Abstract
A key factor in today’s urban wealth is the means by which cities reduce costs of communication. Rapid progress in transport, information and communication technologies lowered the costs of production at distance. Still, in 2009 metropolitan areas were responsible for 85 % of US employment, income and production. The significance of personal communication for innovation is a fundamental aspect of the current economic success of cities. The economic structure of cities varies; diversified cities focusing on producing ideas and specialised cities focusing on producing products successfully coexist in the US. Is communication equally important and valued within both city types?
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- 1.
M is the productivity effect of operating in the local dominant industry. This effect increases with the specialisation level of the city.
- 2.
- 3.
As explained in the next section, data limit us to measure communication at the occupational level.
- 4.
RDI is defined as \( RD{I_i}=\frac{1}{{\mathop{\sum}\nolimits_j{E_{ij }}/{E_j}}} \) where \( {E_{ij }} \) represents employment in industry j in city i and \( {E_j} \) national employment in industry j.
- 5.
In our dataset diversified cities are as well larger than specialised cities, which is discussed in Sect. 11.4.2.
- 6.
Appendix A provides insight in the original scaling of the variables, Table 11.14 present the correlations between the variables.
- 7.
Charlot and Duranton (2004) instrument communication job tasks with the use of computers and internet at the work floor. The Current Population Survey includes similar information for the year 2000. However, we cannot rule out possible endogeneity of computer use. Workers may sort by ability into communication and computer intensive jobs for the same reasoning. Specification tests underline that computer use at the job is endogenous.
- 8.
The importance of communication is measured at the occupation level and independent of location.
- 9.
F-statistics are generated for the additional instruments only (communication and population in 1930).
- 10.
The index is defined as in Acemoglu and Autor (2011). The index is standardised with mean 0 and standard deviation 1. Data The appendix describes the measurement of this index.
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Acknowledgement
I thank Steven Brakman, Harry Garretsen, Andrea Jaeger, Jasper de Jong, Bas ter Weel, two anonymous referees and seminar participants at the SOM conference and the Tinbergen Workshop for many insightful comments.
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Appendices
Data Appendix
1.1 Data Source
1.1.1 Current Population Survey | May Outgoing Rotation Group
The May Outgoing Rotation Group (MORG) of the Current Population Survey is used as these files include detailed information about earnings and working hours. The files contain individual information about employment and other labour-market variables. For instance it contains information on occupation, industry, hours worked, earnings, education, unionisation and a wide variety of demographic variables. Detailed information about this dataset can be found at http://www.census.gov/cps/.
1.1.2 ONET Skill Survey
Detailed information about the performance of communication job tasks and other job activities is gathered from the ONET Skill Database (www.onetcenter.org). The 3.0 version is used for this paper. For each occupation this database provides information about the importance of workers abilities, interest, knowledge, skills, work activities and work context. Work activities are defined as ‘General types of job behaviours occurring on multiple jobs.’, work context as ‘Physical and social factors that influence the nature of work’. Work activities are scaled from 0 to 6 and work context from 0 to 100. To obtain similar scores, we standardized all work activities and context with mean 0 and standard deviation 1.
1.1.3 Local Area Unemployment Statistics
To compute employment figures for Metropolitan Statistical Areas (MSAs), we gather county employment figures from the Local Area Unemployment Statistics of the Bureau of Labor Statistics (BLS). Counties are merged into MSAs following the 1990 definition of the Census. Details on the construction of the city classifications are given below.
1.2 Classifications
1.2.1 Cities
Cities are classified by Metropolitan Statistical Areas in the Current Population Survey. MSAs are defined by the nature of their economic activity. The MSA classifications are updated over time following the scope of regional economic activity. We add several city characteristics to the MSA information provided by the CPS which leads to definition issues. To define time consistent MSA definitions we use the 1990 definition of the Census which combines counties into MSAs. As county borders do not change over time, our MSA classification represents cities, which do not change in geographical size over time. Thus, additional city information covers a time consistent MSA definition. This city classification leads to a sample of 168 MSAs, which borders are stable over time.
1.2.2 Industries
Our industry classification includes 142 three-digit and 11 two-digit industries. The distribution of industries across cities equals the County Business Patterns distribution.
1.2.3 Occupations
The occupational classification includes 326 three-digit and 10 two-digit occupations and follows the classification of Autor and Dorn (2010). To match information from the ONET Skill Survey to the Current Population Survey, the occupation classification from the ONET is matched to these 326 occupations. The occupation classification of ONET varies over time, the classification of ONET version 3.0 provides the most accurate match to the CPS and it used in this paper.
Variables
Additional Figures
Native inhabitants in specialised and diversified cities (Note: source Current Population Survey 2009. City level data, n = 168. The correlations are respectively 0.30 (0.00) and −0.23 (0.00) and significant at the 1 % level. RSI and RDI are measured as described in Sect. 11.3. Natives are defined as workers born in the US and are measured as share of employment)
Communication and native inhabitants (Note: source Current Population Survey 2009. City level data, n = 168. The correlation is −0.08 (0.34) and not significant. Communication is measured as the average score on the Communication-Index as defined in Sect. 11.4. Natives are defined as workers born in the US and are measured as share of employment)
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Kok, S. (2014). Returns to Communication in Specialised and Diversified US Cities. In: Kourtit, K., Nijkamp, P., Stimson, R. (eds) Applied Regional Growth and Innovation Models. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37819-5_11
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