Why are certain models like H and GGM susceptible to overstated productivity levels at the firm level?

Certain models, such as the H model and the Gordon Growth Model (GGM), are particularly vulnerable to overstated productivity levels at the firm level because they depend on firm-level aggregated data. These models typically utilize past performance metrics to project future growth rates, and when aggregated data is used, it can mask variations between different departments or product lines within a firm.

When firm-level data is aggregated, it may lead to a distorted picture of productivity because it averages out highs and lows, potentially leading to optimistic assessments of growth potential. For example, if a few divisions are performing exceptionally well while others are underperforming, the overall aggregated data may suggest robust growth prospects that do not reflect the challenges faced by weaker segments of the business.

This reliance on aggregated data does not consider the intricate dynamics and potential inefficiencies within the firm, which can result in overly positive projections of productivity and financial performance when using models like the H model and GGM. Thus, ensuring accuracy in projections requires a nuanced understanding of the firm’s operational landscape beyond what aggregated data can provide.