The Importance of Access to Coal in the

Industrial Revolution

Kilian Heilmann

September 14, 2014

Abstract

I study the importance of access to coal by using historical population data asa proxy for economic development. I calculate the cost-distance to coal fields byincorporating geographical data on navigable rivers and turnpike roads. Regress-ing county-level percentage population growth on cost distance does not suggestany significant relationship between coal access and economic growth. Replacingpopulation growth with life expectancy as the dependent variable confirms the zerocorrelation with geographic features.

1 Introduction

There is little doubt that the Industrial Revolution ran on coal and data shows that

coal production increased by 1800% between 1700 and 1860 (Church, 1986), but the causal

effect of coal reserves on the vast increase of economic production during the 18th and 19th

century has been subject to discussion. Pomeranz (2000) argues that the availability of

coal near population centers was the key factor in explaining why the Industrial Revolution

happened in Britain and not for example in the coal-poor Netherlands which at that time

had similarly well-developed institutions as Britain. Church (1986) even claimed that

“[...] it is difficult to exaggerate the importance of coal to the British economy between

1830 and 1913.”1 Yet the importance of coal has been challenged by recent research.

Clark and Jacks (2007), using time series on coal prices at pithead, argue that Britain’s

access to coal reserves had a negligible effect and that demand for energy could have

easily been satisfied by traditional sources such as water, wind and firewood. This would

imply that coal was of minor importance for the onset of industrialization and that the

Industrial Revolution started in Britain for reasons other than geographical advantages

in coal resources.

Empirical work on this question has been hindered by the sheer unavailability of

economic data. Even if there exist estimates of economic production as in the case for

1Church (1986), page 758, as cited in Clark and Jacks (2007).

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Britain, the data is difficult to compare to the sparse records for other countries during

the period of interest. This paper tries to shed light on the question by using historical

population data for 19th century Britain and a consistent approach to measure proximity

to coal reserves. The main idea of the paper is the following notion: If the availability of

coal mattered on the big scale, it should have mattered on a small scale too. This means

that we should expect to see a positive relationship between distance to coal supplies and

economic output not only between countries, but also within countries, namely within

Britain. Comparing the economic performance of different cities and regions within a

single country further abstracts from other confounding country characteristics as all

British cities were subject to the same political and legal environment.

This project continues a research program that is concerned with geography’s effect

on economic outcomes. A variety of papers has argued for and against the importance

of geophysical features, with the effect being thought of either directly and indirectly

(through institutions) working on economic output. Crosby (1986) links the well-being

of many British colonies to their similarity with the British climate, allowing established

agricultural technologies to be easily implemented and thus spur growth. Mellinger et al

(2000) support this theory by showing that climate and coastal proximity are major deter-

minants of economic development. Redding and Venables (2002) emphasize the important

negative effect of geographical isolation, such as distance to the nearest coast. They ar-

gue that remoteness drastically increases trading costs which in return negatively affects

economic prosperity. McCord and Sachs (2013) estimate the importance of geographi-

cal features and natural resources and show that alongside institutions and technology,

geography can explain countries’ timing of achieving middle income status. Acemoglu

et al (2002) in contrast argue that geography influences the economy at most indirectly

through institutions. Easterly and Levine (2003) support this view, finding that the cli-

mate in tropical regions indirectly determines cross-country income through institutions,

but they cannot document a direct effect.

This paper aims to contribute to the literature on the relationship between economic

development and geography by addressing coal resources, a geographical advantage which

has not been studied before. Proxying economic development with county-level population

data for the 19th century and regressing these on a cost distance measure to the nearest

coal fields, the results show that there is no significant effect of closer coal resources on

population growth. This suggests that the geography of coal fields did not determine

population patterns in Industrial Revolution Britain.

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The paper proceeds as follows. Section 2 explains the data sources and data manip-

ulation. Section 3 develops the empirical framework and presents the regression results.

Section 4 concludes and discusses the findings.

2 Data

2.1 Data Sources

The variable of interest in this study is local economic growth during the Industrial

Revolution. However, since there is no GDP data available for this period, I follow

the usual practice in the economic history literature to proxy economic development with

population growth (e.g. Hanlon and Miscio, 2014). British population data in a consistent

fashion is available for the 19th century and drawn from the British censuses conducted

every ten years starting from 1801. These decennial censuses enumerated the population

of the entire United Kingdom and even though the implementation of the census varied

over time, the data is believed to be of rather high quality (Lawton, 1978). In the absence

of direct measurement, population data is believed to capture economic development well

in the absence of migration barriers. This is true for 19th century Britain, a period in

which there was high internal migration between cities with little government influence

on industrial policy (Long and Ferrie, 2003).

The unit of investigation is the population number at the level of an Ancient County.

The Ancient Counties (also known as Historic Counties) are ceremonial divisions that

were created in the based on earlier kingdoms. Even though they ceased to serve as

administrative counties in 1889, they were still used in the 1901 Census and that cen-

sus reports their population over the whole 19th century. The Ancient Counties have

the advantage that they experienced relatively little change in their boundaries. Even

though the Counties (Detached Parts) Act of 1844 eliminated many exclaves the county

boundaries featured, the area change was rather small.

To calculate a consistent measure of cost distance to the nearest coal fields, I use

historic geographical data drawn from several sources. Pope (1989) provides geographic

data for Industrial Revolution Britain. I am using the printed maps of navigable rivers

as of 1750, the turnpike road network as of 1750 and a map showing coal fields and coal

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Table 1: Table of Means

Variable Obs Mean Std. Dev. Min Max

Population Percentage Change 520 0.095 0.244 -2.321 2.406Cost Distance to Coal 520 1 0.938 0 4.584Cost Distance to London 520 1 0.576 0.064 2.433Initial Population in 1801 52 171,530 175,403 16,300 859,133

output from 1700-1830. Geographical data on the exact borders of the Ancient Counties

are downloaded from the Historic County Borders Project.2

2.2 Data Manipulation

Processing the raw geographical data to obtain the dataset used in the regression is

performed in ArcMap 10.1. At first, I georeference the scanned maps using the inbuilt

georeferencing tools to obtain polyline shapefiles for the river and turnpike road network

as well as a polygone shapefile indicating the coal fields of Britain. The maps do not

come with a description of which projection is used, but projecting the national boundary

shapefile to an equi-area conic projection and creating a large set of control points yields

a very close congruence between the scanned map and the Ancient County shapefile.

After having obtained the shapefiles, I convert them to rasters and assign cost values

to each mode of transportation. As a first guess, I normalize river transport to a cost

of 1 and assign ocean transport the cost of 2, transportation via turnpikes the cost of 5,

and all other pixels the value of 7. I then combine the rasters to a single cost raster that

contains the cost of crossing each pixel in the raster. Using the Cost Distance calculator

embedded in ArcGIS results in a raster file containing the cost distance to the nearest

coal field for each point in the United Kingdom (Figure 3).

In a next step I create centroids for each ancient county and link them with the cost

to reach the closest coal field from the cost distance raster using the Extract Values tool.

In the same fashion, I calculate the cost distance to reach the capital in London.

As a robustness check I create a different measure of access to coal taking into account

not only the distance to the nearest coal fields, but also the size of it. I first convert

the polygon features for coal fields, rivers and turnpike roads into points and then use

2Publicly available at http://www.county-borders.co.uk/.

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the Kernel Density tool to measure the “density” of those features for each location in

the UK. For coal, this measure takes into account all surrounding coal fields where closer

fields have a higher weight than those farther away. Figure 4 shows that the strongest

concentration of British coal reserves is in the Midwest area which receives a much higher

weight than the smaller and more isolated fields of North East and South West England.

3 Econometric Setup and Results

3.1 Population Change

I calculate county-level percentage changes in population between each census and

regress them on the coal distance measure for the county’s centroid. Given London’s

importance as an administrative and economic center of the United Kingdom, I control

for the distance to London to capture any effects of the closeness to the capital. Since

the distance measures can only be interpreted relatively, I normalize the unit to be equal

to one standard deviation. The regression equation is given by

∆ log (popit) = α + β1distance coali + β2distance londoni + εit. (1)

The results in Table 2 show that there is no significant relationship between either

distance variable and the percentage population change over the 19th century. The nega-

tive coefficients indicate that more remote regions experience less population growth, but

these two variables perform badly at explaining population patterns. Including decade

fixed effects does not change the results.

To account for changing importance of coal, I estimate the same coefficients for each

decade between the censuses and plot the results in Figure 2. It becomes evident that

the coefficient is imprecisely estimated for all periods except for the period preceding

the census of 1861 which seems to drive the results for the aggregate specification. In

conclusion, none of the estimates suggests a significant relationship between access to

coal fields and population growth on the county level. This is consistent with an earlier

finding that high skill workers left cities polluted by high industrial coal use (Hanlon,

2014).

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Figure 1: Geographical and Infrastructure Features

Map combining rivers, turnpike roads, coal fields, and county features (EquidistantConic Projection)

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Table 2: Regression Results

Dependent Variable: Percentage Change in Population(1) (2)

Cost Distance to Coal -0.0112 -0.0112(-1.05) (-1.06)

Cost Distance to London -0.0041 -0.0041(-0.38) (-0.39)

Constant 0.114∗∗∗ 0.137∗∗∗

(4.70) (3.46)

Time Fixed Effects no yes

N 520 520R2 0.0024 0.0452

t statistics in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

3.2 Alternative Specification: Life Expectancy

Instead of using population changes to proxy changes in economic development, I use

life expectancy. Previous research has shown that there is a significant positive relation-

ship between life expectancy and GDP per capita over time (Preston, 1974). Szreter

and Mooney (1998) provide estimates for the expectation of life at birth for 16 large and

medium-size cities in five ten-year intervals starting from 1851-1860 and reaching until

1891-1900. There is considerable variation in life expectancy both between cities and

within cities over time (Table 4). Szreter and Mooney also estimate total population of

these cities for each decade. City sizes range in the initial from just 51,000 for Eccleshall

to a stunning 2.5 million people living in London.

I regress percentage changes of life expectancy on the cost distance to coal fields. The

simple correlation between those two variables is essentially zero and estimated with low

precision (See Table 3, Panel A). Adding control variables (total population of the city

at the beginning of each decade) and decade fixed effects does not change the results.

With the coefficients very close to zero and high standard errors, none of the regression

setups suggests any significant effect of access to coal on longevity. The large jump in the

R2 measure after adding decade fixed effects suggests that simple time trends that are

common for all cities drive the changes in life expectancy. The estimates suffer from a low

number of observations and low variation in the cost distance variable. As can be seen

from Figure 4, most of the cities are located very close or even within coal fields, thus

creating a cost distance of zero. Furthermore, the cities are very close to each other; in

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Figure 2: Estimated Coefficients on Cost Distance to Coal

fact, what used to be separate entities back in the 18th century has grown to become one

urban area (e.g. Liverpool/West Derby, Manchester/Chorlton, Bristol/Clifton), further

increasing the problem of limited variation in the distance measure.

To mitigate at least the problem of zero cost distance, I rerun the regressions using the

alternative distance measure which provides more variation in the independent variables.

The distance measures created by the kernel density calculations result in different values

for the coal access variable depending on the density of coal fields close by. Controlling

for population size, access to roads, and navigable rivers, the weak and insignificant

relationship between coal access and longevity is confirmed. Again it seems that most of

the variation in life expectancy can be explained by decade-specific effects. This leads us

to conclude that the distance to coal did not have a significant effect on longevity for the

different English cities in the sample regardless of which distance measure is used.

4 Conclusion

Regressing the percentage change of population size for British counties during the

19th century on the constructed measures for access to coal did not suggest any significant

relationship between these two variables. Negative coefficients on distances show that

more remote counties experienced slightly lower population, but neither the least cost

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Table 3: Regression Results

Dependent Variable: Percentage Change in Life Expectancy at Birth

Panel 1 Panel 2

Variable VariableCost Distance -0.0000 -0.0000 -0.0000 Coal Kernel -0.0177 -0.0000

(-0.11) (-0.96) (-0.23) (-0.44) (-0.01)Population 0.0000 0.0000 Population 0.0000 -0.0099

(1.00) (0.18) (0.87) (-0.35)River Kernel 0.0000 0.0000

(-0.89) (-0.06)Turnpike Kernel 0.0000 0.0000

(0.76) (0.92)Decade FE no no yes Decade FE no yesObservations 64 64 64 Observations 64 64R2 0.0002 0.0162 0.5298 R2 0.0220 0.5389

Coefficient estimates of the regressions with t-statistics given in parentheses.R2 refers to unadjusted R2 measure.

distance to coal fields nor proximity to the capital in London can reasonably explain

different population patterns. The same result is true if the dependent variable is replaced

by life expectancy data, another proxy for economic development, where the variation in

longevity appears to be largely being explained by common time trends.

These results can be interpreted such that closeness to coal was not a necessary con-

dition for economic growth during the Industrial Revolution. This is not to say that

geographical features were not important, but that inner-British variation in coal endow-

ment played little to no role for county growth patterns. Areas further away from coal and

government centers grew at similar rates as those near coal fields and there are several

potential reasons that are consistent with this finding.

Firstly, these counties might have experienced economic growth in industries that were

not dependent on coal, confirming the notion that highly skilled workers fled the negative

health effects of the coal cities. Secondly, these counties might still have depended on coal-

intensive industries, but that the British transportation system at the time was efficient

enough to distribute coal from its sources to the production centers. This seems reasonable

given that proximity to the coast and the large amount of navigable rivers for most of

the country enabled coal to be shipped on a large scale by water as compared to costly

overland transport.

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5 References

Acemoglu, Daron, Simon Johnson, and James A. Robinson (2002): Reversal of For-

tune. Geography and Institutions in the Making of the Modern World Income Distribu-

tion, The Quarterly Journal of Economics 117 (4), p. 1231-1294.

Church, Roy (1986): The History of the British Coal Industry vol.3, 1830-1913, Ox-

ford: Clarendon Press.

Clark, Gregory and David Jacks (2007): Coal and the Industrial Revolution 1700-1869,

European Review of Economic History 11 (1), p. 39-72.

Crosby, Alfred W. (1986): Ecological Imperialism. The Biological Expansion of Eu-

rope, 900-1900, Cambridge University Press.

Easterly, William and Ross Levine (2003): Tropics, Germs, and Crops. The Role of

Endowments in Economic Development, Journal of Monetary Economics, 50 (1), p. 3-39.

Hanlon, W. Walker and Antonio Miscio (2014): Agglomeration. A Dynamic Approach,

Working Paper.

Hanlon, W. Walker (2014): The Impact of Industrial Pollution on City Growth.

Lessons from the Dark Satanic Mills, Working Paper.

Lawton, Richard (1978): The Census and Social Structure. An interpretative Guide

to 19th Century Censuses for England and Wales, London: Routledge.

Long, Jason and Joseph Ferrie (2003): Labour Mobility, Oxford Encyclopedia of Eco-

nomic History, New York: Oxford University Press.

McCord, Gordon G. and Jeffrey D. Sachs (2013): Development, Structure, and Trans-

formation: Some Evidence on Comparative Economic Growth, NBER Working Paper

19512.

Mellinger, Andrew, Jeffrey D. Sachs, and John Luke Gallup (2000): Climate, Coastal

Proximity, and Development, in Clark, Gordon L., Maryann P. Feldman, and Meric S.

Gertler, eds.: The Oxford Handbook of Economic Geography, p. 169-194, 2000.

Pomeranz, Kenneth (2000): The Great Divergence: China, Europe and the Making

of the Modern World Economy, Princeton: Princeton University Press.

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Pope, Rex (1989): Atlas of British Social and Economic History Since c.1700, London:

Routledge.

Preston, Samuel H. (1975): The Changing Relation between Mortality and Level of

Economic Development, Population Studies 29 (2), p. 231-248.

Redding, Stephen and Anthony J. Venables (2002): The Economics of Isolation and

Distance, Nordic Journal of Political Economy 28, p. 93-108.

Szreter, Simon and Graham Mooney (1998): Urbanization, Mortality, and the Stan-

dard of Living Debate. New Estimates of the Expectation of Life at Birth in Nineteenth-

Century British Cities, The Economic History Review 51 (1), p. 84-112.

11

Appendix

Table 4: Mortality and Population Data

Life Expectancy at Birth Total Population (in thousands)City 1851-60 61-70 71-80 81-90 91-1900 1851-60 61-70 71-80 81-90 91-1900

Bristol 35 36 37 39 43 66 64 60 57 106Clifton 42 42 45 48 50 86 111 147 180 167Sheffield 34 33 35 38 39 116 146 173 194 216Eccleshall 40 40 42 43 46 51 75 101 126 157Newcastle 34 34 37 40 42 101 121 141 174 214Gateshead 37 39 39 42 44 54 70 93 118 150Leeds 34 34 37 39 41 109 128 177 207 237Hunslet 38 36 39 42 40 99 118 124 150 179Bradford 37 36 38 42 44 189 227 285 327 349Birmingham 35 35 37 39 38 193 222 239 246 245Aston 42 42 43 46 45 84 124 178 234 283Manchester 30 29 32 35 36 236 247 255 282 310Chorlton 37 36 38 41 42 147 190 235 279 319Liverpool 27 25 28 29 30 264 254 224 184 153West Derby 38 35 39 40 41 190 284 410 525 615London 38 38 40 43 44 2,583 3,029 3,535 4,014 4,389

Data Source: Szreter and Mooney (1998)

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Figure 3: Cost Distance to Coal Reserves

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Figure 4: Density Plots

Distribution of the density of turnpike road network, the navigable rivers network, andcoalfields as measured by the kernel density tool. More intense colors indicate a higherconcentration in the surrounding area.

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