Urban Tool - Forecast Food Demand Maria Caterina Bramati Sapienza University of Rome mariacaterina.bramati@uniroma1.it 1
Some 2014 Key Figures • 54% world’s population resides in Urban areas • Most urbanized regions: North America (82% urban population), Latin America (80% urban population) • Least urbanized regions: Africa (40%), Asia (48%) • Rapid growth of urban population foreseen by 2050, 90% of the increase concentrated in Africa and Asia • 28 mega-cities with > 10million inhabitants, by 2030 the world is projected to have 41 mega-cities. [ World Urbanization Prospects - 2014 Revision ] 2
Urban prosperity*(1) Defined on the basis of 5 dimensions: 1. Economic growth (productivity, income, employment) 2. Infrastructure (water, sanitation, road network , ICT…) 3. Social services (education, health, recreation, safety…) 4. Reduction of Poverty and Inequalities 5. Environmental protection and preservation *UN-Habitat State of the World’s cities 2012/2013 3
Urban prosperity*(2) Chronic inequalities and mass poverty in cities due to: - Insufficient infrastructure - Poor public services - Inadequate connectivity - Poor governance - Fragile institutions *UN-Habitat State of the World’s cities 2012/2013 4
“…sustainable development challenges will be increasingly concentrated in cities, particularly in the lower-middle-income countries where the pace of urbanization is fastest…” “…rapid and unplanned growth threatens sustainable development when the necessary infrastructure is not developed or when policies are not implemented to ensure that the benefits of city life are equitably shared .” [ World Urbanization Prospects - 2014 Revision ] 5
AIM of the TOOL To raise the awareness of policy makers about the future ROAD Infrastructure need through the 1. Estimation of the food demand in cities and of the number of daily truck journeys needed to supply cities. 2. Projection of future needs of Road Infrastructure according of various scenarios of urban growth . 6
OUTLINE • Main assumptions about the demand function • Estimation and Scenario Building functions • Case Studies • Advanced users • Open issues • Floor Discussion 7
The Econometric Model: Main assumptions • Log-linear food item demand function • estimated for 3 income groups (low-mid-high) • Mean for transportation: 10-ton truck • Food item demand is function of - market prices (including substitute and complements food items), - household income - household size 8
The Econometric Model: Main parameters Parameters are obtained after LS estimation using LSMS country household data. They are - By geographical area - By food item - Set by default, unless the user decides otherwise Parameters: • Price elasticities to food consumption • Income elasticities • Household Size coefficient 9
However… (some issues about default parameters) Data used: household food consumption in urban areas of • Brazil • Tanzania • Malawi • Ethiopia • Iraq • Tajikstan • Bulgaria → timeliness → geographical areas 10
URBAN TOOL An interface running in Excel 11
URBAN TOOL Functions 1. Estimate aggregate urban food demand by single food item, time horizon, geographical area of cities and different income-level of city inhabitants. 2. Forecast aggregate food demand and transport needs as number of daily truck journeys for a given nutritional composition. 3. Assessment of current capacity to face future demand using scenarios related to population , inflation and income growth rates. 12
URBAN TOOL Reporting 1. Output from estimation (a separate sheet with computation is generated). 2. Graphs of food item and aggregate demand for each of the income groups. 3. Pivot Tables with the aggregate food demand and transport needs as number of daily truck journeys. Possible to save reports creating new .xls files 13
URBAN TOOL Remarks • Stepwise procedure: steps are numbered in increasing order. Skipping steps will generate errors. • Compulsory actions (marked by *): input values related to city features. • Free choice of parameters : the user can choose whether using default parameters or enter other coefficients. 14
URBAN TOOL 15
READY? Let’s go for a drive! 16
URBAN TOOL Case Study 1: Lilongwe (Malawi) AIM: Estimate daily demand of cereals and rice 17
URBAN TOOL Case Study 1: Lilongwe (Malawi) AIM: Estimate daily demand of cereals and rice Use function 1 Input needed : - Lilongwe inhabitants - Average household size - Per capita daily income $ US - Market prices/kg $ US [if possible by income group] 18
URBAN TOOL Case Study 1: Lilongwe (Malawi) AIM: Estimate daily demand of cereals and rice KEY Figures (2014) - Lilongwe inhabitants : 867469 - Average household size: 4.45 - Per capita daily income: 2.41 $ US - Market price of Cereals: 0.64 $ US/kg - Market price of Rice: 1.05 $ US/kg [Source: The WorldBank, LSMS] 19
URBAN TOOL Case Study 1: Lilongwe (Malawi) AIM: Estimate daily demand of cereals and rice By income group low middle high inhabitants 650601.8 173493.8 43373.45 average hh size 4.45 4.45 4.45 per capita daily income 2 8 82 [Source: The WorldBank, LSMS] 20
URBAN TOOL Case Study 1: Lilongwe (Malawi) AIM: Estimate daily demand of cereals and rice Change Elasticities for Cereals (Set parameters) New Default Income 1.062 2.718 Price 0.824 0.607 Houshold size 1.22 1.22 [Source: Maganga et al. (2014) ] 21
URBAN TOOL Case Study 1: Lilongwe (Malawi) AIM: Estimate daily demand of cereals and rice Maize daily consumption in Lilongwe (tons) 2003 2014 Maize 316.09 468.43 [Source: FAO (2014) ] 22
URBAN TOOL Case Study 2: São Paulo São Paulo is the 7th most populous city in the world defined according to the concept of city proper (an urban locality without its suburbs). The cities ahead of it in order of population are: Shanghai, Karachi, Istanbul, Mumbai, Beijing and Moscow. Source: IBGE 2010 Census data 23
URBAN TOOL Case Study 2: São Paulo Transport • The average traffic jams on Friday evenings is 180km (112 miles) and as long as 295km (183 miles) on bad days according to local traffic engineers. • Commuters are stuck in traffic hold-ups for an average of two and a half hours daily. • Despite the traffic jams São Paulo continues to experience ever increasing traffic circulation and surpassed 7m vehicles in March 2011. 24
URBAN TOOL Case Study 2: São Paulo AIM: Estimate daily aggregate food demand Use function 2 Input needed : - São Paulo inhabitants - Average household size - Per capita daily income - Food market price for each item 25
URBAN TOOL Case Study 2: São Paulo AIM: Estimate daily aggregate food demand KEY Figures (2014) - São Paulo inhabitants : 20830857 - Average household size: 3.58 - Per capita daily income: 5.73 $ US [Source: The WorldBank, LSMS] 26
Case Study 2: São Paulo AIM: Estimate daily aggregate food demand 2014 Food market prices Item Prices $ US/kg Cereals 1.40 Rice 1.16 Roots and Tubers 1.18 Meats and Meat Products 5.62 Fish 18.00 Dairy Products 0.90 Fruits and Vegetables 1.56 Other Food 1.66 27
URBAN TOOL Case Study 3: Lima In metropolitan Lima, 7% of the population lives in a tugurio (inner-city barrio) and 47% in a squatter 28
URBAN TOOL Case Study 3: Lima AIM: Estimate daily aggregate food demand Use function 2 and change default dietary composition Input needed : - Lima inhabitants - Average household size - Per capita daily income [ by income group ] - Food market price for each item - Dietary composition 29
URBAN TOOL Case Study 3: Lima 2014 Figures by income group low middle high inhabitants 3888828 4861035.5 972207.1 average hh size 5.5 4.7 3.9 per capita daily income 2 10 30 [Source: The WorldBank, LSMS] 30
Case Study 3: Lima AIM: Estimate daily aggregate food demand 2014 Food market prices and dietary composition Item Prices $ US/kg Low % Middle % High % Cereals 2.6 4.9 4.6 3.2 Rice 1.03 15.9 13.0 10.4 Roots and Tubers 0.88 9.5 8.9 7.7 Meat 5.72 26.0 30.1 35.7 Fish 7.34 7.2 6.4 6.4 Dairy Products 1.22 13.1 15.0 15.8 Fruits & Vegetables 1.38 15.2 16.1 16.4 Other Food 1.80 8.3 5.6 4.4 31
URBAN TOOL Case Study 4: Dar-Es-Salaam Dar es Salaam is on track to become Africa's fastest-growing urban center. 32
URBAN TOOL Case Study 4: Dar-Es-Salaam • Dar es Salaam is on track to become Africa's fastest- growing urban center. • New York City added roughly 4 million residents in the past 100 years. Dar es Salaam will add 21 million over a similar span. 33
URBAN TOOL Case Study 4: Dar-Es-Salaam The city’s road network totals about 1,950 km in length, of which 1120 km (less than 60%) is paved, and is inadequate to satisfy its population density, spatial expansion and transportation needs. Dar es Salaam hosts about 52% of Tanzania’s vehicl es, and has a traffic density growth rate of over 6.3% per year. (JICA, 1995; Kanyama et al., 2004). 34
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