Contract analysis

III. QUALIFICATIONS AND BACKGROUND A. Professional Position

1. I am the Lead Consultant and Senior Vice President at Corbin Risk Advisors LLC ("CRA"), a litigation consulting firm specializing in expert economic and statistical analysis. CRA's principal offices are located at 1901 N. Moore Street, Suite 1020, Arlington, Virginia 22209. CRA provides analytical and consulting services to law firms, corporations, and governmental entities across a range of complex litigation and

2. I received my Doctor of Philosophy (Ph.D.) in Economics, with a specialization in labor economics, from the Rutledge School of Social Sciences at Hargrove University, Charlottesville, Virginia, in 2007. My doctoral dissertation, titled "Heterogeneous Labor Supply Responses to Platform-Based Compensation Structures," was supervised by Professor Alan M. Whitford and was awarded the Hargrove Department of Economics D

3. I received my Master of Arts (M.A.) in Applied Statistics from Caldwell Polytechnic Institute, Atlanta, Georgia, in 2003. My master's thesis focused on the application of hierarchical linear modeling techniques to multi-site employment data.

4. I received my Bachelor of Arts (B.A.), summa cum laude, with a double major in Mathematics and Economics from Wentworth College, Boston, Massachusetts, in 2001. I was elected to Phi Beta Kappa during my junior year. C. Professional Experience

5. I have over eighteen (18) years of professional experience in the fields of labor economics, statistical analysis, and employment litigation consulting. Over the course of my career, I have provided expert economic and statistical analysis in more than forty-five (45) FLSA collective action and state-law class action matters involving claims of misclassification, off-the-clock work, unpaid overtime, and related wa

6. I have been deposed or provided trial testimony in approximately twenty (20) cases in federal and state courts across the United States, including the United States District Courts for the Southern District of New York, the Northern District of Illinois, the Central District of California, the Eastern District of Pennsylvania, the Middle District of Florida, and the Western District of Texas, as well as state cour

7. I have authored or co-authored more than a dozen articles published in peer-reviewed academic and professional journals on topics including worker classification, wage and hour economics, labor market analysis, and the economics of platform-based work arrangements. My published work has appeared in the Journal of Labor Economics, the Industrial and Labor Relations Review, the Journal of Applied Econometrics, and t

8. Prior to joining CRA in 2014, I served as an Associate Professor of Economics at Dunmore University, Richmond, Virginia, where I taught graduate and undergraduate courses in labor economics, econometrics, and applied statistics from 2007 through 2014. During my academic tenure, I supervised multiple doctoral dissertations and master's theses related to labor economics and employment policy. D. Engagement in This M

9. I was retained by the law firm of Calderwood & Harkness LLP on behalf of Defendant Stonefield Logistics Group Inc. pursuant to an Expert Retention Agreement dated May 8, 2025. My engagement in this matter is governed by the terms of that agreement, which provides for compensation at a rate of $550 per hour for my time and $375 per hour for the time of associate analysts working under my direction. My compensation,

10. I was asked to analyze whether the approximately 1,187 individuals who performed delivery driving services for SLG under Independent Contractor Operating Agreements ("ICOAs") during the period from April 30, 2022 through the present (the "Relevant Period") are "similarly situated" for purposes of FLSA conditional certification. More specifically, I was asked to analyze the following areas: (1) variance in weekly

11. In forming my opinions, I reviewed and considered the following categories of materials: (a) RouteSync dispatch records for all 1,187 drivers for the period April 30, 2022 through June 30, 2025, including delivery offer data, acceptance and rejection records, delivery completion timestamps, and assignment histories; (b) GPS telematics data from SLG-provided tracking devices installed in driver vehicles across all

12. My analyses employ standard and widely accepted statistical techniques, including descriptive statistics (mean, median, standard deviation, range, interquartile range, coefficient of variation), one-way analysis of variance ("ANOVA"), coefficient of variation analysis, ordinary least squares ("OLS") regression analysis, and chi-square tests of independence. All statistical analyses were performed using Penmark Sy

13. Opinion 1 (Weekly Hours Worked): The 1,187 drivers in the proposed collective exhibit statistically significant variation in weekly hours worked across and within SLG's fourteen (14) distribution hubs. The mean weekly hours worked across the proposed collective is approximately 47.3 hours, with a standard deviation of approximately 12.8 hours and a coefficient of variation of approximately 27.1%. The named Plaint

14. Opinion 2 (Gross Weekly Pay): Gross weekly pay varies substantially across drivers and hubs, with a mean of approximately $1,078, a standard deviation of approximately $412, and a coefficient of variation of approximately 38.2%. This variation reflects individualized economic circumstances — including each driver's delivery mix, volume, and effort — rather than a uniform pay practice susceptible to collective res

15. Opinion 3 (Delivery Acceptance Rates): Delivery acceptance rates vary widely across the proposed collective, with a standard deviation of approximately 6.8 percentage points around a mean of approximately 91.3%. Approximately 14.6% of driver-months fell below the 85% contractual threshold during the Relevant Period, and many of those drivers continued operating without formal consequence. This variation demonstra

16. Opinion 4 (Route Deviation Frequency): Route deviation data demonstrates that drivers exercised significant and variable degrees of autonomy in how they performed deliveries. The mean route deviation frequency was approximately 3.7 per driver per week, with a coefficient of variation of approximately 78.4% — an extremely high level of dispersion. The named Plaintiff's average deviation frequency of approximately

17. Opinion 5 (Multi-Platform Engagement): A material subset of the proposed collective — approximately 247 drivers (20.8%) — showed evidence of engagement with competing delivery platforms or other outside economic activity during the Relevant Period. Multi-platform engagement rates varied dramatically by hub, ranging from approximately 7% at HUB-13 (Lubbock) to approximately 31% at HUB-05 (Austin). This variation i

V. ANALYSIS OF WEEKLY HOURS WORKED A. Data Source and Methodology

18. To calculate weekly hours worked for each driver, I relied upon two complementary data sources: (a) GPS telematics data captured by SLG-provided tracking devices installed in each driver's vehicle, and (b) RouteSync login and logout timestamps recorded each time a driver commenced or concluded a delivery session through the RouteSync dispatch platform. By cross-referencing these two independent data streams, I wa

19. The data analyzed spans the full Relevant Period — from April 30, 2022 through June 30, 2025 — encompassing approximately 161 calendar weeks. During this period, the 1,187 unique drivers in the proposed collective were active for varying durations, reflecting the natural turnover and intermittent engagement characteristic of an independent contractor delivery network. The total number of driver-week observations

20. For purposes of my analysis, I defined "hours worked" in a given week as the total time elapsed from the first delivery dispatch acceptance to the last delivery completion during that week, net of breaks exceeding thirty (30) minutes, as identified through gaps in GPS movement data. This definition is consistent with standard approaches to measuring compensable time under Department of Labor guidance and is delib

21. Across all 97,634 driver-week observations, the mean weekly hours worked was approximately 47.3 hours. The median weekly hours worked was approximately 44.1 hours — meaningfully lower than the mean, indicating a right-skewed distribution in which a subset of high-hour drivers pulls the mean upward. The standard deviation was approximately 12.8 hours, and the range extended from approximately 15 hours per week (at

22. I note that Plaintiff alleges in his declaration that he "typically" worked approximately 58 hours per week. My analysis of SLG's data confirms that Plaintiff Delano's average weekly hours during the Relevant Period was approximately 58.2 hours. However, this figure is far above the collective mean of 47.3 hours and above the collective median of 44.1 hours. In fact, Plaintiff Delano's average weekly hours places

23. I performed a hub-by-hub analysis of mean weekly hours worked across all fourteen (14) SLG distribution hubs. The results, summarized in Exhibit B and detailed below, reveal substantial variation in mean hours across hubs:

24. As the table above illustrates, mean weekly hours ranged from approximately 35.4 hours at HUB-13 (Lubbock) to approximately 52.4 hours at HUB-01 (Fort Worth) — a spread of 17.0 hours between the highest and lowest hub means. The three largest Texas hubs (Fort Worth, Dallas, and Houston) all exhibited mean weekly hours above 49 hours, while the smaller and more geographically remote hubs (Lubbock, Shreveport, Litt

25. I performed a one-way analysis of variance ("ANOVA") to test whether the differences in mean weekly hours across hubs were statistically significant. The ANOVA yielded an F-statistic of 42.87 with 13 and 97,620 degrees of freedom, and a p-value of less than 0.001. This result is highly statistically significant and indicates that between-hub differences in mean weekly hours are not attributable to chance. The bet

26. Although between-hub variance is substantial, within-hub variance is also significant, as discussed in the following paragraphs. This dual finding — of both between-hub and within-hub heterogeneity — indicates that the proposed collective is heterogeneous at every level of aggregation. D. Within-Hub Variation

27. Even within individual hubs, the standard deviations in weekly hours worked are large, ranging from approximately 9.0 hours at HUB-11 (Shreveport) to approximately 13.1 hours at HUB-01 (Fort Worth). These within-hub standard deviations are comparable in magnitude to the overall standard deviation of 12.8 hours, confirming that substantial variation in weekly hours exists among drivers operating out of the same fa

28. To illustrate this point concretely, I examined the distribution of weekly hours worked at HUB-01 (Fort Worth, Texas) — the hub where the named Plaintiff, Marcus Delano, performed his delivery services. Among the 142 drivers at HUB-01, the mean weekly hours worked was approximately 52.4 hours, the median was approximately 50.8 hours, the standard deviation was approximately 13.1 hours, and the range extended from

29. Across the entire proposed collective, approximately 38.2% of all driver-week observations reflected fewer than 40 hours worked during the applicable week. This finding is of considerable significance for the conditional certification inquiry. Under Plaintiff's theory, the proposed collective members are entitled to overtime compensation because they were misclassified as independent contractors and, as putative

30. Named Plaintiff Marcus Delano's average of approximately 58 hours per week places him at approximately the 91st percentile of the distribution. His experience of routinely working well in excess of 40 hours per week is not shared by the majority of the proposed collective. To the extent Plaintiff holds himself out as representative of the collective, the data do not support that characterization. E. Temporal Vari

31. Hours worked by individual drivers also fluctuated significantly over time. I analyzed the within-driver temporal variation by calculating the coefficient of variation of weekly hours for each individual driver across his or her active weeks during the Relevant Period. The within-driver coefficients of variation ranged from approximately 15% to approximately 45%, with a median of approximately 28%. This indicates

32. Moreover, seasonal patterns in hours worked differed meaningfully by hub. The Houston and Dallas hubs (HUB-02 and HUB-03) exhibited pronounced holiday-season spikes in weekly hours, with fourth-quarter average hours approximately 15–20% higher than first-quarter average hours. By contrast, the smaller and more rural hubs — such as Lubbock (HUB-13), Little Rock (HUB-10), and Shreveport (HUB-11) — showed comparativ

VI. ANALYSIS OF GROSS WEEKLY PAY A. Data Source and Methodology

33. To analyze gross weekly pay, I relied upon SLG's per-delivery compensation records for all 1,187 drivers during the Relevant Period. Under the ICOA, drivers are compensated on a per-delivery basis rather than an hourly or salaried basis. The per-delivery rate is not uniform; rather, it varies based on three factors: (a) package weight, (b) delivery distance from the distribution hub, and (c) delivery type, classi

34. For each driver, I calculated weekly gross pay by aggregating all per-delivery payments received during each calendar week in which the driver was active. The resulting data set encompasses the same approximately 97,634 driver-week observations described in connection with the hours-worked analysis above. B. Aggregate Statistics

35. Across all driver-week observations, the mean weekly gross pay was approximately $1,078. The median weekly gross pay was approximately $987, indicating a right-skewed distribution in which a subset of higher-earning drivers pulls the mean upward. The standard deviation was approximately $412, and the range extended from approximately $210 per week (reflecting drivers who completed only a small number of deliverie

36. Mean weekly gross pay varied substantially across SLG's fourteen (14) distribution hubs. The three largest Texas hubs — Fort Worth (HUB-01), Dallas (HUB-02), and Houston (HUB-03) — exhibited the highest mean weekly gross pay, ranging from approximately $1,145 at HUB-02 (Dallas) to approximately $1,218 at HUB-03 (Houston). These hubs also had the highest concentrations of white-glove delivery opportunities, which

37. I performed a one-way ANOVA to test whether the differences in mean weekly gross pay across hubs were statistically significant. The ANOVA yielded an F-statistic of 57.14 with 13 and 97,620 degrees of freedom, and a p-value of less than 0.001. This result is highly statistically significant and confirms that between-hub differences in mean weekly pay are not attributable to random variation. The detailed ANOVA re

38. To better understand the drivers of pay variation, I analyzed the proportion of white-glove versus standard residential deliveries performed by each driver across the Relevant Period. This analysis revealed that delivery mix is a primary determinant of gross weekly pay and effective hourly earnings. White-glove deliveries, which require additional skills such as furniture assembly, in-home placement, and customer

39. Drivers who actively pursued and completed a higher proportion of white-glove deliveries earned materially more than drivers who performed primarily standard residential deliveries. This difference reflects individualized economic choice: under the ICOA, drivers are not assigned to a particular delivery type but rather have the ability to accept or decline individual delivery offers based on the delivery type, lo

40. Named Plaintiff Marcus Delano performed primarily standard residential deliveries during the Relevant Period. His per-delivery rate averaged approximately $21.50, and his average weekly gross pay was approximately $1,247 (reflecting his high volume of deliveries, consistent with his above-average weekly hours). However, his effective hourly rate — calculated as gross weekly pay divided by weekly hours worked — wa

41. The substantial variation in gross weekly pay across the proposed collective reflects individualized delivery mix, volume, and effort — not a uniform pay practice susceptible to collective adjudication. The effective hourly rates imputed from the data range from approximately $15.80 to approximately $32.40, a spread that is more than two-to-one. Under Plaintiff's theory, overtime compensation must be calculated b

42. This calculation cannot be performed on a collective basis. Each driver's regular rate would need to be determined individually, and any overtime damages would require a driver-by-driver inquiry into the number of hours exceeding 40 in each week and the applicable regular rate for that week. This is precisely the type of individualized inquiry that courts have recognized as fatal to conditional certification unde

43. Named Plaintiff Delano's average weekly gross pay of $1,247 and effective hourly rate of approximately $21.50 per hour are not representative of the proposed collective. His pay reflects his high-hour, standard-residential delivery work pattern, which differs materially from the delivery patterns, compensation levels, and effective rates experienced by the majority of the proposed collective.

44. The hub-level pay variation further underscores the heterogeneity of the proposed collective. Drivers at the three largest Texas hubs earned, on average, approximately 50–80% more per week than drivers at the smallest hubs. This variation is attributable in substantial part to differences in delivery volume, white-glove opportunity concentration, and local market conditions — all factors that are hub-specific and

45. In sum, the pay data confirm that the proposed collective is not a group of individuals subject to a common pay practice. To the contrary, gross pay and effective hourly rates are driven by individualized choices and circumstances that vary dramatically across drivers and hubs.

VII. ANALYSIS OF DELIVERY ACCEPTANCE RATES A. The 85% Minimum Acceptance Rate

46. Under all three versions of the ICOA, SLG requires drivers to maintain a minimum delivery acceptance rate of 85% per rolling 30-day period. The acceptance rate is calculated as the number of delivery offers accepted by the driver divided by the total number of delivery offers extended to the driver through the RouteSync platform during the applicable 30-day period.

47. The ICOA specifies that a driver whose acceptance rate falls below 85% during any rolling 30-day period "may be subject to a performance review and, upon repeated failure to maintain the minimum acceptance rate, may be subject to termination of this Agreement." Critically, falling below the 85% threshold does not trigger automatic termination. Rather, it triggers a discretionary "performance review" process, the

48. Plaintiff alleges in his Motion for Conditional Certification that the 85% minimum acceptance rate constitutes an instrument of employer control over scheduling, compelling drivers to accept substantially all delivery offers and thereby negating their purported ability to set their own hours and workload. My analysis of the data does not support this characterization. B. Data and Methodology

49. I used RouteSync dispatch data to calculate each driver's monthly acceptance rate — defined as deliveries accepted divided by deliveries offered — for each rolling 30-day period during the Relevant Period. This calculation was performed for all 1,187 drivers in the proposed collective. The resulting data set encompasses approximately 32,750 driver-month observations. C. Distribution of Acceptance Rates

50. Across all driver-month observations, the mean acceptance rate was approximately 91.3%. The median acceptance rate was approximately 92.7%, indicating a slightly left-skewed distribution with a tail of lower-acceptance-rate drivers. The standard deviation was approximately 6.8 percentage points, and the range extended from approximately 72% to 100%.

51. A meaningful number of driver-month observations reflected acceptance rates below the 85% contractual threshold. Specifically, approximately 14.6% of all driver-month observations (approximately 4,782 out of 32,750 driver-months) showed acceptance rates below 85% at some point during the Relevant Period. This is a substantial figure, representing nearly one in seven driver-months.

52. I further examined the consequences experienced by drivers who fell below the 85% threshold. Of the approximately 4,782 driver-months in which the acceptance rate fell below 85%, SLG records indicate that formal performance reviews were initiated in only approximately 612 instances — roughly 12.8% of sub-threshold driver-months. In the remaining approximately 87.2% of instances in which a driver's acceptance rate

53. These findings indicate that the 85% minimum acceptance rate was not rigidly or uniformly enforced. A significant number of drivers operated below the threshold for extended periods — in some cases, for multiple consecutive months — without formal consequence. Some drivers maintained acceptance rates in the mid-70% range for quarters at a time while continuing to receive delivery offers and compensation through R

54. Acceptance rate patterns differed significantly across SLG's fourteen (14) distribution hubs. Larger hubs with higher delivery volume — particularly Houston (HUB-03), Dallas (HUB-02), and Fort Worth (HUB-01) — exhibited higher mean acceptance rates (ranging from approximately 92.5% to 94.1%) and lower variance. This pattern is consistent with the greater availability of delivery offers at high-volume hubs: when d

55. Smaller hubs with lower delivery volume — particularly Lubbock (HUB-13), Shreveport (HUB-11), and Little Rock (HUB-10) — exhibited lower mean acceptance rates (ranging from approximately 86.3% to 88.7%) and higher variance. Drivers at these hubs were more likely to receive delivery offers that were unattractive due to long distances, low per-delivery compensation, or inconvenient timing, and were correspondingly