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This paper examines the impact of commuting and mass transport on the London labour market in the 1930s using data from the New Survey of London Life and Labour. It analyzes the distance individuals commuted, their transport costs, access to public transport, and the returns and time spent on commuting. The study aims to understand how improvements in transport infrastructure have transformed working class lives and the functioning of labour markets.
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The impact of commuting and mass transport on the London labour market: evidence from the New Survey of London Life and Labour Andrew Seltzer (Royal Holloway, LSE, IZA, CEH , ICS) Jonathan Wadsworth (Royal Holloway, CEP, IZA, LOWER) Jessica Bean (Denison University)
Commuting, earnings, and city structure • Distinguishing characteristics of urban areas include economies of scale and economies of agglomeration • Larger factories have lower unit costs • Complementarities between skilled workers (more productive in the presence of other skilled workers) • As a result urban jobs tend to be packed together very densely • In the absence of transport infrastructure workers need to live close to work – this has huge costs due to crowding externalities • Concentration of poverty in Victorian slums – Booth Report (1890) • Workers tended to live very near their work • Public transport was expensive, slow, had rudimentary networks, and did not serve working class areas • Commuting infrastructure was fairly “local” – it was difficult to commute across London • As a first approximation, much (most?) scholarship of 19th century British Economic History follows from these two facts • Growth of income & employment concentration due to the industrial revolution (Crafts 1997, Williamson 1981) • Urban disamenities during the industrial revolution associated with crowding (Humphries 2010, Komlos 1998)
Did the ability to commute after about 1900-1910 transformed working class lives? • Good transport infrastructure means that workers can live in the suburbs and commute in to work • 20th century improvements to London’s public transport • Most of the modern Underground network built between 1900 and 1910 • Trains, bus, and tram networks “filled in” after 1900 • Dramatic increases in speed and frequency and declines in cost per mile across all forms of transport • Emigration to the outer boroughs and declining urban population density • Inner London boroughs maximize their population in the 1900 Census, steady decline thereafter • Disamentiesof crowding decline (e.g. health improves) • New industries from the 1920s typically located outside the urban centre • Rise of “reverse commuting” • Improved transport infrastructure is likely to improve the functioning of labour markets • Fast, lower cost commuting allows workers to search over wider areas • Better matches between workers and firms – implies higher productivity • Increasing the number of potential employers breaks local monopsony power • “Isolated” employers do not have to pay higher wages as an offset to commuting costs
Changes in availability, speed and cost of public transport, 1900-29
What we do in this paper • Use data from the New Survey of London Life and Labour to examine working class commuting in the 1930s • We can observe the distance individuals commuted and their transport cost (imperfectly measured) • We also observe access to public transport • Good data on the London Underground, trains, and trams • Distance to the nearest station or stop • Number of stations or stops that are “near” each home and residence • Data on access to buses is much more difficult to obtain • The routes and stops change over time • Route-master buses are hop on/hop off – anywhere on route is potentially a stop • But – access to buses is near universal by 1930 • Implies little variation in distances to buses across individuals • We examine “who” commutes and how far • We examine the returns to commuting • We estimate the time spent commuting
Data: The New Survey of London Life and Labour • Follow-up to the Booth Survey – focus on poverty • Survey of working class households (max income £250) in 1928-32 (mostly 1929 and 1930) • Survey covered 30 Metropolitan Boroughs, 8 Municipal Boroughs • Approximately equal to Zones 1-3 on the Underground • Record cards of residents of municipal boroughs of Tottenham & Walthamstow have been lost • The adjacent boroughs of Leyton and Hornsey comprise slightly over 3 percent of the NSLLL sample • Residents of City of London were not surveyed • Not very many working class households lived in the City, so little sampling bias • Individual level micro-data for 26,915 households and 94,136 individuals, about 2 percent of London’s working class population • Background information about all individuals (place of birth, place of residence, age, sex, relationship to head of household) • Employment information for 49,445 income earners (wages earned last week, hours last week, normal wages and hours, employer, occupation, skill level, place of work, transport expenditures) • There is also information about housing (rent paid, number of bedrooms, whether there is a kitchen, etc.) • This data was originally coded as it appears on the record cards by Roy Bailey, Dudley Baines, Tim Hatton, Paul Johnson, and Anna Leith
Information about Commuting in the NSLLL • Only direct information is expenditures on transportation in the previous week. But…. • Lots of missing observations, costs often not easily monetisable (e.g. “bicycle” or “employer pays” or “varies”) • Potential measurement error due to possible inclusion of non-work transport, transport costs paid by employer, etc. • May not be very serious, expenditures are almost always divisible by 6 • Respondent may not have been the commuter – measurement error & missing information • Conceptual issue that this only includes monetary costs not time costs • Another conceptual issue is that this doesn’t say much about the nature of commuting
Information about Commuting in the NSLLL • Alternative is to measure the distance commuted • NSLLL contains home address and workplace location for most workers • This has been recorded in the BBHJL data • We have added GIS information for residence and workplace • Home addresses are exact & recorded by enumerators • Use a single centroid for each street • Location of common-name streets based on Borough and employment information • We have been able to locate 99%+ streets • We feel that home GIS are VERY accurate • Small amount of measurement error because we only know the street, not address on the street • Most residences are on short streets, so the measurement error is small • Work addresses are reported by respondents in response to the enumerators’ questions • Usually one respondent per household – may not be the worker • Transcription errors by enumerators (“Green Street” or “Queen Street”) • Often reported as a place name rather than a specific street (e.g. “Bethnal Green”, about 50% of answers are place names) • Use a single centroid for each place or street (unless a specific address is given) • Much less accurate than home GIS • Longer streets, places are inherently bigger than streets, more places that we can’t locate • This is mostly measurement error for our analysis • Biases will tend to be in the direction of overestimating commuting distances
Distances, continued • We add in the latitudes and longitudes of every underground and train station and tram stop • Use Great Circle Distance to calculate distance from home to a) work, b) closest underground station, c) closest rail station, d) closest tram stop, e) centre of London (Charing Cross Station or Bank of England) • We also divide London into grids of about 500 meters squared and look at the number of train/underground/tram stations or stops in the home and workplace grid.
Appendix: GIS coding of street addresses • This is very time consuming. I don’t recommend it to anyone!! • MANY residences and some workplaces on no-longer extent streets (about 25% of street addresses) • Destruction during the blitz • Clearances after the War • Most no-longer extent addresses are due to a change of the layout and not just a renaming of the street • Need to locate addresses on old maps and find the corresponding spot on a map with GIS • The Booth maps and notebooks are useful for location https://booth.lse.ac.uk/notebooks/search?q=george+road%2C+islington&mode=any • Side-by-side historical and contemporary maps with GIS can be found at https://maps.nls.uk/geo/explore/side-by-side/#zoom=17&lat=51.5212&lon=-0.1079&layers=176&right=BingRoad
Finding a no longer extent street, ex Flower and Dean Street in the East End
Mean distances (ex. Commuting) in the NSLLL data (kilometres)
The nature of commuting • About 42% of employees worked in the same borough where they lived • Most commuting between boroughs either 1) worked in an adjacent borough or 2) worked in the City of London or 3) worked in another wealthy and central borough • The net commuting movement was from poorer residential boroughs to richer workplace boroughs (more commute from Fulham to Kensington than the reverse) • Many live south of the Themes (about 32% of residents of 10 boroughs) and work north of the river, only about 3% of those living in 29 boroughs north of the river work south • There is a net commute towards the centre, but many workers work locally, commute out, or commute across London • Commutes toward the centre (37.7%) – workplace is at least 1km closer to centre than residence • Works locally (29.1%) – commute is less than 1km • Commutes out (16.4%) – workplace is at least 1km further from centre than residence • Commutes across (16.8%) – commutes at least 1km, workplace and home have a similar centrality
Commuting distance by borough of residence (left) and borough of work (right)
Summary • Commuting in 1930 follows a pattern similar to today • On aggregate in towards the centre, but many are local, reverse-commuting, or cross commuting • Shorter average commutes for residents of the wealthy central boroughs, longer average commutes for the most peripheral boroughs • Longer average commutes for workers in the wealthy central boroughs, shortest commutes for workers in the (relatively impoverished) East End • Commutes are much shorter than the modern average of about 11km, but • The sample is restricted to the working class. More skilled workers commute further. • The NSLLL sample is restricted to about Zones 1-3 on the Underground, modern “London” goes beyond Zone 6. Residents of the periphery commute further.
Commuting and earnings, a thought exercise • For the time being assume that a worker’s residence is (exogenously) fixed. • Obviously not realistic, we will return to this later • Relationship between commuting (existence of commuting infrastructure) and earnings • Search theory - better matching between workers and firms (Gibbins and Manning 2006) • Workers are more suited to some employers than other. In a competitive labour market they earn more if there is a better match. • If they can only search very locally, the likelihood of a good match is low. Geographically extending the search (because of cheap and fast public transport) improves matches. • Implies longer commutes lead to higher incomes • Breaks employers’ local monopsony power (Bhaskar, Manning & To) • If an employee’s next best alternative is distant and costly to get to, local employers can pay less than the market wage. Fast and cheap public transport make it easier to get to an alternative employer. • Implies proximity of residence to public transport leads to higher earnings • Compensating differential for isolated workplaces (Gibbins and Manning 2006) • If it costly to get to a workplace location employers in that location will have to pay higher wages to attract workers • Implies inaccessibility of workplace (distance from public transport) leads to higher earnings
A big empirical issues with these models • Assumption of exogenous home location – implies commuting causes earnings, rather than the other way around • But reverse causation is also possible • High earnings may enable workers to live closer to work • High earners may live in suburbs and commute in to work • These factors may have been limited because of the “tightness” of the housing rental market in London circa 1930 • Also the location of public transport access is a policy choice that depended in part on local labour market conditions • Build the transport to pre-existing work centres • Implies location of work drives the location of transport, rather than the reverse • Natural experiment based on London clay • Technologically only possible to build underground tunnels where the is London clay near the surface • Most of the area south of the river has sand and silt near the surface • We want to isolate the effect of commuting on earnings and not merely the net two-way relationship • This is the returns to public investment in commuting infrastructure • In the empirical analysis we consider heads of household and non-head separately • Long tradition in urban economics of arguing that households establish residence based on the workplace of the head • In the data we can directly identify the head of each house, also the higher earner in each house • Residence of non-heads-of-household, particularly relatively young children may be fairly exogenous • Also consider the London clay areas and the sand and silt areas separately
Measuring the relationship between commuting and earnings • Use Mincer Wage Regressions(hedonic wage regressions) • W – earnings in the previous week (hundredths of old pence) • X – control variables • Age, age2, age not reported, sex, hours worked last week, born in England, born in London, born in same borough as current residence, born in adjacent borough. Some Specifications dummy for head of household, skill level, occupation, borough of workplace • DW – distance commuted to work • DCENT – centrality, distance from home to Charing Cross (official centre of London) or Bank of England (Commerical centre) • DU – distance to nearest underground station (from both home and work) • – distance to nearest train station (from both home and work) • (from both home and work) • Run regressions for full sample and for several sub-samples • Sub-samples selected to allay concerns about endogeneity in the location decision
Determinants of earnings • The regressions support traditional models of human capital (age, skill, etc.) – control variables have expected signs • There are substantial returns to commuting • Estimated as about 1-2 percent per kilometre • Robust to specification, somewhat heterogeneous across individuals • Higher for non-heads of household • The estimated returns to living close to the underground, train, or tram are small and insignificant • Suggests monopsony is not important • The estimated returns to working close to the underground, train, or tram are small and insignificant • Suggests that compensating differentials are not so important (e.g. workplace places in London are not especially geographically isolated)
Robustness checks • Replace head with highest income earner • Replace distance to nearest transport with number of stops/stations in 500*500 meter grid (1500*1500 meter grid) • Add expenditures on transportation as an independent variable • Replace occupation with skill level • Separate regressions for “sand and silt” regions, eastern areas w/o underground • Include distance variable interactions • e.g. does access to the underground matter less if you live the centre
Future research • Time Use • Commuting larger distances takes time! • We can obtain reasonable estimates of the time spent commuting by workers in the same • Mapquest between home and work • Public transport is a little faster than in 1930, but we probably slightly overestimate distances • Estimate directly using linear programming based on known average transport speed in 1930 • We don’t have micro-data for early periods, but the Booth report suggests very short commutes • Suggests the decline of the workweek has been exaggerated – work time was (partly) replaced by commuting time • Commuting and housing markets – does ability to commute increase housing cost? • Access to public transport gives better access to labour markets • Theory of rents says that people will pay for this, and this should be reflected in housing costs • In 2014 “A nearby tube station adds £42,000 to the price of a London house” (Guardian) • Need to control for other factors affecting rents • NSLLL has data on rents and housing amenities (bedrooms, kitchen, outdoor space, parlour, etc.) • Can obtain data on local amenities from maps (coal plants, river, parks, etc)
