Economic Complexity and the Wealth of Nations

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1 Economic Complexity and the Wealth of Nations Cesar A. Hidalgo ABC Career Development Professor MIT Media Lab Faculty Associate, Center for International Development Harvard University

2 EARTH WIND WATER FIRE

3 LAND LABOR CAPITAL HUMAN CAPITAL

4 A tale of two countries 1970 Korea ($3363) Peru ($6090) According to UNCTAD Physical Capital Per Worker: Land Per Worker [arable has]: Korea Peru Korea Peru $7100 $35703 (+402%) (+236%) Human Capital [years of schooling]: (+22%) 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)

5 A tale of two countries 2003 Korea ($22854) Peru ($6263) According to UNCTAD Korea Peru Korea Physical Capital Per Worker: $95692 $19943 (+380%) Land Per Worker [arable has]: Human Capital [years of schooling]: (+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)

6 We are left with puzzles What the he? How did Korea went from ½ the per capita income of Peru, to 4 times Peru s Income?!

7 Science Theory Observation 4 LAME FACTS Tunnel

8 Municipalities Countries Looking at Networks Instead What if instead of scratching our heads because of the dynamics of a few variables, we tried to understand the world by studying the structure and dynamics of hundreds of thousands of related variables? Industries Log 10 (NumberOfFirms) Product Log 10 (RCA) Industry Location Data

9 Hausmann Hidalgo (2011) Journal of Economic Growth

10 Hausmann Hidalgo (2011) Journal of Economic Growth

11 Hausmann Hidalgo (2011) Journal of Economic Growth

12 Hausmann Hidalgo (2011) Journal of Economic Growth

13 Science Theory What would be a model that would account for them? Observation 4 LAME FACTS Tunnel

14 Personbytes/Capabilities/Legos & Products Hidalgo, Hausmann PNAS (2009)

15 COUNTRIES & personbytes/legos Hidalgo, Hausmann PNAS (2009)

16 This Bi-Partite Observable Countries Products Hidalgo, Hausmann PNAS (2009)

17 The ROOT of the problem: connecting the structure of the observable network with the structure of its primitive components C ca P pa M cp M cp =1 if P pa is a subset of C ca

18 some simplifying assumptions 20 N a r N a q N p N c

19 Number of Products Theoretical Prediction 1: The diversification of a country increases with the number of capabilities that country has. Hausmann and Hidalgo (2011) JOEG Theory Simulation Number of Capabilities

20 Ubiquity ( 1 ) Theoretical Prediction 2: The ubiquity of a product decreases with the number of capabilities it requires. Hausmann and Hidalgo (2011) JOEG Theory Simulation Number of Capabilities Required by a Product

21 Average Product Ubiquity Theoretical Prediction 3: The average ubiquity of a country s products decreases with its diversity Diversification Hausmann and Hidalgo (2011) JOEG

22 Cumulative Probability Theoretical Prediction 4: (Numerical) The model predicts the probability of products being co-exported given their ubiquities Proximity (Minimum of Conditional Probability of CoExport) Hausmann and Hidalgo (2011) JOEG

23 Theoretical Prediction 5: Distribution of ubiquities Probability Product is Exported by v countries

24 Theoretical Prediction 6: Distribution of diversifications Probability Country exports u products

25 5 out of 6 Simplifying assumption.. All countries have the same probability of having a capability = r

26 Theoretical Prediction 6 (revisited): (Numerical) Diversification Distribution

27 56 out of 6 What are the implications of this model. (Why should u care?)

28 What data cannot do Relating diversity of capabilities to the diversity of products. or N a ->1 or N a ->infinity Hausmann and Hidalgo (2011) In Press

29 Fraction of Products that is Possible And our world? Is one with traps Fraction of Capabilities Available

30 So what about Korea and Peru? Diversification Average Ubiquity AFG AGO ALB ARG AUS AUT BDI BEN BFA BGR BHR BHS BLZ BMU BOL BRA BRB CAF CAN CHL CHN CIV CMR COL CRI CYP DNK DOM DZA ECU EGY ESP ETH FIN GABFJI GBR GHA GIN GMB GRC GTM GUY HKG HND HTI HUN IDN IND IRL IRN IRQ ISL ISR ITA JAM JOR JPN KEN KHM KNA KOR KWT LBN LBR LBY LKA MAC MAR MDG MEX MLI MLT MNG MOZ MUS MWI MYS NCL NER NGA NIC NLD NOR NPL NZL OMN PAK PAN PER PHL PNG POL PRT PRY ROM RWA SAU SDN SEN SGP SLE SLV SOM SUR SWE SYC SYR TGO THA TTO TUN TUR TZA UGA URY USA VEN WSM ZAF ZMB ZWE AFG AGO ALB ARG ARM AUS AUT AZE BDI BEN BFA BGD BGR BHR BHS BIH BLR BLZ BMU BOL BRA BRB CAF CAN CHL CHN CIV CMR COL CRI CYP CZE DEU DNK DOM DZA ECU EGY ESP EST ETH FIN FJI GAB GBR GEO GHA GIN GMB GRC GTM GUY HKG HND HRV HTI HUN IDN IND IRL IRN IRQ ISL ISR ITA JAM JOR JPN KAZ KEN KGZ KHM KNA KOR KWT LBN LBR LBY LKA LTU LVA MAC MAR MDA MDG MEX MKD MLI MLT MNG MOZ MUS MWI MYS NCL NER NGA NIC NLD NOR NPL NZL OMN PAK PAN PER PHL PNG POL PRT PRY ROM RUS RWA SAU SDN SEN SGP SLE SLV SOM SUR SVK SVN SWE SYC SYR TGO THA TJK TKM TTO TUN TUR TZA UGA UKR URY USA VEN WSM YEM ZAF ZMB ZWE 2000 Diversification Average Ubiquity

31 How about the reverse problem? THOUGHT WORLD

32 In Lego Language In Network Language k k c,0 c, n p 1 k c,0 M cp p M, cp k k p,0 p, n 1, c k M p, n cp 1 k p,0 c M cp k c, n 1 and (Simple) Few Capabilities and and and = = (Complex) Diverse Set of Capabilities Hidalgo, Hausmann (2009) PNAS 106(26):

33

34 Income Per Capita Input Diversity Explains Income Per Capita Economic Complexity Hidalgo, Hausmann (2009) PNAS 106(26):

35 Excesses of Input Diversity Predict Future Growth Complexity in 1985 (Controlling for GDP per capita at ppp) Hidalgo, Hausmann (2009) PNAS 106(26):

36 atlas.media.mit.edu

37 The Atlas then shows that the contribution of the Economic Complexity Index (ECI) to predict future economic growth is 500% to 2000% larger than current measures of governance (World Bank), Education, and Competitiveness (WEF) 6 Dimensions of Mental Order 12 Pillars of Mental Order

38 Economic Complexity Top 20.

39 Per Capita Growth to Top 20.

40

41 Korea s Economy Was Always More Complex than that of Peru

42 How does diversity come together? 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 to Nearby Products?!

43 The Product Space

44 CA Hidalgo, B Klinger, A-L Barabasi, R Hausmann. Science (2007) Hausmann, Hidalgo et al. The Atlas of Economic Complexity (2011)

45 atlas.media.mit.edu

46 atlas.media.mit.edu

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48 atlas.media.mit.edu

49 atlas.media.mit.edu

50 atlas.media.mit.edu

51 atlas.media.mit.edu

52 atlas.media.mit.edu

53 atlas.media.mit.edu

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56 atlas.media.mit.edu

57 Become a Explorer in the Economic Complexity Observatory at: ATLAS.MEDIA.MIT.EDU

58 How many stable elements are known? 82 What is the average number of isotopes per element? 27 How many stable isotopes are known? 275 How many dimensions define a measure of competitiveness? 12 How many dimensions define governance? 6 Human Development? 3 Measures of complex social systems have a number of dimensions that is always divisible by 3 (and also by 2 when larger than 3).

59 Identifying Capabilities/Personbytes C {set of countries in which it is present} {set of products that require it}

60 Annotating Personbytes (Network Factorization 1 parameter) ORIGINAL MATRIX M MODELED MATRIX N TP P TP P TN N ~ 87% ~ 86% C P Yildirim M, Hausmann R, Hidalgo CA (In Progress)

61 Number of Products DISTRIBUTION OF UBIQUITIES Number of Countries Yildirim M, Hausmann R, Hidalgo CA (In Progress) Diversification (Factorized Matrix) Ubiquity (Factorized Matrix) Ubiquity DISTRIBUTION 150 OF DIVERSIFICATION Ubiquity (Country Product Matrix) Diversification Diversification (Country Product Matrix)

62 Proximities (Based on Personbytes) Product Centrality Sum of Product Proximities (Based on Personbytes) Does the Product Space make Sense? Proximities (Based on CoExports) Product Centrality Sum of Product Proximities (Based on CoExports) Yildirim M, Hausmann R, Hidalgo CA (In Progress)

63 What About Korea and Peru?

64 Thank You Very Much! ALEX SIMOES JAVASCRIPT WIZARD AND MASTER OF MEDIA ARTS AND SCIENCES AT THE MIT MEDIA LAB SEBASTIAN BUSTOS ECONOMETRICS MAGICIAN AND FELLOW AT HARVARD S CENTER FOR INTERNATIONAL DEVELOPMENT MUHAMMED YILDIRIM ALGEBRAIC GENIUS AND FELLOW AT HARVARD S CENTER FOR INTERNATIONAL DEVELOPMENT RICARDO HAUSMANN MASTERMIND ECONOMIST AND DIRECTOR OF HARVARD S CENTER FOR INTERNATIONAL DEVELOPMENT

65 SOME PREDICTED QUESTIONS (1) WHAT ABOUT SERVICES? (2) WHAT ABOUT TASK TRADE? ARE YOU IMPUTTING TOO MUCH FROM COUNTRIES THAT JUST DO ASSEMBLY? (3) ARE COUNTRIES THE BEST UNIT? WOULDN T CITIES/REGIONS BE BETTER? (4) THIS IS NOT ECONOMICS! (in a somehow upset tone) (answer: It is not.) (5) WHAT ABOUT SCALE, IS COMPLEXITY JUST BEING BIGGER (GDP, POP)? (6) WHAT TOOLS DID U USE TO MAKE THE GRAPHICS? (7) ARE YOU ADVOCATING FOR GOVERNMENT TO PICK INDUSTRIES? (in a somehow upset tone) (8) DOES THIS MEAN THAT U SHOULD ADD VALUE TO YOUR RAW MATERIALS? (9) WHAT ARE THE POLICY IMPLICATIONS? (Question asked by someone that walked into the talk only for the last 2 minutes but still wants to make a question )

66 Method of Reflections: Predicts network properties of future products Hidalgo, Hausmann (2009) PNAS

67 Method of Reflections: Predicts network properties of future products Hidalgo, Hausmann (2009) PNAS

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