1. Benjamin LeRoy
May 2, 2015
Datathon Competition: Blood Glucose Meters Market
1. Guiding assumptions
Discussing our Data and Goal: We are trying to determine what the effect of the merging of two national
firms would be on the regional and national level, and if such a merger would create a monopolistic advantage
for the merged firms. We will refer to our two potentially merging companies as WS (Wellness Solutions) and
HH (Holistic Health). HH is centered in Palo Alto, and WS is centered in Atlanta.
We are specifically interested in the blood glucose meters market. The data from each firm holds orders
of a subset of large buyers in different regions across the country. Since the good is a durable good (or sells
fast enough) we don’t see much effect of the time between orders to the quantity of good bought.
General Assumptions:
Market: We’ve that the markets operate in competitive spirit, with no price fixing between producers.
Regions: We’ve also assumed that regional/geographical locations define markets more so than national
market. This is easily justified by the following graph which displays different clustering of prices and
quantities order depending upon the region (The referenced chart is specifically HH- see Appendix 1).
2. Methodology used in quantitative analysis We used a few methods to analyze our results.
1. Specifically incorporating in the DOJ’s HHI metric to analysis market concentration. HHI: i(pi)2
where pi is the percentage of the market controlled by company i.
2. We used linear regression to analysis the price elasticity for customers in different regions.
3. We also did analysis on marginal costs (Margin = Price˘Cost
Price )
4. In general we examined different characteristics of the clients and how they effected the models
3. Discussion of results
For each geographic region we calculated HHI to examine the market concentration and using the HHI
analysis metrics on page 19 of the the DOJ’s FTC Merge Guidelines to make decision of future exploration.
To calculate the market share of the merged company I just summed to two companies current market share
per industry.
Total.U.S. Pacific.Western Midwest Southeast Northeast
Original HHI: 961.4568 2318 1586 1054 918
After Merger HHI: 1138.0123 2758 2090 1234 998
Difference of HHIs: 176.5556 440 504 180 80
HHI analysis: For the data above, we can conclude that the DOJ guidelines would encourage us to
investigate the effects of the merger in the Pacific West and the Midwest (the guidelines are on page 19).
Chart of the results:
Region: Total USA Pacific West
Decision (on continued investigation): Stop Continue
Reason: Unconcentrated Market Concentrated + high difference
continued:
1

2. Midwest Southwest Northwest
Continue Stop Stop
Concentrated + high difference Unconcentrated Market Small difference +Unconcentrated
In line with the HHI results we decided not to pursue analysis of the merger’s effect on the regional markets
in the Northeast and Southeast, and to also not analysis the effect of the merger on the United States’ market
as a whole.
Elasticity of Demand: In the West and Midwest markets, we saw, after running a basic linear regression
that it didn’t appear much, if any response of unit purchased to prices. I did notice some bi modal clustering
in certain markets, but with hierarchical and kmeans clustering it didn’t see to be able to distinguish such
clusterings.
An example of the linear regressions displaying strong price inelastic, I ran the ordinary linear regression
model in the Western Market for HH, and saw that the coefficient for quantity was actually predicted to be
-.135, and got a p-value of .893. In other words, it’s pretty safe to assume that the quantity purchased is not
linearly related to the price. We see the same results in other markets.
Future Analysis
a. Midwest
In the Midwest we actually see that HH is just selling to Healthcare companies, and in for WS we only see
them selling to Retailer companies.
Since the market is segmented, and we didn’t see competition in between the two companies to cover each
segment, I also don’t have space to graph it here, but the code below examines the values in each companies’
Midwestern region and sees only one class as reported above.
class_w_mw<-unique(wdata_new$cust_category[wdata_new$region==unique(wdata_new$region)[2]])
class_h_mw<-unique(hdata_new$cust_type[hdata_new$region==unique(hdata_new$region)[2]])
We are not in danger of problems in the Midwest from the merger related to reduction of inter competition
(that could have been between the two companies).
There is still a worry about monopolistic tendencies. If we examine the distribution of costs per unit, they
are step-wise (not important enough to actually show).
Moreover, average costs for both groups very similar (see below table), I’ve decided to argue that the effect
of a merger in the Midwest would probably be small. I understand I probably missed monopolistic effects
that occur from potential returns to scale encouraging the company to produce less, but I think that in the
negotiations of pricing, we will see little deviation from the present.
Table 2: Midwest
Company Average Costs
WS 236.3311
HH 232.3333
b. West
In the West, we see a high change in the HHI index, and that our merger caused the market in the West to
become Highly Concentrated (which and HHI increase of 440).
More importantly, both companies currently compete in both subsections of the market (with Healthcare and
Retail clients), see Appendix 2.
In the West, if we compare the two companies’ per unit pricing, costs and margins we get the some interesting
data. Since HH is headquartered in the West (Palo Alto) it makes sense that we’d see a decline lower per-unit
2

3. cost and more variance in the prices. Below is a graph with HH’s data on the left and WS’s data the right.
Although you are unable to see it above, there is clustering around the higher price values in both companies
data, suggesting some strength in companies ability to negotiate prices.
More interestingly, we see a lot of differences between the two companies’ margins.
Table 3: West Coast Margins
Company Mean Variance
HH 0.1744106 0.0011346
WS 0.1229045 0.0001988
We see a larger difference in the margins of the two companies. If the costs to produce goods for WS was
the same as HH, we’d see the margin of WS’s customers become around 0.162287 with a new variance of
1.3111·10−4
. Compared to the old WS margin, that’s a 32.0431856 percent increase in the average profit
margin, and moreover that percent increase of WS’s profit.
Although I cannot think about how to predict the change in pricing strategies, a large change in the marginal
cost curve would probably hurt consumers if the merged companies acted monopolistic, specifically in this
case since the consumers are pretty inelastic to price.
4. Caveats and limitations to analysis The data provided was very pretty but is kinda hard to see
trends with companies that order in the same number of times, and so few companies.
As mentioned in the Midwestern market commentary, I think more time is needed in this area to see the
effects on the merger, since I stopped looking at the data after I realized the the firms were competing in
different submarkets based on type of client.
Other commentary, since this good is a healthcare good, and the demand for such a good is very inelastic, I
would suggest that infringing on the market will less reasoning than on average probably would be wise to
protect consumers since the product is a “need” not a want.
5. Final recommendations and next steps The DOJ should do a few things, 1) talk to the merging
companies about the problems on the West Coast that we won’t accept, encouraging them to either sell 1
part of the company in the west coast, or do a spin off 2) The DOJ should also do more research on the
comments in # 4 and possibly also collect data about HH and WS’s competitors.
Appendix:
0 100 300
220280
HH Customers Clustering
Appendix 1
units
perunitprice
Pacific
Midwest
Northeast
Southeast
0 200 400 600
240280
HH on left and WS on right
Appendix: 2
units
price
Healthcare Provider
Retailer
3