Abstract
This paper constructs an original database on physical capital, labor, education, GDP, innovations, technology spillovers, and institutions to analyze the proximate determinants of British economic growth since 1270. Several approaches are taken in the paper to tackle endogeneity. We show that education has been the most important driver of income growth during the period 1270–2010, followed by knowledge stock and fixed capital, while institutions have not been robust determinants of growth. The contribution of education has been equally important before and after the first Industrial Revolution. Overall, the results give strong support to the predictions of Unified Growth Theories.
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Notes
Apprenticeships and on-the-job training are also important sources of human capital. Madsen (2016) also considers apprenticeships in his estimations; however, the lack of long continuous data makes it difficult to make firm conclusions from the estimates.
A potential concern in this specification is the assumption that the coefficient of capital stock is constant when it may actually have increased over time and, as a result, underestimates the contribution to income of capital stock and overestimates the contribution of the other variables. However, Clark’s (2010) estimates of capital’s income share give no indication of an upward trend in capital’s income share over time. Clark (2010, Table 13) finds capital’s income share to be 31.9% in 1270 and 27.2% in 1860.
Annual hours worked times number of full-time employed would, in theory, be a better measure of labor inputs than employment because it accounts for all dimensions of labor inputs. However, the data on annual hours are controversial and have raised lots of debate in the literature. De Vries (1994) advances the idea that the labor supply increased substantially in North-Western Europe during the Industrial Revolution and earlier; particularly due to more working days per year and increased child labor. Allen and Weisdorf (2011) argue that working days have increased substantially since 1270 and were heavily influenced by the removal of 49 holy days in 1536 associated with the Protestant Reformation. Clark and Van Der Werf (1998), on the other hand, argue that there is no clear evidence of an increase in working days between the middle ages and the nineteenth century. Allen and Weisdorf (2011) compute the working year by estimating how many urban and rural laborer work days are required to achieve a fixed basket of basic consumption. Thus, the days worked a year is essentially a constant times the inverse of real wages, and workers are, consequently, assumed to increase the demand for leisure while holding consumption constant in response to increased real wages; an assumption that is very strong indeed. Regressions using various measures of GDP per hour worked as the dependent variable give results that are consistent to the results obtained above (see, for regression results, Madsen 2016).
In the early nineteenth century and before, there is quite a lot of information about endowed primary and secondary schools but very little data available for the great many private-venture schools including classical grammar, non-classical schools with courses of education aimed at business and navigation and such, as well elementary schools, parish schools, dissenter’s schools and dame schools etc. Although no doubt numerous, these schools were often short lived and left very few, if any, records. A further problem is that when data is available on the number of students, it most often only includes the students studying for free and, thus, excludes the fee paying students who were often in the majority (where they are shown), though their numbers also vary wildly from none upwards.
Historically, reading was always taught first and learning to write was a separate skill that was taught after reading was mastered, if it was taught at all. Spufford (1979), using Cressy’s (1977) literacy statistics inferred from the ability to sign marriage certificates between 1580 and 1700, asserts that those who could sign were educated at a minimum of at least 2, and probably 3, years of education. Citing Cressy, Spufford notes the following ‘literacy’ statistics based on the ability to write: Females 11%, labourers 15%, husbandmen 21%, tradesmen 56% and yeomen 65%. However, Spufford (1979) suggests that these figures ignore a much larger percentage of mainly lower class people, who would only have attended school for 1 or 2 years, but who would have learnt to read in the sixteenth and seventeenth centuries, as well as females who were often only taught to read regardless of the length of their education. Spufford (1979) supports this assertion with evidence citing numerous examples of very poor, and working class readers, the enormous quantity, and the widespread distribution of chapbooks (small very cheap books aimed predominantly at the working classes), as well as almanacs and ballad sheets. Furthermore, Raymond (2003) suggests that as “the abililty to read print was more common than the ability to read manuscripts and the ability to write.....the many tradesmen, craftsmen and even artisans who lived in London and were able to read became the new patrons of the cheap print” (p. 47). Note, however, that chapbooks and almanacs were distributed in large numbers across the whole country, and there is no reason to believe that the target audience was any different outside of London.
Between 1250 and 1350 in Holkham, for example, there are records of a significant number of transactions of peasants and towns people, suggesting that even people from socially lower ranks were given incentives to learn to read and write (Britnell 2004). Furthermore, there was a substantial increase in manorial record keeping from the thirteenth century, recording such things as cash receipts, expenses, and inventories (Britnell 2004, p. 273).
As in almost all other chronologies of great innovations, Ochoa and Corey (1997) do not discuss the criteria used to select significant innovations. To investigate whether the results are sensitive to alternative classification systems, the stock of knowledge computed from the classification of great innovations of Hellemans and Bunch (1991), Gascoigne (1984), and Asimov (1982) were used as alternative measures of knowledge stock in the regressions reported in Madsen (2016). The principal results are unaffected by this consideration.
The j-group (post-1800 estimates) consists of the 18 OECD core OECD members (Canada, the US, Japan, Australia, New Zealand, Austria, Belgium, Denmark, Finland, France, Germany, Italy, the Netherlands, Norway, Spain, Sweden, Switzerland and the UK), while the 16 countries in the k-group (pre-1800 estimates) consist of India, the US, Denmark, Canada, Belgium, France, Germany, Poland, Russia, Portugal, Italy, West Indies, Turkey, and Argentina.
Per capita income at the time at which the working age population with a primary education, \(i \in (0-49)\), or cohort income, is estimated as follows:
$$\begin{aligned} ({Y/P})_t^P =\frac{\mathop \sum \nolimits _{i\,=\,0}^{49} \left[ {Pop_{15+i} \mathop \sum \nolimits _{j\,=\,4}^9 \left[ {\left( {\frac{Y}{Pop}} \right) \cdot GER^{P}} \right] _{t-i-j} } \right] }{50\left[ {\mathop \sum \nolimits _{i\,=\,0}^{49} Pop_{15+i} } \right] \cdot \left[ {\mathop \sum \nolimits _{j\,=\,4}^9 [{GER^{P}}]_{t-i-j} } \right] }, \end{aligned}$$(6)where \({(Y/Pop)}^{p}\) is per capita income for the working age population with a primary education (henceforth, cohort income), \({Pop}_{15+i}\) is the size of the population aged \(15+i\), and \({\textit{GER}}^{P}\) is gross enrollment rates at the primary level, estimated as student enrollment at a given year divided by the population of primary school enrollment age. The term, \(Pop_{n,15+i} \mathop \sum \nolimits _{j\,=\,4}^9 GER_{t-i-j}^P \), is the total primary educational attainment of the 15+i age cohort at time t, where \(\mathop \sum \nolimits _{j\,=\,4}^9 GER_{t-i-j}^P \) is primary school educational attainment of this age cohort. For the 64 year olds in 1570, for example, the primary educational attainment is the sum of GERs over the period 1512–1518. The term \(Pop_{15+i} \mathop \sum \nolimits _{j\,=\,4}^9 [ {( {\frac{Y}{Pop}} )\cdot GER^{P}} ]_{t-i-j} \), is the weighted sum of per capita income at the time at which the population of working age did its primary education, weighted by the fraction of each working age cohort that was enrolled in primary school at grade \(j - 3,\,j = 3-9\).
The equations for secondary and tertiary education are not shown for brevity, however, they follow the same principle as Eq. (6). The school ages are 6–11 for primary schooling, 12–14 for secondary schooling up to around 1902. School reforms in the end of the nineteenth century and the beginning of the twentieth century change the years of schooling at the primary, secondary and the tertiary levels from the 6–4–5 model to the 7–5–5 model. This is incorporated into the estimates of educational attainment with breaking point in 1902. Note that (Y/Pop) in Eq. (6) is used in the per capita income regressions, while Y is used instead of (Y/Pop) in the income regressions.
See, for example, Weber (1905); Becker et al. (1990); Aghion and Howitt (1992, 2009); Goodfriend and McDermott (1998); Galor and Weil (2000); Galor and Moav (2002, 2004); Tamura (2002); Cervellati and Sunde (2005, 2011); Clark (2005, 2007); Boucekkine et al. (2007); Baten and van Zanden (2008); Khan and Sokoloff (2008); Galor (2011); and Squicciarini and Voigtländer (2015).
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Helpful comments and suggestions from Olivier Accominotti, Yann Algan, Steven Broadberry, Markus Bruekner, Neil Cummins, David Fielding, John Gibson, Serguei Guriev, Pietro Peretto, Romain Ranciere, Max Schulze, seminar participants at London School of Economics, Singapore National University, Queensland University of Technology, Sciences-Po Paris, Paris School of Economics, University of Southern Denmark, University of Science Malaysia, participants at the Otago Development Workshop December 2013, The Australasia Development Economics Workshop, Perth (Australia) 2014, The Australasia Public Choice Conference in Singapore, December 2013, and, particularly, Oded Galor and seven referees, are gratefully acknowledged. Stoja Andric, Lisa Chan, Christian Stassen Eriksen, Nancy Kong, Thandi Ndhlela, Christian Rothmann, Ainura Tursunalieva, Cong Wang, Eric Yan, and especially Paula Madsen provided excellent research assistance. Jakob Madsen conducted a great deal of the work on this paper while he was a resident of IMéRA, University of Aix-Marseille in the second half of 2016 and is grateful for the hospitality. Jakob Madsen acknowledges financial support from the Australian Research Council (ARC Discovery Grant Nos. DP110101871 and DP150100061).
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Madsen, J.B., Murtin, F. British economic growth since 1270: the role of education. J Econ Growth 22, 229–272 (2017). https://doi.org/10.1007/s10887-017-9145-z
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DOI: https://doi.org/10.1007/s10887-017-9145-z