated, requiring additional paperwork for families, 67 and may take weeks for the money to be returned. 68 Families using these payment platforms may be less willing to add a substantial amount to their accounts, due to the difficulty of accessing refunds, which may result in incurring additional per-transaction fees.

5.4 Fee Burden

Fees charged by payment platforms affect all families, though low-income families may be disproportionately impacted depending on the fee type and how often they make deposits over the course of a school year. Based on sample averages, school lunch payment processors nationwide may be collecting more than $100 million each year in transaction fees alone. 69 The total fee revenue collected by payment processors could be higher, after including revenue from other fees or additional lunchtime expenses. For families paying for their child’s lunch, these fees may pose a significant additional expense.

Flat transaction fees, as opposed to percentage fees, are much more prevalent among sampled school districts. By nature, flat fees have a regressive impact on lower-income users. Payment platforms appear to charge the same transaction fee for all users, regardless of whether a student receives free or reduced price lunch. Flat transaction fees are also much more expensive for users who make deposits more frequently, compared to those who can afford to deposit more money less frequently. 70 An industry-sponsored survey found that 60 percent of users on online school payment portals make two or more deposits per month, amounting to approximately 18 deposits per year. 71 Conversations with school district administrators suggested that some families may be using these online services much more often, up to once a week. 72 Although some families are able to deposit significant amounts into their child’s account at the beginning of a school year, that option might not be available for families living paycheck to paycheck. Frequent deposits can exacerbate the regressive effect of flat fees for families who do not have the financial flexibility to pre-load hundreds of dollars into their child’s lunch account at one time.

Table 3 shows three scenarios of potential fee burdens associated with full-priced school lunch costs. The below scenarios are based on two different levels of deposit frequency (twice per month, or biweekly, and three times a year), the average flat and percentage fee rates from the CFPB sample ($2.37 and 4.4 percent), the average full-price cost of a middle school lunch ($3.00), 73 and the average length of a school year (180 school days). 74

Table 3: Sample Costs for Families Paying Full Price for School Lunch with Online Payments

Families who pay full price for school meals and make two deposits a month into their child’s lunch account would incur over $42 in fees over the course of a school year. For these families, for every $1 they spent on school lunch, they paid $0.08 to the company processing their payments. Families who instead make just three payments a year end up paying much less in fees, around $7. In this case, for every $1 spent on school lunch, they paid just over $0.01 to a payment processor.

Table 4 shows three scenarios of potential fee burdens associated with reduced priced lunches, which cost $0.40 per lunch on average. 75 Since transaction fees appear to be the same across the board regardless of whether a student is eligible for free or reduced price lunch, families who pay for reduced price lunch pay more in fees relative to their school lunch costs during a school year.

Table 4: Sample Costs for Families paying Reduced Price for School Lunch with Online Payments

Families who pay for reduced price lunch and make two deposits a month into their child’s school lunch account would still incur over $42 in fees over the course of a school year. For these families, every $1 spent on school meals for their child corresponds to $0.60 that was paid to a payment processor. Families who can afford to make just three payments a year still pay $7 in fees, which amounts to about $0.10 for every $1 paid for school lunch.

6. Conclusion

Every day, families of school-aged children across the country spend millions of dollars on school lunch. Many caregivers opt to use online platforms to deposit money into their children’s accounts, incurring average fees of $2.37 or 4.4% of the total deposit per transaction. These fees are widespread, regressive, and may be burdensome for families and districts, who have little control over fee rates and few opportunities to shop around.

School food authorities participating in the USDA’s National School Lunch Program are required to provide fee-free avenues to pay for school lunch and inform families about all available payment methods, including associated fees. However, these fee-free options are not always well advertised or accessible. Despite requirements from the USDA, families may be paying more in fees than they would choose to if they had access to comparably convenient payment options with lower or no fees. Although school districts are able to negotiate fees while contracting with payment platforms, payment processors appear to have broad control over the fees they charge. Few school districts have been successful in ultimately lowering fees for families.

School districts face limited options. The market for school-related payment processing is dominated by a few market leaders and school food authorities may be locked in to using a certain payment platform due to its connection to the back-end service managing their school nutrition program. In turn, families have little choice in the payment platform offered by their school district and may be particularly vulnerable to harmful practices, including those that may violate federal consumer protection law.

Appendix A: Methodology

Sample construction.

This report analyzed data from the 300 largest public school districts by enrollment according to Fall 2021 data from the National Center for Education Statistics (NCES). 76 The CFPB examined the website of each school district in the dataset to identify publicly available information on lunch payment processor partnerships and fees. The CFPB also searched the websites of associated payment processors. This research was conducted between December 2023 and April 2024.

Once a school district was identified, the CFPB recorded the URL for the relevant district website, whether a payment processor is used for online school meal payments, whether the district offered free lunch for all students during the 2023-2024 school year, whether there is a fee associated with online payments, the fee category (e.g., flat fee or percentage), the fee amount, and relevant URLs. School districts within the sample that use only alternative channels to inform families of online lunch payment options, such as direct-to-family newsletters or printed resources distributed at the beginning of the school year, are not adequately captured in this dataset. Only cases where a payment processor for school lunch payments could definitively be identified are observed in the sample.

The CFPB also included descriptive statistics for each school district in the sample, including public high school graduation rates, total number of English language learners, the share of students eligible for free or reduced price lunches, the poverty rate of 5-to 17-year-olds within the district, and the number of schools for each district. For the sample of the 300 largest districts, this data comes from the National Center for Education Statistics. 77

Table 5 contains a comparison between descriptive statistics of the CFPB sample and public school districts nationwide.

Table 5: Comparison between National and CFPB Sample Descriptive Statistics

NA appears where a calculation is not applicable.

The CFPB sample, which includes data from the 300 largest school districts by fall 2021 enrollment, is not representative of the full population of public schools across the country. The CFPB dataset overrepresents large school districts, with the sample average school district size (55,782) far exceeding the national average (3,484). According to data from the National Center for Education Statistics, 71 percent of school districts in the U.S. had fewer than 2,500 enrolled students in the fall of 2021. However, these schools serve just 16.7 percent of the total number of enrolled students nationwide. The CFPB sample captures 36.1 percent of total student enrollment, while featuring only 2.25 percent of school districts. The CFPB dataset reflects nationwide trends for the percent of students eligible for free and reduced price lunch at about 48.5 percent.

In addition to the sample of the 300 largest school districts, the CFPB also analyzed data from a sample of 50 rural 79 public school districts, selected randomly from all U.S. counties with a nonmetro 2023 Rural-Urban Continuum Code (RUCC), 80 then subsequently matched with a corresponding district. 81 This rural sample was used to verify that smaller school districts, serving fewer students, also use these payment products for school lunch costs. Figures from the rural sample were not used to generate fee ranges or averages reported in the body of this issue spotlight. Among 50 rural school districts, 29 disclose a partnership with a third-party payment processor. MySchoolBucks is also the largest provider in the rural sample with 8 district partnerships. The rural sample is not large enough to analyze other information, such as fee averages.

Appendix B: Ownership Structures of Payment Processors

To generate Figure 1, the CFPB identified 20 lunch payment processing platforms in use at public schools in the United States. The CFPB gathered an initial list of platforms by searching for key words and phrases (including “online school lunch payment,” “lunch payment platform,” and “pay lunch online”) and examining the first five pages of search results. Platforms discovered through the construction of the school district sample (see Appendix A) were added to the list. The CFPB initially identified 26 platforms through these data collection methods. The CFPB then validated the list by, for each platform: (1) noting any public marketing statements by the processor or related companies confirming that they serve U.S. public schools; (2) finding U.S. public school districts that confirm usage of the platform on their websites; and (3) identifying any mergers or acquisitions with other platforms. The CFPB removed platforms that were not confirmed to serve U.S. public schools or have been merged into other existing platforms, paring the list down to 20 platforms. The CFPB then analyzed the ownership structures of the payment platforms to identify their parent and affiliate companies, both through examining their websites and the public securities filings of related entities.

EMS LINQ, Inc. owns two payment platforms that the CFPB identified—LINQ Connect, the third largest platform in the CFPB sample, and K-12 Payment Center. 82 The CFPB additionally found two lunch payment processors that have been consolidated into LINQ Connect: Titan, which EMS LINQ acquired in a $75 million leveraged buyout in 2020; and MealsPlus, which was previously marketed as a “LINQ solution” but merged into LINQ Connect in 2021. 83 Some school district webpages and public resources continue to use these older product names, and as of February 2024 the website of MealsPlus continues to exist, though most of its buttons redirect to LINQ Connect. In its public marketing materials, LINQ claims to serve 30 percent of U.S. school districts through its suite of K-12 business products. 84

Additionally, Constellation Software Inc., a Canadian conglomerate, owns N. Harris Computer Corporation, which owns Harris School Solutions, which operates two platforms: MealTime and EZSchoolPay. 85

Five of the payment platforms identified by the CFPB are registered ISOs of Wells Fargo. These platforms include: MySchoolBucks, (a product of Heartland Payment Systems and a subsidiary of Global Payments Direct, Inc.), 86 PaySchools and SchoolPay (products of i3 Verticals), and MySchoolAccount and RevTrak (products of Vanco Payments). 87 Heartland Payment Systems is an ISO of Wells Fargo and the Bancorp Bank, and Global Payments Direct, Inc. is an ISO of Wells Fargo and BMO Harris Bank. i3 Verticals is an ISO of Wells Fargo, RBS Worldplay, Deutsche Bank, Merrick Bank, BMO Harris Bank, and Fifth Third Bank. 88 Vanco Payments is an ISO solely of Wells Fargo. 89

The remaining 11 platforms identified by the CFPB do not appear to belong to companies offering multiple K-12 lunch payment processors, but many are owned by subsidiaries of large, publicly traded holding companies or marketed as part of a suite of K-12 information management products. Cybersoft Technologies owns SchoolCafé. 90 Roper Technologies, a publicly traded software and technological holding company, owns MyPaymentsPlus. 91 PCS Revenue Control Systems, a tech company specializing in K-12 nutrition software, owns PayPAMS. 92 Community Brands HoldCo LLC, a cloud-based software conglomerate, owns MySchoolWallet. 93 FocalTech, Inc., an information technology and e-commerce services provider, owns School Payment Portal. 94 Southwest Business Corporation, a diversified financial services company, owns e-Funds for Schools. 95 Computer Systems Design, Inc., a food and nutrition management software provider, owns LunchMoneyNow. 96 KEV Group, an international school activity fund management company, owns SchoolCash Online. 97 Two separate, independent companies named after their products own MealManage and CheddarUp. 98 Finally, PowerSchool Holdings, a publicly traded comprehensive K-12 software company, owns PowerLunch. 99

U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, Enrollment in public elementary and secondary schools, (retrieved April 2024), https://nces.ed.gov/programs/digest/d13/tables/dt13_203.20.asp .

The requirement extends to schools participating in the U.S. Department of Agriculture’s National School Meal Program. As of February 2024, the National School Lunch Program served 30 million students daily in public and nonprofit private schools, and residential child care institutions, See U.S. Department of Agriculture, Food and Nutrition Service, National School Lunch Program, (retrieved June 2024), https://www.fns.usda.gov/nslp ; See U.S. Department of Agriculture, Food and Nutrition Service, Child Nutrition Tables, (last updated February 2024, retrieved February 2024), https://www.fns.usda.gov/pd/child-nutrition-tables .

Consumer Financial Protection Bureau, Supervisory Highlights: Junk Fees Update Special Edition, Issue 31, Fall 2023 (Oct. 2023), https://www.consumerfinance.gov/data-research/research-reports/supervisory-highlights-junk-fees-update-special-edition-issue-31-fall-2023/ , at 15.

Sometimes school districts will partner with multiple companies, often having one contract with a payment processor that covers school lunch and other food-related payments and a separate contract with a different payment processor that covers other academic or extracurricular fees.

The CFPB conducted a series of unstructured interviews with school district officials around the country from February through March 2024. In these interviews, school district officials mentioned consistently choosing to contract with payment processors that enable online payments for school lunches due to the perceived increase in efficiency, accuracy, and security online platforms would provide. School districts officials indicated they may contract with payment processors to alleviate the need for employees to handle cash or checks and mitigate the perceived risk of fraud or theft.

Federal Reserve Bank of Boston, A fatal cash crash? Conditions were ripe for it after the pandemic hit, but it didn’t happen , Lindsay, Jay, (November 2, 2023), https://www.bostonfed.org/news-and-events/news/2023/11/cash-crash-pandemic-increasing-credit-card-use-diary-of-consumer-payment-choice.aspx

88 percent of public schools in the country participate in the USDA School Meal Program, as of October 2022, estimated by the U.S. Department of Education’s National Center for Education Statistics in their School Pulse Survey. The same survey found that 69 percent of public schools report a majority of their students as participating in the USDA School Meal Program. See National Center for Education Statistics, School Pulse Panel, (retrieved February 2024), https://ies.ed.gov/schoolsurvey/spp/ ; U.S. Department of Agriculture, Food and Nutrition Service, National School Lunch Program (NSLP) Fact Sheet, (last updated April 2023, retrieved February 2024), https://www.fns.usda.gov/nslp/nslp-fact-sheet .

As of 2022, around 90,000 schools participated in the National School Lunch Program and/or the School Breakfast Program, with many schools participating in both. Of participating schools, 4% of those participating in the NSLP are private schools and 3% of those participating in SBP are private. See U.S. Department of Agriculture, Food and Nutrition Service, Child Nutrition Tables, (last updated February 2024, retrieved February 2024), https://www.fns.usda.gov/pd/child-nutrition-tables ; Congressional Research Service, School Meals and Other Child Nutrition Programs: Background and Funding, (accessed Jun. 12, 2024), https://crsreports.congress.gov/product/pdf/R/R46234 .

U.S. Department of Agriculture, Economic Research Service, Child Nutrition Programs: National School Lunch Program, (accessed Mar. 4, 2024), https://www.ers.usda.gov/topics/food-nutrition-assistance/child-nutrition-programs/national-school-lunch-program/ .

By some estimates, eligibility requirements may fail to account for families’ complex socioeconomic realities, such as debt burdens, health and medication costs, and the high cost of living in many urban areas. See Pearce, Allie; Alleyne, Akilah; Neal, Anona, 5 States Addressing Child Hunger and Food Insecurity With Free School Meals for All, https://www.americanprogress.org/article/5-states-addressing-child-hunger-and-food-insecurity-with-free-school-meals-for-all

School Nutrition Association, School Meal Statistics, (accessed Mar. 2024), https://schoolnutrition.org/about-school-meals/school-meal-statistics .

The average cost estimate is based on the length of an average school year, which is 180 days. See U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, Average number of hours in the school day and average number of days in the school year for public schools, (last updated 2007-08, retrieved May 2024), https://nces.ed.gov/surveys/sass/tables/sass0708_035_s1s.asp .

Based on averages, 8.5 million students paying full price ($3.00) for school lunch spend about $25.5 million daily on school lunch. 1.1 million students paying reduced price ($0.40) for school lunch spend about $440,000 daily on school lunch. Together, this amounts to just over $25.9 million. This estimate does not include any costs for additional lunches purchased or a la carte items, which are generally not reimbursable for schools participating in the USDA School Meal Program and are not counted in daily participation totals.

A “school food authority” is the governing body responsible for the administration of one or more schools and has the legal authority to operate the schools’ nutrition program(s). In this report, “school district” and “school food authority” or “SFA” may be used interchangeably when describing the relationship between school district entities and the companies they contract with to provide online payment capabilities. See 7 C.F.R. § 210.2 “School food authority,” (accessed Feb. 2024), https://www.ecfr.gov/current/title-7/subtitle-B/chapter-II/subchapter-A/part-210/subpart-A/section-210.2 .

In a March 2024 CFPB market monitoring meeting with a payment processor, company officials indicated that as many as a third of students pay for lunch using funds electronically loaded to their account.

The CFPB conducted a series of unstructured interviews with school district officials around the country from February through March 2024.

See 12 U.S.C. 5481(15)(A)(vii). 12 U.S.C. 5481(6) defines “covered person” as (A) any person that engages in offering or providing a consumer financial product or service; and (B) any affiliate of a person described in subparagraph A if such affiliate acts as a service provider to such person.

CFPA section 1036(a)(1)(B), 12 U.S.C. 5536(a)(1)(B). In CFPA section 1031, Congress prohibited covered persons and service providers from committing or engaging in unfair, deceptive, or abusive acts or practices in connection with the offering or provision of consumer financial products or services. CFPA sections 1031(c) & (d) set forth the general standard for determining whether an act or practice is unfair or abusive. See 12 U.S.C. §§ 5531(c) & (d).

FNS Instruction 782-6 Rev. 1, Fees for Lunchroom Services (U.S.D.A. 2010), https://www.fns.usda.gov/cn/fees-lunchroom-services .

U.S. Department of Agriculture, Food and Nutrition Service, Memo SP02-2015: Online Fees in the School Meal Programs , (Oct. 8, 2014), https://www.fns.usda.gov/cn/online-fees-school-meal-programs .

U.S. Department of Agriculture, Food and Nutrition Service, Memo SP23-2017: Unpaid Meal Charges: Guidance and Q&A , (March 23, 2017), https://www.fns.usda.gov/cn/unpaid-meal-charges-guidance-qas .

While cash remained the third-most-used payment instrument in 2023, its use as a payment instrument has dropped 48 percent since the first iteration of the Federal Reserve’s Diary of Consumer Payment Choice in 2016. In 2023, consumers continued to increase the share of payments made online or remotely. See The Federal Reserve, 2024 Findings from the Diary of Consumer Payment Choice , Bayeh, Berhan; Cubides, Emily; and, O’Brien, Shaun, https://www.frbservices.org/binaries/content/assets/crsocms/news/research/2024-diary-of-consumer-payment-choice.pdf .

See Appendix B for additional information on company ownership structures.

For example, EMS LINQ, Inc. operates both LINQ Connect and K-12 Payment Center. I3 Verticals operates both PaySchools and SchoolPay. Vanco Payments operates both MySchoolAccount and RevTrak. Harris School Solutions, which is a subsidiary of Constellation Software Inc. (a large holdings conglomerate), operates both MealTime and EZSchoolPay. See Appendix B for additional information on company ownership structures.

Independent Sales Organizations (ISOs) provide payment processing services on behalf of a financial institution that acquires funds from a transaction. ISOs help the financial institutions they serve generate revenue through acquirer mark-ups, ACH transaction fees, and other fees charged for facilitating a transaction. ISOs are also called Merchant Service Providers (MSPs). For more information, see U.S. Department of the Treasury, Office of the Comptroller of the Currency, Comptroller's Handbook: Merchant Processing, Safety and Soundness (Version 1.0), (Aug. 2014), https://www.occ.treas.gov/publications-and-resources/publications/comptrollers-handbook/files/merchant-processing/pub-ch-merchant-processing.pdf .

MySchoolBucks is a product of Heartland Payment Systems, which is a registered ISO of Wells Fargo and the Bancorp Bank, and a subsidiary of Global Payments Direct, Inc., which is an ISO of Wells Fargo and BMO Harris Bank. Wells Fargo also has payment relations with four other platforms: PaySchools and SchoolPay, which are products of i3 Verticals, a registered ISO of Wells Fargo and several other banks; and MySchoolAccount and RevTrak, which are products of Vanco Payments, a registered ISO solely of Wells Fargo. (See Appendix B for more information). Wells Fargo is also a significant player in the higher education payment processing space, generating revenue from ISOs that help colleges embed tuition payment plan processing functions into student portals such as Nelnet, TouchNet (Heartland), and ECSI (Heartland). See Consumer Financial Protection Bureau, Tuition Payment Plans in Higher Education, (Sep. 2023), https://files.consumerfinance.gov/f/documents/cfpb_tuition_payment_plan_report_2023-09.pdf .

District of Columbia Public Schools partners with LINQ Connect to enable families to make school lunch-related payments without transaction fees. The school district subsidizes the full fee that would otherwise be paid by individual families. See District of Columbia Public Schools, “Meal Prices and Payment,” (accessed March 2024), https://dcps.dc.gov/page/meal-prices-and-payment ; The 2018-2019 contract between the School District of Escambia County, FL and Heartland Payment Systems, LLC provides two "fee model” options in the contract: “District Absorbed” or “Parent Paid.” This contract uses “Parent Paid.” See The School District of Escambia County Purchasing Department, Online Payment Processing for Hosted School Nutrition and Point of Sale Software , (accessed Apr. 2024). https://www.escambia.k12.fl.us/board/PDF%2018/June/06_19_18_regmtg/V_b_2_E_13.pdf .

USDA policy memoranda SP 02-1015: Online Fees in School Meals Programs , and SP 23-2017: Unpaid Meal Charges: Guidance and Q&A , allow school food authorities to pay fees associated with using an online system on behalf of families as an alternative to charging parents fees for such services. See U.S. Department of Agriculture, Food and Nutrition Service, Memo SP02-2015: Online Fees in the School Meal Programs , (Oct. 8, 2014), https://www.fns.usda.gov/cn/online-fees-school-meal-programs ; U.S. Department of Agriculture, Food and Nutrition Service, Memo SP23-2017: Unpaid Meal Charges: Guidance and Q&A , (March 23, 2017), https://www.fns.usda.gov/cn/unpaid-meal-charges-guidance-qas .

The Minneapolis Public Schools website describes how the district uses funds to subsidize part of the transaction fee for online lunch payments: “LINQ Connect charges a processing fee of $2.60 for each online payment transaction, no matter the amount. MPS pays $1.60 of this fee and the family pays $1.” See Minneapolis Public Schools, Eating at School, (accessed Feb. 2024), https://www.mpschools.org/departments/cws/menus/eating .

The 2015 contract between Stamford, CT Public Schools and Heartland School Solutions notes that the line-item cost of “MySchoolBucks Payment Services" is $0.00 for the school district, see Stamford Public Schools Purchasing Department, BID/RFP/Contract Award – Recommendation , (accessed May 2024), https://stamfordapps.org/boecontracts/Docs/Contracts/VENDOR%20CONTRACTS/FY20-21/181105%20Heartland%20School%20Solutions%20TIPS%20contract.pdf ; The 2022 contract between Chapel Hill- Carrboro, NC Public Schools and EMS LINQ also shows a line item charge of $0.00 for “K12 Payment Center Meals & Fees,” see Chapel Hill-Carrboro City Schools Board of Education, Agenda Abstract , (accessed May 2024), https://chccs.granicus.com/MetaViewer.php?view_id=2&clip_id=550&meta_id=36302 .

In 2018, Hawaii Public Schools stated that they “negotiated a $0.13 convenience fee with the new vendor, saving parents $0.67. With the new meal payment system, the transaction fee is also lower at 1.99 percent. The previous transaction fee was 5 percent.” According to reporting, the Hawaii State Department of Education switched from SchoolCafé to EZSchoolPay in 2018, when their contract with the former payment company lapsed. See Hawaii State Department of Education, Hawaii public schools to launch new online lunch payment system, (May 2, 2018), https://www.hawaiipublicschools.org/ConnectWithUs/MediaRoom/PressReleases/Pages/2018-eTrition-online-payments.aspx ; In 2017, Charles County Public Schools stated that they “recently negotiated an agreement with My Payments Plus to eliminate fees for system users” after charging a 3.75% service fee per transaction with the same platform. See Charles County Public Schools, CCPS Eliminates Service Fees for My Payments Plus , (December 22, 2017), https://www.ccboe.com/about/public-info-media/details/~board/press-releases/post/ccps-eliminates-service-fees-for-my-payments-plus .

The CFPB conducted a series of unstructured interviews with school district officials from around the country from February through March 2024.

For example , “Registering online with MyPaymentsPlus allows you to view your student’s account balances, purchase history, and payment history online, and even be notified by email when account balances fall below a designated amount. You do not have to make prepayments to use these features.” See Cobb County School District, “Food and Nutrition Services,” (accessed Mar. 2024), https://www.cobbk12.org/foodservices/page/45098/paying-for-meals ; “Myschoolbucks.com is an online payment portal specially designed to allow parents to make quick and easy online payments to their children's school accounts. The system allows parents to manage their children's lunch accounts, including viewing food selection.” See Coconut Creek Elementary (Broward County Public Schools), “Set Up Your Child’s Online Meal Payment Account via MySchoolBucks,” (accessed Mar. 2024), https://www.browardschools.com/site/default.aspx?PageType=3&DomainID=27&ModuleInstanceID=3466&ViewID=6446EE88-D30C-497E-9316-3F8874B3E108&RenderLoc=0&FlexDataID=235479&PageID=47 .

Some payment processors use different names to distinguish fee types. This report refers to any fees that are processed on a per-transaction basis as “transaction fees.” Other fee types are explained in Section 4.2.2.

“LINQ may charge a fee in connection with the Services and/or transactions processed through the Services. The Fee will apply to each one-time, automated, and scheduled payment.” See EMS LINQ Inc., LINQ Connect Terms of Service , (accessed Feb. 2024), https://linqconnect.com/main/terms ; “If you use MySchoolBucks to add funds to your child’s account, you may pay a program fee for the convenience of using our online service.” See MySchoolBucks, Terms of Service , (accessed Feb. 2024), https://login.myschoolbucks.com/users/etc/getterms.action?clientID=schoolbucks ; “SchoolCafé imposes a convenience fee on every payment made using the Service. The convenience fee, an amount or a percentage of the payment, is set solely at the discretion of SchoolCafé and can be changed at any time without notice.” See SchoolCafé, Terms of Service , (accessed Feb. 2024), https://www.schoolcafe.com/ .

“We [Heartland Payment Systems, Inc.] understand that instead of passing the convenience fees along to the parents, FCPS plans to absorb those fees directly. In light of that, we are able to offer more favorable pricing than what was contained in our original proposal…When compared to our original pricing, we reduced the number of volume-based tiers and lowered the fee for each tier.” See Fairfax County Public Schools, Acceptance Agreement Attachment D, (Aug. 12, 2014), https://www.fairfaxcounty.gov/cregister/DownloadPDF.aspx?AttachmentID=926546c7-25aa-4447-a696-885077c7f569 .

Information on payment processing websites suggests that available payment methods ultimately depend on the contract between the payment processor and school district.

A recent The Nilson Report notes that the weighted average of processing fees that merchants paid in 2023 was 1.53 percent of purchase volume on all credit, debit, and prepaid general purpose and private label cards. Fees related to debit card transactions are typically lower. Since 2011, debit card fees are capped by the Federal Reserve at $0.21 plus 0.05% of the transaction value for covered issuers. According to data from 2022, average interchange fees for all debit transactions (both exempt and covered) were $0.34 or 0.73% of the average transaction value. See The Nilson Report, Issue 1259 (Mar 2024); The Federal Reserve, Regulation II (Debit Card Interchange Fees and Routing) , (accessed April 2024), https://www.federalreserve.gov/paymentsystems/regii-average-interchange-fee.htm .

The 2022 Payments Cost Benchmarking Survey found the median cost of initiating and receiving an ACH payment for all businesses to be between 26 cents and 50 cents. See Nacha, ACH Costs are a Fraction of Check Costs for Businesses, AFP Survey Shows , (accessed April 2024), https://www.nacha.org/news/ach-costs-are-fraction-check-costs-businesses-afp-survey-shows .

For example, Poudre School District, (accessed May 2024), https://www.psdschools.org/schools/student-fees-charges/pay-feescharges-online ; For example, Plano School District, (accessed May 2024), https://www.pisd.edu/Page/3841 .

The sample constructed by the CFPB is based on the 300 largest school districts in the United States by enrollment during the 2021-2022 school year. Enrollment data is from the National Center for Education Statistics. See Appendix A: Methodology for more information.

For example, “There is an additional fee of 2.9% +$0.25 per transaction (for debit or credit card).” See Indian Prairie School District #204, PushCoin , (accessed March 2024), https://www.ipsd.org/Page/2623 .

Most payment processors charge one fee regardless of payment method, others have fees that vary. For example, Chandler Unified School District has information on their website that specifies, “When using MySchoolBucks.com platform to fund student accounts, the following fees will be charged to the user: Funded by credit card/debit card: $3.25 per transaction; Funded by e-check/bank account transaction: $2.75.” See Chandler Unified School District, “Online Student Meal Payment Account,” (accessed April 2024), https://www.cusd80.com/Page/118077 .

For example, Pinellas County Schools, (accessed Mar. 2024), https://www.pcsb.org/Page/40505 .

For example, Deer Valley Unified School District, (accessed Mar. 2024), https://www.dvusd.org/studentaccounts .

For example, Virginia Beach City School District, (accessed Mar. 2024), https://www.vbschools.com/families/food-and-nutrition-services ; Omaha School District, (accessed March 2024), https://www.ops.org/Page/319 ; Beaverton 48J School District, https://www.beaverton.k12.or.us/departments/nutrition-services/student-account ; Douglas County School District, (accessed Mar. 2024), https://www.dcsd.net/departments/nutrition-services .

“Beginning next week, some parents in your district will see a Fall 2023 Program Fee of $2.50 at their next transaction on MySchoolBucks.” See Escambia County Public Schools, Food Services, (accessed Mar. 2024), https://www.escambiaschools.org/Page/802 .

“My School Bucks now has an option available to pay an annual, one-time flat fee of $12.95 for a single student or $26.95 for a family. The “OnePay” option gives you unlimited transactions for 12 months, instead of the per-transaction fee of $2.75 for individual transactions.” See McKinney ISD, Meal Prices & Payments , (accessed March 2024), https://www.mckinneyisd.net/school-nutrition/meal-prices-payments/ ; Chandler Unified School District, Online Student Meal Payment Account , (accessed March 2024), https://www.cusd80.com/Page/118077 .

“If you are only transferring funds between siblings enrolled in CCSD, you have the option of doing so online via MyPaymentsPlus.com for any registered students on your account. There is a $2.99 convenience fee for this service for a full year of access to transfers.” See Cobb County School District, Food and Nutrition Services , (accessed March 2024), https://www.cobbk12.org/foodservices/page/45098/paying-for-meals .

The average cost of a middle school lunch nationwide is $3.00. Over the course of 180-day school year, this would amount to $540 per year. Adding $540 to an account with a per-transaction cap of $200 would take three separate transactions, each incurring its own transaction fee.

Education Data Initiative, “School Lunch Debt Statistics,” Hanson, Melanie, (accessed June 2024), https://educationdata.org/school-lunch-debt ; U.S. Department of Agriculture Economic Research Service, Cost of school meals and households’ difficulty paying for expenses: Evidence from the Household Pulse Survey , Toossi, S., (accessed June 2024), https://www.ers.usda.gov/webdocs/publications/106915/eb-37.pdf?v=6431.1 .

“Of course, meal payments can also be made by sending cash or checks (payable to the school cafeteria) to school with your child.” See Forsyth School District, Food & Nutrition Services , (accessible Mar. 2024), https://www.forsyth.k12.ga.us/page/401 ; “Meals may still be prepaid by depositing cash into student accounts.” See Spring ISD, Online Payment Option Available for Meals , (accessed Mar. 2024), https://www.springisd.org/page/online-payments ; “Families without credit/debit cards can add money to their students’ account by bringing a check to the cafeteria staff at the school. There is no processing fee charged for these transactions.” See Minneapolis Public Schools, Eating at School , (accessed Mar. 2024), https://www.mpschools.org/departments/cws/menus/eating .

“This school year (SY2023-24) students will be able to purchase items a la carte, such as milk. To do so, they need to have money loaded onto their MySchoolBucks account. No cash will be accepted.” See SDU46, School Breakfast & Lunch Menus , (accessed Mar. 2024), https://www.u-46.org/Page/9190 ; “Meals may be purchased with cash or by using the online payment system, [SchoolCafe]…The Pasadena Independent School District Nutrition Services will not accept personal checks.” See Pasadena Independent School District, Meal Price & Online Payments , (accessed Mar. 2024), https://www.pasadenaisd.org/13013_4 .

According to USDA guidance, SFAs cannot exclusively use an online system for payment. Those that do use an online system must provide an alternative option to meet the needs of families who do not have access to a computer or who prefer to make their payment in person. See U.S. Department of Agriculture, Food and Nutrition Service, Memo SP 23-2017: Unpaid Meal Charges: Guidance and Q&A , (March 23, 2017), https://www.fns.usda.gov/cn/unpaid-meal-charges-guidance-qas .

For example, one school district specifies that they do not accept cash but will accept cashier’s checks or money orders to pay for school lunch. These banking services may be difficult to access for some families, making it more expensive to avoid fees. See Atlanta Public Schools, Nutrition Pre-Payment Options , (accessed Mar. 2024), https://www.atlantapublicschools.us/domain/14255 ; “Many types of bill payments incur costs for consumers… Some payment instruments can be costly to obtain, such as money orders and checks, while others can be costly to use, such as some credit cards. Consumers usually incur the highest costs when paying a bill in person (regardless of payment instrument) due to transportation costs and the lowest costs when paying over the phone or online; paying through the mail, which incurs postage costs, is somewhere in between.” See also Federal Reserve Bank of Kansas City , When Paying Bills, Low-Income Consumers Incur Higher Costs, (accessed Mar. 2024), https://www.kansascityfed.org/research/payments-system-research-briefings/when-paying-bills-low-income-consumers-incur-higher-costs/ .

For example, The Lewisville ISD website includes information warning caregivers that they must turn off auto-replenish and low balance alerts on their RevTrak payment platform before requesting a refund from the school district. See Lewisville ISD, Refunds, A la Carte Policy, and LISD Employee Accounts , (accessed April 2024), https://www.lisd.net/Page/22806 .

“If you are not satisfied with any good or service purchased using the Services, you agree to resolve the issue exclusively with the Student’s School… You agree that you will not seek and are not entitled to a refund from LINQ.” See EMS LINQ Inc., LINQ Connect Terms of Service , (accessed Feb. 2024), https://linqconnect.com/main/terms ; “You agree that you will not seek and are not entitled to a refund from HPS. If you would like a refund of any kind from you Student’s school or school district, you must contact your student’s school or school district.” See MySchoolBucks, Terms of Service , (accessed Feb. 2024), https://login.myschoolbucks.com/users/etc/getterms.action?clientID=schoolbucks ; “All issues relating to unused funds on a student account should be addressed directly with the students’ school districts SchoolCafé cannot be held liable to the Users for any unused funds.” See SchoolCafé, Terms of Service , (accessed Feb. 2024), https://www.schoolcafe.com .

On their website, MySchoolBucks specifies that “Payments placed through MySchoolBucks are quickly expedited to your school’s bank for deposit. All funds are housed by your district office.” See MySchoolBucks, Top Support Questions , (accessed March 2024), https://www.myschoolbucks.com/ver2/etc/getcontacts.action?clientKey=ZZHKFGWNS605S8L#:~:text=All%20funds%20are%20housed%20by,department%20to%20obtain%20a%20refund .

For example, caregivers seeking a refund for school lunch account balances are required to submit a W-9 form to the Albuquerque Public Schools. See Albuquerque Public Schools, Food and Nutrition Services, (accessed April 2024), https://www.aps.edu/food-and-nutrition-services/school-menus-and-prices .

For example, information on the Spring Branch Independent School District website says that refunds can take 2-3 weeks to process. See Spring Branch ISD, Payments and Refunds , (accessed April 2024), https://www.springbranchisd.com/about/departments/finance/school-nutrition-services/payments-and-refunds ; Since refunds at Prince William County schools are sent in the form of checks via mail, they take between 4 and 6 weeks to process. See PWCS Nutrition, (accessed April 2024), https://www.pwcsnutrition.com/index.php?sid=0408101731444083&page=prepaidacct .

Estimates are made based on national averages, including average cost of school lunch ($3.00 or $0.40), length of a school year (180 days), and sample averages of flat fee prevalence compared to percentage fee prevalence (74 percent and 26 percent, respectively), average fee rates ($2.37 or 4.4 percent), and the proportion of schools in the sample that enable electronic payments through a payment platform (87 percent). Using USDA data describing daily participation in the National School Lunch program (8.5 million for full-priced lunch and 1.1 million for reduced price lunch) and insight from a payment processing company estimating that as many as a third of students paying for lunch do so using funds added to their account electronically, the CFPB estimated that 315,810 students pay for reduced price lunch and 2.4 million students pay for full price lunch using online payment methods. Using these estimates, as well as the annual fee costs for a student paying for full or reduced price lunch every school day, and the relative prevalence of flat and percentage fees in the CFPB sample, the CFPB estimates that companies collect between $28 million and $92 million in fees from students paying for full-priced lunch and between $1.9 million and $10.2 million from students paying for reduced price lunch in transaction fees each year. The lower end of the estimated fee range is based on families making just 3 payments per year. The higher end of the range is based on families making payments every other week, or 18 deposits a year. This estimated range in transaction fee revenue is just for transaction fees incurred while paying for a student’s first lunch, without including any transaction fees for additional meals or a la carte purchases.

EMS LINQ, Inc., 2023 K-12 Payments Survey Report at 9, (accessed Feb. 2024), https://www.linq.com/report/2024-k12-payments-survey-report/ .

School Nutrition Association, School Meal Statistics: School Meal Prices and Unpaid Meals, (accessed Mar. 4, 2024), https://schoolnutrition.org/about-school-meals/school-meal-statistics/ .

U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, Average number of hours in the school day and average number of days in the school year for public schools, (last updated 2007-08, retrieved May 2024), https://nces.ed.gov/surveys/sass/tables/sass0708_035_s1s.asp .

National Center for Education Statistics, Digest of Education Statistics, “Table 215.10 – Selected statistics on enrollment, staff, and graduates in public school districts enrolling more than 15,000 students in fall 2021 (1990 through 2021),” https://nces.ed.gov/programs/digest/d22/tables/dt22_215.10.asp , “Table 215.20 – Revenues, expenditures, poverty rate, and Title I allocations of public school districts enrolling more than 15,000 students in fall 2021,” https://nces.ed.gov/programs/digest/d22/tables/dt22_215.20.asp .

Nationwide statistics are from Tables 204.10, 214.20, and 214.40 of the most recent edition of the Digest of Education Statistics from the National Center for Education Statistics. All statistics are from the 2021-2022 academic year. See National Center for Education Statistics, Digest of Education Statistics , (accessed February 2024), https://nces.ed.gov/programs/digest/current_tables.asp .

In this report, “rural” is used interchangeably with “nonmetro” as classified in the Rural-Urban Continuum Codes documented by the USDA.

The 2023 Rural-Urban Continuum Codes distinguish U.S. metropolitan counties by the population size of their metro area, and nonmetropolitan counties by their degree of urbanization and adjacence to a metro area. The division of counties as either metro or nonmetro, based on the 2023 Office of Management and Budget (OMB) delineation of metro areas, is further subdivided into three metro and six nonmetro categories. Counties with an RUCC of greater than or equal to 4 are considered nonmetro, with counties classified as a 9 exhibiting the least urbanization. All told, the 2023 Rural-Urban Continuum Codes include 1,186 metro counties and 1,958 nonmetro counties in U.S. States and the District of Columbia. See U.S. Department of Agriculture, Rural-Urban Continuum Codes Documentation , (accessed Feb. 2024), https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/ .

The random sampling was performed using the RAND function in Microsoft Excel. Each county with an RUCC of equal to or greater than 4 was assigned a random string of numbers, which was then ordered from Smallest to Largest. The first 50 counties in this randomly assigned order were chosen and matched with a school district in that county. For more information about the RAND function in Excel, see Microsoft, Excel: RAND Function, (accessed Mar. 4, 2024), https://support.microsoft.com/en-us/office/rand-function-4cbfa695-8869-4788-8d90-021ea9f5be73 .

EMS LINQ, Inc., LINQ Connect: Online Portal for K-12, (accessed Feb. 2024), https://www.linq.com/solutions/nutrition/district-nutrition/front-of-house/online-portal/ ; K-12 Payment Center, About Us, (accessed Feb. 2024), https://www.k12paymentcenter.com/Home/AboutUs ; MealsPlus, Site Banner, (accessed Feb. 2024), https://www.mealsplus.com/welcome-to-meals-plus/ .

Globe Newswire, LINQ & TITAN School Solutions Announce Merger, (Nov. 10, 2020), https://www.globenewswire.com/en/news-release/2020/11/10/2123594/0/en/LINQ-TITAN-School-Solutions-Announce-Merger.html ; Stradling Law, TITAN School Solutions Completes $75 Million Acquisition by EMS LINQ, (accessed Feb. 2024), https://www.stradlinglaw.com/experience/titan-school-solutions-completes-dollar75-million-acquisition-by-ems-linq.html ; MealsPlus, Site Banner, (accessed Feb. 2024), https://www.mealsplus.com/welcome-to-meals-plus/ ; @MealsPlus, Twitter/X (Dec. 18, 2020), https://twitter.com/MealsPlus/status/1339933615807971329 .

EMS LINQ, Inc., Home Page, (accessed Feb. 2024), https://www.linq.com/ .

Harris School Solutions, MealTime: School Nutrition Program Management Software, (accessed Feb. 2024), https://harrisschoolsolutions.com/products/mealtime-elementor/ ; Harris School Solutions, EZSchoolPay: Your Digital, Full-Cycle School Payment Software, (accessed Feb. 2024), https://harrisschoolsolutions.com/products/mealtime-elementor/ ; Harris Computer, Public Sector Solutions, (accessed Feb. 2024), https://www.harriscomputer.com/public-sector ; Constellation Software, Inc., Our Companies, (accessed Feb. 2024), https://www.csisoftware.com/our-companies .

MySchoolBucks, Site Banner, (accessed Feb. 2024), https://www.myschoolbucks.com/ ; Heartland Payment Systems, Site Banner, (accessed Feb. 2024), https://www.heartland.us/about/about-us ; Global Payments Direct, Inc., Site Banner, (accessed Feb. 2024), https://www.globalpayments.com/ .

i3 Verticals, LLC, Education Products and Site Banner, (accessed Feb. 2024), https://www.i3verticals.com/education/ ; MySchoolAccount, Site Banner, (accessed Feb. 2024), https://www.myschoolaccount.com/ ; Vanco Payments, RevTrak: Vanco’s Online School Payment System and Site Banner, (accessed Feb. 2024), https://www.vancopayments.com/education/online-payment-processing .

i3 Verticals LLC, Site Banner, (accessed Feb. 2024), https://investors.i3verticals.com/ .

Vanco Payments, RevTrak: Vanco’s Online School Payment System and Site Banner, (accessed Feb. 2024), https://www.vancopayments.com/education/online-payment-processing .

Cybersoft Technologies, About, (accessed Feb. 2024), https://www.cybersoft.net/about/ ; SchoolCafé, About, (accessed Feb. 2024), https://SchoolCafék12.com/about/ .

MyPaymentsPlus is a product of Horizon Software, which operates as a unit of Roper Technologies (ROP: NYSE). See MyPaymentsPlus, Site Banner, (accessed Feb. 2024), https://www.mypaymentsplus.com/welcome ; Horizon Software, Online Payments: MyPaymentsPlus, (accessed Feb. 2024), https://horizonsoftware.com/online-payments ; Horizon Software, About, (accessed Feb. 2024), https://horizonsoftware.com/about-us .

PCS Revenue Control Systems, PayPAMS Family Portal, (accessed Feb. 2024), https://pcsrcs.com/pcs-solutions/parent-account-portal/ .

MySchoolWallet is a product of Diamond Mind, which is part of Community Brands HoldCo LLC. See Diamond Mind Inc., Introducing MySchoolWallet, (accessed Feb. 2024), https://www.diamondmindinc.com/resources/product-videos/introducing-myschoolwallet/ ; Community Brands, Solutions: School Accounting, (accessed Feb. 2024), https://www.communitybrands.com/solutions/school-accounting/ ; Community Brands, Our Brands, (accessed Feb. 2024), https://www.communitybrands.com/company/our-brands/ .

School Payment Portal is a product of LunchTime Software, which is an affiliate of Focal Tech Inc. See Focal Tech Inc., Contact Us, (accessed Feb. 2024), https://www.focaltechinc.com/Contact-Us#contact ; School Payment Portal, Site banner, (accessed Feb. 2024), https://www.schoolpaymentportal.com/Default.aspx .

e-Funds for Schools is a product of Magic-Wrighter, which was acquired by SWIVEL Transactions LLC in 2023, which is a subsidiary of Southwest Business Corporation (SWBC). See e-Funds for Schools, About Us, (accessed Feb. 2024), https://efundsforschools.com/about-us/ ; Magic-Wrighter, About Us, (accessed Feb. 2024), https://www.magicwrighter.com/about-us/ ; SWIVEL, SWBC’s SWIVEL Acquires Magic-Wrighter, Inc. (Dec. 5, 2023), https://www.getswivel.io/press-releases/swbcs-swivel-acquires-magic-wrighter-inc/ ; Southwest Business Corporation, Payment Solutions, (accessed Feb. 2024), https://www.swbc.com/payment-solutions .

Computer Systems Design, Inc., Lunch Money Now, (accessed Feb. 2024), https://systemsdesignusa.com/SDwp/sd/software/lunch-money-now/ .

KEV Group, Products, (accessed Feb. 2024), https://kevgroup.com/products/ ; SchoolCash Online, Home page, (accessed Feb. 2024), https://www.schoolcashonline.com/ .

MealManage, About, (accessed Feb. 2024), https://www.mealmanage.com/about.php ; CheddarUp, About, (accessed Feb. 2024), https://www.cheddarup.com/about-cheddar-up/ .

PowerSchool offers K-12 information management portals that integrate other lunch payment processors. The CFPB identified many lunch payment processors that offer software integrations with PowerSchool’s online portal (for which they must pay an annual fee proportionate to their user base). PowerLunch is the user-facing name of the portal’s lunch payment module. See PowerSchool Holdings, Inc., Investor Relations, (accessed Feb. 2024), https://investors.powerschool.com/home/default.aspx ; U.S. Securities and Exchange Commission, “Letter from PowerSchool Holdings, Inc., re: Draft Registration Statement on Form S-1, CIK No. 0001835681,” EDGAR Archives, (Dec. 22, 2020), https://www.sec.gov/Archives/edgar/data/1835681/000095012321000843/filename1.htm at p. 12.

New Data: Germany’s Baby Boomers Flip the Script on Digital Wallet Usage

Download the Report Digital Wallets Beyond Financial Transactions: Germany

By completing this form, I have read and acknowledged the  Terms and Conditions   and agree that PYMNTS.com may contact me at the email address above.

Consumers in Germany are embracing their digital wallets for payments. Nearly half (47%) use them for online shopping for example. Surprisingly, baby boomers and seniors in Germany are more likely to use one for online shopping than Generation Z consumers.

However, facilitating payments and transfers is just one way consumers can use these tools. These wallets can be anywhere that a smartphone is. This is a fact that cannot be overlooked, and it can drive adoption.

PYMNTS Intelligence finds that once consumers in Germany store credentials and use a digital wallet — to verify their identities or present credentials to access an event, among other uses — most state high satisfaction with their experience.

This suggests that the German market is ready for digital wallets’ next evolution.

These are just some of the findings detailed in “ Digital Wallets Beyond Financial Transactions: Germany Edition ,” a PYMNTS Intelligence and Google Wallet collaboration. This report examines consumer perceptions and use of these wallets in the last year and into the future in the German market. It draws on insights from a survey of 2,302 consumers in Germany conducted from Jan. 11 to Feb. 5.

Inside “Digital Wallets Beyond Financial Transactions: Germany Edition”:

• What tasks consumers in Germany are completing using these wallets
• Which generations are using these wallets the most
• How consumers in Germany are using these tools for travel and transportation
• How using these wallets for identification verification could be more convenient for consumers, especially when they are on the road

Once consumers use a digital wallet to store credentials or show their IDs, most have positive experiences doing so. Download the report to learn what’s next for these wallets in Germany.

This report is the third in a series exploring how consumers in various major economies are using digital wallets. Check out the series archive to learn about how use in Germany compares to other countries.

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The 5 Most Common Factors That Motivate Women To Cheat—By A Psychologist

Do women cheat for the same reasons as men? New research reveals the answer.

When it comes to understanding infidelity, the motivations behind men’s unfaithfulness have been extensively studied. Academics and the public alike are well aware of the potential reasons as to why men cheat—from psychological and evolutionary perspectives, as well as personal ones. But, what’s relatively understudied are the dynamics in which women are inclined to cheat. Their motivations have long been debated, with a clear consensus seemingly out of reach.

However, recent research from the journal Evolution and Human Behavior tested the many debated hypotheses regarding the driving factors behind women’s infidelity. The study’s ultimate findings highlighted a range of motivations, but five factors stood out most prominently. These are the most common motivations behind women’s infidelity.

1. Their Relationship Is No Longer Fulfilling

Overall, women’s most frequently given excuse for their infidelity is being unhappy in their current relationship—with a whopping 64.66% of women in the study having endorsed this reasoning.

When probed about their decision to cheat in this regard, one participant candidly shared, “To be honest, I was with my partner just because I was feeling lonely, and then I didn’t know how to break up with him. I never liked him, but I was vulnerable." This sentiment captures a significant aspect of why many women stray—feeling trapped in a relationship that doesn’t fulfill their emotional needs.

In contrast, only 30.43% of men cited relationship dissatisfaction as their primary motivation for cheating. This discrepancy points to a crucial difference: a lack of fulfillment in a relationship—whether due to ongoing conflict, a lack of intimacy or simply attachment that has eroded over time—seems to push women toward infidelity far more than it does men. When the emotional connection falters, it seems women might seek solace and validation outside their relationship.

2. Their Partner Seems Uninterested In Them

The second most common motivation for women’s infidelity—nominated by 22.41% of participants—is the feeling that their partner is no longer interested in them or invested in their relationship.

According to one such participant who felt this way, “I felt neglected by my partner at the time; he always worked late and didn’t have time for me and our child. I just found myself getting closer to another man who was always available for me when I needed help.” These feelings of abandonment can drive women to seek some kind of connection elsewhere—especially when their primary relationship feels lacking.

Staggeringly, only 5.07% of men reported that they cheated for this reason, indicating that women are four times more likely to stray due to feeling neglected. Clearly, for women, feeling unseen or unheard in a relationship places them at a much higher risk of infidelity compared to men.

When women feel alone in their relationships—whether it be emotionally, in terms of childcare or in domestic labor—the desire for attention and care can lead them to form connections outside their primary relationship. This may be in search of the validation and support they miss at home.

3. They Want Revenge For A Cheating Partner

Intriguingly, 15.52% of women admitted that they cheated purely because they knew their partner was already cheating. One participant, when asked why she cheated, confessed that it was an act of retaliation: “I found an email of his where he was apparently looking for other women to date on Craigslist.”

This is another distinct area where men and women differ significantly, with only 2.9% of men citing this as their reason for infidelity—five times less than women. It seems that, for women, revenge is a dish best served cold.

Giving their partner a taste of their own medicine appears to be a compelling driving force, perhaps because it levels the playing field, or maybe because it provides a small sense of vindication. Whatever the reason, it seems that when women feel wronged, the urge to seek retribution can be a powerful motivator—pushing them to cheat as a form of emotional retaliation.

4. They’re Sexually Dissatisfied

The fourth most endorsed reason behind women's infidelity, at 8.62%, was sexual dissatisfaction. Unsurprisingly, this is quite similar to the men in the study, with 9.42% of them citing the same reason.

One participant shared how this dissatisfaction drove her to explore other options: “I wanted to know I was still desirable and that other men wanted me if my partner ever cheated on me with another woman. Plus, I wanted to be experienced in sex.”

Whether it’s a lack of good sex or the infrequency thereof, a fulfilling sex life seems just as important to women as it is to men. When this aspect of a relationship becomes dull, women, like men, may use any means necessary to satisfy it. The drive for a passionate and satisfying sex life can be a powerful motivator—and when it’s missing, women aren’t above seeking that satisfaction outside their relationship.

5. They’re Bored

One participant’s excuse for her infidelity perfectly sums up the fifth motivator: “I was overall just bored in the relationship.” This sentiment is echoed by 7.76% of women in the study who cited boredom as their motivation. And, in a similar vein, 5.17% of women attributed their infidelity to the need for novelty. It seems that when the routine of a relationship becomes monotonous, the allure of something new and exciting can be a powerful pull.

In terms of boredom, men and women are nearly tied—with 5.8% of men also citing this as their reason for cheating. However, men are almost twice as likely to cheat out of the need for novelty, scoring at 10.87%.

It’s clear that when a relationship starts to feel like stagnant water—with nothing new or exciting happening sexually, emotionally or in terms of present and future plans—women, like men, may seek stimulation elsewhere. Whether consciously or not, the search for something new can reveal that the fun and excitement they’ve been craving lies outside their relationship. From here, one thing leads to another; the quest for novelty can advance into infidelity, as the desire for a fresh, invigorating experience might become too strong to resist.

Concerned that you might fall in line with these statistics? Take this test to find out, and receive science-backed answers from a psychologist: Propensity Towards Infidelity Scale

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PSR stands firm over reimbursement model for APP fraud

As the banking industry chafes against upcoming rules governing payback to victims of authorised push payment fraud, the Payment Systems Regulator has released new data which shows that reimbursement for victims still depends largely on who they bank with.

In 2023, 4.5 billion transactions were made using the Faster Payments system. According to the PSR, users reported 252,626 cases of APP scams totalling almost £341m. The report shows the percentage of APP scam cases that were fully and partially reimbursed by each firm. Under the existing voluntary reimbursement framework, 67% of money lost to APP scams was reimbursed. While this has improved between 2022 (61%) and 2023 (67%), the regulator says there is still an inconsistent approach by firms when it comes to reimbursing victims. By volume of cases, Nationwide fully reimbursed 96% of the APP scam cases reported to it, followed by TSB which fully reimbursed 95% of cases and Barclays which fully reimbursed in 82% of cases. In contrast, only three percent of cases reported to AIB were fully reimbursed, while Danske Bank fully reimbursed seven percent, and Monzo just nine percent. In terms of value of APP losses, TSB reimbursed 88% of APP scam losses to customers in 2023. Nationwide reimbursed 87% and HSBC reimbursed 76%. Again, the data shows that the chances of getting your money back depends on who you bank with, as AIB Group reimbursed just 9% of APP scam losses, Danske Bank 13%, and Monzo 17%. David Geale, managing director of the PSR, says: “Today’s report highlights how payment firms tackled APP scams and the way they treated those who fell victim in 2023. We can see some positive changes with more victims being reimbursed than in 2022. But there is still more to do - particularly for some smaller firms which have much higher rates of receiving fraud than larger firms. “Our new mandatory reimbursement measures will dramatically increase protection for consumers. These come into effect on 7 October 2024, and we are already seeing payment firms innovating and improving their controls, which is key to preventing scams from happening in the first place.”

Currently, only the sending firm makes any reimbursement, ignoring the vital role receiving firms play in preventing scammers from accessing the UK’s payments systems. Under tthe new model, the cost of reimbursement will be split 50:50 between sending and receiving firms - putting incentives in at the receiving end for the first time. Banking industry lobby groups have been pushing for delays to the introduction of the new rules and want the cap on payouts decreased from £415,000 to £30,000, warning that the costs to smaller firms will be overly-onerous and damaging to competition. UK Finance has highlighted the role of Big Tech in facilitating APP scams. Ben Donaldson, managing director of economic crime at UK Finance says: “Reimbursement is important in the fight against fraud, but it does not solve the problem on its own. Our focus has to be protecting consumers in the first place and that means looking at where fraud originates.

"Our data shows that over 90 per cent of APP fraud starts online or over the phone, through social media, fake messages and calls. Despite this, the technology and telecommunications sectors bear no responsibility for reimbursing victims. That needs to change and these sectors also need to tackle the criminal activity that proliferates on their platforms, sites and networks.”

Sponsored: [Impact Study] Payment Fraud in 2024: Who is Liable?

Comments: (2)

Well done David Geale and the PSR for sticking to the plan.

The need is clear and evidence based. Only when the right incentives are in place will we see PSPs change their behavior and fully assume their duty to customers and society by being more proactive in their efforts to reduce economic crime. 

The cost of fraud to victims, the national and global economy is enormous - and payment service providers are best placed to deploy preventative measures.

If firms can't afford to protect their customers and the associated liability for the business they write, then they either need a better business model and/or processes, or should leave the market to others that do.

Report abuse

... or they should earn more float income by delaying all payments for doing greater due diligence, and thereby stiff 99.994% diligent payors who don't get scammed due to the fault of 0.006% (252626/4.5B*100%) negligent payors who do get scammed.

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Gunman at Trump Rally Was Often a Step Ahead of the Secret Service

Text messages, obtained exclusively by The Times, indicate that some law enforcement officers were aware of Thomas Crooks earlier than previously known. And he was aware of them.

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By Haley Willis Aric Toler David A. Fahrenthold and Adam Goldman

The reporters analyzed video clips, photos and law enforcement documents to supplement key interviews for this article.

Nearly 100 minutes before former President Donald J. Trump took the stage in Butler, Pa., a local countersniper who was part of the broader security detail let his colleagues know his shift was ending.

“Guys I am out. Be safe,” he texted to a group of colleagues at 4:19 p.m. on July 13. He exited the second floor of a warehouse that overlooked the campaign rally site, leaving two other countersnipers behind.

Outside, the officer noticed a young man with long stringy hair sitting on a picnic table near the warehouse. So at 4:26 p.m., he texted his colleagues about the man, who was outside the fenced area of the Butler Fair Show grounds where Mr. Trump was to appear. He said that the person would have seen him come out with his rifle and “knows you guys are up there.”

The countersniper who sent the texts confirmed to The New York Times that the individual he saw was later identified as the gunman.

By 5:10 p.m., the young man was no longer on the picnic table. He was right below the countersnipers, who were upstairs in a warehouse owned by AGR International. One of the countersnipers took pictures of him, according to a law enforcement after-action report, which along with the texts from the Beaver County Emergency Services Unit was provided to The Times by the office of Senator Charles E. Grassley, Republican of Iowa. The text messages were independently verified by The Times.

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Tax Payments by Undocumented Immigrants

Carl Davis Research Director

Marco Guzman Senior Policy Analyst

Emma Sifre Senior Data Analyst

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Key Findings

• Undocumented immigrants paid $96.7 billion in federal, state, and local taxes in 2022. Most of that amount, $59.4 billion, was paid to the federal government while the remaining $37.3 billion was paid to state and local governments.
• Undocumented immigrants paid federal, state, and local taxes of $8,889 per person in 2022. In other words, for every 1 million undocumented immigrants who reside in the country, public services receive $8.9 billion in additional tax revenue.
• More than a third of the tax dollars paid by undocumented immigrants go toward payroll taxes dedicated to funding programs that these workers are barred from accessing. Undocumented immigrants paid $25.7 billion in Social Security taxes, $6.4 billion in Medicare taxes, and $1.8 billion in unemployment insurance taxes in 2022.
• At the state and local levels, slightly less than half (46 percent, or $15.1 billion) of the tax payments made by undocumented immigrants are through sales and excise taxes levied on their purchases. Most other payments are made through property taxes, such as those levied on homeowners and renters (31 percent, or $10.4 billion), or through personal and business income taxes (21 percent, or $7.0 billion).
• Six states raised more than $1 billion each in tax revenue from undocumented immigrants living within their borders. Those states are California ($8.5 billion), Texas ($4.9 billion), New York ($3.1 billion), Florida ($1.8 billion), Illinois ($1.5 billion), and New Jersey ($1.3 billion).
• In a large majority of states (40), undocumented immigrants pay higher state and local tax rates than the top 1 percent of households living within their borders.
• Income tax payments by undocumented immigrants are affected by laws that require them to pay more than otherwise similarly situated U.S. citizens. Undocumented immigrants are often barred from receiving meaningful tax credits and sometimes do not claim refunds they are owed due to lack of awareness, concern about their immigration status, or insufficient access to tax preparation assistance.
• Providing access to work authorization for undocumented immigrants would increase their tax contributions both because their wages would rise and because their rates of tax compliance would increase. Under a scenario where work authorization is provided to all current undocumented immigrants, their tax contributions would rise by $40.2 billion per year to $136.9 billion. Most of the new revenue raised in this scenario ($33.1 billion) would flow to the federal government while the remainder ($7.1 billion) would flow to states and localities.

Introduction

Immigration has always been an important part of the story of the United States. Today is certainly no exception.

Debates over immigration policy raise a huge array of issues that are fundamental to life in the U.S. To shed light on just one of those issues, this study undertakes the most thorough examination to date of the federal, state, and local tax payments made by undocumented immigrants.

To accomplish this, the study combines well-established techniques for estimating the size and tax-relevant characteristics of the undocumented population with the trove of data underlying ITEP’s comprehensive studies of U.S. tax incidence. [1] In doing so, it arrives at nationwide estimates of the overall tax contributions of the estimated 10.9 million undocumented immigrants living in the U.S. as of 2022, as well as state-by-state estimates for those immigrants’ payments of state and local taxes. [2] The report also forecasts the growth in tax contributions that would occur under a scenario in which these taxpayers were granted work authorization.

Current Tax Payments by Undocumented Immigrants

Federal, state, and local governments in the U.S. levy a wide array of taxes and most of those taxes affect undocumented immigrants in some fashion. Much like their neighbors, undocumented immigrants pay sales and excise taxes on goods and services like utilities, household products, and gasoline. They pay property taxes either directly on their homes or indirectly when these taxes are folded into the price of their monthly rent. And they pay income and payroll taxes through automatic withholding from their paychecks or by filing income tax returns using Individual Taxpayer Identification Numbers (ITINs). [3]

Using the method described in detail at the end of this report, we estimate that undocumented immigrants paid $96.7 billion in U.S. taxes in 2022, including $59.4 billion in payments to the federal government and $37.3 billion in payments to states and localities. Those tax payments are disaggregated by major category in Figure 1.

Given that the undocumented population included 10.9 million people in 2022, this $96.7 billion tax payment is equivalent to $8,889 per person. In other words, this analysis finds that for every 1 million undocumented immigrants who reside in the country, public services receive $8.9 billion in additional tax revenue. It bears noting that this figure includes only the taxes borne by undocumented immigrants and that other research attempting to quantify the significance of immigrants to the economy more broadly points toward a higher revenue impact per person. [4]

In total, the tax contribution of undocumented immigrants amounted to 26.1 percent of their incomes in 2022. This figure is close to the 26.4 percent rate facing the median income group of the overall U.S. population. [5] This closeness is the net result of factors that tend to lower the tax contributions of undocumented immigrants relative to U.S. citizens (such as lower incomes, lower income tax compliance, and lower smoking rates), as well as factors that tend to increase the contributions of those immigrants (such as tax credit restrictions, reduced likelihood of claiming refunds owed, and lower prevalence of tax-preferred retirement and investment income). These issues are discussed in more detail in the report methodology.

Most of the tax dollars paid by undocumented immigrants are collected through levies applied to their incomes. This includes broad income taxes as well as narrower payroll taxes levied on workers’ earnings that are dedicated to specific programs. It is well established that undocumented workers contribute to the solvency of major social insurance programs through their tax contributions. [6] They pay taxes that fund Social Security, Medicare, and Unemployment Insurance, among other programs, despite their exclusion from most of those benefits. [7] Figure 2 details the tax contributions that undocumented immigrants make under major social insurance programs.

Social Insurance Taxes Attributed to Undocumented Immigrants

Note: Figures include both the employer and employee share of these taxes. Unemployment Insurance Tax figure includes both state and federal components.

Source: Institute on Taxation and Economic Policy

While the federal government collects the bulk of its revenue through various kinds of income taxes, states and localities levy a wider array of tax types. Nearly 39 percent of the total tax dollars paid by undocumented immigrants are to state and local governments, for a total of $37.3 billion in 2022. Figure 3 disaggregates those payments by broad tax category.

The bulk of state and local tax payments by undocumented immigrants occur through sales and excise taxes on their purchases. The total state and local tax contribution of these families, in 2022, included $15.1 billion in sales and excise taxes, $10.4 billion in property taxes, $7.0 billion in personal and business income taxes, and $0.5 billion in other taxes to the states in which they live. Undocumented immigrants also paid another $4.2 billion in taxes to states aside from the ones in which they reside, mostly by making taxable purchases when traveling across state lines or by purchasing items from businesses located in other states that have passed some of their tax expense along to their consumers.

The undocumented immigrant population, and its tax contributions, are relatively concentrated in just a few states. Six states raised more than $1 billion each in tax revenue from undocumented immigrants in 2022, and together those states made up nearly two-thirds (64 percent) of all state and local tax collections from undocumented immigrants in that year. Those states are California ($8.5 billion), Texas ($4.9 billion), New York ($3.1 billion), Florida ($1.8 billion), Illinois ($1.5 billion), and New Jersey ($1.3 billion).

Measured relative to their incomes, undocumented immigrants nationwide paid an average effective state and local tax rate of 8.9 percent toward funding public infrastructure, services, and institutions in their home states. To put this in perspective, the nation’s most affluent taxpayers (those in the top 1 percent of the income scale) paid an average nationwide effective tax rate of just 7.2 percent to their home states. [8] Appendix Table 4 provides effective tax rate data by state and reveals that 40 states collect higher tax rates, relative to income, from undocumented immigrants than from the top 1 percent of households living within their borders.

State-by-state data on the tax contributions of undocumented immigrants can be found in Figure 4 and in the appendix to this report.

State and Local Tax Contributions by Undocumented Immigrants

Current Contributions and Potential Contributions if Granted Legal Status

*Figures may not sum to totals due to rounding. **National total differs from the all states sum because it includes taxes paid by residents of one state to governments of another state.

In many respects, undocumented immigrants face a harsher tax code than legal residents. They often pay taxes that are dedicated to funding programs from which they are barred from participating because of their immigration status. In addition, undocumented immigrants and the citizen members of their families are ineligible for the federal Earned Income Tax Credit (EITC). [9] While some states have moved to make more taxpayers eligible for state EITCs regardless of immigration status, most states still exclude taxpayers filing with ITINs. On top of that, only qualifying taxpayers with children with Social Security Numbers (SSNs) qualify for the federal Child Tax Credit (CTC) and a few states with CTCs have chosen to mimic this restriction in their own CTCs. While some kids — with valid taxpayer identification numbers — may qualify for the Credit for Other Dependents, the credit value is only one-fourth the size of the federal CTC and is not refundable. [10]

Steps toward more immigrant-inclusive tax policies have been uneven in recent years. On the one hand, the 2017 Trump tax law added the SSN requirement for the Child Tax Credit that has barred many immigrant children and their families from benefiting. On the other hand, a growing number of states have chosen a more inclusive path with their own tax credits in recent years. Roughly one-third of states with EITCs and most states with CTCs have written their tax laws to be inclusive of children who do not qualify for an SSN. [11]

Effect of Work Authorization on Undocumented Immigrant Tax Contributions

Undocumented immigrants work without authorization and, as a result, their tax contributions are lower than what would be paid by a worker with legal status in an otherwise comparable position. Granting work authorization to undocumented immigrants would increase their tax contributions for two reasons.

First, income tax revenues would increase because legal status would lessen barriers to complying with existing income tax laws. Second, the data demonstrate that immigrants with employment authorization earn higher wages than undocumented immigrants. [12] Greater access to job opportunities and higher-level education would provide immigrants with the opportunity to earn substantially higher wages which would have the effect of raising taxable earnings, consumption, and property ownership.

We estimate that providing access to work authorization to the currently undocumented population would boost their overall tax contribution by $40.2 billion per year, from $96.7 billion to $136.9 billion. As seen in Figure 5, $33.1 billion of that increase would occur through higher federal tax payments while the other $7.1 billion would occur through higher tax payments to states and localities. Disaggregation of the state and local figure by state is available in Figure 4 and Appendix Table 3.

Recipients of Deferred Action for Childhood Arrivals (DACA) already have access to work authorization and are therefore not included in these estimates of expanded access to work authorization, or in the other estimates contained in this report.

Undocumented immigrants pay substantial amounts toward the funding of public infrastructure, institutions, and services. Specifically, we find that in 2022, undocumented immigrants paid $96.7 billion in taxes at the federal, state, and local levels. More than a third of that amount, $33.9 billion, went toward funding social insurance programs that these individuals are barred from accessing because of their immigration status.

In total, the federal tax contribution of undocumented immigrants amounted to $59.4 billion in 2022 while the state and local tax contribution stood at $37.3 billion. These figures make clear that immigration policy choices have substantial implications for public revenue at all levels of government.

See Appendix A for detailed state-level estimates of the state and local portion of tax contributions made by undocumented immigrants.

See Appendix B for the methodology used to calculate the estimates contained in this report.

Appendix A: State-Level Disaggregation of the State and Local Tax Contributions of Undocumented Immigrants

APPENDIX TABLE 1.

State and Local Tax Contributions by the Undocumented Immigrant Population in 2022

(Scroll right for more)

*Includes state share of Unemployment Insurance (UI) taxes. **Figures may not sum to totals due to rounding. ***National total differs from the all states sum because it includes taxes paid by residents of one state to state and local governments in other states.

APPENDIX TABLE 2.

Potential State and Local Tax Contributions by the Currently Undocumented Population if Legal Status is Granted

APPENDIX TABLE 3.

Change in State and Local Tax Contributions by the Currently Undocumented Population if Legal State is Granted

APPENDIX TABLE 4.

State and Local Effective Tax Rate Data for the Currently Undocumented Population, and Comparison to Each State’s Top 1% of Taxpayers

*Figures may not sum to totals due to rounding. **National total differs from the all states sum because it includes taxes paid by residents of one state to state and local governments in other states.

APPENDIX TABLE 5.

Population and Income Data for the Undocumented Immigrant Population

Appendix B: Methodology

The methodology underlying this report involves three broad components. The first is construction of a data file containing income and other tax-relevant economic and demographic data for the undocumented population. The second is application of federal, state, and local tax parameters to the data in that file, with certain adjustments to reflect the ways in which undocumented immigrants interact with the tax code. The third component of the work is to adjust both the underlying economic data and the applicable tax parameters to reflect the likely impact that granting legal status would have on the economic profile and tax contributions of currently undocumented immigrants.

Each of these three steps is described below, followed by a discussion of how the methodology underlying this report differs from ITEP’s most recent prior study of this issue (Gee et al. 2017).

Construction of the Undocumented Immigrant Data File

The analysis begins with our estimates of the economic profile of undocumented immigrants in each state, which is based on our analysis of the U.S. Census Bureau’s American Community Survey (ACS) PUMS 2018-2022 5-year extract. It is a variation on the residual method employed by the Department of Homeland Security (Baker 2021) and of similar methods employed by other researchers (Passel and Cohn 2018; Van Hook et al. 2023; Warren 2024).

The method utilizes demographic, employment, and other social and economic characteristics to make a series of ‘logical edits’ to the entire population of the United States that leaves us with a pool of individuals who are very likely undocumented. The logical edits we employed take place over several iterations, which are listed below.

Round 1 : Identify the entire pool of potential non-citizen residents of the United States as a starting point for the analysis. This includes anyone who:

• Is part of the civilian noninstitutionalized population.
• Arrived in the U.S. after 1980.
• Reported their citizenship status as ‘Not a citizen of the U.S.,’ report being recently naturalized (within the last 3 years for spouses of U.S citizens and within 6 years for all other reported naturalized citizens), or report being a naturalized citizen whose country of origin is Mexico. [13]
• Listed their country of birth as Cuba and their date of entry to the U.S. as after 2017, before which all Cuban immigrants are assumed to have taken advantage of the Cuban Adjustment Act.

Round 2 : Disqualify people within this universe who likely have lawful permanent residence or temporary authorization to reside in the United States. For some categories of immigration status, these determinations are based on eligibility and then matched to administrative totals, such as those provided in Baker and Miller (2022). This includes anyone who:

• Is a likely recipient of Deferred Action for Childhood Arrivals (DACA), based on age, year of entry into the United States, and occupational information.
• Works in an industry with extensive licensing requirements or strict citizenship requirements, such as the medical field, the legal field, the U.S. government, or the military.
• Likely has a category of work authorization based on a combination of educational attainment and occupations related to highly skilled, religious, or diplomatic work.
• Is a foreign student.
• Is a foreign spouse of a U.S. citizen or a likely visa-holder.
• May be a refugee based on country of birth, year of entry, and DHS-reported refugee arrivals.
• Comes from a country designated by DHS for having Temporary Protected Status.

Round 3 : Disqualify people within this universe who receive public assistance for which undocumented individuals are ineligible.

We then adjust for undercounting of the undocumented population in the ACS. It is well established that the foreign-born population is consistently undercounted compared to the native-born population. We adjust for an expected undercount of 13 percent for those immigrants who arrived in the most recent year, with that rate declining by 7.5 percent in each prior year of arrival, in line with Baker (2021). We also make an additional adjustment, based on the work of Warren (2024), to account for more severe undercounting of immigrants from El Salvador, Guatemala, and Honduras in 2020, 2021, and 2022. The final step in our calculation is a slight adjustment to bring our population total in line with the 2022 population count of 10.9 million found in Warren (2024), which builds on the work of Warren and Warren (2013). The result is a 2022-level population total, with detailed economic and demographic information supplied by the larger sample size available in the 5-year, 2018-2022 ACS data.

After identifying undocumented individuals, it is necessary to group those individuals into tax units—which are persons or groups of people who file one tax return or, for nonfilers, who would file one tax return if they were to file. Tax units are the standard unit of analysis in ITEP’s research and in the research of most other organizations that engage in tax modeling (see, for example, JCT 2023 and Gillette et al. 2023). The ACS household is a conceptually different unit of analysis from a tax unit. Tax units can either be smaller or larger than the Census definition of households, though on average they are smaller because the latter can include roommates or multigenerational families that file more than one tax return. ITEP translates ACS households into tax units using an algorithm similar to those described in Cilke (1994) and Rohaly et al. (2005). ITEP uses information about individual relationships, ages, marital status, and incomes to determine dependents, heads of households, spouses, and filing statuses. We then group these people into tax units.

This methodology produces detailed information on tax units in the ACS with undocumented individuals and their economic profiles. For our tax modeling, the most important component of that economic profile is income level, which is taken from the ACS with certain adjustments to account for consistent underreporting of income (particularly self-employment income) in Census surveys, as discussed in Hurst et al. (2014) and Rothbaum (2015). We compute income for undocumented immigrants within seven income groups in each state: the bottom four quintiles as well as the next 15 percent, next 4 percent, and top 1 percent of tax units overall. We use this information to compute the tax contributions of undocumented immigrants across all tax types using the approaches described below.

Application of Tax Parameters to the Data File

The method used in this analysis involves applying modified versions of effective tax rates obtained from three sources: the seventh edition of ITEP’s Who Pays? report, which measures the impact of state and local taxes on families at every income level (ITEP 2024), a subsequent report examining federal tax impacts by income level (Wamhoff 2024), and custom runs of the ITEP Microsimulation Tax Model completed for this study. Those tax rates are applied to the undocumented immigrant data file with adjustments as described below that reflect economic, demographic, behavioral, and statutory factors that impact the tax contributions of undocumented immigrants. In most cases, we use tax rates calculated for the non-senior population as the starting point of our analysis because 97 percent of the undocumented population is below the age of 65 and retirement income makes up an extremely small share (less than 1 percent) of the total income flowing to undocumented immigrants.

Individual income and payroll taxes

This analysis of the individual income and payroll tax contributions of undocumented immigrants relies in part on our estimates of the distribution of income, by source, among those immigrants. After calculating the income received by undocumented immigrants within each income band, we apply modified versions of our population-wide effective income tax rates to each of those bands.

The first step in modifying those tax rates is to remove the federal Earned Income Tax Credit (EITC) and most state EITCs from the tax rates facing undocumented immigrants. This is necessary because the federal government and most states prohibit filers who do not have a valid SSN from claiming the EITC.

We also scale back the amount of federal Child Tax Credit (CTC) claimed by undocumented families to reflect a provision of federal law that limits eligibility based on the citizenship status of otherwise qualifying children. State CTCs are also scaled back in the small number of states that mirror this provision in their own laws. The CTC adjustment is done by identifying the share of children in undocumented tax units who we expect are ineligible for state CTCs based on citizenship status, and then scaling down potential CTC claims by that share.

These refinements to the EITC and CTC provide us with a series of effective income tax rates, by income level, that better reflect the income tax laws that apply to undocumented immigrants.

The next step in the calculation requires an adjustment to account for the administrative factors confronting undocumented immigrants as they navigate federal, state, and local tax systems. Those factors can yield either higher, or lower, tax contributions by undocumented individuals than would be the case among similarly situated U.S. citizens.

It is widely understood that undocumented immigrants exhibit a lower income tax compliance rate than other households, though perhaps not as low as is commonly thought. The literature on this subject has coalesced around a compliance rate in the range of 50 to 75 percent (CBO 2007). Past ITEP studies, for instance, have adopted a 50 percent assumption in the interest of conservative estimation (Gee et al. 2017). The few studies that have attempted formal measurement of the compliance rate, however, generally suggest a rate significantly above the 50 percent level.

For example, a survey of more than 700 undocumented immigrants from Mexico by Cornelius and Lewis (2006) found that 75 percent paid federal income taxes via withholding, filing an income tax return, or both. This finding aligns closely with earlier work by North and Houstoun (1976) which, in a survey of nearly 800 undocumented immigrants, found that 73 percent paid federal income tax via withholding.

But the income tax compliance rate does not provide a full picture of the tax contributions of undocumented immigrants. In the literature on this subject, the income tax compliance rate typically refers only to the share of undocumented immigrants who pay income tax through withholding or filing returns. For purposes of revenue estimation, however, it is necessary to look not at the share of tax units who pay, but rather at the share of taxes paid relative to the share of taxes owed. We will refer to this share as the “contribution rate.” The overall compliance rate differs from the overall contribution rate because some undocumented individuals pay more income tax than they owe. North and Houstoun (1976), for example, found that while 73 percent of undocumented immigrants paid federal income tax via withholding, just 32 percent of undocumented immigrants filed an income tax return. While the tax filing process has changed significantly for undocumented immigrants since the 1970s, it is clear that a significant number of undocumented immigrants still pay through withholding without filing returns. Because most taxpayers see more tax withheld from their paychecks than they owe and receive a refund upon filing, this suggests that a meaningful number of undocumented immigrants are overpaying federal, state, and local income taxes.

This phenomenon is widespread and has been the subject of some study. The Comptroller of Maryland, for instance, uses the term “unallocated withholding” to refer to tax withholding from individuals who do not file income tax returns (Comptroller of Maryland 2021). Using confidential data from information returns, one study conducted by an official at the Congressional Joint Committee on Taxation found that, at the federal level, 2.7 million people had $7.1 billion in federal income withheld from the paychecks in 2011 and yet failed to file a return despite having incomes above the filing threshold (Cilke 2014). Another study conducted for the IRS Statistics of Income Division estimated that nonfilers failed to claim $3.8 billion in refunds of their withholding in 2005, even before considering the impact of the EITC and CTC (Lawrence et al. 2011). While there are a variety of reasons that a person might choose not to file a return, there is no doubt that a meaningful number of undocumented immigrants are among this group of income tax over-payers.

With this research in mind, we target a 60 percent contribution rate for the undocumented population under the federal individual income tax—a value slightly below the midpoint of the 50 to 75 percent range described earlier. To be clear, the 60 percent contribution rate used in this study implies an income tax compliance rate somewhat below 60 percent because some undocumented immigrants who comply with the tax law pay more income tax than they owe (a fact that bolsters the contribution rate without impacting the compliance rate). Available data do not allow us to translate our contribution rate into a compliance rate and, indeed, such a translation is not needed for the calculations underlying the estimates presented in this report. A sensitivity analysis examining alternative contribution rates of 50 and 75 percent is provided later in this methodology.

The first step in achieving our 60 percent contribution rate target is to derive contribution rates, by income source, for the broader U.S. population. U.S. citizens, much like their undocumented immigrant neighbors, do not exhibit perfect compliance with federal tax law. The IRS estimates that the overall net contribution rate for all federal taxes was 86 percent in 2021 (Krause 2023). For the individual income tax, the net contribution rate is likely closer to 82 percent. These rates vary significantly across individuals based largely on the forms of income they receive. Taxes owed are more likely to be paid on sources of income with robust third-party reporting requirements, such as salaries and wages (Johns and Slemrod 2010; Krause et al. 2023). Our analysis suggests that the average U.S. resident with an income profile in line with that seen in the undocumented population exhibits a contribution rate of 92 percent. This is above the population-wide rate of 82 percent mentioned above because undocumented immigrants receive an unusually large share of their income from salaries and wages.

With contribution rates for the overall U.S. population in hand, we then devise a second set of contribution rates specifically for the undocumented population that allow us to achieve our 60 percent target for the contribution rate under the federal individual income tax. The fact that these contribution rates are constructed separately for each kind of income has the advantage of allowing us to employ different contribution rates to different tax bases. Unemployment insurance taxes, for example, exhibit somewhat higher contribution rates than Social Security and Medicare taxes because the former apply only to wages while the latter include self-employment income that is more likely to go unreported.

Both the employer and employee share of payroll taxes are included in this analysis as there is broad consensus among tax modelers that these taxes are ultimately borne by the employee (Department of the Treasury 2021; CBO 2023). This approach is consistent with our approach to other forms of indirect taxation. For example, motor fuel taxes (discussed below) are remitted by a small number of fuel suppliers, but the final incidence of these taxes is widely understood to fall on fuel consumers and their impact is therefore presented as such.

Sales, excise, and most other consumption taxes

Taxes on purchases made by undocumented immigrants make up the largest share of their state and local tax contributions. These payments are made both through general sales taxes, which apply to a wide range of purchases, as well as through selective taxes levied on narrow categories of goods and services such as alcohol, tobacco, motor fuel, and utilities. These taxes on spending are often referred to as consumption taxes. While the federal government does not levy a broad consumption tax, it does tax certain narrow categories of purchases such as alcohol, tobacco, and motor fuel.

ITEP’s consumption tax model is described in the methodology section of our most recent Who Pays? report (ITEP 2024). The primary data source underlying the model is the Bureau of Labor Statistics’ Consumer Expenditure Survey (CEX), though it is supplemented with data from a variety of other sources. Crucially, the model provides estimates not just the sales and excise taxes paid directly by individuals on their own purchases, but also the sizeable amount of consumption taxes that are paid by businesses on their inputs (Phillips and Ibaid 2019). These taxes are ultimately borne by those businesses’ consumers, workers, and owners—and a portion of those tax payments therefore come from undocumented immigrants.

The ITEP model produces effective tax rates for all consumption tax types, by income level, which provide a key input to our analysis. This analysis assumes that undocumented immigrants’ spending habits are broadly similar to those of U.S. citizens with similar levels of income, with a few exceptions outlined below that reduce the amount of sales and excise tax paid.

We assign 15 percent of income earned by undocumented immigrants to remittances to family members living in other nations. That income is considered unavailable for taxable consumption. The body of research into remittances made by immigrants living in the United States has produced a wide range of estimates depending on the methods used and the populations being studied. Yang (2015) summarizes several studies that find remittances as a share of earnings for various migrant populations in the U.S. as low as 1.4 percent of earnings and as high as 37.7 percent of earnings. Our 15 percent estimate is well within this range and is calibrated to match the United Nations’ 2019 estimate of the share of migrant earnings devoted to remittances worldwide.

To calculate the amount of sales, excise, and other consumption taxes paid by undocumented immigrants, we apply a modified version of the effective consumption tax rates calculated in ITEP (2024) and Wamhoff (2024) to the portion of income earned by undocumented immigrants that is not devoted to remittances. This calculation is performed separately for each of our seven income groupings. A sensitivity analysis examining alternative remittance values of 10 and 20 percent is provided later in this methodology.

Consumption taxes on tobacco

Our federal, state, and local tobacco tax estimates account for the below-average smoking rates observed among immigrants to the U.S. as demonstrated in Bosdriesz et al. (2013) and Azagba et al. (2019). In most states, tobacco is subject to higher effective tax rates than other types of purchases and thus it is important that we avoid overstating the amount of undocumented immigrant spending occurring in this high-tax category. The overall tax rate charged on tobacco is also bolstered with federal excise taxes. Our calculations apply tobacco usage rates among the undocumented population at one half the rate seen among the broader U.S. population.

Vehicle-related taxes

Our analysis of the ACS finds that undocumented immigrants are less likely to own vehicles than other individuals living in the U.S. Other researchers have observed this as well (Cho 2022). The significance of this finding to tax revenue measurement, however, is not entirely clear. While there is little doubt that undocumented immigrants are spending less than average on vehicle-related expenses, the tax impact of that depends on whether foregone spending in that category is instead directed toward taxable spending in another category, or toward a nontaxable purpose such as spending in an exempt category (e.g., public transportation fares) or an increase in personal savings. We err on the side of slightly underestimating the tax contributions of undocumented immigrants by assuming the latter.

More specifically, we reduce sales and excise tax contributions made through taxation of vehicle purchases, repairs, insurance, and motor fuel using ratios that reflect the lower number of vehicles owned by tax units with at least one undocumented individual. We also perform this adjustment for vehicle property taxes and registration charges, and for driver’s license charges in states that allow undocumented immigrants to obtain such licenses. These license charges are set to zero in states that prohibit undocumented immigrants from obtaining driver’s licenses (NCSL 2023).

Residential property taxes

Residential property taxes paid directly by undocumented homeowners and indirectly by undocumented renters make up the second largest component of this group’s state and local tax contribution, after sales and excise taxes on their purchases.

Our analysis of ACS data indicates that undocumented individuals are less likely to own their homes than other U.S. residents. Other researchers have made similar findings (Gelatt and Zong 2018).

After controlling for income level, our review of the ACS data did not uncover consistent, meaningful differences between the average property tax bill paid by undocumented homeowners and the average bill facing other homeowners. We therefore assign undocumented homeowners within each of our seven income groups the same effective property tax rate as all homeowners within that income group.

We then perform a similar calculation for the portion of the undocumented population that does not own homes. Renters are widely understood to pay at least a portion of the property tax levied on their homes as landlords pass along the cost of property taxes in the form of higher rents. We assume in each state that half of the tax is borne by the renter while the other half is borne by the landlord. We are aware of studies finding pass-through percentages both higher and lower than this amount but have concluded that this is roughly the midpoint estimate of the best available literature and, in particular, it is close in line with the estimates produced by Orr (1970), Hyman and Pasour (1973), and Black (1974).

Other included taxes

A wide array of federal, state, and local taxes is included in this study. Our approach to the bulk of those taxes is outlined above. Most other tax types, such as business property taxes, corporate income taxes, and severance taxes are indirect taxes that are formally imposed on business entities but are ultimately borne by people: specifically, by business owners in the form of a reduction in the return on their investments, by employees in the form of lower compensation, or by consumers in the form of higher prices. The parties who ultimately pay different types of indirect taxes vary based on the design of the tax and the nature of the industry being taxed (ITEP 2024).

For the labor share of these indirect taxes, we apply effective tax rates to undocumented immigrants within each income group consistent with the rates paid by the broader population of tax units within that group. For the consumer share, we apply reduced effective tax rates within each group that reflect the lower consumption level occurring due to remittances. For the capital share of these taxes, we reduce the effective tax rate faced by undocumented immigrants to reflect the fact that these immigrants exhibit lower levels of capital ownership than other U.S. residents at the same income level. Specifically, we scale down the capital tax rates by 60 percent based on our analysis of the ratios of capital income to total income in the undocumented population and the broader U.S. population. This adjustment, combined with the fact that undocumented immigrants are disproportionately found in the lower income groups where capital taxes tend to have little impact, means that taxes shifted to labor and consumption have a comparatively larger impact than taxes borne by owners of capital.

Omitted revenue sources

This analysis does not attempt to calculate tax payments made by undocumented immigrants through the federal Net Investment Income Tax, federal excise taxes on airfare, or estate and inheritance taxes levied at all levels of government. While it is clear that undocumented immigrants pay a non-zero amount of at least some of these levies, available data do not allow for reliable estimates and the revenue raised is likely to be low.

The analysis omits a wide array of non-tax revenues paid by undocumented immigrants such as public transportation fares, public parking fees, toll road charges, and college tuition. Including these non-tax revenue contributions would reveal undocumented immigrants to have even greater significance to federal, state, and local revenue streams than is found in this report.

Taxes paid to other states

The bulk of the state and local results reported in this study show the distribution of state and local taxes paid by undocumented immigrants to the states in which they live. This analysis allows lawmakers to understand how undocumented immigrants who live in their states are contributing toward funding the infrastructure, institutions, and services that their states provide.

Some state and local taxes, however, are “exported” to residents of other states. This happens through a variety of channels, such as when a person travels to another state and makes a taxable purchase or, more often, when a business pays a tax and its ultimate incidence is on consumers or firm owners located in another state. From a national perspective, it is worth examining these taxes as well to better understand the full state and local tax contribution made by undocumented immigrants.

We measure undocumented immigrants’ payment of exported taxes using the same kinds of adjustments applied to the measurement of in-state tax contributions, with an added downward adjustment of 50 percent to the direct portion of sales and excise taxes paid by visitors to other states. This adjustment is meant to reflect the fact that lower vehicle ownership, lower access to drivers’ licenses, and fear of deportation likely combine to lessen the amount of travel to other states undertaken by undocumented immigrants.

Sensitivity Analysis for Calculation of Current Tax Contributions

The results presented in this study are relatively insensitive to alternative assumptions regarding the income tax contribution rate (which affects income and payroll tax collections) and the remittance value (which affects consumption tax collections by lowering disposable income).

The base case presented in this study employs a 60 percent income tax contribution rate and 15 percent remittance value and yields a tax revenue estimate of $96.7 billion.

Under a more pessimistic set of alternative assumptions, with a 50 percent contribution rate (a value that we expect is likely too low) and a 20 percent remittance value (which we expect is likely too high), we instead see a revenue yield of $86.4 billion in 2022, or 10.6 percent less than in the base case.

On the other hand, if we apply a higher income tax contribution rate at 75 percent, and a lower remittance value at 20 percent, the resultant revenue figure is $111.7 billion, or 15.5 percent more than in the base case.

Figure 6 provides all nine pairs of possible assumptions for these two values.

Data and Tax Parameter Adjustments Used to Calculate Tax Contributions if Legal Status is Granted

This analysis examines both the current tax contribution of undocumented immigrants and this group’s likely tax contribution if it is granted legal status broadly as part of a comprehensive immigration reform. We modify four indicators in performing the latter calculation: earnings level, personal income tax compliance, eligibility for state Earned Income Tax Credits (EITC), and eligibility for Child Tax Credits (CTC).

Earnings boost : This study accounts for the fact that having the authority to work legally in the United States would increase undocumented immigrants’ wages and thus increase the taxes paid by those immigrants. A literature review by the Fiscal Policy Institute documented that legal immigrants are consistently found to have higher wages than undocumented immigrants and that gaining legal status is likely to boost the wages of affected workers by 6 to 15 percent (FPI 2013). A Congressional Budget Office report on the economic impact of immigration reform estimated the eventual wage boost to be 12 percent (CBO 2013).

This study applies a conservative estimate of a 10 percent wage increase from granting legal status to all undocumented immigrants. An increase in income would directly result in higher income tax payments from the currently undocumented population, and it would bring higher sales and property tax payments on the portion of that income directed toward consumption and housing.

In the face of uncertainty regarding the degree to which legal status would raise homeownership and vehicle ownership rates in the currently undocumented population, we do not apply any adjustments to these rates in calculating the additional tax contribution that would occur if legal status is granted. This suggests that our revenue figure in the full legal status scenario is likely to underestimate the increased tax contributions.

Personal income tax compliance : As explained above, our calculations apply an income tax contribution rate of 60 percent among undocumented immigrants. To calculate the anticipated income tax revenue gain from allowing undocumented immigrants to work in the U.S. legally, this analysis assumes that legal status would cause the formerly undocumented population to exhibit a state income tax compliance rate of 92 percent, a level on par with the contribution rate seen among people with an income profile that matches the one seen in the undocumented population.

Earned Income Tax Credit (EITC) eligibility : All members of a tax unit must have valid SSNs to receive the federal EITC and most state EITCs. This analysis assumes that undocumented immigrants do not claim state EITCs under current law in states where ITIN filers are disallowed from doing so, as documented in Davis and Butkus (2023a). The analysis also assumes that, under a scenario where legal status is granted, currently undocumented immigrants who otherwise meet the EITC eligibility requirements will begin to claim state EITCs for which they become eligible at the same rate observed in the rest of the population. That rate varies by state but, nationally, tends to hover between 75 and 80 percent (IRS 2024).

Child Tax Credit (CTC) eligibility : Most state CTCs are available to income tax filers broadly, without restrictions based on citizenship or immigration status. As documented in Davis and Butkus (2023b), however, some states with CTCs have rules mirroring the federal provision restricting CTC eligibility to children with valid SSNs. In these states, the calculations underlying this analysis only allow for a CTC for tax units with qualifying children. Granting these children legal status could therefore expand CTC claims in some states, which is reflected in this analysis.

Comparison to ITEP’s Prior Estimates of the State and Local Tax Contributions of Undocumented Immigrants

This report represents the first time that ITEP has quantified the federal tax contributions of undocumented immigrants. It is also the first time ITEP has measured the tax contributions that undocumented immigrants living in one state make (directly or indirectly) to the governments of other states. Prior ITEP research, however, has quantified the state and local tax contributions of undocumented immigrants to the states in which they reside (Gee et al. 2017). A brief discussion of the method and conclusions of ITEP’s prior research, relative to the comparable portions of this study, is provided below.

The analysis presented in this report finds that undocumented immigrants pay significantly more state and local taxes to their home states ($33.1 billion) than reported in ITEP’s prior research ($11.7 billion). The most important driver of this finding is an increase in our estimate of the amount of income earned by undocumented immigrants. Part of that increase is a result of wage growth that took place between 2014 (the base year of the previous study) and 2022 (the base year of this study). That wage growth is in large part a reflection of changes in the broader economy that increased wages for most workers in recent years. For undocumented immigrants specifically, wage growth may be bolstered further by the fact that the typical undocumented immigrant has deeper roots in the U.S. than was the case in 2014, as the median duration of U.S. residence among this population has increased during this time (Passel and Cohn 2019). In addition, we have also improved the technique, described above, that we use to estimate the amount of income earned by undocumented immigrants. Our new method is better suited to estimating the amount of income flowing to middle- and upper-income undocumented immigrants—income which we expect was understated in our prior estimates.

The analysis in this report also incorporates several changes to the calculations of tax amounts paid by undocumented immigrants. For instance, the tax calculations in this edition include tax policies enacted through the end of 2023, whereas the 2017 edition included changes enacted through 2014. This edition also includes estimates for some additional taxes—such as unemployment insurance taxes, motor vehicle property taxes, and taxes on business income and property—that were excluded from the 2017 edition. This edition uses a somewhat higher estimate for personal income tax compliance and somewhat lower estimate for sales and excise tax payments relative to income than the previous edition. The sales and excise tax change is the result of an upward revision in the amount of remittances sent to family members living in other countries.

Taken together, these improvements to our tax calculations have led to a modest increase in our estimate of the effective state and local tax rate facing undocumented immigrants. While our 2017 report found that undocumented immigrants paid an average rate of 8.0 percent to their home states, the comparable figure from this report is 8.9 percent.

Appendix B References

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[1] Davis, Carl, et al. “Who Pays? A Distributional Analysis of the Tax Systems in All 50 States, 7th ed.,” Institute on Taxation and Economic Policy, January 2024. https://itep.org/whopays-7th-edition/ .Wamhoff, Steve. “Who Pays Taxes in America in 2024.” Institute on Taxation and Economic Policy, April 2024. https://itep.org/who-pays-taxes-in-america-in-2024/ .

[2] See methodology section for more information on the calculation of estimated tax payments by undocumented immigrants.

[3] See methodology section for more information about current personal income tax compliance.

[4] Clemens, Michael A. “The Economic and Fiscal Effects on the United States from Reducing Numbers of Refugees and Asylum Seekers,” Oxford Review of Economic Policy, Vol. 38 (3), 449-486.

[5] Wamhoff, Steve. “Who Pays Taxes in America in 2024.” Institute on Taxation and Economic Policy, April 2024. https://itep.org/who-pays-taxes-in-america-in-2024/ .

[6] Goss, Stephen, et al. “Effects of Unauthorized Immigration on the Actuarial Status of Social Security Trust Funds,” Social Security Administration, April 2013. https://www.ssa.gov/oact/NOTES/pdf_notes/note151.pdf .

Ranker, Lynsie, et al. “Keeping Medicare Solvent: How Immigrants Subsidize Medicare’s Trust Fund for All U.S. Seniors,” New American Economy, April 2021. https://research.newamericaneconomy.org/wp-content/uploads/sites/2/2021/05/NAE_Medicare_Report.pdf .

[7] Broder, Tanya, and Gabrielle Lessard. “Overview of Immigrant Eligibility for Federal Programs,” National Immigration Law Center, October 2023. https://www.nilc.org/wp-content/uploads/2023/10/overview-immeligfedprograms-2023-10-01.pdf . [If you go to the landing page, you get the most current version, which is now May 2024: Overview of Immigrant Eligibility for Federal Programs – National Immigration Law Center ( nilc.org )]

[8] Davis, Carl, et al. “Who Pays? A Distributional Analysis of the Tax Systems in All 50 States, 7th ed.”

[9] Guzman, Marco, and Emma Sifre. “Improving Refundable Tax Credits by Making them Immigrant-Inclusive,” Pittsburgh Tax Review, Vol. 21 (2), 205-223.

[10] Internal Revenue Service. “What You Need to Know about CTC, ACTC, and ODC,” March 14, 2024. https://www.eitc.irs.gov/other-refundable-credits-toolkit/what-you-need-to-know-about-ctc-and-actc/what-you-need-to-know .

[11] Davis, Aidan, and Neva Butkus. “Boosting Income, Improving Equity: State Earned Income Tax Credits in 2023,” Institute on Taxation and Economic Policy, September 12, 2023. https://itep.org/boosting-incomes-improving-equity-state-earned-income-tax-credits-in-2023/ . Davis, Aidan, and Neva Butkus. “States are Boosting Economic Security with Child Tax Credits in 2023,” Institute on Taxation and Economic Policy, September 12, 2023. https://itep.org/states-are-boosting-economic-security-with-child-tax-credits-in-2023/ .

[12] Borjas, George. “The Earnings of Undocumented Immigrants,” National Bureau of Economic Research Working Paper 23236, March 2017. https://www.nber.org/system/files/working_papers/w23236/w23236.pdf .

[13] Census survey data tend to overestimate the number of naturalized citizens when compared to INS administrative data. This is true of most reported recent naturalizations and naturalizations from Central America (Passel et al. 1997). More recent research suggests this may be more limited to those whose country of origin is Mexico (Van Hook and Bachmeier 2013). For a broader discussion of this literature, see Brown et al. (2019).

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