Industrial Revolution, Industrious Revolution and Industrial Enlightenment

The pre-industrial era witnessed a number of ground-breaking innovations and improvements, but they were typically generated by learning by doing. Producers learned that things worked, but had limited understanding of why things worked. From the seventeenth century, decisive efforts were directed towards gaining more and better knowledge of the ‘laws of nature’. However, it is wrong to believe that the British Industrial Revolution, the period 1770–1830, was based on scientific discoveries. Decisive steps were taken in that period towards a more profound understanding of nature, but these accomplishments had little immediate impact on production technologies. The iconic invention of the eighteenth century, the steam engine, is the exception that confirms this rule. The steam engine developed by Thomas Newcomen (1663–1729) relied on the results of scientific inquiry from the preceding century by the Italians Galileo Galilei (1564–1642) and Evangelista Torricelli (1608–97), the Dutchman Christiaan Huygens (1629–95), and Otto von Guericke (1602–86), a German, regarding atmospheric pressure, the weight of air and the nature of a vacuum. Contemporaries of Newcomen made significant contributions, in particular the French inventor Denis Papin (1647–1712?), who invented the piston. In the first generation of steam engines, the steam was condensed in a cylinder, which created a vacuum, and then the piston was pushed into the cylinder by atmospheric pressure.

The massive breakthrough of technologies, which sprang out of abstract theoretical inquiry coupled with empirical testing, did not arrive until the second half of the nineteenth century and mostly in the closing decades of that century. There is no denying, however, that systematic experiments, often combined with limited or flawed theoretical knowledge, became more common before and during the Industrial Revolution.

These misconceptions regarding the role of science contributed to very optimistic assessments of economic growth in the traditional historical narrative of what made Britain ‘the first industrial nation’.

Random processes can be passed through linear systems in much the same way as deterministic signals can. A time-invariant linear system is described in the time domain by an impulse response function, and in the frequency domain by the Fourier transform of the impulse response function. In a sense we shall see that Fourier transforms provide a diagonalization of WSS random processes, just as the Karhunen–Loève expansion allows for the diagonalization of a random process defined on a finite interval. While a m.s. continuous random process on a finite interval has a finite average energy, a WSS random process has a finite mean average energy per unit time, called the power.

Nearly all the definitions and results of this chapter can be carried through in either discrete time or continuous time. The set of frequencies relevant for continuous-time random processes is all of ℝ, while the set of frequencies relevant for discrete-time random processes is the interval [−π, π]. For ease of notation we shall primarily concentrate on continuous-time processes and systems in the first two sections, and give the corresponding definition for discrete time in the third section.

Representations of baseband random processes and narrowband random processes are discussed in Sections 8.4 and 8.5. Roughly speaking, baseband random processes are those which have power only in low frequencies. A baseband random process can be recovered from samples taken at a sampling frequency that is at least twice as large as the largest frequency component of the process. Thus, operations and statistical calculations for a continuous-time baseband process can be reduced to considerations for the discrete-time sampled process. Roughly speaking, narrowband random processes are those processes which have power only in a band (i.e. interval) of frequencies. A narrowband random process can be represented as a baseband random process that is modulated by a deterministic sinusoid. Complex random processes naturally arise as baseband equivalent processes for real-valued narrowband random processes. A related discussion of complex random processes is given in the last section of the chapter.

The origins of money

We have learned that one major cause of productivity increase in pre-industrial economies is the gains from division of labour resulting from occupational diversification in an economy where regions and nations exploit their comparative advantages. But these gains cannot be reaped without exchange between increasingly specialized producers. Money, as a means of exchange, developed alongside the occupational and regional division of labour. The first money, some five or six thousand years ago, did not consist of stamped coins, but rather of standardized ingots of metal which were generally accepted as a means of payment. The Chinese and Greek civilizations introduced coins which were stamped like a modern coin. To understand the advantages of money it is worth looking at its historical antecedent and alternative. Direct bilateral exchange of one commodity for another, so-called barter, requires coincidence of wants between trading partners. It means that if you want to exchange a pair of shoes for wheat you have to find someone who has wheat and wants a pair of shoes. The matching process necessary to detect coincidence of wants will be very time-consuming, and time matters because it is scarce and has alternative uses. Barter will not only be associated with high search costs, but will also reduce the volume of trade to below its potential level because trade must be balanced. However, the volumes participants want to trade need not balance and in those cases the ‘minimum’ trader will determine the volume of trade. For example, a weaver might find a baker willing to exchange bread for cloth at an agreed price, but the weaver might not be willing to buy as much bread as the baker wants to sell. After all, bread is more perishable than cloth and is typically bought daily in small quantities. The volume traded when relying on bilateral balanced trade will thus, in this particular example, be constrained by the cloth maker, the ‘minimum’ trader.

This book evolved over the years from the lectures I have given and give to my students at the Department of Economics in Copenhagen. I have, however, attempted to write a book for a wider audience who are searching for a very concise introduction to European economic history which is in tune with recent research. I make use of a few basic and simple economic tools which turn out to be very effective in the interpretation of history. The book offers a panoramic view rather than close-ups. However, the analytical framework will be useful in further studies of the specialized literature. For readers with little background knowledge in economics I provide a glossary defining key concepts, which are marked in bold, for example barter. Economic ideas demanding more attention are explained in the text or in appendices.

This is a work of synthesis, but it attempts to give challenging and new insights. I am indebted to generations of economic historians as well as to a great many of my contemporaries. That normally shows itself in endless footnotes, which not only interrupt the narrative flow but also drown the general historical trends amidst all the details. Instead, I have chosen to end each chapter with a selective list of references which is also a suggestion for further reading. Authors I am particularly influenced by are referred to in the main text.

A large number of colleagues have guided me. Cormac Ó Gráda has as usual been a very stimulating critic and Paul Sharp has not only saved me from embarrassing grammatical errors but is also the co-author of two chapters. I would also like to thank Carl-Johan Dalgaard, Bodil Ejrnæs, Giovanni Federico, Christian Groth, Tim Guinnane, Ingrid Henriksen, Derek Keene, Markus Lampe, Barbro Nedstam and Jacob Weisdorf for helpful comments and suggestions.

Mette Bjarnholt was my research assistant during the initial phase of the project and Marc Klemp and Mekdim D. Regassa in the final stage and they have all been enthusiastic and good to have around.

Markov processes are useful for modeling a variety of dynamical systems. Often questions involving the long-time behavior of such systems are of interest, such as whether the process has a limiting distribution, or whether time averages constructed using the process are asymptotically the same as statistical averages.

Examples with finite state space

Recall that a probability distribution π on S is an equilibrium probability distribution for a time-homogeneous Markov process X if π = πH(t) for all t. In the discrete-time case, this condition reduces to π = πP. We shall see in this section that under certain natural conditions, the existence of an equilibrium probability distribution is related to whether the distribution of X(t) converges as t → ∞. Existence of an equilibrium distribution is also connected to the mean time needed for X to return to its starting state. To motivate the conditions that will be imposed, we begin by considering four examples of finite state processes. Then the relevant definitions are given for finite or countably infinite state space, and propositions regarding convergence are presented.

Example 6.1 Consider the discrete-time Markov process with the one-step probability diagram shown in Figure 6.1. Note that the process can't escape from the set of states S1 = {a, b, c, d, e}, so that if the initial state X(0) is in S1 with probability one, then the limiting distribution is supported by S1. Similarly if the initial state X(0) is in S2 = {f, g, h} with probability one, then the limiting distribution is supported by S2. Thus, the limiting distribution is not unique for this process. The natural way to deal with this problem is to decompose the original problem into two problems. That is, consider a Markov process on S1, and then consider a Markov process on S2.

Does the distribution of X(0) necessarily converge if X(0) ∈ S1 with probability one? The answer is no.

Why is an international monetary system necessary?

In Chapter 7, we discussed why money is important for economies – without it, all trade is based on barter and is limited due to the need for coincidence of wants. The same is true on an international scale. Normally, countries do not share currencies (although the euro, which we will return to below, is an important exception to this rule). Nevertheless, they must be able to convert their currencies if trade is not to be restricted to barter. Hence the need for an international monetary system.

In fact, without an international monetary system, trade will normally be restricted to balanced bilateral trade. Suppose for example that Denmark wishes to import 10 billion kroner worth of goods from Norway. It is important that the countries are able to barter, i.e. that Norway actually desires goods from Denmark in return. Even if this is the case, it might be that Norway only desires 5 billion kroner worth of goods from Denmark. In the absence of an international monetary system it is impossible for Norway to lend the difference to Denmark, i.e. there are no channels for international credit, and Denmark's imports are thus restricted to just 5 billion kroner. Trade is thus restricted, and countries are disadvantaged, since they cannot realize fully the gains from trade and specialization discussed in the previous chapter.

There are additional advantages to an international monetary system. In particular, as explained in Box 9.1, without a well-functioning international monetary system, domestic saving must equal domestic investment and thus foreign investment is impossible. Foreign investment is desirable, either by domestic investors abroad, or by foreign investors at home, if the return to investments differs at home and abroad.

History provides plenty of evidence for the disadvantages of the restrictions placed on the world economy by poorly functioning international monetary systems: at these times trade volumes and foreign investment have suffered.

The geo-economic continuity of Europe

The formation of Europe was a long historical process which involved political, cultural and economic forces. The most striking fact is the geo-economic persistence and continuity of Europe during the last two millennia. We will deal with the integrative impact of trade as well as its border-maintaining effect in shaping and maintaining Europe. Trade was the cohesive force when political, religious and military conflicts threatened to tear Europe apart.

If we let the core of Europe be defined by the borders of the European Union, we can trace back the origins of that geographical entity to the Roman and Carolingian empires, the latter emerging in the ninth century, several centuries after the collapse of the Roman Empire. (See Maps 1.1–1.3.) About 80 per cent of the total population of the Roman Empire around the year 100 AD lived within the present (2010) borders of the European Union. It stretched from the Atlantic coast to the Black Sea. Ireland, the northern periphery of Europe, Scandinavia and Russia were touched by neither the Roman nor the Carolingian rulers. Russia's relationship to Europe has remained ambivalent throughout its history, with periods of self-imposed isolation as well as enthusiastic embracing of European ideals, and Scandinavia was late in joining the European Union; in fact Norway is still making up its mind whether to join or not.

The Carolingian Empire represented the revival of political order after the disintegration of the Roman Empire, and also the emergence on the political scene of Germanic peoples, who amalgamated their own traditions with the adopted culture, law and language of their Roman predecessors in their south and westward push. Germanic tribes also advanced towards the east, but kept their own language and pushed the Slavic languages back eastward when they subjected the indigenous peoples and their land.

Understanding pre-industrial growth

We will now combine elements in Malthusian and Smithian explanations as developed in Chapters 2 and 3 to enhance our understanding of the nature of pre-industrial economic growth in those critical phases when the land constraint actually became binding at least locally, say, before the Black Death and in the seventeenth to nineteenth centuries. This new view acknowledges diminishing returns from labour in agriculture as the rural population grows and if the tilled land/labour ratio falls, but we also explicitly acknowledge technological change, that is, the useful application of new knowledge. Furthermore there are Smithian gains from specialization triggered off by division of labour stimulated by increasing ‘the extent of the market’, that is an increase in aggregate demand. If we have resource constraints and technological change the story will become fundamentally different. Technological growth is present if we can produce more goods today than were produced yesterday, with the resources used in production held constant. Technological progress and division of labour enable the economy to have both positive population growth and constant or increasing per capita income. The intuition here is that the effects of diminishing returns are offset by technological change. Figure 4.1 below explains in a simple way how the mechanism works.

Positive population growth has two effects with opposing signs, plus or minus, as to the impact on output or income per head. If the economy is using all available land there will be diminishing returns from labour, which will affect output and income per head negatively. However, as long as positive population growth is increasing aggregate demand (= income per head times the number of people) in the economy, division of labour will be stimulated and hence income per head. There are good reasons to believe that population growth actually increases aggregate demand because, as we noted in Chapter 3, there is strong persistence in wage levels.