Author: Carl McClain

In Texas, Languageline Solutions , Walmart / Sam s , and USAA are seeking new hires

For May 2020 in the state of Texas, Languageline Solutions posted the largest number of job openings in the state of Texas, with 1157 openings, followed by Walmart / Sam s (with 1025 openings) and USAA (with 723 openings). In total, these employers posted 2905 new job openings this month in Texas. Last month, Languageline Solutions , Walmart / Sam s , and Christus Health had the largest number of job openings in Texas.

The largest share of these job openings were in the San Antonio area.
These different employers each had their own demands for employees from a variety of occupations. For example, Languageline Solutions were, in particular seeking qualified Interpreters and Translators , with over 1156 job postings this month. For Walmart / Sam s , Training/Development Specialists were in high demand, with over 180 job postings this month. And at USAA , Financial Specialists, NEC were in high demand, with over 129 job postings this month.

Texas Employers on the Lookout for Registered Nurses, Software Developers, Application, and Sales Reps, Exc Tech/Sci Product.

In May 2020 Registered Nurses are in high demand in Texas, with 6275 openings, the largest number of active job openings. Other occupations in high demand include Software Developers, Application , with 5903 active openings, and Sales Reps, Exc Tech/Sci Product , with 4973 active openings. Last Month, April 2020 , the jobs with the largest number of openings were Registered Nurses , Sales Reps, Exc Tech/Sci Product , and Software Developers, Application .

May saw increased demand for Driver/Sales Workers with the largest number of new job postings by prospective employers, over 350 in the past few weeks. Middle School Teachers also saw large increases in openings, with 225 new posted positions, followed by Elementary School Teachers with 169 new posted positions within the past few weeks.

Texas Job Market Sees Increased Demand for Medical, Supply Chain Workers Amid Coronavirus

The 2020 coronavirus outbreak has sparked severe shocks to the United States labor market. Social distancing policies, designed to slow the spread of the disease, are leading to large layoffs in specific industries, like bars and restaurants. Many more employees in other sectors face the prospect of unemployment or temporary furloughs. Despite this economic strain, employers, particularly those in Medical and Supply Chain services, are expanding to meet new demand. These sectors continue to post job opportunities long after policymakers mandated the closure of non-essential services or issued “shelter-in-place” orders.

Evidence from Texas over the past half-month reveals both predictable and unexpected trends in new job opportunities. It may come as a surprise that, even in this “lockdown economy,” there is still help wanted.

Beginning on March 18th, Texas began implementing statewide social distancing policies, though some areas began issuing such orders days earlier. Cities and counties across the state gradually adopted “shelter-in-place” orders in March.  By March 31st, a statewide order asked residents to stay home, except if they participated in “essential services and activities.”

But within the past two weeks, Texas employers posted over 66,000 new job openings.

Daily job postings are one indicator of up-to-date labor market demand, available from a variety of sources (most notably online).  The Texas Workforce Commission (“TWC”) is the state agency responsible for managing and providing workforce development services to employers and potential employees in Texas.  One service the TWC provides is access to databases of up-to-date job postings for different occupations and employers within the state. These job postings can come from the TWC itself, or from third party sites like Monster or Indeed. This information is extraordinarily valuable to data scientists.

The top 10 in demand occupations cover a variety of occupations, but are heavily concentrated in the healthcare, supply chain, and IT sectors.

Given the stresses to the healthcare system, its little surprise that hospitals are looking for more front-line staff. Registered Nurses were the highest in demand occupation, with over 3,000 new job listings since March 23rd.

 

Retail supply chains are also expanding employment.  Sales Representatives for Wholesalers and Manufacturers, with over 2,300 new listings, was the second highest in demand occupation. Other logistical occupations saw large numbers of new openings, particularly for Truck Drivers, with over 1,200 new job postings since March 23rd.

Anecdotally, supermarkets and retail chains have been hiring more employees to meet increased demand for groceries and other supplies. Evidence from jobs posted since March 23rd would support this finding, with large increases in new listings for Customer Service Representatives (over 1,700), Supervisors of Retail Sales Workers (over 1,600), and Retail Salespersons (also over 1,600).

Finally, with the increase in service sector employees working from home, it should not be surprise that IT workers are also in high demand. Application Developers (over 2,100 new listings) and employees for general Computer Occupations (with 1,800 new listings) have both seen large increases in openings since March 23rd.

EmployStats will be closely monitoring daily job postings as the coronavirus outbreak continues.

EmployStats Publishes Big Data Book

Big Data permeates our society, but how will it affect U.S. courts? In civil litigation, attorneys and experts are increasingly reliant on analyzing of large volumes of electronic data, which provide information and insight into legal disputes that could not be obtained through traditional sources. There are limitless sources of Big Data: time and payroll records, medical reimbursements, stock prices, GPS histories, job openings, credit data, sales receipts, and social media posts just to name a few.  Experts must navigate complex databases and often messy data to generate reliable quantitative results. Attorneys must always keep an eye on how such evidence is used at trial. Big Data analyses also present new legal and public policy challenges in areas like privacy and cybersecurity, while advances continue in artificial intelligence and algorithmic design.  For these and many other topics, Employstats has a roadmap on the past, present, and future of Big Data in our legal system.

Order your copy of Dr. Dwight Steward and Dr. Roberto Cavazos’ book on Big Data Analytics in U.S. Courts!

Simpson’s Paradox in Action

Data Analytics can sometimes be a frustrating game of smoke and mirrors, where outputs change based on the tiniest alterations in perspective. The classic example is Simpson’s paradox.

Simpson’s paradox is a common statistical phenomenon which occurs whenever high-level and subdivided data produce different findings. The data itself may be error free, but how one looks at it may lead to contradictory conclusions. A dataset results in a Simpson’s paradox when a “higher level” data cut reveals one finding, which is reversed at a “lower level” data cut. Famous examples include acceptance rates by gender to a college, which vary by academic department, or mortality rates for certain medical procedures, which vary based on the severity of the medical case. The presence of such a paradox does not mean one conclusion is necessarily wrong; rather, the presence of a paradox in the data warrants further investigation.

“Lurking variables” (or “confounding variables”) are one key to understanding Simpson’s paradox. Lurking variables are those which significantly affect variables of interest, like the outputs in a data set, but which are not controlled for in an analysis. These lurking variables often bias analytical outputs and exaggerate correlations. However, improperly “stratifying data” is also key to Simpson’s paradox. Aggressively sub-dividing data into statistically insignificant groupings or controlling for unrelated variables can generate inconclusive findings. Both forces operate in opposing directions. The solution to the paradox is to find the data cut which is most relevant to answering the given question, after controlling for significant variables.

EmployStats recently worked on an arbitration case out of Massachusetts, where the Plaintiffs alleged that a new evaluation system negatively impacted older and minority teachers more than their peers in a major public school district. One report provided by the Defense examined individual evaluators in individual years, arguing that evaluators were responsible for determining the outcome of teacher evaluations. This report determined, based on that data cut, the new evaluation system showed no statistical signs of bias. By contrast, the EmployStats team systematically analyzed all evaluations, controlling for different factors such as teacher experience, the type of school, and student demographics. The team found that the evaluations, at an overall level and after controlling for a variety of variables, demonstrated a statistically significant pattern of biases against older and minority teachers.

The EmployStats team then examined the Defense’s report. The team found that if all the evalulator’s results were jointly tested, the results showed strong, statistically significant biases against older and minority teachers, which matched the Plaintiff’s assertions. If the evaluators really were a lurking variable, then specific evaluators should have driven a significant number of results. Instead, the data supported the hypothesis that the evaluation system itself was the cause of signs of bias.

To see how EmployStats can assist you with similar statistics cases, please visit www.EmployStats.com or give us a call at 512-476-3711.  Follow our blog and find us on social media: @employstatsnews

Benford’s Law and Fraud Detection

Civil fraud cases hinge on litigants proving where specific fraudulent activity occurred. Tax returns, sales records, expense reports, or any other large financial data set can be manipulated. In many instances of fraud, the accused party diverts funds or creates transactions, intending to make their fraud appear as ordinary or random entries. More clever fraudsters ensure no values are duplicated or input highly specific dollar and cent amounts. Such ‘random’ numbers, to them, may appear normal, but few understand or replicate the natural distribution of numbers known as Benford’s Law.

A staple of forensic accounting, Benford’s Law is a useful tool for litigants in establishing patterns of fraudulent activity.

Benford’s Law states that, for any data set of numbers, the number 1 will be the leading numeral about 30% of the time, the number 2 will be the leading numeral about 18% of the time, and each subsequent number (3-9) will be a leading number with decreasing frequency.  This decreasing frequency of numbers, from 1 though 9, can be represented by a curve that looks like this:

Frequency of each leading digit predicted by Benford’s Law.

For example, according to Benford’s Law, one would expect that more street addresses start with a 1 than a 8 or 3; such hypothesis can be tested and proven. The same pattern holds for any number of phenomenon: country populations, telephone numbers, passengers on a plane, or the volumes of trades. This predicted distribution permeates many aspects of numbers and big data sets. But Benford’s Law is not absolute: it does require larger data sets, and that all the leading digits (1-9) must have a theoretically equal chance of being the leading digit. Benford’s Law, for example, would not apply to a data set where only 4s or 9s are the leading number. Financial data sets do comport with a Benford distribution.

In accounting and financial auditing, Benford’s Law is used to test a data set’s authenticity. False transaction data is typically tampered by changing values or adding additional fake data. The test, therefore, is an early indicator if a data set has been altered or artificially created. Computer generated random numbers will tend to show an equal distribution of leading digits. Even manually created false entries will tend to have some sort of underlying pattern. A person may, for example, input more fake leading digits based on numbers closer to their typing fingers (5 and 6).

An examiner would compare the distribution of leading digits in the data set, and the Benford distrubtion. Then, the examiner would statistically test if the proportion of leading numbers in the data set matches a Benford distribution. The resulting “Z-scores” give a measure of how distorted these distributions are, with higher “Z-scores” implying a more distorted data set, which implies artificially created data.

If a data set violates Benford’s Law, that alone does not prove such transactions numbers fraudulent. But, a violation does give auditors, economists, and fact finders an additional reason to scrutinize individual transactions.

Case Update: Mileage Reimbursement

The scope of Wage and Hour cases can extend beyond traditional claims on overtime or off-the-clock work. The same analytical principles can extend, for example, to cases involving employee reimbursements. EmployStats has recently worked on a case in California where the Plaintiffs allege they were not reimbursed for routine miles traveled in personal vehicles between job sites, despite the Defendant’s stated policy.

The EmployStats team assessed the Plantiffs’ theory of liability and estimated unreimbursed expenses based off of the available case data on mileage, parking, and toll charges. The analysis presented to the court showed a significant difference between stated and actual reimbursements for miles traveled by the Plantiffs. Based off of the analysis and other evidence at trial, the court certified the Plaintiff class.

The EmployStats Wage and Hour Consulting team’s trial plan is as follows:

  1. First, the EmployStats team would survey a statistically representative sample of class members about the existence of unreimbursed miles, using a random sampling methodology to eliminate potential bias.
  2. Next, the team would use a similar statistical sampling methodology to determine the typical miles traveled by the class members, and combining this resulting data with mapping platforms (ex. Google Maps API) to calculate distances in miles traveled between job locations.
  3. Finally, Employstats would tabulate damages based off of these results, using publicly available data on reimbursement rates for miles traveled in personal vehicles.
A copy of the court’s order can be found though the link here: McLeod v Bank of America Court Order – Dwight Steward PhD Statistical Sampling Plan
To see how EmployStats can assist you with similar employment or statistics cases, please visit www.EmployStats.com or give us a call at 512-476-3711.  Follow our blog and find us on social media! @employstatsnews

Data Analytics and the Law: Putting it Together

This series on data analytics in litigation emphasized how best practices help secure reliable, valid, and defensible results based off of “Big Data.” Whether it is inter-corporate litigation, class actions, or whistleblower cases, electronic data is a source of key insights. Courts hold wide discretion in admitting statistical evidence, which is why opposing expert witnesses scrutinize or defend results so rigorously. There is generally accepted knowledge on the techniques, models, and coding languages for generating analytical results from “Big Data.” However, the underlying assumptions of a data analysis are biased. These assumptions are largest potential source of error, leading parties to confuse, generalize, or even misrepresent their results. Litigants need to be aware of and challenge such underlying assumptions, especially in their own data-driven evidence.

 

When it comes to big data cases, the parties and their expert witnesses should be readily prepared with continuous probing questions. Where (and on what program) are the data stored, how they are interconnected, and how “clean” they are, directly impact the final analysis. These stages can be overlooked, leading parties to miss key variables or spend additional time cleaning up fragmented data sets. When the data are available, litigants should not miss on opportunities due to lack of preparation or foresight. When data do not exist or they do not support a given assertion, a party should readily examine its next best alternative.

 

When the proper analysis is compiled and presented, the litigating parties must remind the court of the big picture: how the analysis directly relates to the case. Do the results prove a consistent pattern of “deviation” from a given norm? In other instances, an analysis referencing monetary values can serve as a party’s anchor for calculating damages.

 

In Big Data cases, the data should be used to reveal facts, rather than be molded to fit assertions.

Data Analytics and the Law: Analysis

For data-based evidence, the analysis is the heart of the content: the output of the data compiled for a case. In most instances, the analytics do not need to be complex. Indeed, powerful results can be derived by simply calculating summary statistics (mean, median, standard deviation). More complicated techniques, like regressions, time-series models, and pattern analyses, do require a background in statistics and coding languages. But even the most robust results are ineffective if an opposing witness successfully argues they are immaterial to the case. Whether simple or complex, litigants and expert witnesses should ensure an analysis is both relevant and robust against criticism.

 

What type of result would provide evidence of a party’s assertion? The admissibility and validity of statistical evidence varies by jurisdiction. In general, data-based evidence should be as straightforward as possible; more complex models should only be used when necessary. Superfluous analytics are distractions, leading to expert witnesses “boiling the ocean” in search of additional evidence. Additionally, courts still approach statistical techniques with some skepticism, despite their acceptance in other fields.

 

If more complex techniques are necessary, like regressions, litigants must be confident in their methods. For example, what kind of regression will be used? Which variables are “relevant” as inputs? What is the output, and how does it relate to a party’s assertion of fact? Parties need to link outputs, big or small, to a “therefore” moment: “the analysis gave us a result, therefore it is proof of our assertion in the following ways.” Importantly, this refocuses the judge or jury’s attention to the relevance of the output, rather than its complex derivation.

 

Does the analysis match the scope of the complaint or a fact in dispute? Is the certified class all employees, or just a subset of in a company? Is the location a state, or a county within a state? If the defendant is accused of committing fraud, for how many years? Generalizing from a smaller or tangential analysis is inherently risky, and an easy target for opposing witnesses. If given a choice, avoid conjecture. Do not assume that an analysis in one area, for one class, or for one time automatically applies to another.

 

A key component of analytical and statistical work is replicability. In fields such as finance, insurance, or large scale employment cases, the analysis of both parties should be replicable. Outside parties should be able to analyze the same data and obtain the same results. In addition, replicability can expose error, slights of hand, or outright manipulation.

 

Data-based evidence requires focus, clarity, and appropriate analytical techniques, otherwise an output is just another number.

Data Analytics and the Law: Cleaning Data

After acquiring and merging data, litigants will want to rush to an analysis. But raw datasets, no matter how perfectly constructed, are inevitably riddled with errors. Such errors can potentially bias or invalidate results. Data cleaning, the process which ensures a slice of data is correct, consistent, and usable, is a vital step for any data-based evidence.

 

There is a often quoted rule in data science which says 80% of one’s time is spent cleaning and manipulating data, while only 20% is spent actually analyzing it. Spelling mistakes, outliers, duplicates, extra spaces, missing values, the list of potential complications is near infinite. Corrections should be recorded at every stage, ideally in scripts of the program being used (ex. R, SAS, SQL, STATA); data cleaning scripts leave behind a structured, defensible record. Different types of data will require different types of cleaning, but a structured approach will produce error free analytical results.

 

One should start with simple observations. Look at batches of random rows, what values are stored for a given variable, and are these values consistent? Some rows may format phone numbers differently, inconsistently capitalize, or round values. How many values are null, and are there patterns in null entries? Calculate summary statistics for variables, are there obvious mistakes (ex. negative time values)? After an assessment, cleaning can begin.

 

Fixing structural errors is straightforward: input values with particular spellings, capitalization, split values (ex. data containing ‘N/A’ and ‘Not Available’), or formatting issues (ex. numbers stored as strings rather than integers) can be systematically reformatted. Duplicate observations, common when datasets are merged, can be easily removed.

 

However, data cleaning is not entirely objective. Reasonable assumptions must be made when handling irrelevant observations, outliers, and missing values. If class X or transaction type Y is excluded from litigation, its reasonable to remove their observations. However, one cannot automatically assume Z, a similar class, can be removed as well. Outliers function the same way. What legal reasoning do I have to remove this value from my dataset? Suspicious measurements are a good excuse; but, just because a value is too big or too small, that alone does not make it reasonable to remove.

 

Missing data is a difficult problem: how many missing or null values are acceptable for this analysis to still produce robust results? Should you ignore missing values, or should you generate values based off of similar data points? There is no easy answer.  Both approaches assume missing observations are similar to the rest of the dataset. But the fact that the observations are missing data is informative in of itself. A more cautious stance, the one with the least assumptions, will inevitably be easier to defend in court.

 

Skipping data cleaning, and assuming perfect data, casts doubt on any final product. Data-based evidence follows the maxim “garbage in, garbage out.”