Economics

EmployStats economist Dr. Roberto Cavazos finds that economic loss from pay-per-click (PPC) is on the rise as fraudulent activity risks have increased as many large corporations use this platform. According to Dr. Cavazos, approximately $144 Billion is spent on paid and social search globally. However, according to empirical-based research, 14% of PPC was fraudulent making the economic loss to $23.7 Billion by the end of 2020.

What exactly is PPC fraud? According to bigcommercec.com, PPC fraud can be defined as individuals, computer programs, or generated scripts exploiting online advertisers by repeatedly clicking on a PPC advertisement to generate fraudulent charges. Dr. Cavazos stated there are several techniques used to perform these acts including competitor clicks, clicks by automated clicking tools, and robots or other deceptive software. To counteract these invalid clicks, Google is taking a proactive approach by automatically filtering any click that is considered invalid, however many clients find this safeguard to be a lot more complicated than beneficial.

Dr. Cavazos reported the economic loss that has amounted from PPC globally has made a substantial dent in the market. The total PPC fraud loss for the U.S. in 2019 was $7,700,000,000 and $9,060,800,000 in 2020. By the end of 2020, the expected loss for the largest eCommerce sector was $3.8 Billion on PPC campaigns. This was based on the 17% fraudulent clicks that were found across multiple eCommerce campaigns. Dr. Cavazos shared even though global web sales amounted to an astounding $3.4 trillion in 2019, the projected loss from PPC fraud is still a sizeable amount. Despite the global loss, paid search remains the most-used digital advertising format bringing in 47% of total digital revenues.

The rise of PPC platform spending has opened a new route for commitment of fraudulent activity. Each platform experiences their own issues with fraudulent activity making it difficult to recognize. Ending PPC fraud has become a top priority when it comes to enhancing and augmenting future campaigns. While PPC has been a successful marketing tactic, it poses risks for fraudulent behavior requiring an understanding of the consequence of the use of PPC and steps to be taken to minimize those risks. Dr. Cavazos believes these losses are borne by businesses making the smaller ones most vulnerable to suffering PPC fraud.

Our very own Research Associate Proma Paromita recently participated in an online training course for Stata. Stata is an integrated statistical software package that provides services for data manipulation, visualization, statistics, and automated reporting.

Here at EmployStats our researchers are always working with huge data sets that need to be analyzed and formatted. This course provided Proma with a better understanding of Stata programming and insight on how to more efficiently dissect large quantities of data.

In a sit-down interview Proma discussed her experience and general layout of the course. She explained the scaffolding of course content beginning with the basics and increasing with complexity as the course continued. Proma described the course as concise with practical examples resulting in a perfect tool to utilize for future Stata programming. Stata offers many resources on its website and YouTube channel to help individuals navigate challenges by accessing helpful information.

As a takeaway from the course Proma believes understanding the syntax is more beneficial than memorizing it. Additionally, she stressed it is essential to understand the data that you are working with in order to produce tangible results.

This holiday season, EmployStats had the opportunity to participate in the Boys & Girls Clubs of San Antonio annual Reindeer Brigade. As families continue to navigate recovery from these challenging past 18 months, many are still working to recover, making this year’s program more important than ever. To help spread some holiday cheer, EmployStats donated to twelve children, making their holiday wishes come true. Our company is a proud donor and encourages others to participate. Happy holidays from EmployStats!

About Emma Dooley

Emma received her Bachelor of Arts in Communication from Texas A&M University in the year of 2021. She enjoyed her Intro to Social Media and Crisis Communication courses. She finished up her last semester as a Teaching Assistant for a Research Methods course that she had taken previously that semester.

Emma’s favorites include:

Hobbies:

• Fashion
• Reading
• Discovering new places to eat

Types of Movies and Books:

• Horror Movies
• True Crime/Young Adult Novels

Favorite Book/Podcast:

• A Stolen Life by Jaycee Lee Dugard
• Crime Junkie

Favorite Foods:

• Sushi
• Italian food

We are extremely thrilled to have Emma on our team. We offer our warmest welcome to our newest team member.

About Proma Paromita

Proma received her Bachelor of Arts in Economics from the Asian University for Women in Bangladesh in the year 2018. After receiving her Bachelor’s degree, Proma traveled to Richardson, TX to attend The University of Texas at Dallas to receive her Master of Science in Econometrics and Quantitative Economics this year. She enjoyed her applied regression and labor economics courses, as well as listening to the University’s podcast.

Proma’s favorites include:

Hobbies:

Origami

Handcrafts

Reading

Types of Movies and Books

Thriller Movies

Detective Novels

Favorite Book

Big Little Lies by Liane Moriarty

Favorite Foods

Pasta 

Chicken Wings

Favorite Quote

“I think therefore I am” said by René Descartes

We are extremely excited to have Proma on our team. We offer our warmest welcome to our new team member.

The Career Evolution of the Sex Gap in Wages: Discrimination vs. Human Capital Investment was written by David Neumark and Giannina Vaccaro for the National Bureau of Economic Research (NBER).

Abstract

Several studies find that there is little sex gap in wages at labor market entry, and that the sex gap in wages emerges (and grows) with time in the labor market. This evidence is consistent with (i) there is little or no sex discrimination in wages at labor market entry, and (ii) the emergence of the sex gap in wages with time in the labor market reflects differences between men and women in human capital investment (and other decisions), with women investing less early in their careers. Indeed, some economists explicitly interpret the evidence this way. We show that this interpretation ignores two fundamental implications of the human capital model, and that differences in investment can complicate the interpretation of both the starting sex gap in wages (or absence of a gap), and the differences in “returns” to experience. We then estimate stylized structural models of human capital investment and wage growth to identify the effects of discrimination and differences in human capital investment, and find evidence more consistent with discrimination reducing women’s wages at labor market entry.

Read – The Career Evolution of the Sex Gap in Wages: Discrimination vs. Human Capital Investment

• Find the paper on the NBER website here.
• Find the paper on the SSRN website here.

Data Mining is one of the many buzzwords floating about in the data science ether, a noun high on enthusiasm, but typically low on specifics. It is often described as a cross between statistics, analytics, and machine learning (yet another buzzword). Data mining is not, as is often believed, a process that extracts data. It is more accurate to say that data mining is a process of extracting unobserved patterns from data. Such patterns and information can represent real value in unlikely circumstances.

Those who work in economics and the law may find themselves confused by, and suspicious of, the latest fads in computer science and analytics. Indeed, concepts in econometrics and statistics are already difficult to convey to judges, juries, and the general public. Expecting a jury composed entirely of mathematics professors is fanciful, so the average economist and lawyer must find a way to convincingly say that X output from Y method is reliable, and presents an accurate account of the facts. In that instance, why make a courtroom analysis even more remote with “data mining” or “machine learning”? Why risk bamboozling a jury, especially with concepts that even the expert witness struggles to understand? The answer is that data mining and machine learning open up new possibilities for economists in the courtroom, if used for the right reasons and articulated in the right manner.

Consider the following case study:

A class action lawsuit is filed against a major Fortune 500 company, alleging gender discrimination. In the complaint, the plaintiffs allege that female executives are, on average, paid less than men. One of the allegations is that starting salaries for women are lower than men, and this bias against women persists as they continue working and advancing at this company. After constructing several different statistical models, the plaintiff’s expert witness economist confirms that the starting salaries for women are, on average, several percentage points lower than men. This pay gap is statistically significant, the findings are robust, and the regressions control for a variety of different employment factors, such as the employee’s department, age, education, and salary grade.

However, the defense now raises an objection in the following vein: “Of course men and women at our firm have different starting salaries. The men we hire tend to have more relevant prior job experience than women.” An employee with more relevant experience would (one would suspect) be paid more than an employee with less relevant prior experience. In that case, the perceived pay gap would not be discriminatory, but a result of an as-of-yet unaccounted variable. So, how can the expert economist quantify relevant prior job experience?

For larger firms, one source could be the employees’ job applications. In this case, each job application was filed electronically and can be read into a data analytics programs. These job applications list the last dozen job titles the employee held, prior to their position at this company. Now the expert economist lets out a small groan. In all, there are tens of thousands of unique job titles. It would be difficult (or if not difficult, silly) to add every single prior job title as a control in the model. So, it would make sense to organize these prior job titles into defined categories. But how?

This is one instance where new techniques in data science come into play.

Adela Botello discusses what she has been up to since starting with EmployStats in September.

“To help improve my skills as an Operations Assistant, I am enrolled in an Introduction to Financial Account Course. In college, I registered for two accounting courses; however, my memory in how to debit and credit different accounts has become very hazy. In college, I would assume everyone had select courses they dreaded or simply wished they did not have to take. For me, accounting was that dreadful course. The first couple weeks of class I had to bribe myself to attend, but after those couple weeks, I began to enjoy the functions of accounting. I think all of the “rules” and the organization of journal entries pulled me in. Accounting was not my passion, but something I became to appreciate. 

Since I only had to take two accounting classes for my Economics degree, I have not thought about accounting since. So, when I was offered this position, I decided to pull out my old notes and start refreshing my memory. Not only did I look over my notes, we enrolled me in a Coursera course. The amount of courses available was incredible! It took me a few hours to find a course that met my needs because the possibilities were endless. The specialization I chose was: Introduction to Finance and Accounting Specialization. This specialization has a total of four courses, but you are able to only enroll in what you need. For me, I only needed to take two of the four courses. Both courses are taught by Professor Brian Bushee of University of Pennsylvania, and I really enjoy learning from him. 

The first course is about “master(ing) the technical skills needed to analyze financial statements and disclosures for use in financial analysis…”. The items we discuss include: the income statement, the balance sheet, the statement of cash flows, and analyzing different types of transactions. The course is broken down into four weeks, and each week ends with “homework”, which is really only a quiz about that week’s content. Professor Bushee has created videos, not longer than 20 minutes, to learn the course. At the end of each video, he offers pdf files of the slides he uses, which has helped me fully understand topics I had issues with while watching the videos. 

Coursera has great tools when it comes to note taking and layout of the website. During videos, I am able to take a screenshot and Coursera will note exactly what the professor is saying at the time of the screenshot. So, not only am I able to go look at screenshots of notes I took, I can also read a small subscript of what was being said during the video. It has been very convenient because I can simply go to my “Notes” tab and review for the end of week quizzes a lot faster than having to rewatch several videos. 

My experience with Coursera and this particular course has been extremely helpful and a great review of topics I had previously learned. Once I finish the courses, I will receive a certificate of completion and recognition of my new learned skills. The new skills will help me manage the accounts receivable, accounts payable, and payroll for EmployStats. As well, I will fully understand how invoices are incorporated into different accounts. These courses will prepare me for daily categorization of expenses, auditing of expenses, bank account balancing, and bank account reconciliations. This course will improve my financial analysis skills and help me achieve the task of recognizing underutilized and/or inefficient services and products. I am excited to use these new skills and knowledge towards my work with Employstats!”

David Neumark of the University of California, Irvine, and EmployStats academic affiliate, has been featured in the finance and economics section of The Economist’s 2020 edition.  The article focuses on the topic of wage floors and the cause and effect of increasing minimum wage requirements. A minimum wage policy raises wages for workers, but the money required to support higher minimum wages has a potential to hit poorer bosses’ harder.

Professor Neumark’s paper, co-authored by Lev Drucker and Katya Mazirov, referenced in The Economist article, examines the potential effect increasing the minimum wage may have on businesses.  The author’s paper titled “Who Pays for and Who Benefits from Minimum Wage Increases? Evidence from Israeli Tax Data on Business Owners and Workers” offers insight into potential unintended consequences of increased wage floors.

The Economist article can be found here.

Drucker, Mazirov, and Neumark’s paper can be found here.

Complex wage and hour litigation often involves significant data management and sophisticated analyses in order to assess potential liability and damages. This article highlights common wage and hour data management issues, sampling and surveying, as well as provides a case study as an example of the use of sampling in an overtime misclassification case.

Download Dr. Dwight Steward and Matt Rigling’s paper on wage and hour expert economists here!

Economics and Statistics Experts in Wage and Hour Litigation