Introduction It has been a long-standing premise that infrastructure drives economic growth, which eventually alleviates poverty. However, the lack of reliable subnational data in many developing countries has long made it challenging to evaluate the impacts of location-specific interventions to infrastructure and the economy. In Sri Lanka, the Integrated Road Investment program (iRoad) and Second Integrated Road Investment program (iRoad 2) funded by the Asian Development Bank (ADB) are designed to improve access to the road network in rural areas, thereby increasing the involvement of the rural population in nationwide economic and social development. The two programs aim to upgrade and maintain approximately 6,450 kilometers (km) of rural access roads to all-weather standards, rehabilitate and maintain about 750 km of national roads, and improve the capacity of road agencies. To measure the effects of the program, it would be ideal to have frequent, granular, and representative socioeconomic records to track households, individuals (e.g., wage, agriculture income), or economic outcomes (e.g., gross output or value added) and see how figures compare to statistics from previous years. Unfortunately, because of its scale, the required data is often difficult to obtain in Sri Lanka, especially in the frequency and granularity that would make for easy analysis. Thankfully, recent developments in remote sensing have made it possible to use nonconventional sources as an alternative to local economic activity analysis. In a paper published by ADB, technical experts make an argument for using remote sensing data to measure economic impacts of projects. This includes using data from nighttime lights, which can tell the local story of economic development. Potential of Nighttime Lights Data Nighttime lights provide a promising data set for various reasons. First of all, they can be detected by satellites, making data available and making this technique a good proxy for economic outputs on a wide range of issues. Processed nighttime lights data products are usually publicly available and ready for application, whereas the cost and time needed to conduct new surveys are usually high, if not prohibitive. Secondly, nighttime lights can offer highly frequent data. For example, the figures used in this study are available monthly since April 2012. Finally, nighttime lights data can be extremely specific and granular, with 15 arc seconds or 465 meters x 465 meters at the equator, which is even smaller than most administrative units or Grama Niladhari Divisions in the country. One can make intelligent comparisons to validate and utilize nighttime lights data by combining the administrative unit division-level nighttime lights data and iRoad connectivity data, like area of coverage, and several other datasets, including (i) the 2012 Population Census that includes Grama Niladhari Division-level information on the number of households, population, and labor force; (ii) road roughness data for iRoad segments before and after construction; and (iii) geolocations of socioeconomic centers in Southern Province that the iRoad program intends to connect to a less developed area. Table 1: iRoad Program Coverage in Southern Province Variables 2015 2016 2017 2018 2019 2020 Number of GNDs with iRoad started 443 443 443 443 443 443 Number of GNDs with iRoad completed 0 0 87 357 357 409 Number of households with iRoad completed 17,872 108,685 108,685 123,856 Total population with iRoad completed 67,880 424,406 424,406 483,026 Female population with iRoad completed 35,420 219,301 219,301 249,553 Male population with iRoad completed 32,460 205,105 205,105 233,473 Working population with iRoad completed 40,705 255,634 255,634 290,253 % Households with iRoad completed 2.8% 17.3% 17.3% 19.7% % Working population with iRoad completed 2.7% 17.1% 17.1% 19.4% GND = Grama Niladhari Division, iRoad = Integrated Road Investment Program.Note: The number and share of households, population, and working population are estimated using 2012 Population Census data. The percentage values represent the total values in GNDs connected with iRoad as a share of the total in Southern Province. Source: Asian Development Bank estimates. Findings from Data Analyses Based on insight analyses from the working paper, Southern Province was much brighter in 2020 than it was in 2012. Not only did more areas change from completely dark to luminous, but the luminous areas were brighter as well. Specifically, upon comparing nighttime lights pixel level data in 2020 with 2012, about 10% more areas showed positive luminosity values (the amount of light emitted by an object in a given time period). On average, the luminosity value in a pixel is more than 200% higher in 2020. Areas that saw the most expansion and increase in luminosity are along the coast (i.e., around Galle Harbor and Hambantota Harbor), around socioeconomic centers, and in some cases, along areas with iRoad segments. There are, however, some areas with little shift in luminosity over the decade, such as the northeast area and some northern inland areas. Intuitively, Grama Niladhari Divisions hosting more labor and with a larger population create more economic outputs and consume more electricity, and those with more households with electricity emit more nighttime lights. Comparing granular nighttime lights data with economic census data shows that a 10% increase in lights is associated with about a 3% increase in the labor force, population, or households with electricity. The study finds that the program results in better and more consistent rural road conditions. All else being equal, Grama Niladhari Divisions connected with iRoad in the Southern Province on average showed 12% higher nighttime lights in the second year after iRoad completion. Taking from the association between nighttime lights and labor force presence, this 12% coefficient translates into 2.6% higher economic activity. Figure 1: VIIRS Nighttime Lights and iRoad in 2012 and 2020 iRoad = Integrated Road Investment Program, VIIRS = Visible and Infrared Imaging Radiometer Suite.Source: Asian Development Bank. Implications Overall, analyzing remote sensing data sets like nighttime lights has made it possible to leverage the use of nonconventional data points as an alternative to local economic activity data. These help determine gains and milestones and direct priorities for future development programs. In countries or areas that lack granular data for economic comparisons, like Sri Lanka, using remote-sensing data can provide verifiable data to work with. However, future research should still be done to generate more data that can provide benchmarks of progress. It would be ideal to utilize economic census on employment and labor income, household survey data on agriculture input and output, vis á vis remote sensing data (e.g., daytime satellite images for traffic counts) to get a more holistic picture of development impact. Eventually, when more data is available, better analysis can be made to measure the impact of interventions and identify underlying channels that could further inform complementary policies for infrastructure or economic investments, like Sri Lanka’s iRoad project. The authors thank Ravi Peri, former director of the Transport and Communications Division of the South Asia Department, Asian Development Bank, and Chen Chen, country director of the Sri Lanka Resident Mission, for their strategic guidance; R.W.R. Permasiri, secretary to the Ministry of Transport and Highways of Sri Lanka, for the support; Yi Jiang and Zhigang Li for detailed comments and discussions on an earlier draft; and Takashi Yamano and Eugenia Co Go for constructive feedback. The authors also thank the Road Development Authority of Sri Lanka for providing data and other support, and Ma. Adelle Gia Toledo Arbo, Marjorie Villanueva Remolador, and Haoxin Zhao for their excellent research assistance. Resources Asian Development Bank. Sri Lanka: Integrated Road Investment Program. L. Chen, Y. Lu, and A. Nanayakkara. 2021. Rural Road Connectivity and Local Economic Activity: Evidence from Sri Lanka’s Integrated Road Investment Program. ADB South Asia Working Paper Series. No. 87. Manila: Asian Development Bank. S. Aggarwal. 2018. Do Rural Roads Create Pathways Out of Poverty? Evidence from India. Journal of Development Economics. 133. pp. 375–395. S. Asher and P. Novosad. 2020. Rural Roads and Local Economic Development. American Economic Review.110 (3). pp. 797–823. Ask the Experts Liming Chen Urban Economist, Water and Urban Development Sector Office, Sectors Group, Asian Development Bank Liming Chen’s research interests include urbanization, transport infrastructure, and trade. He obtained his PhD in Economics from the University of California, Santa Barbara in the United States and his bachelor’s degree in Economics from Fudan University in the People’s Republic of China. Yang Lu Transport Specialist, Transport Sector Office, Sectors Group, Asian Development Bank Yang Lu’s research interests include traffic operations and management and big data analytics in transport. He obtained his PhD and MS in Civil Engineering from the University of Maryland, College Park in the United States, and his bachelor’s degree in Automation from Beijing Jiaotong University in the People’s Republic of China. Aruna Nanayakkara Senior Project Officer (Transport), Sri Lanka Resident Mission, South Asia Department, Asian Development Bank Aruna Nanayakkara is a senior project officer (Transport) at Sri Lanka Resident Mission, ADB. He is a professionally qualified civil engineer. Asian Development Bank (ADB) The Asian Development Bank is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 69 members—49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. Follow Asian Development Bank (ADB) on Leave your question or comment in the section below: View the discussion thread.