Growth

This webpage introduces our on-going research project on economic growth of the entire human history. The first objective is to explain why the Industrial Revolution occurred and why it occurred in England in late 18^th century. This is probably the most fundamental question in economic history. Building on that, the second objective is to explain why the demographic transition occured in the late 19^th century, which preceded the modern growth. To be able to explain the data of about a millennium, we develop Unified Growth (UG) models with multiple equilibria, either exogenously given as in Foreman-Peck & Zhou (2018) or endogenously evolved as in Foreman-Peck & Zhou (2021).

1. The Industrial Revolution and Late Marriage

A central question of economics is why some nations experienced economic growth and are now rich, when others have not and are poor. We answer this question by estimating and simulating a structural model of the English economy.

Our explanation is based on the observation that western Europe experienced the earliest modern economic growth and also showed a uniquely high female age at first marriage – around 25 – from at the latest the 15^th century (Foreman-Peck, Explorations in Economic History, 2011). Whereas real wages actually began a sustained rise during the first Industrial Revolution. Without the contribution of late marriage, average living standards in England would not have risen by 1870 (see the black dashed line).

We utilise long time-series evidence dating back to 1300 and test the hypothesis that this West European Marriage Pattern was an essential reason for England’s precocious economic development. Persistent high mortality in the 14^th and 15^th centuries and massive mortality shocks such as the Black Death lowered life expectations. Subsequently as survival chances improved, especially for children, a given completed family size could be achieved with a smaller number of births. In an environment without artificial birth control, a rise in the age at first marriage of females ensured this reduction in fertility.

Later marriage not only constrained the number of births but also provided greater opportunities for female informal learning, especially through ‘service’. A high proportion of unmarried females between the ages of 15 and 25 left home and worked elsewhere, instead of bearing children, as in other societies. This widened female horizons compared with a passage from the parental household directly into demanding motherhood and housekeeping. Throughout this period the family was the principal institution for educating and training future workers. Schooling was not compulsory until 1880 in England. In the early nineteenth century few children attended any school regularly and few remained at school for more than one and a half years. Such skills and work discipline as were learned were passed on and built up over the generations primarily by the family. Our paper shows how, over the centuries, the gradual rise of this human capital raised productivity and eventually brought about the Industrial Revolution.

The following figure illustrates the conceptual framework of our dynamic, stochastic general equilibrium (DSGE) model constructed from microeconomic rationality principle. We go on to show that the model, consistent with the long but unbalanced or fragmentary demographic and economic time series available for England, can explain the onset of the Industrial Revolution. Growth is triggered by the high mortality of the 14^th century and the subsequent re-balancing of power that broke the feudal system. In the model the gradual accumulation of human capital eventually launches a strong growth in real wages. The merit of our identified structural model is that it can support counterfactuals implied by our central hypothesis, and the simulated method of moments estimation permits a closer connection between theory and data than simple calibration allows.

This is not just a historical question, because many developing countries share similar modern experience. A particularly relevant example is China, which imposed a "One Child and Late Marriage" policy since 1980s. The Chinese growth miracle in 1980s-2010s can be partly attributed to this change. The only difference is that it is done through policy intervention rather than economic rationality. In comparison, India, which did not impose a similar birth control policy during the same time, underwent a much slower growth.

You can read our paper published in the Economic History Review. Here is a nontechnical abstract and slides. This paper is presented in a series of international conferences, including European Historical Economics Society conference (Tübingen, 2017), International Symposium on Quantitative History (Kaifeng, 2017), the Royal Economic Society conference (Brighton, 2016), European Economic Association conference (Toulouse, 2014) etc. The microdata, macrodata, the Stata code and the Matlab code are available on the research page.

2. Demographic Transition and Child costs

After the first Industrial Revolution, accumulation of human capital started to speed up while growth of population began to slow down. Throughout the 19^th century, many European countries entered the phase of demographic transition. We attempt to explain an extended growth history of England (1086-2016) using a UG model with endogenously evolving equilibria.

Two fundamental mechanisms are essential to our explanation. First, building on Foreman-Peck & Zhou (2018), we further assume that negative population growth (particularly that triggered by the Black Death) selects for the removal the portion of the population whose preferences render them “less fit”. Second, major mortality events both raise surviving child costs and eliminate agents with lower willingness to choose smaller families with high child quality. We show that the data imply an increasing trade-off between child quantity and quality, with the elasticity of substitution between quantity and quality rising as extreme mortality impacts (shown in the graph below). As this elasticity increases, the Malthusian demand for number of children responds less to higher wages, and the negative effect of human capital growth on the demand for children becomes stronger. These effects are conducive to economic growth because they increasingly constrain population expansion and enhance human capital formation.

Our estimated DSGE model with evolutionary feature can well match the long history of data. As shown below, the observed data (red lines) mostly lie in the 90% confidence intervals (grey bands) of the model predictions (black lines). The discrepancy between the model predictions and the collapse of first-time marriage age in the late 15th century may reflect problems with the baseline data (here a small sample of Inquisition Post-mortems, Russell, 1948) rather than shortcomings of the model. Similarly, with the childless rate which apparently shoots up in the 17th century and collapses in the 18th century.

The following graph simulates the time paths of log real wage if the elasticity of substitution (s) does not evolve. It shows that with an unchanging initial elasticity of substitution (of 0.5) earnings do not recover the 15th century peak until almost the end of the twentieth century. By contrast, with an unchanging unit elasticity of substitution, earnings rise far too strongly to match the data or our model predictions.

To tell a more detailed story of the driving forces of the evolving prices of child number and quality, we estimate the contributions of some well-documented auxiliary processes. The next figure shows that the simulated Crude Birth Rates (CBRs) under various ways of fixing auxiliary processes, does not decline substantially in the late 19th century. The conventional demographic transition story is that mortality falls and then births (CBR) fall with a lag. Had mortality remained at 1850 levels, along with the wage premium and schooling, crude birth rate would have risen. But on its own lower mortality did not contribute to the decline of CBR because the higher target family size offsets the smaller number of births necessary to achieve a target. The single factor contributing most to CBR decline was schooling/child labour. Mortality decline would have raised CBR substantially had it not been for the rise in opportunity cost of schooling (driven by technology), though the wage premium and female literacy also made a substantial contribution to the fall in the family target.

Please read our published paper in the Journal of Population Economics. Data, codes, and supplementary materials can be found in the "Download" tab of the working paper version.