Economic Development in High Definition Ricardo Hausmann & Cesar A. Hidalgo Harvard Kennedy School & Center for International Development, Harvard University

The Media Laboratory, Massachusetts Institute of Technology (MIT) & Center for International Development, Harvard University

Agenda • • • • • •

Restating the economic development puzzle The standard view Our take Implications Applications for business Implications for governments

1 - Germany: The Melander family of Bargteheide Food expenditure: 375.39 Euros or USD$500.07 p/week

2 - Mexico: The Casales family of Cuernavaca Food expenditure: 1,862.78 Mexican Pesos or USD$189.09 p/week

3 - Egypt: The Ahmed family of Cairo Food expenditure: 387.85 Egyptian Pounds or USD $68.53 p/week

7 - Ecuador: The Ayme family of Tingo Food expenditure for one week: $31.55

5 - Chad: The Aboubakar family of Breidjing Camp Food expenditure: 685 CFA Francs or USD$1.23 p/week

1952

2005

The great divergence

Share of World GDP, current US$ .2 .4 .6 .8

The end of divergence? High Income

The end of divergence

Middle Income

0

Low Income 1960

1970

1980

1990 year

2000

2010

The development puzzle • • • • •

Rich countries became rich Why did poor countries not keep up? Why are some of them catching up quickly? How can countries accelerate the process? How can we help them?

What is the world made out of?

The atomic theory of matter • How many types of atoms? • Do they combine? If so, how? • Why?

Do atoms combine?

LAND

LABOR

CAPITAL

HUMAN CAPITAL

Capital

Output

Labor

Y=F(K,L)

=

Why not just turn lead into gold?

Implications • The world has many products but they are all made with few things • We need to accelerate the rate at which we accumulate these things • Human capital – education and health • Physical capital – banking, micro-finance, • Infrastructure • Governance • Openness

What government? • “Little else is requisite to carry a state to the highest degree of opulence from the lowest barbarism but peace, easy taxes, and a tolerable administration of justice: all the rest being brought about by the natural course of things.” • Adam Smith, Lecture in 1755 •

If it is so easy, why has it not happened?

Who makes what? The structure of exports

…independently of the classification scheme

6 years time series

42 years time series

6 years time series

Locations (Sorted by Diversity)

Happens within countries too: Chile 0 50

-0.2

100 150

-0.4

200

-0.6

250 -0.8

300 100

200 300 400 500 Industries (Sorted by Ubiquity)

600

700

-1

…or Turkey 200 400 600 800 1000 1200 1400 1600 1800 2000 10

20

30

40

50

60

70

80

Question: if all is made from putty, why do we observe this structure?

The alternative paradigm • The world is made out of many different kinds of atoms • Products are like molecules • Atoms are like capabilities or functionalities

Countries

Products

Some terminology Diversification

Ubiquity

Degree (Countries)

Degree (Products)

kc = ∑ M cp p

kc1=3

kc2=4

kc3=1

k p = ∑ M cp c

Product p1

kp1=2

Product p2

kp2=2

Product p3

kp3=1

Country C1

Country C2

Country C3 Product p4

Hidalgo CA, Hausmann R Proc. Natl. Acad Sci. (2009) 106(26):10570-10575

kp4=3

Countries

Capabilities

Products

Intuition • Countries that have more capabilities will be able to make more products – They would be more diversified

• Products that require more capabilities will be made by fewer countries – Products will be less ubiquitous

• Countries that have more capabilities will be able to make products that are less ubiquitous • Diversification of countries and ubiquity of products are negatively correlated – They are indirect measures of the capability set of countries

(Year 2000)

kc,1

Average number of countries that make the same products

Ubiquity of a Country’s Products

Evidence of the Connection between the diversity of inputs and that of outputs

kc,0 Hidalgo, Hausmann (2009) PNAS 106(26):10570-10575

Diversification of a country/ Number of Products a country makes

It also works within countries: Chile Diversity-Average Ubiquity Municipalities 300

FOR CHILE 2008

OLLAGUE

COLCHANE

GENERAL LAGOS

k1 (Average Ubiquity)

PRIMAVERA

250

200

150

SAN ROSENDO SANTORTEL GREGORIO LAGUNASIERRA BLANCAGORDA QUILACO CAMARONES ALTO BIOANTUCO BIO CAMINA PEMUCO HUALAIHUE FUTALEUFU LAGO TORRES DEVERDE PAINE PUTRE CURARREHUE TIMAUKEL NINHUE GALVARINO TREHUACO COCHRANE ERCILLA SAN FABIAN MELIPEUCO HUARA PALENA O'HIGGINS COBQUECURA PERQUENCO NIQUEN EMPEDRADO CONTULMO PUQUELDON QUILLECO NAVIDAD PICA ALTO DEL CARMEN SAAVEDRA JUAN FERNANDEZ ANTARTIDA MARIA ELENA RANQUIL RIO IBANEZ PORTEZUELO LA ESTRELLA LUMACO MARCHIGUE GUAITECAS PUMANQUE VICHUQUEN ALHUE LAGO RANCO RIO HURTADO TEODORO SCHMIDT SANTA JUANA NEGRETE COINCO TIRUA PAREDONES ELPINTO CARMEN CANELA PAPUDO PELLUHUE CHOLCHOL SAN DE JUAN DE LA COSTA CABO HORNOS LONQUIMAY PAIHUANO PEDRO SAN DE RAFAEL ATACAMA LA HIGUERASAN CUREPTO CHANCO SAN PEDRO DE MELIPILLA ANDACOLLO LOS SAUCES LOLOL ISLA DE PASCUA PUERTO OCTAY YERBAS RINCONADA BUENAS MAFIL TOLTEN PUNITAQUI PANQUEHUE PALMILLA FRESIA FLORIDA FREIRINA CURACO DE VELEZ PENCAHUE HUALANE SANCHILE PABLO LITUECHE RIO CLARO EL TABO CHICO PUYEHUE PELARCO SAN NICOLAS LOS ALAMOS COMBARBALA SANTA BARBARA TUCAPEL QUEMCHI QUIRIHUE PERALILLO FUTRONO COIHUECO CHAITEN TIERRA CUNCO AMARILLA TIL-TIL RAUCO VILLA ALEGRE COCHAMO QUEILEN SAN RENAICO IGNACIO LICANTEN PUREN CALLE LARGA MARIA PINTO PETORCA CARAHUE RETIRO CHEPICA SANTAYUMBEL RIO MARIA NEGRO YUNGAY OLMUE COELEMU GORBEA SAGRADA MULCHEN FAMILIA CATEMU CODEGUA HUASCO LOS QUILLON MUERMOS MEJILLONES PLACILLA LONGAVI LAJA CISNES QUINTA TILCOCO COLLIPULLI QUINCHAO NOGALES COLBUN NANCAGUA Sin InformaciónCORRAL PUCHUNCAVI COLTAUCO PEUMO ZAPALLAR PAILLACO HIJUELAS EL QUISCO HUALQUI NACIMIENTO MAULLIN MALLOA CABILDO CHANARAL PICHIDEGUA SAN TRAIGUEN ESTEBAN ROMERAL FREIRE MARIQUINA POZO ALMONTE LOS LLAY-LLAY LAGOS LEBU LANCO MAULE PUTAENDO BULNES FRUTILLAR DALCAHUE OLIVAR CURACAUTIN MONTE REQUINOA PATRIA DIEGO DE ALMAGRO VILCUN CHILLAN PURRANQUE VIEJO LA CRUZ LLANQUIHUE CHONCHI PORVENIR CABRERO TOCOPILLA ALGARROBO TALTAL PITRUFQUEN TENOIMPERIAL CARTAGENA SANTO DOMINGO LOS VILOS NUEVA SAN FCO DE MOSTAZAL DONIHUE PICHILEMU RIO BUENO CURANILAHUE RIO VERDE LAS MACHALI CABRAS VICUNA LONCOCHE QUELLON ELCLEMENTE MONTE CALBUCO CURACAVI PANGUIPULLI SAN ISLA DE MAIPO CANETE SAN JOSE PIRQUE CALERA DE TANGO CALDERA MAIPO ARAUCO LAUTARO LOTA SALAMANCA GRANEROS PENCO CHIMBARONGO TOME ILLAPEL MOLINALA LIGUA CAUQUENES PUERTO QUINTERO NATALES AYSEN SAN JAVIER VICTORIA CASABLANCA PARRAL PUCON LA UNION PADRE ANGOL LAS CASAS CONSTITUCION SAN VICENTE T-T ALTO HOSPICIO PADRE HURTADO LIMACHE HUALPEN VALLENAR ANCUD CONCHIGUAYANTE CONSAN CARLOS LO ESPEJO VILLARRICA LA CALERA RENGO SANTA CRUZ PAINE BUIN TALAGANTE LOSAN PRADO CASTRO LOS ANDES RAMON CERRO NAVIACORONEL COYHAIQUE PENAFLOR COLINA LINARES OVALLE VILLAVARAS ALEMANA PUERTO QUILLOTA SAN FELIPE LA SAN PEDRO DE FERNANDO LA PAZ LAMPA P PINTANA AGUIRRE RENCA CERDA SAN MELIPILLA LA GRANJACALAMA EL LO BOSQUE SAN ANTONIO BARNECHEA PUDAHUEL QUILPUE CERRILLOS HUECHURABA COPIAPO CONCHALI TALCAHUANO LAJOAQUIN REINA PENALOLEN SAN MACUL LOS ANGELES INDEPENDENCIA EST CENTRAL COQUIMBO CHILLAN SAN MIGUEL LA CISTERNA OSORNO QUILICURA CURICO QUINTA NORMAL PUNTA LA VALDIVIA SERENA ARENAS ARICA RECOLETA IQUIQUE RANCAGUA LA FLORIDA TALCA VITACURA VALPARAISO SAN BERNARDO NUNOA ANTOFAGASTA PUENTE ALTO PUERTO TEMUCO MONTT CONCEPCION VINA DEL MAR MAIPU

100 0

PROVIDENCIA LAS CONDES SANTIAGO

100

200

400 300 k0 (Diversification)

500

600

…and Turkey Diversity and average ubiquity of Turkey's cities 60

ARDAHAN TUNCELİ

k1 (Average ubiquity) 30 40 50

BİNGÖL IĞDIR HAKKARİ BAYBURT AĞRI GÜMÜŞHANE MUŞ SİİRT BİTLİS ERZİNCAN

20

RİZE NİĞDE SİNOP KARS ŞIRNAK ARTVİN BARTIN BATMAN ADIYAMAN KASTAMONU ERZURUM VAN KARABÜK AKSARAY KIRŞEHİR AMASYA OSMANİYE GİRESUN KİLİS KARAMAN DİYARBAKIR ELAZIĞ ŞANLIURFA MARDİN NEVŞEHİR SİVAS TOKAT BURDUR ORDU DÜZCE YOZGAT ÇANAKKALE MUĞLA MALATYA ZONGULDAK ÇANKIRI ISPARTA EDİRNE BOLU KAHRAMANMARAŞ AFYON KIRIKKALE UŞAK TRABZON HATAY KÜTAHYA YALOVA KIRKLARELİ AYDIN ÇORUM ANTALYA DENİZLİ SAMSUN İÇEL BİLECİK SAKARYA BALIKESİR ESKİŞEHİR KAYSERİ MANİSA GAZİANTEP ADANA TEKİRDAĞ KONYA BURSA KOCAELİ

ANKARAİZMİR

10

İSTANBUL

0

500

1000 k0 (diversification)

1500

What are these capabilities?

To make a product you need more than Capital and Labor

Public Inputs

Tradable Inputs Leather Tanner

Labor Skills Leather Cutters

Leather Pressers

Sawing Sole Making Shapers

Certifying Body Trade Agreements Roads Technical Education Ports Power Tax Regulation

Norms trust

teamwork Manufacturing & Management Certifications

Private Inputs

DIVERSITY of Capabilities

MATTERS

Income Per Capita

Diversity Explains Income Per Capita

Economic Complexity

Diversity Predicts Future Growth

Complexity in 1985 (Controlling for GDP per capita at ppp) Hidalgo, Hausmann (2009) PNAS 106(26):10570-10575

The development puzzle

What makes growth difficult? The chicken and egg problem

• You cannot make new products because you lack the capabilities • You don’t want to accumulate the capabilities because the products that need them are not being made -Because of other missing capabilities • How does the world deal with this? • By moving towards “nearby” products

If products require similar capabilities, they should be exported by the same countries For Example:

CA Hidalgo, B Klinger, A-L Barabasi, R Hausmann. Science (2007)

How do monkeys jump?

CA Hidalgo, B Klinger, A-L Barabasi, R Hausmann. Science (2007)

CA Hidalgo, B Klinger, A-L Barabasi, R Hausmann. Science (2007)

CA Hidalgo, B Klinger, A-L Barabasi, R Hausmann. Science (2007)

CA Hidalgo, B Klinger, A-L Barabasi, R Hausmann. Science (2007)

China

1975

1985

2000

Patterns of Comparative Advantage

Hidalgo et a. Science (2007)

16

Not all countries are equally positioned in the product space

ITAFRA USA ESP POL CZE DEU BEL NLD AUT GBR TURSVK SVN PRT THA CHE DNK SWE BGR JPN HRV MEX GRC HUN ROM ZAF CAN EST LVA BRA HKG LTU IDN FIN UKR NZL BLR TUN ARG KOR GTM JOR COL LUX MKD AUS CRI MYS HND ISR SGP MDAPAK MAR URY ALB PER RUS MUS IRL KGZ SEN PHL CHL TZA UGA LCA NOR GEO NIC MLT IRN BOL ECU TGO ARM KAZ PAN ZMB VCT MWI MNG GUYCPV GMB DMA TTO BEN AZE ISL SAU NER MDV VEN KNA BLZ BDI

13

Open Forest (log) 14

15

IND

CHN

12

SDN

4

6

8 GDP per capita (log)

10

12

Since the set of products that it is accessible to a country depends on its position in the Product Space, the position of monkeys determines the products that a country will make. Malaysia 1990

Chile 1990 High Density in Chile Low Density in Malaysia

High Density in Malaysia Low Density Chile

Monkeys jump to nearby trees!!!! Malaysia 2000

Chile 2000

Exported by Chile Not exported by Malaysia

Exported by Malaysia Not Exported By Chile

Locations (Sorted by Diversity)

What is done in Chile and where? 0 50

-0.2

100 150

-0.4

200

-0.6

250 -0.8

300 100

200 300 400 500 Industries (Sorted by Ubiquity)

600

700

-1

Yet, all missing industries are not equally likely to pop up... Highly likely

Locations (Sorted by Diversity)

Holes 3

0 50

-10

100 -20

150 200

-30

250 -40

300 100

400 500 200 300 Industries (Sorted by Ubiquity)

600

700

Highly unlikely

What new industries appeared in which locations Locations (Sorted by Diversity)

New Industry Location 2005-2008 1

4 Ovalle, (74k urban, 98k total) 0.8 Social Services without Lodging

50 100

0.6

150 200

3 Curacautin, (12k urban, 18k total) Large Department Store for the Sale Of Garments and Houseware

250 300 100

1 A restaurant in General Lagos (1,200 people)

200 300 400 500 Industries (Sorted by Ubiquity)

2 Lanco, (15,000 people) Rental of Construction Equipment with Operatives

600

0.4

General Lagos, Chile

0.2 700

0

Ovalle Curacautin Lanco

How does this view change the way we think about development puzzles?

Percentage of the products that a country can make

What has caused the Great Divergence and why is it narrowing now?

Percentage of the capabilities that a country has

The problem of development, restated • Development is about the accumulation of productive capabilities or functionalities • …and expressing them in more products • … and in products that require more capabilities – i.e. products that are more complex

• But it faces a “chicken and egg” problem • …solved by moving preferentially towards nearby goods • Development is only very partially about “adding value to your raw materials” – Finland

• In the development process people and firms specialize, but societies diversify – A rural medical facility vs. a major hospital

The problem of development, restated • The product space is very heterogeneous – Some parts are dense and others are sparse

• Not all countries have it equally easy • The product space is being crawled by many monkeys independently – The more, the merrier – We need to empower them and help them solve problems

• A map of the product space can make it easier for monkeys to move – What products do I have nearby? – Which ones are attractive? What are strategic?

1970

A tale of two countries

Peru

Korea

($6090)

($3363)

According to UNCTAD Physical Capital Per Worker: Land Per Worker [arable has]: Human Capital [years of schooling]:

Korea Peru

$7100 0.19 4.76

$35703 0.64 3.89

Korea (+22%)

Peru

(+402%) (+236%)

Revealed Factor Intensity at the Product Level. M Shirotoru, B. Tumurchudur, O Cadot. United Nations. Policy Issues in International Trade and Commodities Study Series No 44 (2010)

2003

A tale of two countries

Peru

Korea

($6263)

($22854)

According to UNCTAD Physical Capital Per Worker: Land Per Worker [arable has]: Human Capital [years of schooling]:

Korea Peru

$95692 0.07 10.68

$19943 0.35 7.58

Korea

(+380%) (+40%)

Peru (+400%)

Revealed Factor Intensity at the Product Level. M Shirotoru, B. Tumurchudur, O Cadot. United Nations. Policy Issues in International Trade and Commodities Study Series No 44 (2010)

What the he____? How did Korea go from 1/2 the per capita income of Peru, to 4 times Peru’s income?!

If it does not look right

It might be because of the way you look at things

WAIT! Korea’s Export Structure Looks Very Different from that of Peru.

Now there is no surprise.

We want numbers! We do not care about dots in a weird network chart!

How about rankings?

What the he____? How did Korea go from being 40 positions above Peru in the economic complexity ranking to being 50 positions above Peru?!

What else..

Are there other conspicuous differences we are missing?

They are kind of in different parts of the world

Why the h___ would that matters?

Did you know that countries are significantly more likely to start exporting a product that its neighbors where already exporting?

We did not! That is why we just finished writing a paper about it 

There are maps that help us orient ourselves when we think about the evolution of economic progress But these are different for every country and product, for every industry and location.

We do not sail in the open sea without proper maps and instruments

Yet we expect development work to find its course based only on instructions provided by those that remain ashore

After we get chalk on our hands to discover what maps need to be built and how..

The question is how do we make these tools available and accessible..

Introducing the Economic Complexity O bservatory

APPS FOR: Productive Structure Analysis:

What do you produce? how much you produce of what? how is production growing? where can it take place? Who are the actors that can make it happen?

Bilateral Trade Analysis:

Who are you trading with? In what products? What are new possible destinations? Who are your guys working in similar destinations?

Import Analysis:

Are your imports in particular products too big? Too small? Are imports showing the existence of growing markets in your country that your own production could potentially fulfill?

Regional Application:

Adding a dataset is easy, and it is possible to make these tools available to any municipality, state and export promotion agency in the world.

THANKS! Juan Jimenez Sam Asher Ricardo Hausmann PhD,

Dany Bahar

Ozan Acar

Nikhil Sahni

Director, Center for International Development, Harvard University Professor of the Practice of Economic Development, Harvard Kennedy School

Jasmina Beganovic

JP Chauvin Rodrigo Wagner

Cesar A. Hidalgo PhD, Alex Simoes Phil Salesses Emily Batt Michele Coscia Andres Zahler Asahi Broadcast Corporation Career Development Professor Assistant Professor, The Media Laboratory, Massachusetts Institute of Technology Faculty Associate, Center for International Development, Harvard University

CHL

PRODY-EXPY (000) -10 0 10 20

Higher PRODY (more attractive)

Closer (i.e. using more of the country’s existing capabilities) 1.5

2

2.5 Density (inverse) Petroleum Forest Animal Prods L Intensive Machinery zero

3

Raw Materials Tropical Ag Cereals K Intensive Chemicals

3.5

Higher Strategic Value (would lead to a larger increase in open forest)

Strategic Value (000) 5 10 15 20

CHL

0

Closer (i.e. using more of the country’s existing capabilities)

1.5

2

2.5 Density (inverse) Petroleum Forest Animal Prods L Intensive Machinery

Raw Materials Tropical Ag Cereals K Intensive Chemicals

3

3.5

2000

4000

Sophistication 6000 8000

10000

12000

Pakistan: looking at the nearest sectors (above the 90 percentile in proximity)

5000

6000

7000 8000 StrategicValue

9000

10000

Circles are proportional to global trade in that industry Colors are proportional to unskilled labor intensity