Rigorous studies on place-based policies in developing countries are much scarcer. Interestingly, their findings tend to support the economic gains of these interventions. This is partly because, by research design, these studies try to overcome the two issues affecting the evaluation outcomes. They either investigate targeted areas that are believed to have the potential to spearhead growth or examine policies that are generous in nature. As a case in point, Lu, Wang and Zhu (2019) look at special economic zones (SEZs) in China, which on average, are areas with geographic and socioeconomic advantages. The interventions lasted decades and were also overarching, including infrastructure investment and institutional reforms. Many zones received subsidies from the central government as well. They find zones have a positive effect locally—on the location of firms, investment, employment, productivity and wages, and create a net benefit nationally.<sup>1</sup> In the case of India, Chaurey (2017) studies the 2003 central tax incentives targeting two states, Himachal Pradesh and Uttarakhand, and Shenoy (2018) investigates a broader set of centrally sponsored interventions targeting Uttarakhand, including both fiscal incentives and infrastructure investment, and over the same period. Himachal Pradesh and Uttarakhand were among the smaller, poorer and less developed states in India. However, these interventions lasted for a decade and were among one of the world’s most generous place-based policies in scale. Chaurey (2017) shows that these tax incentives led to large increases in the number of firms, employment, investment and output in the manufacturing industries in the targeted states relative to other states. Shenoy (2018) finds that nighttime light intensity rose sharply in the targeted state relative to the neighboring state, implying about 28 percent increase in output.

While both studies confirm the effectiveness of the interventions at the local level, they are limited by their respective data and corresponding research designs. Relying on the Annual Survey of Industries, the main results of Chaurey (2017) are based on formal companies of the manufacturing industries, which may bias the results by omitting services sectors and informal companies. The Annual Survey of Industries are not spatially granular either, which poses a challenge to Chaurey’s (2017) most stringent specification that relies on boundary discontinuity but compares district-level outcomes (Baum-Snow and Ferrira 2015). The findings of Shenoy (2018) depend on nighttime light intensity data, which are spatially much more granular, and more conducive to boundary discontinuity design. However, nighttime light intensity remains a proxy for economic activities. Additionally, nighttime light data measure aggregated economic output and are not conducive to investigate the specific ways that the interventions affect economic agents’ behaviors.

In this paper, we revisit the question on the effectiveness of the centrally sponsored interventions in Uttarakhand and address several limitations of previous studies. First, we use the 2013 round of the Economic Census to cover all modern economic activities (non-farm activities) in both the formal and informal sectors. Second, we follow a rigorous boundary discontinuity design and compare outcomes at the town and village level while controlling for distance to the border. Most importantly, we investigate outcomes at the town-sector level and explore the heterogeneity across industries in the incentives. Our results shed light on the specific channels through which these interventions affect employment and firm locations.

> 1 Alder et al. (2013) and Wang (2013) also find evidence supporting the effectiveness of the SEZs in China.

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