2. I H S – Institute for Housing and Urban Developmnet Studies GROUP 02 Ahmed Al-Rubea Luiz Gustavo Packer Hintz Ruiwen Kong Wondimagegnehu Girma GEOGRAPHICAL ECONOMICS BRAZIL MODELLING

3. Erasmus Universiteit RotterdamIHS – Institute for Housing and Urban Development Studies 1- 2 SELECTED CITIES

4. Erasmus Universiteit RotterdamIHS – Institute for Housing and Urban Development Studies 1- 3 GEOGRAPHY

5. Erasmus Universiteit RotterdamIHS – Institute for Housing and Urban Development Studies 1- 4 POLICY OPTIONS  Policy options to be considered depending on data availability  Build roads connecting regions  Build road connecting to the Pacific Ocean (port of Lima in Peru) and therefore to Asian markets  Reduce transport costs by reducing toll charges in privatized roads  Reduce transport costs by reducing trade barriers on imports and exports  Build more roads in main cities, thus reducing congestion  Lower housing prices in selected cities in the hinterland

6. Erasmus Universiteit RotterdamIHS – Institute for Housing and Urban Development Studies 1- 5 SIMULATION PARAMETERS   share of income spent on manufactures   love-of-variety effect on consumers   share of labor force in the manufacturing industry   fixed labor input requirement   marginal labor input requirement  1-12  fraction of laborers in the food sector located in regions 1–12, respectively   value of one iteration to the next T  transport costs L  total labor force   represents external economies of scale N 1-12  total number of manufacturing firms in regions 1–12, respectively l ir  amount of labor required in city r to produce x i units of a variety

7. Erasmus Universiteit RotterdamIHS – Institute for Housing and Urban Development Studies 1- 6 PRAMETER ESTIMATION  T  transport costs  CIF value of imports from the point of entry, inclusive of cost, insurance, and freight  FOB measures the value of imports inclusive of all charges in the exporting port  [(CIF/FOB) – 1] x 100%  International ports to be considered: Rotterdam, Shanghai, New York, Lima, and Buenos Aires  Data sources  Brazilian Chamber of Commerce  Brazilian Chamber of International Commerce  Concessionaries of privatized roads (toll charges)

8. Erasmus Universiteit RotterdamIHS – Institute for Housing and Urban Development Studies 1- 7 PRAMETER ESTIMATION  Available Data L  total labor force   share of labor force in the manufacturing industry  1-12  fraction of laborers in the food sector located in regions 1–12, respectively N 1-12  total number of manufacturing firms in regions 1–12, respectively l ir  amount of labor required in city r to produce x i units of a variety  Data sources  Brazilian Institute of Geography and Statistics (IBGE)  Brazilian Institute of Applied Economic Research (IPEA)

9. Erasmus Universiteit RotterdamIHS – Institute for Housing and Urban Development Studies 1- 8 PRAMETER ESTIMATION  ??  What proxies could be used in the estimation of the following parameters ?  (love-of-variety effect on consumers),  (fixed labor input requirement),  (marginal labor input requirement), l ir (amount of labor required in city r to produce x i units of a variety)  Regarding T, is it necessary to model two way transport costs (e.g. from the port of Santos to the port of Shanghai and vice-versa) ?  In modelling with congestion, can the ratio of #cars/Km roads, or urbanization level be used as a proxy to estimate  (external economies of scale) ?  What are the adjustments necessary to model the introduction of food transport costs ?  Suppose we wish to substitute the food sector for the housing sector, what are the consequences for parameter estimation (Helpman model) ?  k 0 (?), H k (housing stock in region k), D jk (proxy for distance ?), err j (error ?)