Leveraging information for action
Measurement is valuable for action not just because it informs decisions and policy design, but because it enables institutionalized incentives
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Our cities are getting smarter by the day. Land use? India now has its own sharp eye in the sky with last week’s launch of Cartosat-2E, capable of capturing objects as small as 60cm-long. “Primarily it will provide useful space-based data for town planners, creators of urban infrastructure, for agriculture and project monitoring, and for decision makers in Smart City and AMRUT,” said a senior Indian Space Research Organization (ISRO) official quoted in The Hindu.
Congestion? The data for tracking it over time, by neighbourhood, and overall across agglomerations are out there what with Google Maps, Wayz, mobile sensors mounted on public buses and autorickshaws, traffic counting at intersections. Potholes? Crowd-sourced tracking options leveraging cellphone gyroscopes are emerging around the world. Google has filed for a patent on one that uses cars’ sensors as well.
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Air quality? Sensors are proliferating as public, private sector, academic, and community projects invest in understanding the air that we breathe. Satellite readings of haze add to the mix. Flood risk? Between drones giving fine-grained readings of topography, satellites supplying data on surface materials that shape water flow, and crowd-sourced mapping of de facto drainage, we can know more than ever.
Economic health? The revamped Periodic Labour Force Survey under the National Sample Survey Organization largely slipped under the radar screen when it started in April, but will generate quarterly data on the state of the labour market in urban areas (annual for the rest of the country).
The aggregation of intelligence seems only to be accelerating. The pan-city proposals under the Smart Cities mission read like a list of monitoring apps: for traffic, parking, street lights, air quality, water supply and energy use, overall “command and control”.
But being smart is not enough. We need the information to also deliver capability: the ability to convert decisions into actions that contribute to economic growth, health and welfare, resilience, and overall quality of life.
Moving from smart to capable cities will require a range of changes beyond amplified information flow. The political reform agenda for cities, from integrated leadership by mayors to greater accountability via elections is well known. Even within the administrative approach to upgrading cities, however, we need a shift in how we evaluate and use data in public administration, public-private partnerships, and other underpinnings for urban development.
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“What gets measured gets done,” goes the old adage, but consider the mechanics underneath the saying and what they require from the institutional ecosystem around measurement.
Measurement is valuable for action not just because it informs decisions and policy design, but because it enables institutionalized incentives. The ability to measure an outcome gives managers the ability to set and enforce the performance criteria for the workforce they oversee—bonuses based on streetlights’ functionality, for example. It allows regulators to define, determine, and disincentivize violations—think fines for extra swimming pools or open space reserve violations, as impartially spotted by a satellite. It allows markets to form—trade in pollution control certificates, for example, linked to demonstrable plant-level emissions. It allows outcome-based contracts to be written for public-private partnerships that link payment to actual achievements of public goals. We could have a bus transport public-private partnership that is based on performance in delivering accessibility services, rather than simply maintaining a vehicle fleet on predetermined routes and schedules that may or may not match evolving economic geographies.
Each of these examples, however, assumes that the measurement has certain properties other than simply existence and accuracy. It assumes that both parties have the confidence to agree that the information is credible and accurate enough to define potentially substantial financial rewards. It assumes that the measurement can be replicated and audited in case of dispute. It assumes that the measurement process is robust enough to maintain its accuracy in the face of pressure for a “better number” or efforts to “game the system”.
Imagine, for example, a city roads surfacing contract with total payment discounted for potholes based on data crowd-sourced from cellphone gyroscopes. In principle, the link between road surface durability and payment should motivate the contractor to avoid cutting corners. But would both contractor and city accept crowd-sourced data as a basis for determining the flow of public funds? In case of a hitch in the data flow—a failure of the app to process a week or a neighbourhood’s worth of data, for example—who becomes liable? What’s to stop canny public administrators from sending out search squads for bumps to increase the basis for discount? Unless the app designers had developed a filter for swerving to discount pothole readings, the data could be gamed. These issues may seem trivial, but in practice, they will determine the real incentive power of the contract—the capability to leverage intelligence about potholes to generate action to avoid them.
We need to move beyond the excitement of simply being able to measure new things, to think harder about how we create the validation, protection, and other protocols required to actually make these measurements useful for actions beyond simply knowing.
Jessica Seddon is managing director of Okapi Research and Advisory and visiting fellow at IDFC Institute. She writes fortnightly on patterns in public affairs.
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