Is it Up? Down? Output? Effort? Value creation?
We seem to be reading a great deal about national, sectoral, and firm-level productivity since the pandemic started (although in my case that could be the reinforcing activity of newsfeed algorithms).
A consistent challenge for me is language and definitions. Virtually no one defines the terms they are using when they write about productivity. When they are defining it, they aren’t questioning it.
I find this lack of clarity and curiosity most common in popular writing about economics. People write about debt, inflation, GDP, and in this case, productivity, uncritically and unquestioningly using received models and language. This seems to me a failure of good journalism when the piece in question pretends to be an analysis.
A point of entry (Part 1)
My first reflection is inspired by this piece in Business Insider: Productivity is way up, by Neil Dutta.
This article stands out from the majority in this space, as Dutta goes to some lengths to explain terms and question orthodoxies. Recommended.
Main Focus
The article discusses the (supposedly) transformative impact of artificial intelligence (AI) and other technological advancements on productivity in traditional business sectors. It highlights the rush of the tech bro community to position technological innovation as the driver for enhancing labor productivity.
Key Insights
1. Productivity Rise: Despite the hype around AI, Dutta suggests that the recent increase in American productivity is attributable more to traditional factors like economic growth, labour force stabilization, training, equipment upgrades.
2. Economic Impact: Enhanced productivity is critical as it allows for higher economic growth without corresponding increases in inflation—what Dutta calls economic nirvana (more on that in the next article in this series).
3. Long-Term Outlook: Technology plays a role, but is one of many factors contributing to productivity improvements. The potential for a significant productivity boom is supported by a combination of better technology, more experienced workers, and increased capital investment.
Foundations First
What, in its most basic form, is productivity?
The Foundation: A Ratio
First, productivity isn’t output. Output is a one-dimensional metric measuring a quantity produced in a given time period.
Properly understood, productivity refers to the efficiency with which inputs are transformed into outputs, whether in the context of a national economy or a firm-level operation. At the national level, productivity is typically measured as the ratio of a total output (such as GDP) to a total input (such as the number of hours worked). This metric helps us assess how effectively a country utilizes its labor and capital to produce goods and services.
At the firm level, productivity measures the output per unit of input, where the ratio is the number of goods produced per labor hour or the revenue generated per employee, for example
Analogically you could even apply this ratio to personal productivity: it is the relationship between the resources you have available to you – time, energy, education, etc., – and your output in work product and actions that move your life in the direction you want to be going.
The standard productivity ratio, while useful, often oversimplifies the complexity of what truly constitutes productivity.
For example, the analyses I’ve seen rarely capture qualitative factors such as the quality of goods produced, the level of customer satisfaction, or the impact of innovation investment costs on the inputs. These qualitative outputs can significantly affect the way in which inputs are perceived to be ‘used’, and the long-term success and sustainability of a business. Similarly, at the national level, increases in GDP might not reflect improvements in the well-being of citizens or sustainability of growth.
Most pundits are silent on all of that.
Moreover, the ‘normal’ conception of productivity has challenges in capturing the latency effect at play in the investment in innovation. When the ratio is sampled using short time windows, the potential and future return on effort and capital expended in innovation does not show up, reducing apparent productivity.
Other factors difficult to capture include the environmental impact of increased outputs, the social costs of the disruption of workforces when driving for certain financial efficiencies, and shifting global supply chains.
The productivity ratio is a snapshot of efficiency. It is a blunt instrument of limited value when considering the systems-level complexity of the inputs and outputs measured in the context of long-term investment in innovation, and social and environmental impacts. But it is a place to start a conversation.
Monday Morning
Here are some practical takeaways and action items suggested by today’s exploration.
1. Reconsider Your Productivity Metrics
Action: Reexamine productivity metrics that focus solely on output per labor hour or simple cost efficiencies. Start a conversation about qualitative measures that reflect things like customer satisfaction, employee engagement, and product quality. Reconsider especially the time window/snapshot framing of your metrics. Do they capture longer-term payoffs in current investments in employee development and innovation?
Why: We want to introduce the value of long-term sustainability, innovation, value creation and competitive advantage.
2. Invest in Employee Development
Action: Prioritize ongoing training and employee development to ensure your workforce can develop new approaches and support new technologies that improve productivity over the long haul while maintaining quality of life for employees.
Why: As Dutta suggests, the stability and capability of the workforce are significant factors in productivity. Well-trained employees are more productive and can drive innovation. Investing in people in this way also improves the chances your other investments in productivity (machinery, processes, software, etc.) actually yield their expected returns. Investing in development signals that we believe the long-term success of our people matters.
3. Invest in Technology Wisely
Action: Invest in technology solutions that complement and enhance your existing business processes without disrupting them.
Why: We talk too much about ‘disruptive technologies’ and ‘transformations’ as if they were always a good thing. They often aren’t. Big, transformative bets are loaded with risk, and most of them fail. A big bet on AI in business fits squarely into this minefield. Integrating technology should focus not only on cutting-edge tools but also on how these tools can improve existing operations and seamlessly integrate with current practices.
4. Adopt a Systems Thinking Approach
Action: Improve your understanding of the interconnectedness of functions and their impact on productivity. This includes assessing how changes in one area of the business affect others and considering external factors like environmental and social impacts. Reading a book like Donella Meadows’ Thinking in Systems: A Primer is a great place to start.
Why: Systems thinking can help identify hidden inefficiencies and opportunities for improvement that traditional productivity metrics overlook. It also helps in making more informed decisions that consider the long-term impacts of business operations. Systems thinking also helps us understand how investments and incentives can tend to create either reinforcing or balancing feedback loops in productivity dynamics. Adopting an informed systems thinking lens can help us weigh the various factors (digital technologies, automation, training, etc.) impacting our productivity.