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
USAspending.gov is a valuable resource to inform business strategy and investment decisions for firms focused on the Federal contracting market. In my previous posts, I’ve highlighted how to wrangle the tens of gigabytes of Federal obligation transactions to mine valuable insights about the market.
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”
This analysis is inspired by investment banker and Federal M&A guru Bob Kipps of Kipps-DeSanto (now a Capital One company). In a recent conversation, Bob noted how many segments of the Federal contracting market are heavily weighted toward smaller businesses and the largest businesses; thus, making it increasingly difficult for companies in the middle scale that neither have the advantages of the set-aside designators nor the scale or brand names of the largest firms they compete against.
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
Source: https://www.usaspending.gov/download_center/award_data_archive
GFY 2020, Archive download 02/07/2021
Big Picture Context
In GFY220, the Federal Government obligated ~$666B in funds across ~85k contractors (at the parent firm level if multi-division or multiple independent units).
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).
Why do the orange and blue lines diverge around the 35% of market share mark?
One can see that the IT and Telecom Services segment follows a pattern more skewed to the smaller firms in the market though that is relative since the ticks on the x-axis are $5B/year!
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.
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.
Advantages of Scale
The two charts highlight bifurcation of the market between the firms <$1B/year and those >$1B/year in scale. The bar chart plots the average obligations per prime contractor in each bin vs the contractor size bins on the x-axis.
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
104 contractors are competing for 12% of the market in the $200M-$1B segment while 33 contractors are competing for 30% of the market in the $1B-$5B segment. The average IT and Telecom obligations per contractor in the $5B-$10B segment is almost two times the previous category with only a 1/4 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).
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
(filtered with > $50M/year in IT and Telecom Obligations)
Competitor Count By Contractor Size by Department
GFY2020 IT and Telecom Service (PSC_Cat “D”) Total Obligations
Conclusion
The graphs and tables show that the IT and Telecom Services (PSC_Cat “D”) sub-market within the overall Federal contracting market skews toward firms below $200M/year and prime contractors greater than $1B/year in size as Bob Kipps noted.
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
