This paper analyzes how the very different patterns of growth across the 41 counties of England [data for Scotland and Wales are sparse, for early wages in particular] between the 1760s and 1830s can be explained by a wide range of potential variables.

We find that industrialization occurred in areas that began with low wages but high mechanical skills, whereas other variables, such as literacy, banks, and proximity to coal, have little explanatory power. Against the view that living standards were stagnant during the Industrial Revolution, we find that real wages rose sharply in the industrializing north and declined in the previously prosperous south.

Abstract
Skills and the Industrial Revolution
Regional Integration and the Accumulation of Skill
The Great Reversal and the Living-Standards Puzzle: England 1760^–₇₀1830_194ya
Regression Result: Textiles
Regression Result: Metallurgy
Regression Result: Traditional Industry
Conclusion.
Appendix A: Spatial Standard Errors and Semiparametric Regressions
Appendix B: Transportation
Appendix C: Data Sources and Construction
References

…For many contemporaries, by contrast, Britain’s success was rooted in its uniquely deep and diverse pool of artisans, in metalworking especially, whose skills could be readily adapted to developing the new machinery and manufacturing processes that began to appear in the mid-eighteenth century… The technology of the late 18^th century is often dismissed as having been fairly rudimentary (which raises the question of why it was not invented a good deal earlier). In fact, the two iconic machines of early industrialization—Arkwright’s spinning frame, with its intricately meshing train of gears, spindles, and rollers, and Watt’s steam engine, with its precisely bored cylinder and complicated valves—were unusually complex technologies by the standards of the time, and each relied on co-opting local artisanal skill for its success.

Our approach is to focus on a simple process where the accumulation of artisan skill drives technological progress, in a way that mirrors the historical pattern of early industrialization. Specifically, as transport networks began to improve and English markets integrated from the late 17^th century onward, regions specialized according to their comparative advantage. This meant that areas of poor agricultural potential (reflected in their low wages) increasingly specialized in manufacturing activities. Naturally, many of these proto-industrial activities, such as making nails or low-quality textiles, required only rudimentary skills and offered no possibilities of technological advances.

However, a few forms of manufacturing—especially in exacting forms of metalwork such as watchmaking, iron founding, and toolmaking—created pools of skilled and versatile workers, artisans whose skills could be readily be adapted and transferred to the increasingly sophisticated machinery and manufacturing processes of the early Industrial Revolution. This simple framework leads to the specific empirical prediction that successful industrialization relied on existing concentrations of suitable skills and that these concentrations were to be found primarily in low-wage areas already specializing in technologically demanding production, in metalworking especially.

…Turning to England, we show below that it was the low-wage north that mechanized, whereas the high-wage regions in the south that had dominated the textile industry for centuries lacked the technical skills to adopt the new machinery and slid into terminal decline. As a result, living standards rose substantially in the north, overtaking the previously prosperous south, where real wages fell markedly. The widespread notion that average national living standards were static during the Industrial Revolution (Feinstein1998) is simply a statistical artifact of aggregating two regions that were moving in sharply opposite directions…Strikingly, the fact that the early textile industry made little use of coal made it highly unusual among British industries of the time.

…In this paper, we introduce a simple way to circumvent these [autocorrelation] difficulties by employing a semiparametric regression that incorporates a spatial smoothing term. Semiparametric regressions go back to Engle et al 1986_38ya, who added to linear regressions a smoothing spline that optimally adapted itself to fit time trends of unknown functional form. Despite their elegance, simplicity, and power, semiparametric regressions never took off in economics. However, they have continued to be actively developed in statistics, under the name “generalized additive models” (Wood2017), and have become popular in machine learning as estimators that are often almost as powerful as black-box techniques but whose results are immediately interpretable (James et al 2021, 289–310). Where Engle et al 1986 added a one-dimensional spline in time, we add a two-dimensional one in longitude and latitude. This allows us to separate out the spatial structure of the regression as a nuisance variable and then carry out standard inference on the parameters that interest us.