living and working in cities assignment

This is why people live, work and stay in a growing city

In the next decade, nearly half of all global GDP growth will come from around 400 cities. Image:  REUTERS/Bobby Yip

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Pearly Siffel

.chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} Explore and monitor how .chakra .wef-15eoq1r{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;color:#F7DB5E;}@media screen and (min-width:56.5rem){.chakra .wef-15eoq1r{font-size:1.125rem;}} Cities and Urbanization is affecting economies, industries and global issues

.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved with our crowdsourced digital platform to deliver impact at scale

Stay up to date:, cities and urbanization.

In the next decade, nearly half of all global GDP growth will come from around 400 cities and growing economies that are on a trajectory to reach 1 billion new consumers.

With such rapid growth comes fierce competition for highly skilled talent. Although economic opportunities make these cities appear attractive financially, economic growth alone cannot guarantee they will be able to attract – and keep – ambitious, mobile and driven workers. What must employers, both in the public and private sector, therefore, do to attract the best people and motivate them to stay?

To answer this question, Mercer’s commissioned a study, People First: Driving Growth in Emerging Megacities , which surveyed 7,200 workers and 577 employers across 15 cities in seven countries to investigate the under-examined, yet highly critical, human and social reasons for why people move to, within or out of a growing city.

While most city studies rely upon secondary research or academic papers analysing economic data and investments, technological advancements, and movement to smart cities, this study examined employer and worker views in growth cities to discover meaningful gaps and points of tension, and to identify practical solutions for employers to effectively compete for talent.

Fifteen current and future megacities were shortlisted based upon their strong projected GDP and population growth for the next decade. Today, the 15 cities – Belo Horizonte, Curitiba, Chengdu, Hangzhou, Nanjing, Qingdao, Ahmedabad, Chennai, Hyderabad, Kolkata, Nairobi, Guadalajara, Monterrey, Casablanca and Lagos – have a combined population of 114 million. By 2030, this is expected to grow by 30% to 150 million – equivalent to the size of Poland or that of Denmark, Finland, Belgium and the Netherlands combined.

As part of the study, employers and workers were asked to rank 20 decision-making factors by importance against four vital and interrelated pillars – human, health, money and work.

Meaningful gaps were discovered between what workers value and what employers believe is important to people when deciding whether to move to, stay or leave a city. The research suggests that what matters most to workers depends on the decision the individual is trying to make.

When deciding in which city to live and work, people rank human factors as the most important. They rate life satisfaction as two times more important than employers realize. This is their number one consideration, followed by safety and security and proximity to family and friends in fourth. Third on their list is pay and bonuses – often a proxy for quality of life. Employers place twice as much weight on career and job opportunities twice as workers do.

However, when deciding on a neighborhood, people place equal importance on all four pillars and care about proximity to supermarkets, banks, public transportation, schools and healthcare. Employers, by contrast, put more emphasis on income and career incentives when attracting the best talent from other cities and fail to listen to the entirety of people’s needs, including very pressing social and human desires.

Employers have a critical role to play in addressing all dimensions of workers’ needs and some are already making the effort.

“In India, some of the more enlightened employers are taking it upon themselves to either do their own training or talk about training programs in Singapore that they would avail for their more experienced staff,” says Alice Charles, project lead, Cities at the World Economic Forum.

Similar strategies are being deployed in China, says XinYing He, HR director at UECHAIRS: “In terms of effective approaches that we are taking to attract and retain talent in growth cities, we help the whole family settle in so that the employees will feel a stronger sense of belonging to the company.”

To understand the nuances of workers in the 15 cities, the study investigates people’s needs based on their demographics, life stage, career progression, predisposition to lifelong learning, aspirations and level of financial security. This results in a segmentation study of five personas – confident achievers, white-collar professionals and graduates, struggling vocationals, business owners and skilled tradespeople, and professional families.

While overall satisfaction with life ranks as the number one, most important factor across all five personas, each persona has a unique set of needs and drivers. Employers cannot discount the significant risk in making sweeping generalizations about people and treating them as one homogenous group. A deeper understanding of workers’ needs is critical, and employers must tailor their solutions, approaches and communications to ensure employee needs are met and that they feel empathetically understood.

Based on each of the 15 cities’ current performance against the four pillars, they were grouped into three stages of progress:

• Advanced cities that scored well in all four pillars with a small-to-medium gap between workers’ expectations and the city’s performance; progressing cities that have a mid-size gap;
• Progressing cities that have a mid-size gap;
• Approaching cities that are low across all four dimensions and have the biggest gaps and tensions. approaching cities that are low across all four dimensions and have the biggest gaps and tensions.

Looking at the cities’ performance against workers’ expectations reveals that while overall cities are doing well on culture, education, skills development and economic aspects, there are significant tensions across three factors: security, safety and lack of violence; affordable housing; and transportation, traffic and mobility. Another striking observation is that even within a country, life satisfaction varies significantly across cities – underscoring the importance of meeting this essential need if growth cities and businesses are to compete for highly skilled workers.

In many of the growth cities, companies are finding new ways to ensure workers’ well-being. But for cities to future-proof themselves with the kind of workers that make them competitive and enable them to leapfrog larger, tier-one cities, a coordinated effort is required from governments and businesses.

According to the study’s findings, workers do not expect any one group to be responsible for addressing the systemic issues of a city. They expect the city or local government (79%) to lead along with the support of the national or federal government (74%) and large businesses (57%).

“We are seeing the breakdown of government providing for the people,” says Hope Frank, Mercer’s global experience officer.

“For the first time, businesses have been asked to take the hero’s journey and make the world a better place for their employees. There is now a clear mandate for CEOs to, first and foremost, create a positive journey and a fulfilling future for their people. Companies that are able to rise to the occasion will be the ones that successfully attract and retain top talent.”

Have you read?

The 5 biggest challenges cities will face in the future, why every resident of us cities should live within 10 minutes of a park, these companies are helping employees get on the property ladder.

To accurately represent workers’ needs, employers must broaden their thinking and recognize that career and job opportunities are not sole motivators in talent attraction and retention. If employers do not represent the needs of workers, there is no guarantee cities will do so in a way that will be truly attractive for people to move in and remain.

To address systemic issues at scale and create environments in which people can thrive, governments, businesses and policy-makers must join forces – together they can better understand people’s needs, tackle the complex problems of rapid urbanization, find solutions to succeed in new and untold ways, and allow people to live healthier and happier lives.

Alternatively, they risk getting left behind.

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Related topics:

The agenda .chakra .wef-n7bacu{margin-top:16px;margin-bottom:16px;line-height:1.388;font-weight:400;} weekly.

A weekly update of the most important issues driving the global agenda

.chakra .wef-1dtnjt5{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;} More on Cities and Urbanization .chakra .wef-17xejub{-webkit-flex:1;-ms-flex:1;flex:1;justify-self:stretch;-webkit-align-self:stretch;-ms-flex-item-align:stretch;align-self:stretch;} .chakra .wef-nr1rr4{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;white-space:normal;vertical-align:middle;text-transform:uppercase;font-size:0.75rem;border-radius:0.25rem;font-weight:700;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;line-height:1.2;-webkit-letter-spacing:1.25px;-moz-letter-spacing:1.25px;-ms-letter-spacing:1.25px;letter-spacing:1.25px;background:none;padding:0px;color:#B3B3B3;-webkit-box-decoration-break:clone;box-decoration-break:clone;-webkit-box-decoration-break:clone;}@media screen and (min-width:37.5rem){.chakra .wef-nr1rr4{font-size:0.875rem;}}@media screen and (min-width:56.5rem){.chakra .wef-nr1rr4{font-size:1rem;}} See all

From São Paulo to Venice: 15 cities with ambitious zero-carbon projects

Victoria Masterson

April 12, 2024

The world is facing a 70% supply shortfall in low-carbon buildings, new report says – here’s why

April 11, 2024

How cities can flex their purchasing power to stimulate innovation

Sam Markey and Andrew Watkins

April 5, 2024

Global earthquakes underscore the importance of resilient infrastructure

H. Kit Miyamoto, Olivia Nielsen, Ommid Saberi and Guido Licciardi

March 27, 2024

Paris aims to host the most sustainable Olympics ever – here's how the city is preparing

March 26, 2024

City streets ranked, low-carbon building trends and other urban transformation stories you need to read

Lisa Chamberlain

March 25, 2024

• Search Menu
• Advance articles
• Featured articles
• Virtual Issues
• Browse content in B - History of Economic Thought, Methodology, and Heterodox Approaches
• Browse content in B4 - Economic Methodology
• B49 - Other
• Browse content in C - Mathematical and Quantitative Methods
• Browse content in C0 - General
• C01 - Econometrics
• Browse content in C1 - Econometric and Statistical Methods and Methodology: General
• C10 - General
• C11 - Bayesian Analysis: General
• C12 - Hypothesis Testing: General
• C13 - Estimation: General
• C14 - Semiparametric and Nonparametric Methods: General
• C15 - Statistical Simulation Methods: General
• Browse content in C2 - Single Equation Models; Single Variables
• C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
• C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
• C23 - Panel Data Models; Spatio-temporal Models
• C26 - Instrumental Variables (IV) Estimation
• Browse content in C3 - Multiple or Simultaneous Equation Models; Multiple Variables
• C31 - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
• C33 - Panel Data Models; Spatio-temporal Models
• C34 - Truncated and Censored Models; Switching Regression Models
• C35 - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
• C36 - Instrumental Variables (IV) Estimation
• Browse content in C4 - Econometric and Statistical Methods: Special Topics
• C41 - Duration Analysis; Optimal Timing Strategies
• C44 - Operations Research; Statistical Decision Theory
• Browse content in C5 - Econometric Modeling
• C50 - General
• C51 - Model Construction and Estimation
• C52 - Model Evaluation, Validation, and Selection
• C53 - Forecasting and Prediction Methods; Simulation Methods
• C54 - Quantitative Policy Modeling
• C55 - Large Data Sets: Modeling and Analysis
• C57 - Econometrics of Games and Auctions
• Browse content in C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
• C61 - Optimization Techniques; Programming Models; Dynamic Analysis
• C62 - Existence and Stability Conditions of Equilibrium
• C63 - Computational Techniques; Simulation Modeling
• C67 - Input-Output Models
• Browse content in C7 - Game Theory and Bargaining Theory
• C70 - General
• C72 - Noncooperative Games
• C73 - Stochastic and Dynamic Games; Evolutionary Games; Repeated Games
• C78 - Bargaining Theory; Matching Theory
• Browse content in C8 - Data Collection and Data Estimation Methodology; Computer Programs
• C83 - Survey Methods; Sampling Methods
• Browse content in C9 - Design of Experiments
• C90 - General
• C91 - Laboratory, Individual Behavior
• C92 - Laboratory, Group Behavior
• C93 - Field Experiments
• Browse content in D - Microeconomics
• Browse content in D0 - General
• D01 - Microeconomic Behavior: Underlying Principles
• D02 - Institutions: Design, Formation, Operations, and Impact
• D03 - Behavioral Microeconomics: Underlying Principles
• Browse content in D1 - Household Behavior and Family Economics
• D11 - Consumer Economics: Theory
• D12 - Consumer Economics: Empirical Analysis
• D13 - Household Production and Intrahousehold Allocation
• D14 - Household Saving; Personal Finance
• D15 - Intertemporal Household Choice: Life Cycle Models and Saving
• D18 - Consumer Protection
• Browse content in D2 - Production and Organizations
• D21 - Firm Behavior: Theory
• D22 - Firm Behavior: Empirical Analysis
• D23 - Organizational Behavior; Transaction Costs; Property Rights
• D24 - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
• D25 - Intertemporal Firm Choice: Investment, Capacity, and Financing
• D29 - Other
• Browse content in D3 - Distribution
• D30 - General
• D31 - Personal Income, Wealth, and Their Distributions
• Browse content in D4 - Market Structure, Pricing, and Design
• D40 - General
• D41 - Perfect Competition
• D42 - Monopoly
• D43 - Oligopoly and Other Forms of Market Imperfection
• D44 - Auctions
• D47 - Market Design
• Browse content in D5 - General Equilibrium and Disequilibrium
• D50 - General
• D51 - Exchange and Production Economies
• D52 - Incomplete Markets
• D53 - Financial Markets
• D57 - Input-Output Tables and Analysis
• D58 - Computable and Other Applied General Equilibrium Models
• Browse content in D6 - Welfare Economics
• D60 - General
• D61 - Allocative Efficiency; Cost-Benefit Analysis
• D62 - Externalities
• D63 - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
• D64 - Altruism; Philanthropy
• Browse content in D7 - Analysis of Collective Decision-Making
• D70 - General
• D71 - Social Choice; Clubs; Committees; Associations
• D72 - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
• D73 - Bureaucracy; Administrative Processes in Public Organizations; Corruption
• D74 - Conflict; Conflict Resolution; Alliances; Revolutions
• D78 - Positive Analysis of Policy Formulation and Implementation
• Browse content in D8 - Information, Knowledge, and Uncertainty
• D80 - General
• D81 - Criteria for Decision-Making under Risk and Uncertainty
• D82 - Asymmetric and Private Information; Mechanism Design
• D83 - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
• D84 - Expectations; Speculations
• D85 - Network Formation and Analysis: Theory
• D86 - Economics of Contract: Theory
• Browse content in D9 - Micro-Based Behavioral Economics
• D90 - General
• D91 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
• D92 - Intertemporal Firm Choice, Investment, Capacity, and Financing
• Browse content in E - Macroeconomics and Monetary Economics
• Browse content in E0 - General
• E03 - Behavioral Macroeconomics
• Browse content in E1 - General Aggregative Models
• E10 - General
• E12 - Keynes; Keynesian; Post-Keynesian
• E13 - Neoclassical
• E17 - Forecasting and Simulation: Models and Applications
• Browse content in E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy
• E20 - General
• E21 - Consumption; Saving; Wealth
• E22 - Investment; Capital; Intangible Capital; Capacity
• E23 - Production
• E24 - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
• E25 - Aggregate Factor Income Distribution
• E26 - Informal Economy; Underground Economy
• E27 - Forecasting and Simulation: Models and Applications
• Browse content in E3 - Prices, Business Fluctuations, and Cycles
• E30 - General
• E31 - Price Level; Inflation; Deflation
• E32 - Business Fluctuations; Cycles
• Browse content in E4 - Money and Interest Rates
• E40 - General
• E41 - Demand for Money
• E42 - Monetary Systems; Standards; Regimes; Government and the Monetary System; Payment Systems
• E43 - Interest Rates: Determination, Term Structure, and Effects
• E44 - Financial Markets and the Macroeconomy
• E49 - Other
• Browse content in E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit
• E50 - General
• E51 - Money Supply; Credit; Money Multipliers
• E52 - Monetary Policy
• E58 - Central Banks and Their Policies
• Browse content in E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
• E60 - General
• E61 - Policy Objectives; Policy Designs and Consistency; Policy Coordination
• E62 - Fiscal Policy
• E65 - Studies of Particular Policy Episodes
• Browse content in E7 - Macro-Based Behavioral Economics
• E70 - General
• E71 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy
• Browse content in F - International Economics
• Browse content in F1 - Trade
• F10 - General
• F11 - Neoclassical Models of Trade
• F12 - Models of Trade with Imperfect Competition and Scale Economies; Fragmentation
• F13 - Trade Policy; International Trade Organizations
• F14 - Empirical Studies of Trade
• F15 - Economic Integration
• F16 - Trade and Labor Market Interactions
• F17 - Trade Forecasting and Simulation
• F18 - Trade and Environment
• F19 - Other
• Browse content in F2 - International Factor Movements and International Business
• F21 - International Investment; Long-Term Capital Movements
• F22 - International Migration
• F23 - Multinational Firms; International Business
• Browse content in F3 - International Finance
• F30 - General
• F31 - Foreign Exchange
• F32 - Current Account Adjustment; Short-Term Capital Movements
• F33 - International Monetary Arrangements and Institutions
• F34 - International Lending and Debt Problems
• F36 - Financial Aspects of Economic Integration
• Browse content in F4 - Macroeconomic Aspects of International Trade and Finance
• F40 - General
• F41 - Open Economy Macroeconomics
• F42 - International Policy Coordination and Transmission
• F43 - Economic Growth of Open Economies
• F44 - International Business Cycles
• Browse content in F5 - International Relations, National Security, and International Political Economy
• F50 - General
• F53 - International Agreements and Observance; International Organizations
• Browse content in F6 - Economic Impacts of Globalization
• F60 - General
• F63 - Economic Development
• F64 - Environment
• F65 - Finance
• Browse content in G - Financial Economics
• Browse content in G0 - General
• G01 - Financial Crises
• G02 - Behavioral Finance: Underlying Principles
• Browse content in G1 - General Financial Markets
• G10 - General
• G11 - Portfolio Choice; Investment Decisions
• G12 - Asset Pricing; Trading volume; Bond Interest Rates
• G13 - Contingent Pricing; Futures Pricing
• G14 - Information and Market Efficiency; Event Studies; Insider Trading
• G15 - International Financial Markets
• G18 - Government Policy and Regulation
• Browse content in G2 - Financial Institutions and Services
• G20 - General
• G21 - Banks; Depository Institutions; Micro Finance Institutions; Mortgages
• G22 - Insurance; Insurance Companies; Actuarial Studies
• G23 - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
• G24 - Investment Banking; Venture Capital; Brokerage; Ratings and Ratings Agencies
• G28 - Government Policy and Regulation
• Browse content in G3 - Corporate Finance and Governance
• G30 - General
• G31 - Capital Budgeting; Fixed Investment and Inventory Studies; Capacity
• G32 - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
• G33 - Bankruptcy; Liquidation
• G34 - Mergers; Acquisitions; Restructuring; Corporate Governance
• G38 - Government Policy and Regulation
• Browse content in G4 - Behavioral Finance
• G40 - General
• G41 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
• Browse content in H - Public Economics
• Browse content in H0 - General
• H00 - General
• Browse content in H1 - Structure and Scope of Government
• H11 - Structure, Scope, and Performance of Government
• Browse content in H2 - Taxation, Subsidies, and Revenue
• H20 - General
• H21 - Efficiency; Optimal Taxation
• H23 - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
• H24 - Personal Income and Other Nonbusiness Taxes and Subsidies; includes inheritance and gift taxes
• H25 - Business Taxes and Subsidies
• H26 - Tax Evasion and Avoidance
• Browse content in H3 - Fiscal Policies and Behavior of Economic Agents
• H30 - General
• H31 - Household
• Browse content in H4 - Publicly Provided Goods
• H41 - Public Goods
• Browse content in H5 - National Government Expenditures and Related Policies
• H50 - General
• H51 - Government Expenditures and Health
• H52 - Government Expenditures and Education
• H53 - Government Expenditures and Welfare Programs
• H55 - Social Security and Public Pensions
• H56 - National Security and War
• Browse content in H6 - National Budget, Deficit, and Debt
• H60 - General
• H63 - Debt; Debt Management; Sovereign Debt
• Browse content in H7 - State and Local Government; Intergovernmental Relations
• H71 - State and Local Taxation, Subsidies, and Revenue
• H75 - State and Local Government: Health; Education; Welfare; Public Pensions
• Browse content in H8 - Miscellaneous Issues
• H81 - Governmental Loans; Loan Guarantees; Credits; Grants; Bailouts
• Browse content in I - Health, Education, and Welfare
• Browse content in I0 - General
• I00 - General
• Browse content in I1 - Health
• I10 - General
• I11 - Analysis of Health Care Markets
• I12 - Health Behavior
• I13 - Health Insurance, Public and Private
• I14 - Health and Inequality
• I15 - Health and Economic Development
• I18 - Government Policy; Regulation; Public Health
• Browse content in I2 - Education and Research Institutions
• I20 - General
• I21 - Analysis of Education
• I22 - Educational Finance; Financial Aid
• I23 - Higher Education; Research Institutions
• I24 - Education and Inequality
• I25 - Education and Economic Development
• I26 - Returns to Education
• I28 - Government Policy
• Browse content in I3 - Welfare, Well-Being, and Poverty
• I30 - General
• I31 - General Welfare
• I32 - Measurement and Analysis of Poverty
• I38 - Government Policy; Provision and Effects of Welfare Programs
• I39 - Other
• Browse content in J - Labor and Demographic Economics
• Browse content in J0 - General
• J00 - General
• J01 - Labor Economics: General
• J08 - Labor Economics Policies
• Browse content in J1 - Demographic Economics
• J10 - General
• J11 - Demographic Trends, Macroeconomic Effects, and Forecasts
• J12 - Marriage; Marital Dissolution; Family Structure; Domestic Abuse
• J13 - Fertility; Family Planning; Child Care; Children; Youth
• J14 - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
• J15 - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
• J16 - Economics of Gender; Non-labor Discrimination
• J17 - Value of Life; Forgone Income
• Browse content in J2 - Demand and Supply of Labor
• J20 - General
• J21 - Labor Force and Employment, Size, and Structure
• J22 - Time Allocation and Labor Supply
• J23 - Labor Demand
• J24 - Human Capital; Skills; Occupational Choice; Labor Productivity
• Browse content in J3 - Wages, Compensation, and Labor Costs
• J30 - General
• J31 - Wage Level and Structure; Wage Differentials
• J32 - Nonwage Labor Costs and Benefits; Retirement Plans; Private Pensions
• J33 - Compensation Packages; Payment Methods
• J38 - Public Policy
• Browse content in J4 - Particular Labor Markets
• J41 - Labor Contracts
• J42 - Monopsony; Segmented Labor Markets
• J44 - Professional Labor Markets; Occupational Licensing
• J47 - Coercive Labor Markets
• Browse content in J5 - Labor-Management Relations, Trade Unions, and Collective Bargaining
• J50 - General
• J52 - Dispute Resolution: Strikes, Arbitration, and Mediation; Collective Bargaining
• Browse content in J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers
• J60 - General
• J61 - Geographic Labor Mobility; Immigrant Workers
• J62 - Job, Occupational, and Intergenerational Mobility
• J63 - Turnover; Vacancies; Layoffs
• J64 - Unemployment: Models, Duration, Incidence, and Job Search
• J65 - Unemployment Insurance; Severance Pay; Plant Closings
• J68 - Public Policy
• Browse content in J7 - Labor Discrimination
• J71 - Discrimination
• Browse content in J8 - Labor Standards: National and International
• J81 - Working Conditions
• J82 - Labor Force Composition
• J83 - Workers' Rights
• Browse content in K - Law and Economics
• Browse content in K0 - General
• K00 - General
• Browse content in K1 - Basic Areas of Law
• K14 - Criminal Law
• Browse content in K3 - Other Substantive Areas of Law
• K31 - Labor Law
• K33 - International Law
• K35 - Personal Bankruptcy Law
• Browse content in K4 - Legal Procedure, the Legal System, and Illegal Behavior
• K40 - General
• K41 - Litigation Process
• K42 - Illegal Behavior and the Enforcement of Law
• Browse content in L - Industrial Organization
• Browse content in L0 - General
• L00 - General
• Browse content in L1 - Market Structure, Firm Strategy, and Market Performance
• L10 - General
• L11 - Production, Pricing, and Market Structure; Size Distribution of Firms
• L12 - Monopoly; Monopolization Strategies
• L13 - Oligopoly and Other Imperfect Markets
• L14 - Transactional Relationships; Contracts and Reputation; Networks
• L15 - Information and Product Quality; Standardization and Compatibility
• Browse content in L2 - Firm Objectives, Organization, and Behavior
• L20 - General
• L22 - Firm Organization and Market Structure
• L23 - Organization of Production
• L25 - Firm Performance: Size, Diversification, and Scope
• Browse content in L3 - Nonprofit Organizations and Public Enterprise
• L31 - Nonprofit Institutions; NGOs; Social Entrepreneurship
• Browse content in L4 - Antitrust Issues and Policies
• L41 - Monopolization; Horizontal Anticompetitive Practices
• L42 - Vertical Restraints; Resale Price Maintenance; Quantity Discounts
• L43 - Legal Monopolies and Regulation or Deregulation
• Browse content in L5 - Regulation and Industrial Policy
• L50 - General
• L51 - Economics of Regulation
• Browse content in L6 - Industry Studies: Manufacturing
• L60 - General
• L62 - Automobiles; Other Transportation Equipment; Related Parts and Equipment
• L63 - Microelectronics; Computers; Communications Equipment
• Browse content in L7 - Industry Studies: Primary Products and Construction
• L71 - Mining, Extraction, and Refining: Hydrocarbon Fuels
• Browse content in L8 - Industry Studies: Services
• L81 - Retail and Wholesale Trade; e-Commerce
• L82 - Entertainment; Media
• Browse content in L9 - Industry Studies: Transportation and Utilities
• L93 - Air Transportation
• L94 - Electric Utilities
• L96 - Telecommunications
• Browse content in M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
• Browse content in M0 - General
• M00 - General
• Browse content in M1 - Business Administration
• M11 - Production Management
• M14 - Corporate Culture; Social Responsibility
• Browse content in M2 - Business Economics
• M21 - Business Economics
• Browse content in M3 - Marketing and Advertising
• M31 - Marketing
• M37 - Advertising
• Browse content in M5 - Personnel Economics
• M50 - General
• M51 - Firm Employment Decisions; Promotions
• M52 - Compensation and Compensation Methods and Their Effects
• M54 - Labor Management
• M55 - Labor Contracting Devices
• Browse content in N - Economic History
• Browse content in N1 - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations
• N10 - General, International, or Comparative
• N13 - Europe: Pre-1913
• Browse content in N2 - Financial Markets and Institutions
• N20 - General, International, or Comparative
• Browse content in N3 - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy
• N31 - U.S.; Canada: Pre-1913
• N32 - U.S.; Canada: 1913-
• N33 - Europe: Pre-1913
• N34 - Europe: 1913-
• Browse content in N4 - Government, War, Law, International Relations, and Regulation
• N42 - U.S.; Canada: 1913-
• N43 - Europe: Pre-1913
• N44 - Europe: 1913-
• N45 - Asia including Middle East
• Browse content in N9 - Regional and Urban History
• N90 - General, International, or Comparative
• N92 - U.S.; Canada: 1913-
• N94 - Europe: 1913-
• Browse content in O - Economic Development, Innovation, Technological Change, and Growth
• Browse content in O1 - Economic Development
• O10 - General
• O11 - Macroeconomic Analyses of Economic Development
• O12 - Microeconomic Analyses of Economic Development
• O13 - Agriculture; Natural Resources; Energy; Environment; Other Primary Products
• O14 - Industrialization; Manufacturing and Service Industries; Choice of Technology
• O15 - Human Resources; Human Development; Income Distribution; Migration
• O16 - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
• O17 - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements
• O18 - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
• Browse content in O2 - Development Planning and Policy
• O23 - Fiscal and Monetary Policy in Development
• Browse content in O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
• O30 - General
• O31 - Innovation and Invention: Processes and Incentives
• O32 - Management of Technological Innovation and R&D
• O33 - Technological Change: Choices and Consequences; Diffusion Processes
• O34 - Intellectual Property and Intellectual Capital
• O38 - Government Policy
• Browse content in O4 - Economic Growth and Aggregate Productivity
• O40 - General
• O41 - One, Two, and Multisector Growth Models
• O42 - Monetary Growth Models
• O43 - Institutions and Growth
• O44 - Environment and Growth
• O47 - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
• Browse content in O5 - Economywide Country Studies
• O51 - U.S.; Canada
• O55 - Africa
• Browse content in P - Economic Systems
• Browse content in P0 - General
• P00 - General
• Browse content in P2 - Socialist Systems and Transitional Economies
• P26 - Political Economy; Property Rights
• Browse content in Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
• Browse content in Q1 - Agriculture
• Q15 - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
• Q16 - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
• Browse content in Q3 - Nonrenewable Resources and Conservation
• Q33 - Resource Booms
• Browse content in Q4 - Energy
• Q41 - Demand and Supply; Prices
• Q43 - Energy and the Macroeconomy
• Browse content in Q5 - Environmental Economics
• Q51 - Valuation of Environmental Effects
• Q53 - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
• Q54 - Climate; Natural Disasters; Global Warming
• Q55 - Technological Innovation
• Q56 - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
• Q58 - Government Policy
• Browse content in R - Urban, Rural, Regional, Real Estate, and Transportation Economics
• Browse content in R1 - General Regional Economics
• R10 - General
• R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
• R12 - Size and Spatial Distributions of Regional Economic Activity
• R13 - General Equilibrium and Welfare Economic Analysis of Regional Economies
• R15 - Econometric and Input-Output Models; Other Models
• Browse content in R2 - Household Analysis
• R21 - Housing Demand
• R23 - Regional Migration; Regional Labor Markets; Population; Neighborhood Characteristics
• Browse content in R3 - Real Estate Markets, Spatial Production Analysis, and Firm Location
• R31 - Housing Supply and Markets
• R33 - Nonagricultural and Nonresidential Real Estate Markets
• Browse content in R4 - Transportation Economics
• R41 - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
• R48 - Government Pricing and Policy
• Browse content in R5 - Regional Government Analysis
• R51 - Finance in Urban and Rural Economies
• Browse content in Z - Other Special Topics
• Browse content in Z1 - Cultural Economics; Economic Sociology; Economic Anthropology
• Z10 - General
• Z12 - Religion
• Z13 - Economic Sociology; Economic Anthropology; Social and Economic Stratification
• Author Guidelines
• Submission Site
• Open Access
• About The Review of Economic Studies
• Editorial Board
• Advertising and Corporate Services
• Self-Archiving Policy
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

1. introduction, 3. static benefits of bigger cities, 4. dynamic benefits of bigger cities, 5. the interaction between ability and the learning benefits of bigger cities, 7. conclusions, acknowledgments, 8 supplementary data.

• < Previous

Learning by Working in Big Cities

• Article contents
• Figures & tables
• Supplementary Data

Jorge De La Roca, Diego Puga, Learning by Working in Big Cities, The Review of Economic Studies , Volume 84, Issue 1, January 2017, Pages 106–142, https://doi.org/10.1093/restud/rdw031

• Permissions Icon Permissions

Individual earnings are higher in bigger cities.We consider three reasons: spatial sorting of initially more productive workers, static advantages from workers' current location, and learning by working in bigger cities. Using rich administrative data for Spain, we find that workers in bigger cities do not have higher initial unobserved ability as reflected in fixed effects. Instead, they obtain an immediate static premium and accumulate more valuable experience. The additional value of experience in bigger cities persists after leaving and is stronger for those with higher initial ability. This explains both the higher mean and greater dispersion of earnings in bigger cities.

Quantifying the productive advantages of bigger cities and understanding their nature are among the most fundamental questions in urban economics. The productive advantages of bigger cities manifest in the higher productivity of establishments located in them ( e.g . Henderson, 2003 ; Combes et al ., 2012a ). They also show up in workers’ earnings. Workers in bigger cities earn more than workers in smaller cities and rural areas. Figure 1 plots mean annual earnings for male employees against city size for Spanish urban areas. Workers in Madrid earn 31,000 euros annually on average, which is 21% more than workers in Valencia (the country’s third biggest city), 46% more than workers in Santiago de Compostela (the median-sized city), and 55% more than workers in rural areas. The relationship between earnings and city size is just as strong in other developed countries. 1 Moreover, differences remain large even when we compare workers with the same education and years of experience and in the same industry.

Mean earnings and city size

Looking at workers’ earnings instead of at firms’ productivity is worthwhile because it can be informative about the nature of the productive advantages that bigger cities provide. There are three broad reasons why firms may be willing to pay more to workers in bigger cities. First, there may be some static advantages associated with bigger cities that are enjoyed while working there and lost upon moving away. These static agglomeration economies have received the most attention (see Duranton and Puga, 2004 , for a review of possible mechanisms and Rosenthal and Strange, 2004 ; Puga, 2010 , and Holmes, 2010 , for summaries of the evidence). Secondly, workers who are inherently more productive may choose to locate in bigger cities. Evidence on such sorting is mixed, but some recent accounts ( e.g. Combes et al ., 2008 ) suggest it may be as important in magnitude as static agglomeration economies. Thirdly, a key advantage of cities is that they facilitate experimentation and learning ( Glaeser, 1999 ; Duranton and Puga, 2001 ). In particular, bigger cities may provide workers with opportunities to accumulate more valuable experience. Since these dynamic advantages are transformed in higher human capital, they may remain beneficial even when a worker relocates.

In this article, we simultaneously examine these three potential sources of the city size earnings premium: static advantages, sorting based on initial ability, and dynamic advantages. For this purpose, we use a rich administrative data set for Spain that follows workers over time and across locations throughout their careers, thus allowing us to compare the earnings of workers in cities of different sizes, while controlling for measures of ability and the experience previously acquired in various other cities.

To facilitate a comparison with previous studies, we begin our empirical analysis in section 3 with a simple pooled ordinary least squares (OLS) estimation of the static advantages of bigger cities. For this, we estimate a regression of log earnings on worker and job characteristics and city fixed effects. In a second stage, we regress the estimated city fixed effects on a measure of log city size. This yields a pooled-OLS elasticity of the earnings premium with respect to city size of |$0.0455$|⁠ . The first stage of this estimation ignores both the possible sorting of workers with higher unobserved ability into bigger cities as well as any additional value of experience accumulated in bigger cities. Thus, this basic estimation strategy produces a biased estimate of the static advantages of bigger cities and no assessment of the possible importance of dynamic advantages or sorting.

Glaeser and Maré (2001) and, more recently, Combes et al . (2008) introduce worker fixed effects to address the issue of workers sorting on unobserved ability into bigger cities. When we follow this strategy, the estimated elasticity of the earnings premium with respect to city size drops substantially to |$0.0241$|⁠ , in line with their findings. This decline is usually interpreted as evidence of more productive workers sorting into bigger cities ( e.g. Combes et al ., 2008 ). We show instead that this drop can be explained by workers’ sorting on ability, by the importance of dynamic benefits in bigger cities, or by a combination of both.

We then introduce dynamic benefits of bigger cities into the analysis in section 4 . Our augmented specification for log earnings now provides a joint estimation of the static and dynamic advantages of bigger cities, while allowing for unobserved worker heterogeneity. By tracking the complete workplace location histories of a large panel of workers, we let the value of experience vary depending on both where it was acquired and where it is being used. Experience accumulated in bigger cities is substantially more valuable than experience accumulated in smaller cities. Furthermore, the additional value of experience acquired in bigger cities is maintained when workers relocate to smaller cities. This suggests there are important learning benefits to working in bigger cities that get embedded in workers’ human capital.

Our results indicate that where workers acquire experience matters more than where they use it. Nevertheless, for workers who relocate from small to big cities, previous experience is more highly valued in their new job location. This finding has implications for earnings profiles at different stages of workers’ life cycle: more experienced workers obtain a higher immediate gain upon relocating to one of the biggest cities but then see their earnings increase more slowly than less experienced workers.

In section 5 , a final generalization of our log earnings specification explores heterogeneity across workers in the dynamic advantages of bigger cities. 3 Our estimates show that the additional value of experience acquired in bigger cities is even greater for workers with higher ability, as proxied by their worker fixed effects.

Once we address the sources of bias in the first stage of the log earnings estimation, we proceed to estimate again the elasticity of earnings with respect to city size. We now distinguish between a short-term elasticity that captures the static advantages of bigger cities— i.e. the boost in earnings workers obtain upon moving into a big city—and a medium-term elasticity that further encompasses the learning benefits that workers get after working in a big city for several years. The estimated medium-term elasticity of |$0.0510$| is more than twice as large as the short-term elasticity of |$0.0223$| implying that, in the medium term, about half of the gains from working in bigger cities are static and about half are dynamic.

We show that the higher value of experience acquired in bigger cities can almost fully account for the difference between pooled OLS and fixed-effects estimates of the static earnings premium of bigger cities. This suggests that, while the dynamic advantages of bigger cities are important, sorting may play a minor role. To verify this implication, in section 6 , we compare the distribution of workers’ ability across cities of different sizes. This exercise relates to recent studies that also compare workers’ skills across big and small cities, either by looking at levels of education ( e.g. Berry and Glaeser, 2005 ), at broader measures of skills ( e.g. Bacolod et al ., 2009 ), at measures of skills derived from a spatial equilibrium model ( e.g . Eeckhout et al ., 2014 ), or at estimated worker fixed effects ( e.g. Combes et al ., 2012b ). We focus on worker fixed effects because we are interested in capturing time-invariant ability net of the extra value of big city experience.

We find sorting based on unobservables to be much less important than previously thought. Although there is clear sorting on observables by broad occupational skill groups (we use five categories), within these broad groups, there is little further sorting on unobserved ability. Workers in big and small cities are not particularly different to start with; it is largely working in cities of different sizes that makes their earnings diverge. Workers attain a static earnings premium upon arrival in a bigger city and accumulate more valuable experience as they spend more time working there. This finding is consistent with the counterfactual simulations of the structural model in Baum-Snow and Pavan (2012) , which suggest that returns to experience and wage-level effects are the most important mechanisms contributing to the overall city size earnings premium. 4 Because these gains are stronger for workers with higher unobserved ability, this combination of effects explains not only the higher mean but also the greater dispersion of earnings in bigger cities that Combes et al . (2012b); , Baum-Snow and Pavan (2013) and Eeckhout et al . (2014) emphasize.

Employment histories and earnings

Our main data set is Spain’s Continuous Sample of Employment Histories ( Muestra Continua de Vidas Laborales or MCVL). This is an administrative data set with longitudinal information obtained by matching social security, income tax, and census records for a 4% non-stratified random sample of the population who in a given year have any relationship with Spain’s Social Security (individuals who are working, receiving unemployment benefits, or receiving a pension).

The unit of observation in the social security data contained in the MCVL is any change in the individual’s labour market status or any variation in job characteristics (including changes in occupation or contractual conditions within the same firm). The data record all changes since the date of first employment, or since 1980 for earlier entrants. Using this information, we construct a panel with monthly observations tracking the working life of individuals in the sample. On each date, we know the individual’s labour market status and, if working, the occupation and type of contract, working hours expressed as a percentage of a full-time equivalent job, the establishment’s sector of activity at the NACE three-digit level, and the establishment’s location. Furthermore, by exploiting the panel dimension, we can construct precise measures of tenure and experience, calculated as the actual number of days the individual has been employed, respectively, in the same establishment and overall. We can also track cumulative experience in different locations or sets of locations.

The MCVL also includes earnings data obtained from income tax records. Gross labour earnings are recorded separately for each job and are not subjected to any censoring. Each source of labour income is matched between income tax records and social security records based on both employee and employer (anonymized) identifiers. This allows us to compute monthly labour earnings, expressed as euros per day of full-time equivalent work. 5

Each MCVL edition includes social security records for the complete labour market history of individuals included in that edition, but only includes income tax records for the year of that particular MCVL edition. Thus, we combine multiple editions of the MCVL, beginning with the first produced, for 2004, to construct a panel that has the complete labour market history since 1980 and uncensored earnings since 2004 for a random sample of approximately 4% of all individuals who have worked, received benefits or a pension in Spain at any point since 2004. This is possible because the criterion for inclusion in the MCVL (based on the individual’s permanent Tax Identification Number) as well as the algorithm used to construct the individual’s anonymized identifier are maintained across MCVL editions. Combining multiple waves has the additional advantage of maintaining the representativeness of the sample throughout the study period, by enlarging the sample to include individuals who have an affiliation with the Social Security in one year but not in another. 6

A crucial feature of the MCVL for our purposes is that workers can be tracked across space based on their workplace location. Social Security legislation requires employers to keep separate contribution account codes for each province in which they conduct business. Furthermore, within a province, a municipality identification code is provided if the workplace establishment is located in a municipality with population greater than 40,000 inhabitants.

The MCVL also provides individual characteristics contained in social security records, such as age and gender, and also matched characteristics contained in Spain’s Continuous Census of Population (Padrón Continuo), such as country of birth, nationality, and educational attainment. 7

2.1. Sample restrictions

Our starting sample is a monthly data set for men aged 18 and over with Spanish citizenship born in Spain since 1962 and employed at any point between January 2004 and December 2009. We focus on men due to the huge changes experienced by Spain’s female labour force during the period over which we track labour market experience. Most notably, the participation rate for prime-age women (25–54) increased from 30% in 1980 to 77% in 2009. Nevertheless, some results for women are provided in section 4 . We leave out those born before 1962 because we cannot track their full labour histories. We also leave out foreign-born workers because we do not have their labour histories before immigrating to Spain and because they are likely to be quite different from natives. We track workers over time throughout their working lives to compute their job tenure and their work experience in different urban areas, but study their earnings only when employed in 2004–2009. In particular, we regress individual monthly earnings in 2004–2009 on a set of characteristics that capture the complete prior labour history of each individual. 8 We exclude spells workers spend as self-employed because labour earnings are not available during such periods, but still include job spells as employees for the same individuals. This initial sample has 246,941 workers and 11,885,511 monthly observations.

Job spells in the Basque Country and Navarre are excluded because we do not have earnings data from income tax records for them as these autonomous regions collect income taxes independently from Spain’s national government. We also exclude job spells in three small urban areas and in rural areas because workplace location is not available for municipalities with population below 40,000—and because our focus is comparing urban areas of different sizes. Nevertheless, the days worked in urban areas within the Basque Country or Navarre, in the three small excluded urban areas, or in rural areas anywhere in the country are still counted when computing cumulative experience (both overall experience and experience by location). These restrictions reduce the sample to 185,628 workers and 7,504,602 monthly observations.

Job spells in agriculture, fishing, mining, and other extractive industries are excluded because these activities are typically rural and are covered by special social security regimes where workers tend to self-report earnings and the number of working days recorded is not reliable. Job spells in the public sector, international organizations, and in education and health services are also left out because earnings in these sectors are heavily regulated by the national and regional governments. Apprenticeship contracts and certain rare contract types are also excluded. Finally, we drop workers who have not worked at least 30 days in any year. This yields our final sample of 157,113 workers and 6,263,446 monthly observations.

2.2. Urban areas

We use official urban area definitions, constructed by Spain’s Ministry of Housing in 2008 and maintained unchanged since then. The 85 urban areas account for 68% of Spain’s population and 10% of its surface. Four urban areas have populations above 1 million, Madrid being the largest with 5,966,067 inhabitants in 2009. At the other end, Teruel is the smallest with 35,396 inhabitants in 2009. Urban areas contain 747 municipalities out of the over 8,000 that exhaustively cover Spain. There is large variation in the number of municipalities per urban area. The urban area of Barcelona is made up of 165 municipalities, while 21 urban areas contain a single municipality.

Three urban areas (Sant Feliú de Guixols, Soria, and Teruel) have no municipality with a population of at least 40,000, and are not included in the analysis since they cannot be identified in the MCVL. We must also exclude the four urban areas in the Basque Country and Navarre (Bilbao, San Sebastián, Vitoria and Pamplona) because we lack earnings from tax returns data since the Basque Country and Navarre collect income taxes independently. Last, we exclude Ceuta and Melilla given their special enclave status in continental Africa. This leaves 76 urban areas for which we carry out our analysis.

To measure the size of each urban area, we calculate the number of people within 10 km of the average person in the urban area. We do so on the basis of the 1-km-resolution population grid for Spain in 2006 created by Goerlich and Cantarino (2013) . They begin with population data from Spain’s Continuous Census of Population (Padrón Continuo) at the level of the approximately 35,000 census tracts (áreas censales) that cover Spain. Within each tract, they allocate population to 1 |$\times$| 1 km cells based on the location of buildings as recorded in high-resolution remote sensing data. We take each |$1\times 1$| km cell in the urban area, trace a circle of radius 10 km around the cell (encompassing both areas inside and outside the urban area), count population in that circle, and average this count over all cells in the urban area weighting by the population in each cell. This yields the number of people within 10 km of the average person in the urban area.

Our measure of city size is very highly correlated with a simple population count (the correlation being |$0.94$|⁠ ), but deals more naturally with unusual urban areas, in particular those that are polycentric. Most urban areas in Spain comprise a single densely populated urban centre and contiguous areas that are closely bound to the centre by commuting and employment patterns. However, a handful of urban areas are made up of multiple urban centres. A simple population count for these polycentric urban areas tends to exaggerate their scale, because to maintain contiguity they incorporate large intermediate areas that are often only weakly connected to the various centres. For instance, the urban area of Asturias incorporates the cities of Gijón, Oviedo, Avilés, Mieres, and Langreo as well as large areas in between. A simple population count would rank the urban area of Asturias sixth in terms of its 2009 population (835,231), just ahead of Zaragoza (741,132). Our measure of scale ranks Asturias nineteenth in terms of people within 10 km of the average person (203,817) and Zaragoza fifth (583,774), which is arguably a more accurate characterization of their relative scale. Our measure of city size also has some advantages over density, another common measure of urban scale, because it is less subject to the noise introduced by urban boundaries which are drawn with very different degree of tightness around built-up areas. This noise arises because some of the underlying areas on the basis of which urban definitions are drawn (municipalities in our case) include large green areas well beyond the edge of the city, which gives them an unusually large surface area and artificially lowers their density.

It is worth emphasizing that we assign workers to urban areas at each point in time based on the municipality of their workplace. Thus, when we talk about migrations we refer to workers taking a job in a different urban area. Each year about 7% of workers change jobs across urban areas throughout our study period. 9

Equation ( 1 ) allows for a static earnings premium associated with currently working in a bigger city, if the city fixed effect |$\sigma_c$| is positively correlated with city size. It also allows for the sorting of more productive workers into bigger cities, if the worker fixed effect |$\mu_i$| is positively correlated with city size. Finally, it lets the experience accumulated in city |$j$| to have a different value which may be positively correlated with city size. This value of experience |$\delta_{jc}$| is indexed by both |$j$| (the city where experience was acquired) and |$c$| (the city where the worker currently works). In our estimations, we also allow experience to have a non-linear effect on log earnings but to simplify the exposition we only include linear terms in equation ( 1 ). 11

We shall eventually estimate an equation like ( 1 ). However, to facilitate comparisons with earlier studies and to highlight the importance of considering the dynamic advantages of bigger cities, we begin by estimating simpler and more restrictive equations that allow only for static benefits.

3.1. Static pooled estimation

Compared with equation ( 1 ), in equation ( 2 ) the worker fixed effect |$\mu_i$| and the terms capturing the differential value of experience for each city |$\smash{\sum_{j=1}^{C}} \delta_{jc} e_{ijt}$| are missing. We can estimate equation ( 11 ) by ordinary least squares using the pooled panel of workers.

Column (1) in Table 1 shows the results of such estimation. As we would expect, log earnings are concave in overall experience and tenure in the firm and increase monotonically with occupational skills. 12 Having tertiary education and working under a full-time and permanent contract are also associated with higher earnings.

Estimation of the static city size earnings premium

Notes : All specifications include a constant term. Columns (1) and (3) include month–year indicators, two-digit sector indicators, and contract-type indicators. Coefficients are reported with robust standard errors in parenthesis, which are clustered by worker in columns (1) and (3). |$^{***}$|⁠ , |$^{**}$|⁠ , and |$^*$| indicate significance at the 1, 5, and 10% levels. The |$R^2$| reported in column (3) is within workers. Worker values of experience and tenure are calculated on the basis of actual days worked and expressed in years.

Static OLS estimation of the city size premium

Figure 2 plots the city fixed effects estimated in column (1) against log city size. We find notable geographic differences in earnings even for observationally equivalent workers. For instance, a worker in Madrid earns 18% more than a worker with the same observable characteristics in Utrera—the smallest city in our sample. The largest earning differential of 34% is found between workers in Barcelona and Lugo. Column (2) in Table 1 regresses the city fixed effects estimated in column (1) on our measure of log city size. This yields an elasticity of the earnings premium with respect to city size of |$0.0455$|⁠ . This pooled OLS estimate of the elasticity of the earnings premium with respect to city size reflects that doubling city size is associated with an approximate increase of 5% in earnings over an above any differences attributable to differences in education, overall experience, occupation, sector, or tenure in the firm. City size is a powerful predictor of differences in earnings as it can explain about a quarter of the variation that is left after controlling for observable worker characteristics ( ⁠|$R^2$| of |$0.2406$| in column (2). 13

The pooled OLS estimate of the elasticity of interest, |$0.046$| in column (2), is in line with previous estimates that use worker-level data with similar sample restrictions. Combes et al . (2010) find an elasticity of |$0.051$| for France while Glaeser and Resseger (2010) obtain an elasticity of |$0.041$| for the U.S. 14

Equation ( 5 ) shows that a static cross-section or pooled OLS estimation of |$\sigma_c$| suffers from two key potential sources of bias. First, it ignores sorting, and thus the earnings premium for city |$c$|⁠ , |$\sigma_c$|⁠ , is biased upwards if individuals with high unobserved ability, |$\mu_i$|⁠ , are more likely to work there, so that |$\text{Cov}(\iota_{ict},\,\mu_i)>0$| (and biased downwards in the opposite case). Secondly, it ignores dynamic effects, and thus the earnings premium for city |$c$|⁠ , |$\sigma_c$|⁠ , is biased upwards if individuals with more valuable experience, |$\sum_{j=1}^{C} \delta_{jc} e_{ijt}$|⁠ , are more likely to work there, so that |$\text{Cov}(\iota_{ict},\, \smash{\sum_{j=1}^{C}} \delta_{jc} e_{ijt}) > 0$| (and biased downwards in the opposite case). 15

To see how these biases work more clearly, it is useful to consider a simple example. Suppose there are just two cities, one big and one small. Everyone working in the big city enjoys an instantaneous (static) log wage premium of |$\sigma$|⁠ . Workers in the big city have higher unobserved ability, which increases their log wage by |$\mu$|⁠ . Otherwise, all workers are initially identical. Over time, experience accumulated in the big city increases log wage by |$\delta$| per period relative to having worked in the small city instead. For now, assume there is no migration. If there are |$n$| time periods, then the pooled OLS estimate of the static big city premium |$\sigma$| has probability limit |$\text{plim}\,\hat{\sigma}_{\text{pooled}} = \sigma + \mu + \frac{1+n}{2}\delta$|⁠ . Thus, a pooled OLS regression overestimates the actual premium by the value of higher unobserved worker ability in the big city ( ⁠|$\mu$|⁠ ) and the higher average value of accumulated experience in the big city ( ⁠|$\frac{1+n}{2}\delta$|⁠ ).

3.2. Static fixed-effects estimation

Note that |$\sigma_c$| is now estimated only on the basis of migrants—for workers who are always observed in the same city |$\iota_{ict} = \bar{\iota}_{ic} = 1$| every period—while all other coefficients are estimated by exploiting time variation and job changes within workers’ lives. 16

In column (3) of Table 1 , we present results for this specification, which adds worker fixed effects to the pooled OLS specification of column (1). Then, in column (4) we regress the city fixed effects from column (3) on our measure of log city size. The estimated elasticity of the earnings premium with respect to city size of column (4) drops substantially relative to column (2), from |$0.0455$| to |$0.0241$|⁠ . 17 This drop is in line with previous studies. When worker fixed effects are introduced, Combes et al . (2010) see a decline in the elasticity of 35%, while Mion and Naticchioni (2009) report a larger drop of 66% for Italy. Our estimated drop of 47% lies in between both.

Worker fixed effects take care of unobserved worker heterogeneity. However, the estimate of |$\sigma_c$| is still biased because dynamic effects are ignored. The earnings premium for city |$c$| is biased upwards if the value of workers’ experience tends to be above their individual averages in the periods when they are located in city |$c$|⁠ . It is biased downwards when the reverse is true.

Again, to see how this bias works more clearly, it is instructive to use the same simple two-city example as for the pooled OLS estimate. Like before, assume everyone working in the big city enjoys an instantaneous (static) log wage premium of |$\sigma$|⁠ . Workers in the big city have higher unobserved ability, which increases their log wage by |$\mu$|⁠ . Otherwise, all workers are initially identical. Over time, experience accumulated in the big city increases log wage by |$\delta$| per period relative to having worked in the small city instead. Since with worker fixed effects |$\sigma_c$| are estimated only on the basis of migrants, we add migration to the example. Consider two opposite cases.

First, suppose all migration is from the small to the big city and takes place after migrants have worked in the small city for the first |$m$| periods of the total of |$n$| periods. The fixed-effects estimate of the static big city premium |$\sigma$| is now estimated by comparing the earnings of migrants before and after moving and has probability limit |$\text{plim} \; \hat{\sigma}_{\text{{FE}}} = \sigma + \frac{1+n-m}{2}\delta$|⁠ . With all migrants moving from the small to the big city, the fixed-effects regression overestimates the actual static premium ( ⁠|$\sigma$|⁠ ) by the average extra value of the experience migrants accumulate by working in the big city after moving there ( ⁠|$\frac{1+n-m}{2}\delta$|⁠ ). The estimation of equation ( 6 ) forces the earnings premium to be a pure jump at the time of moving, while in the example the premium actually has both static and dynamic components. Not trying to separately measure the dynamic component not only ignores it, but also makes the static part seem larger than it is.

Consider next the case where all migration is in the opposite direction, from the big to the small city. Suppose migration still takes place after migrants have worked in the big city for the first |$m$| periods of the total of |$n$| periods. Now, we also need to know whether the extra value of experience accumulated in the big city is fully portable or only partially so. Assume only a fraction |$\theta$| is portable, where |$0\leqslant\theta\leqslant 1$|⁠ . The fixed-effects estimate of the static big city premium |$\sigma$| then has probability limit |$\text{plim} \; \hat{\sigma}_{\text{{FE}}} = \sigma + \left( \frac{1+m}{2} - \theta m \right) \delta$|⁠ . With all migrants moving from the big to the small city, the fixed-effects regression differs from the actual static premium ( ⁠|$\sigma$|⁠ ) by the difference between the value of the average big city experience for migrants prior to moving |$\frac{1+m}{2}\delta$| and the (depreciated) value of the big city experience that migrants take with them after leaving the big city |$ \theta m \delta$|⁠ . If the additional value of experience accumulated in big cities is sufficiently portable, |$\sigma$| is underestimated on the basis of migrants from big to small cities. 18 By forcing both the static and dynamic premium to be captured by a discrete jump, the jump now appears to be smaller than it is. Moreover, the dynamic part is still not separately measured.

This example shows that the estimation with worker fixed effects deals with the possible sorting of workers across cities on time-invariant unobservable characteristics. However, the estimates of city fixed effects are still biased due to the omission of dynamic benefits. This, in turn, biases any estimate of the static earnings premium associated with currently working in bigger cities. Migrants from small to big cities tend to bias the static city size premium upwards (their average wage difference across cities is “too high” because when in big cities they benefit from the more valuable experience they are accumulating there). Migrants from big to small cities tend to bias the static city size premium downwards (their average wage difference across cities is “too low” because when in small cities they still benefit from the more valuable experience accumulated in big cities).

In practice, the bias is likely to be small if the sample is more or less balanced in terms of migration flows across cities of different sizes, and the learning benefits of bigger cities are highly portable (in the example, if |$\theta$| is close to 1). The first condition, that migration is balanced, holds in our data and, likely, in many other contexts. 19 The second condition, that the learning benefits of bigger cities are highly portable, is one that we can only verify by estimating the fully fledged specification of equation ( 1 ).

Combes et al . (2008) interpret the drop in the elasticity of the earnings premium with respect to city size (in our case, the drop in the elasticity between columns (2) and (4) in Table 1 ) as evidence of the importance of sorting by more productive workers into bigger cities. However, we have shown that by ignoring the dynamic component of the premium, we can affect the magnitude of the bias in the estimated static city size premium. The lower static earnings premium found when using worker fixed effects could thus reflect either the importance of sorting by workers across cities in a way that is systematically related to unobserved ability, or the importance of learning by working in bigger cities, or a combination of both. We cannot know unless we simultaneously consider the static and the dynamic components of the earnings premium while allowing for unobserved worker heterogeneity. However, the main reason to study the dynamic component explicitly is that it may be an important part of the benefits that bigger cities provide in the medium term. Thus, we wish to quantify the magnitude of these dynamic benefits.

Estimation of the dynamic and static city size earnings premia

Notes : All regressions include a constant term. Column (1) includes month–year indicators, two-digit sector indicators, and contract-type indicators. Coefficients are reported with robust standard errors in parenthesis, which are clustered by worker in column (1). |$^{***}$|⁠ , |$^{**}$|⁠ , and |$^*$| indicate significance at the 1, 5, and 10% levels. The |$R^2$| reported in column (1) is within workers. Worker values of experience and tenure are calculated on the basis of actual days worked and expressed in years. City medium-term premium calculated for workers’ average experience in one city (7.72 years).

We now turn to a joint estimation of the static and dynamic components of the earnings premium of bigger cities while allowing for unobserved worker heterogeneity. This involves our full earnings specification of equation ( 1 ), in which the value of a worker’s experience is allowed to vary depending both on where it was acquired and on where the worker is currently employed. In column (1) of Table 2 , we add to the first-stage specification of column (3) of Table 1 the experience accumulated in the two biggest cities—Madrid and Barcelona. We also add the experience accumulated in the next three biggest cities—Valencia, Sevilla, and Zaragoza. We still include overall experience in the specification, so that it now captures the value of experience acquired outside of the five biggest cities. 20 Just as we included the square of experience in earlier specifications to let the value of additional experience decay for workers with more experience, we also now interact experience in the two biggest cities and experience in the third to fifth biggest cities with overall experience. 21 Our results indicate that experience accumulated in bigger cities is more valuable than experience accumulated elsewhere. For instance, the first year of experience in Madrid or Barcelona raises earnings by 3.1% relative to having worked that same year in a city below the top five ( i.e. , |$e^{0.0309-0.0008} - 1$|⁠ ). The first year of experience in a city ranked third to fifth raises earnings by 1.5% relative to having worked that same year in a city below the top five. We have also tried finer groupings of cities by size (not reported), but found no significant differences in the value of experience within the reported groupings ( e.g. between Madrid and Barcelona).

We also allow for the value of experience accumulated in bigger cities to vary depending on where it is used. For this purpose, we include interactions between years of experience accumulated in each of three city size classes (first to second biggest, third to fifth biggest, and outside the top five) and an indicator for currently working in one of the five biggest cities. We also include further interactions with overall experience to allow for non-linear effects. Our results show that the value of experience acquired in the two biggest cities, as reflected in earnings, is not significantly different if a worker moves away to work in a city below the top five. The same finding holds for the value of experience acquired in the third to fifth biggest cities. Both results suggest that the additional value of experience acquired in bigger cities is highly portable. At the same time, the positive and statistically significant coefficient on the interaction between experience acquired outside the five biggest cities and an indicator for currently working in the five biggest cities shows that, for workers relocating from smaller cities to the biggest, previous experience is more highly valued in their new job location.

Overall, where workers acquire experience matters more than where they use it. A first year of experience raises earnings an additional 3.1% if this was acquired in the two biggest cities instead of outside the top five, regardless of where the worker is currently employed. In comparison, a first year of experience raises earnings an additional 0.6% if this is subsequently used in the five biggest cities instead of outside the top five, and only when that experience was gathered outside the five biggest cities. As noted above, experience acquired in the two biggest cities is equally valuable everywhere, as is experience acquired in the third to fifth biggest. Thus, while moving from a small to a big city brings additional rewards to previous experience, the main effect is that any additional experience gathered in the big city is substantially more valuable and will remain so anywhere.

4.1. Earnings profiles

An illustrative way to present our results is to plot the evolution of earnings for workers in cities of different sizes, calculated on the basis of the coefficients estimated in column (1) of Table 2 . In panel (a) of Figure 3 , the higher solid line depicts the earnings profile over 10 years of an individual with no prior experience working in Madrid (the largest city) relative to the earnings of a worker with identical characteristics (both observable and time-invariant unobservable) who instead works in Santiago de Compostela (the median-sized city). To be clear, the top solid line does not represent how fast earnings rise in absolute terms while working in Madrid, they represent how much faster they rise when working in Madrid than when working in Santiago.

Earnings profiles relative to median-sized city

For the worker in Madrid, the profile of relative earnings has an intercept and a slope component. The intercept captures the percentage difference in earnings between an individual working in Madrid and an individual working in Santiago, when both have no prior work experience and have the same observable characteristics and worker fixed effect. This is calculated as the exponential of the difference in estimated city fixed effects for Madrid and Santiago from the specification in column (1) of Table 2 , expressed in percentage terms. The slope component captures the rising gap in earnings between these individuals as they each accumulate experience in a different city. This is calculated on the basis of the estimated coefficients for experience in the first to second biggest cities and experience in the first to second biggest cities |$\times$| experience in column (1) of Table 2 .

Figure 3 shows that a worker in Madrid initially earns 9% more than a worker in Santiago, and this gap then widens considerably, so that after 10 years the difference in earnings reaches 36%. The lower solid line depicts the earnings profile over 10 years of an individual working in Sevilla (the fourth largest city) relative to the earnings of a worker in Santiago. There is also a substantial gap in the profile of relative earnings, although smaller in magnitude than in the case of Madrid: an initial earnings differential of 3% and of 14% after 10 years.

The dashed lines in panel (a) of Figure 3 illustrate the portability of the learning advantages of bigger cities. The top dashed line plots the difference in earnings between two individuals with no prior work experience and identical characteristics, one who works in Madrid for 5 years and then moves to Santiago and another one who works in Santiago during the entire 10-year period. Up until year 5, the relative earnings profile of the worker who begins in Madrid and then relocates is the same as that of a worker who always works in Madrid as captured by the top solid line discussed above. 22 At that point, he relocates to Santiago, and his relative earnings drop as a result of the Santiago fixed effect replacing the Madrid fixed effect, and of the value of the experience he acquired over the 5 years in Madrid changing following his relocation (recall we let the value of experience vary depending not only on where it was acquired but also on where it is being used). Since there is only a minor change in the value of experience acquired in Madrid after moving, the 8.6% drop in earnings following relocation is almost identical to the initial 9.4% earnings gap between Madrid and Santiago. The worker is able to retain the 14.5% higher earnings resulting from the more valuable experience accumulated over 5 years in Madrid after relocating to Santiago. 23

From that point onwards, the additional value of the experience acquired in Madrid depreciates slightly but a substantial gap remains relative to the benchmark of having always worked in Santiago. 24 Someone moving to Santiago after 5 years in Sevilla exhibits a qualitatively similar relative profile, although with smaller magnitudes.

The evolution of earnings portrayed in panel (a) of Figure 3 shows that much of the earnings premium that bigger cities offer is not instantaneous, but instead accumulates over time and is highly portable. This perspective contrasts with the usual static view that earlier estimations of this premium have adopted. This static view is summarized in panel (b) of Figure 3 . Once again, we depict the profile of relative earnings for a worker in Madrid or Sevilla relative to a worker in Santiago, but now on the basis of column (3) of Table 1 instead of column (1) of Table 2 . In this view, implicit in the standard fixed-effects estimation without city-specific experience, relative earnings for a worker in Madrid exhibit only a constant difference with respect to Santiago: a static premium of 11% gained immediately when starting to work in Madrid and lost immediately upon departure. 25

Our findings reveal that the premium of working in bigger cities has a sizeable dynamic component and that workers do not lose this component when moving to smaller cities. This latter result strongly suggests that a learning mechanism is indeed behind the accumulation of the premium.

Earnings profiles relative to median-sized city, worker with and without prior experience

In Figure 4 , we explore how the earnings premium of working in bigger cities varies depending on the worker’s prior experience. The higher solid line is the same as in panel (a) of Figure 3 , plotting the difference in earnings between two individuals with no prior work experience and identical characteristics, one who works in Madrid during the entire 10-year period and another one who works in Santiago. The higher dashed line compares instead two individuals with 5 years of previous work experience in Santiago and identical characteristics, one who migrates to Madrid and works there during the next 10 years and another one who remains in Santiago. The dashed line comparing experienced workers has a higher intercept and a flatter subsequent profile than the solid line comparing inexperienced workers. This is because the 5 years of prior work experience in Santiago bring 3% higher returns in Madrid than in Santiago. However, a worker with 5 years of prior work experience benefits less from acquiring additional experience in Madrid than an inexperienced worker (over 10 years, the gain in earnings from acquiring experience in Madrid instead of Santiago is 31% for a worker with 5 years of prior work experience in Santiago and 36% for a worker with no prior work experience).

4.2. Short-term and medium-term city size earnings premia

After having addressed two key sources of bias in the estimation of city fixed effects in an earnings regression (by including worker fixed effects and by allowing the value of experience to vary depending on where it is acquired and used), we can now estimate the elasticity of the static earnings premium with respect to city size in the second stage of our estimation. In column (2) of Table 2 , we regress the city indicators estimated in column (1) on log city size and obtain an elasticity of |$0.0223$|⁠ . This estimate is not significantly different from the static fixed-effects estimate in column (4) of Table 1 . As we already stated, the bias in the static fixed-effects estimate would tend to be small if the direction of migration flows is balanced (as in our data) and the learning benefits of bigger cities are portable. The estimates of our dynamic specification show that experience accumulated in bigger cities remains roughly just as valuable when workers relocate. This is good news, because it implies that existing fixed-effects estimates of the static gains from bigger cities are accurate and robust to the existence of important dynamic effects.

Studying the static earnings premium from currently working in bigger cities alone, however, ignores that there are also important dynamic gains. To study a longer horizon, we can estimate a medium-term earnings premium that incorporates both static and dynamic components. To this end, we add to each city fixed effect the estimated value of experience accumulated in that same city evaluated at the average experience in a single location for workers in our sample (7.72 years). The estimated elasticity of this medium-term earnings premium with respect to city size, presented in column (3) of Table 2 , is |$0.0510$|⁠ .

When comparing the |$0.0510$| elasticity of the medium-term earnings premium with respect to city size in column (3) of Table 2 with the |$0.0223$| elasticity of the short-term static premium in column (2) we notice that in the medium term, about half of the gains from working in bigger cities are static and about half are dynamic.

Note also that the |$0.0510$| elasticity of the medium-term earnings premium with respect to city size in column (3) of Table 2 is not significantly different from the standard static pooled OLS estimate in column (2) of Table 1 . This suggests that the drop in the estimated elasticity between a standard static pooled OLS estimation and a standard static fixed-effects estimation is not due to sorting but to dynamic effects. When estimating the medium-term elasticity, we have brought dynamic effects in (by incorporating the additional value of experience acquired in bigger cities evaluated at the mean experience in a single location into the second stage), but left sorting on unobserved time-invariant ability out (by including worker fixed effects in the first stage). The fact that this takes us back from the magnitude of the static fixed-effects estimate to the magnitude of the static pooled OLS estimate indicates that learning effects can fully account for the difference.

An alternative way of reaching the same conclusion is to allow the value of experience to vary depending on where it is acquired in the pooled OLS estimation. This amounts to estimating the first-stage specification in column (1) of Table 2 without worker fixed effects. When we then regress the estimated city indicators on log city size, we obtain a static short-term elasticity of |$0.0320$|⁠ . Hence, not including worker fixed effects to deal with sorting but accounting for dynamic effects separately notably reduces the pooled OLS estimate of the static city size premium. Again, this suggests that the drop in the estimated elasticity between a standard static pooled OLS estimation and a standard static fixed effects estimation is mainly to dynamic effects rather than sorting. Finally, if we then add dynamic effects back in to compute the medium-term elasticity based on this extended pooled OLS estimation (by adding to each city fixed effect the estimated value of experience accumulated in that same city evaluated at the average experience) we obtain an elasticity of |$0.0489$|⁠ , reinforcing the conclusion that dynamic effects are behind the difference between existing pooled OLS and fixed-effects estimates.

This finding not only underscores the relevance of the dynamic benefits of bigger cities that this article emphasizes, it also suggests that sorting on unobservables may not be very important. We return to this issue later in the article.

Dynamic fixed-effects estimation of the medium-term city size premium

While our estimate of the medium-term benefit of working in bigger cities resembles a basic pooled OLS estimate, our methodology allows us to separately quantify the static and the dynamic components and to discuss the portability of the dynamic part. Furthermore, the estimation of the combined medium-term effect is more precise. Figure 5 plots the estimated medium-term premium against log city size. Compared with the plot for the pooled OLS specification in Figure 2 , log city size explains a larger share of variation in medium-term earnings across cities ( ⁠|$R^2$| of |$0.3732$| versus |$0.2406$|⁠ ). In fact, we observe that many small- and medium-sized cities now lie closer to the regression line. One reason why some cities are outliers in the pooled OLS estimation is that they have either relatively many or relatively few workers who have accumulated substantial experience in the biggest cities. Workers in cities far above the regression line in Figure 2 , such as Tarragona-Reus, Girona, Manresa, or Huesca have accumulated at least 7% of their overall experience in the five biggest cities. Workers in cities far below the regression line in Figure 2 , such as Santa Cruz de Tenerife, Ourense, Valle de la Orotava, Elda-Petrer, or Lugo have accumulated less than 2% of their overall experience in the five biggest cities. At the same time, the two biggest cities, Madrid and Barcelona, are now further above the regression line reflecting the large returns to experience accumulated there which increase earnings over the medium term.

4.3. Addressing the endogeneity of city sizes

We have addressed the biases arising in the first-stage estimation of column (1) in Table 2 from not taking into account sorting on unobservables nor the differential value of experience accumulated in bigger cities. However, a potential source of bias remains in the second-stage estimation of columns (2) and (3). The association between the earnings premium and city size is subject to endogeneity concerns. More precisely, an omitted variable bias could arise if some city characteristic simultaneously boosts earnings and attracts workers to the city, thus increasing its size. We may also face a reverse causality problem if higher earnings similarly lead to an increase in city size.

The extant literature has already addressed this endogeneity concern and found it to be of small practical importance ( Ciccone and Hall, 1996 ; Combes et al ., 2010 ). Relative city sizes are very stable over time ( Eaton and Eckstein, 1997 ; Black and Henderson, 2003 ). If certain cities are large for some historical reason that is unrelated with the current earnings premium (other than through size itself), we need not be too concerned about the endogeneity of city sizes. Thus, following Ciccone and Hall (1996) , we instrument current city size using historical city-size data. In particular, our population instrument counts the number of people within 10 km of the average resident in a city back in 1900. 26

Following Combes et al . (2010) , we also use land fertility data. The argument for using land fertility as an instrument is that fertility was an important driver of relative city sizes back when the country was mostly agricultural, and these relative size differences have persisted, but land fertility is not directly important for production today (agriculture accounted for 60% of employment in Spain in 1900 compared with 4% in 2009). In particular, we use as an instrument the percentage of land within 25 km of the city centre that has high potential quality. Potential land quality refers to the inherent physical quality of the land resources for agriculture, biomass production, and vegetation growth, prior to any modern intervention such as irrigation. 27

In addition to these instruments used in previous studies, we incorporate four additional instruments. A city’s ability to grow is limited by the availability of land suitable for construction. Saiz (2010) studies the geographical determinants of land supply in the U.S. and shows that land supply is greatly affected by how much land around a city is covered by water or has slopes greater than 15%. Thus, we also use as instruments the percentage of land within 25 km of the city centre that is covered by oceans, rivers, or lakes and the percentage that has slopes greater than 15%. 28 The next instrument we include is motivated by the work of Goerlich and Mas (2009) . They document how small municipalities with high elevation, of which there are many in Spain, lost population to nearby urban areas over the course of the twentieth century. An urban area’s current size, for a given size in 1900, could thus be affected by having high-elevation areas nearby. The instrument we use to incorporate this fact is the log mean elevation within 25 km of the city centre. Our final instrument deals with historical transportation costs. Roman roads were the basis of Spain’s road network for nearly 1700 years and this may have favoured population growth of cities with more Roman roads. Recent roads built as the country has grown and suburbanized are no longer determined by the Roman road network, and instead seem to be mostly affected by roads built by the Bourbon monarchs in the eighteenth century ( Garcia-López et al ., 2015 ). However, to the extent that relative city sizes are very persistent, Roman roads may help predict relative city sizes today. Thus, we also use as an instrument the number Roman road rays crossing a circle drawn 25 km from city centre. 29

Table 3 gives the first and second stages of our instrumental variable estimation. The first-stage results in column (1) show that the instruments are jointly significant and also individually significant. 30 They are also strong. The |$F$| -statistic (or Kleinberger–Papp rk Wald statistic) for weak identification exceeds all thresholds proposed by Stock and Yogo (2005) for the maximal relative bias and maximal size. The |$LM$| test confirms our instruments are relevant as we reject the null that the model is underidentified. We can also rule out potential endogeneity of the instruments: the Hansen-J test cannot reject the null of the instruments being uncorrelated with the error. Lastly, according to the endogeneity test, the data do not reject the use of OLS.

IV estimation of the dynamic city size earnings premium

Notes : All regressions include a constant term. Column (1) is the first-stage regression of log city size on a set of historical population and geographical instruments. Columns (2) and (3) are second-stage regressions of city premia on instrumented log city size. Coefficients are reported with robust standard errors in parenthesis. |$^{***}$|⁠ , |$^{**}$|⁠ , and |$^*$| indicate significance at the 1, 5, and 10% levels. The |$F$| -statistic (or Kleinberger–Papp rk Wald statistic) reported on the weak instruments identification test exceeds all thresholds proposed by Stock and Yogo (2005) for the maximal relative bias and maximal size.

Column (2) of Table 3 shows that instrumenting has only a small effect on the elasticity of the short-term premium with respect to city size (it is |$0.0203$|⁠ , compared with |$0.0223$| in Table 2 ). Similarly, column (3) shows that the elasticity of the medium-term premium with respect to city size is also almost unchanged by instrumenting (it is |$0.0530$|⁠ , compared with |$0.0510$| in Table 2 ). In fact, a Hausman test fails to reject that instrumental variables are not required to estimate these elasticities. This is in line with the consensus among urban economists that the endogeneity of city sizes ends up not being an important source of concern when estimating the benefits of bigger cities ( Combes et al ., 2010 ).

4.4. Addressing other potential sources of bias

We now report several additional robustness checks we have performed to address other potential sources of bias in our estimates. One remaining source of concern is the possible existence of an “Ashenfelter dip” in earnings prior to migration. Ashenfelter (1978) observed that the earnings of participants in a government training programme often fell immediately before entering the programme. This pre-programme dip in earnings has been found to arise in multiple contexts and when it occurs it can lead to an overestimate of the effect of the programme ( Heckman and Smith, 1999 ). Similarly, our estimates of a city size premium could be upwardly biased if earnings tended to fall immediately prior to workers relocating across cities. To ensure this is not the case, we add to our specification in column (1) of Table 2 indicator variables for workers who relocate across cities for each of the eight quarters prior to and after the migration event. 31 This allows us to establish the time pattern of the effect on migrants’ earnings of working in bigger cities non-parametrically. Figure 6 visualizes these results by showing how the earnings of a worker who works in Santiago for 5 years and then moves to Madrid change in the 3 years prior to leaving Santiago and in the 3 years after arriving in Madrid compared to those of a worker with identical characteristics who remains in Santiago. We can see that there is no indication of an “Ashenfelter dip” in relative earnings prior to migration and that the evolution of the big city earnings premium for the migrant relative to the stayer follows a similar profile to our benchmark parametric specifications.

Non-parametric pre- and post-migration earnings profile relative to median-sized city

Another potential issue when interpreting our results arises from the importance of migrants for our estimation. We have already noted that both migrants and stayers contribute to estimating the values of experience acquired in different cities. However, it is worthwhile checking whether these values differ between movers and stayers. Furthermore, it could be the case that workers tend to move across cities only when they face a job opportunity that offers a particularly promising earnings path at their new destination or when earnings in their current location have followed a particularly disappointing path. If this type of self-selection into migration is important, migrants from small to big cities will typically see a steep earnings increase after they move to the big city, and will tend to bias the estimated big city premium upwards. Migrants from big to small cities will typically see a relatively flat earnings path prior to leaving the big city, and will tend to bias the estimated big city premium downwards. Note that even if such opposing biases arise, they may tend to cancel out since migration flows across cities of different sizes are approximately balanced in our data. Nevertheless, we would like to assess whether differences like these are important. To this end, we augment our specification of column (1) of Table 2 to let the values of experience acquired in different cities vary between stayers, migrants who move into the five biggest cities and migrants who move out of the five biggest cities. More specifically, we interact the experience acquired in the top two cities and in cities ranked third to fifth (as well as their interactions with overall experience) with indicator variables of migrants in both directions. 32 Migrants exhibit higher returns to overall experience, which translate into steeper earnings profiles relative to stayers regardless of their final destination. And yet, what matters for our estimates of the dynamic gains from bigger cities is that the estimated additional value of experience acquired in the two biggest cities or in the third to fifth biggest cities is not statistically different between stayers, migrants to big cities, or migrants from big cities.

Finally, an important sample restriction involves the period being studied. Our estimates are based on regressing individual monthly earnings in 2004–2009 on a set of characteristics that capture the complete prior labour history of each individual. As noted in section 2 , this is because prior to 2004 we have all job characteristics for the worker but lack earnings from income tax data. We would like to check that our findings are not specific to the period 2004–2009, since during the first 4 years of this 6-year period Spain was experiencing an intense housing boom. To this effect, we repeat our estimations for the preceding 6-year period, 1998–2003. 33 Since uncensored income tax are only available from 2004 onwards, estimations for 1998–2003 rely on earnings data from social security records corrected for top and bottom coding following a procedure based on Card et al . (2013) . 34 We obtain similar elasticities of earnings with respect to city size for the period 1998–2003 as in our baseline estimates for 2004–2009. The short-term earnings elasticity of |$0.0247$| is similar to our estimate of |$0.0223$| for the period 2004–2009 in column (2) of Table 2 , whereas the medium-term elasticity of |$0.0439$| is somewhat lower than our estimate of |$0.0510$| in column (3) of Table 2 . One potential reason for this drop in the medium-term elasticity is that for older individuals measures of overall experience and city-specific experience are left-censored in 1998–2003, which may reduce the estimated returns to city-specific experience and, hence, the medium-term earnings premium. 35 On the whole, however, our estimated elasticities of earnings with respect to city size appear to be robust to the period of analysis.

We have also explored removing two other sample restrictions. Our results have focused on men, given the huge changes experienced by Spain’s female labour force during the period over which we track labour market experience. Repeating our estimations for women shows that they have a much lower city size earnings premia than men. In particular, we obtain a medium-term earnings elasticity with respect to city size of |$0.0229$| for women, compared with the |$0.0510$| medium-term elasticity for men in column (3) of Table 2 . It is a well-established fact in the labour literature on gender differences that returns to experience are substantially lower for women, even when using—as we do—measures of actual experience instead of potential experience (Blau and Kahn, 2013). Our estimates for women confirm this finding and show that the same additional experience increases women’s earnings by only about half as much as it increases men’s. Moreover, the additional value of accumulating that additional experience in Madrid or Barcelona as opposed to outside the five biggest cities is also only about half as large for women.

We have also excluded job spells in the public sector, international organizations, and in education and health services because of their heavily regulated earnings. As expected, including job spells in these regulated sectors lowers the magnitude of earnings premia (a reduction from |$0.0510$| to |$0.0431$| in the elasticity of the medium-term earnings premium with respect to city size). This implies that the gains from working in big cities are larger in the private sector.

Following Following Baker (1997) , a large literature emphasizes that there is substantial heterogeneity in earnings profiles across workers, which has crucial implications for income dynamics and choices made over the life cycle (see Meghir and Pistaferri, 2011, for a review). In the previous section, we have shown that an essential part of the advantages associated with bigger cities is that they provide steeper earnings profiles. Given that both higher individual ability and experience acquired in bigger cities can increase earnings faster, we now explore whether there are complementarities between them, i.e. whether more able workers enjoy greater learning advantages from bigger cities.

A simple approach is to classify workers into different ability types based on observables, for instance, their educational attainment or occupational skills. We can then interact indicators for these observable ability types with the differential value of experience in cities of different sizes. When we try this, the estimation results (not reported) show that the additional value of experience accumulated in bigger cities is not significantly different across these types, defined by observable indicators of ability. Given that our dependent variable is log earnings, this implies that accumulating an extra year of experience in Madrid, for example, instead of in Santiago, gives rise to the same percentage increase in earnings for workers with a college degree or in the highest occupational category than for workers with less education or lower occupational skills. This leads us to shift our attention to a broader definition of skills, using worker fixed effects to capture unobserved innate ability.

Table 4 shows the results of our iterative estimation. Relative to column (1) of Table 2 we have added interactions between experience and ability (estimated worker fixed effects). The interactions are statistically significant and large in magnitude.

Estimation of the heterogeneous dynamic and static city size earnings premia

Notes : All regressions include a constant term. Column (1) also includes firm tenure and its square, occupation indicators, month–year indicators, two-digit sector indicators, and contract-type indicators. Coefficients in column (1) are reported with bootstrapped standard errors in parenthesis which are clustered by worker (achieving convergence of coefficients and mean squared error of the estimation in each of the 100 bootstrap iterations). Coefficients in columns (2) and (3) are reported with robust standard errors in parenthesis. |$^{***}$|⁠ , |$^{**}$|⁠ , and |$^*$| indicate significance at the 1, 5, and 10% levels. The |$R^2$| reported in column (1) is within workers. Worker values of experience and tenure are calculated on the basis of actual days worked and expressed in years. City medium-term premium calculated for workers’ average experience in one city (7.72 years).

To get a better sense of the magnitudes implied by the coefficients of Table 4 , Figure 7 uses these to recalculate the earnings profiles of Figure 3 for workers of different ability. The top solid line depicts the difference in earnings between working in Madrid and working in the median-sized city, Santiago de Compostela, for a high-ability worker (in the 75th percentile of the estimated overall worker fixed-effects distribution). The top dashed line repeats the comparison between Madrid and Santiago for a low-ability worker (in the 25th percentile of the estimated overall worker fixed-effects distribution). After 10 years, the difference in earnings between working in Madrid and working in Santiago for the high-ability worker has built up to 39%. For the low-ability worker, the difference is instead 33%. The difference in earnings between Sevilla and Santiago after 10 years is 14% for the high-ability worker and 12% for the low ability worker. 37

Overall, these results reveal that there is a large role for heterogeneity in the dynamic benefits of bigger cities. Experience is more valuable when acquired in bigger cities and this differential value of experience is substantially larger for workers with higher ability.

Our estimations separately consider the static advantages associated with workers’ current location, learning by working in bigger cities and spatial sorting. However, we have so far left sorting mostly in the background. Some of the evidence discussed above suggests that sorting across cities on unobservables is not very important. Nevertheless, it is possible that there is sorting on observables. We would also like to provide more direct evidence that sorting on unobservables is unimportant by comparing the distribution of workers’ ability across cities of different sizes.

The concentration in bigger cities of workers with higher education or higher skills associated with their occupation has been widely documented for the U.S. ( e.g . Berry and Glaeser, 2005 ; Bacolod et al ., 2009 ; Moretti, 2012 ; Davis and Dingel, 2013 ). A similar pattern can be observed in Spain. In Table 5 , we compare the distribution of workers across our five skill categories in cities of different sizes. 38 Very-high-skilled jobs (those requiring at least a bachelors or engineering degree) account for 10.9% of the total in Madrid and Barcelona, compared with 6.3% in the third to fifth biggest cities, and with 3.5% in cities below the top five. High-skilled jobs (those typically requiring at least some college education) also account for a higher share of the total the bigger the city size class. At the other end, workers employed in medium-low-skilled and low-skilled jobs are more prevalent the smaller the city size category. These differences are strong evidence of sorting based on observable worker characteristics. Big cities have more engineers, economists, and lawyers than small cities. However, is it also the case that big cities attract the best within each of these observable categories? To answer this question, we now compare across cities of different sizes the distribution of workers’ ability as measured by their estimated fixed effects from our earnings regressions.

Earnings profiles relative to median-sized city, high- and low-ability worker

Comparison of occupational groups across cities of different sizes

Notes : Employers assign workers into one of ten social security categories which we regroup into five occupational skill categories. Shares are averages of monthly observations in the sample.

Panel (a) in Figure 8 plots the distribution of worker fixed effects in the five biggest cities (solid line) and in cities below the top five (dashed line) based on our full earnings specification with heterogeneous dynamic and static benefits of bigger cities (Table 4 , column (1)), which also controls for occupational skills. Since many workers move across cities, we must take a snapshot on a specific date in order to assign workers to cities. We assign the fixed effect of each individual (estimated using their entire history) to the city where he was working in May 2007. We can see that both distributions look alike (we do a formal comparison below that confirms how close they are). This suggests that there is little sorting on unobservables: the distribution of workers’ innate ability (as measured by their fixed effects), after controlling for our five broad occupational skill categories, is very similar in big and small cities.

Comparisons of worker fixed-effects distributions across cities

Other recent papers also compare measures of workers’ ability that are not directly observed across cities of different sizes, and find relevant differences. In particular, Combes et al . (2012b ) study worker fixed effects from wage regressions for France. The key difference with respect to our comparison in panel (a) of Figure 8 is that their worker fixed effects come from a specification that does not allow the value of experience to differ across cities of different sizes nor for heterogeneous effects. To facilitate the comparison between our results and theirs, we now move towards their specification in two steps.

Panel (b) of Figure 8 repeats the plot of panel (a), but now constrains the dynamic benefits of bigger cities to be homogenous across workers (worker fixed effects in this panel come from Table 2 , column (1)). While the distributions of worker fixed effects in the five biggest cities and the corresponding distribution in smaller cities have approximately the same mean, the distribution in bigger cities exhibits a higher variance. This is the result of forcing experience acquired in bigger cities to be equally valuable for everyone, so the ability of workers at the top of the distribution appears larger than it is (this estimation mixes the extra value that big city experience has for them with their innate ability), while the ability of workers at the bottom of the distribution appears smaller than it is. Hence, by ignoring the heterogeneity of the dynamic benefits of bigger cities we can get the erroneous impression that there is greater dispersion of innate ability in bigger cities.

If we do not take this bias into account, it could appear from the estimated fixed effects that workers in bigger cities have higher ability on average even if the distribution of |$\mu$| in small and big cities were identical. Estimation based on equation ( 11 ) yields instead |$\text{plim} \; \hat{\mu}_i = \mu_i$|⁠ .

The comparison in panel (c) corresponds to the same comparison of fixed effects carried out by Combes et al . (2012b ). They find a higher mean and greater dispersion of worker fixed effects in bigger cities for France, which is also what this panel shows for Spain. The higher mean and variance for bigger cities is amplified in the distribution of log earnings, plotted in panel (d). Combes et al . (2012b ) carefully acknowledge that their estimated fixed effects capture “average skills” over a worker’s lifetime. In contrast, panel (a) separates innate ability from the cumulative effect of the experience acquired in different cities, showing that differences arise as a result of the greater value of experience acquired in bigger cities, and are further amplified for more able workers. Restated, it is not that workers who are inherently more able (within each broad skill category) choose to locate in bigger cities, it is working in bigger cities that eventually makes them more skilled.

Another recent paper comparing skills across cities of different sizes is Eeckhout et al . (2014) . Instead of measuring skills through worker fixed effects, Eeckhout et al . (2014) use real wages as a measure of skills. They argue that if workers are freely mobile across cities, then any spatial differences in utility must correspond to differences in ability. Their comparison resembles that of panel (b), with similar means and greater variance in bigger cities. In their context, this implies that workers at the top of the earnings distribution in bigger cities get paid more than necessary to offset their greater housing costs relative to the workers at the top of the earnings distribution in smaller cities, which would indicate the former are being compensated for being more skilled. Workers at the lower end of the distribution in big cities get paid less than necessary to offset their greater housing costs, which would indicate they are less skilled than their small city counterparts.

Eeckhout et al . (2014) explain greater skill dispersion in bigger cities through what they call extreme skill complementarity, i.e. workers with the highest skills benefit most from having workers with the lowest skills in their same city and vice versa. This explanation is very appealing across different broad observable skill categories. To use one of their examples, a top surgeon or a top lawyer in New York City, given the value of her time, benefits greatly from the ease to hire in that city low-skilled services at her job (catering, administrative assistance) and home (child care, schooling and help in the household). 39 At the same time, the argument is harder to make within occupational skill group, which would imply the top surgeon benefiting particularly from working with a mediocre surgeon. Our results point to a different story within broad skill groups: the innate ability of surgeons or lawyers in big cities and in smaller places is not that different to start with, it is working in bigger cities and the experience this provides that makes those working there better over time on average. Since big city experience not only improves skills but also benefits most those with higher innate ability, this also creates a greater dispersion of earnings within occupational group in bigger cities. 40

Comparison of earnings and worker fixed-effects distributions, five biggest versus other cities

Notes : The table applies the methodology of Combes et al . (2012a ) to approximate the distribution of worker fixed effects in the five biggest cities, |$F_B(\mu_i)$|⁠ , by taking the distribution of worker fixed effects in smaller cities, |$\smash{F_S(\mu_i)}$|⁠ , shifting it by an amount |$A$|⁠ , and dilating it by a factor |$D$|⁠ . |$\hat{A}$| and |$\hat{D}$| are estimated to minimize the mean quantile difference between the actual big city distribution |$F_B(\mu_i)$| and the shifted and dilated small city distribution |$\smash{F_S\left((\mu_i-A)/D\right)}$|⁠ . |$M(0,\,1)$| is the total mean quantile difference between |$F_B(\mu_i)$| and |$F_S(\mu_i)$|⁠ . |$\smash{R^{2}=1-M(\hat{A},\,\hat{D})/M(0,\,1)}$| is the fraction of this difference that can be explained by shifting and dilating |$F_S(\mu_i)$|⁠ . Coefficients are reported with bootstrapped standard errors in parenthesis (re-estimating worker fixed effects in each of the 100 bootstrap iterations). |$^{***}$|⁠ , |$^{**}$|⁠ , and |$^*$| indicate significance at the 1, 5, and 10% levels.

Table 6 performs a formal comparison of the plotted distributions, using the methodology developed by Combes et al . (2012a ) to approximate two distributions. In particular, we approximate the distribution of worker fixed effects in the five biggest cities, |$F_B(\mu_i)$|⁠ , by taking the distribution of worker fixed effects in smaller cities, |$\smash{F_S(\mu_i)}$|⁠ , shifting it by an amount |$A$|⁠ , and dilating it by a factor |$D$|⁠ . |$\hat{A}$| and |$\hat{D}$| are estimated to minimize the mean quantile difference between the actual big city distribution |$F_B(\mu_i)$| and the shifted and dilated small city distribution |$\smash{F_S\left((\mu_i-A)/D\right)}$|⁠ . 41

The top row compares the distributions of worker fixed effects from our full specification with heterogeneous dynamic and static benefits of bigger cities (Table 4 , column (1). The second row forces these benefits to be homogenous across workers. The third row constrains the benefits of bigger cities to be purely static. The bottom row compares log earnings. The table confirms what was visually apparent from Figure 8 .

Starting from the bottom row, earnings are higher on average in bigger cities. The shift parameter is |$\hat{A}=0.2210$|⁠ , indicating that average earnings are 24.7% ( i.e. |$e^{0.2210} - 1$|⁠ ) higher in the five biggest cities. Earnings are also more dispersed in bigger cities. The dilation parameter is |$\hat{D}=1.2153$| indicating that the distribution of earnings in the five biggest cities is amplified by that factor relative to the distribution in smaller cities.

Moving one row up, the distribution of worker fixed effects from a static specification also exhibits a higher mean and greater dispersion in bigger cities. However, the estimated shift and dilation parameters are smaller than those for earnings, and the distributions are more similar (the mean squared quantile difference is |$5.6e-02$| instead of |$0.1149$|⁠ ). To facilitate the comparison with Combes et al . (2012b ), the only controls included in this specification are the sector of employment, age, and the square of age. The greater similarity of the resulting worker fixed-effect distributions than that of the log earnings distributions indicates that sector and age account for an important fraction of differences in earnings across cities.

The next row up introduces dynamic effects. This brings the distributions even closer (the mean squared quantile difference is reduced by another order of magnitude). The estimated shift parameter is not statistically significantly different from zero, indicating both distributions are centred on the same mean. However, the distribution of worker fixed effects is still more dispersed in the five biggest cities ( ⁠|$\hat{D}=1.1633$|⁠ ).

The top row corresponds to our full specification. Once we allow experience in bigger cities to be more valuable and workers with higher innate ability to take greater advantage of this, worker fixed effects exhibit very similar distributions in big and small cities (the mean squared quantile difference is reduced by almost another order of magnitude). The estimated shift parameter is not statistically significantly different from zero, indicating both distributions have the same mean. The dilation parameter shows that there is slightly more dispersion in bigger cities. However, the value is substantially closer to |$1$| (which would mean no additional dispersion in bigger cities) than before. 42

Several recent studies ( Combes et al. , 2012b ; Baum-Snow and Pavan, 2013 ; Eeckhout et al ., 2014 ) emphasize that earnings are higher on average and also exhibit greater dispersion in bigger cities. Our results in this section indicate this is partly due to the concentration of specific sectors and occupations in them (controlling for them and other observables takes us from panel (d) to panel (c) in Figure 8 ) and partly due to the greater value of experience in bigger cities and the complementarity between big city experience and individual ability (controlling for them takes us to panel (a), where the distributions become very similar). Thus, within very broad occupational skill groups, there appears to be little sorting by innate ability. Instead, workers in bigger cities attain higher earnings on average precisely thanks to working there, which provides them with static advantages and also allows them to accumulate more valuable experience. Because more able workers benefit the most and less able workers the least from working in bigger cities, a similar distribution of underlying ability translates into greater dispersion of earnings in bigger cities. In sum, workers in big and small cities are not particularly different in unobservable skills to start with, it is working in cities of different sizes that makes their earnings diverge.

We have examined three reasons why firms may be willing to pay more to workers in bigger cities. First, there may be some static advantages associated with bigger cities. Secondly, bigger cities may allow workers to accumulate more valuable experience. Thirdly, workers who are inherently more productive may choose to locate in bigger cities. Using a large and rich panel data set for workers in Spain, we provide a quantitative assessment of the importance of each of these three mechanisms in generating earnings differentials across cities of different sizes.

We find that there are substantial static and dynamic advantages from working in bigger cities. The medium-term elasticity of earnings (after 7 years) with respect to city size is close to |$0.05$|⁠ . About one-half of these gains are static and tied to currently working in a bigger city. About another half accrues over time as workers accumulate more valuable experience in bigger cities. Furthermore, workers are able to take these dynamic gains with them when they relocate, which we interpret as evidence that learning in bigger cities is important. Workers with more education and higher skills are disproportionately present in bigger cities, but within broad skill categories it is not the case that more able workers sort into bigger cities.

In the process of deriving our results, we also make some methodological progress. We confirm that estimations of the static city size premium that use worker fixed effects to address sorting, but ignore the learning advantages of bigger cities, provide an accurate estimate of the purely static gains. However, besides not capturing learning, they overestimate the importance of sorting because they mix innate ability with the extra value of big city experience. Once we disentangle innate ability and the value of accumulated experience, cities of different sizes have quite similar distributions of unobserved worker ability.

Overall, we conclude that workers in big and small cities are not particularly different in terms of innate unobserved ability. It is working in cities of different sizes that makes their earnings diverge. The combination of static gains and learning advantages together with the fact that higher-ability workers benefit more from bigger cities explain why the distribution of earnings in bigger cities has higher mean and higher variance.

Thanks to Nathaniel Baum-Snow, Stéphane Bonhomme, Pierre-Philippe Combes, Lewis Dijktra, Gilles Duranton, Jason Faberman, Miquel-Ángel García-López, Thomas Holmes, Elena Manresa, Alvin Murphy, Vernon Henderson, and three anonymous referees for helpful comments and discussions. Funding from the European Commission’s Seventh Research Framework Programme through the European Research Council’s Advanced Grant ‘Spatial Spikes’ (contract number 269868), Spain’s Ministerio de Economía y Competividad (grant ECO2013-41755-P), the Banco de España Excellence Programme, the Comunidad de Madrid (grant S2007/HUM/0448 PROCIUDAD-CM) and the IMDEA Ciencias Sociales and Madrimasd Foundations is gratefully acknowledged. This research uses anonymized administrative data from the Muestra Continua de Vidas Laborales con Datos Fiscales (MCVL) with the permission of Spain’s Dirección General de Ordenación de la Seguridad Social. The replication files for this article are available at http://diegopuga.org/data/mcvl/ and also as supplementary material. In addition to the replication files, interested researchers will need to obtain access to the MCVL data by applying to Spain’s Dirección General de Ordenación de la Seguridad Social.

Supplementary Data are available at Review of Economic Studies online.

Ashenfelter, O. ( 1978 ), “Estimating the Effect of Training Programs on Earnings” , The Review of Economics and Statistics , 60 , 47 – 57 .

Google Scholar

Ayuso, J. and Restoy, F. ( 2007 ), “House Prices and Rents in Spain: does the Discount Factor Matter?” , Journal of Housing Economics , 16 , 291 – 308 .

Bacolod, M. , Blum, B. and Strange, W. C . ( 2009 ), “Skills and the City” , Journal of Urban Economics , 65 , 127 – 135 .

Baker, M. ( 1997 ), “Growth-Rate Heterogeneity and the Covariance Structure of Life-cycle Earnings” , Journal of Labor Economics , 15 , 338 – 375 .

Baker, M. and Solon, G. ( 2003 ), “Earnings Dynamics and Inequality among Canadian Men, 1976–1992: Evidence from Longitudinal Income Tax Records” , Journal of Labor Economics , 21 , 267 – 288 .

Baum-Snow, N. and Pavan, R. ( 2012 ), “Understanding the City Size Wage Gap” , Review of Economic Studies , 79 , 88 – 127 .

Baum-Snow, N. and Pavan, R. ( 2013 ), “Inequality and City Size” , Review of Economics and Statistics , 95 , 1535 – 1548 .

Berry, S. and Glaeser, E. L . ( 2005 ), “The Divergence in Human Capital Levels Across Cities” , Papers in Regional Science , 84 , 407 – 444 .

Black, D. and Henderson, V. ( 2003 ), “Urban Evolution in the USA” , Journal of Economic Geography , 3 , 343 – 372 .

Blau, Fr. and Kahn, L. ( 2013 ), “The Feasibility and Importance of Adding Measures of Actual Experience to Cross-sectional Data Collection” , Journal of Labor Economics , 31 , S17 – S58 .

Card, D. , Heining, J. and Kline, P. ( 2013 ), “Workplace Heterogeneity and the Rise of West German Wage Inequality” , Quarterly Journal of Economics , 128 , 967 – 1015 .

Ciccone, A. and Hall, R. E . ( 1996 ), “Productivity and the Density of Economic Activity” , American Economic Review , 86 , 54 – 70 .

Combes, P.-P. , Duranton, G. and Gobillon, L. ( 2008 ), “Spatial Wage Disparities: Sorting Matters!” , Journal of Urban Economics , 63 , 723 – 742 .

Combes, P.-P. , Duranton, G. , Gobillon, L. ,et al.  ( 2012a ), “The Productivity Advantages of Large Cities: Distinguishing Agglomeration from Firm Selection” , Econometrica , 80 , 2543 – 2594 .

Combes, P.-P. , Duranton, G. , Gobillon, L. ,et al.  ( 2010 ), “Estimating Agglomeration Effects with History, Geology, and Worker Fixed-Effects” , in Glaeser, E. L. (ed.) Agglomeration Economics , ( Chicago, IL: Chicago University ), 15 – 65 .

Google Preview

Combes, P.-P. , Duranton, G. , Gobillon, L. et al.  ( 2012b ), “Sorting and Local Wage and Skill Distributions in France” , Regional Science and Urban Economics , 42 , 913 – 930 .

Davis, D. R . and Dingel, J. I . ( 2013 ), The Comparative Advantage of Cities . Processed , (Columbia University) .

De La Roca, J. , Ottaviano, G. I. P . and Puga, D. ( 2014 ), “City of Dreams” , Processed, (CEMFI) .

Duranton, G. and Puga, D. ( 2001 ), “Nursery Cities: Urban Diversity, Process Innovation, and the Life Cycle of Products” , American Economic Review , 91 , 1454 – 1477 .

Duranton, G. and Puga, D. ( 2004 ), “Micro-Foundations of Urban Agglomeration Economies” , in Henderson, V. and Thisse, J.-F. , , Handbook of Regional and Urban Economics , Vol. 4 . ( Amsterdam: North-Holland ), 2063 – 2117 .

Eaton, J. and Eckstein, Z. ( 1997 ), “Cities and Growth: Theory and Evidence from France and Japan” , Regional Science and Urban Economics , 27 , 443 – 474 .

Eeckhout, J. , Pinheiro, R. and Schmidheiny, K. ( 2014 ), “Spatial Sorting” , Journal of Political Economy , 122 , 554 – 620 .

EUROPEAN ENVIRONMENT AGENCY. ( 1990 ), CORINE Land Quality Project ( Copenhagen: European Environment Agency ).

Garcia-LóPez, M.-Á. Holl, A. and Viladecans-Marsal, E. ( 2015 ), “Suburbanization and Highways in Spain When the Romans and the Bourbons Still Shape its Cities” , Journal of Urban Economics , 85 , 52 – 67 .

Glaeser, E. L . ( 1999 ), “Learning in Cities” , Journal of Urban Economics , 46 , 254 – 277 .

Glaeser, E. L . ( 2008 ), Cities, Agglomeration and Spatial Equilibrium ( Oxford: Oxford University Press ).

Glaeser, E. L . ( 2011 ), Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier, and Happier ( London : MacMillan ).

Glaeser, E. L . and Maré, D. C. ( 2001 ), “Cities and Skills” , Journal of Labor Economics , 19 , 316 – 342 .

Glaeser, E. L . and Resseger, M. G . ( 2010 ), “The Complementarity Between Cities and Skills” , Journal of Regional Science , 50 , 221 – 244 .

Goerlich, F. J . and Cantarino, I. ( 2013 ), “A Population Density Grid for Spain” , International Journal of Geographical Information Science , 27 , 2247 – 2263 .

Goerlich, F. J . and Mas, M. ( 2009 ), “Drivers of Agglomeration: Geography VS. History” , Open Urban Studies Journal , 2 , 28 – 42 .

Goerlich, F. J ., Mas, M. , Azagra, J. et al.  ( 2006 ), La localización de la poblacion española sobre el territorio. Un siglo de cambios: un estudio basado en series homogéneas ( Bilbao, Spain: BBVA Foundation ).

Gould, E. D . ( 2007 ), “Cities, Workers, and Wages: A Structural Analysis of the Urban Wage Premium” , Review of Economic Studies , 74 , 477 – 506 .

Guvenen, F. ( 2009 ), “An Empirical Investigation of Labor Income Processes” , Review of Economic Dynamics , 12 , 58 – 79 .

Heckman, J. J . and Smith, J. A . ( 1999 ), “The Pre-Programme Earnings Dip and the Determinants of Participation in a Social Programme. Implications for Simple Programme Evaluation Strategies” , Economic Journal , 109 , 313 – 348 .

Henderson, J. V . ( 2003 ), “Marshall's Scale Economies” , Journal of Urban Economics , 53 , 1 – 28 .

Holmes, T. J . ( 2010 ), “Structural, Experimentalist, and Descriptive Approaches to Empirical Work in Regional Economics” , Journal of Regional Science , 50 , 5 – 22 .

Jarvis, A. , Reuter, H. I ., Nelson, A. et al.  ( 2008 ), Hole-filled Seamless SRTM Data, Version 4.1 ( Cali, Colombia: International Centre for Tropical Agriculture ).

Mc Cormick, M. , Huang, G. , Zambotti, G. et al.  ( 2008 ), Roman Road Network . ( Cambridge, MA: Digital Atlas of Roman and Medieval Civilizations (DARMC), Center for Geographic Analysis, Harvard University ).

Meghir, C. and Pistaferri, L. ( 2011 ), “Earnings, Consumption and Life Cycle Choices” , in Ashenfelter, O. and Card, D. , Handbook of Labor Economics , Vol. 4 . ( Amsterdam: Elsevier ), 773 – 854 .

MINISTERIO DE EDUCACIóN, CULTURA Y DEPORTE. ( 2013 ), Datos Básicos del Sistema Universitario Español. Curso 2013/2014 . ( Madrid, Spain: Ministerio de Educación, Cultura y Deporte ).

Mion, G. and Naticchioni, P. ( 2009 ), “The Spatial Sorting and Matching of Skills and Firms” , Canadian Journal of Economics , 42 , 28 – 55 .

Moretti, E. ( 2011 ), “Local Labor Markets” , in Ashenfelter, O. and Card, D. , Handbook of Labor Economics , Vol. 4 . ( Amsterdam: Elsevier ), 1237 – 1313 .

Moretti, E. ( 2012 ), The New Geography of Jobs ( New York, NY: Houghton Mifflin Harcourt ).

Puga, D. ( 2010 ), “The Magnitude and Causes of Agglomeration Economies” , Journal of Regional Science , 50 , 203 – 219 .

Rosenthal, S. S . and Strange, W. ( 2004 ), “Evidence on the Nature and Sources of Agglomeration Economies” , in Henderson, V. and Thisse, J.-F. , Handbook of Regional and Urban Economics , Vol. 4 . ( Amsterdam: North-Holland ), 2119 – 2171 .

Saiz, A. ( 2010 ), “The Geographic Determinants of Housing Supply” , Quarterly Journal of Economics , 125 , 1253 – 1296 .

Stock, J. H . and Yogo, M. ( 2005 ), “Testing for Weak Instruments in Linear IV Regression” , in Stock, J. H. and Andrews, D. W. K. , Identification and Inference for Econometric Models: Essays in Honor of Thomas J. Rothenberg . ( Cambridge: Cambridge University Press ), 109 – 120 .

In the U.S., workers in metropolitan areas with population above 1 million earn on average 30% more than workers in rural areas ( Glaeser, 2011 ). In France, workers in Paris earn on average 15% more than workers in other large cities, such as Lyon or Marseille, 35% more than in medium-sized cities, and 60% more than in rural areas ( Combes et al ., 2008 ).

It is worth stressing that it is nominal wages that one ought to study to capture the productive advantages of cities, since they reflect how much more firms are willing to pay in bigger cities to comparable, or even the same, workers. Having higher nominal wages offsetting higher productivity in bigger cities (keeping firms indifferent across locations) is compatible with having no substantial differences in real earnings as higher housing prices tend to offset higher nominal earnings (keeping workers indifferent across locations). See Glaeser (2008) for further elaboration on this point and a thorough treatment of the spatial equilibrium approach to studying cities.

The relevance of heterogeneity in the growth profiles of earnings has been stressed in the macroeconomics and labour economics literature (see, e.g., Baker, 1997 ; Baker and Solon, 2003 and Guvenen, 2009 ). We highlight here the spatial dimension of this heterogeneity in earnings profiles and its interaction with individual ability.

Baum-Snow and Pavan (2012) address unobserved ability by using a three-type mixture model where the probability of a worker being of certain type is non-parametrically identified and depends, among other factors, on the city where he enters the labour market. In our much larger sample (157,000 men observed monthly compared with 1,700 men observed annually), we can estimate a worker fixed effect and let the value of experience in cities of different sizes vary systematically with this fixed effect.

In addition to uncensored earnings from income tax records, the MCVL contains earnings data from social security records going back to 1980. These alternative earnings data are either top or bottom coded for about 13% of observations. We, therefore, use the income tax data to compute monthly earnings, since these are completely uncensored.

More recent editions add individuals who enter the labour force for the first time while they lose those who cease affiliation with the Social Security. Since individuals who stop working remain in the sample while they receive unemployment benefits or a retirement pension, most exits occur when individuals are deceased or leave the country permanently.

A complete national update of the educational attainment of individuals recorded in the Continuous Census of Population was performed in 1996, with a subsequent update by most municipalities in 2001. Further updates used to rely on the information provided by individuals, most often when they completed their registration questionnaire at a new municipality upon moving (a prerequisite for access to local health and education services). However, since 2009 the Ministry of Education directly reports individuals’ highest educational attainment to the National Statistical Institute and this information is used to update the corresponding records in the Continuous Census of Population. It is worth noting that the Ministry of Education data indicate very low mobility to pursue higher education in Spain (Ministerio de Educación, Culturay Deporte, 2013). This is in contrast with the high rates of job-related mobility that, as reported below, are comparable to those of the U.S.

We do not study years prior to 2004 due to the lack of earnings from income tax data. We also do not study years after 2009 due to the extreme impact of the Great Recession on Spain after that year. In particular, our fixed-effects estimations rely on migrants to identify some key coefficients. Migrations across urban areas had remained very stable, with around 7% of workers relocating every year since 1998 through both bad and good times, but plummeted below 3% in the Great Recession. Nevertheless, to check that our estimates are not specific to the period 2004–2009, we also provide comparable results for 1998–2003. Since no income tax data are available prior to 2004, estimations for 1998–2003 rely on earnings data from social security records corrected for top and bottom coding following a procedure based on Card et al . (2013) .

This annual mobility rate is roughly comparable to the one in the U.S. Using individual-level data from the National Longitudinal Survey of Youth 1979, and restricting the sample to male native-born workers between 25 and 45 years old, we calculate that each year around 9% of workers move across metropolitan areas (defined as Core Based Statistical Areas by the Office of Management and Budget) throughout 1983–2010.

The city fixed effect |$\sigma_c$| could also be time-varying and written |$\sigma_{ct}$| instead. We keep it time-invariant here for simplicity. In our estimations, we have tried both having time-varying and time-invariant city fixed effects. We find that the elasticity of time-varying city fixed effects with respect to time-varying city size is the same as the elasticity of time-invariant city fixed effects with respect to time-invariant city size. Thus, we stick with time-invariant city fixed effects to not increase excessively the number of parameters in the richer specifications that we introduce later in the article.

Note that we are not explicitly deriving equation ( 1 ) from a general equilibrium model. Instead, we start directly from a reduced-form expression for earnings that potentially captures the contribution of static advantages, learning and sorting to the premium associated with bigger cities. In follow up work partly motivated by the findings of this article ( De la Roca et al ., 2014 ), we propose an overlapping generations general equilibrium model of urban sorting by workers with heterogeneous ability and self-confidence that see their experience differ in value depending on where it is acquired and used.

Employers assign workers into one of ten social security occupation categories, which we have regrouped into five skill groups. These categories are meant to capture the skills required by the job and not necessarily those acquired by the worker.

We have also estimated the elasticity in a single stage by including log city size directly in the Mincerian specification of log earnings (see Combes et al ., 2008 for a discussion on the advantages of using a two-step procedure). In this case, the estimated elasticity rises slightly to |$0.0512$|⁠ . In addition, we have carried out alternative estimations for the pooled OLS two-stage estimation. First, we try including interactions of city and year indicators in the first stage to address the possibility of such city effects being time-variant. Then, in the second stage we regress all estimated city-year indicators on time-varying log city size and year indicators. The estimated elasticity remains almost unaltered at |$0.0458$|⁠ . Secondly, urban economists have studied agglomeration benefits arising from local specialization in specific sectors in addition to those related to the overall scale of economic activity in a city. Following Combes et al . (2010) , we can account for these potential benefits of specialization by including the share of total employment in the city accounted for by the sector in which the worker is employed as an additional explanatory variable in the first-stage regression. When we do this, the elasticity of the earnings premium with respect to city size is almost unchanged, rising only marginally to |$0.0496$|⁠ . This result indicates that some small but highly specialized cities do pay relatively high wages in the sectors in which they specialize, but that this leads only to a small reduction in the earnings gap between big and small cities. Thirdly, we may be worried about the city fixed effects being estimated on the basis of more observations for bigger cities. This may introduce some heteroscedasticity through sampling errors, which can be dealt with by computing the feasible generalized least squares (FGLS) estimator proposed in appendix C of Combes et al . (2008) . When we do this, the elasticity of the earnings premium with respect to city size is almost unchanged, falling slightly from |$0.0455$| to |$0.0453$|⁠ . Finally, we can estimate two-way clustered standard errors by both worker and city instead of clustering just by worker (note that these clusters are not nested because many workers move across cities). This increases computational requirements by at least one order of magnitude, but does not change the level of statistical significance (at the 1, 5, or 10% level) of any coefficient in the table.

Combes et al . (2010) aggregate individual data into a city sector level data to estimate an elasticity analogous to our pooled OLS result. Mion and Naticchioni (2009) find a lower estimate of this elasticity for Italy ( ⁠|$0.022$|⁠ ).

Strictly speaking, the actual bias in the pooled OLS estimate of |$\sigma_c$|⁠ , |$\hat{\sigma}_{c\;\text{pooled}}$|⁠ , is more complicated because it is not necessarily the case that |$\text{Cov}(\mathbf{x}_{it},\,\mu_i + \smash{\sum_{j=1}^{C}} \delta_{jc} e_{ijt}) = \mathbf{0}$|⁠ , as we have assumed. For instance, even if we do not allow the value of experience to vary by city, we may have overall experience, |$\smash{e_{it}\equiv\sum_{j=1}^{C} e_{ijt}}$|⁠ , as one of the explanatory variables included in |$\mathbf{x}_{it}$| in equation ( 2 ). In this case, |$\delta_{jc}$| measures the differential value of the experience acquired in city |$j$| when working in city |$c$| relative to the general value of experience, which we may denote |$\gamma$|⁠ . Then |$\text{plim}\,\hat{\sigma}_{c\;\text{pooled}} = \sigma_c + \text{Cov}(\iota_{ict},\,\mu_i)/\text{Var}(\iota_{ict}) + \smash{\sum_{j=1}^{C}} \delta_{jc} \text{Cov}(\iota_{ict},\, e_{ijt})/\text{Var}(\iota_{ict})+(\gamma-\hat{\gamma}_{\text{pooled}}) \text{Cov}(\iota_{ict},\, e_{it})/\text{Var}(\iota_{ict})$|⁠ . Relative to the simpler example discussed in the main text, the bias incorporates an additional term |$(\gamma-\hat{\gamma}_{\text{pooled}}) \text{Cov}(\iota_{ict},\, e_{it})/\text{Var}(\iota_{ict})$|⁠ . In practice, this additional term is negligible if |$\text{Cov}(\iota_{ict},\, e_{it})$| is close to zero, that is, if the total number of days of work experience (leaving aside where it was acquired) is not systematically related to workers’ location. In our sample, this is indeed the case: the correlation between mean experience and log city size is not significantly different from |$0$|⁠ .

This can be a source of concern for the estimation of city fixed effects if migrants are not representative of the broader worker population or if the decision to migrate to a particular city depends on shocks specific to a worker-city pair. As long as workers choose their location based on their characteristics (both observable and time-invariant unobservable), on job traits such as the sector and occupation, and on characteristics of the city, the estimation of |$\sigma_c$| will remain unbiased. However, any unobserved time-varying factor that is correlated with the error term in equation ( 6 )—such as a particularly attractive wage offer in another city—will bias the estimation of city fixed effects. Nevertheless, even if people were to migrate only when they got a particularly high wage offer, provided that this affects similarly moves to bigger cities and moves to smaller cities, and that migration flows across cities of different sizes are approximately balanced (as they are in our data), then the actual bias may be small.

The alternative estimations discussed in footnote 13 above result in similar magnitudes of this elasticity. When allowing for city fixed effects to be time-variant it is |$0.0253$|⁠ , when controlling for sectoral specialization it is |$0.0241$|⁠ , and when implementing the FGLS estimator of Combes et al . (2008) it is |$0.0219$|⁠ . The only meaningful change in the elasticity of the earnings premium with respect to city size occurs when we estimate it in a single stage, which gives a lower estimate at |$0.0163$|⁠ . As before, estimating two-way clustered standard errors by both worker and city does not change the level of statistical significance (at the 1, 5, or 10% level) of any coefficient in the table.

Specifically, |$\text{plim} \; \hat{\sigma}_{\text{{FE}}} = \sigma + \left(\frac{1+m}{2} - \theta m \right) \delta < \sigma$| provided that |$\theta>\frac{1}{2} \left( \frac{1}{m} + 1 \right) $|⁠ .

In our sample of 157,113 workers, between 2004 and 2009 there are 40,809 migrations in which a worker takes a job in a different urban area: 8,868 migrations from the five biggest cities to smaller cities, 8,790 migrations from smaller cities to the five biggest cities, and another 23,151 moves between cities of similar sizes.

It is worth noting that city indicators are still estimated on the basis of migrants. However, the value of experience acquired in cities of different sizes is estimated on the basis of both migrants and stayers. This is because, although location does not change for stayers, their experience changes from month to month while working.

In an earlier version of this article, we included the square of experience in the two biggest cities and the square of experience in the third to fifth biggest cities instead of interacting experience in each city size class with overall experience. Results were very similar, but the current specification allows us to capture the different effects of working in big cities for less and more experienced workers, as discussed below.

The profiles coincide over the first 5 years for the worker who stays in Madrid (solid line) and for the worker who subsequently moves to Santiago (dashed line) by construction. However, at the end of this section we introduce further flexibility in the estimation to let the profiles differ between stayers, migrants to big cities, and migrants from big cities and find no significant differences among them.

Our specification allows the discrete loss when moving from Madrid to Santiago to differ from the discrete gain when moving from Santiago to Madrid (through the interactions with the indicator variable “now in 5 biggest”). Our estimates show that these discrete changes are very similar in magnitude. One interpretation is that the static component of the Madrid earnings premium is similar for migrants going in either direction and there is very little depreciation in the dynamic component. However, since depreciation in the dynamic component is identified only on the basis of migrants leaving Madrid, it is difficult to distinguish such depreciation from idiosyncratic differences in the static part for those who leave Madrid. Thus, we cannot rule out that workers self-select into moving from Madrid to Santiago when they have a particular good fit with Santiago so that the static loss is particularly small for them. Recall that we allow the value of experience accumulated in Madrid to depreciate both discretely at the time of moving and over time after the move. If the discrete depreciation at the time of moving away from Madrid coincided with the idiosyncratic difference in the static loss for those who move from Madrid to Santiago, the total discrete loss when moving from Madrid to Santiago could still be roughly the same as the discrete gain when moving from Santiago to Madrid. Nevertheless, the fact that when we let the value of big city experience differ between stayers, migrants to big cities, and migrants from big cities we find no significant differences between them provides some evidence against self-selection having an important effect on our results.

In our specification of column (1) of Table 2 , the depreciation of experience acquired in the two biggest cities after relocation is captured by the interaction between this variable and overall experience, since overall experience keeps increasing after relocation. We have also tried capturing depreciation through interactions between experience in each city size class and the time elapsed since the worker last had a job in that city size-class, but these additional interaction terms are not statistically significant when added to our specification, suggesting that the interaction between experience in each city size class and overall experience already does a good job in capturing depreciation.

Earlier papers arguing that the urban earnings premium has an important dynamic component include Glaeser and Maré (2001) , Gould (2007) , and Baum-Snow and Pavan (2012) . Glaeser and Maré (2001) compare the earnings premium associated with working in a metropolitan area instead of a rural area in the U.S. across migrants with different arrival dates. They find the premium is larger for migrants who, at the time they are observed in the data, have already spent some time in a metropolitan area than for those who have only recently arrived. Relative to their work, instead of comparing earnings in rural areas with those of all urban areas combined, our estimations compare earnings across cities of different sizes; instead of comparing workers with different arrival dates in cities our estimations track workers’ job history in different cities and explicitly allow the value of their experience to vary depending on where it is acquired and used; we also study complementarities between learning in cities and unobservable skills and simultaneously consider static advantages, dynamic advantages, and sorting to explain the earnings premium of bigger cities. Gould (2007) finds in a structural estimation that white-collar workers in U.S. rural areas earn more if they have previously worked in a metropolitan area. Baum-Snow and Pavan (2012) also estimate a structural model and find that returns to work experience in big cities can account for about two-thirds of the wage gap between large and small metropolitan areas in the U.S.

We obtain historical population data from Goerlich et al . (2006) who construct decennial municipality population series using all available censuses from 1900 to 2001, keeping constant the areas of municipalities in 2001. As we do for current urban area size, we measure urban area size in 1900 with the number of people within 10 km of the average person in the urban area. Since we lack a 1-km-resolution population grid for 1900, we distribute population uniformly within the municipality when performing our historical size calculations.

The source of the land quality data is the CORINE Project (Coordination of Information on the Environment), initiated by the European Commission in 1985 and later incorporated by the European Environment Agency into its work programme (European Environment Agency, 1990). We calculate the percentage of land within 25 km of the city centre with high potential quality using Geographic Information Systems (GIS). The city centre is defined as the centroid of the main municipality of the urban area (the municipality that gives the urban area its name or the most populated municipality when the urban area does not take its name from a municipality).

Geographic information on the location of water bodies in and around urban areas is computed using GIS and the digital map of Spain’s hydrography included with Goerlich et al . (2006) . Slope is calculated on the basis of elevation data from the Shuttle Radar Topographic Mission ( Jarvis et al ., 2008 ), which record elevation for points on a grid 3 arc-seconds apart (approximately 90 m).

The number of Roman road rays 25 km from each city centre is computed using GIS and the digital map of Roman roads of McCormick et al . (2008) .

The percentage of water within 25 km of the city centre has a positive sign in the first stage regression. Intuitively, water bodies have a negative effect on land supply but also a positive effect on land demand through their amenity value. The first stage of the instrumental variable estimation suggests that the latter dominates and the net effect of water bodies around a city is positive. While in some other European countries water may also affect city size through navigable waterways used for transportation, Spain does not have any major navigable waterways used historically for transportation. It is for this reason that we use Roman roads instead of historically navigable waterways as an additional instrument.

We also include indicator variables for movers in the third year before and after the migration event. We exclude from the estimation those workers who relocate more than once in 2004–2009.

Again, we drop migrants who move more than once in the estimation period.

Ayuso and Restoy (2007) estimate that during 1998–2002 the price-to-rent ratio for Spanish housing was below its long-run equilibrium. It then shot up markedly above its long-run equilibrium before dropping down again from 2008 onwards.

In particular, since we are interested in worker moves across cities while Card et al . (2013) study worker moves across firms, we treat cities in our procedure as they treat firms in theirs to correct for top and bottom coding. We run 300 Tobit regressions by groups of age, occupation, and year (five age groups |$\times$| ten occupations |$\times$| 6 years) and include as explanatory variables sets of indicator variables for level of education, temporary contract, part-time contract and month. Given that our baseline specification incorporates a worker fixed effect, we further include as in Card et al . (2013) the worker’s mean of log daily wages (excluding the current wage) and the fractions of top or bottom censored wage observations over his career (again excluding the current censoring status). Moreover, since their specification also incorporates firm fixed effects, instead of including the annual mean of wages in the firm and firm size as regressors, we include the annual mean of wages in the city and our measure of city size. Using the coefficients of these Tobit regressions (including the estimated variance), we proceed to simulate earnings only for capped observations. Further details of the estimation and simulation procedures and results are available upon request.

In order to keep constant the ages of individuals in the estimation samples for 1998–2003 and 2004–2009 ( i.e. individuals aged 18–47), we include in the former period individuals who were born between 1957 and 1961 for whom experience is only available since 1980, typically after several years of having entered the labour force.

In our empirical estimations, we include non-linear terms that allow the differential value of experience accumulated in cities of different sizes to vary with the amount of previously acquired experience. The equations in the text omit the interaction terms with overall experience to simplify the exposition and for consistency with our earlier discussion.

Since reference groups for solid and dashed lines are workers with different levels of ability, the reader should not interpret the vertical gap between a solid and a dashed line for the same city as the difference in the earnings between worker types in that city. To obtain such premium, one should further add to the earnings gap the extra value of overall (as opposed to city-specific) experience attained by high-ability workers. See the interactions between experience (or experience squared) and the worker fixed effect in column (1) of Table 4 .

These skill groups are the same we used as controls in our regressions. They are based on categories assigned by employers to workers in their social security filings and are closely related to the level of formal education required for the job. For instance, social security category 1 (our “very-high-skilled occupation” category) corresponds to jobs requiring an engineering or bachelors degree and top managerial jobs. Note that it is the skill required by the job and not those acquired by the worker that determine the social security category. For instance, someone with a law degree will have social security category 1 (our “very-high-skilled occupation” category) if working as a lawyer, and social security category 7 (included in our “medium-low-skilled” category) if working as an office assistant.

A complementary explanation at the low end of the skill distribution has to do with the differential value by skill of big city amenities. If a server at a McDonald’s restaurant in New York City does not make sufficiently more than a server at a McDonald’s in Kansas City to offset the difference in housing costs, it may be not because the server in New York City is that much worse at her job, but because big city amenities (public transportation, an established network of earlier immigrants that helps new low-skilled immigrants settle, etc.) make it worthwhile to remain in a big city even if wages are not that much higher.

Our explanation is consistent with Baum-Snow and Pavan (2013) , who point to the importance of differences in the returns to unobservable skills to explain the higher variance of earnings in bigger cities.

Combes et al . (2012a ) also allow for truncation of one distribution to approximate the other. We find no significant truncation when comparing our two distributions, and so in Table 6 we restrict ourselves to shift and dilation.

Relative to the Combes et al . (2012b ) specification two rows below, the top row of Table 6 makes two changes. First, it introduces dynamic effects from working in bigger cities and allows them to be heterogeneous across workers. Secondly, it introduces additional controls for observable characteristics. It is the first of those changes that makes most of the difference. To confirm this, we have also computed fixed effects removing controls from our full specification (leaving it as in Table 4 , column (1), but without controlling for firm tenure, occupation, sector, nor contract-type). This results in an estimated shift parameter of |$\hat{A}=0.0117$|⁠ , indicating a difference in means for the fixed-effects distribution of just 1.2%. This compares with a difference in means of 0.1% for the fixed-effects distributions of our full specification with controls and a difference in means of 17% for the fixed-effects distributions when we use the Combes et al . (2012b ) specification. The estimated dilation parameter is |$\hat{D}=1.1039$| and the mean squared quantile difference is |$3.1e-03$|⁠ . This confirms that sorting is not very important whether conditional or unconditional on observables, after we take out the effect of accumulating experience in different cities.

Supplementary data

Email alerts, citing articles via.

• Recommend to your Library
• Journals Career Network

Affiliations

• Online ISSN 1467-937X
• Print ISSN 0034-6527
• Copyright © 2024 Review of Economic Studies Ltd
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

To log in and use all the features of Khan Academy, please enable JavaScript in your browser.

World History Project - Origins to the Present

Course: world history project - origins to the present   >   unit 6.

• READ: Why Was Slavery Abolished? Three Theories
• READ: Race and Post-Abolition Societies
• READ: Rise of Proletariat
• BEFORE YOU WATCH: Capitalism and Socialism
• WATCH: Capitalism and Socialism
• READ: Child Labor
• READ: A World Tour of Women's Suffrage

READ: Responses to Industrialization

• Transformation of Labor

First read: preview and skimming for gist

Second read: key ideas and understanding content.

• How did Evangelical Christianity inspire some reformers?
• What was the connection between the anti-slavery movement and the women’s rights movement?
• What were some effects of industrialization which Upton Sinclair highlighted in his book?
• What was life like in the tenements, according to the author?
• What were some of the successes of the reform movement?

Third read: evaluating and corroborating

• How does evidence from this article help you evaluate the production and distribution frame narrative?

Responses to Industrialization

Women’s rights, labor reforms, public health.

• While there were reform movements in other parts of the world, they did not always start for the same reasons. In some cases, these movements happened later. Reformism in the U.S. and Britain were efforts to counteract the negative social effects of industrialization. But many societies in other parts of the world were just beginning to witness the rise of industrial capitalism.

Want to join the conversation?

• Upvote Button navigates to signup page
• Downvote Button navigates to signup page
• Flag Button navigates to signup page

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

The Growing Pains of Urbanization, 1870-1900

Urbanization and Its Challenges

OpenStaxCollege

[latexpage]

Learning Objectives

By the end of this section, you will be able to:

• Explain the growth of American cities in the late nineteenth century
• Identify the key challenges that Americans faced due to urbanization, as well as some of the possible solutions to those challenges

Urbanization occurred rapidly in the second half of the nineteenth century in the United States for a number of reasons. The new technologies of the time led to a massive leap in industrialization, requiring large numbers of workers. New electric lights and powerful machinery allowed factories to run twenty-four hours a day, seven days a week. Workers were forced into grueling twelve-hour shifts, requiring them to live close to the factories.

While the work was dangerous and difficult, many Americans were willing to leave behind the declining prospects of preindustrial agriculture in the hope of better wages in industrial labor. Furthermore, problems ranging from famine to religious persecution led a new wave of immigrants to arrive from central, eastern, and southern Europe, many of whom settled and found work near the cities where they first arrived. Immigrants sought solace and comfort among others who shared the same language and customs, and the nation’s cities became an invaluable economic and cultural resource.

Although cities such as Philadelphia, Boston, and New York sprang up from the initial days of colonial settlement, the explosion in urban population growth did not occur until the mid-nineteenth century ( [link] ). At this time, the attractions of city life, and in particular, employment opportunities, grew exponentially due to rapid changes in industrialization. Before the mid-1800s, factories, such as the early textile mills, had to be located near rivers and seaports, both for the transport of goods and the necessary water power. Production became dependent upon seasonal water flow, with cold, icy winters all but stopping river transportation entirely. The development of the steam engine transformed this need, allowing businesses to locate their factories near urban centers. These factories encouraged more and more people to move to urban areas where jobs were plentiful, but hourly wages were often low and the work was routine and grindingly monotonous.

Eventually, cities developed their own unique characters based on the core industry that spurred their growth. In Pittsburgh, it was steel; in Chicago, it was meat packing; in New York, the garment and financial industries dominated; and Detroit, by the mid-twentieth century, was defined by the automobiles it built. But all cities at this time, regardless of their industry, suffered from the universal problems that rapid expansion brought with it, including concerns over housing and living conditions, transportation, and communication. These issues were almost always rooted in deep class inequalities, shaped by racial divisions, religious differences, and ethnic strife, and distorted by corrupt local politics.

This 1884 Bureau of Labor Statistics report from Boston looks in detail at the wages, living conditions, and moral code of the girls who worked in the clothing factories there.

THE KEYS TO SUCCESSFUL URBANIZATION

As the country grew, certain elements led some towns to morph into large urban centers, while others did not. The following four innovations proved critical in shaping urbanization at the turn of the century: electric lighting, communication improvements, intracity transportation, and the rise of skyscrapers. As people migrated for the new jobs, they often struggled with the absence of basic urban infrastructures, such as better transportation, adequate housing, means of communication, and efficient sources of light and energy. Even the basic necessities, such as fresh water and proper sanitation—often taken for granted in the countryside—presented a greater challenge in urban life.

Electric Lighting

Thomas Edison patented the incandescent light bulb in 1879. This development quickly became common in homes as well as factories, transforming how even lower- and middle-class Americans lived. Although slow to arrive in rural areas of the country, electric power became readily available in cities when the first commercial power plants began to open in 1882. When Nikola Tesla subsequently developed the AC (alternating current) system for the Westinghouse Electric & Manufacturing Company, power supplies for lights and other factory equipment could extend for miles from the power source. AC power transformed the use of electricity, allowing urban centers to physically cover greater areas. In the factories, electric lights permitted operations to run twenty-four hours a day, seven days a week. This increase in production required additional workers, and this demand brought more people to cities.

Gradually, cities began to illuminate the streets with electric lamps to allow the city to remain alight throughout the night. No longer did the pace of life and economic activity slow substantially at sunset, the way it had in smaller towns. The cities, following the factories that drew people there, stayed open all the time.

Communications Improvements

The telephone, patented in 1876, greatly transformed communication both regionally and nationally. The telephone rapidly supplanted the telegraph as the preferred form of communication; by 1900, over 1.5 million telephones were in use around the nation, whether as private lines in the homes of some middle- and upper-class Americans, or jointly used “party lines” in many rural areas. By allowing instant communication over larger distances at any given time, growing telephone networks made urban sprawl possible.

In the same way that electric lights spurred greater factory production and economic growth, the telephone increased business through the more rapid pace of demand. Now, orders could come constantly via telephone, rather than via mail-order. More orders generated greater production, which in turn required still more workers. This demand for additional labor played a key role in urban growth, as expanding companies sought workers to handle the increasing consumer demand for their products.

Intracity Transportation

As cities grew and sprawled outward, a major challenge was efficient travel within the city—from home to factories or shops, and then back again. Most transportation infrastructure was used to connect cities to each other, typically by rail or canal. Prior to the 1880s, the most common form of transportation within cities was the omnibus. This was a large, horse-drawn carriage, often placed on iron or steel tracks to provide a smoother ride. While omnibuses worked adequately in smaller, less congested cities, they were not equipped to handle the larger crowds that developed at the close of the century. The horses had to stop and rest, and horse manure became an ongoing problem.

In 1887, Frank Sprague invented the electric trolley, which worked along the same concept as the omnibus, with a large wagon on tracks, but was powered by electricity rather than horses. The electric trolley could run throughout the day and night, like the factories and the workers who fueled them. But it also modernized less important industrial centers, such as the southern city of Richmond, Virginia. As early as 1873, San Francisco engineers adopted pulley technology from the mining industry to introduce cable cars and turn the city’s steep hills into elegant middle-class communities. However, as crowds continued to grow in the largest cities, such as Chicago and New York, trolleys were unable to move efficiently through the crowds of pedestrians ( [link] ). To avoid this challenge, city planners elevated the trolley lines above the streets, creating elevated trains, or L-trains, as early as 1868 in New York City, and quickly spreading to Boston in 1887 and Chicago in 1892. Finally, as skyscrapers began to dominate the air, transportation evolved one step further to move underground as subways. Boston’s subway system began operating in 1897, and was quickly followed by New York and other cities.

The Rise of Skyscrapers

The last limitation that large cities had to overcome was the ever-increasing need for space. Eastern cities, unlike their midwestern counterparts, could not continue to grow outward, as the land surrounding them was already settled. Geographic limitations such as rivers or the coast also hampered sprawl. And in all cities, citizens needed to be close enough to urban centers to conveniently access work, shops, and other core institutions of urban life. The increasing cost of real estate made upward growth attractive, and so did the prestige that towering buildings carried for the businesses that occupied them. Workers completed the first skyscraper in Chicago, the ten-story Home Insurance Building, in 1885 ( [link] ). Although engineers had the capability to go higher, thanks to new steel construction techniques, they required another vital invention in order to make taller buildings viable: the elevator. In 1889, the Otis Elevator Company, led by inventor James Otis, installed the first electric elevator. This began the skyscraper craze, allowing developers in eastern cities to build and market prestigious real estate in the hearts of crowded eastern metropoles.

Jacob Riis was a Danish immigrant who moved to New York in the late nineteenth century and, after experiencing poverty and joblessness first-hand, ultimately built a career as a police reporter. In the course of his work, he spent much of his time in the slums and tenements of New York’s working poor. Appalled by what he found there, Riis began documenting these scenes of squalor and sharing them through lectures and ultimately through the publication of his book, How the Other Half Lives , in 1890 ( [link] ).

By most contemporary accounts, Riis was an effective storyteller, using drama and racial stereotypes to tell his stories of the ethnic slums he encountered. But while his racial thinking was very much a product of his time, he was also a reformer; he felt strongly that upper and middle-class Americans could and should care about the living conditions of the poor. In his book and lectures, he argued against the immoral landlords and useless laws that allowed dangerous living conditions and high rents. He also suggested remodeling existing tenements or building new ones. He was not alone in his concern for the plight of the poor; other reporters and activists had already brought the issue into the public eye, and Riis’s photographs added a new element to the story.

To tell his stories, Riis used a series of deeply compelling photographs. Riis and his group of amateur photographers moved through the various slums of New York, laboriously setting up their tripods and explosive chemicals to create enough light to take the photographs. His photos and writings shocked the public, made Riis a well-known figure both in his day and beyond, and eventually led to new state legislation curbing abuses in tenements.

THE IMMEDIATE CHALLENGES OF URBAN LIFE

Congestion, pollution, crime, and disease were prevalent problems in all urban centers; city planners and inhabitants alike sought new solutions to the problems caused by rapid urban growth. Living conditions for most working-class urban dwellers were atrocious. They lived in crowded tenement houses and cramped apartments with terrible ventilation and substandard plumbing and sanitation. As a result, disease ran rampant, with typhoid and cholera common. Memphis, Tennessee, experienced waves of cholera (1873) followed by yellow fever (1878 and 1879) that resulted in the loss of over ten thousand lives. By the late 1880s, New York City, Baltimore, Chicago, and New Orleans had all introduced sewage pumping systems to provide efficient waste management. Many cities were also serious fire hazards. An average working-class family of six, with two adults and four children, had at best a two-bedroom tenement. By one 1900 estimate, in the New York City borough of Manhattan alone, there were nearly fifty thousand tenement houses. The photographs of these tenement houses are seen in Jacob Riis’s book, How the Other Half Lives , discussed in the feature above. Citing a study by the New York State Assembly at this time, Riis found New York to be the most densely populated city in the world, with as many as eight hundred residents per square acre in the Lower East Side working-class slums, comprising the Eleventh and Thirteenth Wards.

Visit New York City, Tenement Life to get an impression of the everyday life of tenement dwellers on Manhattan’s Lower East Side.

Churches and civic organizations provided some relief to the challenges of working-class city life. Churches were moved to intervene through their belief in the concept of the social gospel . This philosophy stated that all Christians, whether they were church leaders or social reformers, should be as concerned about the conditions of life in the secular world as the afterlife, and the Reverend Washington Gladden was a major advocate. Rather than preaching sermons on heaven and hell, Gladden talked about social changes of the time, urging other preachers to follow his lead. He advocated for improvements in daily life and encouraged Americans of all classes to work together for the betterment of society. His sermons included the message to “love thy neighbor” and held that all Americans had to work together to help the masses. As a result of his influence, churches began to include gymnasiums and libraries as well as offer evening classes on hygiene and health care. Other religious organizations like the Salvation Army and the Young Men’s Christian Association (YMCA) expanded their reach in American cities at this time as well. Beginning in the 1870s, these organizations began providing community services and other benefits to the urban poor.

In the secular sphere, the settlement house movement of the 1890s provided additional relief. Pioneering women such as Jane Addams in Chicago and Lillian Wald in New York led this early progressive reform movement in the United States, building upon ideas originally fashioned by social reformers in England. With no particular religious bent, they worked to create settlement houses in urban centers where they could help the working class, and in particular, working-class women, find aid. Their help included child daycare, evening classes, libraries, gym facilities, and free health care. Addams opened her now-famous Hull House ( [link] ) in Chicago in 1889, and Wald’s Henry Street Settlement opened in New York six years later. The movement spread quickly to other cities, where they not only provided relief to working-class women but also offered employment opportunities for women graduating college in the growing field of social work. Oftentimes, living in the settlement houses among the women they helped, these college graduates experienced the equivalent of living social classrooms in which to practice their skills, which also frequently caused friction with immigrant women who had their own ideas of reform and self-improvement.

The success of the settlement house movement later became the basis of a political agenda that included pressure for housing laws, child labor laws, and worker’s compensation laws, among others. Florence Kelley, who originally worked with Addams in Chicago, later joined Wald’s efforts in New York; together, they created the National Child Labor Committee and advocated for the subsequent creation of the Children’s Bureau in the U.S. Department of Labor in 1912. Julia Lathrop—herself a former resident of Hull House—became the first woman to head a federal government agency, when President William Howard Taft appointed her to run the bureau. Settlement house workers also became influential leaders in the women’s suffrage movement as well as the antiwar movement during World War I.

Jane Addams was a social activist whose work took many forms. She is perhaps best known as the founder of Hull House in Chicago, which later became a model for settlement houses throughout the country. Here, she reflects on the role that the settlement played.

Life in the Settlement discovers above all what has been called ‘the extraordinary pliability of human nature,’ and it seems impossible to set any bounds to the moral capabilities which might unfold under ideal civic and educational conditions. But in order to obtain these conditions, the Settlement recognizes the need of cooperation, both with the radical and the conservative, and from the very nature of the case the Settlement cannot limit its friends to any one political party or economic school.

The Settlement casts side none of those things which cultivated men have come to consider reasonable and goodly, but it insists that those belong as well to that great body of people who, because of toilsome and underpaid labor, are unable to procure them for themselves. Added to this is a profound conviction that the common stock of intellectual enjoyment should not be difficult of access because of the economic position of him who would approach it, that those ‘best results of civilization’ upon which depend the finer and freer aspects of living must be incorporated into our common life and have free mobility through all elements of society if we would have our democracy endure.

The educational activities of a Settlement, as well its philanthropic, civic, and social undertakings, are but differing manifestations of the attempt to socialize democracy, as is the very existence of the Settlement itself.

In addition to her pioneering work in the settlement house movement, Addams also was active in the women’s suffrage movement as well as an outspoken proponent for international peace efforts. She was instrumental in the relief effort after World War I, a commitment that led to her winning the Nobel Peace Prize in 1931.

Section Summary

Urbanization spread rapidly in the mid-nineteenth century due to a confluence of factors. New technologies, such as electricity and steam engines, transformed factory work, allowing factories to move closer to urban centers and away from the rivers that had previously been vital sources of both water power and transportation. The growth of factories—as well as innovations such as electric lighting, which allowed them to run at all hours of the day and night—created a massive need for workers, who poured in from both rural areas of the United States and from eastern and southern Europe. As cities grew, they were unable to cope with this rapid influx of workers, and the living conditions for the working class were terrible. Tight living quarters, with inadequate plumbing and sanitation, led to widespread illness. Churches, civic organizations, and the secular settlement house movement all sought to provide some relief to the urban working class, but conditions remained brutal for many new city dwellers.

Review Questions

Which of the following four elements was not essential for creating massive urban growth in late nineteenth-century America?

Which of the following did the settlement house movement offer as a means of relief for working-class women?

What technological and economic factors combined to lead to the explosive growth of American cities at this time?

At the end of the nineteenth century, a confluence of events made urban life more desirable and more possible. Technologies such as electricity and the telephone allowed factories to build and grow in cities, and skyscrapers enabled the relatively small geographic areas to continue expanding. The new demand for workers spurred a massive influx of job-seekers from both rural areas of the United States and from eastern and southern Europe. Urban housing—as well as services such as transportation and sanitation—expanded accordingly, though cities struggled to cope with the surging demand. Together, technological innovations and an exploding population led American cities to grow as never before.

Urbanization and Its Challenges Copyright © 2014 by OpenStaxCollege is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Don't miss tomorrow's smart cities industry news

Let Smart Cities Dive's free newsletter keep you informed, straight from your inbox.

3 key themes that will drive cities' strategies for job creation

"The future of work in America" report assessed how technology and automation will impact labor fields, productivity and economic development in cities.

The labor market looks different than it did 20 years ago and the next wave of technology could accelerate the rate of change in the coming decades, according to a report from the McKinsey Global Institute.

" The future of work in America ," which examined 315 cities and more than 3,000 counties, indicates that automation is changing humans' jobs and the skills needed to perform them.

The report found that in the decade since the Great Recession, 25 "megacities" and their surrounding areas — 96 million people, or 30% of the US population — generated two-thirds of America's job growth. Those same 25 cities could capture 60% of of the nation's job growth through 2030.

The study suggests that the biggest change with automation will not be machines completely replacing people, but rather altering the type of work they do. The time people save by using machines for some tasks, often routine or repetitive tasks, can be put toward different, higher-value activities. In this way, machines will make human labor more productive, not obsolete.

"All Americans will need to cultivate new skills to remain relevant in a more digital and knowledge-intensive economy," the report says. 

The report offers three key themes that can drive cities' strategies for job creation in the coming years:

1. Technology will cause jobs in some occupations to shrink and others to grow

Nearly 40% of America's current jobs are in categories that could shrink by 2030. The main thing these jobs have in common is that they involve routine or physical tasks. Jobs at risk of displacement include office support, food service, production work and machine operations, retail sales and customer service roles.  

But technology also will create jobs, mainly in healthcare, STEM, business or legal professionals, creatives or arts management and work requiring personal interaction. Shifts already are occurring and jobs are being added in areas that make use of new technologies such as software developers, information security analysts, solar panel installers and wind turbine technicians. Healthcare jobs will remain in high demand, especially those that cater to aging Baby Boomers such as hearing aid technicians.  Increasing affluence in society is creating demand for jobs as well. Personal services including massage therapists and fitness trainers are on the rise.

The report predicts growth in the transportation sector despite autonomous vehicle (AV) development. Researchers indicate it will take years to overcome the technical and regulatory barriers to AV deployment and for companies to replace their transportation assets already on the road.

About 8%-9% of the jobs technology will help create by 2030 will be ones we can't yet imagine because they barely exist today.

2. Job growth and loss from automation will be universal, but it will not be felt evenly

Even though America could experience positive net job growth in the coming years, the jobs won't appear in the same places as right now. As demand for skills changes, high-wage jobs are expected to expand and the loss of middle wage jobs evident over the last 20 years is expected to continue. The report flags "notable inequality" among regions and demographics.    The places where new jobs do appear might not align with the skills of workers in those regions, so communities will face the challenge of addressing local mismatches and helping workers gain the new skills necessary to succeed.  The report notes that technology could increase disparities among and within communities. Because "local economies have been on diverging trajectories for years," they are coming into the automation age from different starting points, and not all communities will have the momentum to offset job displacement.

An already occurring trend will intensify: Rural areas will experience slower job growth and even losses, while growth occurs in urban areas and their suburbs, namely the 25 megacities. Automation could amplify variances across demographic groups. Those with a high school degree or less education are four times more likely than those with bachelors degrees to hold jobs that can be automated. Hispanic and African American workers might be the hardest hit, researchers say, with 12 million jobs in those demographics potentially displaced. Young people represent a group with 15 million at-risk jobs and people over 50 represent a group with 11.5 million jobs at risk. "[E]ven the most thriving cities will need to connect marginalized populations with better opportunities," the report says. Although gender representation changes within occupations over time, men could have a higher job displacement rate than women by 2030 because of the occupations where they are concentrated. For example, women are expected to experience job growth because of their heavy concentrations in healthcare and personal care. However, men's displacement could be stabilized by their heavier representation in tech-forward jobs that have yet to emerge.

3. Local public sector leaders must work with private sector leaders and educators to overcome challenges

Technology will bring opportunities for innovation, productivity and inclusive growth, but all sectors will have to work together for a community to successfully realize those benefits. Even prosperous cities have populations struggling to find their place in the new economy and keep up with the increasing cost of living. Each city is different and partners will have to work together to overcome the area's unique set of labor challenges. "They can draw on a common toolbox of solutions, but the priorities vary from place to place — from affordable housing in major cities to digital infrastructure that enables remote work in rural counties," the report says. But the common thread is collaboration on job matching and mobility, skills and training, support for workers in transition and overall economic development and job creation. Retraining workers in new and higher-level skills as well as fostering lifelong learning are two strategies viewed as critical for successfully transitioning to the more automated, tech-forward workforce.

Recommended Reading

Smart Cities Dive news delivered to your inbox

Get the free daily newsletter read by industry experts

• Select user consent: By signing up to receive our newsletter, you agree to our Terms of Use and Privacy Policy . You can unsubscribe at anytime.

Editors' picks

Heat pumps would cut energy bills for majority of US homes: NREL research

Even so, “we need work to bring down the cost of installing heat pumps,” said a senior research engineer at the National Renewable Energy Laboratory.

Can you fix a Phoenix water shortage? A new drought simulation game lets players try.

As the desert city endures a drought that has stretched on since the turn of the century, water resource specialists want the community to understand the trade-offs necessary in water management. 

Keep up with the story. Subscribe to the Smart Cities Dive free daily newsletter

Company Announcements

• 5 ways cities can accelerate EV charger deployments equitably By Iulia Gheorghiu
• DOE awards $27M for communities to plan a clean-energy future By Ysabelle Kempe
• How cities are building out public EV charging infrastructure By Joe Burns
• Rooftop heat pump technologies can save US businesses $5B annually: DOE By Nish Amarnath

City Life – Why people prefer to live in cities?

City living can have many positive impacts compared to rural life. Because of these benefits, most of the world’s population live in cities now. While the rural lifestyle is less stressful, city lifestyle has a lot of people and many advantages for people. With a rapid increase in urban growth, cities are becoming lucrative. It’s more likely people will walk in parks or get more exercise in city life than to their rural counterparts. Living in the city is becoming a new normal and people now prefer living in major cities inspite of higher cost of living. There are some negatives to city life, such as stressful lifestyles and fast-paced life. These have to be tolerated to live in a good city. Obesity is also high in urban areas because of loss of exercise. In rural areas, people do a lot of manual work which helps maintain their stress and obesity levels. People might also know each other more personally in rural areas as compared to cities. Although it is important to think of both sides, this article will focus on the advantages of living in the city rather than in rural areas or the countryside.

Also Read: Disadvantages and Issues of living in a City or City Life

Here are some advantages of living in a big city:

• More job opportunities: In cities, there are a lot more jobs and learning  opportunities for you. Although the job market is a lot more competitive, there are a lot of job opportunities and a wide variety of jobs available that you can take part in. If you need to develop a skill, it is also likely that you can do a course to understand your skills and to get that job. If you are educated, then there are more jobs available for labor jobs, clerks, customer service to engineering jobs. This is one of the major reasons many people migrate from rural areas to urban city life.
• More chances of meeting new people: One of the major ways in which people get new opportunities is through meeting people and city life offers this. Wherever you go in the city, you will encounter new people and will be in the mix of meeting someone who can suggest an opportunity for you. There is also a lot of information and knowledge available in cities through the computer and the internet, which might not be available in the rural lifestyle. It’s likely you will become friends with a few people as you become more familiar with the city lifestyle and develop your own group of connections and network. You gain better life experiences as you learn from a larger population.
• A lot of variety and choices: This is because of the abundance and mix of people and culture. In major cities, you find people from all walks of life and countries/cities. Larger populations also help you in making new friends. So there is a melting pot of cultures and people. This suggests that cities are more connected to the world. This will help people to understand their own choices and themselves better in relation to what they like and dislike or which path will give them more opportunities in life.
• Better infrastructure and education opportunities: There are more educational opportunities and a lot of different courses and institutions available in cities. Although they might be expensive, it is also easier to get a bank loan in the city. If you have children, then it might be better to migrate to the city as they have many opportunities and will be exposed to a more modern lifestyle. There is always an opportunity to learn something new or develop something in a city. Since the pay is also higher in cities, with your job it will be easier to save money for the child’s education. Living in the city is more expensive in short term but the returns offered on education and job are virtually guaranteed as compared to rural area.
• More overall facilities and knowledge about health: There are more opportunities for a healthy lifestyle in cities, such as good doctors and vaccination opportunities. People are known to live longer because they have purposeful lives and dedicate themselves to more worthy causes. Even NGOs like UNICEF work in major cities, bringing in more knowledge about health issues where even the poor can get access to flu vaccinations and so forth. There might also be more immunity because of the large population size. There are also many gyms and parks in cities that encourage people to walk and exercise. But, one negative issue is the pollution that might cause ill-health in cities which cities are struggling to tackle.

The benefits and advantages can be clubbed into following categories:

Availability Of Services & Facilities

• Cities usually have hospitals and nursing homes where there are good doctors to take care of us when we have diseases or illnesses. Good medicine is also available in cities.
• Cities have better drainage systems, meaning they usually avoid floods.
• Cities have a very good supply of electricity and often have power plants near to it.
• Cities have good schools and colleges and universities and many books are available which help with good education opportunities.
• Cities often have factories near them for the production of goods and materials for the people.
• The differences are visible even in small town and big towns, a large city will offer more greater choices.

Good Connectivity

• Cities have good means of transport like trains, buses, trams, cars. They also have airplanes in airports for travelling longer distances or for going to other countries.
• Cities have ports for bringing in good and people from far off places.
• Public transport options are available in plenty of cities, meaning that a lot of people can travel together and save fuel and also save money for the same.

Municipality & Proper Sewage Systems

• Water is usually provided well by municipalities and the government.
• Drinking water is often provided by the government which is purified and circulated from filtration plants maintained by the state.
• Cities have good sewage systems for keeping it clean and hygiene is maintained.
• Cities have waste management departments for better waste disposal and management.

Proper Infrastructure

• Cities have big buildings and places of interest. They have offices and headquarters of companies.
• Cities have movie theaters and shopping complexes and places to see. They have parks and markets and restaurants for eating out and hotels for staying.
• More offices in cities mean that more jobs are available there and people often come to cities to earn their living and stay there.
• Cities have libraries to help students and researchers find books in an affordable way and continue their studies.
• Cities have big five — star hotels to provide top — class service and amenities. They have coffee shops, grocery stores, big stadiums for cricket and football matches where a lot of people can sit and watch the games.

Environment Friendly and Cultural Diversity

• With growing concern about the environment, more and more trees are being planted in parks in cities and greenery is being enhanced.
• Cities have a huge diversity of culture due to people from various backgrounds living there together.
• Cities have a lot of cultural places for watching and enjoying festivals and dance shows and music events. Hustle and bustle in urban areas is enjoyed by vast variety of people.

Availability Of Internet And Other Facilities

• With growing internet services, cities have internet connections and provide wireless connectivity to people so that they can connect to the internet.
• Online shopping websites supply goods to most cities via couriers and shopping is thus made easy, by buying from the comfort of the home.
• Modern cities have many apartments instead of individual houses and thus, housing problems are somewhat solved due to many flats in single buildings.
• Cities have better postal service and couriers for sending and receiving letters and things.
• Cities have a better system of police to help us during problems and firemen to help in case of fire.
• In most cities, there are big courts for resolving legal troubles and for getting justice in case of problems and crimes.
• Cities offer great ways of engaging in sports and such activities through clubs and parks. They often have golf courses and fields for athletes.

Also Read: Difference between village life and city life

Other advantages of living in the city:

• Better & Reliable Public Transportation – because of the high population density in urban areas, public transportation is a basic requirement for people living in a city. Good public transit acts as a lifeline for the city and thus needs to be reliable, accessible & efficient. The need of having a private vehicle may be felt, but it’s necessity depends on individuals since most of the daily and routine trips can be made using the public transport. This is one of the many advantages of living in a city. Big cities have more developed transit systems as compared to small towns, but still, they remain much better and reliable than rural area.
• Multiple Housing options – Owning a house in urban areas and especially large cities, is not really possible all the time. It depends on the person’s income & affordability. But urban areas provide wider options, you can always rent or share a house. Co-living is one such emerging concept. Since there are people coming from various places, you have more options available to meet your requirements and budget. Having such diverse housing options is one of the many pros of living in the city. Real estate prices in big cities such as San Francisco, Ney York, London, Mumbai etc. are amongst the highest which results in higher cost of living in such cities however they provide many advantages.
• A fast pace of life – Some of us like to live a fast pace of life. Things should be happening all around us, and we just want to get things done as quickly as possible. This is the way of life in most urban areas. You will find people in a hurry, going from one place to another all the time. Everyone is busy doing something or other. The fast pace of life is one of the reasons for living in a city.
• More Exposure & opportunities – Since cities are home to people from various backgrounds, expectations, background, learning, skill sets, they provide a unique combination of such people. This provides you with more exposure as everyone has their own way of doing work and living life. You get to learn more from others.
• Cultural Diversity & vibrant communities – Some large cities attract people from various cultural backgrounds. The mix becomes so large at times that the own identity of the town or city is hard to identify. Each culture has something to present, and people start experimenting and exploring other cultures as well.
• Shopping/ Specialized Needs – Cities are home to the largest shopping areas and specialized shops, showrooms. This is especially an advantage of living in a big city. No matter what your requirement is or how exclusive it is, you will always find the person or dealer for your needs. Such specialized shops are possible in cities because of the large population. Each type of business/ activity needs a threshold population to sustain itself, as explained in Central Place Theory by Walter Christaller .

Obviously, there are a lot more advantages to city life and also a few disadvantages. For example, increased exposure to various types of pollution , but it is true that a city brings more exposure and connects people to the world in comparison to rural life. There has been a growth in the city population in India and other countries. It is estimated that nearly 70% of the world’s population will live in cities by 2025. This is a large number and suggests that we should be more cautious about finding more opportunities and a better life for people in cities. Although there is a difference between cities themselves and competition between them, migrating from rural life to city life can be both challenging and exciting at the same time. It would be beneficial to adjust to city life and embrace the change because of the vast number of opportunities it offers.

People always tend to choose cities to live in. This is due to the presence of a larger scope for enjoying services and improved facilities and, therefore, enjoying a better quality of living. The benefits of cities are innumerable and are always plus points. Especially for those looking for better opportunities in life and achieving more in the fields they are interested in. With the advancement of technology, cities are becoming bigger and more sophisticated, and more and more opportunities are opening up towards a brighter and happier future.

Related articles:

• Galactic City Model
• Primate City & Primacy Concept | Relationship between city sizes
• What are ghost towns?
• Satellite City | Meaning, Characteristics, & Examples
• Aerotropolis: Airport Centric Cities
• Counter Urbanisation | Meaning, Factors, Effects and Examples

Tags: Advantages of living in a big city, Benefits of living in a city in a city, city life, urban life, benefits of living in an urban area, city life points, urban life vs rural life, city life advantages, city lifestyle benefits

About The Author

Thejas Jagannath

4 thoughts on “city life – why people prefer to live in cities”.

I am totally puzzled that you do not mention entertainment – life in cities is exciting! I have interviewed hundreds of people in developing countries about their migration decisions, and especially young people talk about the availability of sports, clubs, fast internet service, music and other forms of entertainment (cockfighting in Vietnam for example). Why so utilitarian?

THIS HELPED US WIN THE DEBATE. THANKS!!!!!!!!!

Services are cheaper due to the effect of Economies of Scale i.e. buying in bulk.

where are the disadvantages

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Notify me of follow-up comments by email.

Notify me of new posts by email.

Schneider Electric Blog

Home > Life @ Schneider > 5 things I understood after living and working in 5 different cities

Life @ Schneider

5 things I understood after living and working in 5 different cities

February 16, 2022

4 min read |  Employee Voices

This audio was created using Microsoft Azure Speech Services

Written by: Ilias Jumadilov, Corporate Strategy Director at Schneider Electric

When you work in a global company, you get the opportunity to become a global person by working in different cities. In the past 17 years, I had the chance to visit my colleagues on 5 continents.

The only continent that I did not yet visit is Antarctica. We do not have Schneider Electric ’s subsidiary there but we have a customer, the first zero-emission polar station equipped with our energy management solution. Maybe one day I will have the opportunity to visit this customer.

Currently, I work in Hong Kong, a cosmopolitan city with the most skyscrapers in the world. Previously, my offices were in Grenoble, Moscow, Barcelona, and Almaty.

The country where I come from, Kyrgyzstan, is considered a country of nomads. I just put my nomad style at a different level and consider myself an international nomad.

So, what did I understand by living and working in 5 different places?

Each country believes it’s unique and different

And yes, they are! In some essence, it is true as we have different cultures and backgrounds. By visiting so many different cities in the past years, I’ve heard from almost each sales rep that their country is unique and global processes can not be applied to them.

In the end, after a while, through customer visits and several interviews, we discovered that customer relations, sales reps approach, market segmentations are very similar worldwide. Global processes work, and they need limited customization by country.

Working in different cities is like working in different companies

We live in an era where it’s rare to work for the same company for over 15-20 years. When I was visiting Japan some years ago, I met some of my colleagues that worked in the same company for over 20 years. They could not change it as it was not well perceived. One of them was particularly not happy with what he was doing.

I can’t imagine myself working in the same place and position for many years unless I fall in love with what I do.  Over the past 17 years in Schneider, I changed 5 cities , and I considered that somehow I’ve changed 5 companies. Each time, being in a very different environment, culture, language, I am getting a completely new experience and that fresh, exciting anticipation of the unknown.

So I guess, if you feel stuck in the same company, changing geography is can be a good option.

Each time, being in a very different environment, culture, language, I am getting a completely new experience and that fresh, exciting anticipation of the unknown.

Changing cities is good for your salary and opens more opportunities

I know this topic is very much taboo, but changing the country as your new working location can be a great opportunity to discuss your current reward package. At the same time, it can open up many more potential positions for you.

The world is big and small at the same time

While you travel, you can see how globalized we have become. In fact, you will find again your lifestyle with minimum changes in any countries and cities. I could always find Starbucks at the corner to get my Chai Tea Latte or Massimo Dutti to buy a new shirt in each country I have worked. It is both good and bad, as every culture becomes less and less unique.

But the most striking thing is not our shopping or dining experience, but the omnipresence of global problems, such as climate change or poor air quality. Even by traveling to different cities, we can no longer avoid these problems. They are global, they are for all humans. And at some point, by being in so many places you’ll realize how fragile our planet is. In this case, we are not Kyrgyz, French, Russian, or Americans, we are humans, part of humanity that need to solve humanity’s problems.

Country is loved by you thanks to the emotions you experience in it rather than the country itself

If I recall which countries or cities I liked the most, I could not choose one at all. I realized it was always the people I met, the emotions I have experienced that make the place memorable.

When I say people, I meant roughly the 10 people that I met in different cities regularly. For sure, we know way more people during the 4-5 years stay in a foreign country. But what makes your stay special is the few people you have become soulmates and you truly love. Although it may sound strange, it does not matter how many people are in that foreign country or how different they are from you. What matters is you find your 10 soulmates and enjoy your life.

I think people that can discover this planet from different angles are the lucky ones, Yet, at the same time, they have the duty to make this world more connected, have less prejudice, and bridge people’s minds to solve global problems. I am profoundly grateful for what I have understood and could do during my 17 years of work in 5 cities.

Lastly, let me tell you a fun fact. Do you know that our body renews completely at cell level every 7 years? So I guess, I am partially Kyrgyz, Russian, French, Spanish, Kazakh, and Chinese now, not only mentally but also at a physical level.

You can join Schneider Electric all over the world! Visit our careers site and find a job opportunity and a location that suits you! www.se.com/careers  We’re hiring!

Tags: Careers at Schneider , expat , Hong Kong , LifeIsOn

Why Diversity and Inclusion Matter at a Workplace — Living and Working with a Handicap

Are you living your unique life and work?

Brazilian Expat on the Adventure of Living Abroad

Executive ‘Global nomad’ on living in four countries and three continents

Inside Business | New concept for MacArthur Center includes…

Share this:.

• Click to share on Facebook (Opens in new window)
• Click to share on X (Opens in new window)

Inside Business

• Ports and Shipyards

Inside Business | New concept for MacArthur Center includes hotel, retail space; former bus station being transformed, Norfolk mayor says

NORFOLK — Mayor Kenny Alexander shared his vision for the future of MacArthur Center as a mixed-use community anchored by a 400-room military-themed hotel near the end of his State of the City address on Friday.

Alexander encouraged more than 1,200 attendees of the annual event, produced by the Hampton Roads Chamber, to envision a vibrant destination that celebrates the city’s culture, reconnects the city, attracts tourists and ensures economic vitality.

“By optimizing existing assets, we aim to solidify Norfolk as a premier hub for business, living, hospitality and tourism, elevating our city’s appeal to residents and visitors alike,” he said.

The concept includes 518,000 square feet of modern high-rise living for rent or purchase and 47,000 square feet of luxury amenities, he said. Part of the vision is a 2.5-acre pedestrian promenade with more than 172,000 square feet of retail space. He shared site renderings during the presentation.

When asked if a developer was involved and about the timeframe for the project, a Norfolk spokesperson said after the event that more information was not available.

The former Greyhound bus station in the Neon District is also undergoing a transformation into Houndstooth , a 220-unit apartment complex with parking garage, gym, recreation room and rooftop deck.

The project represents a $35 million capital investment with the creation of more than 300 construction jobs, Alexander said. The Neon District posted on Instagram Friday that The Breeden Co., based in Virginia Beach, is the developer on that project, which will include ground-floor retail, live-work units and public areas.

To continue thriving as a vibrant hub of art, culture and entertainment, the mayor said Scope Arena and the Chrysler Hall complex — more than 50 years old — are due for a transformative renovation. Changes at Scope will include up to 1,500 new seats; diverse food, beverage, club and dining options; expanded conference and restrooms; interactive technologies; and improved backstage efficiencies.

Chrysler Hall will see improved plaza accessibility with ramps, stairs and street enhancements; expanded lobbies; new seating configurations; stage, sound, lighting and acoustics upgrades; increased restroom capacity; and expanded food and beverage stations on every level.

Alexander said the city also is looking forward to the redevelopment of Military Circle mall as a dynamic hub bustling with life and vitality centered around community sports, residential and creative office spaces.

“As we reimagine our city, let’s bridge our past, present and future converging with history and innovation,” he said.

He referenced the city as a hub for climate science, higher education, health care, maritime industry, military, art and culture.

Alexander summarized the city’s progress over the past year: maintaining public safety as a top priority, economic development with job creation, a thriving innovation sector and an ongoing dedication toward building coastal resilience and combating homelessness.

“Norfolk has been steady — a true symbol of strength and resilience since 1682,” Alexander said. “Let’s build on these victories and assure Norfolk’s winning spirit prevails for generations to come.”

Sandra J. Pennecke, 757-652-5836, [email protected]

More in Inside Business

National News | USS John C. Stennis leaves Newport News Shipbuilding dry dock, overhaul 65% complete

Business | Hundreds bring pitches and prototypes to ‘Shark Tank’ audition at Portsmouth casino

City Politics | Hampton rehires former economic development director after stints in Richmond, Atlanta area

Inside Business | Chesapeake Regional, during Donate Life Month, honors generosity of organ donors who save lives

Trending nationally.

• Florida COVID death toll nears 2,300 in 2024; experts urge updated vaccines
• The most infamous serial killers all seem to have something in common — they’re from the Midwest
• Trump is about to go on trial in New York. Here’s what to expect
• World’s oldest conjoined twins, who lived in Reading, Pa., have died
• Is California still the world’s 5th largest economy?

• Share full article

For more audio journalism and storytelling, download New York Times Audio , a new iOS app available for news subscribers.

• April 12, 2024   •   34:23 How One Family Lost $900,000 in a Timeshare Scam
• April 11, 2024   •   28:39 The Staggering Success of Trump’s Trial Delay Tactics
• April 10, 2024   •   22:49 Trump’s Abortion Dilemma
• April 9, 2024   •   30:48 How Tesla Planted the Seeds for Its Own Potential Downfall
• April 8, 2024   •   30:28 The Eclipse Chaser
• April 7, 2024 The Sunday Read: ‘What Deathbed Visions Teach Us About Living’
• April 5, 2024   •   29:11 An Engineering Experiment to Cool the Earth
• April 4, 2024   •   32:37 Israel’s Deadly Airstrike on the World Central Kitchen
• April 3, 2024   •   27:42 The Accidental Tax Cutter in Chief
• April 2, 2024   •   29:32 Kids Are Missing School at an Alarming Rate
• April 1, 2024   •   36:14 Ronna McDaniel, TV News and the Trump Problem
• March 29, 2024   •   48:42 Hamas Took Her, and Still Has Her Husband

How One Family Lost $900,000 in a Timeshare Scam

A mexican drug cartel is targeting seniors and their timeshares..

Hosted by Katrin Bennhold

Produced by Asthaa Chaturvedi and Will Reid

With Clare Toeniskoetter and Lynsea Garrison

Edited by Brendan Klinkenberg and Michael Benoist

Original music by Marion Lozano ,  Rowan Niemisto ,  Dan Powell ,  Pat McCusker and Will Reid

Engineered by Chris Wood

Listen and follow The Daily Apple Podcasts | Spotify | Amazon Music

Warning: this episode contains descriptions of violence.

A massive scam targeting older Americans who own timeshare properties has resulted in hundreds of millions of dollars sent to Mexico.

Maria Abi-Habib, an investigative correspondent for The Times, tells the story of a victim who lost everything, and of the criminal group making the scam calls — Jalisco New Generation, one of Mexico’s most violent cartels.

On today’s episode

Maria Abi-Habib , an investigative correspondent for The New York Times based in Mexico City.

Background reading

How a brutal Mexican drug cartel came to target seniors and their timeshares .

There are a lot of ways to listen to The Daily. Here’s how.

We aim to make transcripts available the next workday after an episode’s publication. You can find them at the top of the page.

The Daily is made by Rachel Quester, Lynsea Garrison, Clare Toeniskoetter, Paige Cowett, Michael Simon Johnson, Brad Fisher, Chris Wood, Jessica Cheung, Stella Tan, Alexandra Leigh Young, Lisa Chow, Eric Krupke, Marc Georges, Luke Vander Ploeg, M.J. Davis Lin, Dan Powell, Sydney Harper, Mike Benoist, Liz O. Baylen, Asthaa Chaturvedi, Rachelle Bonja, Diana Nguyen, Marion Lozano, Corey Schreppel, Rob Szypko, Elisheba Ittoop, Mooj Zadie, Patricia Willens, Rowan Niemisto, Jody Becker, Rikki Novetsky, John Ketchum, Nina Feldman, Will Reid, Carlos Prieto, Ben Calhoun, Susan Lee, Lexie Diao, Mary Wilson, Alex Stern, Dan Farrell, Sophia Lanman, Shannon Lin, Diane Wong, Devon Taylor, Alyssa Moxley, Summer Thomad, Olivia Natt, Daniel Ramirez and Brendan Klinkenberg.

Our theme music is by Jim Brunberg and Ben Landsverk of Wonderly. Special thanks to Sam Dolnick, Paula Szuchman, Lisa Tobin, Larissa Anderson, Julia Simon, Sofia Milan, Mahima Chablani, Elizabeth Davis-Moorer, Jeffrey Miranda, Renan Borelli, Maddy Masiello, Isabella Anderson and Nina Lassam.

Katrin Bennhold is the Berlin bureau chief. A former Nieman fellow at Harvard University, she previously reported from London and Paris, covering a range of topics from the rise of populism to gender. More about Katrin Bennhold

Advertisement