Urban Wage Premia, Cost of Living and Collective Bargaining Marianna Belloc Sapienza University of Rome Paolo Naticchioni University of Roma Tre, IZA Claudia Vittori Sapienza University of Rome
Introduction • I benefit from the previous presentation, no details about: Italian bargaining system The computation of the local CPI The Theoretical Framework We make use of a similar CPI We have unique data for employees and self-employed
The Urban Wage Premium • Wide literature on the Urban Wage Premium (UWP) • UWP estimates positive in basically all countries • Possible explanations: • Urbanizations externalities : lower transport costs, and technological and knowledge spillovers etc. (Marshall, 1890, Glaeser, 1998, Kim, 1987, Ciccone and Hall, 1996). • Learning . Human capital accumulation might be faster in cities, with possible positive spillovers on the unskilled (Moretti, 2004). • Sorting . Best workers and best firms are more likely to be located in urban areas (Combes et al, 2008, Mion e Naticchoni, 2009). • Matching . Better quality of the match in dense areas, due to thicker labour markets.
Goal of the paper • Main goal: identifying the role played by collective bargaining on the urban wage premium (UWP). • Collective bargaining tends to make wages homogenous along the space dimension • Cost of living highly heterogeneous in the space dimension, between urban and rural areas ⟶ to address the impact of collective bargaining on the UWP it is crucial to derive measures of local cost of living Hence, two additional pillars in the paper: Collective Bargaining and local cost of living
Why is Italy a perfect case study • Collective Bargaining is a two-tier Multi-Employer system. • Local prices are very heterogeneous. • This is particularly true for housing prices that represents the main driver of local consumer price index (CPI). • Also, papers on productivity shows that agglomeration effects are substantial (Cingano and Schivardi, 2004; Lamorgese and Petrella, 2016). • Additional reason for considering Italy: Excellent data for workers and for housing
ESTIMATION OF A LOCAL PRICE INDEX, LOCAL CPI
Estimation of a local price index • Housing is one of the main driver of the variation in local cost of living : housing costs incorporate economic and non economic factors that make individuals willing to pay more (less) for a given location. • Data from the Osservatorio Mercato Immobiliare (OMI) provide detailed information on housing prices at the municipality level. • Main intuition from Moretti (2013): computing direct and indirect impact of housing on local CPI. • Direct : direct costs of housing • Indirect : the effects of housing on other goods, think about a restaurant or having an haircut
How to compute a local price index • The Local Price index in city c at time t as a weighted average of two price indexes: housing H (direct and indirect impact) and non housing NH : 𝑫𝑸𝑱 𝒅,𝒖 = 𝜸𝑰 𝒅,𝒖 + 𝟐 − 𝜸 𝑶𝑰 𝒖 • 𝜸 is the weight of the housing ( H ) , both direct and indirect • To derive a local 𝑫𝑸𝑱 𝒅,𝒖 we need to compute H c,t , NH t and 𝜸
How to compute H c,t and NH t • The housing price index H c,t is computed using the OMI data on the housing transactions. Public finance approach: regress the price of housing transaction on characteristics (square meter, number of rooms, cadastral category, being in the city center or in the suburbs, state of the property etc). Residuals are averaged by province (or LLM) and year: housing price index depurated by the quality of the properties. • The component non housing price NH t : national variation of all goods except housing, using national CPI by goods by Istat (as in Boeri et al 2016). No variations at the local level. • Period 2005-2015. Base year: 2005
Estimate of the real weight of housing ( 𝜸 ) • Estimate of 𝜸: regress the official ISTAT provincial CPI p,t on the provincial housing price index (HP p,t ), with provincial fixed effects: 𝑫𝑸𝑱 𝒒,𝒖 = 𝜸𝑰 𝒒,𝒖 + 𝝂 𝒒 + 𝜻 • From our estimate: β = 0.34 . • Much bigger than the direct impact of housing, estimated by Istat to be equal to 0.09. • We compute the 𝑫𝑸𝑱 𝒅,𝒖 at the municipality level we introduce 𝜸 in 𝑫𝑸𝑱 𝒅,𝒖 = 𝜸𝑰 𝒅,𝒖 + 𝟐 − 𝜸 𝑶𝑰 𝒖
A local CPI index • By definition, this local CPI index is an approximation of the ‘true’ CPI in a space dimension. • One might argue that in cities individual might react to the higher cost of living consuming less or differently (i.e. living in tiny flat for instance). In this case our local CPI might overestimate the cost of living in cities. • On the other hand, Combes, Duranton, Gobillon (2017) ⤇ the elasticity of urban costs increases with city population much more than in proportional (linear) way. In this case our CPI might underestimate the real cost of living in cities.
Robustness Checks: Alternative CPI indexes a) Computing the β using housing rents instead of housing prices (data by Istat at the provincial level): β increases to 0.5 , and results are even stronger. b) Using an external official but rougher local CPI, provided by ISTAT to compute absolute poverty thresholds ⤇ c) Important point: the CPI affects the magnitude of our impact, not their qualitative interpretation (under the assumption that CPI index is higher in cities).
Two levels of analysis: LLM and municipality • Preferred one: Local labour market level : high variability (611 LLM) and accurate estimate of the local cost of living: the LLM are defined to maximizes the probability that the individual works and lives in the same local unit. • Check: Municipality level: very high variability (8,000 municipalities). Higher probability to imprecisely estimate the local cost of living, which depends also on the municipality where workers live, and on the surrounding municipalities. • Robusteness check (see later): Very similar results
LOCAL CPI DESCRIPTIVE STATISTICS
Clear positive relation between Local CPI and Pop density, by LLM (2005): bubbles are LMM size, in big cities CPI is higher
Data by LLM In dense LMM, price are clearly higher: See Rome, Milan, Naples, Catania, Palermo, Florence, Bologna etc.
Agglomeration variable • Agglomeration measure: population density (ED), population by LLM (or municipality) out of surface in km2 (Combes 2000, Combes et al, 2008, 2011, Mion and Naticchioni, 2009, Matano and Naticchioni, 2012). • As a check we also use employment density : similar results
Data by LLM
Worker data: VisitINPS • VisitINPS: research program started by Tito Boeri, INPS president from 2015. • Three different datasets on : 1. Employees, using an employer-employee dataset • Self-employed, in particular: 2. ‘ Collaboratori ’ , a peculiar type of self-employed, where the worker is still always associated to a firm 3. Standard self- employed (‘ Casse Professionali ’), such as Lawyers, Physicians, Accountants etc.
Employee data: VisitINPS • Universe of the dependent workers in Italy (male). • Period: 2005-2015. • Information of the Municipality where the job is carried out. • One observation per worker per year (highest earnings). • Dropping the outliers in the tails (0.5% by year), and workers attached to the labour market for less than two months per year. • Final sample: around 75 millions of observations .
Within-Between Variance Decomposition Variance of nominal wages for employees does not depend much on differences across macroregions, regions or provinces: wage variability is basically very local, within provinces. Within-Between Variance Decomposition Macro Regions (5) Region (20) Province (103) Between 4.4 4.7 6.2 Within 95.6 95.3 93.8 Total 100.0 100.0 100.0
Nominal and Real Wages definition Two weekly wage variables: • Weekly Nominal Wage ; • Weekly Spatial Real Wage : deflated by using the local CPI. Nominal and Real weekly wages for employee. Year 2005. quantiles Nominal Wages Real Wages 1 401 446 2 428 459 3 444 458 4 472 465 5 511 443 • Clear evidence. Real wages are more compressed.
Data By LLM • Real wages are much lower in cities like Rome, Naples, Milan etc. • And real wages are greater in the South, consistently with Boeri et al (2017).
THE ECONOMETRIC PART
Econometric specification • The main specification is: 𝒎𝒐(𝑿 𝒋(𝒅),𝒖 ) = α + ρ*ln( PopDen i,t )+ β*X + δ r + δ t + u i + ε i,t • ρ : estimate of the UWP. It is an elasticity: variables are in log. • Matrix X : individual controls (age, occupation, part time, fixed term); firm controls (size). • To control for the centralized national bargaining we include (250) dummies for all national contracts (roughly industries) • Year and Regional dummies; • Standard errors clustered at the LLM level. • Dropping CPI outliers at the SLL level: 0.5% in the two tails (to exclude locations like Portofino, Cortina D’Ampezzo , Capri , or L’Aquila after the earthquake in 2009).
UWP Estimates : Nominal Wages
UWP Estimates : Nominal vs Real Wages • In real terms, the UWP becomes negative and substantial!
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