R48 | Macro Paper Warehouse

Optimal Public Transportation Networks: Evidence from the World's Largest Bus Rapid Transit System in Jakarta

Mon, 01 Jan 0001 00:00:00 +0000

This paper studies how commuter preferences over wait times, travel times, and transfers should shape the design of urban bus networks, using the world’s largest Bus Rapid Transit (BRT) system — TransJakarta in Jakarta, Indonesia — as the empirical laboratory. The setting provides unusually rich identification: between January 2016 and February 2020, TransJakarta launched 93 new BRT and non-BRT feeder routes in a staggered, city-wide expansion, during which the operating bus fleet more than doubled from roughly 700 to over 1,600 vehicles. The authors combine over 500 million smart-card tap records, GPS tracking of every bus at 5–10 second intervals, and anonymized smartphone location data covering 35 million weekday trips from 2.3 million devices.

The paper proceeds in three steps. First, the authors classify new route launches into three event types and estimate their causal impact on ridership via difference-in-differences. Event 1: a new direct connection between an origin-destination pair already served by transfer only, with no travel-time improvement — raises BRT ridership by 0.16 log points. Event 2: a new direct connection that also reduces travel time (by 0.29 log points on average) — raises ridership by 0.27 log points. Event 3: additional buses on an already-directly-connected pair, which increases the bus arrival rate by 0.32 log points and reduces wait times — raises ridership by 0.09 log points, implying a ridership elasticity with respect to wait times of approximately −0.29 for BRT. For non-BRT routes the implied wait-time elasticity is −1.05, raising the possibility of multiple equilibria in service levels. Crucially, none of the three event types produce detectable increases in aggregate trip volumes measured by smartphone data, implying the ridership gains reflect modal substitution toward the bus rather than trip generation.

Second, the authors estimate a structural demand model. At its core is a route-choice model in which bus arrivals follow independent Poisson processes, so wait times are exponentially distributed and idiosyncratic. This formulation avoids the red-bus/blue-bus aggregation problem endemic to logit models. Commuters are also allowed to be partially inattentive to routes whose travel time exceeds the fastest available option by more than an estimated threshold. Structural parameters are recovered by classical minimum distance, matching seven reduced-form moments. Key findings: wait time is valued 2.4 times more than time on the bus for BRT routes, and 4.2 times more for non-BRT routes. There is no additional transfer penalty beyond the wait time and travel time costs of the second leg. Commuters pay significantly less attention to options with travel time more than roughly 34–44 percent above the fastest option in their choice set.

Third, the authors use the estimated preference parameters to characterize optimal bus networks. Because the optimization problem is high-dimensional (418 grid cells, 1,536 possible edges, yielding on the order of 10^500 configurations) and exhibits neither global convexity nor simple complementarity, they reformulate the social planner’s problem as a discrete choice over networks with additive logit shocks — effectively sampling from a multinomial logit distribution via simulated annealing. The result: optimal networks cover approximately 66 percent of grid cells versus 42 percent under the actual TransJakarta network, and would give 91 percent of Jakarta residents bus access versus 73 percent currently. Bus frequency in the city center is somewhat lower in the optimal network. Despite commuters’ high sensitivity to wait times, the current network concentrates too many buses in the city center where wait times are already short, rather than extending reach to underserved areas. Comparative statics show that doubling the wait-time cost parameter produces much more concentrated optimal networks (23 percent of origin-destination pairs connected, 41 percent fewer than baseline), while increasing the transfer penalty by the equivalent of 15 minutes of wait time raises the direct-connection share of served pairs from 12 to 16 percent.

Q: What are the three event types and why are they analytically distinct?

A: Event 1 is the launch of the first direct route between an origin-destination pair already connected by transfer, where the direct route is not faster than the existing transfer option; it isolates the effect of directness absent a travel-time change. Event 2 is the same but with a faster direct route (average reduction of 0.29 log points in travel time), combining directness and speed improvements. Event 3 is the launch of a new route that overlaps an existing direct route, increasing bus frequency and cutting wait times (arrival rate up 0.32 log points) without substantially changing travel time or directness. The three events together provide variation across the key dimensions — directness, speed, and frequency — needed to separately identify commuter preference parameters.

Q: What are the main ridership effects and how large are they in levels?

A: For BRT routes, Event 1 raises ridership by 0.16 log points (approximately 19 additional riders per week for a treated origin-destination pair with a baseline of 111 weekly riders), Event 2 by 0.27 log points (approximately 24 additional riders per week), and Event 3 by 0.09 log points (approximately 20 additional riders per week). For non-BRT routes, proportional effects are larger but level effects are similar: Event 1 yields roughly 34 additional weekly riders, Event 2 roughly 21, and Event 3 roughly 15. Event-study graphs show clear, discrete jumps in ridership at route launch with no pre-trends, and some gradual adjustment in the months following.

Q: What does the paper find about aggregate trip generation versus modal substitution?

A: Using smartphone location data to measure all trips regardless of mode, the authors find no statistically significant increase in aggregate trip volumes for any of the three event types. For BRT Event 1, the estimated aggregate-trip coefficient is −0.008 with a standard error of 0.051, allowing rejection at the 95 percent level of any positive impact above roughly 0.091 log points — small relative to the precise 0.11 log-point bus ridership effect in the same sample. The authors interpret this as evidence that the ridership gains over the 10-month post-event window reflect substitution from private modes (motorcycles, cars, taxis) toward TransJakarta rather than trip generation, and they use this null result to justify holding destination choices fixed in the structural model.

Q: How does the model avoid the red-bus/blue-bus aggregation problem?

A: The paper’s route-choice model assumes bus arrivals follow independent Poisson processes, so wait times are exponentially distributed. A key proposition (Proposition 1) proves that splitting one route into two identical routes with half the buses each produces exactly the same choice probabilities and expected utility as the original single route — because the sum of two independent Poisson processes is itself Poisson with the summed rate. Standard logit models fail this invariance because splitting a route creates two options with independent error draws, artificially inflating expected utility. The invariance property is essential for the optimal network design exercise, where the planner freely reallocates buses across routes.

Q: What are the estimated preference parameters and what do they imply about commuter behavior?

A: The paper estimates that wait time is valued 2.4 times more than time on the bus for BRT routes and 4.2 times more for non-BRT routes. There is no additional transfer disutility beyond the wait time and travel time costs implied by the extra leg. Commuters become substantially inattentive to routes with travel time more than approximately 34 percent above the fastest available option (BRT threshold) or 44 percent (non-BRT). The high relative cost of waiting versus riding reflects both the discomfort of waiting at exposed non-BRT stops and the fact that TransJakarta runs without a published schedule, so commuters cannot minimize wait time by timing arrivals.

Q: What explains the non-BRT wait-time elasticity exceeding −1?

A: For non-BRT routes, Event 3 raises ridership by 0.450 log points while raising the bus arrival rate by 0.425 log points, yielding an implied elasticity of ridership with respect to wait times of −1.05. Because the baseline arrival rate for non-BRT treated pairs is 2–4 times lower than for BRT pairs, the absolute reduction in wait time per additional bus is much larger. An elasticity exceeding −1 in absolute value implies that adding buses on some non-BRT routes could increase ridership enough to maintain or even raise average ridership per bus — the extreme form of the Mohring effect — suggesting the possibility of a high-ridership/low-wait-time equilibrium distinct from the current low-ridership/high-wait-time one.

Q: How is the optimal network characterized and what algorithm is used?

A: The social planner chooses a network to maximize utilitarian welfare (average expected utility across all commuters) from the estimated demand model, plus a network-level logit shock capturing cost and other factors outside the model. This transforms the combinatorially explosive optimization into sampling from a multinomial logit distribution over networks, which the authors approximate using simulated annealing. They run the algorithm multiple times to obtain a sample of networks drawn asymptotically from the planner’s distribution, then estimate optimal network characteristics and comparative statics from sample analogs. The theoretical framework is general and, the authors note, applicable to other high-dimensional spatial planning problems where welfare differences can be computed for pairs of counterfactuals.

Q: How does the optimal network differ from the current TransJakarta network?

A: The typical optimal network covers approximately 66 percent of 2km grid cells versus 42 percent for the actual network, and 91 percent of Jakarta residents would have bus access versus 73 percent currently. The optimal network reduces bus frequency in the city center relative to the current network, accepting longer wait times there in order to extend reach to peripheral areas. The paper finds no tension between distributional and efficiency concerns in this setting — expanding coverage improves both aggregate welfare and access for underserved areas.

Q: What do the comparative statics reveal about the sensitivity of optimal network design to preference parameters?

A: Doubling the wait-time cost parameter leads to substantially more concentrated optimal networks: only 23 percent of origin-destination pairs are connected, 41 percent fewer than in the baseline optimal network. This is because higher wait-time costs make it more valuable to concentrate buses on fewer routes to achieve short headways. Increasing the transfer penalty by the equivalent of 15 minutes of wait time raises the share of connected location pairs with a direct (non-transfer) connection from 12 to 16 percent. These comparative statics link micro-level preference parameters to macro-level network topology, clarifying which parameters most influence design choices.

Q: How does the paper validate the destination imputation from tap-in-only smart card data?

A: For the subset of BRT stations where tap-out is enforced (36 percent of stations), the authors estimate bivariate regressions of imputed daily ridership shares against actual observed ridership shares, obtaining R-squared of 0.85. They also show robustness by varying the grid cell size from 500 meters to 2 kilometers, finding no systematic decline in treatment effect magnitudes, which rules out large displacement effects within the network as an explanation for the results.

Q: Does the response to network improvements vary by local poverty rates?

A: The authors interact all six event types with an indicator for above-median poverty rate at the origin grid cell (from SMERU 2014 data), controlling for population. They find no clear pattern of heterogeneity by income level — richer and poorer areas respond similarly to service improvements. The paper notes this absence of heterogeneity as relevant context for interpreting optimal network design: the case for extending reach is not offset by a differential preference for frequency among poorer commuters.

Mohring Effect: The externality arising from ridership responsiveness to wait times — more riders justify more buses, which reduce wait times for all riders, further increasing ridership. The paper estimates a BRT wait-time elasticity of −0.29, confirming the effect operates in Jakarta; for non-BRT the elasticity of −1.05 suggests the possibility of multiple equilibria in service levels.

Negative Exponential Distribution Model (Daganzo 1979): The route-choice model used in the paper, in which bus arrivals on each route follow independent Poisson processes and wait times are exponentially distributed. The model is invariant to aggregation of identical routes (avoids the red-bus/blue-bus problem) and yields tractable closed-form expressions for choice probabilities and expected utility.

Partial Inattention: The model feature whereby commuters assign near-zero effective arrival rates to bus options whose travel time exceeds the fastest available option by more than an estimated threshold (34–44 percent depending on route type). Captures the empirical finding that commuters in a large, complex network do not appear to consider all available options.

Event Types (1, 2, 3): The paper’s taxonomy of service improvements induced by new route launches. Event 1 isolates the value of directness (new direct route, no speed gain). Event 2 combines directness and speed (new direct route that is also faster). Event 3 isolates the value of frequency (additional buses on an already-direct route, reducing wait time without changing travel time).

Optimal Network Characterization via Social Planner’s Logit: The paper’s approach to the combinatorially intractable network optimization problem. The planner is modeled as making a logit discrete choice over all possible networks, with welfare from the demand model plus a network-level idiosyncratic shock. Sampling via simulated annealing yields estimates of optimal network characteristics and comparative statics without requiring identification of a single globally optimal network.

Network Concentration vs. Extensiveness Tradeoff: The core design tension the paper formalizes — for a fixed bus fleet, concentrating buses on fewer routes reduces wait times on served routes but leaves more areas without coverage, while spreading buses across more routes extends reach at the cost of longer headways. The estimated preference parameters (high wait-time sensitivity) make this tradeoff non-trivial; nonetheless, the paper finds the current network is too concentrated relative to the optimum.

Pigovian Transport Pricing in Practice

Mon, 01 Jan 0001 00:00:00 +0000

This paper reports on the MOBIS experiment, a large-scale randomized controlled trial (RCT) implementing a multi-modal Pigovian transport pricing scheme in urban areas of German- and French-speaking Switzerland. The central research question is whether a first-best transport pricing scheme — one that charges users the full marginal external costs of their travel choices, varying across time, space, and mode — generates meaningful behavioral responses, and how those responses compare to a pure information intervention.

The study recruited participants from urban areas, requiring them to be between 18 and 65 years old and to use a car at least two days per week. After contacting over 90,000 individuals and an initial online screening of 21,800 respondents, 3,656 participants completed the RCT. Each participant agreed to have their daily travel tracked via a smartphone app (“Catch-My-Day”) for eight weeks: four weeks of observation followed by four weeks of treatment. Assignment to treatment and control groups was fully randomized without stratification.

The pricing treatment gave participants a budget equal to their observed external costs during the observation period plus a 20% buffer, from which the external costs of their actual travel were deducted in real time; any remaining balance was theirs to keep. External costs were computed across all modes using official Swiss Federal Roads Office monetization factors, including congestion (via a MATSim-based average marginal cost approach), CO2 climate costs (CHF 136.08/ton), health costs from air pollution (PM10 and NOx), and accident and physical activity effects for active and public modes. Public transport also carried a peak-hour surcharge of CHF 0.10/km for congested zone-pairs. A second “information-only” treatment provided identical information about external costs but imposed no financial charge. A control group received only weekly summaries of kilometers traveled by mode.

The regression framework is a difference-in-differences specification with person, calendar-day, and day-of-study fixed effects, estimated in levels for external-cost outcomes (due to negative values from walking’s net external benefit) and via Poisson Pseudo-Maximum Likelihood for non-negative outcomes.

The pricing treatment reduced total external costs by CHF 0.215 per day (p < 0.01), a 5.1% reduction relative to the control group. The average private cost of transport for the control group during the treatment period was CHF 25.72 per day; the external cost was CHF 4.22 per day, implying that Pigovian pricing raised total transport costs by 16.4% on average. The implied price elasticity of external costs with respect to this price increase is -0.31. The reduction is attributable to mode substitution toward public transport and active modes and to departure time shifting away from peak hours, but not to a reduction in total distance traveled.

The information-only treatment produced a coefficient of -0.087, which is not statistically significant at conventional levels for the full sample. The differential effect of adding pricing to information is -0.127 (marginally significant, p < 0.1), with the pricing increment particularly important for reducing congestion costs. Sensitivity analysis shows that removing the control group and time fixed effects inflates the before-vs.-after elasticity to between -0.57 and -0.71, substantially larger than the preferred estimate of -0.31, underscoring the importance of the experimental design.

Heterogeneity analysis reveals that men respond more strongly than women, German speakers more than French speakers, participants under 30 more than older participants, and those with above-median altruistic values respond significantly even to information alone. Correct knowledge of the definition of external costs (present in 45% of the sample) is a key driver of the pricing treatment effect. These scope conditions — mode availability, urban Swiss context, short 4-week treatment window, mandatory car use eligibility, and the specific external cost monetization framework — bound the generalizability of the elasticity estimate.

Q: What is the main treatment effect of the Pigovian pricing scheme on external transport costs? A: The pricing treatment reduced total external costs by CHF 0.215 per day, which is a 5.1% reduction relative to the control group (p < 0.01). About half of the reduction came from health costs, with congestion and climate costs following in magnitude. The implied elasticity of external costs with respect to the Pigovian price increase is -0.31, meaning a 10% increase in total transport costs from Pigovian pricing would reduce external costs by approximately 3.1% in the short run.

Q: How was the Pigovian price increase calculated, and what was its magnitude relative to private costs? A: The average private cost of transport for the control group during the treatment period was CHF 25.72 per day, and the average external cost was CHF 4.22 per day. The external cost thus represents 16.4% of total (private plus external) transport costs, and dividing the 5.1% reduction in external costs by this 16.4% price increase yields the elasticity of -0.31.

Q: What mechanisms drove the reduction in external costs? A: The reduction resulted from a combination of mode substitution — a shift away from car use toward public transport and active modes — and departure time shifting away from peak hours. Critically, total distance traveled did not decline; the behavioral adjustment operated entirely through changes in how and when people traveled, not in how much.

Q: What was the effect of the information-only treatment? A: The information-only treatment produced a coefficient of -0.087 CHF per day, which was not statistically significant at conventional levels for the full sample. It was statistically significant only for subgroups, notably participants with above-median altruistic values. The differential effect of adding pricing to information (alpha_P minus alpha_I = -0.127) was marginally significant (p < 0.1) and was particularly concentrated in congestion cost reductions, suggesting that the monetary incentive is especially important for internalizing the congestion externality.

Q: Why is the control group critical, and how does removing it affect the estimated elasticity? A: The tracking data show a seasonal negative trend in external costs over the study period; without a control group, this trend would be incorrectly attributed to the treatment, inflating the estimated effect. When both day-of-study and calendar-day fixed effects are removed (approximating a before-vs.-after design without a control group), the estimated elasticity rises to between -0.57 and -0.71, roughly double the preferred estimate of -0.31. This highlights that most prior studies in the literature, which lack control groups, are likely to overestimate treatment effects.

Q: What heterogeneity is observed in the treatment response? A: Men respond more strongly than women to both treatments, with the gender gap particularly pronounced for congestion costs. German speakers respond more strongly than French speakers. Participants under age 30 show stronger responses than older participants. Those scoring above the median on an altruistic values index respond significantly not only to pricing but also to information alone. Participants who correctly defined external costs (45% of the sample) drive the pricing treatment effect; a causal forest analysis confirms knowledge of external costs, age below 30, and language region as key heterogeneity drivers.

Q: How were external costs computed across modes, and what are the key monetization parameters? A: For private road transport, GPS tracks were map-matched using Graphhopper and processed via MATSim modules; emission factors came from the HBEFA 3.3 database, and congestion was assessed via an average marginal cost approach incorporating spillback effects. Externalities were monetized at CHF 136.08/ton for CO2, CHF 515,497–1,358,461/ton for PM10 (rural vs. urban), CHF 7,109/ton for NOx (regional), and a value of travel time savings of CHF 25.77/hour. For other modes, per-km values from the Swiss Federal Roads Office were applied. Walking carries net external benefits (negative external costs), while cycling carries small net external costs because accident costs exceed physical activity benefits.

Q: How was public transport priced in the experiment, and why was it simplified? A: A second-best zonal peak-hour surcharge of CHF 0.10/km was applied to public transport stages between zone-pairs experiencing peak demand, with peak windows set at 7–9 am and 5–7 pm. Full first-best pricing of public transport crowding was deemed infeasible because crowding effects are highly heterogeneous spatially and temporally, often concentrated in very short windows on specific lines, making aggregate distribution unreasonable.

Q: Was there evidence of gaming the mode detection system? A: Because participants could manually correct the app’s algorithmic mode assignments — and the pricing group had an incentive to overclaim low-cost modes — the potential for strategic misreporting was examined. While the analysis could not rule out some gaming, the main results were shown to be robust to excluding potential gamers, suggesting that gaming did not materially distort the treatment effect estimates.

Q: What does the study imply for transport pricing policy? A: The elasticity of -0.31 provides a benchmark for policymakers: a full Pigovian pricing scheme that raises total transport costs by about 16% can be expected to reduce external costs by about 5% in the short run in an urban context. The finding that congestion costs respond more to pricing than to information alone suggests the monetary component is essential for this externality. Heterogeneous responses — particularly the weaker responses by women and French speakers — have distributional implications. The experiment is a proof of concept that first-best transport pricing can generate meaningful behavioral responses, but scaling it would require addressing privacy concerns from GPS tracking, technical infrastructure, and political economy challenges.

Pigovian transport pricing: A pricing scheme that charges each user the marginal external costs of their transport choices — including health, climate, congestion, and noise costs — as they vary across time, space, and mode, intended to internalize the gap between private and social costs of travel.

External costs of transport: Costs borne by society rather than the individual traveler, including congestion (delay imposed on others), climate damages (CO2 emissions), health costs (local air pollution, accidents), and noise; in this paper, computed in real time from tracked trips using official Swiss monetization values.

Average treatment effect (ATE): The difference-in-differences estimate of the causal effect of the pricing or information treatment on outcomes, identified from the randomized assignment and controlling for person, calendar-day, and day-of-study fixed effects.

Mode substitution: The behavioral response in which travelers shift from higher-external-cost modes (primarily car) to lower-external-cost modes (public transport, walking, cycling) in response to pricing, as distinct from reducing total travel distance.

Departure time shifting: The behavioral response in which travelers adjust when they depart to avoid peak-hour congestion surcharges, contributing to reduced congestion externalities without reducing total distance traveled.

Information-only treatment: An experimental arm receiving identical information about external costs as the pricing group but facing no financial charge, used to isolate the informational component of the pricing treatment from the monetary incentive component.

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

Mon, 01 Jan 0001 00:00:00 +0000

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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