
==== Front
SSM Popul Health
SSM Popul Health
SSM - Population Health
2352-8273
Elsevier

S2352-8273(24)00107-1
10.1016/j.ssmph.2024.101706
101706
Regular Article
The health legacy of coal mining: Analysis of mortality rates over time in England and Wales (1981–2019)
Shaikh Matthew matthew.shaikh@wu.ac.at

Vienna University of Economics and Business, Welthandelsplatz 1, 1020, Vienna, Austria
13 8 2024
9 2024
13 8 2024
27 10170631 10 2023
6 6 2024
8 8 2024
© 2024 The Author
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

– Coal mining areas in the UK continue to suffer worse health outcomes despite the industry disappearing by the early 1990s. Unemployment and deprivation are cited as key explanations. However, as the health effects of hazardous working environments continue after the industry's closure, it is unclear to what extent this ongoing health deficit is due to the legacy health effect of coal mining versus socioeconomic factors, including unemployment and deprivation.

Methods

– I isolate the legacy health effect of coal mining using a matching research design. Coal mining areas are paired with non-mining areas using propensity score matching. This creates a sample of socioeconomically similar local authority districts in England and Wales. I estimate the effect of coal mining on male and female age-standardised period mortality rates for 1981–2019, analysing temporal dynamics and testing for convergence.

Results

– I find an initial coal mining effect in 1981 on male (female) mortality rates of 122.6 (66.5) deaths per 100,000. This effect decreases by 91% (70%) during this period, indicating convergence in mortality rates. The timing of this convergence is consistent with that of the industry's closure, with higher convergence rates observed during the 1990s.

Conclusions

– These results provide evidence for a legacy health effect on mortality from coal mining and convergence in mortality rates between 1981 and 2019. This effect is important when explaining the health deficit experienced by coal mining areas. Furthermore, as coal mining areas tend to be more deprived, these results also shed light on relevant mechanisms driving recent health inequality in the UK.

Highlights

• I compare socioeconomically similar local areas using propensity score matching.

• Initially higher mortality rates are observed in coal mining areas.

• Convergence in mortality rates between coal mining and non-mining areas occurs.

• Legacy health effects are relevant in explaining coal mining areas' health deficit.

Keywords

Health inequality
Coal mining
Deindustrialization
Mortality
Matching
England
==== Body
pmc1 Introduction

Heavy industry and mining have long been associated with poor health. Deindustrialization in high-income countries has resulted in the closure of some of the most health damaging industries, including coal mining, resulting in a reduction of work related mortality and deaths from harmful air pollution (Carnell et al., 2019; Coggon et al., 2010). However, industrial areas continue to experience worse health outcomes than non-industrial areas (Audureau et al., 2013; Rind & Jones, 2015; Scheiring et al., 2023; Walsh et al., 2010). Following the closure of heavy industry and mining, industrial areas have been characterised by unemployment and deprivation, with corresponding negative health effects (Carstairs & Morris, 1989; Jin et al., 1995). Nevertheless, as the health impact of such industries will continue to be felt long after their disappearance, it is unclear to what extent the post closure health deficit experienced by industrial areas is a result of the health legacy of industry, and how much is a result of subsequent unemployment and deprivation. Separating these effects and assessing their impact is important to understanding the ongoing health deficit in industrial areas.

Coal mining areas in the UK are no exception. These areas continue to suffer from worse health outcomes in spite of the industry disappearing by the early 1990s. Life expectancy is around one year less than average and the percentage of the population reporting poor health is between 11% and 36% higher than average, with unemployment and deprivation cited as key explanations (Beatty et al., 2019; Foden et al., 2014; Shucksmith et al., 2010). Riva et al. (2011) assess self-rated health in 2004–6 controlling for individual and area level socioeconomic factors, finding a “coalfield effect” of unexplained worse health in coal mining areas. This continuing health gap (controlling for socioeconomic factors), and the apparent lack of convergence in health outcomes, is puzzling given the considerable elapsed time since the industry's closure. It is this unexpected health deficit that I investigate in this paper, with the research question: what is the health legacy of the coal mining industry and how does it change over time? To answer this question, it is necessary to identify the causal mechanisms affecting health in coal mining areas and assess their temporal dynamics. With 5.7 million people (9% of the total population) in Great Britain living in coal mining areas (Beatty et al., 2019), understanding the dynamics of this health difference is substantively important.

These causal mechanisms can be divided into two groups. Firstly, the health effects of the hazardous working environment in coal mines and their legacy. Coal mining is one of the most dangerous industries to work in. Miners are exposed to harmful coal dust, resulting in higher respiratory and lung disease mortality rates (Almberg et al., 2023; Miller & MacCalman, 2010) and face injury and fatality rates are up to two to three times higher compared to other occupations (Bennett & Passmore, 1984; Margolis, 2010; Toscano & Windau, 1993; Zimmerman, 1981). Such impacts have lasting effects on health and will continue to be felt after the industry's closure. Secondly, the health effects of subsequent unemployment and deprivation, as well as community breakdown and decrease in social capital. Coal mining was an important industry in Great Britain and geographically widely spread. It employed around 700,000 people before experiencing substantial decline in the 1960s (Ritchie, 2019) and was abruptly closed during the 1980s, constituting a “shock” to the local economy. The disappearance of mining jobs resulted in a lasting increase in unemployment and deprivation in affected areas (Beatty et al., 2019; Beatty & Fothergill, 1996; Foden et al., 2014; Shucksmith et al., 2010). This closure of the British coal mining industry also resulted in a loss of community and social capital which have been shown to affect health (Ehsan et al., 2019; Fagg et al., 2008; Strangleman, 2001). These two groups of mechanisms have different temporal dynamics. While the mines operate, workers will suffer the negative health effects of the hazardous working environment. Following closure, these effects will continue but gradually reduce over time, as ex-miners suffer no additional negative health impacts and new would-be miners are not exposed to such hazardous environments. However, the local population will suffer health consequences from the resulting unemployment, deprivation and social capital loss after the industry's closure.

In this paper I analyse the legacy health effect of the coal mining industry in England and Wales and investigate the temporal dynamics of this effect. To separate this industry legacy health effect from that of unemployment, deprivation and social capital, I use a matching research design. I create counterfactual local-areas pairs of coal mining and non-mining areas with similar socioeconomic conditions over time using propensity score matching. For this sample of matched local areas, I analyse the effect of coal mining on male and female age-standardised period mortality rates (ASMR) for 1981 to 2019. I assess, firstly, whether coal mining areas experience higher mortality rates during this period, and secondly, whether any convergence in mortality rates occurs between coal mining areas and their socioeconomically similar non-mining counterparts. In this way, I isolate the legacy health effect of industry from unemployment, deprivation and social capital effects.

This paper makes various contributions to the literature. To my knowledge this is the first study to use a matching causal inference research design to investigate the legacy health effect of the coal mining industry in the UK, separating this legacy effect from unemployment, deprivation and social capital effects. It analyses the temporal dynamics of this effect, testing for convergence in mortality rates over an almost forty-year period following the industry's closure. This adds to our knowledge of the different health mechanisms operating in coal mining areas and how they change over time. Furthermore, as these areas tend to be more deprived, this paper investigates one of the underlying mechanisms driving recent health inequality in the UK.

2 Methods

I define local areas as local authority districts in England and Wales (using 2021 boundaries). This gives 329 districts with an average population of 160,000 (excluding the City of London and Isles of Scilly due to their size and data availability). Taking the period 1981 to 2019, I obtain a longitudinal data set with 39 annual time periods and a total of 12,831 observations. All district boundary changes are taken into account and data converted to 2021 boundaries for analysis (see Appendix A.1).

2.1 Mortality in England and Wales

As a measure of health, I use sex-specific age-standardised period mortality rates (ASMR) for each year between 1981 and 2019. I obtain annual deaths from all causes by district and 5-year age band from the UK Office for National Statistics (ONS). I calculate age specific mortality rates and apply the European Standard Population 2013, to give male and female annual ASMR for all-cause mortality for each district 1981–2019.

2.2 Coal mining areas

To identify coal mining areas, I quantify the concentration of the industry prior to its decline during the 1960s. I use the recently digitalised 1961 Census and identify mining areas as districts where the mining employment rate (per 1000 people) in 1961 is in the upper quartile of the distribution. To differentiate between coal and other types of mining, I use detailed industry employment data from the Census of Employment business survey from 1981 (earliest year data is freely available), identifying relevant Standard Industry Classification codes for coal mining (see Appendix A.2.1). I identify coal mining areas as having mining employment rate in 1961 in the upper quartile and coal mining employment rate greater than other mining employment rate in 1981, thereby identifying areas where coal was the primary mining activity. I take this as my primary area identification method. I retrieve all data from the ONS nomis platform unless stated otherwise (https://www.nomisweb.co.uk/).

Previous studies use the 1981 Census in their identification of coal mining areas. This captures the distribution of the industry prior to the final phase out in the 1980s but does not consider the decline observed during the 1960s. In the literature, coalfield areas have been defined as continuous groups of wards (small geographical areas) where at least 10% of the resident males in employment were employed in the energy and water sector in 1981 (Beatty & Fothergill, 1996). Using this definition, the Coalfield Regeneration Trust (CRT) identifies 55 English coalfield districts (48 using current district boundaries) targeted for regeneration programmes that Riva et al. (2011) use as their identification of coalfields (Welsh districts are not included in the CRT definition). I use two alternative identification methods to align with previous studies and to test the robustness of my results. Firstly, I identify coal mining areas as having the percentage of employed males working in the energy and water sector (hereafter referred to as mining) in 1981 in the upper quartile of the distribution, and coal mining employment rate greater than other mining employment rate in 1981. I label this method “1981”. Secondly, I define coal mining areas as coalfield districts identified by the CRT.

These area identification methods give similar results (see Table 1 and Fig. 1). The primary and 1981 methods identify the same districts as coal mining areas, with the primary method identifying an additional three districts. The CRT method largely overlaps with these methods. However, it does not identify 13 coal mining districts in Wales (as the CRT's definition only covers England) and misses 4 in England. It also identifies 8 districts as coal mining areas whose primary mining activity is not coal or have employment below one of the upper quartile employment cut-offs. Key statistics are similar with mean mining employment well above population means.Table 1 Identification of coal mining areas.

Table 1Area identification	Country	Number of districts	Mining employment rate (per 1000) in 1961	% Employed males working in mining in 1981	
Mean	SD	Min	Max	Mean	SD	Min	Max	
Primary	Eng & Wal	57	62.04	41.31	8.95	195.71	13.49	9.39	3.23	46.05	
1981	Eng & Wal	54	64.79	40.70	8.95	195.71	14.05	9.34	3.73	46.05	
CRT Coalfields	England	48	59.63	44.34	3.44	195.71	13.38	10.17	2.01	46.05	


	
All districts	Eng & Wal	329	13.26	28.75	0.00	195.71	4.46	5.82	0.75	46.05	

Fig. 1 Identification of coal mining areas in England and Wales. Note: Solid lines indicate upper quartile boundaries used as cut-offs. Values of 0.01 are assigned to districts with mining employment rate in 1961 below 0.01 for scaling.

Fig. 1

The geographical distribution of the coal mining industry shows a central belt running from Yorkshire to the Midlands, along with areas in the North East, North West and Wales (see Appendix A.2.2). This geographical distribution is very similar to those found in literature (Beatty et al., 2019; Beatty & Fothergill, 1996; Riva et al., 2011).

2.3 Socioeconomic factors

Socioeconomic factors that affect mortality can be both contextual, where you live, and compositional, who you are (Bambra, 2016). I include both types of factors, specifically household income, deprivation, social cohesion, air quality, population density and past manufacturing employment. For income, I use average gross disposable household income (GDHI) for each district available from 1997 to 2020 (Office for National Statistics, 2023), imputing values prior to 1997 (see Appendix A.3).

As a measure of deprivation, I use the Carstairs and Morris (CM) index (Carstairs & Morris, 1989). This index is composed of four equally weighted standardised variables; proportion of the residents without access to a car, proportion living in overcrowded accommodation, male unemployment rate, and proportion of residents classified as low social class. I use Census data from 1981 to 2021, imputing values between census years. I use this index as it can be calculated for any geography and year where constituent variables are available. As alternative measures, I use normalised male unemployment rate and male employment rate using the same methodology.

To account for the community breakdown and social capital loss that occurred in coal mining areas following pit closures, I use Congdon's (1996) measure of social fragmentation, following Riva et al. (2011). This has been shown to affect health independently from economic and deprivation variables (Fagg et al., 2008). This index is similarly composed of four standardised variables; proportion of the population living alone, proportion of unmarried adults, proportion of residents in private rented accommodation, and proportion of residents moving into the district in the previous 12 months. I obtain all data from the Census 1981–2021 and impute values between census years.

To account for urban-rural mortality differences I include population density. I use population estimates for each year and district land area (land area data from the 2011 Census (Office for National Statistics, 2013)). To measure air quality, I use a proxy variable of carbon dioxide emissions which exhibit co-pollutant elasticities with various health damaging air pollutants (Zwickl et al., 2021). I obtain total carbon dioxide emission concentrations for each district 2005–2019 (Department for Business, Energy & Industrial Strategy, 2020), imputing missing values. Heavy industry and manufacturing workers tend to suffer from higher mortality and higher incidence of various health issues (Katikireddi et al., 2017; McDonald et al., 2005). To account for health effects from these industries, such as asbestos exposure in shipbuilding (Goswami et al., 2013), I include the past concentration of the manufacturing industry as a covariate control using the 1961 Census and calculating the “production” employment rate for each district. For models that use the alternative coal mining area identification methods I use the percentage of employed males working in manufacturing from the 1981 Census.

2.4 Descriptive statistics

Coal mining areas have different profiles from other areas (Table 2). These areas suffer from higher male and female mortality rates, both in the past (1981) and more recently (2019). This gap has narrowed for male mortality, showing some convergence in the mean area mortality rate. This is not the case for female mortality where the difference in mean area mortality rate is roughly the same in 1981 and 2019. The socioeconomic profiles of these two groups are also different. Coal mining areas are characterised by lower average income, higher deprivation and higher unemployment rates. These areas also tend to be more rural with corresponding lower carbon dioxide emissions.Table 2 Descriptive statistics.

Table 2Variable	Coal mining areas	All other areas	Difference (coal mining - other)	
Mean	SD	Mean	SD	Mean	p-value	
Number of districts	57		272				


	


	
Mining employment rate (per 1000) in 1961	62.04	41.31	3.04	6.53	59.00	<0.001	
% Employed males working in mining in 1981	13.49	9.39	2.57	1.44	10.92	<0.001	


	


	
Mortality rate (ASMR, deaths per 100,000)	


	
Female 1981	1409	113	1311	121	98.6	<0.001	
Female 2019	878	77	776	106	101.7	<0.001	
Decrease	531		534		−3.1		


	
Male 1981	2259	208	2083	199	176.2	<0.001	
Male 2019	1153	103	1030	135	122.8	<0.001	
Decrease	1106		1052		53.4		


	


	
Socioeconomic profile (1981–2019)	


	
Income (thousand GBP per person)	10.7	1.2	13.4	3.5	−2.6	<0.001	
CO2 emissions (kT per km2)	7.1	5.3	13.6	21.8	−6.4	<0.001	
Population density (people per ha)	7.8	6.7	16.7	23.0	−8.8	<0.001	
Manufacturing employment rate (per 1000) in 1961	195.3	55.6	187.4	70.6	7.9	0.36	
Male unemployment rate (%)	8.6	2.3	7.1	2.9	1.5	<0.001	
Male employment rate (%)	63.9	4.6	67.6	5.2	−3.8	<0.001	


	
Deprivation	%		%		% points		
% in lowest quintile	2%		24%		−22.1		
% in highest quintile	25%		19%		5.4		


	
Social fragmentation	%		%		% points		
% in lowest quintile	42%		15%		26.7		
% in highest quintile	4%		24%		−20.0		
							
Note: Coal mining areas are those identified using the primary area identification method. All other areas are districts not identified as coal mining areas. All socioeconomic variables are average values for 1981–2019.

2.5 Matching

The aim of this paper is to separate the legacy health effect of coal mining from unemployment, deprivation and social capital effects. Estimation via standard regression controlling for relevant covariates may be problematic, as the socioeconomic profiles of coal mining and other areas differ substantially (Table 2). There is an issue of common support with 64 (24%) other areas having incomes higher than the coal mining area with the highest income. This means that high-income districts, together with low-income districts, jointly set model parameters that estimate the effect of coal mining on mortality. Essentially, high-income districts in London and the South East are used as potential counterfactuals for coal mining areas in Yorkshire and the North East that on average have lower income and higher deprivation. Results will be model dependent and unbiased estimation may be problematic, particularly with the presence of control units far outside the covariate range of treated units (Ho et al., 2007; Sekhon, 2009).

I use matching to reduce model dependence and mitigate this common support issue. Matching aims to mimic a randomised experiment by creating treatment and control groups that are similar on observable characteristics, where treatment assignment is as-if-random. I use propensity score matching to reduce the covariate imbalance between coal mining and non-mining areas, matching socioeconomically similar areas to create a series of district pairs. The matching algorithm prioritises area similarity by choosing pairs with the minimum propensity score difference first, within a caliper. The treatment group is composed of coal mining areas identified using the methods detailed in section 2.2. For the control group, I restrict other areas to non-mining areas that have negligible mining employment (coal or otherwise). For the primary area identification method, I define non-mining areas as areas with the mining employment rate in 1961 below the median value. This definition ensures that non-mining areas have negligible mining employment while also giving a sizeable control pool of districts (see Appendix A.4.1).

For the alternative area identification methods, I follow a similar procedure. For the 1981 alternative I define non-mining areas as districts with percentage of employed males working in mining in 1981 below the median value and exclude districts with mining employment rates in 1961 in the upper quartile (removing areas with significant mining employment in 1961). For the CRT alternative, I include all districts not identified as CRT Coalfield districts as non-mining areas. This gives three sets of treatment and controls pools, where treatment is defined as the presence of the coal mining industry followed by its subsequent disappearance, and control is defined as never having coal mines in the area. From these pools, I obtain matched pairs of districts using the propensity score obtained via a logit model, regressing a coal mining area binary indicator on the socioeconomic covariates outlined above (income, carbon dioxide emissions, population density, deprivation, social fragmentation and manufacturing employment rate) using average values for 1981–2019.

This procedure results in 27 matched pairs of districts using the primary area identification method. Importantly, covariate balance is substantially improved and results in the selection of coal mining and non-mining areas with similar socioeconomic conditions over time (see Appendices A.4.2 and A.4.3). There is now a lack of districts in London and the surrounding area in the South East included (Fig. 2). The alternative area identification methods show similar matching results.Fig. 2 Map of matched districts.

Note: Coal mining areas are shown in blue, non-mining areas in red. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

2.6 Statistical methods

I use a feasible generalised least squares (FGLS) random effects estimator as the model contains both time-variant and invariant regressors. This estimator accounts for AR(1) type serial correlation in the errors (Wooldridge, 2012). Serial correlation results from the fact that coal mining areas are time invariant and that mortality rates are likely to be correlated over time due the cumulative nature of health impacts.

I regress sex-specific male and female mortality rates on a coal mining area binary indicator. I test for convergence in mortality rates by interacting this indicator with a linear time variable. General improvements in mortality rates over time common to all areas are accounted for by adding a time trend, including a quadratic term to account for non-linearities. These general improvements include, for example, improvements in cardiovascular mortality and in the National Health Service. This regression is given by equation (1).(1) ASMRit=α+β1CoalMiningi+β2CoalMiningi*t+θ1t+θ2t2+γjCovijt+εit

For district i, year t ϵ {0, 1, …, 38} (equivalent to 1981 to 2019), where ASMRit is the sex-specific age-standardised mortality rate, CoalMiningi is the coal mining area binary indicator and Covijt are the j socioeconomic covariates outlined above.

The quantities of interest in this estimation are β1 and β2. β1 indicates the effect of coal mining on mortality rates in 1981 (at t = 0), positive coefficients will show an initial effect of higher mortality in coal mining areas. β2 is the parameter for the interaction term between coal mining and time. A β2 coefficient of the opposite sign to that of the coal mining indicator (β1) will indicate a diminishing gap, i.e. convergence, in mortality rates over time. At any given time, the effect of coal mining on mortality and its duration will depend on a combination of these two parameters (β1 + β2*t).

3 Results

3.1 Estimation results

I display regression results in Table 3 using the primary area identification method and the two alternatives, running the models for samples with matched districts only (models 1–3 and 7–9) and comparing results to samples using all districts (models 4–6 and 10–12). I also plot the marginal effect coefficients and 95% confidence intervals for the coal mining effect in 1981 and 2019 (Fig. 3).Table 3 Regression estimation results (dependent variable: age-standardised mortality rate).

Table 3Dep var = ASMR (deaths per 100,00)	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	
Sex	Male	Male	Male	Male	Male	Male	Female	Female	Female	Female	Female	Female	
Sample	Matched	Matched	Matched	All districts	All districts	All districts	Matched	Matched	Matched	All districts	All districts	All districts	
Area identification method	Primary	1981	CRT	Primary	1981	CRT	Primary	1981	CRT	Primary	1981	CRT	
Coal mining area	122.6a	146.1a	103.7a	142.0a	140.0a	141.9a	66.49a	66.08a	50.61a	63.98a	62.62a	67.34a	
	(22.10)	(23.07)	(20.10)	(13.07)	(13.28)	(14.11)	(16.02)	(16.16)	(14.19)	(9.362)	(9.518)	(10.11)	
Coal mining area * t	−2.946a	−3.539a	−2.952a	−2.643a	−2.486a	−2.785a	−1.221a	−1.108a	−1.004a	−0.0830	−0.0184	−0.391b	
	(0.568)	(0.683)	(0.561)	(0.317)	(0.324)	(0.342)	(0.370)	(0.418)	(0.360)	(0.209)	(0.213)	(0.225)	


	
Mortality rate convergence (1981–2019)	
ASMR	111.9	134.5	112.2	100.4	94.5	105.8	46.4	42.1	38.2	3.2	0.7	14.9	
Percentage	91%	92%	108%	71%	67%	75%	70%	64%	75%	5%	1%	22%	


	
Observations	2106	1716	2496	12,831	12,831	11,973	2106	1716	2496	12,831	12,831	11,973	
Number of districts	54	44	64	329	329	307	54	44	64	329	329	307	
R2 Overall	0.888	0.892	0.888	0.884	0.884	0.883	0.790	0.826	0.810	0.811	0.813	0.811	
Note: All models include covariate controls and quadratic time trend terms as per equation (1). Models using the CRT area identification are run for English districts only.

Standard errors in parentheses.

**p < 0.05.

a p < 0.01.

b p < 0.1.

Fig. 3 Coal mining marginal effects 1981, 2019. Note: Coefficients and 95% confidence intervals displayed.

Fig. 3

For the matched sample models an initial mortality gap is observed, with higher mortality rates in coal mining areas, followed by convergence in mortality rates over time. Estimated coefficients for the coal mining effect are 122.6 (103.7 and 146.1 for alternative specifications) deaths per 100,000 for male mortality rates and 66.5 (50.6 and 66.1 for alternative specifications) for female mortality rates. All coefficients are statistically significant at the 5% level. For the matched sample models, the interaction term is negative and statistically significant for both male and female rates, indicating an additional decrease in mortality rates for coal mining areas over time. This combination indicates initially higher mortality rates in coal mining areas followed by a reduction of this mortality gap over time, i.e. convergence. Male mortality rates show elimination of 91% (92% and 108% for alternative specifications) of the mortality rate gap in 1981, indicating almost complete convergence for 1981 to 2019. Female mortality rates show partial convergence, with 70% (64% and 75% for alternative specifications) of the initial mortality rate difference eliminated, though the 95% confidence interval of the coal mining marginal effect in 2019 includes zero, meaning complete convergence cannot be ruled out.

Comparing estimation results for the matched versus all districts samples shows some variation in results. Coal mining areas continue to exhibit initially higher mortality rates. However, the amount of convergence decreases when including all districts. For male mortality rates the interaction term coefficient decreases in magnitude, indicating convergence but at a lower level (67–75%). For female mortality rates, this interaction term decreases in size and becomes statistically insignificant (at the 5% level), indicating a lack of convergence for models that include all districts.

3.2 Effect timing

These models estimate convergence in mortality rates as a linear function of time. However, the rate of convergence need not be constant over time. To allow for variation in the rate of convergence, I estimate a flexible model interacting the coal mining indicator with a series of year dummies, shown by equation (2). This estimates a coal mining effect parameter for each time period. I plot the three-year rolling average coal mining marginal effects for male and female mortality rate for the matched samples (Fig. 4).(2) ASMRit=α+β1CoalMiningi+∑t=138β2tCoalMiningi*Yeart+∑t=138θtYeart+γjCovijt+εit

Fig. 4 Coal mining marginal effects, flexible model.

Fig. 4

For male mortality rates, the coal mining effect decreases throughout this period. Initially the effect decreases slowly, indicating a low convergence rate, until the early 1990s. The effect subsequently decreases at a higher rate, indicating a higher rate of convergence, until the early 2000s, after which the curve flattens and there is little further convergence. This leaves a small coal mining effect remaining at the end of the period, of approximately 30 deaths per 1000.

The pattern for female mortality rates shows some similarities. However, the effect of coal mining initially increases, indicating divergence in mortality, peaking in the early 1990s. The effect then decreases at a slightly declining rate over time, indicating convergence at a rate decreasing over time.

3.3 Robustness tests

I further test the robustness of these results using several robustness tests (see Appendix A.5 for regression tables and marginal effects plots). Firstly, I vary the covariate values used in the propensity score matching model. I use 1981 and 2019 values for socioeconomic variables rather than average values. Results remain largely consistent, particularly for male mortality rates, though there is more variation in the coefficients for female mortality rates. Examining the socioeconomic profiles over time for matched coal mining versus non-mining areas, all three matching procedures result in samples with similar profiles albeit via different matching pairs (see Appendix A.5.3).

Secondly, I use male unemployment and employment rate variables as alternative measures to the CM deprivation index. While this index is a robust indicator of mortality, it has been shown to be less predictive of excess mortality over time (Hanlon et al., 2005). Unemployment rates have been shown to not entirely reflect the employment situation following the coal mine closures, with substantial increases in long-term sickness rates and retirement rates of working age males in coal mining areas, as former miners withdrew from the labour market (Beatty & Fothergill, 1996). I therefore use the male unemployment rate and male employment rate as alternatives to the CM deprivation index. Results remain consistent with those from the primary specifications, again with more variation for female mortality rates.

4 Discussion

These results provide evidence for a legacy health effect of coal mining on mortality, progressively reducing over time following the industry's closure. The initially higher mortality rate in coal mining areas is followed by convergence in mortality rates for 1981 to 2019 between coal mining areas and socioeconomically similar non-mining areas. The timing of this mortality rate convergence appears consistent with the timing of the industry's decline in the UK. Low convergence rate in male mortality rates is initially observed, with convergence likely partly due to coal mining employment reductions during the 1960s. Following the industry's closure, the 1990s sees an increase in the convergence rate before reducing from the early 2000s onwards. Female convergence rates follow a similar pattern, though they show an initial divergence in mortality rates until the early 1990s. The size of this effect and the level of convergence is substantial. The primary linear model shows an initial effect in 1981 for male (female) mortality rates of 122.6 (66.5) deaths per 100,000, equivalent to 5.8% (5.0%) of the mean area mortality rate in 1981. The reduction in the coal mining effect during this period is 111.9 (46.4) deaths per 100,000, equivalent to 10.5% (8.7%) of the overall reduction in mean area mortality rate.

These results also impact on our knowledge of contemporary health inequality in the UK and the underlying mechanisms. The literature finds significant health inequality between the most and least deprived areas (Carstairs & Morris, 1989; Kraftman et al., 2021; Norman et al., 2011). As coal mining areas tend to be more deprived, the legacy health effect of coal mining will be one of the mechanisms driving this inequality. Furthermore, health inequality has increased over recent years, with mortality rates increasing in the most deprived areas since 2012 (Walsh et al., 2020). This increase has occurred alongside a decrease in the legacy health effect of coal mining, affecting more deprived areas. As such health inequality driven by other factors (such as unemployment) may have increased by more than previously thought.

4.1 Strengths and limitations

This is the first study to my knowledge that employs a matching causal inference research design to isolate the legacy health effect of the coal mining industry in the UK and analyse its temporal dynamics. Using longitudinal data to estimate effect dynamics enables me to enrich previous findings with one of a progressive decrease in the coal mining effect on mortality. Through the use of matching, I estimate effects for coal mining and non-mining areas that are socioeconomically similar, greatly improving covariate balance and removing districts with very different socioeconomic characteristics. This differs from previous studies, which examine the effect of coal mining on health outcomes at particular points in time, either controlling for socioeconomic differences by adding covariates to regressions (Riva et al., 2011) or simply looking at differences in means (Beatty et al., 2019; Foden et al., 2014; Shucksmith et al., 2010). Matching reduces the model dependency of estimates, meaning results are less driven by particular modelling assumptions, making inferences more reliable. Thus, by restricting my sample to matched pairs of districts with similar socioeconomic profiles, I am better able to assess the impact of different causal mechanisms affecting health in coal mining areas.

While this research design aims to isolate coal mining legacy health effects from other mechanisms, remaining alternative explanations cannot be entirely ruled out and may contribute to explaining the observed coal mining effect. Other explanations must result from, and be correlated with, coal mine closures and not be sufficiently accounted for by the covariates included. Of course, the variables used (household income, deprivation and social fragmentation indices) may not fully capture the unemployment, deprivation and social capital loss that occurred following coal mine closures. If this is the case, then the estimated coal mining effect will contain the part of these factors not captured by the variables. A second possible explanation is local pollution effects of coal mines (Pless-Mulloli et al., 1998) disappearing with mine closures, that is not sufficiently captured through the proxy variable carbon dioxide emissions, for which I impute values prior to 2005. Thirdly, unemployment induced out-migration following coal mine closures, or subsequent in-migration due to reduced house prices and increased commuting, could provide an alternative explanation. However, the evidence on out-migration is mixed (Beatty & Fothergill, 1996; Green, 1994), with Hollywood (2002) finding a lack of migration among miners. Finally, it is possible that the observed convergence in mortality rates occurs due to improved NHS access and care improvements in coal mining areas. However, such improvements would have to be specific to coal mining areas, above and beyond improvements in more deprived areas.

Analysis at local authority district level is consistent with previous studies that assess health outcomes in coal mining areas (Riva et al., 2011; Shucksmith et al., 2010). Analysis at this spatial scale may bundle together coal mining and non-mining local areas in the same district, potentially weakening effects. However, identification at the district scale shows mining as an important sector in the local labour market. Coal mining areas have an average of almost 15% rising to over 45% of employed males working in coal mining in 1981. Using districts also minimises the impact of migration, as UK Census Flow data shows that most migration occurs within districts. Migration will thus become more problematic using smaller local areas, which may not effectively capture the industry's effect on health over such a long time period.

4.2 Further research

The theoretical case for higher female mortality in coal mining areas and subsequent convergence is less obvious than for male mortality. It was almost exclusively men that worked in the mines and suffered the direct health consequences from the hazardous working environment. There is some evidence for a female specific coal mining health effect, with wives and cohabitors of miners exhibiting a higher prevalence of respiratory and asbestos related health issues (Goswami et al., 2013; Higgins et al., 1959). The smaller yet similar trend in female mortality rates could also be explained by a common factor affecting both men and women, potentially in combination with sex specific mechanisms. Possible common factors that could affect both male and female mortality rates include the alternative explanations highlighted above, namely pollution, migration, improved NHS access and any residual effect of unemployment, deprivation and social capital loss not captured by the variables used. Future research could investigate female specific effects and the possibility of one of the aforementioned common factors driving the observed trends.

5 Conclusion

Results in this paper show initially higher male and female mortality rates in coal mining areas that converge with those of socioeconomically similar non-mining areas over time. This provides evidence for a legacy health effect of coal mining, with possible alternative explanations discussed. The coal mining effect in 1981 for male (female) mortality rates is 122.6 (66.5) deaths per 100,000. Male mortality rates almost fully converge, with 91% of the initial difference eliminated by 2019. Female mortality rates also converge but only partially, with 70% of the initial gap eliminated by 2019. Convergence timing follows the timing of the coal mining industry's closure. The rate of convergence is highest during the 1990s, immediately following the industry's closure, before reducing from the early 2000s as health gains are realised. Applying various robustness tests, effects are largely stable for male mortality rates, with more variation observed for female mortality rates.

The size of this coal mining effect on mortality and the amount of convergence is substantial. The effect for 1981 equates to 5.8% for males (5.0% for females) of the mean area mortality rate in 1981, and convergence is equivalent to 10.5% (8.7%) of the overall reduction in mean area mortality rate observed in this period. As such, this effect is important when explaining the health deficit experienced by coal mining areas. Furthermore, as coal mining areas tend to be more deprived, this paper sheds light on important health mechanisms underlying recent health inequality in the UK.

CRediT authorship contribution statement

Matthew Shaikh: Writing – review & editing, Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A.1. Local authority district boundaries

A small minority of districts change boundaries over time. All recent boundary changes have been to merge districts. These cases were identified (via district code changes) and data summed/weighted averages taken. This applies to all data except 1961 Census data which were mapped to 2021 district boundaries via small area (Output Area) lookups downloaded from the ONS Open Geography Portal.

A.2. Coal mining areas identification

A.2.1. Mining Standard Industry Classification codes (SICs)

Fig. 5 UK SIC 1980.

Fig. 5Note: Coal mining identified as 4d SIC codes 1113, 1114. Other mining defined as all other SIC codes under 2d SIC codes 11, 12, 13, 21, 23.

A.2.2. Coal mining areas geographical distribution

Fig. 6 Map of coal mining areas.

Fig. 6Note: Blue areas indicate coal mining areas via the primary area identification method.

A.3. Imputation strategy

A.3.1. Household income and carbon dioxide emissions

I impute missing values by regressing income/carbon dioxide emissions (CO2e) on a time variable for each district, running various models. I evaluate these models using the following criteria: (i) zero negative predicted out of sample values and minimal number of predicted values that do not follow the expected trend (increasing over time for income, decreasing for CO2e); (ii) sum of the in sample squared errors; (iii) examination of the development of mean and standard deviations, and its comparison to GDP per capita (Macrotrends, n.d.) and various harmful air pollutants (Department for Environment, Food and Rural Affairs (Defra), 2010) trends over this period. The final models used are log(income) = α + β1 time + β2 time2; and CO2e = α + β1 time.

A.3.2. Deprivation and social fragmentation

I assume a linear trend for constituent variables between census years for each standardised variable, re-standardising and summing variables for the imputed years.

A.4. Matching

A.4.1. Coal mining and non-mining area pools

Table 4 Coal mining areas definition for matching using primary identification method.

Table 4		Mining employment rate (per 1000) in 1961	
Area	N	Mean	SD	Min	Max	
Non-mining	164	0.52	0.40	0.00	1.47	
Coal mining	57	62.04	41.31	8.95	195.71	

A.4.2. Matching covariate balance

Fig. 7 Matching covariate balance.

Covariate balance pre and post matching between coal mining and non-mining areas improves substantially.

Fig. 7

A.4.3. Matching covariate development over time

Fig. 8 Matching covariate development over time.

Covariate values post matching (a) show similar development over time for coal mining (treatment) and non-mining (control) areas. This contrasts with pre matching covariate values (b) which show dissimilar development of covariate values.

Fig. 8Note: Mean values for coal mining areas are in blue, non-mining areas in red. Black lines give mean values (solid) and ± one standard deviation (dashed) for all districts in England and Wales.

A.5. Robustness tests

A.5.1. Estimation results

Table 5 Estimation results for robustness tests. Models (1) and (6) show model from main results.

Table 5Dep var = ASMR (deaths per 100,00)	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	
Sex	Male	Male	Male	Male	Male	Female	Female	Female	Female	Female	
Sample	Matched	Matched	Matched	Matched	Matched	Matched	Matched	Matched	Matched	Matched	
Model specification	Primary	Covs 1981	Covs 2019	Unemp rate	Emp rate	Primary	Covs 1981	Covs 2019	Unemp rate	Emp rate	
Coal mining area	122.6∗∗∗	114.7∗∗∗	127.1∗∗∗	128.5∗∗∗	121.0∗∗∗	66.49∗∗∗	56.71∗∗∗	51.75∗∗∗	69.52∗∗∗	71.49∗∗∗	
	(22.10)	(20.92)	(21.98)	(21.84)	(25.36)	(16.02)	(14.97)	(14.48)	(15.19)	(17.24)	
Coal mining area * t	−2.946∗∗∗	−2.593∗∗∗	−3.115∗∗∗	−3.460∗∗∗	−3.553∗∗∗	−1.221∗∗∗	−0.632∗	−0.786∗∗	−1.347∗∗∗	−1.804∗∗∗	
	(0.568)	(0.591)	(0.573)	(0.576)	(0.617)	(0.370)	(0.381)	(0.351)	(0.376)	(0.387)	


	
Mortality rate convergence (1981–2019)	
ASMR	111.9	98.5	118.4	131.5	135.0	46.4	24.0	29.9	51.2	68.6	
Percentage	91%	86%	93%	102%	112%	70%	42%	58%	74%	96%	


	
Observations	2106	1950	2028	2106	1872	2106	1950	2028	2106	1872	
Number of districts	54	50	52	54	48	54	50	52	54	48	
R2 Overall	0.888	0.891	0.888	0.886	0.875	0.790	0.814	0.818	0.799	0.791	
Note: All estimations include time trends and control variables.

Standard errors in parentheses.

∗∗∗ p < 0.01.

∗∗ p < 0.05.

∗ p < 0.1.

A.5.2. Coal mining marginal effects

Fig. 9 Coal mining marginal effects 1981, 2019.

Fig. 9Note: Point estimates and 95% confidence intervals displayed.

A.5.3. Matching covariate development over time

Fig. 10 Matching covariate development over time for different covariate values used in matching.

Fig. 10Note: Mean values for coal mining areas are in blue, non-mining areas in red. Black lines give mean values (solid) and ± one standard deviation (dashed) for all districts in England and Wales.

Data availability

Data will be made available on request.
==== Refs
References

Almberg K.S. Halldin C.N. Friedman L.S. Go L.H.T. Rose C.S. Hall N.B. Cohen R.A. Increased odds of mortality from non-malignant respiratory disease and lung cancer are highest among US coal miners born after 1939 Occupational and Environmental Medicine 80 3 2023 121 128 10.1136/oemed-2022-108539 36635098
Audureau E. Rican S. Coste J. From deindustrialization to individual health-related quality of life: Multilevel evidence of contextual predictors, mediators and modulators across French regions, 2003 Health & Place 22 2013 140 152 10.1016/j.healthplace.2013.03.004 23703375
Bambra C. Health divides: Where you live can kill you (illustrated edition) 2016 Policy Press
Beatty C. Fothergill S. Labour market adjustment in areas of chronic industrial decline: The case of the UK coalfields Regional Studies 30 7 1996 627 640 10.1080/00343409612331349928
Beatty C. Fothergill S. Gore A. The state of the coalfields 2019: Economic and social conditions in the former coalfields of England, Scotland and Wales 2019 Sheffield Hallam University shura.shu.ac.uk/25272/1/state-of-the-coalfields-2019.pdf
Bennett J.D. Passmore D.L. Correlates of coal mine accidents and injuries: A literature review Accident Analysis & Prevention 16 1 1984 37 45 10.1016/0001-4575(84)90004-6
Carnell E. Vieno M. Vardoulakis S. Beck R. Heaviside C. Tomlinson S. Dragosits U. Heal M.R. Reis S. Modelling public health improvements as a result of air pollution control policies in the UK over four decades—1970 to 2010 Environmental Research Letters 14 7 2019 074001 10.1088/1748-9326/ab1542
Carstairs V. Morris R. Deprivation: Explaining differences in mortality between scotland and England and Wales BMJ 299 6704 1989 886 889 10.1136/bmj.299.6704.886 2510878
Coggon D. Harris E.C. Brown T. Rice S. Palmer K.T. Work-related mortality in England and Wales, 1979–2000 Occupational and Environmental Medicine 67 12 2010 816 822 10.1136/oem.2009.052670 20573846
Congdon P. Suicide and parasuicide in London: A small-area study Urban Studies 33 1 1996 137 158 10.1080/00420989650012194
Department for Business, Energy & Industrial Strategy. (2020, June 25). UK local authority and regional carbon dioxide emissions national statistics: 2005 to 2018. GOV.UK. https://www.gov.uk/government/statistics/uk-local-authority-and-regional-carbon-dioxide-emissions-national-statistics-2005-to-2018.
Department for Environment, Food and Rural Affairs (Defra). (2010, April 8). Air Quality Library- Defra, UK (UK; United Kingdom). Department for Environment, Food and Rural Affairs (Defra), Nobel House, 17 Smith Square, London SW1P 3JR helpline@defra.gsi.gov.uk. https://uk-air.defra.gov.uk/library/reports?report_id=621.
Ehsan A. Klaas H.S. Bastianen A. Spini D. Social capital and health: A systematic review of systematic reviews SSM - Population Health 8 100425 2019 10.1016/j.ssmph.2019.100425
Fagg J. Curtis S. Stansfeld S.A. Cattell V. Tupuola A.-M. Arephin M. Area social fragmentation, social support for individuals and psychosocial health in young adults: Evidence from a national survey in England Social Science & Medicine 66 2 2008 242 254 10.1016/j.socscimed.2007.07.032 17988774
Foden M. Fothergill S. Gore T. The state of the coalfields: Economic and social conditions in the former mining communities of England, Scotland and Wales 2014 Sheffield Hallam University https://www.shu.ac.uk/centre-regional-economic-social-research/publications/the-state-of-the-coalfields-economic-and-social-conditions-in-the-former-mining-communities
Goswami E. Craven V. Dahlstrom D.L. Alexander D. Mowat F. Domestic asbestos exposure: A review of epidemiologic and exposure data International Journal of Environmental Research and Public Health 10 11 2013 11 10.3390/ijerph10115629
Green A.E. The role of migration in labour-market adjustment: The British experience in the 1980s Environment and Planning: Economy and Space 26 10 1994 1563 1577 10.1068/a261563
Hanlon P. Lawder R.S. Buchanan D. Redpath A. Walsh D. Wood R. Bain M. Brewster D.H. Chalmers J. Walsh D. Why is mortality higher in scotland than in England and Wales? Decreasing influence of socioeconomic deprivation between 1981 and 2001 supports the existence of a ‘scottish effect’ Journal of Public Health 27 2 2005 199 204 10.1093/pubmed/fdi002 15774571
Higgins I.T.T. Cochrane A.L. Gilson J.C. Wood C.H. Population studies of chronic respiratory disease: A comparison of miners, foundryworkers, and others in staveley, Derbyshire Occupational and Environmental Medicine 16 4 1959 255 268 10.1136/oem.16.4.255
Ho D.E. Imai K. King G. Stuart E.A. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference Political Analysis 15 3 2007 199 236 10.1093/pan/mpl013
Hollywood E. Mining, migration and immobility: Towards an understanding of the relationship between migration and occupation in the context of the UK mining industry International Journal of Population Geography 8 4 2002 297 314 10.1002/ijpg.264
Jin R.L. Shah C.P. Svoboda T.J. The impact of unemployment on health: A review of the evidence Canadian Medical Association Journal: Canadian Medical Association Journal 153 5 1995 529 540 7641151
Katikireddi S.V. Leyland A.H. McKee M. Ralston K. Stuckler D. Patterns of mortality by occupation in the UK, 1991–2011: A comparative analysis of linked census and mortality records The Lancet Public Health 2 11 2017 e501 e512 10.1016/S2468-2667(17)30193-7 29130073
Kraftman L. Hardelid P. Banks J. Age specific trends in mortality disparities by socio-economic deprivation in small geographical areas of England, 2002-2018: A retrospective registry study The Lancet Regional Health – Europe 7 2021 10.1016/j.lanepe.2021.100136
Macrotrends. (n.d.). U.K. GDP Per Capita 1960-2023. Retrieved 30 October 2023, from https://www.macrotrends.net/countries/GBR/united-kingdom/gdp-per-capita.
Margolis K.A. Underground coal mining injury: A look at how age and experience relate to days lost from work following an injury Safety Science 48 4 2010 417 421 10.1016/j.ssci.2009.12.015
McDonald J.C. Chen Y. Zekveld C. Cherry N.M. Incidence by occupation and industry of acute work related respiratory diseases in the UK, 1992–2001 Occupational and Environmental Medicine 62 12 2005 836 842 10.1136/oem.2004.019489 16299091
Miller B.G. MacCalman L. Cause-specific mortality in British coal workers and exposure to respirable dust and quartz Occupational and Environmental Medicine 67 4 2010 270 276 10.1136/oem.2009.046151 19819863
Norman P. Boyle P. Exeter D. Feng Z. Popham F. Rising premature mortality in the UK's persistently deprived areas: Only a Scottish phenomenon? Social Science & Medicine 73 11 2011 1575 1584 10.1016/j.socscimed.2011.09.034 22030211
Office for National Statistics 2011 census: Population estimates by five-year age bands, and household estimates, for local authorities in the United Kingdom—Office for national statistics https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/2011censuspopulationestimatesbyfiveyearagebandsandhouseholdestimatesforlocalauthoritiesintheunitedkingdom 2013
Office for National Statistics Regional gross disposable household income: Local authorities by ITL1 region—Office for National Statistics https://www.ons.gov.uk/economy/regionalaccounts/grossdisposablehouseholdincome/datasets/regionalgrossdisposablehouseholdincomelocalauthoritiesbyitl1region 2023
Pless-Mulloli T. Phillimore P. Moffatt S. Bhopal R. Foy C. Dunn C. Tate J. Lung cancer, proximity to industry, and poverty in northeast England Environmental Health Perspectives 106 4 1998 189 196 10.1289/ehp.98106189 9485483
Rind E. Jones A. “I used to be as fit as a linnet” – beliefs, attitudes, and environmental supportiveness for physical activity in former mining areas in the North-East of England Social Science & Medicine 126 2015 110 118 10.1016/j.socscimed.2014.12.002 25541186
Ritchie H. The death of UK coal in five charts. Our World in Data https://ourworldindata.org/death-uk-coal 2019
Riva M. Terashima M. Curtis S. Shucksmith J. Carlebach S. Coalfield health effects: Variation in health across former coalfield areas in England Health & Place 17 2 2011 588 597 10.1016/j.healthplace.2010.12.016 21277820
Scheiring G. Azarova A. Irdam D. Doniec K. McKee M. Stuckler D. King L. Deindustrialisation and the post-socialist mortality crisis Cambridge Journal of Economics 47 2 2023 341 372 10.1093/cje/beac072
Sekhon J.S. Opiates for the matches: Matching methods for causal inference Annual Review of Political Science 12 1 2009 487 508 10.1146/annurev.polisci.11.060606.135444
Shucksmith J. Carlebach S. Riva M. Curtis S. Hunter D.J. Blackman T. Hudson R. Health inequalities in ex-coalfield/industrial communities Improvement and Development Agency, London 2010
Strangleman T. Networks, place and identities in post-industrial mining communities International Journal of Urban and Regional Research 25 2 2001 253 267 10.1111/1468-2427.00310
Toscano G. Windau J. Fatal work injuries: Results from the 1992 national census Monthly Labor Review 116 10 1993 39 48 JSTOR
Walsh D. McCartney G. Minton J. Parkinson J. Shipton D. Whyte B. Changing mortality trends in countries and cities of the UK: A population-based trend analysis BMJ Open 10 11 2020 e038135 10.1136/bmjopen-2020-038135
Walsh D. Taulbut M. Hanlon P. The aftershock of deindustrialization—trends in mortality in Scotland and other parts of post-industrial Europe The European Journal of Public Health 20 1 2010 58 64 10.1093/eurpub/ckp063 19528189
Wooldridge J.M. Introductory econometrics: A modern approach 5th ed. 2012 Cengage Learning
Zimmerman W.F. Safety evaluation methodology for advanced coal extraction systems 1981 JPL-PUB 81–54 https://ntrs.nasa.gov/citations/19810021955
Zwickl K. Sturn S. Boyce J.K. Effects of carbon mitigation on Co-pollutants at industrial facilities in europe Energy Journal 42 1 2021 10.5547/01956574.42.5.kzwi
