The goal of this chapter is to prepare the ground for theorizing the diversity and spatiality of markets, (necessarily) dealing with, but seeking to move concertedly beyond, the restrictive optic of the orthodox model. It seeks to do so by way of three steps. The following section opens up some preliminary questions about the place of markets and their actually existing historical geography. Next, the chapter turns to the challenge of decentring the market, of moving beyond the idea that the market properly and eternally belongs at the centre of the economic universe. What is the scope for understanding “real markets”, both conceptually and empirically, without recourse to the idealized model of the “pure” market, and all its downstream distortions? Finally, the chapter addresses the question of alternative “mappings” of markets, taking some cues from the work of Karl Polanyi, but urging a more thoroughgoing reconstruction. The chapter's conclusion acknowledges that the “where?” of markets remains a demanding theoretical question, such that what follows serves to inform some different points of departure, not the destination itself. New answers to this “where?” question, though properly a matter for what Polanyi characterizes as the “substantivist” investigation of real-world market formation, re-formation and transformation, ought to recognize, inter alia: first, the relational character of markets, which are codependent on non-market modes of coordination and regulation; second, their “instituted” form, necessary in function but variable in practice; and not least, third, their always out-of-equilibrium processual dynamics and contradictions.

MISPLACED MARKETS

Contrary to the received mythology of the market, in which trucking, bartering and exchanging are naturalized as something resembling primeval urges, markets were actually born as deliberately constructed boundary objects. The first of these economic institutions were, literally, provincial constructions: designated sites, spaces and settings for rule-governed forms of exchange that, in feudal times, were commonly found at crossroads, at the edge of town or in the borderlands between jurisdictions, where they might be marked by a boundary stone.

MARKETS HAVE THE FLOOR

Most formal economic exchanges are now mediated one way or another by digital technologies. For those that are not, few remain that are not facilitated by, or at least accounted for and archived in, digital systems. Yet the rapid growth of digitized exchanges is not only about new modes of mediation. Digital intermediation has replaced face-to-face and other pre-digital exchanges, but, more importantly, digital technologies have afforded new kinds of work and commodities, new kinds of firms, production and consumption and – most importantly for this chapter – new kinds of markets for exchange.

In this context, how might we conceptualize the geographical political economy of markets or marketization? One possibility is in the nascent literature on digital platforms and platform capitalism (Langley & Leyshon 2017; Srnicek 2017). Platforms have deservedly attracted quite a lot of attention, not least in the ways they organize or constitute new socio-economic spaces in and through firms such as Amazon, Google, Facebook and Airbnb. Firms such as these are particularly relevant, as they tend not to produce easily discernible commodities but, rather, serve as web-based marketplaces for goods and services produced by others. At the same time, these new market places have expanded the division of labour, allowing a new class of workers and capitalists to manipulate, buy and sell information about people and commodities, and even to commodify that information itself. To date, however, the relationship between what I will call this new merchant class, space and the process of marketization has received little attention.

My approach to these problems begins with asking whether the geography of platforms is unique to the internet age. And, if, as I argue, the spatial transformations that accompany digital platform economies are not entirely new, what might we learn from the historical geographies of platformed market exchange? In fact, we can learn quite a lot about digital commerce in general and digital platforms in particular by looking back to a previous revolution in information and communications technologies – the development of the electric telegraph – and its relationship with the reorganization of commercial space in the middle to late nineteenth century.

Many of our news stories seem to be about global warming, pollution, species loss and protests about the threat of global extinction. These stories arise from a fear that the system that has given us productivity growth is out of control. There are also stories about continuing productivity problems in different economies, not least the UK. These stories assume that we have to find a way to get even more productivity growth. This chapter is about this contradiction and the question of whether productivity growth can go on forever. In the first part we ask can the planet support unlimited productivity growth? This question necessarily challenges the narrow view that many economists hold of the economic system. In the second part we accept, for the sake of argument, this narrower view but ask to what extent is productivity growth in advanced capitalism already slowing down and could we reverse it if we wanted to?

Productivity and planetary possibilities

The economist Kenneth Boulding is supposed to have said, “anyone who believes exponential growth can go on forever in a finite world is either a madman or an economist”. The normal laws of physics are not suspended in economics, but when conventional economists look to the future they tend to ignore these laws. They have the implicit, sometimes explicit, view that the economy is some kind of perpetual motion machine that can go on getting bigger forever.

Some people think about this problem in terms of population. We know that the magic of productivity growth has enabled the world's population to grow. But the problem is not really the number of people. In fact, the rate of the world's population growth has been slowing since the early 1960s. Absolute numbers are still rising, so in the next decades the world's population will peak, perhaps at around 10 billion. Numbers will then start to fall. Birth rates have been falling sharply for a long time and, in some places, they are already below replacement levels (Dorling 2013).

The problem is that we all want the same high standard of living and to maintain it. Table 4.1 shows estimates of the numbers of people who have ever lived and the share of all the output they have produced and consumed during the time they were and are alive.

Productivity appears to be a simple calculation, but what do we measure and how do we add things together? This chapter is about these numbers and how they are arrived at. Sounds boring? Let us try again. This is a chapter about whether we should measure cooking, toilet training, sex, drugs and “rock and roll”, and how. It is about discrimination and misogyny. It is about tax dodging, cheating and lying, even cooking the “national books”. It is also about the new layers of complexity that are growing as a result of the organizational shift from creating new value to forms of “rent seeking”. Debt seems to be everywhere. State licences create “illusory” value in intellectual property. Modern business is rife with subsidy extraction. Tax dodging is endemic, speculation commonplace (Standing 2016; Mazzucato 2018a). Capitalism is moving ever further away from any textbook model and everyone is struggling more to get the “data” and to make sense of it to logically and to consistently measure real outputs and real inputs. Things are not as simple as they might seem.

Measuring matters

For thousands of years, day-to-day measurements were done by rule of thumb, sometimes using local measures. Even everyday conceptions of time were vague. The rise of capitalism demanded more precision. This was partly a question of technology, but it was also an economic requirement. Companies wanted to know their accounts and new states their populations. Common national and international systems of physical measurement were developed. Pioneers began to ask questions about what the size of outputs and inputs might be in a “nation”: not just the population but the size of the labour force, the area of land being farmed and the total amount of coal or iron being produced. Investigators became interested in understanding the ways in which tools were being used and machine production organized. Charles Babbage, having thought about the basic idea of a computer, perhaps in anticipation of the idea of garbage in–garbage out, was anxious to accurately describe, measure and count what went on in workshops and factories in early nineteenth-century Britain.

We have seen that there are significant gaps in the productivity levels between economies. These gaps developed qualitatively, and then quantitatively, as capitalism took off and more and more of the world was dragged into its orbit. This produced the “great divergence” or “divergence big time”. The gap between the most productive and least productive economies in the world economy has continued to grow.

Between the extremes, states compete to catch up or minimize the threat of falling back. The economies that have closed the gap are sometimes grouped into “convergence clubs”. Talk of “clubs” implies that there is some commonality, some membership criteria, but critics quickly pointed out that economies were being grouped together once they had grown or failed to grow. They were clubs “after the event” not “before it”.

The catching-up process of productivity growth certainly involves some common elements. But it has taken place, and is taking place, in a dynamic global economy so the possibilities and constraints have also varied over time and place. Moses Abramovitz argued that an economy not only needs the capability to catch up, it needs the opportunity (Abramovitz 1986). In this chapter we will focus on economies at the top of the global productivity hierarchy, their past productivity growth and some aspects of the current debates about their productivity performance.

Britain and the United States: the leaders of the gang?

In the last two centuries two economies have dominated any global productivity race: Britain and the United States. Britain's dominance lasted until the late nineteenth century, but since then the US has set, and continues to set, the global productivity benchmarks.

Britain's early productivity lead was bound up with it being “the first industrial nation”. However, the underlying productivity story is rather different from that often suggested. Table 5.1 shows some very long-run data for the British economy. These estimates look more precise than they really are, but we can use them to make some important points about the emergence of Britain's early productivity lead.

Note first the contrast between the rate of productivity growth before 1700 and that which came after, which is further evidence of the productivity shift that occurred with industrialization. Notice too that labour productivity growth after 1700 picked up in all sectors.

Our productivity journey has allowed us to see the benefits of productivity growth. We have also seen the complications of its measurement. We have looked at the debates over the causes of productivity growth and the extent to which productivity has a dark side. In Chapter 4 we argued that the past cannot be a guide to the future. Now, having explored some of the variety of actual productivity experiences, we can also see more clearly why we need to think beyond the conventional views of productivity growth as the solution to global problems.

This was the point that Tawney, whom we quoted in Chapter 1, made a century ago. Productivity is a means, not an end, and it is a means that can also be part of the problem that it is said to cure. Tawney was not alone in thinking this. In a 1930 essay, the economist John Maynard Keynes talked about what he called “the economic possibilities for our grandchildren”. He was confident that the productivity successes to come would release them from the need to look for never-ending productivity growth. Already by 1930 he had problems with those who wanted to argue that “jam is not jam unless it is a case of jam tomorrow and never jam to-day”. This argument would look even more foolish in “a hundred years [when] the economic problem would be solved”. In this future world we would have so much that we would need to use fewer inputs, not least our labour. “Three-hour shifts or a fifteen-hour week … three hours a day is quite enough to satisfy the old Adam in most of us!” (Keynes 1931 [1930]: 370). In a new world of leisure, we would have the opportunity to do many different things. Today we are the grandchildren that Keynes wrote about. Some people reading this book will be the great grandchildren, perhaps the great, great grandchildren of those alive in 1930. Keynes was right. We have achieved the miraculous levels of wealth in the rich world that he thought would release us from need and the focus on ever more material wealth.

Productivity is one of the most important concepts in economics, but it took a long time to get established. The first use of the word in The Times seems to have been in 1858. Nearly seven years later it appeared for a second time in a discussion from the great chemist Liebig. He was asking whether the productivity of the soil could (in addition to using farmyard manure, the ground bone dust of animals and humans, Peruvian guano and new chemicals) be improved by the use of “Metropolitan sewage” (The Times, 25 January 1865). The real world of productivity can take us to many such strange places, but these often get elided in the discussions of economists. Since 1945 productivity has become something of an obsession, so much so that when it was suggested that I write a book on productivity my immediate reaction was that a good introduction must already exist. But while books have discussed different aspects of “productivity”, there have been few attempts to draw these strands together in a single work focused on productivity and the economy.

What qualifications do I have to try to produce such a synthesis? At first I thought none. Then I realized that in one way or another the question of productivity has been a thread running through much of what I have taught and researched over the years.

One aspect of this has been my interest in the patterns of the global economy, their history and the work of Angus Maddison. I first encountered Maddison as a result of an interest in Russia and Japan, about which he wrote a pioneering comparative study. I followed his work as he began to create a database of global gross domestic product (GDP) pushing the figures back as far as he could. Today, partly in his footsteps, we have a variety of databases that contain a mass of historical and contemporary data that feed into our understanding of productivity. I draw on some of them here and there is a short note on them at the start of the bibliography to guide readers.

Africa, the world's poorest continent, is where productivity remains the lowest. Once largely ignored by the rest of the world, today its problems attract attention from many mainstream economists. A billion people – one-seventh of the world's population – live there. Some are incredibly rich. Some are called middle class, but being “middle class” in Africa is very different from in the advanced world: your income may well be less than the minimum wage in the UK, but enough to have an inside toilet and a generator for when the official electricity fails, as it does on a regular basis (Hammond 2014). Most are poor, although the level of poverty overall has declined (by how much is disputed). Yet Africa is where the first human societies were created. Poverty in Africa, Jared Diamond once wrote, is “the opposite of what one would expect from the runner first off the block” (Diamond 2005).

Our interest here is sub-Saharan Africa. Table 7.1. shows the comparative data for sub-Saharan African productivity levels measured as a percentage of the US level. Looking at individual economies, we see some of the lowest levels of productivity in the world. Productivity is advancing, but more slowly than in most of the rest of the world. Table 7.2 shows the result: in relative terms Africa has fallen further behind.

Views of progress in Africa have swung between “Afro-optimism” and “Afro-pessimism”. In the 1960s, optimism was in the ascendant. Then, from 1970 to around 2000, pessimism became dominant. In 2000, The Economist magazine declared that Africa was “the hopeless continent”. Many in Africa were offended, but some who lived there were just as brutal: “Wherever we look”, wrote one Nigerian civil rights activist in 1999, “Africa spells failure on all fronts: political, economic, and moral” (Kukah 1999). Growth picked up in the 2000s. This led to talk of “Africa rising” and “African lions” (there are no tigers in Africa), but the surge seemed to falter. The pessimistic view is still widespread among economists who focus on the “African growth tragedy” and its “chronic growth failure”. This failure has become a stylized fact of economic history.

We have seen that productivity is a ratio. The numerator is some measure of output, the denominator is some measure of input. There are physical productivity measures. We can calculate the tonnage of wheat grown per hectare; we can count the number of cars produced per worker; we could calculate the number of examination passes per 100 students. Leave aside whether this would capture or explain everything we are interested in. These physical measures of productivity can only take us so far. Economists are more interested in inputs as a cost and outputs as value. This potentially gives us the three different types of productivity measure, as set out in Figure 2.1.

If we take land productivity, we can still have a single-input measure that divides the monetary value of output by a physical input or a value input. In agriculture the value of agricultural output per unit of land input is an example. We can also look at labour productivity in terms of the value of output per person (effectively an output per head calculation), per worker or, best of all if the data are available, per hour worked. These measures are some of the most basic and widely used measures of productivity. When it comes to the productivity of capital things are different. We cannot add up a plough, a hammer and a computer. We can only measure the value of output against the value of the capital used. To measure the value of output against all inputs too, we need prices. We cannot add hectares of land to numbers of workers and machines as inputs. And we cannot add the weight of wheat to the number of cars or the meals cooked as outputs. We have no choice but to add up the cost of the inputs and the value of outputs.

Table 2.1 sets out the basic productivity measures that economists use following this logic (OECD 2001).

Notice that there is an additional element shown in Table 2.1. This is the idea of an unknown component, or “knowledge” component, embodied in the changing technology of doing things.

Paul Krugman is one of the world's best-known economists. He has a Nobel Prize in economics and a regular economics column. He once said, “productivity isn't everything, but, in the long run, it is almost everything”. Few economists have not heard this quote. The reason productivity is so important, Krugman continued, is that “a country's ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker” (Krugman 1994a: 11). This makes the idea of productivity look pretty important, and it is. But it has also been suggested that today the word most associated with productivity is puzzle. Productivity is a puzzle because it is a problem to understand conceptually; it is a problem to measure; it is a problem to explain and it is a problem to know how to improve it. Much of this book is about this productivity puzzle, but first we need to ask why productivity matters so much.

Imagine that we double the inputs we put into the economy. We might hope to double the output. But getting the same amount out as you put in is not very impressive. It is an example of what is called extensive growth. What we really want is more from the extra bits we put in. We want more output per hectare of land we use, more output per worker and more output per unit of capital. This is called intensive growth. Until the late nineteenth century, economists tended to talk of productiveness as a catch-all term. They were not very careful to distinguish between extensive growth and increasing what you get out per unit that is put in. But in the late nineteenth century they began to use the term productivity for this second effect. Productivity here is efficiency, it is a ratio of inputs to outputs and it is the driving force of intensive growth.

We can get more out separately from each of the factors of production. There is the land input, and this might lead us to think of land productivity. There is labour: the physical and nervous energy we expend.

What is the story in the larger part of the world where productivity has risen but not so much as to allow the types of gains that have created the wealth of the developed world? In this chapter we are going to focus on three cases: South Korea, China and India. They each started late and had different degrees of success in raising their productivity; so much so that they are sometimes seen as examples of miracle productivity growth. But first we need to put their stories into the wider perspective of the global productivity pattern.

Most of the high-productivity economies today are members of the OECD. It was formed in 1961 as a “Western” group in the midst of the Cold War. Today it has 36 members with an average per capita nominal income of some $41,000. Unfortunately, the population of the OECD countries today makes up only 17 per cent of the world's population. High productivity is not an explicit membership requirement of the OECD. Politics matters too. This explains why Russia is still not a member. But the high productivity of the OECD economies makes them a huge force in the global economy. In Asia only Japan and South Korea are members. In Central and South America only Chile and Mexico. No African country is a member of the OECD.

Table 6.1 shows the productivity gaps which exist today. We are again measuring these in terms of GDP per person employed because the poorer economies lack good enough data to calculate GDP per hour worked. Some regional categories are ragbag ones. There is a big gap, for example, between the economies with oil in the Middle East and North Africa and those without it. Still, the contrasts are sharp. We see here the leading role of the US and the relatively high position of the OECD as a whole, even allowing for some members with weaker productivity levels. The five BRICS are a coming together of Brazil, Russia, India, China and South Africa. The BRICS countries have size – their combined population amounts to 42 per cent of the global population – but their average nominal GDP per capita is just under $7,000. They are also far from moving forwards at the same speed and their performance and level is dominated by China.

To crown our discussions in the previous chapters, we will now recalculate the size of the government sector in several alternative ways and compare these results with the official data. It will become apparent that the conventional measures are too narrowly defined. Consequently, if the methodological treatment of national accounts were changed, the findings of economic researchers would probably show different results on the relationship between government and other economic variables such as inflation, growth and unemployment.

Our analysis is, of course, limited by the current data availability. In spite of several data gaps, we have enough information to show that the officially presented figures constitute the lowest estimation of the size of government. Although our results are less favourable than those presented by national statistical offices, when it comes to redistribution or debt ratio, we believe that they provide a more realistic picture on both the size and the indebtedness of government.

Thus, we will use the aggregates recalculated in the previous chapter to present shares built on a different methodological approach. Of course, the way that relevant aggregates have been recalculated above might seem too radical, as well as the results. Nevertheless, as will be shown below, even conventionally quantified aggregates can be rearranged in a meaningful way, such that the size of government would be strikingly different than as officially presented. At the end of this chapter, we will also discuss the relation of statistics to relevant theoretical concepts.

GOVERNMENT EXPENDITURES RECALCULATED

Let’s start with the size of government in terms of total expenditures. Although the expenditures of public corporations are missing in our aggregates, we can still at least put the conventionally measured government sector into a different perspective by counting various government expenditures which are currently left out of macroeconomic models. Table 8.1 presents the results of the recalculation indicating the share between the recalculated government expenditure (see Table 7.2) and the recalculated GDP (see Table 6.5).

Table 8.1 illustrates the part of the recalculated GDP that is redistributed through the government accounts in the form of expenditure.

In this chapter, we shall make two steps beyond the conventional methodology. First, we shall extend the coverage of the government sector by including all public institutions as identified by the concept of control in national accounts; second, we will take into the accounts other kinds of expenditures which conventionally fall outside the definition. Government aggregates, recalculated in this way, will then be compared with nominal GDP as it is defined by current methodology.

Before going further, it is worth emphasizing that the analysis below is based, of course, on the currently existing data. Data availability poses, as usual for any empirical analysis, a more or less serious limitation. Simply put, not all useful and required data are reachable. For our purposes, this is mainly the case of public non-financial corporations and some of the central bank’s indicators. Unfortunately, lack of data does not allow us to recalculate all aggregates which would be desirable. Nevertheless, it opens a room for future research, once the missing data is available.

GOVERNMENT EXPENDITURES EXTENDED

First, let’s look at one of the most popular expressions of the size of government, in terms of total government expenditure. Although the designation “total” suggests that the government outlays are recorded in their entirety, this is not the case. Some categories of expenditures are excluded on the grounds that they will generate revenue in the future. In the national accounts terminology, these are categorized as “pure financial transactions”. Certain parts of government outlays thus remain hidden from the aggregate of “total expenditures”; technically speaking, they are identifiable only from the financial account in the system of national accounts, i.e. “below the line” and in the balance sheets. This concerns mainly financial investments in private or public companies through purchases of shares, provisions of loans and bond purchases as well as bond issuances which are not caused by a need to cover excessive expenditures. Despite the presumed recoverability, acquisitions of financial assets constitute, of course, a redistribution favouring one group of fellow citizens at the cost of others.