Blog #11 The “Kipps Curve” — Understanding Federal Agency Buying Preferences

TL;DR — Uncover competitive insights from information and analytics using Federal contracting #opendata. Analyze > 100GB’s of US Federal spending open data with a few lines of Python code.

Previous Blog Post in this series can be found at this link.

Introduction

This post will focus on understanding Department buying patterns for IT-related services. One of the key insights from analyzing Department and Agency-level spending from this perspective is to highlight whether there are biases toward firms of a certain scale.

The “Kipps Curve”

The analysis I present here is an effort to understand what that looks like in practice using USAspending.gov data to review the current state of the market in the government fiscal year 2020 (GFY2020) that ended September 30, 2020.

For simplicity, I focus my attention on the product or service codes where the dominate type of work has been designated as IT and Telecom Services (also known as the “D” PSC_Cat codes). That was a $49B market in GFY2020 and has been growing faster than the overall Federal contracting market.

It is especially hard for successful businesses that graduate from the protections of set-aside programs when they enter “into the wild” of full and open competition against the largest firms in the market. The analysis below makes it even starker how big a challenge there is for firms between $200m/year to $1B/year to compete and scale.

Analysis

GFY 2020, Archive download 02/07/2021

Big Picture Context

The curves that follow show the cumulative percentage of the total obligations by the estimated prime-contractor federal total revenue.

The x-axis plots the size of the prime contractor as the sum of all obligations they were issued across all work as a prime contractor. They are ordered from smallest to largest. The y-axis is the cumulative percent of spending for a specific product or service category work (PSC_Cat) as designated by the contracting agency. Each dot on the chart represents a contractor and plots them by ordered size along the x-axis and by their cumulative % of the total along the y-axis. The largest Federal contractor on the right completes the trail of dots to reach 100% of all obligations in that category. This first figure displays all Federal obligations for all goods and services in blue (~85k contractor-dots). The orange dots represent obligations tagged as PSC_Cat “D” for IT and Telecom Services (~6k contractor-dots).

Figure 1. PSC_Cat D IT and Telecom Services Spending vs Overall Obligations.

Why do the orange and blue lines diverge around the 35% of market share mark?

The upper right dot is an overlay of a blue and orange dot representing the largest contractor. If you look to the left of the blue dot representing the second largest contractor, you can see that the largest contractor has greater than 10% market share (100% vs ~89% => ~11%). Each jump in altitude from one dot to the next represents the incremental market share of that firm. One can see from the blue dots that the largest firm has much larger market share than #2. In the case of the orange dots that represent PSC_Cat “D” IT and Telecom spending, one can see that the largest contractor has relatively little market share but there is a large jump from #3 to #2.

Let’s look at a much smaller portion of the graph above.

Figure 2. IT and Telecom Services vs Overall Market — Smallest firms to $200M/year in size

Even with this magnification of the chart in Figure 1, one can see that IT and Telecom Services spending is skewed toward the smaller firms vs the overall Federal market.

This next chart magnifies the segment below $10B. 30% of the IT and Telecom Services market is spent on firms $100M/year or smaller. And, ~38% of the spending for IT and Telecom is spent on firm less than $200M/year in size. ~50% of the spending goes to firms greater than $1B/year in size. A small number of firms between $1B-$1.5B in size capture about 12% of the market while firms between $200M-$1B capture about 12% also.

Figure 3. IT and Telecom Services vs Overall Market — Smallest firms to $10B/year in size

Advantages of Scale

Figure 4. IT and Telecom Services — Average Obligations Per Contractor by Contractor Size Bin

The number above the bars represent the number of companies with prime contract obligations in that size bin. Note the large jump in average obligations per contractor from the $200m-$1B bin to the >$1B bins.

3 Times the Spending and 1/3 the Number of Competitors

Note the scaling of the reduced number of competitors at each stage. The $25-$100M segment has 1/10 the competitors of the <$25M segment. The $100M-$1B segment has about 40% of the previous segment. The $1B-$5B segment has 33/(95+104) ~ 16% of the competition.

The next diagram displays the same information for all obligations of all types of products and services across all types of work (~$666B in GFY2020).

Figure 5. All Products and Services — Average Obligations Per Contractor by Contractor Size Bin

The next tables describe the distribution of PSC_Cat “D” IT and Telecom spending across Departments that spend more than $50M/year on that type of work. The top line “*ALL” computes the distribution of PSC_Cat “D” obligations across all Federal Departments and every spending level. It provides a reference point to compare the other Department percentages.

Market Share by Contractor Size by Department

Competitor Count By Contractor Size by Department

GFY2020 IT and Telecom Service (PSC_Cat “D”) Total Obligations

Conclusion

The Department level spending distribution table highlights different preferences by Departments in their choices and willingness to select firms of different size to meet their IT needs. For instance, the SBA spends ~94% of its ~$257M/year in IT on prime contractors less than $100M/year in size (77 companies). Which makes sense given their mission. In contrast, the VA spend ~40% of its ~$4.8B/year in IT obligations on firms between $1B-$5B/year (16 companies).

A logical next step is to take this analysis to the Agency level inside the largest Departments to better understand the preferences at that level and the competitive intensity of those sub-segments of the market.

What are the implications for the firms in that $200M/year-$1B/year range and those $100M/year and below firms hoping to thrive as they graduate “into the wild” of full and open competition?

I welcome feedback and your thooughts. Also, please note any errors that I’ve made. I am eager to fix them. What should I look at next?

Previous Blog Post in this series can be found at this link.

Appendix:

MIT Open Source License Copyright 2020 Leif C Ulstrup

When working with USAspending.gov data, see this note embedded on the USAspending.gov site about attribution of D&B data and “D&B Open Data” that is embedded in the download data and USAspending.gov website reports such as the one below — https://www.usaspending.gov/db_info

Steps Followed

Step 1: Download the GFY2020 Archive (see earlier blog on that topic)

Step 2: Read the CSV files into Python Pandas via Dask and apply data wrangling cleanup (see earlier blog on this topic)

Step 3: Filter the transactions to identify parent companies with prime obligations in product_or_services_code (“PSC_Cat”) that start with “D” for IT and Telecom services

Step 4: Compute the Federal-wide total prime obligations by parent firm identified in the previous step

Step 5: Narrow the analysis to a specific Department/Agency/PSC_Cat

Step 6: Compute total obligations for that Department/Agency/PSC_Cat by Contractor

Step 7: Sort Contractors by Total Federal Prime Obligations

Step 8: Analyze and Plot

Strategy, emerging technology, innovation, and management advisor https://www.primehookllc.com/about-us.html, https://www.american.edu/kogod/faculty/ulstrup.cfm

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