--- title: "Mobility and Congestion in Urban India: Akbar, Couture, Duranton & Storeygard (2023)" description: >- Distilled: Develops city-level vehicular speed indices decomposable into uncongested speed and a congestion factor, applied to 57 million simulated Google Maps trips in 180 Indian cities. Uncongested speed explains 70 percent of cross-city speed variance; congestion explains only 13 percent, overturning the view that slow Indian cities are primarily congested. American Economic Review 2023, paywalled. Seven core results with source locators, datasets used, the index methodology, and the empirical specifications. sidebar: label: Akbar-Couture-Duranton-Storeygard 2023 order: 1 tags: [paper-summary, urban-economics, transportation, congestion, india, developing-countries, panel-regression, cross-section, peer-reviewed, unreplicated, data:google-maps, data:openstreetmap, data:india-census, data:ghsl] paper: authors: Prottoy Akbar, Victor Couture, Gilles Duranton, Adam Storeygard authorList: - { family: Akbar, given: Prottoy, orcid: "0000-0003-3332-7600", affiliation: Aalto University and Helsinki Graduate School of Economics } - { family: Couture, given: Victor, affiliation: Vancouver School of Economics, University of British Columbia } - { family: Duranton, given: Gilles, orcid: "0000-0002-5261-9927", affiliation: Wharton School, University of Pennsylvania } - { family: Storeygard, given: Adam, orcid: "0009-0001-1525-1043", affiliation: Department of Economics, Tufts University } year: 2023 venue: American Economic Review 113(4), April 2023, 1083-1111 venueShort: AER 2023 doi: 10.1257/aer.20181662 jel: codes: [O15, O18, R23, R41] assignedBy: claude-sonnet-4-6 date: 2026-06-24 topics: - Urban Transport and Accessibility - Transportation Planning and Optimization - Human Mobility and Location-Based Analysis dataAccess: licensed-commercial outcome: - city-level vehicular speed index - city-level uncongested speed index - congestion factor across Indian cities - welfare gains from speed improvement outcomeClass: [urban-mobility] license: >- All rights reserved (standard AER copyright; Crossref licence block empty for this work; no open-access licence asserted; paywalled via AEA website) licenseShort: paywalled access: paywalled machineAccess: "blocked-paywall (AEA website, 2026-06-24)" redistribution: extract-only resultsCount: 7 citedByCount: 58 methods: role: both contributes: city-speed-index family: descriptive buildsFrom: [panel-regression] identification: descriptive contributionType: [new-method, new-data, new-fact, measurement] introducesData: true scope: region: India (US for comparison) period: 2019-06..2019-11 frequency: mixed dataType: [administrative, other] granularity: [aggregate, transaction] n: "57,103,181 trip instances, 180 Indian cities, 2,730,969 unique trips, June-November 2019" findings: - ref: R1 outcome: cross-city variation in city vehicular speed index metric: r-squared value: "0.701 (uncongested speed share of speed index variance, 180 cities, all trips); 0.126 for congestion factor; -0.086 covariance (Table 4, p. 1102)" direction: positive vsBenchmark: "uncongested speed explains 5.6x more cross-city variance than congestion (Table 4)" - ref: R2 outcome: cross-city variation in city vehicular speed index metric: r-squared value: "0.567 vs 0.259 (uncongested vs congestion share of speed variance at high-peak hours 6-8 PM, Table 4, p. 1102)" direction: positive vsBenchmark: "uncongested speed explains 2.2x more variance than congestion even at peak hours" - ref: R3 outcome: city-level vehicular speed index metric: elasticity value: "log population elasticity = -0.15 (SE 0.016); log area elasticity = +0.17 (SE 0.017) (Table 5, col. 1, p. 1104)" direction: negative - ref: R4 outcome: city-level vehicular speed index metric: elasticity value: "0.069 (SE 0.016) for log major roads on speed index (Table 5, col. 2); 0.077 (SE 0.016) on uncongested speed (col. 5); ~0 (0.0074, SE 0.0038) on congestion factor (col. 8); p. 1104" direction: positive - ref: R5 outcome: city-level vehicular speed index metric: coefficient value: "earnings coefficient 0.028 (SE 0.0088); earnings-squared -0.0021 (SE 0.00053); speed peaks at 8th income decile (Table 5, col. 3, p. 1104)" direction: mixed - ref: R6 outcome: vehicular speed comparison India vs US metric: coefficient value: "US city trips 70% faster on average than Indian city trips; within-country speed decile spread 36% in India vs 25% in US (p. 1108)" direction: negative vsBenchmark: "US cities as benchmark" - ref: R7 outcome: welfare gains from uncongested speed improvement metric: level value: "Population-weighted average yearly gain ~Rs 1,157 (~$16) per vehicle commuter from a 10% uncongested speed improvement; up to Rs 2,696 (~$38) in Delhi (p. 1102-1103)" direction: positive vsBenchmark: "many times larger than gains from optimal congestion pricing (Akbar and Duranton 2018; Kreindler 2018)" resultType: new-finding replicationCode: url: https://doi.org/10.3886/E182681V1 status: available relatesTo: - { cite: "Couture, Duranton, and Turner (2018)", doi: '10.1162/rest_a_00744', relation: extends, note: "applies and extends their US city speed-index methodology to 180 Indian cities, using the same GM-trip approach" } - { cite: "Duranton and Turner (2011)", relation: builds-on, note: "the fundamental law of road congestion underpins the finding that more major roads leave the congestion factor near zero" } - { cite: "Akbar and Duranton (2018)", relation: builds-on, note: "prior single-city congestion measurement work in Bogota that motivates the cross-city approach" } - { cite: "Kreindler (2018)", doi: '10.1126/science.aan2747', relation: cites, note: "welfare gains from optimal congestion pricing in Bangalore are smaller than one percent of travel cost, far below uncongested-speed gains" } - { cite: "Brownstone and Small (2005)", relation: cites, note: "travel-time reliability valuation framework used to contextualize the welfare calculations" } openQuestions: - "Speed is only one component of accessibility; the other is proximity to destinations. The authors note ongoing work on complementary accessibility measures for Indian cities (p. 1109)." - "The investigation leaves for future research a deeper analysis of population growth and congestion: population growth is associated with more congestion but also faster uncongested travel, and the net effect on speed is not established causally (p. 1106)." - "Country-specific studies are needed: the comparison with the US implies that policies effective there may not transfer, and other developing countries may exhibit their own distinctive patterns (p. 1108-1109)." extraction: - by: paper-distiller (claude-sonnet-4-6) date: 2026-06-24 role: extracted note: >- Full text read from local PDF (pp. 1083-1111); seven results extracted from tables and narrative. Crossref licence block confirmed empty (paywalled). Not human-verified. Not reproduced. Replication data is publicly archived at ICPSR (doi:10.3886/E182681V1) but has not been run here. - by: paper-verifier (claude-sonnet-4-6) date: 2026-06-24 role: verified note: >- Locators and reported magnitudes re-checked against source PDF (pp. 1083-1111). All seven Core results rows confirmed against Table 4 (p. 1102) and Table 5 (p. 1104); R6 narrative confirmed p. 1108; R7 values confirmed pp. 1102-1103. Equations (1)-(3) and both empirical specifications verified term-by-term. Two fixes applied: JEL codes corrected (O15 was missing from [O18, R41, R23]; abstract shows O15, O18, R23, R41); R4 locator expanded from "cols. 2 and 8" to "cols. 2, 5, and 8" and findings value updated to include all three column magnitudes (congestion factor 0.0074, SE 0.0038 from col. 8). No em-dashes or colorful adjectives found. licenceVerification: - source: Crossref REST API works/10.1257/aer.20181662 checked: 2026-06-24 by: paper-distiller (claude-sonnet-4-6) found: "license[] block empty; no open-access licence asserted; paper published in AER under standard AEA copyright; access is paywalled" rightsSignalConflict: false --- **What this is.** The paper's core results, the speed-index methodology (equations and decomposition), and the empirical specifications: enough to understand what was found and how, without reading all 29 pages. To replicate or extend, read the original at [doi.org/10.1257/aer.20181662](https://doi.org/10.1257/aer.20181662) or download the replication archive at [doi.org/10.3886/E182681V1](https://doi.org/10.3886/E182681V1). ## TL;DR The paper develops a methodology to estimate city-level vehicular speed indices from 57 million simulated Google Maps trips in 180 large Indian cities (population above 300,000 as of 2018, data collected June to November 2019). The headline index is exactly decomposable into an uncongested speed component and a congestion factor. Across the 180 cities, uncongested speed explains 70 percent of the cross-city variance in overall speed; the congestion factor explains only 13 percent. This holds even at peak hours (6-8 PM), where uncongested speed still accounts for 57 percent of variance versus 26 percent for congestion. Slower Indian cities are slower at all hours, not primarily because of traffic. Population density is the dominant correlate of lower speed (working mostly through uncongested speed), while more major roads, a gridded network, and street lighting increase uncongested speed. A hill-shaped relationship between city income and speed reflects two opposing forces: higher income raises uncongested speed through better infrastructure but raises congestion in the upper half of the income distribution. Indian city trips are 70 percent slower on average than comparable US trips. ## Core results Magnitudes are as reported; SE in parentheses. Locators point into the source PDF. | # | Result | Locator | Magnitude | |---|---|---|---| | R1 | **Uncongested speed explains 70% of cross-city speed variance**; congestion explains only 13% (all trips, 180 cities) | Table 4, p. 1102 | Variance shares: uncongested = 0.701, congestion = 0.126, covariance = -0.086 | | R2 | **At high-peak hours (6-8 PM), uncongested speed still explains 57% of variance** vs 26% for congestion | Table 4, p. 1102 | Variance shares, high-peak: uncongested = 0.567, congestion = 0.259 | | R3 | **Population elasticity of city speed = -0.15**; city area elasticity = +0.17 (density is the binding constraint) | Table 5, col. 1, p. 1104 | log population: -0.15 (0.016); log area: 0.17 (0.017); R\*\*2 = 0.47 | | R4 | **More major roads increase speed via uncongested speed, not congestion** (consistent with the fundamental law) | Table 5, cols. 2, 5, and 8, p. 1104 | log major roads: 0.069 (0.016) on speed; 0.077 (0.016) on uncongested speed; ~0 on congestion factor | | R5 | **Hill-shaped income-speed relationship**: speed rises with city earnings up to the 8th decile, then falls | Table 5, col. 3, p. 1104 | earnings: 0.028 (0.0088); earnings\*\*2: -0.0021 (0.00053); turning point at 8th decile | | R6 | **Indian city trips are 70% slower than US city trips** on average; within-country decile spread is larger in India (36%) than the US (25%) | p. 1108 | US mean trip speed ~70% above India mean; US trip elasticity on trip length is 25-50% larger | | R7 | **Welfare gain from 10% uncongested speed improvement** far exceeds gains from optimal congestion pricing | p. 1102-1103 | Population-weighted average gain ~Rs 1,157 (~$16) per vehicle commuter per year; Rs 2,696 (~$38) in Delhi | **Overall (paper's conclusion).** Slow Indian cities are slow at all times of day because of low uncongested speed, not primarily because of traffic congestion. Policy interventions that target uncongested speed (road quality, network design, street lighting) generate substantially larger welfare gains than congestion pricing or ride-sharing promotion. Prior welfare estimates of optimal congestion pricing -- for example Kreindler (2018) for Bangalore and Akbar and Duranton (2018) for Bogota -- are below one percent of travel costs, far below uncongested-speed gains; Brownstone and Small (2005) provide the travel-time reliability valuation framework used to contextualize the welfare calculations. The finding challenges popular reports (e.g., Chin et al. 2018) that characterize certain Indian cities as exceptionally congested: Kolkata is in fact among the least congested but is the second slowest city due to its low uncongested speed. ## Theory / model The paper has no formal structural economic model. It conceptualizes travel as a consumption problem in which travelers select trips, and city-level speed serves as a price index for the cost of a typical trip. The conceptual framework identifies two components of that price: uncongested speed (the inherent ability of the road network to move vehicles in the absence of traffic) and a congestion factor (the additional delay imposed by other vehicles at peak times). **Key conceptual result (decomposition identity, p. 1095, equation 3).** Because uncongested and congested speed are defined over identical trip lengths, the following exact additive decomposition holds at the city level: $$ \hat{f}_c^{fe} = \hat{m}_c^{fe} - \hat{s}_c^{fe} \tag{3} $$ where $$\hat{s}_c^{fe}$$ is the estimated speed index, $$\hat{m}_c^{fe}$$ is the uncongested speed index, and $$\hat{f}_c^{fe}$$ is the congestion factor. All three are city fixed effects from separate OLS regressions run on the same sample with the same covariates; they therefore add up algebraically, enabling exact variance decomposition. **Identification.** The paper does not claim causal identification. City-speed indices are estimated by conditioning on trip characteristics (length, distance to center, time of day, day of week, weather, trip type, road class), so the city fixed effect captures the component of speed attributable to the city after holding these trip-level factors constant. Cross-city correlates of the indices (population, roads, income, topography) are interpreted as associations, not causal effects. ## Method The method has two stages: constructing a comparable speed index per city, and decomposing it into uncongested speed and congestion. **Stage 1: City speed index (pp. 1093-1094).** The naïve approach, a length-weighted average speed for city $$c$$: $$ S_c^m = \frac{\sum_{i \in c} D_i}{\sum_{i \in c} T_i} \tag{1} $$ is not comparable across cities because trip length and distance to the city center vary systematically. The paper instead estimates a log-linear regression of trip speed on city fixed effects and a vector of trip characteristics $$\mathbf{X}_i$$ (equation 2, p. 1093): $$ \log S_i = \alpha \mathbf{X}_i' + s_{c(i)}^{fe} + \epsilon_i \tag{2} $$ where $$S_i = D_i / T_i$$ is the speed of trip instance $$i$$, $$c(i)$$ is its city, and $$s_c^{fe}$$ is the city fixed effect used as the speed index. The index $$\hat{S}_c^{fe} = \exp(\hat{s}_c^{fe} + \hat{\phi}^2/2)$$ is a predicted speed for a typical comparable trip in city $$c$$. The same regression is re-estimated twice more: once with log uncongested speed ($$S_i^{nt} = D_i / T_i^{nt}$$, where $$T_i^{nt}$$ is GM's no-traffic duration) as the dependent variable to obtain $$\hat{m}_c^{fe}$$, and once with the log congestion delay $$\log T_i - \log T_i^{nt}$$ to obtain $$\hat{f}_c^{fe}$$. The additive identity in equation (3) follows immediately. This builds on the panel-regression tradition and adapts the price-index approach of Couture, Duranton, and Turner (2018) to a developing-country setting. The finding that more major roads raise uncongested speed without reducing the congestion factor is consistent with the fundamental law of road congestion advanced by Duranton and Turner (2011): new road capacity attracts new traffic and leaves congestion unchanged in aggregate. **Stage 2: Reliability.** Trip-time unreliability is measured as the ratio of the 90th to 50th percentile of the travel-time distribution across different weekday instances of the same trip, after conditioning on city-specific weekday effects (p. 1095). Unreliability is then used as a dependent variable in a variant of equation (2). ## Empirical specifications **Benchmark trip-level regression (Table 2, p. 1097).** The core specification regresses log trip speed on city fixed effects, day and time of day effects (30-minute bins), log trip length, log distance to city center, and weather controls: $$ \log S_i = \alpha_1 \log D_i + \alpha_2 \log \text{Dist}_{ci} + \text{time}_{t(i)} + \text{day}_{d(i)} + \text{type}_{k(i)} + \text{weather}_{w(i)} + s_{c(i)}^{fe} + \epsilon_i $$ OLS, N = 57,103,181 trip instances (all trips) or 41,249,209 (Intents-weighted weekday trips), 180 cities. Standard errors are robust. Column (1) without weather controls yields trip-length elasticity 0.22 (SE 0.0046); adding distance to center (column 3) raises the distance elasticity to 0.078. Columns (5-7) add route-level controls (gradient, road class shares, intersections, turns against traffic, establishment density) to obtain "narrow" city fixed effects that isolate indirect effects. **City-level correlates regression (Table 5, p. 1104).** The extracted city fixed effects from equation (2) are regressed on city characteristics with 180 city-level observations and a constant: $$ \hat{s}_c^{fe} = \beta_1 \log \text{Pop}_c + \beta_2 \log \text{Area}_c + \beta_3 \text{Geography}_c + \beta_4 \text{Roads}_c + \beta_5 \text{Earnings}_c + \beta_6 \text{Earnings}_c^2 + u_c $$ The same specification is estimated three times (columns 1-3 for speed index, 4-6 for uncongested speed, 7-9 for congestion factor). Coefficients in columns 1-3 equal the analogous coefficients in columns 4-6 minus those in columns 7-9 (from the decomposition identity). Geography variables include elevation variance, water length; roads include log major roads, log street lights, and a grid-conformity index (network shape). Robust standard errors throughout; R\*\*2 reaches 0.64 in the full speed-index specification. The same specification is run for US metro areas (online Appendix M) for cross-country comparison. ## Datasets used | Dataset | Role in paper | Wiki page | |---|---|---| | Google Maps simulated trips (57M+ instances) | Primary speed data: GM's recommended route duration with and without traffic for 2,730,969 unique origin-destination pairs sampled across 180 Indian cities; also 52M US instances for comparison | no page yet | | OpenStreetMap via OSMnx | Road network classification (motorways, primary, secondary, tertiary, residential), intersection counts, turns against traffic, network shape (grid index) | no page yet | | India Census 2011 | City population (sum of town/village level), car and motorcycle ownership shares, road inventory (paved/unpaved, street lights), commute mode shares, earnings from National Sample Survey 2011-2012 | no page yet | | Global Human Settlement Layer (GHSL) | Urban extent and city boundary delineation; built-up area pixels used to define the 180-city sample | no page yet | | Intents Mobi actual-trip app | Validation: 90,894 actual weekday trips in 89 cities by professional drivers, used to verify GM speed patterns and calibrate time-of-day weighting | no page yet | | Meteostat | Weather conditions at time of each trip instance (rain, thunderstorms, wind, humidity, temperature), used as controls | no page yet | | DMSP nightlight satellite data | Proxy for urban extent and street lighting; used alongside OSM street light data | no page yet | Sample: 180 Indian cities with 2011 census population above 300,000 (after dropping one city with defective boundary data), June 5 to November 13, 2019. Each trip sampled across 21 instances spanning times of day and days of the week. ## When to read the full paper Read the original at [doi.org/10.1257/aer.20181662](https://doi.org/10.1257/aer.20181662) if you are: replicating or extending the speed-index methodology to other countries (the online appendices give full details on trip sampling, city boundary construction, and robustness variants including Laspeyres-type indices and discrete-choice models); studying urban transportation policy in developing countries (the paper discusses implications for congestion pricing, ride-sharing, and road investment); comparing Indian and US urban mobility in detail (Appendix M); or examining walking and transit in India (Appendix A). The replication archive at Akbar et al. (2023) (ICPSR, doi:10.3886/E182681V1) provides data and code. ## Attribution and rights Source: peer-reviewed, *American Economic Review* 113(4), April 2023. This distillation was extracted by an LLM on 2026-06-24 and is **not human-verified or independently reproduced**. The verbatim PDF is under standard AEA copyright (all rights reserved); only this extract-only summary is provided here. > Akbar, Prottoy, Victor Couture, Gilles Duranton, and Adam Storeygard. > "Mobility and Congestion in Urban India." > *American Economic Review* 113, no. 4 (April 2023): 1083-1111. > DOI: 10.1257/aer.20181662. > Replication data: doi:10.3886/E182681V1 (ICPSR).