Broadband Adoption, Productivity Dynamics and Labour Demand: Is There a Skill Bias in Italy?

(1)

Broadband Adoption, Productivity Dynamics and Labour Demand: Is There a Skill Bias in Italy?

Emanuela Ciapanna and Fabrizio Colonna

Bank of Italy

November 30, 2018

(2)

Introduction

Research Question

How does broadband (high speed) internet affect firm’s productivity labour productivity of skilled vs. unskilled workers (skill workforce composition)

Total Factor Productivity

(3)

Introduction

Research Question

How does broadband (high speed) internet affect firm’s productivity labour productivity of skilled vs. unskilled workers (skill workforce composition)

Total Factor Productivity

(4)

Introduction

The paper in a nutshell

What is BB? High Speed internet Technology (> 2Mbs) introduced in Italy by Telecom Italia in 1999 and rapidly covered virtually whole country;

Internet speed varies significantly across locations, even within the same municipality, due to technical characteristics

Exploit speedspatial heterogeneity to identify impact of internet speed connection on firms’ productivity

Micro data to estimate a nested CES Production Function;

Accounting for substituability/complementarity b/w skilled and unskilled labour

Immediately

higher productivity of skilled workers higher share of skilled labor

In the long run

Higher levels of TFP Larger average firms’ size

(10)

Introduction

Immediately

higher productivity of skilled workers

higher share of skilled labor In the long run

(11)

Introduction

Immediately

In the long run

(12)

Introduction

Immediately

In the long run

(13)

Introduction

Immediately

In the long run

Higher levels of TFP

Larger average firms’ size

(14)

Introduction

Immediately

In the long run

(15)

Outline

1 Reference Literature

2 Data and Identification Strategy

3 Stylized facts on labor demand

4 Empirical Model: firms’ performance

5 Results

6 Concluding Remarks and way forward

(16)

Reference Literature

1 Effects of ICT in general on labor market outcomes (Acemoglu and Autor, 2011; Autor and Dorn,2013; Michaels, Natraj, and Van Reenen, 2014)

2 Specific effects of internet BB on economic performance and labor market outcomes (Czernich et al., 2011; Akerman et al., 2015)

3 Empirical Strategy: IV distance from the Exchange (Falck et al., 2014;Billari et al., 2017) and CES production function (Kasahara et al., 2013)

(17)

Reference Literature

(18)

Reference Literature

(19)

Data

Invind Dataset, representative sample of 20+ italian firms (1995-2017):

Balance sheet Information (Cerved)

Workforce Information, administrative data (INPS)

Unique Dataset of Top Speed available at firm’s address (Address point geocoding) provided by TIM

(20)

Data

(21)

Data

(22)

Broadband ADSL Technology

(23)

Broadband ADSL Technology

Copper degradation→Firm’s speed depends on

(24)

Broadband ADSL Technology

1 on phone exchange speed

(25)

Broadband ADSL Technology

1 on phone exchange speed

(26)

Broadband xDSL Technology

Figure:The speed capacity-distance trade-off

(27)

Data

(28)

Data

(a)Local Exchanges, source: TIM (b)Firms, source: INPS-Invind, 2014 Figure:Distribution of local telephone exchanges and sample firms, Italy.

(29)

Different speeds for different tasks

Table:Example: BB capacity needs for a 50 employees firm in hospitality as of 2018

Task Speed (Mbps)

Web 8

email 5

Video conference 10 Share file services 4

Cloud storage 5

Cloud Office productivity 3

HD Imaging 2.5

Remote/ virtual Desktop 6.4

Total 51.9 Mbps Download/45.2 Upload

(30)

More BB capacity for more complexity

Table:Example: BB-enabled Product and Process Innovation, Manufacturing Product Innovation

Low complexity High complexity Crowdsourcing 3D-Digital modeling Collaboration tools Digital simulation

Process Innovation

Low complexity High complexity Customer engagement Cloud based sales tools

Social media Customer/Supplier integration

Marketing Smart factory

(31)

Empirical strategy: Baseline Equation

BB speed and labor demand outcomes

Y_it =β^speedit+γ^Xit+ςt+ηit

where

speed_it maximum BB speed at address point X_it controls and province and sector FE ςt is time FE

ηit firm-specific shock iid

(32)

Potential bias sources

1 Reverse causality: larger/more productive firms have higher BB speed

=_⇒Look for exogenous supply shifter

IV is distance of facility from closest local exchange

Unique Dataset of geolocal position of the universe of exchanges provided by TIM

2 Omitted variable bias: firms having higher speed BB enjoy large network economies

=_⇒Control for average local speed

Control for number of exchanges in a 3-km radius

(33)

Potential bias sources

(34)

Potential bias sources

(35)

Potential bias sources

(36)

Potential bias sources

(37)

Potential bias sources

(38)

Potential bias sources

(39)

IV: Identification Assumption

Exclusion restriction: distance from exchange influences firms’ outcome ONLY through BB capacity

Local exchange in Italy date back to 1945 as part of telephone network (BB was born in 1999)

they responded to a logic of "Universal service" for telephone ^Neer their number and location remains fixed over time

exclude firms born after 1999

(40)

IV: Identification Assumption

they responded to a logic of "Universal service" for telephone ^Neer

their number and location remains fixed over time exclude firms born after 1999

(41)

IV: Identification Assumption

(42)

IV: Identification Assumption

(43)

Distance vs. density

(44)

IV Regression

Y_it =_β_tspeed_IVi+_λdens_i+_γX_it+_ς_t+_ν_it

where

speed_IVi is the first stage fit

dens_i is BB density, i.e. number of LTEs within a radius of 3Km from facility X_it controls and firm fixed effect

ςt is time FE

νit firm-specific shock iid

(45)

IV Regression

Y_it =_β_tspeed_IVi+_λdens_i+_γX_it+_ς_t+_ν_it

where

speed_IVi is the first stage fit

dens_i is BB density, i.e. number of LTEs within a radius of 3Km from facility X_it controls and firm fixed effect

ςt is time FE

νit firm-specific shock iid β

(46)

First Stage

Table:First Stage

Variable Download Speed

distance -1.567***

(0.053)

dist² 0.204***

(0.012)

Province fix. effect yes

Industry fix. effect yes

R-squared 0.187

F-statistics of instruments 52.7

Obs 2250

Average Size

White Collars Managers

(53)

Occupational tasks and professional qualifications

Figure:ISCO tasks by professional qualifications

(54)

Labor outcomes: differences between firms closer/farther

than 2 Km from LTE

(55)

Employment: Average size

(56)

Employment: Skill Shares

(57)

MDL of Cobb-Douglas production function with embedded CES aggregators

We extend the theoretical framework of Kasahara et al. (2013)

Y_it =L^α_it^lK_it^α^k"^ω^it

Y_it is VA of firm i at year t;

L_it and K_it are labor and capital inputs

"^ω^it is unobserved TFP

(58)

MDL of Cobb-Douglas production function with embedded CES aggregators

Labor is a composite input of skilled L^S

and unskilled L^U units:

L_it =

θ_it^σ¹^L^S_it^σ−^σ¹+ (1− θit)^σ¹L^U_it ^σ−^σ¹

_σ−^σ1

θit is relative skilled labor productivity (skilled labor share in the Cobb-Douglas case)

σis the elasticity of substitution between skilled and unskilled labour

(59)

2-step Estimation

1 Estimateσ^andθitfrom correlation b/w labor share and relative min wages

2 Estimateαl,αk andωusing Wooldridge or LP methodology

(60)

2-step Estimation

1 Estimateσ^andθitfrom correlation b/w labor share and relative min wages

2 Estimateαl,αk andωusing Wooldridge or LP methodology

(61)

First step

Given wages of skilled and unskilled labor W_t^S, W_t^U Rearranging FOC with respect to L^U and L^S:

ln

L^U_it L^S_it

=_σln

W_it^S W_it^U

+ln

₁

− θit

θit

we have

l_it=σ^wit+ln

₁

− θit

θit

+u_it

u_it firm-specific labour demand shock, unforeseen before t and independent of w_i_,_t,θi,t

Industry-level minimum contractual wages

σ: skilled labor shares elasticity to skilled labour wage θit are residual

(62)

First step

ln

L^U_it L^S_it

=_σln

W_it^S W_it^U

+ln

₁

− θit

θit

we have

l_it=σ^wit+ln

₁

− θit

θit

+u_it

σ: skilled labor shares elasticity to skilled labour wage θit are residual

(63)

First step

ln

L^U_it L^S_it

=_σln

W_it^S W_it^U

+ln

₁

− θit

θit

we have

l_it=σ^wit+ln

₁

− θit

θit

+u_it

Industry-level minimum contractual wages σ

θit are residual

(64)

First step

ln

L^U_it L^S_it

=_σln

W_it^S W_it^U

+ln

₁

− θit

θit

we have

l_it=σ^wit+ln

₁

− θit

θit

+u_it

σ: skilled labor shares elasticity to skilled labour wage

(65)

First step

Given

ln

L^U_it L^S_it

=σ^ln

W_it^S W_it^U

+ln

₁

− θit

θit

we have

l_it=_σw_it+ln

₁

− θit

θit

+u_it

θit =βi+βt+βstspeed_IV_,_i+βdtdens_i+v_it

v_it firm-specific shock to TFP, unforeseen before t and independent of w_i_,_t,θi,t

Impact of speed on (relatively) skilled labour productivity

(66)

First step

Given

ln

L^U_it L^S_it

=σ^ln

W_it^S W_it^U

+ln

₁

− θit

θit

we have

l_it=_σw_it+ln

₁

− θit

θit

+u_it

θit =βi+βt+βstspeed_IV_,_i+βdtdens_i+v_it

v_it firm-specific shock to TFP, unforeseen before t and independent of w_i_,_t,θi,t

Impact of speed on (relatively) skilled labour productivity

(67)

2nd Step

Plugging_σ,ˆ _θˆ_in

L_it=

θ_it^σ¹^L^S_it^σ−^σ¹ + (1− θ

σ1

it )L^U_it ^σ−^σ¹

_σ−^σ₁

and taking logs:

y_it=_α_kk_it+_α_ll_it+_ω_it

ωit=_ξ_t+_ξ_i+_γ_ω_ω_it₋₁+_γ_stspeed_IV_,_i+_γ_dtdens_i+v_it where

ξt,ξi are time and firm TFP shock,

speed_it is available BB speed of firm i in year t,

u_it firm-specific shock to TFP, unforeseen before t and independent ofξt

(68)

Results

Baseline High Speed year>2000 High Speed

& t>2000

σ ^0.56 ^0.56 ^0.56 ^0.56

αl 0.45 0.03 0.02 0.12***

αk 0.13 0.05 0.02 0.01

(69)

Results

(70)

Results

(71)

Gains from High Speed

(2000-2007) Enhance higher skilled labour productivity Firms adjust skilled labour ratio

(2008-) Efficiency gain

Firms with Low-Speed had to adjust skill ratio by cutting employment Firms with High-Speed experienced TFP gains and kept employment level

(72)

Cobb Douglas vs. CES

Baseline High Speed year>2000 High Speed

& t>2000

CES

σ ^0.56 ^0.56 ^0.56 ^0.56

αl 0.45 0.03 0.02 0.12***

αk 0.13 0.05 0.02 0.01

CD

σ ¹ ¹ ¹ ¹

αl 0.46 -0.01 -0.11*** 0.02

αk 0.15 0.02 0.04 -0.05

(73)

Cobb Douglas vs. CES

(74)

Cobb Douglas vs. CES

(75)

CES vs Cobb-Douglas

With higher skilled/unskilled complementarity:

Output elasticity not fixed, depend on relative input ratio

Complementarity increases output elasticity to relative scarce input CD doesn’t fully disentagle output contribution of TFP and skill ratio

(76)

Concluding remarks and way forward

Effects of different BB capacity (not only availability of connection) among Italian firms on labor demand and firm performance

novel and rich data-set

technology-based IV: minimum distance firm-LTE

BB as a GPT leads to skill biased technological change (change in work organization, ICT adoption, process-digitization)

higher share of skilled labor higher TFP

next step: exogenous labor supply shifter to investigate different workers’

behavior

(77)

Thanks for the attention!

(78)

Introduction and Motivation

Once upon a time...

Return

Figure:Test of the first Italian automatic telephone exchange of Prati di Castello, Rome

(79)

Introduction and Motivation

Once upon a time...

’20s-’30s: Division of the telephone service into five zones, first duplex telephone, radio link experiments (STIPEL)

1940-’45: Heavy damage to infrastructure due to bombing: significant fall in subscriptions

1945-’50: Adoption of coaxial cables, radio links andnew generation of automatic telephone exchanges

1952: every Italian town has access to the national telephone network (Universal service principle)

1991: Activation of ISDN network for integrated digital data and voice transmission via telephone lines

1999: Introduction of ADSL technology for fast Internet connections

(80)

[

(81)

label=hs] ^Return

(82)

Employment: Hirings, by skill

(83)

Employment: Shares of Blue Collar on Hirings

(84)

Employment: Separation Rates

(85)

[

(86)

label=sd] ^Return

Figure:The speed capacity-distance trade-off