Slum Upgrading and Long-Run Urban Development: Evidence from Indonesia

This paper estimates the long-term causal effects of the Kampung Improvement Program (KIP), one of the world’s largest slum upgrading programs, on urban development in Jakarta, Indonesia. KIP ran from 1969 to 1984 across three staggered waves (Pelita I-III), covered 110 square kilometers (25% of Jakarta’s area), and served approximately 5 million residents at a total cost of roughly $500 million (2015 USD). The program provided basic physical upgrades — paved roads and footpaths, sanitation and drainage, and community buildings such as schools and health clinics — along with a verbal non-eviction guarantee for 15 years. Residents were not relocated.

The central research question is whether preserving slums through upgrading entails long-run dynamic inefficiency: as Jakarta formalizes, do KIP areas lag behind non-KIP areas in ways that generate opportunity costs from land misallocation?

The authors assemble high-resolution data on KIP policy boundaries, current assessed land values (nearly 20,000 sub-blocks), building heights from a novel photographic survey of 19,518 pixels stratified across Jakarta, and multiple novel measures of informality — a rank-based photographic index (0 to 4), an attributes-based index across fifteen binary characteristics, and administrative data on unregistered land-parcel titles. They also use digitized historical maps from 1937 and 1959 to identify pre-KIP kampung boundaries.

Two empirical strategies address program selection bias (KIP planners prioritized the worst-condition kampungs first). The first restricts the sample to historical kampungs that existed before KIP and includes locality fixed effects, comparing treated kampungs against nearby untreated ones within the same neighborhood. The second is a boundary discontinuity design (BDD) comparing observations within 200 meters of KIP boundaries. Both strategies include eighteen predetermined controls for historical landmarks, infrastructure, and topography including flood proneness.

Average effects (robust across both strategies): KIP areas today have land values approximately 14-17 log points (roughly 15%) lower than observably equivalent non-KIP areas, and are about 8-12 percentage points less likely to contain buildings taller than three floors — half the control-group mean of 0.24. KIP areas are more informal across all three informality metrics: the rank-based index is higher by 0.29 standard deviations, the attributes-based index by 0.05 SD units, and the share of unregistered parcels is 3 percentage points higher. Building heights corroborate the land-value finding: imputing the hedonic value of missing tall buildings in KIP accounts for approximately 90% of the aggregate land-value impact ($2.2 billion of $2.4 billion).

Heterogeneity by real estate potential is a central finding. The authors construct a predicted land index for 2,058 hamlets in Jakarta using non-KIP land values. In the lowest quintile (Q5), KIP areas show a positive and statistically significant effect of +10 log points on land values, consistent with direct capitalization of the upgrades. This effect reverses in higher-potential areas: the estimate reaches -28 log points in Q2 and -30 log points in Q1, as non-KIP neighborhoods formalize while KIP areas lag.

Surplus calculations integrating land values, building heights, horizontal built-up coverage (35% for KIP vs. 18% for non-KIP), and demand and supply elasticities reveal that 90% of total surplus losses are concentrated in the top two quintiles (Q1 and Q2), which comprise 47% of KIP’s coverage area. In Q1, KIP surplus is lower by $2,369 per square meter; in Q2, the gap is $1,044 per square meter. In the bottom two quintiles, KIP delivers greater surplus (up to +$347 per square meter in Q5), covering an estimated 3 million residents across 57 square kilometers.

Mechanisms consistent with delayed formalization include significantly higher population density in KIP areas (+33 log points, or 39%) and greater land fragmentation (+9 parcels per pixel relative to a non-KIP mean of 19), both of which raise relocation and land assembly costs. The original KIP investments show no differential effect by type or intensity after four decades, consistent with their 15-year projected useful life. Endogenous sorting is ruled out as a confounder: if anything, educational attainment is slightly higher in KIP areas.

Q: What is the Kampung Improvement Program (KIP) and what did it provide? A: KIP was a slum upgrading program implemented in Jakarta, Indonesia from 1969 to 1984 across three five-year plan waves (Pelita I, II, III). It covered 110 square kilometers and 5 million residents at a total cost of approximately $500 million (2015 USD). The program provided three categories of basic physical improvements — vehicular and pedestrian road access, sanitation and drainage infrastructure, and community buildings (schools, health clinics) — along with a verbal non-eviction guarantee for 15 years. Crucially, upgrades were designed to be basic, with a planned useful life of only 15 years, to avoid attracting higher-income groups.

Q: What is the core research question and theoretical concern motivating the paper? A: The paper asks whether slum upgrading programs, while immediately beneficial to residents, entail dynamic inefficiency by delaying formalization as cities develop. The concern is that preserving slums through upgrades and non-eviction guarantees can create opportunity costs from land misallocation when surrounding areas formalize and redevelop into higher-value formal structures. This is framed as a trade-off between the direct welfare benefits of upgrading (affordable in-situ housing for millions) and the long-run costs to urban land productivity.

Q: How does the paper address the selection bias problem — KIP targeted the worst-condition kampungs first? A: Two complementary strategies are used. First, the historical kampung specification restricts the sample to areas that were kampungs before KIP (from 1937 and 1959 maps) and includes locality fixed effects, so treated and control units are compared within the same neighborhood and share the same real estate market by assumption. Second, a boundary discontinuity design (BDD) compares observations within 200 meters of KIP boundaries with boundary fixed effects and quadratic distance controls. A falsification test using sequential KIP waves confirms the approach: the raw data shows a monotonic pattern (Wave I worst: -0.40 log points, Wave II: -0.29, Wave III: -0.17) consistent with selection bias, but this pattern disappears in the historical kampung specification (Wave I: -0.13, Wave II: -0.11, Wave III: -0.14), supporting the identification assumption.

Q: What are the average effects of KIP on land values and building heights? A: In the historical kampung specification, KIP areas have land values 14 log points (approximately 15%) lower than non-KIP historical kampungs within the same locality. The BDD estimate is similar at -17 log points. For building heights, KIP areas are 12 percentage points less likely to contain a building taller than three floors in the historical kampung sample (8 percentage points in the BDD), relative to a non-KIP control mean of 0.24 — meaning KIP areas are roughly half as likely to have tall buildings. The average effect on floors is -1.6 floors, relative to a control mean of 5 floors.

Q: How do the authors validate that land value estimates are not distorted by measurement error in informal areas? A: The authors impute the hedonic value of missing tall buildings in KIP using a hedonic regression estimated solely on non-KIP historical kampungs. KIP areas have 145 fewer buildings with more than ten floors; combined with a 57% price premium for tall buildings (relative to a base price of 13.4 million Rupiahs per square meter), the implied land value loss from missing buildings above ten floors is approximately $1.3 billion, and from buildings between four and ten floors is $0.9 billion, for a total imputed effect of $2.2 billion. This accounts for approximately 90% of the aggregate land value impact from the historical kampung specification ($2.4 billion), assuaging concerns that lower measured land values in KIP reflect data quality differences rather than true price gaps.

Q: How does the KIP effect vary across the distribution of real estate potential? A: The authors construct a predicted land index for 2,058 Jakarta hamlets by regressing non-KIP log land values on hamlet fixed effects, then rank hamlets into quintiles. In Q5 (lowest predicted land values, least likely to formalize), KIP areas show a statistically significant positive effect of +10 log points on land values, consistent with direct capitalization of the upgrades. Moving to higher-potential areas, the effect attenuates and reverses: it is -28 log points in Q2 and -30 log points in Q1, where non-KIP areas have formalized. This cross-sectional pattern traces out the dynamic inefficiency predicted by theory.

Q: What informality measures does the paper construct and what do they show? A: The paper constructs three complementary informality metrics. First, a rank-based photographic index (0 = very formal, 4 = very informal) coded by two trained Jakarta-based research assistants from approximately 28,000 hand-coded photographs, with inter-rater correlation of 0.78. Second, an attributes-based index averaging fifteen binary characteristics across vehicular access, neighborhood appearance, and structural permanence, standardized to a z-score. Third, the area share of unregistered land parcels from the Indonesian National Land Agency’s 2020 digital land maps. KIP areas score higher on all three: the rank-based index is higher by 0.29 SD units, the attributes-based index by 0.05 SD units, and the unregistered parcel share is higher by 3 percentage points.

Q: What mechanisms explain why KIP areas remain informal and have lower land values? A: The paper identifies three mutually reinforcing mechanisms. First, KIP areas have significantly higher population density (+33 log points or 39% in the historical kampung sample, equivalent to 51 more people per pixel), which raises relocation costs. Second, KIP areas have greater land fragmentation, with 9 more parcels per pixel relative to a non-KIP mean of 19, exacerbating holdout problems during land assembly; a back-of-the-envelope calculation attributes a 9% land value effect (60% of the total 15% effect) to this channel. Third, the verbal non-eviction guarantees and improved conditions likely strengthened residents’ tenure perceptions and encouraged them to stay, leading to sub-division of parcels over time. The original KIP investments show no differential effect by type after four decades, consistent with their designed 15-year useful life, and KIP areas have similar access to public amenities today.

Q: How does the paper calculate surplus and what are the results? A: The surplus framework compares KIP (informal, tends to stay informal) against non-KIP counterfactuals (more likely formal) on three dimensions: non-KIP areas have (i) higher land values, (ii) taller structures, but (iii) lower horizontal built-up coverage than slums (18% vs. 35% for KIP). Consumer surplus uses a linear demand approximation with elasticity of 0.2 for non-KIP and 0.16 for KIP (backed out from differences in housing budget shares). Producer surplus integrates a Cobb-Douglas supply curve with elasticities of 1.4 (formal) and 1.3 (informal). In Q1, KIP property value is $1,873 per square meter vs. $3,098 for non-KIP, a difference of $1,225 in value terms and $2,369 in surplus terms. The surplus gap falls to $1,044 in Q2, and halves again in Q3, becoming positive (+$347 per square meter) in Q5. Ninety percent of total surplus losses are concentrated in Q1 and Q2, which cover 47% of KIP’s area.

Q: What do the case studies of kampung clearances illustrate? A: Three Jakarta kampungs cleared in 2015-2016 are examined. Kampung Bukit Duri (Q5, lowest real estate potential) shows a surplus difference of +$572 per square meter in favor of KIP — meaning clearance there is socially inefficient. Kali Pessangrahan (Q3) shows a surplus difference of -$307. Kalijodo (Q2) shows -$910 per square meter, suggesting sizable societal gains from formalization. However, even in Kalijodo, residents were relocated 24 km away to Marunda (a Q5 area), where consumer surplus is only 46% of Kalijodo’s — illustrating that societal gains from formalization do not automatically translate into Pareto improvements for evicted residents.

Q: What robustness checks address alternative explanations? A: The paper runs several tests. A placebo BDD using 45 non-KIP historical kampung boundaries finds no significant discontinuity, ruling out the hypothesis that slums generically have persistently lower land values. Bandwidth robustness shows consistent BDD estimates from 150 to 500 meters. Tests for spatial spillovers find no spatial decay pattern in land values near KIP boundaries, consistent with the prevalence of gated communities in formal Jakarta minimizing neighborhood contamination. Endogenous sorting is examined using 2010 Census data on 10 million individuals: educational attainment is slightly higher in KIP, and in-migration is slightly lower (1-2 percentage points below mean) with migrants having slightly more years of schooling — both inconsistent with an explanation based on low-skill sorting into KIP. Direct congestion effects from population density are also ruled out by estimating spatial decay around 45 dense non-KIP informal hamlets, finding no decay large enough to explain the land-value effects.

Q: What are the policy implications for slum upgrading in other developing countries? A: The paper’s framework suggests that slum upgrading’s cost-benefit balance depends critically on where the upgraded area sits in the real estate potential distribution. In low-potential areas (bottom quintiles of the land index), upgrading delivers net surplus even decades later and implicitly provides affordable housing at scale to millions of residents. In high-potential areas (top quintiles), the opportunity costs from delayed formalization can be large — up to $2,369 per square meter in surplus terms — and the paper suggests that stronger land market institutions to share surplus with informal residents could partially mitigate these costs. The paper also notes that formalization involves complex institutional and political challenges: relocating millions of kampung residents is logistically difficult, compensation is frequently inadequate or absent, and land assembly faces severe holdout problems.

Dynamic inefficiency in cities: The phenomenon, in the context of this paper, whereby preserving informal slum settlements through upgrading delays their formalization, generating opportunity costs from land misallocation as surrounding formal areas develop. Distinguished from static inefficiency: KIP may raise resident welfare while simultaneously reducing aggregate land productivity.

Slum upgrading: A policy providing basic public goods improvements (roads, sanitation, community buildings) and tenure security (typically verbal non-eviction guarantees) to existing slum residents in situ, without relocating them. Contrasted with formalization (redevelopment) and sites-and-services programs.

Boundary discontinuity design (BDD): The paper’s second identification strategy, comparing outcomes for observations within 200 meters on either side of KIP program boundaries, with boundary fixed effects and quadratic distance controls, under the assumption that absent KIP, unobserved real estate potential varies smoothly at program boundaries.

Predicted land index: A hamlet-level index constructed by regressing non-KIP log land values on hamlet fixed effects across 2,058 Jakarta hamlets, used to proxy real estate market potential and rank neighborhoods into quintiles from highest (Q1) to lowest (Q5) development stage.

Informal surplus: The surplus generated within the informal housing sector, including built-up volume from high horizontal coverage (35% for KIP kampungs) and low-cost informal structures, which is destroyed upon formalization and must be weighed against the gains from taller, higher-value formal developments.

Land fragmentation: The number of distinct land parcels per unit area (pixel), measured from Jakarta’s 2011 cadastral maps. Higher fragmentation exacerbates holdout problems in land assembly, raising the cost of redevelopment and contributing to delayed formalization.

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