HIST363: Global Perspectives on Industrialization

In order to shed some additional light on the factors driving the spread of steam power technology in this section we estimate "adoption" equations for eighteenth century steam engines using a cross section of counties. We focus on the late eighteenth century and compare systematically the factors affecting the installation of Newcomen versus Watt engines.

Clearly, the aim is to check whether there were noteworthy differences in the factors driving the diffusion process of the two types of engines. Our dependent variable is the number of steam engines (Newcomen or Watt) erected in each county in the period 1775–1800. In both cases, we have a count variable that is skewed, with a non-negligible number of counties having no (i.e., zero) engines. Accordingly, we will make use of negative binomial regressions for estimating the two models.

The explanatory variables are:

(i) the price of coal prevailing in the county;

(ii) a dummy indicating the level of coal prices in a dichotomous way (i.e. low/high, with low being approximately less than 14 s.). This characterization of the price of coal variable permits us to use in the estimation of the regression equation all the counties (84) and not just the 41 for which coal prices are directly available. The dummy variable has been constructed considering the studies of the coal mining industry of Flinn, von Tunzelmann and Turnbull;

(iii) the number of water-wheels, which can be considered as a proxy for the demand for power (note that in some applications such as ironworks and textiles, steam engines were initially used the operation of water-wheels during drought periods);

(iv) the number of patents in steam technology taken by residents in the county over the period. This variable should capture, admittedly in a rough way, the depth of steam engineering skills existing in the county in question¹⁸;

(v) the number of blast furnaces in operation existing in the county c. 1800;

(vi) the number of cotton mills existing in the county c. 1800;

(vii) the number of wool mills existing in the county c. 1800;

(viii) for the counties with collieries, the output of coal (in 000s of tons) in 1800;

(ix) a dummy variable indicating the industrial counties: these are the counties identified by Wrigley as those where, in the second half of the eighteenth century, employment in manufacturing was growing fastest. By 1831, all these counties had a share of agricultural male employment of less than 20%.

(x) the population of the county in 1801 in (000s).

Admittedly, our set of explanatory variables is far from covering all the potential factors affecting the diffusion of steam technology in the period in question. Coal prices reflect the cost of a unit of power for the adopter of a steam engine. However the coefficient can also reflect the use of the steam engine in coal mines (as in coal mining areas coal was cheap). We try to control for this latter effect by including in the regression also the coal output mined in counties with collieries. Similarly, the number of water wheels is a proxy for the overall demand of power existing in the county but, at the same time, the variable may also capture some "substitution" or "complementarity" effects between steam and water power.¹⁹

The sectoral variables (number of blast furnaces, number of cotton mills, number of woollen mills, output of coal), are used as proxies for the size of different branches of economic activities in various counties and control for the different (steam) power requirements of application sectors. They provide a measure of the size of industries (ironworks, textiles and coal mining) that were among the most intensive users of steam power and are included in order to assess the influence of the production structure of the county on the patterns of engine adoption. Note that our coverage of application sectors cannot by any means considered as exhaustive. Lack of suitable data has prevented us from estimating for a sufficient number of counties the size of other sectors which were very intensive users of steam power, such as breweries and waterworks and canals. In order to address this issue, in some specifications we have included the dummy "industry" taken from Wrigley indicating the counties with the fastest growth in manufacturing employment at the end of the eighteenth century. The variable population is also introduced as an additional control for the different sizes of the counties.

The variable "patents in steam technology" is aimed at capturing the "depth" of steam engineering skills existing in the county in question. Of course this is a very imperfect proxy. As we have already mentioned, the high rates of diffusion for Watt engines estimated in Table 2 were plausibly not only determined by the superior fuel efficiency of the Watt engines, but also by the effectiveness of Boulton and Watt's organisation of steam engine production and marketing techniques. Since the very outset, Boulton and Watt wanted to establish themselves as a leading "national" producer of steam engines.²⁰ Instead, the construction of Newcomen engines was mainly undertaken by local manufactures with rather narrower and less ambitious business horizons.²¹  In this respect, Roll and Dickinson stressed the fundamental role played by Boulton's entrepreneurial and marketing abilities for the success of the partnership.²² Boulton's efforts ensured that Watt engines were quickly adopted in a wide range of industrial applications, which before had not made much use of steam power (breweries, textiles, etc.). For example, the erection of the famous Albion Mills in London is frequently pointed out as an example of a successful marketing strategy which succeeded in triggering the interest in steam power of many industrialists (in particular, breweries) in the London area.²³ Another initiative of Boulton and Watt aimed at broadening the use of steam technology was the publication of small technical booklets (of course only reserved for their customers) providing detailed descriptions of the procedures for erecting and operating their engines. In this way, "distant" customers could hopefully cope with minor technical difficulties without the assistance of Boulton and Watt's men.

Dependent variable is the number of Newcomen engines installed in the period 1775–1800. Standard error in brackets. *, **, *** indicate significance levels of 10, 5, and 1 percent

Furthermore, Boulton and Watt successfully established standard units of measure for both the fuel efficiency (duty) and the power (horsepower) of steam engines. Note that the establishment of a standardized unit of power was an event not only of technical, but especially of economic significance (perhaps one of the main determinants of the successful adoption of the engine in various manufacturing applications). The horsepower unit permitted industrialists to have a rather reliable assessment of their power requirements and it also permitted a rough, but rather effective, cost-benefit analysis of the adoption of various power sources. Rules of thumb soon came into common usage for expressing the power requirements of a number of industrial processes [e.g. in cotton spinning 1 horsepower was typically supposed to drive 100 spindles].

From these considerations it is clear that our econometric exercise can hope to provide just a partial appraisal of the determinants of the usage of steam technology in the late eighteenth century. Hence, the results ought to be regarded with care, taking into account not only the possible influence of factors not included in our set of explanatory variables, but also that the interpretation of the coefficients of the variables included in the econometric model is by no means straightforward.

Tables 3 and 4 report our estimates for the equations having as dependent variable the number of engines. We have estimated the coefficients considering two different forms of the negative binomial density function. In the first case we have assumed a density function with mean equal to $μ$ and variance equal to $μ(1 + δ)$. This case is termed "NB 1" by Cameron and Trivedi. In the second case we have assumed that the negative binomial density function has mean equal to $μ$ and variance equal to $μ(1 + αμ)$. Cameron and Trivedi refer to this model as "NB 2". It is possible to test for the actual existence of "overdispersion" (i.e., that the variance is larger than the mean) by verifying that $α$ or $δ$ are different from zero. In our case this was done by means of a likelihood ratio test that has confirmed the existence of overdispersion supporting our choice of negative binomial estimations.

Table 4 Adoption of Boulton & Watt engines 1775–1800: Negative Binomial Regressions

Dependent variable is the number of Watt engines installed in the period 1775–1800. Standard error in brackets. *, **, *** indicate significance levels of 10, 5, and 1 percent

Table 4 Adoption of Boulton & Watt Engines 1775–1800: Negative Binomial Regressions

Dependent variable is the number of Watt engines installed in the period 1775–1800. Standard error in brackets. *, **, *** indicate significance levels of 10, 5, and 1 percent

In this respect, one can note that the existence of overdispersion points to the fact that the data exhibit a higher degree of cross sectional heterogeneity (i.e. clustering in counties with "high" or "low" number of engines), than the case of a spatially homogeneous Poisson process. In other words, the existence of overdispersion reveals a pattern of spatial clustering among counties in terms of their extent of steam usage that goes beyond what can be accounted for by our set of explanatory variables. One could actually suggest that this cross-sectional heterogeneity can be seen as providing an indication of the existence of county-specific absorptive capabilities affecting the spread of steam technology. In general the NB 1 model should probably be the preferred specification in terms of goodness of fit (pseudo $R^2$), but it is worth noting the coefficients estimated using the NB 1 and NB 2 are consistent with each other.

The price of coal (whose inclusion restricts the sample to 41 counties) is negative and significant in the case of Newcomen engines, where is not significant in case of Watt engines. The marginal effect of the estimated coefficient in the NB 1 specification implies that an increase of the price of coal of 1 shilling would determine a reduction of 0.7 Newcomen engines installed in the county. The coefficient for the coal dummy is negative and significant both for Newcomen and Watt engines. As one could have expected, the negative size of the coefficient for Newcomen engine is larger than the one for Watt engines. In this case, the marginal effects of the estimated coefficients in the NB 1 specification imply that being a high coal price county determines a reduction of 6.2 Newcomen and of 3.6 Watt engines in comparison with a low coal price county with the same characteristics. Our findings, thus, confirm the role of coal prices as the critical variable affecting the choice between Newcomen and Watt engines in our cross-section of counties. Note that the variable coal output has also a positive and significant coefficient of roughly similar size both in Tables 3 and 4.

Curiously enough, in Table 3 the coefficient for number of wool mills variable is significant with a negative sign. This can be accounted for by the peculiarities of the transition to steam power mechanization in the wool textile industry (which was concentrated in Yorkshire (West Riding) and in the West of England). Overall the shift to steam in wool textiles was much slower than in cotton. Furthermore, in this industry, the diffusion of steam technology proceeded at two very different paces in the two areas. In West Riding, atmospheric returning engines were rapidly and rather successfully adopted for power carding and spinning machines (jennies). Table 2 indicates that about Newcomen 100 engines were installed in West Riding by 1800. Instead in the other wool regions of the West of England (Gloucester, Wiltshire) and of Scotland, steam power technology was introduced very slowly. The combined effect of these two contrasting patterns of adoption can help explain why the coefficient for wool mills appears significant with a negative sign in some specifications.

In Table 3 the variable "steam patents" has a positive and significant coefficient, showing the positive influence of the level of engineering skills for the adoption of engines.²⁴ The marginal effect of this coefficient in the NB 1 specification of column 3 implies that having one more patent in steam engineering would determine an increase of 0.4 Newcomen engines installed in the county. There does not seem, instead, to exist a systematic relation between the number of Watt engines and the number of steam patents (the variable is significant only in column 4 of Table 4). This is actually consistent with the fact that Watt engines throughout the eighteenth century (with the exception of "pirate" engines) were installed by only one company owning a proprietary technology.

Finally the coefficient of the variable water-wheels is positive and significant in the models estimated in columns 3 and 4 of Table 3, whereas the variable is not significant in Table 4. This result may probably be accounted for by the fact that Newcomen engines delivering rotary motion where often used to pump water for a wheel, whereas Watt engines were more frequently employed providing directly rotary motion.

In Table 4, the positive and significant coefficient for the variable blast furnaces in the equations for Watt engines is consistent with those historical accounts that have emphasized the rapid adoption of the engine in ironworks type of application. The marginal effect of the coefficient estimated using the NB 1 model in column 3 implies that having one more blast furnace would determine an increase of 0.35 Watt engines installed in the county.