MILAN 11-14 MARCH 2015

SEVENTY-NINTH INTERNATIONAL ATLANTIC ECONOMIC CONFERENCE

INVITED ADDRESS

LUIGI ZINGALESCHICAGO BOOTH SCHOOL OF BUSINESS

“Diagnosing the Italian Disease”

Diagnosing the Italian Disease

December 2014

Bruno PellegrinoUniversity of California

Luigi ZingalesUniversity of Chicago

GDP per Hour Worked (2005 PPP$)

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

25

27.5

30

32.5

35

37.5

40

42.5

45

47.5

50

52.5

EUItalyUS

2005

$ p

er h

our

Motivation• Twenty years ago Italy’s labor productivity

stopped growing. • Understanding why is important for several

reasons:

1. Italy is the sick country of Europe. Difficult for the euro to survive without Italy improving

2. The Italian disease is a more acute form of a European disease-> understand better link between institutions and growth

Sector Level Data EU-KLEMS structural database, – value added, output, inputs, total factor productivity, and

input compensation shares

• at the 3-digit ISIC level for 25 European countries, Australia, South Korea, Japan, and the United States for the period 1970-2012.

• We stop at 2007 to cut out the crisis • Lose 11 countries for lack of capital formation series• 3 for inconsistent data • Down to 15 countries

Growth in GDP / Capita (1994–2006)

Japan

Italy

German

y

France

Denm

ark

Belgiu

m

Austria

United S

tates

Netherl

ands

Australi

a

Spain

United K

ingdom

Sweden

Finlan

d

Irelan

d-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Hour Worked/Employee

Employment/Population

GDP/Hour

GDP p.Capita

Sectors• We aggregate sectors 50 to 52 (wholesale and

retail trade) to merge some explanatory variables in the dataset that are available at industry-level.

• We use the aggregate sector 70t74 instead of 70 (real estate) and 71t74 (other business services) for problems in the attribution of real estate assets

• We drop, as customary, public sector and social services (sectors 75-99)

• Left with 23 sectors

Total Factor Productivity Value Added/Hour

Sector Name Code Italy Average Italy Average

Agriculture, Hunting, Forestry And Fishing 01t05 1.0% 1.8% 2.3% 3.2%

Mining And Quarrying 10t14 -2.6% -1.4% 0.1% -0.2%

Food Products, Beverages And Tobacco 15t16 -0.4% -0.7% 0.7% 0.6%

Textiles, Leather And Footwear 17t19 -0.8% 0.7% 0.5% 2.3%

Wood And Products Of Wood And Cork 20 1.8% 0.6% 2.5% 1.6%

Paper, Printing And Publishing 21t22 -0.8% 0.0% 0.9% 1.7%

Coke, petroleum products and nuclear fuel 23 -12.8% -0.3% -11.0% 2.5%

Chemicals 24 0.3% 1.8% 0.8% 4.2%

Rubber and Plastic Products 25 0.1% 1.3% 1.0% 2.7%

Other Non-Metallic Mineral Products 26 0.1% 1.0% 1.7% 2.8%

Basic Metals And Fabricated Metal Products 27t28 0.1% 0.8% 0.6% 1.7%

Machinery And Equipment, N.E.C. 29 -0.9% 1.7% -0.4% 3.4%

Electrical And Optical Equipment 30t33 -0.8% 8.8% 0.4% 11.2%

Transport Equipment 34t35 0.1% 2.2% 0.5% 3.6%

Manufacturing N.E.C. And Recycling 36t37 0.0% 1.1% 0.7% 2.5%

Electricity Gas And, Water Supply 40t41 0.2% 1.1% 2.7% 3.8%

Construction 45 -1.3% -1.3% -0.5% -0.6%

Wholesale and Retail Trade 50t52 -1.0% 1.8% 0.7% 3.1%

Hotels And Restaurants 55 -1.5% -0.2% -0.8% 0.4%

Transport And Storage 60t63 -0.6% 0.5% 0.1% 1.5%

Post And Telecommunications 64 5.9% 3.1% 8.9% 6.3%

Financial Intermediation 65t67 1.4% 1.0% 2.3% 3.0%

Other Business Service Activities 70t74 -0.6% -0.6% -3.1% 0.1%

Firm Level Data • EFIGE (European Firms in a Global

Environment), developed by Altomonte and Aquilante (2012) for the think-tank Bruegel.

• It contains balance sheet data for over 14,000 firms from 6 European countries (Austria, France, Germany, Hungary, Italy, Spain, UK).

• EFIGE has a short time span and does not allow us to study the dynamics of productivity growth.

• Yet, it allows us to observe key features of the businesses’ organizational model more directly.

Italy’s productivity growth gap

Explanations Based on Traditional Italian Characteristics

1. “Bad” firm demographic

– “Wrong” sectors and too small a size

2. Lack of labor flexibility

3. Government inefficiency

4. Quality of human capital

Size and Sectors

Country Actual Predicted (Sector)

Predicted (Size)

Predicted (Sector & Size)

Australia 30.0% 18.0% 21.8% 22.6%

Austria 22.8% 20.7% 20.0% 23.1%

Belgium 13.8% 18.3% 20.3% 21.0%

Denmark 11.4% 20.6% 19.0% 21.6%

Finland 32.0% 23.4% 20.2% 25.1%

France 20.5% 18.9% 20.0% 19.5%

Germany 19.9% 20.3% 18.3% 21.5%

Ireland 36.2% 23.5% 19.5% 24.4%

Italy 1.8% 20.9% 27.0% 30.0%

Japan 24.8% 19.9% 23.0% 25.9%

Netherlands 22.8% 17.4% 18.8% 16.4%

Spain 2.1% 18.6% 24.4% 24.2%

Sweden 37.6% 21.0% 20.2% 23.9%

United Kingdom 28.0% 16.6% 18.2% 16.3%

United States 16.9% 18.0% 14.4% 11.7%

Lack of Labor Flexibility

Need for Labor Flexibility

Table 5. TFP growth and Labor Market Regulation

Government Inefficiency

Impact of PA on a Sector

• For the period 2000-2007 we count all news regarding a sector in Reuters, Thomson, Bloomberg, FT, WSJ, and Dow Jones (Factiva tag).

• We re-compute from the same sources the news regarding the sector having government as topic.

• We take the ratio of the two

Public Sector Dependence Scores (Factiva)

Basic Metals And Fabricated Metal Products

Textiles, Leather And Footwear

Hotels And Restaurants

Paper, Printing And Publishing

Machinery And Equipment, N.E.C.

Coke, petroleum products and nuclear fuel

Transport And Storage

Electrical, Optical & Medical Equipment

Other Manufacturing & Recycling

Construction

Chemicals (inc. Pharma)

0.0% 2.5% 5.0% 7.5% 10.0%

Table 6. TFP growth and public sector performance

TFP Growth and Human Capital Growth

(1) (2)

Δlog PIAAC 2.80*** (.870)

Δlog PIAAC × Labor Compensation Share -5.67 (4.05)

Country-Clustered Standard Errors

Country-Fixed Effects

Sector-Fixed Effects

Observations 345 345

R-squared .256 .365

*significant at 10% confidence, **significant at 5% confidence, ***significant at 1% confidence

Trade-Based Explanations • In the short term, a decrease in external demand for Italian

products can adversely affect productivity through several channels1. Scale effect

2. Embedded technological progress

3. Impact on profitability

4. Labor adjustment costs

• In the long term, if there is a permanent drop in demand for Italian products, firms will eventually adjust or close. – If they adjust, they will be forced to increase productivity. – If they close, the least productive firms will close first, increasing

the average productivity simply through a compositional effect.

Table 8. Capital Accumulation, Firm Size Growth and the Trade Balance

Table 9. Productivity growth, Employment Protection, Firm Size and China

Table 10. Innovation and Foreign Competition

Figure 5. The ICT Revolution

Table 11. Productivity growth and ICT capital growth

Ability to Exploit IT Revolution • Bloom et al. (2012) productivity gap between

US and EU due to a combination of IT and management.

• Bresnahan et al (2002): complementarities between IT and workplace reorganization.

• Institutional factors (size, organization, low labor flexibility, large black market economy) may have prevented Italy from taking full advantage of the ICT revolution.

Networked Readiness Index

• World Economic Forum measures “Networked Readiness”:

• Networked Readiness Index =

1/4 Environment subindex

+ 1/4 Readiness subindex

+ 1/4 Usage subindex

+ 1/4 Impact subindex

Networked Readiness

ICT Contribution to Growth, by Sector

Table 12. Productivity growth, ICT capital and Networked Readiness

Table 13. Productivity growth, ICT capital, and Networked Readiness

What is Network Readiness?

• Quality of managers? – Quality of management schools – Number of GMAT takes /population

• Meritocratic selection:

i) perceived favoritism in officials’ decision making.

ii) the degree of meritocracy in the selection of private sector managers.

We average these two variables to form a proxy for meritocracy

Meritocracy

Table 14. Productivity growth, ICT capital accumulation and Management

ICT and productivity

• The impact of ICT on productivity is crucially mediated by management

• As Garicano and Heaton (2010) show enjoying the benefits of technology requires 1) Measurable goals

2) Internal accountability

3) Middle management empowerment

4) Rewards

=> performance-based, meritocratic management

Compstat Introduced by the New York Police Department in 1994 by Commissioner William Bratton.

• the real time mapping of crime by time and place

• (notorious) early morning meetings

Weisburd: (1) statement of the measurable

goals of the department; (2) internal accountability,

particulary through Compstat meetings

(3) geographic organization of command-- district commanders have authority and resources to accomplish their goals over their areas;

(4) empowerment of middle managers;

(5) data driven problem identification and assessment;

(6) innovative problem solving tactics.

Firm-Level IT usage

To quantify a firm’s level of IT usage, we count the number of “yes” answers to the following questions: – Does the firm have access to a broadband connection

(high-speed transmission of digital content)?– Does the firm use IT systems/solutions for internal

information management (e.g. SAP / CMS)?– Does the firm use IT systems/solutions for E-

commerce (e.g. SAP / CMS)?– Does the firm use IT systems/solutions for

management of the sales/purchase network?

Firm-Level Performance Manag.

• Mimicking Bandiera et al (2008) we extract the first principal component from the following six dummy variables: 1. the firm’s CEO belongs to the controlling family (-)

2. the firm is family-managed (-)

3. management is de-centralized (+)

4. the firm uses bonuses to incentivize managers (+)

5. the firm has sought a third-party quality certif. (+)

6. at least one of the firm’s executives has worked more than one year abroad (+)

Table 15. IT usage and Management Models

Institutions and Incentives

Conclusions

• The Italian disease appears to be an extreme form of a European disease: – inability to take full advantage of the ICT

revolution

• This disease appears to be linked to the lack of meritocracy and professional performance-based management.

• We still need to explain why these practices are so rare in Italy and Southern Europe.

Conclusions - 2

• Suppose that there is some institutional factor in Southern Europe that makes difficult to keep up with technological change.

• Difficult to keep up with a fixed exchange rate.

• How could have Japan and the United States kept a fixed exchange rate from 1950 to 1990?

• Organizational issues are crucial for the survival of the euro.