Slum Upgrading and Long-run Urban Development: Evidence...

Maisy Wong (Wharton) Slum upgrading and long-run urban development

Slum Upgrading and Long-run Urban Development:

Evidence from Indonesia

Nina Harari Maisy WongWharton Real Estate Wharton Real Estate and NBER

Slum upgrading and urbanization - Lens to understand how cities grow out of informality

➢ Massive urbanization in developing countries

o Cities will add 2.5 billion people by 2050 (UN)

o 1 billion people live in slums (UN)

➢ How to (re)configure cities to facilitate urbanization and promote growth?

o Needs: Housing, infrastructure, services, taxes …

o Challenges:

• Limited resources (esp. land is scarce)

• Weak property rights (titling + institutions)

• Rapid urbanization + coordination failures ➔ congestion, lack of connectivity, riots…

Slum upgrading and urbanization - Lens to understand how cities grow out of informality

➢ Massive urbanization in developing countries developing

o Concerns: inadequate shelter, upward mobility, aggregate growth, mi

o allocation…

➢ How to (re)configure cities to facilitate urbanization and promote growth?

Weak property rights, externalities … + political risk

➢ Policy options:

o Public housing, quotas for cities (hukou), village grants…

o Today: Slum upgrading on-site

• Basic public goods

• Eg. China, India, Indonesia, Bangladesh, Brazil, Kenya, Tanzania…

➢ Pros: Place-based upgrades improve livelihoods of (many) residents, stimulate private investments

➢ Cons: Place-based distortions encourage staying and crowding, persistence of slums

➢ Duranton and Venables (2018):

o Slum re-development costs: land assembly + relocating people

o Distortions ➔ increase density ➔ increase re-development costs

➢ Under weak property rights, place-based distortions can be large enough to reverse direct effects of upgrades on land values

Slum upgrading as place-based investment- Kline and Moretti (2014) under weak property rights (Field, 2007)

◉ Data o Lack of coverage of informal areas (not in administrative data)

◉ Program selection biaso Slum upgrading programs target low quality places

◉ Spatial spillovers “contaminate” boundary discontinuity designs

◉ Long-run GE effects, endogenous sorting …

Empirical challenges to study long-run impacts of slum upgrading

o 5 million beneficiaries, 25% of Jakarta’s area, 1969-1984

o Basic upgrades + 15-year verbal non-eviction guarantee

• Eg. Roads, drains, sanitation, health centers, schools

➢ Setting: Jakarta today, mega-city growing out of informality

➢ Data:

o Policy maps, historical kampungs

o Assessed land values in 2015

o Photos from Google + slums: building heights, informality indices

o Also: 1 million land parcels, 10 million people from 2010 Census

➢ Research design: KIP vs. non-KIP

o Historical kampungs + neighborhood FE’s

o Boundary analysis (200m), assumes smooth market potential today

o Staggered rollout to assess program selection bias

Jakarta’s Kampung Improvement Program (KIP)- World’s largest scale slum upgrading program

KIP today: lower values & heights, more informal

➢ Relative to non-KIP historical kampungs, KIP areas today:

o 12% lower land values $11 billion in aggregate

o 50% fewer tall buildings (>3 fl.), 88% of loss in land values

o More likely informal, more land fragmentation and pop. density (9 more parcels / 11 more households)

o Address threats to ID: selection bias, spillovers, persistent slums

➢ Place-based investments ➔ sunk costs ➔ dynamic inefficiency (Krugman 1991)

o Around 2/3 of KIP areas are central by now

Policy implications

➢ Policy lessons: where/when/how to manage urban transformation

o Slum upgrading suitable for cities at early stages

o Opportunity costs of land use manifest once city faces rapid urbanization and land becomes scarce

o Bounding exercise rationalizes why formalization process is slow

- Relocation costs are high (many people, periphery is far out)

➢ Policy lessons: where/when/how to manage urban transformation

➢ Welfare impact?

• Lower land values in KIP do not imply KIP reduced resident welfare

- Their well-being likely increased: KIP allows the poor to stay in the center for longer

• Welfare calculation needs to consider where people would move absent place-based distortions from KIP

• Land values capture differences in localized externalities between KIP and non-KIP, but differences out more aggregate externalities

- E.g.: inefficient spatial distribution of economic activity and foregone agglomeration, smaller aggregate tax base (lower property tax revenues)…

Policy implications

Related literature

➢ Urban development with informality:

o Bleakley and Lin (2012), Libecap and Lueck (2013), Brooks and Lutz (2016), Hornbeck and Keniston (2017)

o Slums and opportunity cost of land use (Henderson et al. ,2017; Gechterand Tsivanidis, 2018)

➢ Urban renewal and place-based policies:

o Rossi-Hansberg et al. (2010), Kline and Moretti (2014), McIntosh et al. (2018)

o Sites and services in Tanzania (Michaels et al., 2019)

➢ Slums and housing: Field (2007), Feler and Henderson (2011), Marx et

al. (2013, 2016), Brueckner and Lall (2015), Galiani et al. (2015), Barnhardt et al. (2016)

➢ Our contribution:

o Long-run impacts of slum upgrading in a city growing out of informality

o Trade-offs to ex ante planning (Tanzania) vs. ex post upgrading (Jakarta)

o Shed light on barriers to urban development under weak land institutions

Outline

➢ Introduction

➢ Background

➢ Data

➢ Conceptual framework

➢ Empirical strategy and results

➢ Discussion and conclusions

Jakarta, Indonesia- A mega-city growing out of informality

➢ Indonesia:

• 260 million inhabitants

• GDP pc $3,800, 54% urban

➢ Jakarta: 10m people in city, 30m in metropolitan area

• GDP pc $14,000

• Growing city, severe housing shortage

• Property rights: formal and informal (customary)

Jakarta’s urban development history- Expansion of built-up area since independence from Dutch (1945)

1970s 1980s 1990s 2000s Today- Suharto - Oil prices fall - Asian Financial

- Oil revenue surplus (1986) Crisis (1997)

KIP in Jakarta

(1969 – 1984)KIP in other cities

(1990s)

KotaKu

154 cities,

9.7m people

The KIP program in Jakarta

➢ 3 waves: I (1969-1974), II (1974-1979), III (1979-1984)

➢ Coverage: 10,000 ha (25% of the city), 5 million people

➢ Cost: $450-$550 m (2015 USD)

➢ Goal: improve neighborhood conditions

o Basic physical upgrades (estimated useful life ~ 15 years)

o + verbal non-eviction guarantee for 15 years

➢ KIP components:

o Road paving and wideningo Drainage canals, sanitation (flooding concerns)o Health clinics and schools

➢ Selection criteria: scoring rule for public goods (roads, sanitation), neighborhood conditions, age, population density, income;

+ even distribution across 5 Jakarta districts

Kampungs in Jakarta, before and after KIP

1995 World Bank report on KIP- Interviews and surveys (2 KIP kampungs in Jakarta + other cities)

◉ Neighborhood quality improved after KIP:

o Evidence of crowd-in of private investments

o Convergence: “non-KIP kampungs [had] caught up”

◉ Strengthened perceptions of tenure security:

o 47% of claimed ownership rights in KIP (32% in non-KIP)

o Physical upgrades “were crucial to establishing the permanence of the kampungs''.

◉ Resident composition appeared stable

o “KIP did not encourage an influx of higher-income groups into the kampungs, as had originally been feared”

o Average length of stay for KIP residents was 27 years

Outline

➢ Introduction

➢ Background

➢ Data

Core datasets:1. Maps: KIP, historical slums2. 2015 assessed land values3. Photos: heights, informality

Auxiliary:4. 2011 land parcels5. 2010 Population Census 6. Land use, amenities …7. Geographic, distance controls

Policy maps: KIP coverage

Jakarta Department of Housing , 2011

Policy maps: KIP boundaries and assets

Historical maps: Kampungs in 1959

Assembled data: treated and control slums

KIP areas

Historical slums(from 1937, 1959 maps)

Outline

➢ Introduction

➢ Background

➢ Data

Core datasets:1. Maps: KIP, historical slums

2. 2015 assessed land values3. Photos: heights, informality

Maisy Wong (Wharton) Slum upgrading and long-run urban development 23

➢ Market-based assessment:

• Goal: Property taxes

• Start from broker data / listings, other sources

• Adjustments (hedonic, field visits)

• Subtract cost of structure based on engineering cost

Assessed sub-blocks

Assessed land values, 2015N = 19,862 sub-blocks

- Correlation with property transaction prices

Validation check:

compare with 4000 manually geo-referenced property transaction prices from Brickz website

Assessed land values, 2015

Outline

➢ Introduction

➢ Background

➢ Data

Core datasets:1. Maps: KIP, historical slums

2. 2015 assessed land values3. Photos: heights, informality

N= 7,104 pixels

Photo survey for building height + informality

➢ Sampled 7,104 pixels (75m x 75m grid cells)

o From full grid of Jakarta: 89,463 pixels

➢ Google StreetView + field photos ➔ 28,416 photos

o For each pixel: take 4 photos (4 angles) from centroid

o Photos from the field to overcome coverage bias in Google:

• 19% photos: for slums

• 5% photos: private gated developments

Goal: real quantities/quality measure for key estimation samples

(i) Historical kampung sample(ii) Boundary sample*Stratified by distance terciles

N = 5280 pixels in historical kampungs- Each pixel: 75m x 75m

Repeat the same for boundary sample➔ Full photo sample: 7,104 pixels

N= 7,104 pixels

Photo survey for building height + informality

➢ Sampled 7,104 pixels (75m x 75m grid cells)

o From full grid of Jakarta: 89,463 pixels

➢ Google StreetView + field photos ➔ 28,416 photos

o For each pixel: take 4 photos (4 angles) from centroid

o Photos from the field to overcome coverage bias in Google:

• 19% photos: for slums

• 5% photos: private gated developments

➢ Outcomes:

o 1(tallest building in pixel > 3 floors)

• Measure number of floors (skyscrapers: call, elevators)

o Rank-based informality index

• Subjective ranking by 2 RA’s ➔ averaged (robust to RA FE’s)

o Attribute-based informality index

• Vehicular access

• Structures

• Appearance

Informality index- Ranking-based

0 = very formal 1

4 = very informal2 3

Informality index- For robustness: attribute-based

Manually code presence of:

➢ Access: • Paved road• Unpaved road• Damaged road pavement• Road accessible by car• Garden

➢ Neighborhood quality:• Exposed wires• Drainage canals• Trash

➢ Quality of structures:• Permanent wall• Non-permanent wall• Unfinished wall• Unfinished buildings• Damaged wall• Permanent fence• Rust

➢ Code attributes: 0=good, 1= bad

➢ Average of z-scores

Auxiliary data (later)

➢ Full Population Census data, 2010:

• 10 million people in Jakarta

• Smallest geo-referenced location unit: hamlet

• Population counts/density, education, some info on migration

➢ Cadastral maps (2011): 1 million land parcels

➢ Administrative land use map, 2015: retail, office

➢ Openstreetmap, 2017: schools, bus stops, hospitals, police stations

➢ Slum household survey, 2015 (used for policy calculations)

➢ Other: topography, flood-proneneness, zoning, distance to landmarks…

Summary statistics

KIP vs. non-KIP differences cannot explain main results

Outline

➢ Introduction

➢ Background

➢ Data

➢ 2 neighborhoods, (K)ampung and (F)ormal, differ by:

• Prices (p)

• Building heights (ℎ)

• “Formality”(ℎ > തℎ)

• Amenities (A): public goods, etc.

Building blocks:

1. Housing supply: fixed costs to re-develop slums

>Ownership disputes, holdout problems, land assembly costs, relocation costs

2. Housing demand

3. Spatial equilibrium ➔ Kampung vs. Formal neighborhoods

4. Comparing KIP vs. non-KIP

• KIP = physical upgrades + non-eviction guarantee

Rosen-Roback spatial equilibrium model+ Fixed cost to re-develop slums

Case study: Setia Budi- Compare historical slums with and without KIP- Short run: KIP improves quality of life, land values

Massive urbanization ➔ aggregate demand shock- 2/3 of KIP areas are central by now

Over time: KIP residents stay, sub-divide land ➔ more dense and fragmented ➔ higher re-location and land assembly costs

blue = very formalred = very informal

Long-run:Non-KIP neighborhoods formalize, KIP stays informal

Long-run: control vs. KIP

log 𝑝ഥ𝑃′(𝒄)

log ℎ

log 𝑝ത𝑃(𝒄)

log ℎ

KIPControl

Caveats to model

➢ Perfect mobility and homogeneous agents

• Absent this, no perfect equalization of utility but same impacts on prices and heights

➢ No localized externalities

• …but limited evidence of those in the data

➢ No city-level externalities

• Foregone amenities from formalization also include agglomeration benefits

➢ Future research: micro-found formalization costs

• Population density, land assembly costs

Outline

➢ Introduction

➢ Background

➢ Data

Yij = a+ b 1(KIPij) + 𝜉𝑗 + εij

➢ i = sub-block (land values) or pixel (for heights)

➢ j = geographic unit

➢ 𝜉𝑗 = index of unobs. market potential for neighborhood j

➢ Controls:

➢ Granular fixed effects: 200m boundary pairs, locality, hamlets

➢ Xij = topographic and distance controls

➢ Estimation samples:

o Historical kampungs: KIP vs. non-KIP

o Boundary analysis: 200m

o Other samples: full sample (heterogeneity), pop. census

Empirical strategy- Y: land values, building heights

Results: a roadmap

1. Main results

a. Land values Log (assessed land values, Rupiahs/sqm)

b. Building heights 1 (tallest building in pixel > 3 floors)

2. Threats to IDa. Program selection bias Staggered roll-out of KIP

b. Spillovers, admin. boundaries

c. Persistence of slums

3. Barriers to formalization Land fragmentation, population density

4. Other potential channels

a. Amenities

b. Educational attainment

5. Other robustness checks

a. Endogenous sorting

b. Displacement and full sample

c. Standard errors

d. Selection: land values, heights

Land values: -12% in KIP

Treatment effect x avg. land value in control x total KIP area =

-12% x 89 $ per sq foot x 10,000 hectares = $11b

- 196 locality FE’s, 124 boundary FE’s

- Boundary discontinuity design

Empirical strategy

KIP: half as many tall buildings (1 if > 3 floors)-12pp (relative to control group mean of 0.24)

KIP areas have half as many tall buildings

Nr of floors

+ bunching at 2 floors

Height results validate land values result- Data issues with slums: land values not accurate, selective coverage

Translating height effects to land values

1. Hedonic regression (land values data from non-KIP areas)

2. Impute loss in values (heights data from representative sample)

➔ Missing high-rises in KIP explain 88% of difference in land values

Nr of floors

Results: a roadmap

1. Main results

a. Land values -12% lower in KIP

b. Building heights 50% fewer tall buildings, 88% of values effect

2. Threats to IDa. Program selection bias Staggered roll-out of KIP

3. Barriers to formalization

a. Amenities

c. Standard errors

KIP Non-KIP

Δ𝑌

𝑌𝑖𝑗 = 𝛼 + 𝛽𝐾𝐼𝑃𝑖𝑗 + 𝜉𝑗 + 𝜀𝑖𝑗

Δ𝑌 = 𝛽 + 𝐸(𝜉𝑗 𝐾𝐼𝑃) − 𝐸(𝜉𝑗 𝑛𝑜𝑛𝐾𝐼𝑃)

Selection bias

Using staggered roll-out to assess selection bias

Scoring rule implies: DI < DII < DIII

KIP I KIP II KIP III Non-KIP

D𝐼𝐼

D𝐼𝐼𝐼

Monotonic pattern consistent with scoring rule

Pattern disappears: historical kampungs + locality FE’s

KIP wavesRed (wave I), blue (wave II), green (wave III)

Patterns robust to controlling for het. treatment by waves

No monotonic pattern for heights- Full photo sample: already restricted to historical kampungs/BDD

Results: a roadmap

1. Main results

a. Land values 12% lower in KIP

2. Threats to IDa. Program selection bias No selection pattern in historical sample + granular FE’s

3. Barriers to formalization Land fragmentation, population density

a. Amenities

c. Standard errors

Spatial spillovers from KIP onto controls (Turner et al., 2014)

Each point corresponds to a coefficient and 95 percent confidence interval for coefficients

on distance bins, historical sample with locality fixed effects.

- Decay away from KIP boundaries. Not large relative to 12% effect

KIP boundaries same as administrative boundaries? No

KIP effect vs. Persistent of slum effectFalsification test: placebo non-KIP, historical slum borders

- Is slum/non-Slum effect large enough to explain away KIP/non-KIP effect?

- Includes 41 (45) boundary pair FE’s - Consistent w/ common demand shocks associated with massive urbanization

Results: a roadmap

1. Main results

3. Barriers to formalization a. KIP is more informal today (photos survey)

b. Greater land fragmentation, popDensity

c. Het. analysis: also present in periphery

d. Migration patterns point to KIP residents staying

e. Direct congestion effect not large enough

a. Amenities, education

- Varying degrees of informality

Very formal Very informal

Informality: KIP areas are more informal

Informality: KIP areas more likely to be kampungs

KIP areas + 15 pp. (+37%) more likely to have index >1

- 1(Kampung)=1 informality index>1

Informality: KIP areas more likely to be kampungs

KIP areas + 15 pp. (+37%) more likely to have index >1

- 1(Kampung)=1 informality index>1

Effect of KIP domains on the attribute-based index

Results: a roadmap

1. Main results

b. Greater parcel and population density

Cadastral maps of parcels, 2011- Parcel count as proxy of land assembly costs

Effect of KIP on parcel and population density

➢ Per pixel: 9 more parcels, 46 more people (11 households)

Fragmented places have lower land values

- 9 parcels ➔ 9% lower land values (75% of 12% effect)

- 38% of height effect

Heterogeneous analysis for parcel and pop density

➢ Difference because KIP caused crowding or non-KIP formalized?➢ Restrict to places that are informal or periphery

KIP grid roads

KIP paved roads

KIP footpaths

Presence of grid roads reverse direct KIP effect- Regularity and coordination of plots (Libecap and Lueck, 2011)

Results: a roadmap

1. Main results

- Where do the extra people in KIP come from? - Not detected in proxies of fertility nor mortality

KIP and population density

-No differential in-migration into KIP (if anything, more stayers)-Migrant defined by district of birth, or origin district 5-years ago

- Where do the extra people in KIP come from?- Not explained by migrants

KIP and population density

Results: a roadmap

1. Main results

Is congestion reducing land values?

- As we move away from high-density non-KIP hamlets, cannot detect large enough decay in land values to explain -12% effect

Effect on land values of being at different distance bins to 45 non-KIP hamlets with population density above median

Results: a roadmap

1. Main results

2. Threats to ID

3. Barriers to formalization KIP areas more informal, more parcel and pop. density

a. Amenities i. Initial KIP amenitiesii. Current public amenitiesiii. Current private amenities (retail, office density)

c. Standard errors

- Heterogeneous treatment effects by KIP component

➢ No differential effects

KIP-provided amenities: likely depreciated

➢ Intensity of KIP investments within 500 m of each obs.

- From Openstreetmap: public schools

Current public amenities: likely converged

Current “private” amenities: higher in non-KIP

Results: a roadmap

1. Main results

2. Threats to ID

a. Amenities > Convergence of access to public amenities> Non-KIP areas more formal (retail, office density)

b. Educational attainment KIP residents slightly more schooling (biased against)

c. Standard errors

KIP residents are slightly more educated

- Universe of current residents age >25, matched to hamlets

- Biased against lower land values

Stayers in KIP have slightly more schooling

- Restrict to those born in the district

KIP has fewer migrants. Migrants seem more educated

Results: a roadmap

1. Main results

2. Threats to ID

a. Amenities >Convergence of access to public amenities>Non-KIP areas more formal (retail, office density)

b. Educational attainment >KIP residents slightly more schooling (biased against)

c. Standard errors

Results: a roadmap

1. Main results

2. Threats to ID

a. Amenities Convergence of access to public amenitiesNon-KIP areas more formal (retail, office density)

c. Standard errors

Sorting: migration rates, migrants’ education

- Migrants into KIP neighborhoods are slightly more educated

Consistent with high share of long-term stayers in 1995 WB report and own 2016 hh survey (>30 years)

Results: a roadmap

1. Main results

2. Threats to ID

b. Displacement and full sample Central KIP worse than middle non-KIP

c. Standard errors

Heterogeneous effects by distance to the city center- Central KIP worse than middle non-KIP

2/3 of KIP areas are central by now

Robustness to full sample analysis- Effects do not cancel out in full sample

Results: a roadmap

1. Main results

2. Threats to ID

c. Standard errors Robust to Conley se’s, coarser or finer clustering

d. Selection: land values, heightse. Drop hamlets with Dutch settlements

Outline

➢ Introduction

➢ Background

➢ Data

o Bounding exercise rationalizes why formalization of slums is slow o Surplus per resident not enough to compensate for relocation

Policy implications

➢ Benefits for slum residentso 1 million households ➔ Surplus per resident ~ $11,000, 3 times hh income

➢ Costs to relocate from central to periphery

o Difference in access to amenities and jobs

o At least difference in rental costs (US$2,160, 2015 hh survey)

o If residents expect to live more than 6 years

➔ Relocation costs > surplus per resident

➢ Rationalizes why formalization of slums is so slow

Bounding exercise: $11b agg. opportunity cost of land

Conclusion-Slum upgrading in city growing out of informality

➢ Novel causal estimates of the long-term impacts of a large-scale slum upgrading program using granular data

o 12% lower land values, half as many tall buildings

o Delayed formalization in KIP, greater parcel and population density

➢ Policy lessons:

o How to foster urban transformation w/ weak property rights?

o Dynamic inefficiency from place-based investments

• SR benefits, LR costs from place-based distortions

o Where/when/how to implement place-based investments?

➢ Future work:

o Slum upgrading in other citieso Place-based targeting with slumso Property rights + economic livelihoods of slum dwellerso Inter-generational mobility and slums

Thank you!

Slum Upgrading and Long-run Urban Development: Evidence...

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