Critical Loss
Analysis (CLA) has two main applications in antitrust policy, assisting the
market definition of relevant markets and in horizontal merger control in
identifying unilateral price effects of mergers.

If a firm
increases their prices, they will experience a fall in sales (negative effect
on profits) and a higher profit margin (positive effect on profits). As both
work in the opposite directions, CLA operationalise the SSNIP test in
establishing how much fall in the hypothetical monopolist’s sales to make the
hypothetical price increase unprofitable.

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To identify
the balance between the two effects of supply-side and demand-side substitution,
CLA calculates the percentage fall in quantity.

If the
hypothetical firm has a large profit margin, the firm will suffer a greater
loss in profits from a price increase. Hence, a smaller critical loss is needed
to make a price increase by a hypothetical monopolist unprofitable.

With regards
to merger control, the focus is not whether the hypothetical monopolist is able
to increase prices and be profitable, but whether they are able to perform it
post-merge.The estimated
critical loss is the percentage reduction in quantity such that the two effects
balance out (benefits from the price increase are balanced out by the losses).
The estimated actual loss is the actual percentage reduction in quantity
predicted by increasing prices. If the
estimated actual loss is no greater than the estimated critical loss, the
relevant market is presumed to be narrower than the market currently
considered. This is because it will be profitable to increase prices. Consumers
are unable to easily switch to other substitute goods; hence the candidate market
which is the local radio advertising is the relevant market.

However, we must
note that while own-price elasticity is a key factor in estimating actual loss,
it ignores cross-price elasticity and elasticities should be best estimated by
observing the price changes on unit sales, which requires additional time and
data. Hence, the inference made might not be accurate.