The Economic Implications of the Information Society: The Rise of Information Economies

Every time you stream a movie, transfer money through an app, or book a train ticket online, you are taking part in an economy that barely existed a century ago. The wealth of nations was once measured in tonnes of steel, bushels of wheat, and barrels of oil. Today, a growing share of it is measured in something far less tangible: information. Economists have spent decades trying to count this invisible output, name it, and explain what it means for jobs, trade, and national income. This shift from making things to processing knowledge sits at the heart of what we call the information economy, and understanding how it was measured tells us a great deal about where modern economies are heading.

The knowledge economy and how economists learned to count it 🔗

The idea that knowledge itself could be an economic sector did not arrive overnight. It began with a careful counting exercise by an Austrian-born economist working in the United States.

Fritz Machlup and the knowledge industry 🔗

In 1962, economist Fritz Machlup published The Production and Distribution of Knowledge in the United States, a study that is widely treated as the starting point for the academic study of the information society. Machlup had originally been studying the patent system, but he realised patents were just one small part of a much larger activity he called the knowledge industry. He treated knowledge as a commodity and tried to measure how much of it the economy produced and distributed.

His definition was famously broad. He included education, research and development, the media, and even the production of stationery and typewriters. Using this generous classification, Machlup estimated that in 1958 the knowledge economy was worth about $136 billion, or roughly 29 percent of the US gross national product (GNP). He also found that the share of the labour force engaged in knowledge work had climbed from around 11 percent in 1900 to about 32 percent by 1959, a remarkable structural shift. In doing so, Machlup essentially founded the field of information economics.

Marc Porat and the four-sector model 🔗

The next big leap came in 1977, when Marc Uri Porat produced a nine-volume study for the US Department of Commerce titled The Information Economy: Definition and Measurement. This is generally credited as the first major use of the term information economy. Porat defined information activity as any industry or occupation whose main job is to produce, process, or transmit economically valuable information.

Porat’s most influential contribution was to redraw the map of the economy. The traditional model divided economic activity into three sectors: agriculture, industry, and services. Porat added a fourth, the information sector, and split it into a primary and a secondary part. The primary information sector covers organisations whose output is itself information, such as publishing, telecommunications, computing, and education. The secondary information sector captures the information work done inside non-information firms, such as the managers, accountants, and clerks inside a car factory.

His findings were striking. Porat estimated that in 1967 around 46 percent of US GNP came from information-related activities, and that these activities employed more than 40 percent of the workforce while earning 53 percent of all labour income. By his measure, the United States had effectively crossed over into an information economy when information work began to exceed work in the other sectors.

Later researchers extended his method. A study by Apte and Nath found the US information sector’s share of GNP grew from about 46 percent in 1967 to around 56 percent in 1992, and to roughly 63 percent by 1997. Whatever the exact figure, the direction was clear: information had become the largest single component of the world’s biggest economy.

Shifting economic structures in developed economies 🔗

The rise of the information economy is closely tied to a broader transformation that the sociologist Daniel Bell described in his 1973 book The Coming of Post-Industrial Society. Bell argued that advanced economies were moving through three historical phases: a pre-industrial phase dominated by agriculture, an industrial phase dominated by manufacturing, and a post-industrial phase dominated by services and knowledge.

The decline of manufacturing employment 🔗

According to Britannica, a post-industrial society is marked by a transition from producing goods to producing services, with blue-collar manual workers being replaced by technical and professional workers such as engineers, doctors, and bankers. This pattern is most visible in the countries that industrialised earliest, including the United States, Western Europe, and Japan. In these economies, more than half the workforce now sits in the service sector.

It is important to be precise about what this decline means. The shift does not necessarily mean that manufacturing output collapses; rather, it often reflects a falling need for labour as computing, telecommunications, and management techniques redesign how work is done. Factories did not disappear. They became more automated and required fewer people, while the high-value work of design, marketing, finance, and software moved to the foreground.

Knowledge at the centre 🔗

For Bell, the defining feature of the new society was not just the growth of services but the central role of codified theoretical knowledge. He identified three components of a post-industrial society: a shift from manufacturing to services, the rise of new science-based industries, and the emergence of a new class of technical experts. Universities, research institutes, and professionals became the engines of growth, in the same way that the factory and the assembly-line worker had defined the industrial age.

This is the link that ties Bell’s sociology to Machlup and Porat’s economics. The post-industrial society described by Bell is, in economic terms, the information economy that Machlup and Porat were trying to measure.

Economic transformation in developing countries 🔗

For a long time, the information economy was treated as a feature of rich, post-industrial nations. The interesting story of recent decades is how developing countries have entered this space, sometimes in ways the original theorists did not predict.

India’s leap into services 🔗

India offers one of the clearest examples of a developing economy reshaping itself around information work. The usual development path runs from agriculture to manufacturing and only then to services. India instead registered an unusual shift directly from agriculture towards the service sector, which now contributes over 50 percent of the country’s GDP. This is sometimes described as services-led growth, and it has drawn global attention precisely because it skips a step in the classic model.

The information technology and IT-enabled services industry sits at the core of this transformation. The IT-BPM sector contributed around 7 percent of GDP in the 2024 financial year, a figure estimated to rise towards 10 percent. According to NASSCOM figures, industry revenue rose from about US$167 billion in FY18 to an estimated US$297 billion in FY25, with exports forming the bulk of that total. The sector supports millions of direct jobs and has turned cities like Bengaluru, Hyderabad, and Pune into global hubs for software and outsourcing.

Yet the same data reveals a tension. While services generate a large share of national output, the sector employs only about 30 percent of the workforce, pointing to a widening gap between output and job creation as services become more capital- and technology-intensive. This means India’s information economy creates enormous value but does not always absorb the vast numbers of workers leaving agriculture, a challenge that shapes much of the country’s policy debate.

The wider developing-country pattern 🔗

India is not alone. Other large developing economies, including Brazil, have built growing service and information sectors, supported by expanding telecommunications networks, mobile internet, and digital public services. For these countries, the information economy offers a chance to participate in global value chains without first building the heavy industrial base that defined Western development. The opportunity is real, but so is the risk of leaving behind workers whose skills do not match the new demand.

Criticism of information economy models 🔗

For all their influence, the models built by Machlup and Porat have attracted serious criticism. The core problem is deceptively simple: information is hard to define, and therefore hard to measure.

The problem of broad classification 🔗

The first criticism concerns how widely the net is cast. Machlup’s decision to count typewriters and stationery as part of the knowledge industry struck many as too generous. Porat’s framework faced similar objections. A long-running debate asks, for example, whether government employees should count as information workers simply because they process information. If almost every modern job involves handling information to some degree, the category risks becoming so large that it loses its analytical meaning.

This vagueness was central to the critique offered by the British scholar Frank Webster. Webster acknowledged the scope of Machlup and Porat’s work but worried about the imprecision of definition and the difficulty of identifying exactly when the growth of information activity adds up to a genuine information society. His concern was that lumping very different activities together produces an impressive-looking number that hides more than it reveals.

The problem of measurement 🔗

The second criticism is methodological. Scholars have argued that the work of Porat and Rubin lacks firm theoretical building blocks, relies on questionable measurement systems, and draws an inadequate line between information and non-information activities. Without a strong underlying theory of what information is worth, the resulting percentages can feel arbitrary.

A further practical difficulty is that official statistics were never designed for this purpose. Because national statistical agencies collect data based on a traditional industrial view of the economy, the available figures do not fit neatly into the categories an information economy requires. Researchers must therefore reorganise existing data into information and non-information buckets, and the choices they make at this stage heavily influence the final numbers. Two careful economists using the same raw data can arrive at quite different estimates of the information sector’s size.

These criticisms do not destroy the value of the information economy concept. Even sceptics accept that something important changed in the structure of advanced economies during the twentieth century. What the criticism does is remind us to treat the headline figures, such as “63 percent of GNP”, as useful approximations rather than precise measurements.

Why this matters today 🔗

The debate that Machlup and Porat began is far from settled, and in some ways it has become more urgent. The growth of data, artificial intelligence, and digital platforms has made information an even larger part of economic life than these pioneers could have imagined. Their central insight, that information is a resource which can be produced, traded, and measured like steel or wheat, now shapes how governments plan education, how companies value their assets, and how nations compete. The questions of how to classify information work and how to measure its true contribution remain as relevant as ever, especially for developing economies trying to find their place in a knowledge-driven world.

What do you think? If almost every job today involves processing information in some form, is the “information worker” still a useful category, or has it become too broad to mean anything? And for a country like India, is services-led growth a genuine shortcut to prosperity, or does skipping a strong manufacturing base create problems that the information economy alone cannot solve?

References

1. https://press.princeton.edu/books/paperback/9780691003566/the-production-and-distribution-of-knowledge-in-the-united-states
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