By Winton Bates, on January 4th, 2011
Some readers may think this question is like asking whether the Pope is a Catholic. The question is worth considering, however, because it raises some fairly common misconceptions about the industrial revolution (some of which I held until recently).
My main reason for reading about the industrial revolution has to do with my interest in human flourishing. The industrial revolution led to a massive, unprecedented and ongoing improvement in living standards, beginning in Britain and then spreading to other parts of the world. From that perspective, the industrial revolution tends to be associated with the advent of sustained economic growth.
 However, Joel Mokyr suggests that the best available estimates indicate that growth in per capita income in Britain did not accelerate until the decades after 1830 – well after the beginning of the industrial revolution (‘The Enlightened Economy’, p 256). That makes sense if we define the industrial revolution in terms of the technological innovations which brought about a transformation in the way goods and services were produced in the British economy between 1760 and 1830. One reason why these innovations were not immediately reflected in higher per capita income growth was the rapid growth of population – the English population increased from 6.1 million to 13.1 million between 1760 and 1830 (p.257). Another reason was the initial concentration of major innovations in a relatively small, though rapidly growing, part of the British economy (p. 82).
Information from a table presented by Deirdre McCloskey is graphed below in order to provide some perspective on the contribution of different industries to productivity growth in Britain over the period from 1780 to 1860 (‘Bourgeois Dignity’, p.219).
Figure 1 shows the relatively rapid growth of productivity in some manufacturing industries as well as canals and railways.
Figure 2 shows that despite the more modest productivity growth rate in agriculture, the relatively large size of this sector means that over the period considered its contribution to overall productivity growth was comparable to that of the manufacturing industries with more rapid productivity growth.
So, was the industrial revolution mainly about the growth of manufacturing industry? Perhaps, if we define the industrial revolution so narrowly that it has to refer to the growth of manufacturing industry. If we do that, however, we need another term to describe the processes leading to the advent of economic growth in Britain. Joel Mokyr’s term, the industrial enlightenment, aptly describes the broader processes through which a social climate favourable to innovation was made possible by growing recognition that material progress could be achieved through advances in science and technology.
Mokyr puts the various phases of the industrial revolution in context as follows:
‘The Industrial Revolution was above all a beginning. It cannot be judged on its own grounds without considering what it led to. What is truly significant is not the wave of great inventions made in the years between 1765 and 1800, but the fact that this process did not subsequently fizzle out. Some societies, in Europe and Asia, had witnessed previous clusters of macroinventions, leading to substantial economic changes. … The “classical” Industrial Revolution in the eighteenth was not an altogether novel phenomenon. In contrast, the second and third waves in the nineteenth century, which made continuous technological progress the centrepiece of sustainable economic growth, were something never before witnessed and that constituted a sea change in economic history like few other phenomena ever had’ (p. 83-4).
By Rok Spruk, on September 8th, 2010
Carlos Pereira of the Brookings Institution (link) has reviewed the dismal productivity growth and the consequent macroeconomic indicators in Brazil in the last decade.
“Although there are several expenditures in this category, the one that stands out high above all others is outlays for social security and pensions. Practically one-third of the federal budget is devoted to these expenditures, whereas expenditures in investments were less than 6 percent in 2003. Pensions in Brazil since the 1988 constitution have been notably generous, especially in the civil service. A new group of non-contributing rural pensions was added, contributing to systematic deficits. With about 11.7 percent of GDP, Brazil has one of the highest social security expenditures in the world, especially considering that the Brazilian population is much younger than that of most countries with similar levels of expenditure.“
By Thomas Knapp, on August 26th, 2010
Jacob Lyles at The Distributed Republic:
Open border libertarians like to pretend that the supply of immigrant laborers has no effect on the welfare of existing United States workers. But this is not the case.
Broadly speaking, the welfare of an average Joe who trades his labor for a living depends on two factors. The first is labor productivity which determines how much employers are willing to bid for workers. The second is the supply of labor of similar quality. Increasing the supply of labor with a particular skill set will bid down the wages of workers with substitutable skills. …
There is no a priori reason to think that labor markets are immune to economic incentives.
I’ve never, ever, ever heard a libertarian claim that “the supply of immigrant laborers has no effect on the welfare of existing United States workers.”
On the contrary, an unconstrained ability of workers to move back and forth across imaginary lines drawn on the ground by politicians (”borders”) almost certainly has a great many effects — some of them putatively negative (increased local labor supplies where labor is needed, driving down wages), some of them putatively positive (decreased labor costs driving down prices for everyone), many of them unnoticed, all of them certainly subject to what Mises referred to as the “calculation problem” when it comes to regulation.
The real question is not whether or not Worker A will have less leverage in seeking higher wages if Workers B and C are “allowed” to cross one of those imaginary lines.
The real question is whether or not the state has a legitimate power to distort the market for the alleged benefit of Worker A at the expense of Workers B and C who are forbidden to rent their labor, at the expense of the employer seeking to rent labor, at the expense of those called upon to involuntarily “undo” the distortions with still further distortions, and at the expense of the customer who has to pay more for the product or service because Worker A got the monopoly he wanted.
The libertarian answer to that question, in case you were wondering, is “no.”
By Claus Vistesen, on June 28th, 2010
Blog post series, like the vuvuzela, is the new bacon; it works with everything and with John Hempton’s recent excellent series on the economics of default in the Eurozone and Edward’s recent postings on AFOE in which he pulls out some of our old paper abstracts has inspired me to a series in which I try to pin point exactly how demographics and macroeconomics interact and where I believe we need more focus and work.
When it comes to the overall link between demographics and macroeconomics we already have a number of core workhorse models in the form of the life cycle and life course framework where the former deals with consumption and savings decisions as a function of age and the latter deals, broadly, with life time events and their individual and aggregate importance on economic dynamics. The adequate impact on the macro economy from the dynamics of demographics must then be developed as a function of the attempt to do two things; firstly, to continuously develop the life cycle and life course theories themselves and secondly to seek out new ways to apply life cycle and life course theory to existing macroeconomic problems and themes.
In the first series, I will begin with the latter. Overall, I will highlight 6 areas where demographics enter macroeconomic theory and research as an important variable and I will try to offer my view on where to progress further. I will begin with two classics in the form of growth theory and open economy dynamics.
Growth Theory
Firstly, I need to say that I am not an expert on growth theory and this represents somewhat of a problem since growth theory although somewhat out of vogue at the moment has grown to become an extremely diverse field with a wide number of different schools and discourses. For the purpose here it will suffice to note that most economists today still use some form of the classic production function framework which has its roots in the work by Charles Cobb and Paul Douglas in 1928 and was popularized in 1958 by Solow’s famous article. This is what it looks like;
Where Y is output, K is physical capital, A is the illusive residual or more specifically technology/production function, L is the size of the labour force and H is a measure of human capital. Now, I certainly won’t do any math at this point and it is important to note that the functional form may take many exotic forms (which are not necessarily Cobb-Douglas), but just to give you one example the following is a Cobb-Douglas production function which incorporates human capital as above (here with constant returns to scale);
The key point I want to emphasize here is simply that we have output as a function of some input and that we would like to account for and explain the dynamics and behaviour of this input. How might we imbue this model with reasonable characteristics that reflect demographic dynamics? As it turns out, we already have some pretty solid frameworks to deal with this questions and we can see this by looking at the inputs one at a time.
The evolution of capital (K) – In most traditional models the evolution of capital is simply expressed as the fraction of income save minus any depreciation of the capital stock in the last period and here of course we have several workhorse models to show demographic dynamics that are all wrapped up in the form of the life cycle hypothesis of savings and consumption. Usually and since most of these models are constructed on the basis of Walrasian microfoundations, we have some form of intertemporal optimization problem ticking away in the background which assumes an OLG (overlapping generations) form. The classic model here is the Diamond model who is based on Diamond (1965) which is the father of all OLG models, but over time a plethora of different OLG models have been developed with differing degree of analytical complexity.
The basic problem here though remains the concept of the steady state which means that we must construct model such as to allow the change of capital through time (or its derivative with time) to be 0 in the long run. Please note here that this condition is not imposed on the basis of empirical behaviour but on the basis of (mathematical) analytical tractability. So, apart from the uncertainty surrounding exactly what this ”long run” is it also locks in the analysis and assumes away a large part of the important aspects of even basic life cycle behavior. Specifically, the idea that once reaching a steady state any change in the savings/consumption rate will one have transitory effect and that the economy will automatically (and always) converge to the same growth rate/state as before is a problem. Essentially, the whole idea of a steady state whether be it in the form of an exogenous or endogenous growth theory framework is a huge problem since it is evident that such a thing does not exist. And even if we could establish over a very long run horizon that such an average/constant path is a good approximation we would be ironing out all the interesting and important questions in the process.
The evolution of human capital (H) – The adoption of human capital into the growth theory framework is famously due to a paper by Mankiw, Romer and Weil in 1992 in which human capital is proxied by rates of schooling and thus the perspective becomes one of the quality of human capital and to the extent that the formation of human capital also includes the evolution of the population (or perhaps working age population) we can say that this is a direct way in which demographics enter the framework. Again, we might simply ask here; to what extent does the aggregate quality of human capital in an economy depend on the age structure of its population and here I am not only talking about the level of education but much more broadly about the idea of innovative capacity as a function of population structure.
The evolution of technology (A) – Technology and productivity are famously assumed exogenous in the Neo-Classical tradition while New Growth theory as it was developed in the 1980s and 1990s emphasised the need to specifically account for the evolution of technology. Today, I would venture the claim that there is a consensus that productivity and technology is a function of what we could call, broadly, institutional quality which encompass almost anything imaginable from basic property rights to the level of entrepreneurship. Indeed, a large part of research is still devoted to pinning down exactly which determinants that are most important here both across countries and through time. Now, I would argue that, in the context of standard growth theory, this is where the scope for the study of the effect of population dynamics is largest. Thus I don’t think it is unreasonable to expect the level and evolution of productivity growth and technological development to be a function of the current population structure but also its velocity which is a function of e.g. migration (new inputs?), future working age size etc. Also, this is also where human capital and the evolution of technology is joined at the hip through the idea of innovative capacity and readiness.
As you might have inferred from the exposition above, I have some difficulties with growth theory. I can admire the framework for its internal logic and I can see why it is an important part of a macroeconomist’s toolkit, but I also think that growth theory (as I describe it above) has outlived itself. In this sense, most of the questions that we have as economists when it comes to the evolution of growth and welfare of our economies both individually and through their interaction is not addressed by growth theory. Especially the effect of an ongoing and ruthless process of ageing is completely impossible to analyse in the standard framework. Naturally, I am also being a bit unfair here since the kind of growth theory I am describing above is also too simple to give adequate credit to where the field is today. For example in relation to demographics, I am grossly overlooking important strides in the development of OLG models which have been perfected continuously so that we today have a very large battery of very complex models. But also more generally, growth theory is being used today to produce a lot of useful research. As I say, it remains a key tool in our toolbox.
Yet, the basic growth theoretical setup remains flawed in key a number of un-salvagable ways. Concretely, specifying a production function and specifying the underlying inputs as differential equations through whose solution we reach a steady state equilibrium is not, in my opinion, the way to go. Thus and in an intuitive sense I feel much more at home, for example, in the company of evolutionary growth theorists [2] whose argument and methodology is more agile. In summary then and as I try my utmost not to become a hostage of the notion of a steady state I will simply make the following observations in the context of what we macroeconomists consider the main inputs to growth where the ”age” is simply an unspecified collection/function of variables that pertains to fertility, age structure, mortality etc (and of course a whole slew of other factors, but for the sake of argument let us keep it monocausal here).
Where age in the context of the capital stock relates to the size and evolution of the capital stock as well as savings and investment dynamics, in the context of human capital it may be argued to enter directly, but may also affect the quality of human capital. Finally, I think that the impact of demographics on innovation and especially the idea of velocity of innovation and innovative capacity represents an area which is not well understood. In general though and short of letting some variant of demographics enter directly, I think an important research program would be to examine the effect from demographics on the inputs to growth which we traditionally operate with. Especially, the process unprecedented process of ageing is a completely new phenomenon here in the context of traditional growth theoretical analysis.
Open Economy Dynamics
An enduring feature of macroeconomics is that the entities we study are not black boxes but interdependent entities who interact in very complex ways in the global economy. This statement was true 40-50 years ago and today it is almost a cliché. In fact, for non-macroeconomists it must seem very strange that we still distinguish so strongly between closed and open economy analysis as the use of studying the former must surely be almost nill. I would agree with this statement but simply note that important things do actually happen when we go from a closed to an open economy and the way this transition is operationalised is important in itself.
Now, I could write a lot about this (in fact, I have penned a whole thesis about it), but I will only cover the essentials. What you need to know upfront are two things. The first is that the economic theory used to handle the effect of age structure/demographics on open economy dynamics is again the life cycle framework and, in most cases, we still have a OLG representative agent model taking care of the microfoundations. Secondly, it is important to be aware of the concrete specifics of the transition from a closed to an open economy. Luckily, this can be handled by some very simple algebra from macroeconomics 1-0-1.
The whole point is to find an expression for savings, so for the closed economy we have;
By definition every unit of output has to equal a unit of income, and national income in any given period can either be saved or consumed. This means that national income can either be put aside for saving or consumed through government (G) or private consumption (C). In this way, we define national saving in any given period as;
This is a fundamental result in basic macroeconomics and what is equally fundamental is why this changes in one key aspect when we move into an open economy setting. We then have;
With (x-m) equal to the trade balance and by doing the same exercise above we get;
In this context and remembering that the life cycle hypothesis tries to map consumption and saving as a function of age, the transition from a closed to an open economy becomes crucial in order to see how demographics may affect open economy dynamics. As such, allow me to quote the following passage of my thesis which I find myself coming back to when thinking about this topic;
The best way to think about this [3] is to imagine that savings and investment are in a race governed and controlled, as it were, by the transition in age structure that occurs as a result of the demographic transition. Initially, as the transition sets in with a decline in mortality and where fertility only follows with a lag, investment demand outruns the supply of savings and the economy is running an external deficit. Steadily however, the supply of saving catches up with investment demand which itself begins to decline and thus the external balance moves into a surplus. Finally, the pace of savings accumulation is replaced by outright decumulation (dissaving) and the external balance moves into deficit as savings decline faster than domestic investment demand
This is stylized of course, but especially the idea of the race between savings and investment is a very helpful metaphor. Consider then a closed economy; in such a setting there can be no race as described above since savings and investment will be tied together at all points in time, but in an open economy savings and investment dynamics are exactly what provokes relations between economies and more specifically, the fact that the economies have different preferences for savings and investment at different points in time. This gives a very strong foundation for thinking about how demographics affect open economy dynamics.
Concretely, and in order to tie the argument up on the underlying theory capital flows occur precisely because economies have different intertemporal preferences for consumption and saving and since this intertemporal preference itself is a function of age (through the life cycle/OLG framework) demographics become a driving force for international capital flows.
This as it were is also where the fun begins since exactly how this process should be understood both from the point of view of the individual economy, but also in a global context remains, for all intent and purposes, an unresolved question. Surely, we have studies that use basic life cycle frameworks to simulate capital flows between economies and they do have some intuitive appeal and explanatory power, but they are hampered by, in my opinion, by an inadequate understanding of the life cycle thesis and how exactly it manifests itself. As I noted in the beginning, part of all this also requires a continuous development of the life cycle hypothesis itself and here this becomes important. Personally, I have cast my eyes on two areas of research where I believe that the influence of demographics on open economy dynamics is important.
1 – Global Imbalances
This represents an enduring feature of the global economic system and while everyone can agree that they need to be resolved some way or the other I think that the proper understanding of demographics shows us that they are essentially structural. Especially on the side of surplus economies I have argued (both in my thesis and in genera) why we cannot suddenly expect economies such as Germany and Japan to do their part and crucially, why we should expect more economies to venture down the same path as they are also ageing rapidly. Importantly, this provides a concrete theoretical spin to the question everyone seems to be asking at the moment of who exactly is going to run the deficits? The pessimistic answer here is no-one and herein lies the rub.
2 – Export dependency
This one is essentially the concrete theoretical proposition used to make the argument above on global imbalances. Ageing leads to a decline in domestic demand and in a closed economy there is really not a lot you can do; savings/investment will fall and consumption will be lacklustre since there is no underlying dynamic to feed it other than dissaving. However, in an open economy you can fight this through claims on other economies or put in another way, you can save more than merited by domestic demand and thus you can invest your savings abroad. Note here that technically this is exactly what e.g. Germany and Japan are doing in the sense that their excess savings have to be matched by excess borrowing/investment demand elsewhere.
I am still developing these two areas, but there are plenty of meat on this topic I think. One crucial task is to develop the life cycle hypothesis on the basis of observed behavior of economies as they age and another is to.
Stay tuned for the next post in this series which looks at the influence from demographics on asset prices, demand, and return and composition of consumption. Suggestions and comments on potential omissions on my part are welcome.
—
[1] - Most often operationalized through an OLG framework.
[2] – Evolutionary Growth goes back to this one “Nelson, R.R., Winter, S.G., 1982. An Evolutionary Theory of Economic Change. Harvard University Press, Cambridge, MA” and is a must read I think. The work by Jan Fagerberg is a good place to begin as well as for a more modern exposition.
[3] – I.e. demographics and savings and investment behavior in an open vs closed economy
By Claus Vistesen, on March 26th, 2010
Most, if not all, of the deficit nations that make up the Bretton Woods II edifice and subsequent global imbalances have seen notable housing and construction bubbles as part of their path towards excess leverage. The demand for housing and thus in some sense construction on the aggregate macroeconomic level can be tied to demographics and specifically the idea of a life course [1]. So, could we say that this was one of the channels through which demographics have indirectly affected global imbalances?
I think so and below I will argue why, but first things first. In fact, blame it on that double shot latte that I enjoyed a couple of days ago early in the morning reading this paper by Pedro Gete from Georgetown University, but what follows will be terribly wonkish. Yet, intellectually and theoretically I think it represents an important piece of the puzzle so if you have the energy I welcome you to indulge me.
The Model,
Mathematical economics, rational expectations, and representative agent modeling have taken their share of the flak in the context of the macroeconomic shake-out following the financial crisis. For the most part I agree with the critics, but let the following be a counter example on the general blurred and fuzzy nature of economic modeling. In this way, the model developed and discussed in Gete (2010) is neat, concise, and easily allows for an intuitive expansion of perspective. In short, I like it; a lot! For good measure, here is the abstract;
This paper makes a theoretical and an empirical contribution to the debate on what caused the global imbalances. On the empirical side, I provide different types of evidence to support that housing demand shocks (shocks to the aggregate marginal rate of substitution between housing and tradables) help to explain the global imbalances. On the theory side, I show that shocks to the demand for housing generate trade de
cits without need for the standard ingredients used by others to model housing (wealth effects or trade in capital goods). I model housing as a durable and nontradable good. Countries import tradable goods during periods when more domestic labor is devoted to produce nontradables to smooth consumption between tradables and nontradables. Housing booms are larger if the country can run a trade de
cit because the de
ficit lowers the opportunity cost of building, which is the foregone consumption of tradable goods due to reallocation of labor to the construction sector. Concerning the empirical evidence, I first document that over the last decade there has been a strong cross-country correlation between housing variables and current account dynamics. Second, I show that using the cross-country dynamics of employment in construction as the explanatory variable, the model generates current account dynamics matching recent global imbalances. Finally, I use sign restrictions implied by the model to estimate a vector autoregression and identify the effects of housing demand shocks on the U.S. trade de
cit. The results suggest that housing shocks matter for current account dynamics.
In order to spare my readers the nitty gritty of the model I will not do the math here, but merely describe the model verbally. In this way, the nice aspect of the model and thus intuition in Gete (2010) is that it assumes that housing shocks are exogenous (which of course they aren’t), but by doing this he creates a simple laboratory through which to impose different assumptions on what might actually make shocks to the demand for housing endogenous (hint: this is where demographics come in, but first things first).
The economy in Gete (2010) is very simple. It consists of two sectors, a tradable and a non-tradable (housing), and only one input to production which can be shifted without adjustment costs between the two sectors (but not between countries). Now, in a model like this with exogenous preferences there is really not a lot we can do and this is especially the case if we assume a two economy world where relative preferences between tradables and non-tradables in both economies are the same. In such an economy, we simply solve the respective optimization problems and each economy consumes the same amount of tradables and non-tradables (housing). However, let us introduce a deus ex macina and assume that the demand for housing suddenly increases in one of our economies. What will happen?
Well, those of you with a fondness for core economic theory should start to feel that warm fuzzy feeling just about now while the rest of you … are you even still with me?
Moving back on track, and given that our only input to production is labour, the economy will have to shift labour from the tradables sector to the non-tradables sector and thus production of tradables will give way for production of non-tradables (housing) [2]. But does it also mean that we have to give up consumption of tradables? No, and this is the key mechanism by which Gete (2010) explains how an increase in the demand for housing/construction leads to a trade deficit and thus may explain global imbalances to the extent that the deficit countries are the ones who have seen housing booms. Specifically and in the jargon of the trade, the economy who sees a positive shock to housing demand may smooth consumption of tradables and non-tradables by running a current account deficit; effectively borrowing to consume tradables which it no longer produces itself because labour has been allocated to the construction of housing.
The empirical evidence presented in Gete (2010) is circumstantial although the simulation based on the model turn out quite well. One chart however which shows the proposed relationship quite well is the following which plots the change in the CA/GDP ratio (in % points) on the y-axis against the change in the labor share of construction on the x-axis. Moreover, I have made my own small replication with different data and a slight change methodology (and sample [3]) and the picture is the same.
In this sense the relationship seems solid enough to vindicate the overall claim that raising the weight of housing/construction leads to excess investment over the capacity of domestic savings and thus an external deficit. Note especially that my sample, data, and methodology are quite different from those in Gete (2010) Whether Mr. Gete is right in that it relates to tradables vs. non-tradables is a theoretical discussion in its own right, but I am willing to go with this issue for now although, from a life cycle/permanent income perspective, I would not discount the wealth effect argument entirely.
Endogenizing Housing Schocks, introducing Life Course Theory
Stepping outside the realms of Neo-Classical representative agent modelling and real business cycle simulation, how might we operationalize this argument in a realistic theoretical context [4].
Before moving on, remember the key premise set up by Gete (2010). Preferences are exogenous and thus what this paper really sets out to show is how the distinction between housing as a non-tradable and “other goods” as tradables may lead to capital flows between economies. Specifically, this is put up as an alternative explanation to how an increase in housing demand may generate a trade deficit relative to the traditional account of a strong wealth effect from housing which translates into a demand for consumption today relative to savings tomorrow (or through the fact that it increases permanent income). For the purpose of what follows, this particular distinction is not so important. What is important however is to find a way or mechanism through which to endogenize the preference for housing and thus a way to model this purposed shock.
Enter life course theory.
Life course theory essentially deals with the timing and significance of key events in an individual’s life from birth to death. As it is presented in presented e.g in (ed. Mayer (2006)) it primarily operates on core sociological parameters such as age of leaving school, age of labour market entry, age of retirement, age of marriage (fertility decisions). This makes it a broader framework than the life cycle framework, but in fact the two are tightly joined at the hip. In this sense, one could also imagine more strict economic parameters such as age of first acquisition of house, car, or other “durable” goods. Specifically, one would probably tie this together to the capacity and willingness to take on debt to finance large durable purchases which are, by definition, very rarely financed through non leveraged lump sum transfers.
Slowly but surely we are moving towards a working model here. One way to conceptualize the way demographics may serve to generate international capital flows would then be to take a life course/life cycle perspective of the demographic transition in which an economy harbours the capacity sustain and develop a construction/housing boom which, through the mechanism described in Gete (2010), may serve to facilitate an ongoing trade deficit. Specifically, we could say that an economy must have a certain and relative amount of workers in their most productive age (say 30 to 45) to generate this dynamic which, by far, is not automatic since evidently; for an economy run a large current account deficit there need to be a corresponding pool of foreign savings.
As shown, Gete (2010) provides tentative evidence to suggest how the correlation between a large share of construction as percentage of GDP is closely associated with a trade deficit.
In general however, does my theory square off with reality?
Well, consider the fact that no economy with a median age over 40 have seen a sustainable housing boom which has led into an external deficit. In fact, based on the assumptions laid out above in the small laboratory set up by Gete (2010) and my endogenous imposition through demographics, we could say that as long as there is a balanced amount of economies with relatively stable population pyramids contrary to a group of rapidly ageing economies the system may work. Apart from mercantilist Asia and the petro exporters, I would hold this to be an important part of the source of the underlying stability of Bretton Woods II. The problem is that we are all ageing beyond the threshold where we can reasonably expect the economy to shoulder a large an ongoing deficit based on a rapid increase in construction and housing. This then becomes just another, and very specific, perspective on the gordian knot which is the global economic system with so many would be savers (exporters) and too few economies willing and able to suck up the excess liquidity [5].
Consequently, and if we can say that if the characteristics of a classic external deficit economy based on demographic fundamentals include a large share of construction/housing as a percentage of GDP, the recent economic crisis has drastically limited the peloton of such economies while simultaneous increasing the number of economies more than willing to finance whatever housing bubble there might be left somewhere.
Is this a new proposition then?
Not quite and if we look at the argument as a two step theoretical construct, the first step in terms of linking the demand for housing/construction to demographics is not new. Mankiw and Weil (1989) who examine the effect of the boomer generations move through the population pyramid is a seminal contribution with Green and Hendershott (1996) and related study. Far more interesting however are studies who try to combine both steps and thus tying together construction, demographics, and external account dynamics. Here especially Malmberg and Lindh (1999a and 1999b) is interesting as it shows how the disaggreation of investment reveals significant effects in a classic empirical context à la Higgins (1998) and Lürhmann (2003). Note especially this from the abstract;
Disaggregating investment we find that young cohorts have a positive correlation with housing investment while older but still active cohorts have a positive correlation with business investment. The differences in saving and investment effects are, nevertheless, sufficient to generate persistent and sizeable age effects on the current account. Our results suggest that policies concerning current account balance should take into consideration age distributions and the degree of development.
This would seem to fit not only with the picture laid out in Gete (2010), but also crucially with a strong life course effect and thus an argument to support a strong eye to life course/life cycle dynamics when modelling current account dynamics.
With respect to the global imbalances this perspective also offers a valuable lesson.
In a nutshell, if demographics drive housing and construction activity, and the latter drive current global imbalances it then follows naturally that demographics have something to do with current account imbalances. Yet, it may not be so simple. Consequently, it is dubious to claim that the reason why excess global liquidity was channeled into housing in the first place falls exclusively on the effect from demographic change. However, this would also be the wrong way to present the main argument. In this way, we can say that the extent to which a given economy was able (willing) to respond to excess global liquidity by developing a housing/construction bubble was a function of a specific demographic strucuture. More to the point even; the inability to harbour a housing/construction bubble and thus a matching current account deficit for all this liquidity splashing around is marked by the absense of a certain demographic structure namely that of a median age below 40 (to put it really strict). Basically, and as I have argued time and time again, the unwinding of global imbalances are greatly complicated by the fact the global economy increasingly is going to be populated by economies who cannot be expected to push up domestic demand to meet the propensity to save by others. In fact, this bites itself in the tail since those very same economies are the ones who are now dependent on exports. So, something has to give and while we cannot, yet, export to Mars this still becomes an important externality to consider in the context of the dynamics of the global economy.
This final point is perhaps the most important lesson to take home from my considerations above.
Summary
Well, I told you that this would be wonkish, but even if you have not read all those academic references above I hope that it is still possible to dissect the main message here. Demographics matter, but much more than this a strong methodological foundation in a life course/life cycle framework enables us to see how the demographic transition casts a long and significant shadow over the economic profile of individual economies as well as the aggregate global economy. Of course, this is my all time favorite hobby horse, but I do think that the facts are there to back me up.
As for Gete (2010), I would warmly recommend you to read it especially if you have lost faith in classic economic modelling where, I think, it provides a good example of an intuitive and easily comprehendible model of the world.
For future reference the topic above is naturally one that I will be pursuing vigorously as I move forward, so this is not the last time that I have treated this topic since it has much, much more to offer.
List of References
Gete, Pedro (2010) – Housing Markets and Current Account Dynamics, Working Paper (Georgetown University)
Green R. and P.H. Hendershott (1996) – Age, housing demand, and real house prices. Regional Science and Urban Economics, 26(5):465–480,
Higgins, Matthew (1998) – Demography, National Savings, and International Capital Flows, International
Economic Review, Volume 39 (1998) Issue (Month): 2 (May) pp 343-69
Malmberg, Bo and Lindh, Thomas (1999a) – Age Distributions and the Current Account A Changing
Relation? Working Paper Series 1999:21, Uppsala University, Department of Economic
Malmberg, Bo and Lindh, Thomas (1999b) – Demography and housing demand—what can we learn from residential construction data. Workshop on Age Effects on the Economy, Stockholm, pages 2–3, 1999
Mankiw, N. Gregory and Weil, David N (1989) – The baby boom, the baby bust, and the housing market, Regional Science and Urban Economics Volume 19, Issue 2, May 1989, Pages 235-258
Mayer, Karl Ulrich (2006) – Handbook of the Life Course (review) Social Forces – Volume 84, Number 4, June 2006, pp. 2363-2365
Lürhmann, Melanie (2003) – Demographic Change, Foresight and International Capital Flows, MEA
discussion paper series 03038, Mannheim Research Institute for the Economics of Aging (MEA),
University of Mannheim
—
[1] – No, not life cycle which is also important, but indeed life course.
[2] – Think undergrad microeconomics here with allocation of labour in a two goods economy with a corresponding production possibility frontier and indifference curve.
[3] – I have the following countries; Australia Austria Belgium Czech Republic Denmark Finland France Germany Greece Hungary Ireland Italy Japan Korea Luxembourg Mexico Netherlands Poland Portugal Slovak Republic Spain Sweden Switzerland United Kingdom United States Estonia Slovenia.
[4] – And yes, here I AM implying that RBC simulations and neo-classical economic modelling are not very realistic even if they may hold some intuitive appeal.
[5] – … which again is created by the fact that OECD central banks are in QE mode in an attempt to spur growth in their domestic economies, but where the liquidity simply ends up moving through the back door ending up whereever there is yield to be found. And round and round we go …
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By Rok Spruk, on February 1st, 2010
In NY Times, Paul Krugman (link) wrote about the comparison of European and U.S economic model, concluding that in the last 10 years, the European model of social democracy led to higher standard of living and, compared to U.S in output per hour and standard of living, and relative convergence of European countries relative to the U.S respectively.
The real convergence is a complex mathematical and empirical issue, so I will rather outline the key patterns of GDP per capita gap between the U.S and Europe and the economic explanation of it. I downloaded the data from the IMF and composed a graph which shows the GDP per capita (PPP-adjusted) in European countries as a percentage of the U.S GDP per capita. Switzerland is the only European country whose level of GDP per capita is more than 90 percent of the U.S level. Ireland, where the output contracted by 7.5 percent in 2009 (link), was once the poorest country in the European Union. Today, its GDP per capita reached 85 percent of the U.S level. In spite of the notorious advantages of the Nordic model, the GDP per capita level of all Nordic countries (excluding Norway), is below 80 percent of the U.S level. The UK GDP per capita is also far below the U.S level (75 percent). The levels of GDP per capita of the less developed countries in European Union (Slovenia, Greece, Portugal, Czech Republic and Slovakia) are all below 62 percent of the U.S level.
 Source: IMF, World Economic Outlook
The basic economic question is the length of the gap between the U.S and European countries. To answer the question, we have to set certain assumptions. So, let’s assume that the U.S output will increase by 2 percent in the long run. The economic theory would predict faster growth of less developed countries, since countries with lower levels of standard of living (GDP per capita) tend to follow-up the countries with higher GDP per capita. In economic literature, that is the so-called “catch-up effect”. So, what would happen if the UK economy increased by 3.5 percent in the long run. A quick estimate shows that the time gap between the UK and US is 19 years. So, what happens of the US economy increases by 2 percent in each of the next year while, at the same time, the UK GDP per capita is 75 percent of the U.S level? A fairly quick estimate shows that, if the UK GDP per capita will reach the U.S level in 10 years (although an unlikely scenario), the UK GDP per capita would have to increase by 4.9 percent each year to catch-up the U.S level of GDP per capita. If France’s GDP per capita reached the U.S level in 10 years (assuming 2 percent growth in U.S GDP per capita), it would have to increase the economic growth to 5.3 percent in each of the next 10 years. If the convergence objective is set at 20 years, the French economy would still have to grow at the annual rate higher than 3 percent.
The main question is why the European countries are still behind the U.S level of GDP per capita? There are, of course, many plausible explanations. As far as the GDP per capita is concerned, the difference in the level and growth of productivity is the most important figure in setting conclusions. After all, in the long run, productivity determines the standard of living across countries.
First, the European disease is mostly the result of high tax burden. High tax rates diminished the incentives to work, since each additional hour of labor reduced worker’s marginal productivity. Hence, as professor Mankiw explains, the rise of European leisure (link) is mostly the result of fewer working hours. In addition, early retirement is a common phenomena across Europe. By 2030, each worker will support one retired individual in Germany. The coming of Europe’s pension crisis (link) is a consequence of generous PAYG pension systems. Lower employment-to-population ratio led to higher tax rates to finance the financial liabilities for the retired. In addition, high government spending and periodic budget deficits discouraged productivity growth.
Second, another key to the explanation of the anemic growth rates in Europe is rigidity of the labor market. In many European countries, labor costs are very high (link). If the cost of labor market entry is high, people prefer longer studying and working in the shadow economy. The shares of shadow economy are relatively high in all European countries (link). The highest rates of shadow economy are in the following countries:
1. Slovenia 27%
2. Greece 26%
3. Italy 24%
4. Spain 21%
5. Belgium 20%
6. Germany 15%
7. France 13%
Source: ATKearney (2009), Friedrich Schneider (2005)
Third, Europe’s relative decline compared to the U.S, is not a consequence of the lack of R&D investment. High percentage of R&D investment in the GDP is not a cure for the real cause. In fact, European universities rank far below the top universities in the world. In the field of engineering and computer sciences, the first non-US university is in the 15th rank. Europe’s brain-drain is a known phenomena since many bright European minds immigrate to places such as the U.S, Canada and Australia. The outcome is deteriorating international ranking of universities and low efficiency of R&D expenditure on misguided projects such as the intention of the European Commission to build a “European MIT” (link) to boost Europe’s global technology leadership.
Without higher growth of GDP, productivity and market working hours, European countries will hardly sustain the convergence towards the U.S level of GDP per capita. To boost economic growth, bold structural reforms are required to cut the rates of shadow economy, reduce tax and social security burden, decrease government spending and deregulate the labor markets.
By D H Smith, on January 20th, 2010
This preliminary study started with a blog post I did several months ago entitled “New Jersey, the Sorry State”, a deep dive into Bureau of Labor Statistics data showing that my state is hardly generating employment outside the government sector.
The blame for this sorry state of affairs I heaped on NJ’s political culture, which is high-taxing, heavily-regulating, pro-union, anti-business and Democrat-dominated. As the power of Democrats, the self-proclaimed friends of the working man, has risen in this state, fewer working men have actually had work.
One of my readers suggested extending the work to all states. A daunting prospect, but I have made a start — back to BLS data for 51 deep dives. This time I’m looking longer term, with data from 1990 to the present.
To try to get to grips with party politics in all states through time, I researched affiliations of the governor and two senators and the plurality of the House of Representatives delegations and the state senate and legislatures for each year since 1990, using wikipedia and such other sources as I could find. No doubt there are some errors at this stage, particularly in identifying the leanings of state legislatures 15 or more years ago. These errors are minor; it’s unlikely that I could mistake Idaho for a blue state or Washington for a red state, for example.
Those two next door neighbors bracket my best ranking of the 50 states + DC by political complexion, from most Democrat to most Republican:
>> bluest: WA DC WV MA AR NJ CA MD IL HI DE
>> next: NY VT IA WI RI MI OR CT ME NC
>> middle: NM MN MT LA COPA NH ND IN TN
>> next: SD VA MS NV AL MO NE KS OK FL
>> reddest: KY OH AZ SC WY AK GA UT TX ID
Next best alternative ranking is so similar:
>> bluest: DC WA WV MA AR MD CA HI NJ DE VT
>> next: IL RI NY MI OR CT IA WI LA NM
>> middle: NC ME MN ND MT IN PA VA NV CO
>> next: TN AL SD GA NH KY MS MO FL NE
>> reddest: AZ KS OH TX OK AK SC WY UT ID
Let me point out a few things by way of caveats and highlight a few preliminary conclusions.
Conclusion 1: Government is not just New Jersey’s growth industry; it’s a growth industry in most states, Democrat or Republican. In fact, it is only in a handful of blue states and territories that government employment has been static or falling: MA, MI, NY, DC, and RI.
Conclusion 2: The predominant pattern in the last ten years has been for employment in goods-producing industry to be declining, in service-providing business to be growing somewhat, and in government to be growing fastest of the three. That pattern is seen in 37 states: AL, AK, AZ, AR, CA, CO, CT, DE, FL, GA, IL, IN, IA, KS, KY, MD, MS, MO, NE, NV, NH, NJ, NC, OH, OK, OR, PA, SD, TN, TX, UT, VT, VA, WA, WV, and WI; in MI it was declining but less than other employment. So at bottom, government is growing at the expense of goods production. In the limit, this places fiscal drag on the economy, which reinforces the original trend and makes it worse. That is our New Jersey experience.
Conclusion 3: The states that have experienced the greatest declines in employment in goods-producing industry are (worst first): RI, MI, NJ, CT, NY, NC, OH, ME, MA, and PA. Mostly northeastern/midwestern, mostly unionized, and mostly Democrat.
Conclusion 4: The states that have experienced the best performance in growing employment in goods-producing industry are (worst first): NE, CO, NM, SD, ID, MT, UT, WY, NV, ND. Near runners-up were TX, AZ, and OK. Mostly western, mostly right-to-work, and mostly Republican.
Conclusion 5: Only in Wyoming is employment growth in goods-producing industry positive and higher than either services or government.
Caveat: A Democrat is not the same wherever you go, nor is a Republican. A Maine Republican is a very different animal than a Texas or Wyoming Republican; in fact, some say it is a RINO. A Mississippi Democrat in 2009 is not ever the same as a Massachusetts Democrat, nor a Mississippi Democrat of twenty years ago.
Caveat, speaking of Massachusetts: In connection with the special election there on 1/19/2010, I and many others have taken to calling the Bay State “the bluest of all blue states.” This is incorrect. It yields to the blueness of the Washingtons (state & district) and West Virginia.
Caveat: Employment in goods-producing industry is not a holy grail and need not be the object of all economic policy. If someone leaves a job in the declining textile industry in North Carolina, retrains as a radiological technician and get a better job in that field, no one argues that either that person or the state of North Carolina are worse off. The problem is when employment in the goods-producing sector as a whole is in total headlong decline. That means industry is giving up on a place. That means industry prefers to take its chances with the Chinese Communist than the Michigan Democrats.
Caveat: Productivity has improved in goods producing industry, meaning fewer workers are needed to do the same or greater work. I know. That’s wonderful. But that productivity itself should incentivize capital to come into a place and employ workers who have worked themselves out of their jobs. If it’s not enough, other things are wrong, and the benefit of their productivity is not for workers to share. Politicians must ask the question, what else is needed to attract industry? Republicans ask that question; Democrats ask instead what other self-defeating social costs and regulations they can impose on job-creating enterprise.
Here’s one final caveat, and it is important. I don’t know which way the causation runs. I am not sure whether the growth states of the West are Republican because they are prosperous, or prosperous because they are Republican. I am more certain that employment grows in right-to-work states because it can, without restriction; that’s just economic common sense. Capital goes where it is well treated.
This much is clear. The employment restrictions and the class struggle nonsense offered by those friends of the working man, the Democrats, isn’t offering the working man in the post-industrial Northeast and Midwest any tangible economic return on his long-term political investment.
I say if you want to work, go R. If you want to stand on the unemployment line complaining about the Man, go D.
By Rok Spruk, on November 13th, 2009
On Wednesday, it had been 20 years since the fall of the Berlin wall and the eventual collapse of communist political and economic system in Central and Eastern Europe. However, there is still discussion about economic costs and benefits of German reunification (Wiedervereinigung). I’ve been motivated to open this debate by professor Becker’s analysis (link) on the size of countries and by the recent article in Financial Times by the contributing German economist (link).
Economists in Germany and the rest of the world have long warned against the consequences of the unification of East and West Germany. After the unification, German central bank set the exchange rate at 1:1. Because East German workers’ relative productivity level lagged far behind the West German level, East German workers migrated to West Germany in search of higher wages. When wage rates between West and East Germany were equalized in the absence of productivity catch-up in East Germany, the excess labor supply in the East led to high unemployment and slow changes in the economic structure. As the exchange rate was equalized and wages prevented from the natural adjustment to productivity growth, the unemployment soared as East German manufacturing sector couldn’t employ labor anymore. The unemployed received massive transfer payments which, even more than a decade after the reunification, still present about 4 percent of total German income.
Today, the figures suggest that East German GDP per capita is roughly 70 percent of the Western German level and the unemployment rate exceeds 12 percent – more than twice the Western level. Low population density and high share of rural population are the main structural obstacle to higher productivity growth in the East. The majority of models in economic geography and urban economics suggests that agglomeration economies occur where population density is high. The latter yields significant advantages in terms of spillovers, search cost, factor mobility, know-how and economies of scale. Low population density is a major obstacle in attracting investment mostly because firms are not eager to locate at the periphery in the presence of high search costs and in the absence of high-skilled labor, agglomeration and linkages to economies of scale. In the U.S, for instance, Pittsburgh’s industrial restructuring from resource-based steel industry into knowledge-intensive information technology, biotechnology and software development required agglomeration which combined high-skilled labor, human capital, access to regional and international markets as well as high population density.
In Germany, for example, Hamburg generated the highest GDP per capita (€51,000) among cities and Bavaria (Bayern) generated the highest GDP per capita (€36,000) among German states. Hamburg and Munich, as well as the linking cities located in their vicinity are among the most densely populated areas which enabled them to develop core industries, spillovers, know-how and dynamic knowledge externalities. There is an overwhelming evidence that differences in population density are a good source of growth difference between east and west Germany.
After the unification, German fiscal policymakers favored an expansive fiscal policy which directed federal expenditures into poorer regions of the East to boost the development of infrastructure. However, at an exchange rate 1:1, West German firms were reluctant to invest in East Germany mainly because of higher relative price of labor. As East German workers moved to the Western part of the country, west German firms hired eastern workers. As brain drain became widespread, the convergence of east German income per capita slowed.
East Germany were far better off, if the country remained independent. The reunification of Germany would yield significant economic benefits, if the unification itself were based on close economic integration with the establishment of free trade area and free movement of capital, goods and labor. If East Germany remained independent and retained its own currency without the uncovered exchange rate realignment to to West German exchange rate parity, the relative price of East German labor wouldn’t increase and thus the unemployment rate would be significantly lower than it has been ever since the reunification. Thus, West German firms would easily find attractive investments in East Germany. The process would dramatically reduce disparities in population density compared to the West. Under such scenario, East Germany’s macroeconomic stabilization and institutional reforms would be a lot easier and the overall economic and political transition much less painful.
By Eldon Mast, on November 6th, 2009
John Chambers ignited markets on Thursday. The Cisco CEO joined other leading firms like Apple, Amazon, Alcoa, Intel, and others by stating that, “the quarter was very strong. The recovery is gaining momentum.” Earlier in the week, the institute for supply management speculated that the US GDP is likely now growing at an annualized rate of 4.5%.
Chambers continued, “what we saw is a clear tipping point as our business continued to reflect strong sequential growth trends that meet or exceed expectations during normal economic times.”
Elsewhere on Thursday the US Labor Department said the output per hour of nonfarm workers rose at an annual rate of 9.5% in the quarter, more than four times the average productivity growth rate of the past quarter-century. When taken together with the second quarter’s 6.9% rise, it was the strongest productivity growth rate over a six-month period since 1961.
While unemployment remains high, initial claims for unemployment continue to fall and corporate profits have bounced back significantly from the strong downturn in Q1. As output keeps climbing, employment gains will follow shortly.
Such large productivity gains are quite common at the end of deep recessions and the beginning of recoveries. A healthy pattern is that productivity grows first, then employment rises, and finally wages increase.
It continues to be clear that this recovery will not be a jobless one. In fact on Thursday the government also reported that jobless claims dropped to a 10-month low raising speculation that the national unemployment rate has peaked will begin to fall as soon as next month.
And as we’ve published here since February (and as was witnessed on Thursday), the stock market will continue its move — swift and steep.
By Winton Bates, on July 27th, 2009
I find it hard to take seriously the concept of a happy planet. Is Earth happier than Mars? How would we know? It seems to me that only sentient beings can be happy, but that might just reflect the limited perspective of a sentient being. For all I know a rock might have a completely different perspective.
The happy planet index constructed by the New Economics Foundation (nef) doesn’t actually attempt to compare the happiness of different planets. What it attempts to do is to assess how happy our planet is with what is happening in different countries. I hope that makes you smile because if you take the happy planet index too seriously I think you are at risk of becoming unhappy – and that might make the planet unhappy!
The countries that are given the highest ratings in nef’s index are Costa Rica, Dominican Republic, Jamaica, Guatemala and Vietnam. These places don’t seem to me to offer the ideal of a good life for the people who live in them, even though many of these people say they are satisfied with their lives.
The authors claim that the results show that a good life is possible without “costing the earth”. Andrew Norton has pointed out that the results do not support this conclusion. Average happiness levels are relatively low in several countries that are ranked among the top 50 in the happy planet index.
As defined by the nef the happy planet index is a productivity measure. The numerator (or output measure) is happy life years, measured by multiplying average life satisfaction levels by average life expectancy. The denominator (or input measure) is a linear function of the average “ecological footprint”, which is a measure of the total amount of land required to provide all resource requirements plus the amount of vegetated land required to absorb CO2 emissions.
The basic idea seems to be that “the planet” becomes happier when people in a particular country become happier without using more “land” or when people maintain their current happiness level while using less “land”.
How do we know that this is what makes the planet happier? How do we know that the planet cares whether or not humans are happy?
My point is that the happiness of the planet only exists in the mind of the human who thought up the idea of the happy planet index. There is nothing wrong with trying to imagine what it would be like to be a planet that has feelings, but this is a game that anyone can play. Some people could imagine, for example, that the happiness of the planet will rise if more CO2 is produced. After all, CO2 is food for plants and planets like plants. Don’t they?
It would be possible for everyone on earth to have their own happy planet index that takes account of the things that they imagine that the planet might value. It would probably be preferable, however, to come down to earth and acknowledge that there is potential for everyone on the planet to vary in the extent to which they value various things that are important to them.
If nef’s happy planet index serves a useful purpose I think it is to remind us that surveys that measure our subjective well-being do not necessarily take into account all the things that are important to us. When we report how satisfied we are with life we take account of the things that are most salient to us at the time. We don’t necessarily take into account our own future well-being and the well-being of future generations of family members, let alone the well-being of other relatives and friends, the well-being of other humans, the well-being of animal pets, the well-being of other living things, or other matters that might be important to us.
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