In the past 10, years, due to Milankovitch cyclicity changes in the Earth orbit around the sun the Earth has naturally warmed and should still do so for approximately another 40, years before we would expect to see these effects reversed and the Earth once again move towards another ice-age. However, since the industrial revolution, humans have expelled copious amounts of carbon dioxide into the atmosphere. During the following Interglacial period, the average global temperature slowly rose to In the absence of suitable sanitation and refuse collection, waste from domestic sources caused additional problems.
The impact on the health of urban populations from water-borne diseases like cholera and typhoid, from air pollution, and occupational exposure to hazardous materials was often devastating, and particularly affected working families housed close to the industrial sources.
The levels of pollutants that occurred regularly then would lead to prompt action now, at least in more prosperous countries and localities. Yet in spite of the harm to populations in the vicinity, the impacts of pollution generally remained localised, and by today's standards only a limited range of chemical compounds and materials was used by industry.
Often the simple expedient of dispersing the pollutants more widely, by using a high chimney, for example, seemed sufficient to solve the problem. Making the decision to study can be a big step, which is why you'll want a trusted University. Take a look at all Open University courses.
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Then browse over free courses on OpenLearn and sign up to our newsletter to hear about new free courses as they are released. Every year, thousands of students decide to study with The Open University. These data show that there was substantial variation across cities in the expected level of coal use per worker, even among similarly sized cities.
Sheffield, often cited as the prototypical polluted industrial city, emerges as the most intensive user of coal in the database, followed by other cities specialising in metals such as Birmingham and Wolverhampton. Textile manufacturing towns, such as Manchester and Leeds, show moderate levels, near the average. Commercial and trading cities, such as Liverpool and Bristol, as well as London, use industrial coal less intensively. Bath, a resort town, is the least polluted city in the database.
This section presents a spatial equilibrium model in the Rosen-Roback tradition, but modified in a few important ways in order to fit the empirical setting. The economy is made up of a fixed number of cities, indexed by c. These cities are small open economies that take goods prices as given.
As is standard in spatial equilibrium models, workers and firms can move freely across cities and goods are freely traded. I begin by modeling the demand for labour in cities. They play an important role in the model; by introducing decreasing returns at the city-industry level, they allow multiple cities to be active in an industry even when productivity varies across cities, trade is costless, and markets are perfectly competitive.
It is worth emphasising that the expression in 2 maps directly into the coal use values calculated using 1. The fact that those coal use values do a good job of reproducing observed coal use levels in see Online Appendix A. Put another way, if the model were a poor approximation of the world, then we would not expect coal use estimates based on the structure of the model to do a reasonable job of matching the observed data.
Furthermore, the results in Online Appendix A. In addition to workers, the model is also populated by capitalists who receive the rent from land and local resources.
For simplicity, I assume that capitalists live and spend their income outside the city. Next, I want to incorporate the impact of local industrial pollution into the model. Coal pollution can impact the city by affecting both workers and firms. Coal use can also affect the productivity of local firms. Given the outside option utility, the national coal price, a set of national industry output prices, technology levels, and city industry resources, equilibrium in a city is defined as the set of local wages, resource prices, housing rent and population, and a set of industry employment and coal use levels, such that firms maximise profits, the local markets for resources clear, the housing market clears in each city, and city labour supply equals city labour demand.
Equation 6 forms the basis for the main empirical specifications used in this article. The model suggests that both of these will negatively impact city-industry employment growth, though it is worth noting that the impact of city size may be positive if a city-size agglomeration force is included in the model.
These will be absorbed by year effects in the empirical analysis. On the bottom row of 6 , the first term reflects national industry-level demand or productivity shocks, the building blocks of the Bartik instrument. These can be absorbed by industry-time effects in the main analysis. The final two terms on the bottom row of 6 are the error terms. The structure of these terms makes it clear that I should allow for correlated errors across industries within the same location and time period in the empirical analysis.
The focus of the empirical analysis will be estimating the coefficient on the coal use and city-size terms in 6. As 6 shows, the impact of either coal use or congestion is determined by a combination of several model parameters.
In the empirical analysis, I will estimate a single coefficient reflecting how, together, these parameters govern the relationship between either congestion or coal use and city growth, but I will not be able to identify the component parameters individually.
For further discussion of this expression and its link to the coefficients estimated in the empirical analysis, see Online Appendix A. Online Appendix A. This section begins with an analysis of the impact of coal use on local employment growth, first at the level of city-industries and then at the city level. These are the central results of the article.
Following that, I present a simple counterfactual that can help us think about the implications of coal use for overall urbanisation levels. Finally, I provide some tentative evidence on the channels through which coal use may have affected city growth.
The x axis is the predicted change in city-level industrial coal use over the period, which is generated using the initial composition of city industries interacted with national industry growth rates and measures of industry coal use per worker. The trend line is based on a third-order polynomial. Identification in this estimation approach relies on assumptions that are standard in articles following Bartik , particularly those that rely on variation in industry characteristics such as Diamond The main threat to identification in this approach is that there could be some other industry feature that is both correlated with industry coal use intensity and affects local employment growth.
After presenting the main regression results, I present a variety of additional results including controls for the most likely channels through which the identification assumption might be violated. These additional checks allow me to strengthen identification beyond what is typical within the literature following Bartik An alternative to the reduced-form approach represented by equation 8 is to use the predicted coal use to instrument for the actual change in coal use.
In the main results I prefer the reduced-form approach because it is easier to work with and because the advantages of the IV approach are limited since the variable that one would ideally want to instrument for, the local pollution level, is unobserved. The specification in 8 includes an assumption that the impact of coal use is linear in logs.
There are two available pieces of evidence supporting this functional form. First, this functional form is consistent with the scatterplot shown in Figure 2.
Second, Beach and Hanlon provides evidence that the impact of coal use on mortality is linear in logs. To the extent that the mortality rate is a good indicator of the impact of coal use this suggests that the specification used here is reasonable.
Note that 8 abstracts from heterogeneous industry responses to changing levels of city pollution or city congestion forces—a feature suggested by the theory. While I begin the analysis by abstracting from heterogeneity in the response to coal use across industries, later I will also present results that explore these heterogeneous responses. In relation to the theory, the estimated b 1 coefficient from 8 will reflect the impact of changes in local industrial coal use on city-industry employment growth, which will depend on how coal use affects the city amenity level, how coal use affects firm productivity, as well as the extent to which industries can respond to these effects by shifting employment away from polluted locations.
Similarly, the b 2 coefficient should be interpreted as reflecting the impact of an increase in local employment holding fixed the level of local industrial coal use, i. This estimation approach abstracts from variation in industry coal use intensity across cities. This is driven in part by data constraints, since city-specific industry coal use intensities are not observed. However, even if city-level industry coal use intensity was observed, I would probably not want to incorporate this into the explanatory variable because, as suggested by the theory, this value will be endogenous and dependent on local wage levels.
Abstracting from spatial variation in industry coal use intensity avoids this endogeneity concern. Estimation is done using pooled cross-sections of data after taking differences , an approach that allows me to exploit as much of the available data as possible.
This is vital because the key variation in this study occurs at the city level and only 31 cities are observed in the data. We may be concerned about spatial and serial correlation in this setting. To deal with these potential issues, I allow correlated standard errors across industries within the same city, following Conley and across time within the same city-industry, as in Newey and West I begin the analysis, in Table 1 , by exploring results with differences taken over time periods ranging from one to three decades.
The table includes results for all industries, in Columns 1—3, and for a set of manufacturing industries only, in Columns 4—6. I provide separate results for manufacturing industries only because these produce more tradable products and so are a better fit for the model, and also because some of the control variables that I will introduce later are available for only this set of industries.
Standard errors, in parentheses, allow correlation across industries within a city in a period and serial correlation within a city industry across a number of decades equal to the lag length.
All regressions use data covering each decade from to The regressions for all industries include 26 private-sector industries spanning manufacturing, services, transport and utilities. The results for manufacturing industries are based on 15 industries. Table 1 reveals several important patterns. The most important result for this study is that the coal use variable always has a negative impact on city-industry employment growth.
This impact is clearer when we look over longer time differences, and becomes statistically significant for differences of two or three decades. Note that growth will be larger over a longer period, so we should expect to find larger coefficient estimates, given the same underlying effect, across longer time differences.
In particular, the same effect should generate a coefficient in Column 2 that is twice as large as in Column 1 and an effect in Column 3 that is 1. Given this, the estimated effect of coal use appears to be roughly constant as I extend the time period from two to three decades.
Over the one-decade differences I estimate a smaller effect, which suggests that this may not be a long enough window for city growth to fully reflect the impact of changes in pollution levels.
Table 1 also provides evidence of a negative short-run effect of employment growth in other city-industries that becomes positive over longer periods. This pattern is consistent with a city-size congestion force that weakens over time, together with positive city-size agglomeration benefits. This is reasonable if we think that there are some city features, such as infrastructure, that are difficult to adjust in the short run but can be expanded in the long run.
Finally, it is worth noting that the R 2 values increase as we move to longer differences. This suggests that city-industry employment growth may be subject to idiosyncratic short-run shocks, but that longer-run growth patterns are more closely tied to predictable influences.
Later, I will discuss in more detail the magnitude of the coal use effects documented in Table 1 , but before doing so it is useful to discuss some additional robustness results. Table 2 present the coefficient on the change in log coal use for a variety of robustness results full results are in Online Appendix A. In the top panel, Columns 1—2 present results with a variety of city-level controls these are listed in the table comments.
Of the available controls, I find that cities with higher levels of initial innovation based on patenting and better access to coal reserves grew more rapidly, while larger cities and those with more rain or colder temperatures grew more slowly. Columns 3—4 present results from regressions including city fixed effects. These results make it clear that the patterns that I document are not simply driven by a few slow-growing cities. Finally, Columns 7—8 present results including as a control log employment in each city-industry at the beginning of each period.
All regressions use data covering each decade from to and include the predicted change in city employment as well as industry-time effects. The additional controls in Columns 1—2 are the number of air frost days in each city, rainfall in each city, patents in the city from to , log city population at the beginning of each period, the log of city coal use at the beginning of each period, carboniferous rock deposits within 50 km and a seaport indicator. Columns 7—8 include controls for initial industry size.
The controls in Columns 9—15 are city-level controls based on industry features constructed using the same approach used for city coal use. The controls in Columns 16—20 are for changes in industries sharing buyer or supplier linkages to the observation industry IO in and IO out or using demographically or occupationally similar labour forces. The violence controls are based on city-level mortality due to violence or accidents.
In the middle panel of Table 2 , I present results including a set of controls based on industry characteristics, which are available only for manufacturing industries.
These controls directly address the main identification concern, i. The control variables that I have constructed are the share of high skilled salaried to lower skilled wage workers, average firm size, the share of output exported, the labour cost share, the female worker share and the youth worker share. These industry characteristics are used to construct city-level changes using the exact same approach that was used to construct changes in city coal use using the industry coal per worker data.
These variables are then included as controls in the regressions in Columns 9— Including these variables does not meaningfully affect my main results. In the bottom panel of Table 2 , I include controls based on connections between industries, through input-output channels or labour force similarity. Recent work by Ellison et al. The controls I use reflect, for each industry, the change in local employment in buyer industries, supplier industries, or industries employing workforces that are demographically or occupationally similar.
The results in Columns 18—20 show that including these controls does not alter the main results. Finally, in Columns 21—22, I add controls for the initial level or the change in the rate of violence and industrial accidents in each city based on mortality data.
This addresses concerns that workers in more coal-intensive industries could have brought other undesirable features, such as a propensity for crime, or that coal-using industries could have been more hazardous for workers. As a falsification test, Online Appendix Table A12 presents results looking at the relationship between city-industry employment growth in period t and lagged or leading changes in city coal use. These results suggest that city growth responds to predicted changes in coal use in a period, but not to predicted changes in coal use in previous or future periods.
This provides some confidence in the identification strategy and allows me to rule out substantial dynamic or longer-run effects not captured by my two-decade differences. Also, in Online Appendix A. I conduct two other exercises to assess the stability and statistical significance of the results.
First, in Online Appendix A. Comparing the estimated coal use coefficients from these placebo regressions to the coefficient obtained using the true data implies a confidence level of With the full set of city-level controls, the confidence level implied by the permutation test is Second, I re-run the results, dropping each of the cities in the data using the specification in Column 2 of Table 1. As an additional check, in Online Appendix Table A18 I estimate the impact of coal use separately for five main coal-using industries.
These results show similar estimated coal use impacts across the different industries. This check is important in helping address the concern, recently raised by Goldsmith-Pinkham et al.
Overall, these results consistently show a negative and statistically significant relationship between city coal use and city-industry employment growth, regardless of whether we are focused on all industries or just manufacturing industries.
To interpret these estimates, it is useful to know that the average increase in log predicted city coal use across all periods was 0. Given these results, we should expect a city with an increase in coal use that is one standard deviation above the mean to have a reduction in city-industry employment growth of 21—26 percentage points over two decades.
Average city-industry employment growth across all cities and periods was Thus, a one s. These results imply that rising coal use had a powerful effect on city employment growth. While the results described thus far estimate average effects of coal use across all industries, the theory suggests that these effects are likely to be heterogeneous.
When I run regressions that include the interaction of the coal use variable with industry labour cost share this is what I find. City-level results are presented in Table 3. Columns 1—2 present results obtained by aggregating the private-sector industries used in the main analysis to the city level. Columns 3—4 present results for the entire working population of the city.
These results show that rising city coal use was negatively related to city employment or population growth. As expected, this impact is strongest for private-sector workers and weakest when we include government workers and non-workers such as retirees or family members. This makes sense because these populations and job types are likely to be less mobile in response to variation in local amenities.
Standard errors allow serial correlation across two decades. The data cover 31 cities over each decade from to , with differences taken over year periods. The additional controls included are the number of air frost days in each city, rainfall in each city, patents in the city from to , log city population at the beginning of the period, and log city coal use at the beginning of the period.
The full results show that rainfall and initial city size are negatively related to city growth, while patenting and the initial level of coal use are positively associated with city growth. Was there scope for environmental regulations to reduce the negative externalities of coal use documented above? If so, what impact might these improvements have had on the British urban system?
In an attempt to answer these questions, this section provides a counterfactual analysis of the impact of improved coal use efficiency. Workers who came from the countryside to the cities had to adjust to a very different rhythm of existence, with little personal autonomy. They had to arrive when the factory whistle blew, or else face being locked out and losing their pay, and even being forced to pay fines. Stearns , a historian at George Mason University, explains.
Instead, workers often spent their leisure time at the neighborhood tavern, where alcohol provided an escape from the tedium of their lives. Without much in the way of safety regulation, factories of the Industrial Revolution could be horrifyingly hazardous. As Peter Capuano details in his book Changing Hands: Industry, Evolution and the Reconfiguration of the Victorian Body , workers faced the constant risk of losing a hand in the machinery. He eventually died as a result of the trauma.
Mines of the era, which supplied the coal needed to keep steam-powered machines running, had terrible accidents as well. David M. He was in such awful shape that he required opium to cope with the excruciating pain.
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