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During the course -Network Types (Small World, Scale Free, Random) -Network Models (Erdos-Renyi, Scale-Free, Hierarchical Networks, Duplication Divergence) -Network Properties (Degree, Degree Distribution, Clustering, Degree Correlations, Rich Club Phenomena, Average Path Length, Diameter, Community Structure etc…) -Dynamical Properties (Failure and attack tolerance, epidemic thresholds, etc..) -Social Systems Team assembly and the invisible college, the dynamics of social ties and groups -Biological Systems Molecular Networks (PPI, Metabolic), Disease Networks. We discovered that it was a bit too much for only seven sessions

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Networks, Complexity and Economic Development

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How can we describe the economic development of nations?

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GDP per capita Labor, Land, Capital, Technological Sophistication Production Function Robinson, J. (1953) The production function and the theory of capital, Review of Economic Studies, vol XXI, 1953, pp. 81-106 Technical Change and the Aggregate Production Function RM Solow - The Review of Economics and Statistics, 1957

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DesignerProgrammerHardware Knowledge Webpage Mechanic Fashionable Electronic Southern California Rich people toys

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

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Building the Forest

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d( )=F(C,N,N,N) corr( )=(C N-N N )/sqrt((N-N ) (N-N ) N N ) M.I.( )=H( )+H( )-H( ) R.R.( )= C N/N N Proximity( )= min(C /N, C /N )

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Product

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Maximum Spanning Tree (MST)

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Threshold = 0.5 Number of Links = 2850 Threshold = 0.55 Number of Links = 1525 Threshold = 0.6 Number of Links = 1026 Threshold = 0.7 Number of Links = 829

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Threshold = 0.55 Number of Links = 1525

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

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Patterns of Comparative Advantage

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How do monkeys jump?

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Malaysia 1975

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Malaysia 1980

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Malaysia 1985

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Malaysia 1990

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Malaysia 1995

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Malaysia 2000

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China 197519852000

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1985 High density of Monkeys around this tree Low density of Monkeys around this tree

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CA Hidalgo, B Klinger, A-L Barabasi, R Hausmann. Science (2007) Countries are more likely to jump towards products that are close by

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Malaysia 1990 Chile 1990 High Density in Malaysia Low Density Chile High Density in Chile Low Density in Malaysia

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Malaysia 2000 Chile 2000 Exported by Malaysia Not Exported By Chile Exported by Chile Not exported by Malaysia

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H=Average Density in Countries that transitioned into the Product / Average Density in Countries that did not transition into the product

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Society Products

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A similar story….

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What is matter made of?

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Dimitri Mendeleev Johann Döbereiner John Newlands Alexandre de Chancourtois

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CA Hidalgo, B Klinger, A-L Barabasi, R Hausmann. Science (2007) How good is your neighborhood?

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First economic quantification Country A Country B S a E=- S a log(S a ) H= S 2 a D= S a F(S a )

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1006 product categories 132 countries Quantifying Economic Complexity M a = 1 if country a exports product

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Method of Reflections

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Product 1 Country C 1 Country C 2 Country C 3 Product 2 Product 3 Product k=3 k=4 k=1 Method of Reflections Degree (Countries) Degree (Products) DiversificationUbiquity

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Method of Reflections Product 1 Country C 1 Country C 2 Country C 3 Product 2 Product 3 Product Country C 1 Country C 2 Country C 3 k 1,1 =7/3 k 2,1 =2 k 3,1 =3 k 1 = Standardness: Average ubiquity of products exported by a country Mirror

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k=3 k=4 k=1 Product 1 Product 2 Product 3 Product Country C 1 Country C 2 Country C 3 Product 1 Product 2 Product 3 Product Method of Reflections 1 = Complexity: Average diversification of a products exporters

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Country VariablesProduct Variables k0k1k2k3k4k0k1k2k3k4 Method of Reflections

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Diversification k Standardness k 1 Poorly Diversified & Producing Common Products Highly Diversified & Producing Common Products Highly Diversified & Producing Exclusive Products Poorly Diversified & Producing Rare Products Method of Reflections: k-k 1 diagram

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Ubiquity Produced in Few, Highly Diversified, Countries Produced in Many Highly Diversified Countries Produced in Many Non Diversified Countries Produced in Few Non-Diversified Countries Method of Reflections: diagram Complexity

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Method of Reflections: diagram

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Method of Reflections: Null Models

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Method of Reflections: k-k 1 diagram and null models

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Method of Reflections: k-k 2, k 1 -k 2 diagrams and null models

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Method of Reflections: diagram and null models

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Method of Reflections: diagrams and null models

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Method of Reflections: k, k 1 and GDP per capita (ppp)

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Method of Reflections: and PRODY

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(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) Predicted Variable Growth (85,95) Predictors GDP per capita ppp (1985) -0.001760.000993-0.00206-0.00154-0.00249-0.00223-0.00470-0.00233-0.00244-0.00238-0.00242 (-0.794)(0.533)(-0.882)(-0.497)(-0.735)(-0.688)(-1.478)(-0.758)(-0.849)(-0.804)(-0.831) Entropy (1985) 0.00660***0.00828**0.002000.00322 (3.650)(2.600)(0.931)(0.896) Herfindahl (1985) -0.0273***0.0116-0.004140.00723 (-2.765)(0.760)(-0.406)(0.454) k (1985) 6.62e-05** (2.080) k 1 (1985) -0.000612 (-0.749) k 4 (1985) 0.00169*** (2.866) k 5 (1985) 0.0321* (1.737) k 8 (1985) 0.0338*** (3.075) k 9 (1985) 0.890*** (2.713) k 18 (1985) 0.401***38.88***35.05**37.26***35.57*** (3.453)(2.952)(2.618)(2.849)(2.643) k 19 (1985) 1127***1017**1080***1033*** (2.928)(2.603)(2.829)(2.632) Constant0.01140.01370.006500.0338-0.735*-19.29***-69.21***-23801***-21475**-22808***-21801*** (0.751)(0.776)(0.437)(0.922)(-1.883)(-2.807)(-3.454)(-2.940)(-2.610)(-2.834)(-2.633) N97 Adjusted R 2 0.1950.1150.1920.1180.2060.2470.2020.274 0.268 Method of Reflections: 20 year Growth

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(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) Predicted Variable Growth (85-95--05) Growth (85-95-05) Predictors GDP per capita ppp (85,95) -0.002350.000334-0.002460.00112-0.00395**-0.00311*0.00310**-0.00119-0.00346*-0.00229-0.00343* (-1.322)(0.209)(-1.349)(0.595)(-2.062)(-1.695)(2.188)(-0.707)(-1.899)(-1.327)(-1.850) Entropy (85,95) 0.00699***0.00759***0.00458***0.00434 (4.962)(2.985)(3.002)(1.648) Herfindahl (85,95) -0.0325***0.00422-0.0210**-0.00162 (-3.890)(0.285)(-2.494)(-0.112) k (85,95) 9.75e-05*** (3.967) k 1 (85,95) 0.000916** (2.543).k 4 (85,95) 0.00102*** (5.577) k 5 (85,95) 0.00329*** (5.971) k 8 (85,95) 0.00577*** (5.056) k 9 (85,95) 0.0152*** (5.594) k 18 (85,95) -0.000660***0.455***0.310***0.375***0.311*** (-3.577)(4.306)(2.705)(3.428)(2.695) k 19 (85,95) 1.158***0.789***0.954***0.792*** (4.312)(2.709)(3.433)(2.699) Constant0.01710.02260.0153-0.0186-0.168***-1.178***0.102***-96.21***-65.48***-79.20***-65.78*** (1.356)(1.602)(1.087)(-0.971)(-6.137)(-5.215)(3.107)(-4.308)(-2.705)(-3.428)(-2.695) Observations221 Adjusted R 2 0.1130.0770.1090.0850.1700.1600.0680.1370.1680.1580.164 Method of Reflections: 10 year Growth

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(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) Predicted Variable Growth (85-90-95-00- 05) Predictors GDP per capita ppp (85,90,95,00) -0.00269*0.000292-0.00286*0.000361-0.00393**0.00224*0.00326**0.00310**-0.00257*0.000281-0.00275* (-1.732)(0.211)(-1.785)(0.220)(-2.431)(1.767)(2.553)(2.479)(-1.686)(0.207)(-1.749) Entropy (85,90,95,00) 0.00798***0.00885***0.00759***0.00851*** (6.280)(3.760)(6.060)(3.680) Herfindahl (85,90,95,00) -0.0373***0.00602-0.0351***0.00636 (-4.970)(0.440)(-4.765)(0.474) k (85,90,95,00) 0.000121*** (5.351) k 1 (85,90,95,00) 0.000953*** (2.853) k 4 (85,90,95,00) 0.00113*** (7.074) k 5 (85,90,95,00) 0.00260*** (5.694) k 8 (85,90,95,00) 0.00102*** (3.474) k 9 (85,90,95,00) 0.00312*** (5.504) k 18 (85,90,95,00) -0.0002650.000632**0.000673**0.000647**0.000676** (-1.147)(2.131)(2.359)(2.234)(2.365) k 19 (85,90,95,00) 0.00280***0.00259***0.00265***0.00260*** (4.671)(4.494)(4.523)(4.493) Constant0.01660.0236*0.0142-0.0160-0.173***-0.224***0.0349-0.163***-0.142**-0.132**-0.145*** (1.497)(1.910)(1.144)(-0.933)(-7.646)(-4.198)(0.889)(-2.846)(-2.576)(-2.355)(-2.611) Observations451 Adjusted R 2 0.0900.0620.0890.0710.1360.0710.0130.0570.1270.1010.125 Method of Reflections: 5 year Growth

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Method of Reflections: 5 year growth, fixed country effects (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) Predicted Variables Growth (85-90-95-00- 05) Predictors GDP per capita ppp (85,90,95,00) -0.0585***-0.0581***-0.0595***-0.0773***-0.0863***-0.0891***-0.0651***-0.0899***-0.0868***-0.0884***-0.0867*** (-7.911)(-7.721)(-8.072)(-10.11)(-11.28)(-11.78)(-8.337)(-11.89)(-11.39)(-11.58)(-11.40) Entropy (85,90,95,00) 0.0134***0.0247***0.00706**0.0142** (4.478)(4.037)(2.453)(2.435) Herfindahl (85,90,95,00) -0.0390***0.0585**-0.01810.0360 (-2.842)(2.117)(-1.435)(1.410) k (85,90,95,00) 0.000223*** (3.710) k 1 (85,90,95,00) 0.00238*** (6.549) k 4 (85,90,95,00) 0.000537*** (2.922) k 5 (85,90,95,00) 0.00366*** (9.287) k 8 (85,90,95,00) 0.000611*** (2.801) k 9 (85,90,95,00) 0.00410*** (8.998) k 18 (85,90,95,00) -0.000535***0.000601***0.000653***0.000618***0.000673*** (-2.808)(2.801)(3.051)(2.879)(3.141) k 19 (85,90,95,00) 0.00419***0.00395***0.00410***0.00389*** (8.799)(8.164)(8.521)(8.031) Constant0.467***0.514***0.429***0.594***0.588***0.589***0.651***0.596***0.543***0.585***0.511*** (7.427)(8.147)(6.592)(9.546)(8.165)(8.070)(8.203)(8.310)(7.315)(8.133)(6.583) Observations451 Within R 2 0.20710.17840.21790.29910.33790.33730.17790.33720.34940.34150.3535

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Method of Reflections: Predicts network properties of future products

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TheLegoTheoryofDevelopment

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Lego World Real World

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Countries How many different pieces you have? How rare are your pieces?

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Many different pieces If you have: We might expect you to build many products, including those requiring many different pieces and rare pieces. Few different pieces We expect you to build few products, that are made by many others, as it is likely to have those few pieces High Diversification (k 0 ), Low Standardness (k 1 ) Low Diversfication (k 0 ), High Standardness (k 1 )

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Many different pieces If a product requires: We might expect you to find that product In few countries that have many pieces and therefore build many products Few different pieces We expect to find that products in many Countries, including those with few pieces Low ubiquity (k 0 ), High complexity (k 1 ) High ubiquity (k 0 ), Low complexity (k 1 ) Few rare pieces We expect to find that products in few countries Low Ubiquity (k 0 )

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Concluding Remarks INTRODUCTION: 1.- The product space is heterogeneous and can be approximated by studying export data using network methods. 2.- The heterogeneity of the product space matters for development. (The location of a country in the product space determines their ability to diversify) MAIN BODY 1.- We can quantify the productive structure of countries and the sophistication of products by studying exports as a bipartite network. 2.- This network characterization of productive structure is associated with income and growth, suggesting that it is able to capture some fundamental properties of production. FINALE 1.- We can explain some of this observations using the Lego Theory of Development

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