Rudiger Ahrend and Alexander C. Lembcke OECD/GOV, Regional Development Policy Division OECD, 15 January 2015

What Makes Cities More Productive?

Agglomeration economies and the role of urban governance

What determines productivity in cities? • Focus on cities’ size and their governance structures

Evidence for 5 (+2) OECD countries: • Germany, Mexico, Spain, UK and US • Netherlands and Japan in progress

Coherent and comparative methodology • Use wage microdata and OECD Functional Urban Areas:

Economic links (commuting flows) define a city’s boundary rather than the city’s administrative limits

• Two-step econometric framework account for individual characteristics that determine productivity

Preliminaries: what, who and how?

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Sources of agglomeration economies Reviews by Rosenthal and Strange (2004), Duranton and Puga (2004) and Puga (2010); concepts already present in Marshall (1890). • Sharing facilities, inputs, gains from specialisation

firms may face lower costs for specialised non-traded inputs that are shared locally in a geographical cluster.

• Thicker labour markets: labour market pooling; better matching gain from reduced labour acquisition and training costs in thick local labour markets with abundant specialised labour force

• Knowledge spillovers: learning about and spreading new ideas face-to-face contact can enable tacit knowledge spillovers through increases in the intensity of the interactions with other firms or individuals

Agglomeration economies

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Horizontal administrative fragmentation is common as cities outgrow their historic boundaries (more than 10 local governments in 75% of OECD Metropolitan Areas; more than 100 in 22%)

Potential positive impact: • a larger number of local governments is associated with more choice in the

provision of public services, more tailored solutions and better accountability (Tiebout, 1956).

• Large literature that finds no scale effects for specific public services (Ostrom, 2010) or governmental expenditure (Kalb, 2010).

Potential negative impact: • Policies, investment and services require city-wide coordination (e.g.

Cheshire and Gordon, 1996): e.g. transport; land use; ease of doing business; economic promotion; environmental regulation, etc.

Fragmentation and governance

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Regressing per worker GDP against city size and governance? • Confounds two important effects:

– Cities have the potential to increase their residents productivity – People with higher levels of education and skills tend to live in larger cities

they would be more productive anywhere not just in larger cities

⇒ Need to account for bias arising from non-random sorting of more productive (e.g. skilled) individuals across cities

Use a two-step microdata based approach (Combes, Duranton and Gobillon, 2011) to disentangle the two effects: i. individual (Mincerian) wage regressions to estimate the differential productivity levels of cities, controlling for sorting (on observables) ii. explain the differential city productivity levels found in the first step by regressing them on a number of city explanatory variables

Empirical strategy: Two-step approach

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First step: 𝑦𝑖𝑖𝑐𝑡 = 𝛽𝑋𝑖𝑖𝑐𝑡 + 𝛿𝑖𝑐𝑡 + 𝜀𝑖𝑖𝑐𝑡 yiact: (ln) wage Xiact: gender, education, experience, occupation, part-timing 𝛿𝑖𝑐𝑡: city-year FE Second step:

�̂�𝑖𝑐𝑡 = 𝛿𝑄𝑖𝑐𝑡 + 𝜇𝑍𝑖𝑐 + 𝜃𝑐𝑡 + 𝑢𝑖𝑡 • Allows for flexible country time trends 𝜃ct • Sample limited to years available for all five countries

(2005-2007)

Empirical strategy: Two-step approach

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Explain first stage estimates with a set of relevant city (Functional Urban Area) level controls (𝑄𝑖𝑐𝑡; 𝑍𝑖𝑐)

• agglomeration: (ln) population density and (ln) area • urban governance: horizontal administrative

fragmentation (ln number of municipalities; presence of a governance body; interaction term)

• human capital: share of university graduates • industrial structure: specialisation, composition and

technological intensity • location “fundamentals”: port; capital city; pop in

cities within 300km radius • urban form: population concentration; complexity of

the shape of the urban built-up area

Empirical strategy: City characteristics

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Microdata with info on wages, individual characteristics and detailed residence/workplace location • UK: ASHE first stage (1% sample); city-year controls from QLFS;

2004-2010 • Spain: MCVL; administrative data (4% sample); 2005-2011 • Germany: BA-Employment Panel (2% sample, 1998-2007) • US: IPUMS data; Census 1990, 2000 and American Community

Survey 2005-2007 • Mexico: ENE 2000-2004; ENOE 2005-2010 • Japan: Basic Survey on Wage Structure (~3% of employees,

1989-2013) • Netherlands: Census (SSB); Labour Force Survey (EBB); Firm

registry (EBB) for 2010

Data

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City productivity increases with city size even after controlling for sorting

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Heterogeneity: bigger is better

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Spain

United States

Heterogeneity: borders matter(ed)

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Germany

Mexico

Heterogeneity: distance matters

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Netherlands

United Kingdom

Netherlands

Heterogeneity: distance matters

13 excluding FUAs that border a metropolitan area (light blue)

United Kingdom

Distance matters

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Productivity differentials and distance to London

City productivity and administrative fragmentation

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Pooled regression results, 2005-2007

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(I) (II) (III) (IV) ln(population) 0.038***

(0.005) ln(population density) 0.037*** 0.048*** 0.045***

(0.006) (0.006) (0.006) ln(area) 0.038*** 0.070*** 0.070***

(0.006) (0.009) (0.009) ln(municipalit.) -0.032*** -0.035***

(0.006) (0.006) ln(other FUA pop. in 0.017** catchment area) (0.008) R-Squared 0.76 0.76 0.78 0.78 Observations 1,290 1,290 1,290 1,290 FUAs 430 430 430 430

• Agglomeration benefits : elasticity of around 0.04-0.05(increasing population while holding area constant)

• Administrative fragmentation: elasticity of around -0.035 (holding area and population constant)

All FUAs Metro areas Metro areas Metro areas

ln(pop. density) 0.048*** 0.064*** 0.065*** 0.047***

(0.006) (0.012) (0.012) (0.012)

ln(area) 0.064*** 0.082*** 0.085*** 0.087***

(0.008) (0.012) (0.012) (0.013)

ln(municipalit.) -0.032*** -0.032*** -0.057*** -0.066*** (0.006) (0.010) (0.016) (0.017)

ln(municipalit.) 0.031** 0.036** x govern. body (0.014) (0.015)

governance body -0.079** -0.092**

(0.034) (0.038) Add.controls no no no yes R-Squared 0.779 0.847 0.855 0.880 Observations (FUAs) 1,290 (430) 420 (140) 420 (140) 420 (140)

Fragmentation and governance bodies

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Taking governance bodies into account shows the true fragmentation penalty: -0.06 with governance bodies attenuating half.

Human capital, high-tech and knowledge intensive services oriented cities make their residents more productive Smaller cities can “borrow” agglomeration benefits Benefits are not limited to cities but positively affect accessible regions

Beyond city size and governance

18 Ahrend and Schumann (2014)

Annual per capita GDP growth rates (1995-2010) and driving time to the closest metro area of 2 million or more inhabitants

• First study that quantifies link between (horizontal) administrative fragmentation, the role of governance bodies and productivity in cities, using a comparable and coherent definition for “cities” and a common empirical strategy.

• Size matters: Estimated elasticity for agglomeration benefits around 0.02-0.05 (roughly: 2-5% higher productivity for a doubling in population)

• Cities with fragmented governance structures exhibit lower levels of productivity: Doubling the number of municipalities within a metropolitan area is associated with 6% lower productivity

=> The presence of a governance body halves this penalty • Productivity is higher in cities with more skilled workers; with

larger employment shares in high-tech manufacturing, finance and business services; larger neighbouring cities and lower levels of governmental fragmentation.

City productivity: Results from 5 OECD countries

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The presentation draws from: Ahrend, Farchy, Kaplanis and Lembcke (2014) “What Makes Cities More Productive? Agglomeration economies and the role of urban governance: Evidence from 5 OECD Countries” Ahrend and Schumann (2014) “Does regional economic growth depend on proximity to urban centres?” OECD (2015) The Metropolitan Century: Understanding Urbanisation and its Consequences OECD (2015) Governing the City OECD (forthcoming) Metropolitan Review: Rotterdam-The Hague OECD (2012) Redefining Urban: a new way to measure metropolitan areas

Thank you

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Local productivity, prices and purchasing power

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What explains the heterogeneity in net differentials?

Germany (1999-2007) West Germany (1999-2007)

(L) (M) (N) (O) (L) (M) (N) (O)

Access to coast or -0.012* -0.006 -0.014** -0.005 -0.011 -0.003 -0.012* -0.003

large lake (0.007) (0.007) (0.006) (0.007) (0.008) (0.008) (0.007) (0.008)

UNESCO cultural -0.009* -0.009 -0.008* -0.008* -0.012** -0.013** -0.011** -0.013***

heritage site (0.005) (0.005) (0.005) (0.005) (0.006) (0.006) (0.005) (0.005)

Number of theatre -0.014*** -0.017*** -0.010** -0.010*** -0.015** -0.019** -0.007 -0.008

seats / 1,000 (0.005) (0.005) (0.004) (0.004) (0.007) (0.008) (0.005) (0.005)

Number of theatre 0.001*** 0.001*** 0.001*** 0.001*** 0.001 0.001* 0.000 0.000

seats / 1,000 (sq.) (0.000) (0.000) (0.000) (0.000) (0.001) (0.001) (0.001) (0.001)

University hospital in -0.001 0.000 -0.007 -0.000 -0.004 -0.001 -0.007 0.001

FUA (0.006) (0.007) (0.005) (0.006) (0.006) (0.007) (0.005) (0.006)

Number of universities -0.007*** -0.007*** -0.002 -0.002 -0.008*** -0.009*** -0.004** -0.004**

(0.002) (0.002) (0.001) (0.001) (0.002) (0.003) (0.002) (0.002)

Numb. of nat. league 0.001 0.000 -0.002 -0.003 0.002 0.002 0.000 0.000

football clubs 1995-99 (0.006) (0.006) (0.004) (0.004) (0.006) (0.007) (0.004) (0.004)

Avg. annual level of 0.005** 0.004** 0.003 0.002*

PM10 (0.002) (0.002) (0.002) (0.001)

Share of workers w/ -0.315*** -0.254*** -0.379*** -0.323*** -0.322*** -0.267*** -0.345*** -0.264***

university degree (0.087) (0.085) (0.077) (0.083) (0.088) (0.084) (0.073) (0.066)

R-squared 0.45 0.46 0.39 0.39 0.50 0.54 0.33 0.35

FUAs 109 100 109 100 88 80 88 80

Obs. 981 541 981 541 792 423 792 423

Productivity indicators Aggl. Aggl. All All Aggl. Aggl. All All 22

Bigger cities are more productive

Germany (I) (II) (III) (IV)ln(emp.density) 0.054*** 0.029*** 0.024*** 0.018***

(0.006) (0.005) (0.005) (0.006)ln(area) 0.023*** 0.015*** 0.013*** 0.011**

(0.005) (0.004) (0.004) (0.005)east -0.167*** -0.201*** -0.104*** -0.207***

(0.012) (0.010) (0.009) (0.011)R-squared 0.789 0.885 0.944 0.880

NT = 981 NT = 109

I. Selection of individuals into cities is ignored II. Selection based on observable characteristics (age, education, gender,

occupation, worker status) III. Selection based on observable and time-invariant unobservable

characteristics IV. Specification (II) instrumenting employment density with population

in 1910 (and its square) and fragmentation in 1910

• Selection of more skilled individuals into larger cities • Agglomeration benefits in Germany between 1.5-3.0% for a

doubling in FUA population 23

Summary statistics: Regression controls

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Variables USA Mexico Germany UK Spain

Population (in million inhabitants) 2.470 0.880 0.465 0.457 0.389

(3.115) (2.211) (0.615) (1.169) (0.810)

Surface area (in 1,000 km²) 11.233 4.322 1.208 0.741 1.566

(12.907) (6.989) (1.136) (.878) (2.205)

Population density (in 1,000 inhabitants 0.349 0.414 0.512 0.735 0.625

per km²) (0.379) (0.542) (0.513) (0.518) (0.913)

# local governments per 1,000 inh. 5.000 5.400 44.266 4.337 31.803

(3.800) (10.144) (50.390) (7.003) (52.161)

% of resident living in largest municip. 0.665 0.810 0.522 0.717 0.717

(0.233) (0.232) (0.183) (0.221) (0.165)

Governance body 0.868 0.808 0.833 0.286 0.125

(0.341) (0.402) (0.381) (0.469) (0.354) Herfindahl index for municipal 0.550 0.739 0.327 0.614 0.564

population concentration (0.265) (0.295) (0.216) (0.256) (0.224)

Remoteness 0.770 0.781 0.547 0.534 0.645

(0.273) (0.258) (0.033) (0.032) (0.079)

Spin 37.597 26.929 7.958 5.920 10.465

(42.574) (34.460) (3.173) (2.699) (7.288)

Disconnection 1.065 1.063 0.749 0.732 0.888

(0.388) (0.324) (0.045) (0.043) (0.106)

Range 2.116 1.819 1.137 1.125 1.455

(1.610) (0.944) (0.132) (0.154) (0.317)

Population in catchment area 51,305 88,200 265,560 311,389 57,607

(45,524) (119,364) (133,150) (107,237) (41,566)

% of residents with university degree 0.276 0.156 0.136 0.194 0.122

(0.056) (0.051) (0.041) (0.070) (0.054)

Capital city 0.014 0.013 0.009 0.010 0.013

(0.120) (0.115) (0.096) (0.100) (0.115)

Port city 0.478 0.187 0.174 0.158 0.316

(0.503) (0.392) (0.381) (0.367) (0.468)

Variables USA Mexico Germany UK Spain

Herfindahl Index (industry 2-dig.) 0.060 0.063 0.046 0.050 0.068

(0.006) (0.018) (0.013) (0.009) (0.025)

Agriculture 0.018 0.062 0.008 0.015 0.007

(0.015) (0.056) (0.011) (0.021) (0.006)

High-tech manufacturing 0.022 0.007 0.026 0.021 0.005

(0.020) (0.016) (0.023) (0.016) (0.007)

Med. high-tech manufacturing 0.024 0.036 0.095 0.039 0.026

(0.021) (0.047) (0.064) (0.020) (0.030)

Med. low-tech manufacturing 0.023 0.047 0.060 0.037 0.038

(0.013) (0.027) (0.041) (0.020) (0.041)

Low-tech manufacturing 0.037 0.080 0.052 0.046 0.052

(0.012) (0.055) (0.024) (0.021) (0.049)

Electricity 0.007 0.006 0.005 0.009 0.007

(0.003) (0.004) (0.008) (0.006) (0.005)

Construction 0.077 0.093 0.053 0.084 0.130

(0.016) (0.026) (0.016) (0.018) (0.042)

Trade 0.220 0.209 0.150 0.149 0.203

(0.014) (0.036) (0.022) (0.028) (0.047)

Catering 0.012 0.069 0.022 0.043 0.077

(0.013) (0.037) (0.013) (0.016) (0.052)

Transport & communication 0.061 0.053 0.058 0.070 0.055

(0.014) (0.019) (0.019) (0.029) (0.026)

Finance 0.056 0.010 0.029 0.040 0.035

(0.018) (0.006) (0.017) (0.020) (0.025)

Real estate & business 0.115 0.050 0.144 0.109 0.147

(0.017) (0.018) (0.028) (0.036) (0.042)

Public administration 0.038 0.061 0.064 0.070 0.045

(0.016) (0.043) (0.021) (0.022) (0.040)

Educ., health & social work 0.209 0.099 0.157 0.213 0.102

(0.027) (0.033) (0.036) (0.039) (0.037)

Other services 0.081 0.120 0.044 0.056 0.073

(0.016) (0.028) (0.015) (0.016) (0.020)

FUAs (metropolitan areas) 69 (68) 75 (26) 109 (24) 101 (14) 76 (8)

(I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX)

ln(density) 0.043*** 0.031*** 0.015** 0.046*** 0.032*** 0.016** 0.047*** 0.034*** 0.017**

(0.006) (0.008) (0.007) (0.006) (0.007) (0.007) (0.006) (0.007) (0.007)

ln(area) 0.044*** 0.036*** 0.022*** 0.064*** 0.057*** 0.036*** 0.064*** 0.057*** 0.036***

(0.007) (0.009) (0.007) (0.008) (0.010) (0.008) (0.008) (0.010) (0.008)

ln(municipalit.) -0.036*** -0.041*** -0.029*** -0.034*** -0.038*** -0.026***

(0.007) (0.007) (0.006) (0.008) (0.007) (0.006)

% of pop.in 0.054** 0.063** 0.072*** -0.037 -0.037 -0.001

largest municip. (0.026) (0.025) (0.021) (0.029) (0.027) (0.022)

Herfindahl index for munic. -0.013 -0.010 0.017

population concentration (0.027) (0.026) (0.021)

Administrative fragmentation or concentration?

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(I) (II) (III)

ln(density) 0.047*** 0.035*** 0.016**

(0.006) (0.007) (0.007)

ln(area) 0.050*** 0.044*** 0.024***

(0.008) (0.010) (0.008)

ln(municipalit.) -0.025*** -0.028*** -0.024***

(0.005) (0.005) (0.005) Remoteness 0.085 0.098 -0.017 (0.085) (0.095) (0.079) Spin 0.001*** 0.001*** 0.001*** (0.000) (0.000) (0.000) Disconnection -0.119 -0.11 -0.046 (0.109) (0.093) (0.069) Range -0.008 -0.007 -0.003 (0.021) (0.016) (0.011)

Administration fragmentation or urban form?

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(I) (II) (III) ln(density) 0.052*** 0.039*** 0.020***

(0.006) (0.007) (0.006) ln(area) 0.058*** 0.055*** 0.036*** (0.008) (0.010) (0.008)

ln(municipalit.) x DEU -0.035*** -0.036*** -0.031*** (0.006) (0.006) (0.005)

ln(municipalit.) x ESP -0.008 -0.019* -0.021** (0.010) (0.010) (0.009)

ln(municipalit.) x GBR -0.029** -0.037*** -0.030*** (0.012) (0.012) (0.010)

ln(municipalit.) x MEX -0.066*** -0.066*** -0.047*** (0.013) (0.013) (0.011)

ln(municipalit.) x USA 0.007 0.005 0.006 (0.011) (0.009) (0.008)

Does a single country drive the results?

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USA MEX DEU GBR ESP ln(pop.density) 0.068*** 0.037*** 0.048*** 0.048*** 0.055***

(0.028) (0.010) (0.012) (0.012) (0.011) ln(area) 0.140*** 0.059*** 0.092*** 0.092*** 0.088***

(0.026) (0.011) (0.014) (0.013) (0.013) ln(municipalit.) -0.075*** -0.032* -0.052*** -0.066*** -0.080*** (0.022) (0.020) (0.021) (0.019) (0.016) ln(municipalit.) 0.027* 0.031** 0.024 0.037** 0.049*** × gov. body (0.020) (0.016) (0.019) (0.016) (0.015) Governance -0.054 -0.070** -0.084** -0.092*** -0.111*** body (0.055) (0.042) (0.041) (0.039) (0.036)

Does a single country drive the results?

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% university 0.275***

Graduates (0.073)

Capital 0.028

(0.030)

Port 0.039***

(0.010)

Herfindahl -0.704***

Index (0.266)

Agriculture 0.0808

(0.257)

Catering 0.472**

(0.230)

Other determinants

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Transport & -0.126

communication (0.200)

Finance 0.878***

(0.181)

Real estate 0.410**

& business (0.176)

Public 0.057

administration (0.261)

Educ., health -0.120

& social work (0.154)

Other services 0.535*

(0.275)

High-tech 1.104*** Manufacturing (0.234) Med. high-tech 0.840*** Manufacturing (0.135) Med. low-tech 0.494*** Manufacturing (0.146) Low-tech 0.082 Manufacturing (0.149) Electricity -0.931**

(0.463) Trade 0.223

(0.171)