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DIALOGUE FOR CHANGE

Congestion Pricing to Solve Traffic Jams? Not so Fast!

A study in Bangalore showed that peak-hour pricing would only have a small benefit in terms of traffic speeds.

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The Lieutenant Governor of Delhi recently said that the city is likely to become the first in India to have congestion charges in certain stretches. This column reports results from an experimental pilot in Bangalore that seeks to assess the effectiveness of congestion pricing as a solution to chronic traffic congestion.

Congestion pricing has long been promoted as a promising solution to chronic traffic congestion. In a recent International Growth Centre (IGC) study, I report the results from an experimental pilot of peak-hour congestion pricing policies in Bangalore, India, one of the most congested cities in the world (Kreindler 2018). I found that peak-hour pricing would only marginally improve travel times, and the benefits would be lower still when accounting for schedule costs for commuters induced to travel at different times. A key driver of this surprising result is that in Bangalore, it seems that adding a vehicle on the road increases travel times by a roughly similar amount at any level of congestion.

A Theoretically Attractive Solution for a Big Problem:

Traffic congestion is a chronic problem in large cities across the world. Slow but aggressive traffic, noise, and pollution fumes: this is a daily experience for millions of urban commuters. For example, study participants in Bangalore spent on average 1.5 hours driving each weekday, and the average trip speed was only 14 kilometres (km) per hour.

Economists have long recommended charging road users fees that scale with the amount of congestion. Technologically, this is possible using GPS devices or other car sensors, as well as license plate reading traffic cameras, and some cities are interested in implementing this type of policies. For example, Jakarta is at the bidding stage of installing Electronic Road Pricing to gain control over unruly congestion, while Singapore is transitioning to a pay-per-km model using GPS car units.

Congestion pricing policies are backed by common sense, as well as by economic theory. The idea is that drivers impose “externalities”, namely a cost on society, by slowing down traffic, generating pollution, etc. Typically, drivers do not take this into consideration, which leads to excessive congestion. Judiciously chosen pricing can, in principle, remedy this situation.

However, calculating how much to charge is not straightforward. Charging high fees will always reduce congestion, but this is not necessarily a good thing. These benefits must be weighed against the adjustment costs for drivers. Similarly, how much of an overall improvement in travel times – as well as net of adjustment costs – is also an empirical question.

Focus on Peak-Hour Congestion:

I study congestion pricing in the context of within-day road traffic peak-hours. I implemented an experimental pilot with congestion pricing policies in Bangalore, and I developed a method to assess the impact of peak-hour congestion pricing on how individual drivers would respond, and on city-wide traffic throughout the day1. I focus on commuters who are using their vehicles, and who decide when to travel, within the day2.

This study has four main steps:

  1. A pilot of pricing policies, implemented using a smartphone app
  2. A field experiment to measure how commuters respond to pricing
  3. Measurements of the road externality in Bangalore using app GPS data
  4. A calculation of the impact on traffic of a city-wide policy

For this study, a team of surveyors recruited car and motorcycle (non-commercial) drivers in randomly selected petrol pumps in southern Bangalore. Of the approximately 8,500 eligible drivers, more than a quarter installed the study app on the spot, and, eventually, 497 took part in the experiment with congestion pricing policies. The small response rate is symptomatic of studies in urban areas, and the primary concern is that non-respondents would respond differently to pricing policies. I find that participants are younger than the typical driver approached, which is to be expected given the smartphone app component. Encouragingly, controlling for age there is no difference between participants and non-participants in whether they use a car (vs. motorcycle) or in the predicted vehicle price.

Figure 1. Study area, recruitment locations, and GPS data points (black)

The Congestion Charge Pilot:

The first challenge is to collect accurate data on how people travel. To do this, I used a custom-designed smartphone app that efficiently collects location data throughout the day, which is then converted into time and precise route information for each trip. The data covers over 100,000 individual weekday trips and almost 1 million km of travel inside the city. I later use the same data to implement the pricing policies3.

I designed and implemented two broadly realistic congestion charge policies. Under the first ‘departure time’ policy, trips are charged according to a pay-per-km rate that is zero for off-peak departures, and higher for departures close to the peak hour. Under the second ‘area’ policy, commuters face a flat fee for driving through a small area along their usual route, chosen such that there exists a longer, untolled alternate.

In practice, charges were calculated automatically daily and subtracted from a prepaid virtual account. The account balance was transferred to the participants’ bank account at the end of each week in the study.

Figure 2. Illustration of the ‘departure time’ policy

Measuring Commuter Responses to Congestion Pricing Policies:

The two policies were implemented as part of a randomised field experiment, which allows to rigorously measure how commuters respond to charges. The policies were specifically designed to measure how commuters value two aspects of travel: time spent commuting (value of time for short), and arriving earlier or later than ideal at their destination (schedule costs).

Experimental results show that commuters have a moderate flexibility to adjust trips away from typical work hours in order to save money. Under ‘departure time’ charges, commuters leave earlier in the morning and later in the evening. During the morning interval, participants advance their trips by around 4-6 minutes on average. Under ‘area’ charges, participants cross the congestion area less frequently by switching to longer routes.

I then interpret the results using a model of travel demand. The two key findings are (1) commuters value time spent driving highly. (In other words, congestion is costly.) (2) Commuters have moderate schedule flexibility.

For example, a typical commuter is roughly indifferent to leave one hour earlier than usual in the morning, if that results in faster travel time by 15 minutes.

Measuring the ‘Road Technology’ (Externality):

Optimal congestion pricing depends crucially on the cost that drivers impose on society. I quantify the road speed dimension of this externality using all the GPS data collected on over 120,000 trips. I document that times of the day with higher traffic volume have higher travel times, and this effect is linear. This means that an additional vehicle on the road increases the average trip duration by a constant amount, for any level of existing traffic. This result is in contrast to previous research in transportation engineering in countries such as the US, where this relationship is typically convex. That is, studies focusing on highways typically find that adding a vehicle onto an already congested highway has a larger effect. This result suggests that the road technology in urban Bangalore is fundamentally different from highways in rich countries. This may be due to low speeds in Bangalore even when roads are free, differences in driving style, high fraction of motorcycles, etc. Encouragingly, Akbar and Duranton (2017) recently documented a similar shape relationship in Bogotá, Colombia.

Quantitatively, I find that a half-hour trip during the peak-hour increases the aggregate driving time of everyone else by around 17 minutes.

Figure 3. The ‘road technology’ or the impact of traffic volume on travel times

City-wide Impact of Congestion Pricing:

To make progress on understanding how a city-wide congestion pricing policy would affect traffic, I make several simplifying assumptions. I focus on the distribution of traffic during the day, around the morning peak-hour. I assume that commuters do not change their home and work locations in response to congestion pricing, nor the likelihood to use public transport, and that no trips are cancelled.

I use a model to simulate how a city-wide policy plays out. Commuter responses are based on the field experiment results, and when people travel affects traffic at different times of the day based on the road technology described above. I compare two scenarios: the current, unpriced, and inefficient situation, and the outcome with peak-hour congestion charges chosen optimally.

I find that peak-hour congestion pricing can only deliver small benefits in terms of faster traffic, and almost negligible improvements when schedule costs are taken into account. Specifically, under the optimum policy, average travel times are 2-6% faster depending on the benchmark, while the total trip cost (including both travel time and schedule costs) is, on average, only 0.5% lower – again of only Rs. 5 per commuter per day. Intuitively, the social value of the travel time saved by removing commuters from the peak hour is not significantly larger than the costs to those commuters of travelling at different, inconvenient times.

This conclusion relies essentially on the road technology. I show, using the model, that gains would be significantly higher with a convex technology. However, varying commuter responses while keeping the same road technology does not alter the above conclusion.

Limitations:

This study focused on one margin of commuter adjustment that is plausibly most malleable in the short run, namely trip timings. In future research, it is important to rigorously measure how drivers in cities like Bangalore switch to public transport and adopt carpooling in response to pricing. A longer-run margin of interest is how people factor in commute times and monetary costs in their home- and work-location decisions. In addition, pollution exposure due to traffic slowdowns and motor vehicle accidents are two dimensions along which drivers generate externalities. A comprehensive evaluation requires quantitative estimates of these effects and their costs.

Bangalore shares traffic woes with many cities worldwide. However, it is possible that drivers in other cities may respond differently, for example, due to different types of occupations. Importantly, the magnitude and shape of the road externality may be different, and this requires further research in other settings.

Discussion:

The mere existence of severe traffic congestion does not, by itself, imply that the gains from congestion pricing would be substantial. Indeed, in Bangalore, I showed that peak-hour pricing would only have a small benefit in terms of traffic speeds.

This study provides a method that can be used in any city to understand how congestion pricing would affect road traffic. The first step should be to measure the road technology, or externality, in order to understand the scope for the inefficiency of the current (unpriced) situation.

Secondly, field experiments can be used to measure commuter responses to pricing. Taken together, these two components allow simulating the city-wide impact of pricing on travel times and costs.

Notes

  1. The study was conducted during February-September 2018.
  2. I do not quantify benefits from reduced exposure to pollution, nor switches to public transport. I return to these important issues at the end.
  3. In a real-world policy, charges may be based on data from GPS responders installed in vehicles: Martin and Thornton (2017) study such an experiment in Melbourne, Australia.

Disclaimer: The facts and opinions expressed in this article are strictly the personal opinions of the author. League of India does not assume any responsibility or liability for the accuracy, completeness, suitability, or validity of any information in this article.

Published with permission from Ideas For India (www.ideasforindia.in), an economics and policy portal.

Gabriel Kreindler

Gabriel Kreindler is a doctoral candidate in the economics department at the Massachusetts Institute of Technology. His current research projects include an experimental simulation of road congestion pricing in Delhi. He has worked with Innovations for Poverty Action (IPA) since 2011, in Morocco, Indonesia and India.

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Moving Towards Better Definitions of ‘Urban’ in India

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According to the 2011 Census, 31% of the country is ‘urban’. Using definitions of urbanisation that are different from those used by the government, this column demonstrates that this figure may be an underestimate. It is important to recognise and fix the flaws in the current method of defining urban areas as it forms the basis for important policies such as eligibility for government schemes.

According to the 2011 Census, India is 31% urban – a statistic that is much-relied on to shape development strategies and perceptions about the country. The percent of India governed as urban – that is, administered by urban local bodies such as municipal corporations – is even lower.

In recent research, we compare the urbanisation rate of India using the two government definitions – the administrative definition and Census definition – which can be discretionary in nature, with two alternative definitions that make use of objective population threshold criteria (Tandel, Hiranandani and Kapoor 2016).

We argue that alternative definitions are better suited than the administrative definition used by the government to determine policies like eligibility for Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA).

Administrative and Census Definitions:

State governments ultimately determine the administrative status of settlements. The default category of a settlement is rural and it becomes urban only after the state government converts it following a requisite legal process.

Although there are guidelines that propose population and other criteria in order for settlements to be governed as urban, these are not binding on state governments. As a result, the decisions to convert settlements from rural panchayats to urban local bodies can be arbitrary and may vary across states. There may even be pressures or incentives (such as being able to access rural schemes) to not convert settlements to the urban category, even when they are de facto urban in nature.

The Census of India acknowledges the existence of settlements that are de facto urban but are governed as rural by creating a category called “Census towns” to identify such settlements. The criteria for being a Census town are having a population of at least 5,000, density of at least 400 persons per square kilometre, and at least 75% of the male main1 working population engaged in non-agricultural activities.

However, contrary to common perception, even the Census uses discretion in actually identifying these towns (Pradhan 2013). The Census includes these Census towns together with settlements that are urban as per the administrative definition in its definition of urban.

Alternate Definitions Based on Population Criterion:

As a counterpoint to the administrative definition and the census definition of urban, we study how the scenario would change if India used a population criterion of 5,000 or a population criterion of 2,500 for defining urban. These definitions are used by countries such as Ghana, Qatar, Mexico and Venezuela2.

We find that while India is only 26% urban using the administrative definition, it is 31% urban by the census definition, 47% urban by the 5,000 population definition, and 65% urban by the 2,500 population definition. The differences in urbanisation rates using different definitions are even starker at the state level.

For instance, Kerala goes from being 15% urban by the administrative definition to 99% urban by the 5,000 and 2,500 population definitions.

Urbanisation Rates and Socioeconomic Indicators:

It is difficult to find a precise definition that could capture the true nature of all places. However, studies like Buckley et al. (2009) and Khan (2000) have established a link between urbanisation and socioeconomic indicators, and one way to assess the suitability of various urban definitions is to examine the relationship of urbanisation rates using different definitions with these socioeconomic indicators.

In a system that justifies special treatment to rural areas because they are thought to be more deprived or agrarian, an examination of the relationship between the chosen definition and development or agricultural indicators is warranted.

We conduct a state-level comparison of the relationship between urbanisation rates as measured by the administrative, Census, and alternative definitions, and poverty rates, per capita net state domestic product, and share of working population engaged in agriculture and cultivation.

We find that the census definition and 5,000 population definition have a stronger relationship with these characteristics as compared to the administrative definition.

Eligibility for Government Schemes:

Using inaccurate definitions of urban and rural can be costly since, among other reasons, these categories are used as the basis for determining eligibility for various state and central government schemes, and standards of public goods and services delivery. For instance, MNREGA – the world’s largest employment guarantee programme in terms of a number of beneficiaries (Honorati, Gentilini, and Yemtsov 2015) – makes use of the administrative definition to identify rural areas and allocate funds to them.

A faulty or arbitrary way of defining settlements as rural implies that there is arbitrariness in the way in which people across the country are eligible for an employment guarantee. There is also a possibility that MNREGA funds are being allocated to settlements that are actually urban but are classified as rural by the administrative definition.

We compare the use of MNREGA – which has in-built self-selection mechanisms of providing unskilled manual work at close-to-minimum wage so that only the most deprived people demand it – with the four urban definitions, and find a surprising trend.

According to the administrative definition, more urban states3make more use of the scheme, while according to the other three definitions more rural states make more use of the scheme.

Figure 1. The relationship between MNREGA expenditure and urbanisation in Indian states and Union Territories (UTs)

Note: Eligible population is urban population when using the administrative definition.

This observation prompted us to undertake a district-level study of the relationship between urbanisation rates and MNREGA use. We control for other factors that could affect MNREGA use such as state-level effects, district development indicators, and political characteristics. We find that when using the administrative definition, more urban districts make more use of MNREGA, whereas using the 5,000 and 2,500 population definitions, more rural districts make more use of MNREGA.

There may be several explanations for this. For instance, district administrative staff of more administratively urban districts may be more adept at using government resources, or information asymmetries may be less in more urban districts leading residents to demand more of government schemes.

However, we believe that it is more plausible that the relationship indicates that the administrative definition is a poor indicator of the urban character of the district. This is supported by the fact that alternative definitions relate better with development indicators, and that we do not find evidence that more rural districts by the administrative definition as compared to alternative definitions (in other words, districts in which the administrative definition overestimates rural rates to a greater extent) make more use of MNREGA. This suggests that the scheme’s self-selection mechanisms broadly work in this respect.

Recognise and Fix Flaws in Current Urban Definitions:

Taken together, the results present a strong case that alternative definitions of urban are better suited than the administrative definition to reflect the urban character of settlements in India.

A precise and perfect method for defining urban and rural areas may be difficult and costly. Therefore, it may be more prudent to reduce the stakes of such definitions, to make definitions more reflective of ground realities, and to reduce the scope for political exploitation of subjective categorisation.

One way to reduce the stakes of definitions is to use objective criteria such as poverty rates and proportion of agricultural workers to determine eligibility for government schemes and policies and access to social services, wherever possible. Where this is not possible, instead of a standard definition for all scenarios, it may be worthwhile to explore using different definitions to determine eligibility for particular programmes.

Such an approach is used in countries like the United States and the United Kingdom, where different concepts of rural are used for different government programmes, depending on their objectives.

However, there is still merit in moving towards a more accurate and general definition of urban since characteristics such as population and population density themselves alter the nature of places and prospects of their residents, justifying the need to treat places differently.

Hence, while we adapt and reduce reliance on urban-rural categorisation, it is also important to recognise and fix the flaws in India’s current method of defining urban areas.

Notes:

  1. Main workers are defined by the Census as those who “worked for the major part of the reference period (six months or more)”, as opposed to marginal workers.
  2. More countries, like Argentina and Ethiopia, use a 2,000 cutoff rather than 2,500, but we use the more conservative number as the threshold.
  3. More urban states, by the administrative definition, are states with a higher proportion of their population living in settlements that are categorised as urban by the administrative definition.

Disclaimer: The facts and opinions expressed in this article are strictly the personal opinions of the authors. League of India does not assume any responsibility or liability for the accuracy, completeness, suitability, or validity of any information in this article.

Published with permission from Ideas For India (www.ideasforindia.in), an economics and policy portal.

Komal Hiranandani

Komal Hiranandani is Consulting Senior Associate at IDFC Institute and a graduate student at Cornell University's Applied Economics programme. She also holds a B.A. (Hons) in Government from Georgetown University and is awaiting her L.L.B. (General) degree from Mumbai University. Her previous positions include Programme Officer at Asia Society India Centre.

Mudit Kapoor

Mudit Kapoor is an Associate Professor of Economics at Indian Statistical Institute, Delhi Centre. Before this, he was Assistant Professor of Economics and a Research Fellow at the Indian School of Business (ISB), Hyderabad. Prof. Kapoor’s research interests are in Development Economics, Gender and Political Economy.

Vaidehi Tandel

Vaidehi Tandel is Senior Associate at IDFC Institute, Mumbai. She has a PhD in Economics from the University of Mumbai. She has published co-authored papers in peer-reviewed journals and has co-authored a chapter in an edited book on the Indian economy. Her research interests lie in the areas of new institutional economics, urban economics, urban and metropolitan governance, and political economy.

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First Meeting of Think Tank on Framework for National Policy on E-Commerce Tomorrow

It will provide a credible forum for an inclusive and fact-based dialogue leading to recommendations for informed policy making.

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NEW DELHI: Minister of Commerce & Industry Suresh Prabhu will chair the first meeting of the think tank on the ‘Framework for National Policy on E-commerce’ to be held on April 24, 2018.

Senior officers of the ministries/ departments of the Government of India involved in different aspects of e-commerce; high-level representatives from the industry bodies, e-commerce companies, telecommunication companies and IT companies; Reserve Bank of India; and independent experts have been invited to participate in the meeting.

The think tank on the Framework for National Policy on E-commerce has been established recently by the Department of Commerce. It will provide a credible forum for an inclusive and fact-based dialogue leading to recommendations for informed policy making, so that the country is adequately prepared to take advantage of the opportunities, and meet the challenges, that would arise from the next wave of advancements in the digital economy.

The think tank on the Framework for National Policy on E-commerce will seek to collectively deliberate on the challenges confronting India in the arena of digital economy with a view to developing recommendations for a comprehensive and overarching national policy on e-commerce.

Some of the issues that will be discussed by the think tank include the following aspects of e-commerce and digital economy: physical and digital infrastructure, regulatory regime, taxation policy, data flows, server localisation, intellectual property rights protection, FDI, technology flows, responding to disruptions in industrial organisation, need for skill development and trade-related aspects.

Developments in e-commerce at the WTO and evolving appropriate national position on the underlying issues would be another important dimension of the discussions of the think tank on the Framework for National Policy on E-commerce.

The think tank will explore options for providing a fillip to entrepreneurship in the digital economy. It will identify specific policy interventions for nurturing domestic firms and create jobs in e-commerce.

Representatives of almost fifty organisations are expected to participate in the first meeting of the think tank on the Framework for National Policy on E-commerce to be held on 24 April 2018.

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India Exploring Signing of MoU for ‘Road Information System’

This would be developed on lines of the system run by the Express Highways Information Corporation of South Korea.

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NEW DELHI: Union Minister of Road Transport and Highways Nitin Gadkari has said that India is exploring an agreement with South Korea for the introduction of Highways Information System in the country.

This would be developed on lines of the system run by the Express Highways Information Corporation of South Korea, with integrated monitoring of a highway at a centralized control room.

Inaugurating the 29th National Road Safety Week in New Delhi’s Vigyan Bhavan today, the Minister spelt out the ministry’s priorities in ensuring safety of road users in the country.

He informed that he has fixed a target of bringing down the number of road fatalities to half from the around 1.5 lakh accidental deaths reported when he took over.

Gadkari said though progress has been achieved in this respect, he is still not satisfied and aspires to keep working in this direction.

The Ministry organizes the Road Safety Week every year to create awareness among general public and improve upon the safety of road users.

This year, Road Safety Week is being organized from 23rd to 29th of April and the focus is on schools and commercial drivers.

Addressing the participants Gadkari requested that every student be made a road safety ambassador for his family and for the society. Various activities have been organized for school / college students, drivers and all road users.

The Minister gave away awards to fifteen school children who won the national level essay competition on road safety. The top three winners were given cash prizes of Rs 15,000, Rs 10,000 and Rs 5,000 and a certificate each. Gadkari also administered the road safety pledge to all those present on the occasion.

Outlining the initiatives and steps taken to improve vehicle safety and overall road safety, the Minister said his ministry has adopted the 4E principles of Education, Enforcement, Engineering and Emergency care to address the problem of road safety.

For ensuring vehicular safety, the standards of buses have been upgraded, air bags and speed alert device have been made mandatory for all cars, and every two-wheeler will have ABS to avoid skidding. He said, the Motor Vehicles (Amendment) Bill, 2017, passed by Lok Sabha is waiting to be cleared by Rajya Sabha. This bill comprehensively promotes safety on the road. It would also promote the development of an efficient, seamless and integrated multi-mode public transport system.

The Ministry of Road Transport & Highways has launched a scheme for setting up of Driving Training Centre to provide quality training to commercial vehicle drivers, improve road and environment safety and strengthen overall mobility on roads. 789 road accident black spots have been identified on NHs of which 139 black spots are rectified, work on 233 black spots are awarded and in progress.

Ministry had issued guidelines for taking up of Road Safety Audits on National Highways either as part of all new projects. Detailed Road Safety Audits in a length of 610 km have been sanctioned on National Highways in last two years.

Installation of crash barriers at accident prone locations on National Highways in Hilly Terrain has been sanctioned in a length of 183 Km at a cost of Rs 108.25 crore.

Shri Gadkari also released a paper on road safety by Indian Road Safety Campaign. IRSC is a youth led national mission promoting road safety, led by students and alumni of IIT Delhi. It is currently working on creating awareness on road safety, developing technologies, simplifying laws and improving the post-accident emergency care system.

IRSC organizes an annual road safety championship called as the Safer India Challenge in collaboration with the Ministry of Road Transport and Highways and various state polices involving the youth to come up with innovations in this area.

The Safer India Challenge’18 was launched on the occasion. In addition to this the Minister also released a short story collection on road safety theme entitled “Have a safe journey”

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