FINANCING CITIES IN THE GLOBAL ECONOMYLOCATION AND GEOGRAPHICAL ECONOMICS

Liga MiezeDenis Assimwe KangereBizuneh Gultu LakewCamilo Mendoza

May 2005IHS- Rotterdam

Source: Biological Resources Investigation Institute Alexander Von Humboldt, 2000

• Mid Point between North and South America

• 2 Oceans: Atlantic & Pacific

• Spanish language

ColombiaGeneral Information

Source: IGAC

• Why Colombia?Because of its location ~ could be a bridge between North and South AmericaIn negotiation a Free Trade Treaty with USAAnalysing a Latin American Country

• 1,141,748 km2 inland – 928.660 km2 on water

• Population: 46 million

• 72% in cities

ColombiaGeneral Information

Colombia turned from a mainly rural country to an urban one in the last half century as a Colombia turned from a mainly rural country to an urban one in the last half century as a result of:result of:

• industrialization / diversification of exports (mining products)industrialization / diversification of exports (mining products)• agriculture technology improvementagriculture technology improvement• development of third sector activitiesdevelopment of third sector activities• rural violencerural violence• high birth rateshigh birth rates• revenue concentrationrevenue concentration

YEAR TOTAL URBAN RURAL URBAN RURAL1951 11.600.050 4.504.594 7.095.456 39% 61%1961 15.889.120 7.768.324 8.120.796 49% 51%1971 21.380.505 12.288.015 9.092.490 57% 43%1981 28.646.865 18.145.435 10.501.430 63% 37%1991 36.092.470 24.454.330 11.638.140 68% 32%2001 43.035.394 30.693.455 12.341.939 71% 29%2003 44.531.434 32.017.189 12.514.245 72% 28%

Urbanisation

Pasto

Buenaventura

Cali

Pereira

Medellin

Tunja

Bucaramanga

Monteria

36% of the population live in the selected cities

Bogotá 7,185,889Cali 2,423,381Medellín 2,093,624Barranquilla 1,386,895Cartagena 1,030,149Bucaramanga 577,347Pereira 521,684Santa Marta 447,860Pasto 424,283Montería 348,168Buenaventura 278,960Tunja 126,570Total 16,844,810

Source: Dane

1 2 3 4 5 6 7 8 9 10 11 12Ciudad Bogotá Cali MedellínBarranquillaCartagenaBucaramangaPereiraSanta MartaPasto MonteríaBuenaventuraTunja

1 Bogotá 511 399 985 1090 393 360 952 921 791 566 1202 Cali 511 456 1214 1099 844 234 1241 410 848 120 6313 Medellín 399 456 758 643 478 222 851 866 392 512 5194 Barranquilla 985 1214 758 115 570 980 93 1264 343 1269 8585 Cartagena 1090 1099 643 115 685 865 209 1509 298 1219 9586 Bucaramanga 393 844 478 570 685 634 534 1314 723 840 2737 Pereira 360 234 222 980 865 634 1009 369 614 290 4808 Santa Marta 952 1241 851 93 209 534 1009 1650 436 1362 8079 Pasto 921 410 866 1264 1509 1314 369 1650 1258 530 1041

10 Montería 791 848 392 343 298 723 614 436 1258 904 91111 Buenaventura 566 120 512 1269 1219 840 290 1362 530 904 68612 Tunja 120 631 519 858 958 273 480 807 1041 911 686

DISTANCE (km)

1 2 3 4 5 6 7 8 9 10 11 12Ciudad Bogotá Cali MedellínBarranquillaCartagenaBucaramangaPereiraSanta MartaPasto MonteríaBuenaventuraTunja

1 Bogotá 7.9 6.1 15.2 16.8 7.1 6.5 14.6 14.2 12.2 8.7 2.22 Cali 7.9 7.0 18.7 16.9 15.3 3.6 19.1 6.3 13.0 1.6 9.73 Medellín 6.1 7.0 10.1 8.6 7.4 3.4 11.3 13.3 5.2 7.9 8.04 Barranquilla 15.2 18.7 10.1 1.5 8.8 15.1 1.2 19.4 4.6 19.5 13.25 Cartagena 16.8 16.9 8.6 1.5 10.5 13.3 2.8 23.2 4.0 18.8 14.76 Bucaramanga 7.1 15.3 7.4 8.8 10.5 11.5 8.2 20.2 11.1 12.9 4.27 Pereira 6.5 3.6 3.4 15.1 13.3 11.5 15.5 5.7 9.4 4.5 7.48 Santa Marta 14.6 19.1 11.3 1.2 2.8 8.2 15.5 25.4 5.8 1362.0 12.49 Pasto 14.2 6.3 13.3 19.4 23.2 20.2 5.7 25.4 19.4 8.2 16.0

10 Montería 12.2 13.0 5.2 4.6 4.0 11.1 9.4 5.8 19.4 13.9 14.011 Buenaventura 8.7 1.6 7.9 19.5 18.8 12.9 4.5 21.0 8.2 13.9 10.612 Tunja 2.2 9.7 8.0 13.2 14.7 4.2 7.4 12.4 16.0 14.0 10.6

DISTANCE (time-hours)

Simulation AssumptionsSimulation Assumptions

• Delta 0.67 reflects the percentage of expenses on Delta 0.67 reflects the percentage of expenses on manufacturing manufacturing

• Epsilon as provided by model Epsilon as provided by model • Tau(congestion parameter) small deviation & large Tau(congestion parameter) small deviation & large

deviations useddeviations used• Stop criterion and adjustment speed as provided by the Stop criterion and adjustment speed as provided by the

modelmodel• Initial distribution of mobile& immobile workers based on Initial distribution of mobile& immobile workers based on

the populationthe population• Distances based on time of travel between the different Distances based on time of travel between the different

citiescities

Base Scenario Distribution of WorkersBase Scenario Distribution of Workers

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initial distribution mobile activityFinal distribution mobile activity

Scenario 1Scenario 1• With tau at 0.2 and transport cost fixed at 1.3,we observe a tendency to spread

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initial distribution mobile activityFinal distribution mobile activity

Scenario2Scenario2• With tau increased to 0.3 and transport cost still fixed at 1.3,we observe a tendency to spread to the cities to the far rightWith tau increased to 0.3 and transport cost still fixed at 1.3,we observe a tendency to spread to the cities to the far right

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initial distribution mobile activityFinal distribution mobile activity

Scenario 3Scenario 3• With tau at 0.4 and transport cost fixed at 1.3,we observe a tendency to spreadWith tau at 0.4 and transport cost fixed at 1.3,we observe a tendency to spread

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initial distribution mobile activityFinal distribution mobile activity

ConclusionsConclusions• City 12(Tunja) tends to attract the mobile

workers as the congestion increases

• Promote policies that improve the connectivity of the other cities