Technical Change and the Production Process: A Deep Dive

Imagine a factory that produces smartphones. Today, with the same machines and workers, it manufactures 1,000 units daily. Tomorrow, through better management techniques or improved worker training, the same factory produces 1,200 units without adding any new equipment or hiring more staff. This simple scenario captures the essence of technical change-the ability to get more from what we already have. Understanding how technical change transforms the production process is fundamental to grasping why some economies grow rapidly while others stagnate.

What is technical change in production? 🔗

Technical change represents any improvement in the production process that allows firms to produce more output from the same quantity of inputs, or alternatively, to produce the same output using fewer inputs. Think of it as the economy’s invisible engine of progress. When a textile mill adopts automated looms that enable the same number of workers to produce twice as much fabric, that’s technical change in action.

This concept goes beyond simply buying newer machines. Technical change can manifest through better production methods, improved worker skills, superior organizational practices, or innovative ways of combining existing resources. A restaurant that reorganizes its kitchen layout to reduce preparation time is experiencing technical change just as much as a manufacturer implementing robotic assembly lines.

The visual representation of technical change is powerful and intuitive. On a graph showing the production function, technical change appears as an upward shift of the entire curve over time. This upward movement tells us that for any given level of inputs-whether labor hours or machine time-the economy can now produce more output than before.

Incorporating time into the production function 🔗

Economists model the production relationship mathematically to analyze it rigorously. The traditional production function Y = F(K, L) shows how output (Y) depends on capital (K) and labor (L). But this formulation has a critical limitation: it’s static, frozen in time, unable to capture the dynamic improvements we observe in real economies.

To solve this problem, economists introduce time as an explicit factor in the production function, writing it as Y = F(K, L, t), where ‘t’ represents the passage of time. Here, time doesn’t just track the calendar-it acts as a shifter that captures all the technological improvements, knowledge accumulation, and efficiency gains that occur as years pass. This mathematical device allows the production function to shift upward systematically, reflecting the continuous stream of innovations that characterize modern economies.

Consider India’s telecommunications sector. In the early 2000s, a telecom company might have needed substantial infrastructure and workforce to serve one million customers. Today, with advancements in network technology and digital systems, the same company can serve ten times as many customers with proportionally fewer resources. The ‘t’ in our equation captures these cumulative improvements that have unfolded over two decades.

The concept of effective inputs 🔗

Another sophisticated way to understand technical change is to think of it as augmenting the factors of production themselves. Rather than saying “technology improves,” we can say “our labor and capital become more effective over time.” This perspective leads to a modified production function: Y = F[J(t)K, Z(t)L].

In this formulation, J(t) represents the effectiveness of capital, and Z(t) represents the effectiveness of labor. A machine that cost ₹1 lakh ten years ago might be equivalent to a ₹5 lakh machine today in terms of productive capacity-that’s capital becoming more effective. Similarly, a worker with access to modern software tools might accomplish what previously required five workers-that’s labor augmentation in action.

The beauty of this approach is that it allows the effective quantities of labor and capital to grow even when their physical quantities remain constant. A steel plant doesn’t need to hire more workers or buy more furnaces to increase output if technical progress makes each existing worker and each existing furnace more productive. This perspective helps explain how economies can maintain growth despite stable or even declining populations.

Types of factor-augmenting technical change 🔗

Not all technical progress affects labor and capital equally. Understanding the different patterns of technical change helps us predict how technological advancement will reshape employment, wages, and investment patterns in the economy.

Capital-augmenting technical change 🔗

Capital-augmenting technical progress occurs when innovations primarily enhance the productivity of capital equipment while leaving labor productivity relatively unchanged. Mathematically, this means dJ/dt > 0 while Z=1. Imagine a logistics company that installs GPS tracking and route optimization software in its delivery trucks. The trucks (capital) become dramatically more productive-completing more deliveries per day-but the drivers’ basic skills and effort levels remain essentially the same.

This type of technical change is particularly important in capital-intensive industries like manufacturing, where investments in automation and advanced machinery can yield substantial productivity gains. Indian automobile plants that have adopted robotic welding systems exemplify this pattern: the robots (capital) become far more efficient, while the role of human workers shifts but doesn’t necessarily become more productive per hour worked.

Labour-augmenting technical change 🔗

When technical progress primarily increases labor productivity while capital productivity remains constant (dZ/dt > 0, J=1), we have labour-augmenting or Harrod-neutral technical change. This form is particularly significant because, as economists have demonstrated, it’s the only type of technical change consistent with sustained balanced growth in economies.

Consider software developers equipped with modern integrated development environments and code libraries. The same computer hardware (capital) is used, but each programmer can now accomplish far more in an hour than was possible two decades ago. Education and training programs that enhance worker capabilities without requiring additional capital investment also exemplify labor-augmenting progress. A warehouse worker trained to operate multiple types of forklifts becomes more versatile and productive without any change to the forklifts themselves.

Equally factor-augmenting change 🔗

Sometimes technical progress enhances both capital and labor at the same rate, meaning both J(t) and Z(t) grow proportionally. This balanced improvement-often called Hicks-neutral technical change-maintains the relative productivity of capital and labor. When a restaurant chain simultaneously upgrades its kitchen equipment and implements better staff training programs that proportionally increase both types of productivity, it experiences this balanced form of technical progress.

Linking technical change to total factor productivity 🔗

For economists analyzing growth, a crucial question arises: how much of output growth comes from using more inputs, and how much comes from using inputs more efficiently? This question leads to the concept of Total Factor Productivity (TFP), which represents the portion of output growth not explained by increases in capital or labor.

Under the assumption of constant returns to scale-meaning that doubling all inputs doubles output-the production function can be elegantly simplified to Y = A(t)F(K, L). Here, A(t) captures Total Factor Productivity, the mysterious residual that Robert Solow famously identified as “a measure of our ignorance” about the sources of economic growth.

Recent data from the U.S. Bureau of Labor Statistics shows that TFP growth contributed significantly to output growth, with increases ranging from around one to two percent annually in recent years. This seemingly modest percentage actually represents enormous value creation because it compounds over time without requiring proportional increases in resource consumption.

Understanding TFP in practical terms 🔗

What does TFP actually capture? It encompasses everything that makes an economy more productive beyond simply accumulating more machines and workers. Better management practices, improved logistics networks, more effective regulations, scientific discoveries, software innovations, and even cultural changes that enhance work quality all contribute to TFP growth.

Consider India’s digital payments revolution. The same banks, with largely the same number of employees and branch networks, now process vastly more transactions through UPI and digital wallets. The improved transaction volume doesn’t come primarily from hiring more tellers or building more branches-it comes from technical change embodied in digital infrastructure and changed behaviors. This represents pure TFP growth: more output from the same measured inputs.

Measuring what matters 🔗

Calculating TFP involves a straightforward but powerful accounting exercise. Economists measure how much output grew, then subtract the contributions from labor growth (weighted by labor’s share of income) and capital growth (weighted by capital’s share). What remains-the residual-is TFP growth. If an economy’s output grows by five percent, labor inputs grow by two percent (accounting for 1.4 percentage points of growth at a 0.7 weight), and capital grows by three percent (accounting for 0.9 percentage points at a 0.3 weight), then TFP growth is approximately 2.7 percentage points.

This measurement approach has revealed striking patterns across countries and time periods. Research by William Easterly and Ross Levine found that TFP accounts for about sixty percent of growth in output per worker on average, highlighting that how we use resources matters more than how many resources we have. Countries with similar levels of capital and labor can experience vastly different living standards primarily due to differences in TFP.

What do you think? If technical change and Total Factor Productivity are so important for economic growth, what policies might help developing economies accelerate their TFP growth? How might artificial intelligence and automation affect the balance between capital-augmenting and labor-augmenting technical change in the coming decades?

References

1. https://en.wikipedia.org/wiki/Production_function
2. https://www.economicsdiscussion.net/theory-of-production/technological-progresses-and-the-production-functions-with-diagram/5082
3. https://quickonomics.com/terms/harrod-neutral-technical-progress/
4. https://en.wikipedia.org/wiki/Total_factor_productivity
5. https://www.frbsf.org/research-and-insights/publications/economic-letter/2009/08/growth-accounting-output-recession/
6. https://smiller.faculty.unlv.edu/EFFICIENCY_PRODUCTIVITY_PAPER.pdf