Determinants of population and jobs at a local level

Abstract

This paper explores the determinants of population and jobs at a local level. We consider that employment and population are simultaneously determined and assume that, for population and employees, location determinants vary between professional groups rather than between sectors. We used two-stage least-squares to estimate residential and employment location and tested the model using recent data for municipalities in Catalonia (from 1991 to 2001). Our results show that location patterns depend on professional groups of residents and employees. We also found that, although population and jobs are simultaneously determined, the location of population is more important for the location of jobs than vice versa.

This is a preview of subscription content, access via your institution.

Notes

  1. 1.

    Catalonia is an autonomous region of Spain with about 6 million inhabitants (15% of the Spanish population) and an area of 31,895 km2. It contributes 19% of Spanish GDP. The capital of Catalonia is the city of Barcelona. See Appendix A.3 for more detailed information.

  2. 2.

    We are also not analysing here the population movements such as those due to education (e.g. students at university) or age (e.g. retired people).

  3. 3.

    The report on the urban policy of the Clinton administration (Office of Policy Development and Research 1995) maintained that, since the end of the Second World War, the suburbanisation process in the USA has been initiated by the population (looking for an urban environment with a better quality of life) and followed by the firms.

  4. 4.

    See, for a review, Muth (1971).

  5. 5.

    For instance, they used data from more than 3,000 counties in the USA whereas previous scholars used data only within large metropolitan areas.

  6. 6.

    We determined the centrality level in two different ways: by administrative function [municipalities that are capitals of comarca (CAP) and distance from those municipalities (DISCAP)] and by size [distance from the municipalities with at least 100,000 inhabitants (DIS100)]. A comarca (county) is a territorial administrative unit formed by adjacent municipalities. There are 41 comarques in Catalonia. The average area and population of Catalan comarques are, respectively, 781 km2 and 145,000 inhabitants.

  7. 7.

    The number of municipalities used in our database was 939 (we removed five municipalities because data on them were unavailable). In 1991, the 944 municipalities had an average population of 6,439 inhabitants.

  8. 8.

    A referee has suggested that using the two terms “population” and “workers” could cause confusion because both refer to the economically active population and that we use the terms “day-time” and “night-time” population to distinguish between individuals who live and work in different municipalities and those who live and work in the same municipality. We acknowledge that this suggestion could help to remove confusion but it is also true that most studies in the literature use the terms “population” and “workers” and most readers are familiar with these terms.

  9. 9.

    Also starting from the idea that location patterns are not homogeneous across individuals, Greenwood and Stock (1990) differentiate according to the income group of the population and the type of employment.

  10. 10.

    Specifically, if we take professional group 9 as a reference for the other groups and give them an income level equal to 100, the income levels of the other groups are as follows: group 1 (399.5), group 2 (288.7), group 3 (232.6), group 4 (157.5), group 5 (107.7), group 7 (146.4) and group 8 (149.0). Data for groups 0 and 6 are unavailable (the source is our own elaboration from the Spanish Statistical Institute). We use this data to understand the results of the simultaneous estimation of population and workers.

  11. 11.

    For the sake of simplicity, we will refer to the percentage of each professional group of workers over the total number of workers (WOR) as just workers and the percentage of each professional group of residents over total residents (POP) as just residents or population.

  12. 12.

    Among these variables, we have, for instance: population and employment density, interstate highway density, median school years, median family income (Carlino and Mills 1987) and access to highways, distance to centre, tax revenue per capita and residential and employment activity (Deitz 1998).

  13. 13.

    See Appendix for a description of the variables. Most of these data come from the Institut d’Estadística de Catalunya (Catalan Statistical Institute), which is the official statistical institute of the Autonomous Government of Catalonia.

  14. 14.

    If we consider amenities as “place-specific goods or services that enter the utility functions of residents directly” (Gottlieb 1995, p. 1413), both workers and firms can benefit from them (Schmitt and Henry 2000)—workers by consuming them and firms by paying lower wages because of the existence of such amenities.

  15. 15.

    One exception is the study by Greenwood and Hunt (1989), who demonstrate that jobs and wages are more important than location-specific amenities in explaining net metropolitan migration.

  16. 16.

    See Colwell et al. (2002) for a specific analysis of how location-specific amenities influence household location.

  17. 17.

    Most researchers analysing determinants of population and jobs use instrumental variables or TSLS, but there are other methods, such as the Granger causality test (Mathur and Song 1995), which is used to evaluate whether employment precedes population or vice versa. We assume that, when using instrumental variables, some results could be affected by unobserved municipality effects but, given that we do not have a panel data structure (because our time series are very short), there is no need to estimate a random effects model.

  18. 18.

    In this estimation, period t refers to 2001, period t−1 refers to 1991 and subindex i refers to the municipality.

  19. 19.

    At any rate, it may be that, when working with professional groups, some crossed effects among these groups are not caught. However, we do not consider this problem to be so important for our overall results.

  20. 20.

    Wasteful commuting means that some commuting between two sites is unnecessary. Imagine, for instance, that two individuals living in municipalities “a” and “b”, respectively, are working in the municipalities “b” and “a”, respectively, and commute daily between the two sites. See Hamilton (1982, 1989) for a more detailed explanation.

  21. 21.

    The results also show a positive (but not significant) effect for group 2 and a negative (but not significant) effect for groups 7 and 8.

  22. 22.

    However, it is difficult to obtain data on neighbourhood quality and to measure this phenomenon.

  23. 23.

    For instance, as well as capitals of comarca like Barcelona (1,643,542 inhabitants in 1991) or Reus (87,670), there are others such as Falset (2,603) or Cervera (6,545).

  24. 24.

    These preferences could be due to a desire to reduce commuting costs or to the fact that the kind of urban environment in which these professional activities take place is very similar to a residential environment. We should note that, with these accessibility indexes, we are only talking about the total number of workers: we are not disaggregating them into professional groups. The main issues here are that a municipality can focus on a residential function or on a working function and that, depending on the professional groups of the individuals, the municipality prefers one or both of these environments.

  25. 25.

    For instance, Hansen (1995) maintains that public efforts should focus on helping disadvantaged people rather than on helping specific territories.

  26. 26.

    The Hirshmann–Herfindahl index is defined as \(DIV_j = \sum\limits_{j = 1}^n {s_{ij}^2 }\), where s represents the weight of each industrial sector over the whole industrial sector at each municipality, j represents the sector, and i represents the municipalities.

  27. 27.

    \(AW_z = \frac{{\sum\limits_1^n {entrances_{j \ne z,t} } }}{{Workers_{z,t - 1} }}\) where z,j are municipalities

  28. 28.

    \(AP_z = \frac{{\sum\limits_1^n {entrances_{j \ne z,t} } }}{{Population_{z,t - 1} }}\) where z,j are municipalities

References

  1. Alonso W (1964) Location and land use. Harvard Univ. Press, Cambridge

    Google Scholar 

  2. Blanco C (1963) The determinants of interstate population movements. J Reg Sci 5:77–84

    Article  Google Scholar 

  3. Boarnet M (1994) The monocentric model and employment location. J Urban Econ 36:79–97

    Article  Google Scholar 

  4. Boehm TP, Ihlanfeldt KR (1986) Residential mobility and neighborhood quality. J Reg Sci 26(2):411–424

    Article  Google Scholar 

  5. Carlino GA, Mills ES (1987) The determinants of county growth. J Reg Sci 27(1):39–53

    Article  Google Scholar 

  6. Casado JM (2000) Local labour market areas in Spain: a case study. Reg Stud 34(9):843–856

    Article  Google Scholar 

  7. Colwell PF, Dehring CA, Turnbull GK (2002) Recreation demand and residential location. J Urban Econ 51:418–428

    Article  Google Scholar 

  8. Deitz R (1998) A joint model of residential and employment location in urban areas. J Urban Econ 44:197–215

    Article  Google Scholar 

  9. Freeman DG (2001) Sources of fluctuations in regional growth. Ann Reg Sci 35:249–266

    Article  Google Scholar 

  10. Garreau J (1991) Edge city: life on the new frontier. Doubleday, New York

    Google Scholar 

  11. Glaeser EL, Kolko J, Saiz A (2001) Consumer city. J Econ Geogr 1:27–50

    Article  Google Scholar 

  12. Gottlieb PD (1995) Residential amenities, firm location and economic development. Urban Stud 32(9):1413–1436

    Article  Google Scholar 

  13. Greenwood MJ, Hunt GL (1989) Jobs versus amenities in the analysis of metropolitan migration. J Urban Econ 25:1–16

    Article  Google Scholar 

  14. Greenwood MJ, Stock R (1990) Patterns of change in the intrametropolitan location of population, jobs, and housing: 1950 to 1980. J Urban Econ 28:243–276

    Article  Google Scholar 

  15. Hamilton BW (1982) Wasteful commuting. J Polit Econ 90(5):1035–1053

    Article  Google Scholar 

  16. Hamilton BW (1989) Wasteful commuting again. J Polit Econ 97(6):1497–1504

    Article  Google Scholar 

  17. Hansen N (1995) Addressing regional disparity and equity objectives through regional policies: a skeptical perspective. Pap Reg Sci 74:89–104

    Article  Google Scholar 

  18. Holmberg I, Johansson B, Strömquist U (2003) A simultaneous model of long-term regional job and population changes. In: Andersson AE, Johansson B, Anderson WP (eds) The economics of disappearing distance. Ashgate, Aldershot, pp 161–189

    Google Scholar 

  19. Kain JF (1968) The distribution and movement of jobs and industry. In: Wilson J (ed) The metropolitan enigma. Harvard Univ. Press, Cambridge, pp 1–42

    Google Scholar 

  20. Kan K (2002) Residential mobility with job location uncertainty. J Urban Econ 52:501–523

    Article  Google Scholar 

  21. Lowry IS (1966) Migration and metropolitan growth: two analytical models. Chandler, San Francisco

    Google Scholar 

  22. Mathur VJ, Song F (1995) The dynamics of regional populations and employment growth. Rev Urb Reg Dev Stud 7:70–88

    Article  Google Scholar 

  23. Mills ES (1986) Metropolitan central city population and employment growth during the 1970s. In: Peston MH, Ivandt RE (eds) Prices, competition and equilibrium. Philip Allan, London, pp 268–284

    Google Scholar 

  24. Mills ES, Price R (1984) Metropolitan suburbanization and central city problems. J Urban Econ 15(1):1–17

    Article  Google Scholar 

  25. Muth RF (1968) Differential growth among large U.S. cities. In: Quirk JP, Zarley AM (eds) Papers in quantitative economics. University of Kansas, Lawrence

    Google Scholar 

  26. Muth RF (1971) Migration: chicken or egg? South Econ J 37:295–306

    Article  Google Scholar 

  27. Office of Policy Development and Research (1995) The Clinton administration’s national urban policy report. U.S. Department of Housing and Urban Development

  28. Schmitt B, Henry MS (2000) Size and growth of urban centers in French labor market areas: consequences for rural population and employment. Reg Sci Urban Econ 30:1–21

    Article  Google Scholar 

  29. Simpson W (1987) Workplace location, residential location and urban commuting. Urban Stud 24:119–128

    Article  Google Scholar 

  30. Steinnes D (1977) Causality and intraurban location. J Urban Econ 4:69–79

    Article  Google Scholar 

  31. Steinnes D (1978) A simultaneous econometric model of the intra-urban location of employment and residence. Reg Sci Perspect 8:101–115

    Google Scholar 

  32. Steinnes D (1982) Do ‘people follow jobs’ or do ‘jobs follow people’? A causality issue in urban economics. Urban Stud 19:187–192

    Article  Google Scholar 

  33. Steinnes D, Fisher W (1974) An econometric model of intraurban location. J Reg Sci 14(1):65–80

    Article  Google Scholar 

Download references

Acknowledgements

This research was partially funded by CICYT: SEJ2004-05860/ECON and CICYT: SEJ2004-07824/ECON. I would like to acknowledge the helpful and supportive comments of Agustí Segarra, Miguel Manjón, the Instituto Valenciano de Investigaciones Económicas (IVIE) and participants at the 4th European Urban & Regional Studies Conference. I am also grateful to the Centre for Spatial and Real Estate Economics (CSpREE) of the University of Reading, where a preliminary version of this paper was completed during my stay as a research visitor. I also wish to thank the editor of The Annals of Regional Science and three anonymous referees for helpful comments. Any errors are, of course, my own.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Josep-Maria Arauzo-Carod.

Appendix

Appendix

Professional groups

Group code Description
1 Company directors
2 Technicians, scientists and intellectual professionals
3 Technicians and support professionals
4 Administrative staff
5 Retail and restaurant staff
6 Qualified workers in the farming and fishing industries
7 Manufacturing, construction and mining workers
8 Operators of facilities and machinery
9 Non-qualified workers
0 Armed forces
  1. Catalan Statistical Institute (IDESCAT)

Explanatory variables: definition and sources

Variable Definition Source
Transport infrastructures   
 Accessibility by rail (RAIL) Accessibility to rail network in the same comarca Catalan Government
 Accessibility by highway (HIG) Accessibility to highway network in the same comarca Catalan Government
Labour market   
 Population (POP) Residential population grouped by professional categories (1991, 2001) Censo de Población
 Workers (WOR) Workers grouped by professional categories (1991, 2001) Censo de Población
 Industrial diversity (DIV) Index HHI of diversity of industrial jobs (1991)Footnote

The Hirshmann–Herfindahl index is defined as \(DIV_j = \sum\limits_{j = 1}^n {s_{ij}^2 }\), where s represents the weight of each industrial sector over the whole industrial sector at each municipality, j represents the sector, and i represents the municipalities.

Own elaboration with data from IDESCAT
 Accessibility to workers (AW) Daily entrance of commuters weighted by the number of jobsFootnote

\(AW_z = \frac{{\sum\limits_1^n {entrances_{j \ne z,t} } }}{{Workers_{z,t - 1} }}\) where z,j are municipalities

Censo de Población and Padrón municipal
 Accessibility to population (AP) Daily entrance of commuters weighted by the populationFootnote

\(AP_z = \frac{{\sum\limits_1^n {entrances_{j \ne z,t} } }}{{Population_{z,t - 1} }}\) where z,j are municipalities

Censo de Población and Padrón municipal
 Human capital (HC) Population that has obtained a university degree (%) IDESCAT
Geographical   
 Distance (DISCAP) Distance (km) from the capital of the comarca Catalan Cartographical Institute
 Distance (DIS100) Distance (km) from the nearest city with at least 100,000 inhabitants Catalan Cartographical Institute
Amenities   
 Capital of the comarca (CAP) Dummy that shows whether the municipality is the capital of a comarca IDESCAT
 Coastal municipalities (COAST) Dummy that shows whether the municipality is located on the coast IDESCAT
 Quality of life(QUA) Measured by the mean average area of houses IDESCAT
 Stock of houses (HOU/INH) Stock of houses divided by number of inhabitants IDESCAT
 Retail shops (SHOP) Density of retail shops per square kilometers IDESCAT
 Income level (INC) Income level per capita in the municipality IDESCAT

Some characteristics of Catalan municipalities

  1991 1996
Population Workers Population Workers
Inhabitants N % N % N % N %
Lower than 2,000 386,261 6.4 128,727 5.7 388,762 6.4 126,527 5.8
2,000–10,000 808,385 13.3 300,843 13.4 852,858 14.0 303,704 13.8
10,001–50,000 1,419,153 23.4 473,649 21.1 1,574,307 25.9 516,259 23.5
50,001–100,000 506,438 8.4 211,922 9.4 500,207 8.2 226,964 10.3
100,001–1,500,000 1,295,664 21.4 370,225 16.5 1,265,101 20.8 359,762 16.4
More than 1,500,000 1,643,542 27.1 761,165 33.9 1,508,805 24.8 659,949 30.1
Total 6,059,443 100.0 2,246,531 100.0 6,090,040 100.0 2,193,165 100.0
  1. Catalan Statistical Institute (IDESCAT)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Arauzo-Carod, JM. Determinants of population and jobs at a local level. Ann Reg Sci 41, 87–104 (2007). https://doi.org/10.1007/s00168-006-0058-6

Download citation

JEL Classification

  • R200
  • R300
  • C300