Of Cities and Slums. December 27, EPGE-FGV 2 FRB St. Louis & Wash U. 3 EPGE-FGV

Transcription

1 Of Cities and Slums P C F 1 A M -N 2 L T M P 3 December 27, EPGE-FGV 2 FRB St. Louis & Wash U. 3 EPGE-FGV

2 Disclaimer: Disclaimer. The views expressed here are those of the authors and do not necessarily reflect those of the Federal Reserve Bank of St. Louis or the Federal Reserve System.

3 Introduction Theme: Structural Transformation & Emergence of Slums Causes: Conditions on labor, housing and education markets. Consequences: Slums: Traps or Stepping Stones? This paper: Facts: Emergence of Slums in Brazil. Model: Structural Transformation & Urbanization. When do slums emerge? What are their micro and aggregate implications? Counterfactuals: Macro and Micro Impacts of Policies.

4 Brazilian Facts 1. Urbanization: Emergence of Slums. 2. Structural Transformation: Emergence of Low skill urban jobs. 3. Slums: Aggregate and Individual Persistence. 4. Slums: Better than Rural Areas for Income and Education. 5. Urban Areas: Integration of Labor vs. Schooling Markets. Integrated Labor Markets: Adults in Slums work outside slums. Segmented Schools: Slum children go to schools there. 6. Locations: Large impact of on schooling attainment. Cities Slums Rural areas. 7. City Housing Costs: Barriers to entry for low skill individuals.

5 Value Added per Worker Share of Population A Common View Structural Transformation: Korea 3.6 x Workers outside agriculture Pop. in urban locations Agric. productivity Non Agric. productivity Year

6 Value Added per Worker Share of Population Brazil: Structural Transformation and Urbanization Workers outside agriculture Pop. in urban locations Pop. in slums, Rio de Janeiro Agric. productivity Non Agric. productivity Year

7 Value Added per Worker Share of Population Is Brazil Special? No! Mexico and Other Latin American Countries Workers outside agriculture Pop. in urban locations Pop. in slums Agric. productivity Non Agric. productivity

8 1. Emergence of Slums Urban population living in slums (%) Cities Year Rio de Janeiro São Paulo Belo Horizonte Belém Salvador

9 2. Emergence of Low Skill Urban Jobs Labor distribution by sector and location (%) R U R U R U R U R U Agriculture Manufact Lo-S Serv Hi-S Serv Not def

10 2. Emergence of Low Skill Urban Jobs & Slums Labor distribution by sector and location (%) São Paulo Rio São Paulo Rio Slums City Slums City Slums City Slums City Agriculture Manufact Lo-S Serv Hi-S Serv Not def

11 3. Slums: Aggregate and Individual Persistence Rio de Janeiro: % Migrants/Total Population Slums City

12 4. Slums vs Rural Areas: Income Income Ratios by Education and Location, 2000 Brazil Rio de Janeiro Sao Paulo Education Urban/Rural City/Rural Slum/Rural City/Rural Slum/Rural Average to to to or

13 5. Integration of Labor vs. Schooling Markets. Integrated Labor Markets: Adults in Slums work outside slums. Job Location: Adults Living In Three Slums in Rio (%) Alemão Manquinhos Rocinha Inside slums In the close vicinity Outside slums

14 5. Integration of Labor vs. Schooling Markets. Segmented Schools: Slum children go to schools there School Location: Children Living In Three Slums in Rio (%) Alemão Manguinhos Rocinha Inside slums Outside, <1km away Outside, 1-3km away Outside, >3km Could not locate

15 6. Slums vs Rural Areas vs Cities: Education Children with Father with 0 yrs School

16 6. Slums vs Rural Areas vs Cities: Education Fathers, Schooling 1-4 years Fathers, Schooling 5-8 years Fathers, schooling 9-11 years Fathers, schooling 12+ years

17 7. Housing Costs: Barriers to Entry for the Low-Skilled Ratio of Monthly Rents: 1991 Brazil Rio de Janeiro Sao Paulo Bedrooms Urban/Rural City/Slum City/Rural City/Slum City/Rural

18 Environment Discrete time: t = 1, 2, 3,...OLG. Preferences: Intergenerational, Non-Homothetic: V t = u(c t ) + βe t [z t+1 ], u (c t ) = ( c i t c i ) α i, i {A,M } Heterogenenous Skills: µ t measure over skills z R + Constant population size 0 µ t(dz) = 1. Time Evolution: {µ t } t=1

19 Environment Three Locations: Rural, Sums, City l = R, F, C; U = C + F. Three Occupations: unskilled, qualified, adaptable, j = u, q, a: h u (z) = 1 for all z R + ; { h q 0 if z < zmin, (z) = 1 otherwise; h a (z) = z for all z R +. Two Sectors: i = A, M; Agricultural (Rural): Non-agricultural (Urban): Y A t = X A t L u t, (1) Y M t = X M t (L q t ) η (L a t ) 1 η.

20 Environment Housing Costs: Rural: None. Slum: Proportional to income: τy t (z). City: Fixed Cost, ξ units of non-agricutural goods. Locations: Given µ t ( ): µ t ( ) = µ l t( ), µ l t( ) measure in location l. l {R,F,C } Skill Formation: z Q ( ) Zt l, unbounded support, Zt l Population Dynamics: µ t+1 (B) = l {R,F,C } 0 Q [ z ρ µ l t(dz) µ l t(dz) ( ) B Zt l µ l t (dz). ] 1/ρ.

21 Equilibrium State variable for the country is S t = { µ t, Xt A, Xt M } ; Competitive Equilibrium: Ind.Optimization+Market Clearing Price System: { p M t, w u t, w q t, w a t } ; p A t = 1. Allocations: locations { χ R t (z), χ F t (z), χ C t (z) } occupations, {χ u t (z), χ q t (z), χ a t (z)}, consumptions, c t (z) = { c A t (z), c M t (z) }.

22 Equilibrium Competitive Firms: w u t = X A t. w q t = p M t ηx M t ( ) L a 1 η t. L q t w a t = p M t (1 η) X M t ( L q t L a t ) η Households: max i {A,M } ( c i t c i ) α i s.t. i p i tc i t y t : Demand: ct i = c i + α i [ yt pt i i ptc i i ] ; ( ) Flow Utility: v t (y t ) = θ p M ( yt i ptc i i ), where θ ( p M ) ( ) (α A ) α A 1 αa 1 αa. p M

23 Equilibrium Households: Occupation Choices: Rural: Trivial; Urban: { } (z) = max wt q χ (z zmin ), wt a z. y U t Location Choices: V t (z) = max { V R t (z), V F t (z), V C t (z) } : Vt R (z) = θ (p ) [ ] [ ] M yt R (z) i {A,M } ptc i i + βe t z t+1 Zt R, ( ) [ Vt F (z) = θ p M (1 τ) (y )] [ t R (z) i {A,M } ptc i i + βe t z t+1 Zt F Vt C (z) = θ (p ) [ ] [ ] M yt R (z) pt h i {A,M } ptc i i + βe t z t+1 Zt C. ],

24 Equilibrium Location Thresholds 0 < zt R zt F < : [ ] rural population, µ t 0, z R ( t, slum population, µ t z R t, zt F ] ( city population µ t z F t, ). Urban Occupation: z H t > z min, adaptable labor. Alternative Urban Configurations Urban Locations Urban Jobs Cities Only Cities and Slums High Skill Only z F = z R z min z F > z R > z min High & Low Skill z F = z R < z min z R < z min ; z F > z R ;

25 ear nings by occupations ear nings by occupations Equilibrium: Urban Occupations y Q t (z) y Q t (z) 5 y A t (z) 5 y A t (z) 4 3 y L t (z) 4 Unskilled Low skill Rur al jobs Ur ban jobs 3 Qualified Ur ban jobs High skill Ur ban jobs 2 Unskilled Rur al jobs Qualified Ur ban jobs High skill Ur ban jobs 2 1 R H z z t t skills z Urban High Skill Jobs Only 1 y L (z) t H R z z z t m in t skills z Low & High Skill Urban Jobs Discussion: Growth of Cities: (i) z R < z min vs (ii) z R > z min.

26 Equilibrium: Myopic or Static Existence and Uniqueness. No Slums: z R = X t A + ξp M ( z R ) w a (z R < z min ) Only low-skilled: X A t (1 τ) w a (z R ) < Slums: [ ξp M ( z R ) /τ ] w a (z R ) < z min. Some qualified: ξp M ( z R ) = τw q ( z R ), with z R < z F Some high skilled: τw q ( z R ) < ξp M ( z R ). [ z min, z H ].

27 Equilibrium: Dynamic Assumption E: Empty Regions: if household z moves to an empty region l (i.e. µ l t (B) = 0 for all Borel B) then z Q ( z). Proposition: Under assumption E: (a) Equilibria can only be of the form z R t z F t <. (b) Existence and Uniqueness. Compensating Differentials: Rural-Slum: ( ) e z R ; z R = X t A (1 τ) β [ { Et zt+1 Zt F } Et z t+1 Zt R (1 τ) θ [p M (z R )] ], e ( z F ; z R ) = City-Slum: ( ) ξ ( ) p M z R β [ { E t zt+1 Z C } { t Et zt+1 Zt F τ τθ [p M (z R )] }].

28 Calibration Parameter Value Source/Target Parameter Value Source/Target β Literature X A Normalized α 0.01 Literature X A Data c A Agric.Labor X M Production share z min 11 Baseline X M Production share η 0.6 Income Shares z Γ ( Z l 0, k) τ Data k 2.4 School Distr τ Data Z R " ξ % city pop. Z F 0 1 " ξ % city pop Z C 0 2 " ρ 1 Baseline

29 Calibration Parameter Value Source/Target Parameter Value Source/Target β Literature X A Normalized α 0.01 Literature X A Data c A Agric.Labor X M Production share z min 11 Baseline X M Production share η 0.6 Income Shares z Γ ( Z l 0, k) τ Data k 2.4 School Distr τ Data Z R " ξ % city pop. Z F 0 1 " ξ % city pop Z C 0 2 " ρ 1 Baseline

30 Calibration Baseline Model and Data Brazil Variable Data Model Data Model Slum Pop % 10.96% 18.70% 18.84% City Pop % 56.58% 66.30% 63.63% L A 38.15% 32.46% 16.70% 17.53% Y A /Y 6.85% 4.67% 5.72% 2.29% Avg. years of school., Rural Avg. years of school., Slums NA Avg. years of school., City NA

31 F r equency F r equency Calibration Rural area Favela Rural area Favela City City Years of Schooling Years of Schooling

32 Fr equency Year s of Schooling Census Dat a (Avg. years of school. = 3.13) M odel (Avg. years of school. = 2.53) Fr equency Year s of Schooling Census Dat a (Avg. years of school. = 5.51) M odel (Avg. years of school. = 5.78) Fr equency Year s of Schooling Census Dat a (Avg. years of school. = 9.48) M odel (Avg. years of school. = 9.92) Calibration: Human Capital Distributions, Rural Slum City

33 Counterfactual: Cracking Down Slums Cracking Down Slums Variable τ C 1 = 0.5 τc 1 = 1 τc 2 = 0.5 τc 2 = Slum Pop City Pop L A Y A /Y E ( z Z R ) E ( z Z F ) E ( z Z C )

34 Counterfactual: Urban Housing Changes in Housing Costs in the City Variable ξ1 C = 0.15 ξc 2 = 0.3 ξc 2 = Slum Pop City Pop L A Y A /Y E ( z Z R ) E ( z Z F ) E ( z Z C )

35 Conclusions Examine the emergence of slums in Brazil. A simple model to replicate facts. Ensuing work: Richer quantitative models. Policies.