metadata: authors: 0: author: Beyer,Robert Carl Michael
1: author: Jain,Tarun
2: author: Sinha,Sonalika
count: India
docna: 0: docna: Lights Out ? COVID-19 Containment Policies
            and Economic Activity
docty: Policy Research Working Paper
subtopic: Public Health Promotion,Transport Services,Health Care Services Industry
teratopic: Industry,Health, Nutrition and Population,Transport
abstracts: cdata!: This paper estimates the impact of a
            differential relaxation of COVID-19 containment policies on
            aggregate economic activity in India. Following a uniform
            national lockdown, the Government of India classified all
            districts into three zones with varying containment measures
            in May 2020. Using a difference-in-differences approach, the
            paper estimates the impact of these restrictions on
            nighttime light intensity, a standard high-frequency proxy
            for economic activity. To conduct this analysis,
            pandemic-era, district-level data from a range of novel
            sources are combined -- monthly nighttime lights from global
            satellites; Facebook’s mobility data from individual
            smartphone locations; and high-frequency, household-level
            survey data on income and consumption, supplemented with
            data from the Indian Census and the Reserve Bank of India.
            The analysis finds that nighttime light intensity in May was
            12.4 percent lower for districts with the most severe
            restrictions and 1.7 percent lower for districts with
            intermediate restrictions, compared with districts with the
            least restrictions. The differences were largest in May,
            when the different policies were in place, and slowly
            tapered in June and July. Restricted mobility and lower
            household income are plausible channels for these results.
            Stricter containment measures had larger impacts in
            districts with greater population density of older
            residents, as well as more services employment and bank credit.
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repnme: repnme: Lights Out ? COVID-19 Containment Policies
            and Economic Activity
display_title: Lights Out COVID-19 Containment
            Policies and Economic Activity
listing_relative_url: /research/2020/11/32621548/lights-out-covid-19-containment-policies-economic-activity
topicv3: Transport m1326661 135,Pandemics m1327520 2167,Population, Aging and Health m1327736 640,Health m1504661 3480,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2020-11-30T00:00:00Z
historic_topic: Industry,Health, Nutrition and Population,Transport
origu: EFI-SAR-MTI-MacroFiscal (ESAMU)
no_of_pages: 25
author_keys: 0: author_key: 1501628
model_extractions: Policy Research Working Paper 9485 Lights Out? COVID-19 Containment Policies and Economic Activity _Robert C. M. Beyer Tarun Jain Sonalika Sinha_ South Asia Region Office of the Chief Economist November 2020
Policy Research Working Paper 9485

# **Abstract**

This paper estimates the impact of a differential relaxation of COVID-19 containment policies on aggregate economic activity in India. Following a uniform national lockdown, the Government of India classified all districts into three zones with varying containment measures in May 2020. Using a difference-in-differences approach, the paper estimates the impact of these restrictions on nighttime light intensity, a standard high-frequency proxy for economic activity. To conduct this analysis, pandemic-era, district-level data from a range of novel sources are combined —monthly nighttime lights from global satellites; Facebook’s mobility data from individual smartphone locations; and high-frequency, household-level survey data on income and consumption, supplemented with data from the Indian Census and the Reserve Bank of India. The analysis finds that nighttime light intensity in May was 12.4 percent lower for districts with the most severe restrictions and 1.7 percent lower for districts with intermediate restrictions, compared with districts with the least restrictions. The differences were largest in May, when the different policies were in place, and slowly tapered in June and July. Restricted mobility and lower household income are plausible channels for these results. Stricter containment measures had larger impacts in districts with greater population density of older residents, as well as more services employment and bank credit.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rcmbeyer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Lights Out? COVID-19 Containment Policies and Economic Activity<sup>1</sup>

Robert C. M. Beyer Tarun Jain

## Sonalika Sinha

World Bank Indian Institute of Management Ahmedabad Reserve Bank of India

**Keywords:** Containment policies, COVID-19, nighttime lights, India

**JEL Codes:** E21, H12, I18

> 1Robert C.M. Beyer is Senior Economist at the Macro, Trade and Investment Global Practice, World Bank; Tarun Jain is Associate Professor in Indian Institute of Management Ahmedabad; Sonalika Sinha is Manager (Research) in International Department, Reserve Bank of India (RBI). Contact information for Beyer: rcmbeyer@worldbank.org, Jain: tarunj@iima.ac.in and Sinha: sonalikasinha@rbi.org.in. We thank Roshani Bulkunde for outstanding research assistance and the Indian Institute of Management Ahmedabad for grant support. We are grateful to Viral Acharya, N.R. Prabhala, Rangeet Gosh, Dhruv Sharma, and Rishabh Choudhary, to the participants of the World Bank ’ s 6th South Asia Economic Policy Conference, the SERI COVID-19 Workshop and CAFRAL seminar for helpful comments. The views expressed in the paper are those of the authors and not the institutions to which they belong. The usual disclaimer applies.
# **1 Introduction**

Epidemics and pandemics, including the Coronavirus Disease 2019 (COVID-19), have large impacts on human life and livelihoods. The 1918 influenza, for example, caused large declines in economic output as many productive workers died (Fenske, Gupta, and Yuan 2020; Tumbe 2020). During a pandemic, governments typically intervene to slow its spread, as well as to mitigate the economic impact and revive activity. Thus, understanding the impact of government restrictions on reviving or hindering economic activity is important.

This paper estimates the impact of differential containment policies implemented by the Government of India (GoI) during the COVID-19 pandemic on aggregate economic activity. The first COVID-19 infection in India was reported at the end of January 2020. In March, the GoI implemented one of the most stringent lockdowns globally (Hale et al. 2020). After five weeks of nationwide lockdown, uniform restrictions were replaced with targeted measures that introduced variation across districts in May. The differential relaxation of restrictions across districts permits us to examine the impact of the heterogeneous containment measures. Districts were classified into three zones: those with the most severe restrictions (Red), those with intermediate restrictions (Orange), and those with the least severe ones (Green). Using a difference-indifferences approach, we compare the speed of recovery in the three zones after the uniform national lockdown. Using monthly data of man-made nighttime lights at the district level, we examine the impact of these differential relaxations of restrictions on aggregate economic activity. We also examine the period after the differential lockdown to understand whether the containment measures yielded subsequent gains in economic activity. We validate and explain the aggregate impact by analyzing mobility patterns across districts and household income and consumption. In addition, we compare the impact across different districts by accounting for their population density, the share of services sector employment, outstanding credit per capita, and the average age.

We use nighttime light intensity as our main proxy of economic activity for a number of reasons. First, a number of studies have reported the high correlation of man-made nighttime light intensity with other measures of economic activity (Donaldson and Storeygard 2016). Most notably, Henderson et al. (2012) argue that for countries with poor national income accounts, the optimal estimate of growth is a composite measure with roughly equal weights on conventionally measured growth and growth predicted from nighttime lights. Nighttime lights track economic activity in India closely (Prakash et al. 2019; Beyer et al. 2020), and provide a useful approximation of economic activity at high spatial granularity (Gibson et al. 2017; Chanda and Kabiraj 2020). In a recent study, Chodorow-Reich et al. (2020) use cross-sectional differences 2
in nighttime light growth to assess the effect of the 2016 Indian banknote demonetization. Second, using nighttime lights is particularly appropriate for this study since this measure is available at high spatial granularity at monthly frequency.<sup>2</sup> This allows us to match nighttime lights to the relevant district-level zone classification to determine economic activity in the pre-period in March and April, and to compare it to activity in the post-period in May, June, and July. In contrast, official quarterly GDP estimates and other measures of overall economic activity like electricity consumption are not disaggregated at the district level. Finally, nighttime lights from satellites represent an objective measure of economic activity, which is also immune to biases due to survey non-response which might be correlated with lockdown policies.

We find that nighttime light intensity in India dimmed during the strict national lockdown in March and April and recovered in May and subsequent months. In May, districts with the most severe restrictions witnessed a 12.4% (0.06 standard deviation) lower recovery in nighttime light intensity compared to those with the least restrictions. The recovery for districts with intermediate restrictions was 1.7% (0.01 standard deviation) lower. While remaining in the same direction, the impact of the zone classification tapered off in June and July. Our findings are robust to several logically orthogonal robustness checks. These include a test of the impact of the zonal classification on the pre-period, where we reassuringly find no effect. We restrict the sample to only those districts that border a differently classified zone, to non-metropolitan districts, and those that were not subsequently reclassified in mid-May and find no qualitative change in our results. Finally, a test with placebo zone classifications yields coefficients that are close to zero and insignificant. Facebook mobility data from cellphone locations indicates that restricted mobility is a plausible channel. Monthly household data point to lower household income but not consumption as an important channel for these findings. More developed districts with above median population density, share of employment in services, credit per capita, and mean age experienced larger impacts of the restrictions.

Our study contributes to the growing literature on the economic impacts of government interventions to mitigate pandemics. Deb et al. (2020) find large effects of containment measures to slow the spread of COVID-19 on economic activity across countries. Goolsbee and Syverson (2020) examine the drivers of pandemic-related economic decline in the United States and compare consumer behavior across commuting zones to distinguish between government restrictions and the role of fear – both of which matter. Kong and Prinz (2020) quantify the employment impact of different state-level containment measures in the United States and Petroulakis (2020) shows that high non-routine jobs reduce the probability of job losses. While

> 2While data from the United States Air Force Defense Meteorological Satellite Program (DMSP) using the Operational Linescan System (OLS) are only available at annual frequency, recent data from the Suomi National Polar Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are monthly.

3
disruptive in the short-run, Correia et al. (2020) show that stricter government interventions in cities in the United States during the 1918-19 influenza pandemic had positive economic impacts in the medium run. Beyer et al. (2020) employ state-level daily electricity consumption to assess the economic cost of the uniform lockdown during March and April 2020 in India and use monthly nighttime light intensity to explain drivers of district-level heterogeneity.

We also contribute to the literature on the economic and social effects of the COVID-19 pandemic in India. Mahajan and Tomar (2020) look at the disruption in food supply chains and find a minimal impact on prices but falling food availability. Ravindran and Shah (2020) employ the same zone classification as we do to analyze its impact on gender-based violence and find that complaints are higher in districts with more severe restrictions.

# **2 COVID-19 containment strategy in India**

The first COVID-19 infection was reported in India on January 30, 2020. Through February and March, the Government of India introduced restrictions on international travel, while promoting social distancing. Increasing threat of domestic infection spread prompted the GoI to announce a comprehensive nationwide lockdown starting on March 25, 2020, which was uniform across all states and districts (Bhalla 2020). During this phase, nearly all offices, commercial and private establishments, industrial units, as well as public services were closed. Most transportation services - including international and domestic flights, railways and roadways - were suspended. Hospitality services and educational institutions were shut. This nationwide lockdown was initially announced for three weeks and later extended until May 3, 2020.

Starting May 4, 2020, the Government of India announced “Lockdown 3.0”, where districts were classified into three zone categories based on multiple criteria including the incidence of cases, the extent of testing, and vulnerability to the pandemic more generally (Sudan 2020). During this phase, the GoI classified 130 districts as _Red_ zone districts, 284 as _Orange_ zone districts, and 319 as _Green_ zone districts.<sup>3</sup> Many restrictions aimed at curbing the movement and congregation of people, so that the differences between the zone classifications - especially between Red and Orange zone districts - were largely different mobility restrictions.

> 3Regardless of zonal classification, domestic and international air travel, inter-state bus transport, and metro and local trains remained shut. Only special inter-state trains started operations on May 12. Educational institutions, hotels, movie theaters, malls, gyms, swimming pools and bars were shut, and religious and social gatherings were banned across India.

4
<!-- Start of picture text --> Northwestern region<br><!-- End of picture text --> <!-- Start of picture text --> Northeastern region<br><!-- End of picture text --> <!-- Start of picture text --> Southern region<br><!-- End of picture text --> HO Green zone district NN Orange zone district MM Red zone district ~~ Unknown
state governments.

The ‘Unlock’ phase of containment policies commenced from June 1, 2020. Several restrictions were eased, and primary rule-making authority devolved to state governments. Thus, our analysis compares the severe restrictions which were uniform across the country in March and April 2020, to the time after May 4 when restrictions varied by district, until July 2020.

# **3 Data**

We combine multiple sources of district-level information on nighttime light intensity, household consumption and income, mobility, and district-specific characteristics. This information is merged with the government’s district level zonal classification and COVID-19 infection data. We use 2020 district boundary classifications to match how zonal containment policies and infections are reported.

We extract district level nighttime light data from the VIIRS-DNB Cloud Free Monthly Composites (version 1) provided by the Earth Observation Group at Colorado School of Mines. Due to a wider radiometric detection range and onboard calibration correcting for saturation and blooming effects, these data are more comparable over time than previous nighttime light products. However, the monthly composite still includes some temporary lights like fires and gas flaring and filtering out background noise strengthens the relationship of nighttime lights and economic activity. Following Beyer et al. (2020), we hence consider only lights outside a background noise mask. For the latter, we identify different clusters by removing outlier observations, averaging cells over time, and clustering areas based on their nighttime light intensity.<sup>4</sup> In practice, this approach amounts to setting to zero cells that are distant from homogeneous bright cores. The adjusted monthly data are aggregated to the district level and standardized by area.

We access mobility information from Facebook Data for Good,<sup>5</sup> which is based on individuals who use Facebook on a mobile device, provide their precise location, and are observed for a meaningful period of the day. Facebook quantifies how much people move around by counting the number of level-16 Bing tiles (approximately 600 meters by 600 meters area) they are seen in within a day, with the idea that people seen in fewer tiles are less mobile. The specific metric calculates the percentage of eligible people who are only observed in a single tile during the course of a day, and hence represents small distance mobility.<sup>6</sup> We aggregate Facebook’s tile-level information to match contemporary Indian districts, then invert and standardize the metric.

> 4 The same procedure has been used to aggregate the monthly composite to a clean annual composite (Elvidge et al. 2017). 5 https://dataforgood.fb.com. 6 Maas et al. (2019) provide more details about these data.

6
We use household-level total expenditure (as a proxy for consumption) and total income using the Centre for Monitoring Indian Economy’s Consumer Pyramids Household Survey (CPHS). These data are collected through stratified multi-stage surveys of 2011 census district classification in India, covering 28 states and union territories and 514 districts.<sup>7</sup> The CPHS is conducted every four months with every household surveyed in every round. The latest CPHS wave was conducted during June 2020. The monthly time-series is created by seeking data on income and expenses from households for each of the four months preceding the month of the survey. By putting together all the data from multiple surveys, we create a monthly time-series of income and expenses of households. We correct for response bias in CPHS using weights for non-response, which was a concern during the lockdown period.

All other data are either from Covid19India.org, the 2011 Census, the SHRUG data set (Asher et al. 2019), or the Reserve Bank of India Database on the Indian Economy. We aggregate daily district-level information on infections from Covid19India.org which collates information from the central and state governments, and verifies it against media reports, to monthly data. Since the 2011 district boundaries used by the Census and the SHRUG database are different from the 2020 boundaries used for the zone classification, we convert information from these sources using official orders for rearranging district boundaries and 2011 data on population in the relevant sub-districts. From the Census, we compute the average age for each district. From the SHRUG database, which consolidates village and urban ward characteristics from a large number of official sources (Asher et al. 2019), we obtain the population density and the fraction of workers in the services sector. We also use quarter-end outstanding aggregate credit for scheduled commercial banks from the Reserve Bank of India’s Quarterly Statistics on Deposits and Credit of Scheduled Commercial Banks (Database on Indian Economy). We use per-capita credit to measure access to finance.

Appendix Table 1 describes the summary statistics. In Panel A, the number of observations is 3,665, which reflects 733 districts across India each observed over five months (March 2020 to July 2020). Red zone districts have much higher levels of nighttime lights compared to Orange and Green districts. The levels of mobility are similar across zone types. Simultaneously, Red zone districts had 1,324 average infections, compared to 260 in Orange districts, and 89 in Green districts. These patterns are consistent with higher infection rates in large urban areas with greater international connectivity and lower distancing. Correspondingly, Red zone districts are more populous with greater density, have higher bank credit and a larger fraction of the population employed in the services sector. The mean age in Red, Orange and Green 7 CPHS does not cover Arunachal Pradesh, Nagaland, Manipur, Mizoram, Andaman and Nicobar Islands, Dadra & Nagar Haveli and Daman & Diu. 7
districts is similar. Panel B of Appendix Table 1 also summarizes household-level CPHS variables; means for monthly consumption and income are similar across zone types.

# **4 Specification**

## **4.1 Main analysis**

We use a difference-in-differences specification to estimate the impact of variation in relaxing lockdown restrictions on aggregate economic activity (Goodman-Bacon and Marcus 2020). Our main equation to examine the influence of the different COVID-19 containment policies on macroeconomic activity in district _i_ for month _t_ is as follows: In equation (1), _yit_ is the standardized nighttime light intensity per square kilometer in district _i_ for month _t_ . _Redi_ and _Orangei_ indicate the zonal classification in May 2020, with Green zone districts as the excluded category. _Postt_ indicates the months of May, June and July, which is when the lockdown varied across the country, compared to March and April when the lockdown was uniformly severe. Thus, the OLS estimate for 1 is the marginal effect of red zone compared to green zone districts on changes in nighttime light ββ intensity during the unlock period. Similarly, 2 is the marginal effect of orange versus green zones on ββ changes in nighttime light intensity from March/April to May/June/July 2020.<sup>8</sup> We add controls **X** _it_ for a range of factors that might influence district level light intensity. The spread of infections might impact economic activity in the district, so we include per capita monthly infections as a control variable. **X** _i_ includes the nightlights for each district for every month from January 2013 to February 2020 to control for pre-trends, including seasonality, in the outcome variable. This also helps control for the potential impact of 2019 economic growth, as well as the level of economic development, on changes in nightlight intensity due to COVID-19 infections and the corresponding government containment.

We include district fixed effects in the specification to account for all time-invariant observable and unobservable characteristics of the district that might impact economic activity and nighttime lights. These

> 8 One concern is that economic activity is correlated between neighboring districts, for example due to supply chains, particularly across districts of different zone classifications. If these cross-zone economic factors are important, then the coefficients would be biased towards the null. Therefore, reported coefficients represent a lower bound on the true estimates of zone classifications on nighttime light intensity.

8
include the level of development, transportation links, health facilities, and governance aspects. The independent effect of _Red_ and _Orange_ is absorbed by _DistrictFEi_ and therefore both are omitted from the specification.

State-month fixed effects ( _StateMonthFEit_ ) in equation (1) are the most non-linear way to capture time-variant and invariant state specific factors during the pandemic period. These include state level policies to control the pandemic, since health is a state subject in India. The state-month fixed effects also control for policing and other governance measures used to restrict the movement of people. Such rules were common across districts but varied by state, since law and order is also within the purview of state governments. Finally, represents robust standard errors. The impact of the COVID-19 infections and containment policies might dampen or exacerbate in June DDtt εε and July compared to May. As restrictions started lifting in June, the marginal increase in nighttime lights measure might have been greatest in Red zone districts which experienced economic revival. Conversely, the impact might be exacerbated if the zonal restrictions created a path-dependence (Durlauf 1994), and Red and Orange zone districts could not revive as quickly. Thus, we also estimate a specification where the _Postt_ variable is disaggregated into _Post_ ∗ _May_ , _Post_ ∗ _June_ and _Post_ ∗ _July_ and correspondingly the main variables of interest are _Redi_ ∗ _Mayt_ , _Redi_ ∗ _Junet_ , _Redi_ ∗ _Julyt_ , _Orangei_ ∗ _Mayt_ , _Orangei_ ∗ _Junet_ and _Orangei_ ∗ _Julyt_ .

# **Robustness**

We conduct a number of robustness checks to the main analysis to confirm the effect of zonewise containment policies on nighttime light intensity. First, we analyze the impact of the zone classifications on pre-period outcomes for several months before the pandemic. Zone classifications created in May 2020 in response to the COVID-19 pandemic beginning in March 2020 ought not to impact nighttime intensity in the months prior to the pandemic. We regress the following standard difference-in-differences specification.

Our primary outcome variable remains nighttime light intensity for district _i_ in month _t_ . In this specification **Month** _t_ is a vector of indicators for months from July 2019 to February 2020. If the zone classifications were not a function of preexisting trends in the data, we expect 1 and 2to be close to the 9 ββ ββ
null and statistically insignificant.

Second, to address concerns that variables omitted from the specification do not potentially drive zone classification as well as the economic output of a district, we estimate equation (1) with a restricted sample of districts that border each other but have different containment policies (600 districts). Geographical proximity implies that districts have similar economic, health and cultural characteristics, so the comparison in the restricted sample is more precise (Jain 2017).

Our third analysis omits the 17 districts with the largest metropolitan cities from the sample, since these districts generate a disproportionate share of the nighttime light and also experienced high COVID-19 infections. Results from the remaining sample should more reliably indicate the effect of the zone classification on nighttime light intensity in the average Indian district.

We also check the sensitivity of our results to removing 15 districts where the zone classifications changed after two weeks.

Finally, to ensure that the results are directly attributable to zone classification and not to either unobserved common time-varying characteristics or spurious correlations in the data, we randomly rematch the zone classification to a different district. We then estimate equation (1) with these rematched placebo districts (Card and Giuliano 2013; Jain and Langer 2019). If 1O 2are null and statistically insignificant, we have greater confidence that the main results are due to the zone classifications. ββ ddββ

# **4.2   Channels**

We analyze the impact of zone classifications on intermediate variables – short distance mobility, household income and consumption – that illustrate potential channels through which pandemic containment policies could influence nighttime light intensity. First, we use district level short-distance mobility data from Face- book as a way to measure where and how severely the zonal containment policies impacted movement of individuals. We estimate equation (1) with a disaggregated _Postt_ variable using standardized mobility in district _i_ for month _t_ as the outcome variable _yit_ . Since individual movement across the country was most severely restricted in April, we expect an increase in mobility in subsequent months. In May, we expect lower mobility in Red and Orange zone districts compared to Green zone ones. In June, mobility ought to have revived since mobility restrictions were lifted in June under ‘Unlock 2.0’.

Important channels through which the zone-based containment could affect nighttime lights are lower household income and consumption. To examine this, we estimate the following model with monthly household (indexed by _h_ ) level consumption and income.

10
If income and consumption changes are plausible channels for the main effects, we expect that both coefficients are lower in May and June in Red ( < 0) and Orange ( < 0) zone districts compared to Green zone districts. Conversely, positive coefficients would suggest that the associated γγ1, γγ3 γγ2, γγ4 districts are rebounding faster after the lockdown.

# **1.3. Heterogeneity analysis**

We conduct heterogeneity analysis on a number of dimensions. We divide the main dataset into subsamples with above-median and below-median population density, share of employment in services, average population age, and credit per capita.<sup>9</sup> We then estimate equation (1) on both sub-samples for each of the specified variables.

First, we examine heterogeneity by district-level population density both because it may drive the spread of infections and determine the type and extent of economic activity.

Next, we analyze the role of the sectoral composition in economic activity. The service sector relies significantly on personal interactions and is strongly impacted by containment measures. Consequently services have been hit hardest during the national lockdown (World Bank 2020). We use the share of employment in services to assess whether more severe restrictions continued to impact more strongly the districts with greater services activity.

Our analysis also examines the role of a district’s average population age on the effect of zonal restrictions on economic activity. Mobility restrictions might impact districts with older populations less if older workers travel less for work. Conversely, if older workers are more productive, or if older individuals are more susceptible to the COVID-19 infection, then restrictions due to the zone classifications might disproportionately impact older districts.

Finally, we examine the role of district’s average credit per capita, as a proxy for access to finance. Such districts might have more resources to cope with zone restrictions, decreasing the impact of the mobility restrictions on economic activity. Conversely, the impact of mobility restrictions on high-value businesses might have disproportionate negative impact on aggregate economic activity.

> 9 We use the median within the Red and Orange zone classifications and keep all Green zone districts.

11
**5  Results**

# **5.1 Main results**

Table 1 reports results from estimating equation (1) with the standardized nighttime light intensity per square kilometer as the main outcome variable, March and April 2020 as the pre-period, and May, June and July 2020 as the post-period. Column (1) of Table 1 reports results from the full specification, including all controls and fixed effects. The R-squared is 0.99, suggesting that observable variables and fixed effects in the model explain nearly all the variation in the outcomes. The coefficient on _Post_ is 1.16 ( _p_ < 0.01) indicating a recovery in aggregate economic activity across India as the severe lockdown of March and April transitioned to fewer restrictions in subsequent months.

Our main finding is that the recovery in nighttime light intensity over the entire post period was 0.043 σ lower in Red zone districts than in Green zone districts. Similarly, the recovery in nighttime light intensity in Orange zone districts was 0.009 σ lower ( _p_ < 0.01 for both). Another way to interpret these coefficients is that nighttime light intensity was lower by 8.9% for Red zone districts, and by 1.5% for Orange zone districts compared to Green zone districts. The two coefficients are statistically different from each other, confirming that Red zone districts had significantly lower nighttime light intensity compared to Orange zone districts in the post period. Given the high correlation between economic activity and nighttime light intensity, this implies that economic activity revived faster in Green zone districts than in Red and Orange zone districts.

We examine the impact of containment policies on nighttime lights separately for May, June and July as our _Postt_ months. Column (2) shows that nighttime light intensity was 0.06 σ lower in May ( _p_ < 0.01), 0.040 σ lower in June ( _p_ < 0.01), and 0.026 σ lower in July ( _p_ < 0.05) for Red zone districts. Simultaneously, nighttime light intensity was 0.010 σ lower in May ( _p_ < 0.05), 0.011 σ lower in June ( _p_ < 0.01) and 0.007 σ lower in July ( _p_ > 0.10) for Orange zone districts compared to Green zone districts.

12
Table 1: Effect of zonal classification on change in nightlight intensity

||Night<br>light in|-time<br>tensity|Border<br>districts|No metro<br>districts|Unchanged<br>zones|Shuffled<br>zones|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Red zone district*Post|-0.043<sup>∗∗∗</sup>||-0.035<sup>∗∗∗</sup>|-0.031<sup>∗∗∗</sup>|-0.041<sup>∗∗∗</sup>||
||(0.011)||(0.011)|(0.005)|(0.011)||
|Orange zone district*Post|-0.0094<sup>∗∗∗</sup><br>||-0.011<sup>∗∗∗</sup><br>|-0.0094<sup>∗∗∗</sup><br>|-0.0093<sup>∗∗∗</sup><br>||
||(0.003)||(0.003)|(0.002)|(0.003)||
|Post|1.16<sup>∗∗∗</sup>||1.17<sup>∗∗∗</sup>|1.14<sup>∗∗∗</sup>|1.16<sup>∗∗∗</sup>|1.17<sup>∗∗∗</sup>|
||(0.134)||(0.134)|(0.129)|(0.134)|(0.132)|
|Red zone district*May||-0.060<sup>∗∗∗</sup><br>(0.020)|||||
|Orange zone district*May||-0.010<sup>∗∗</sup>|||||
|||(0.005)|||||
|Post * May||1.07<sup>∗∗∗</sup><br>(0.139)|||||
|Red zone district*June||-0.040<sup>∗∗∗</sup><br>(0.011)|||||
|Orange zone district*June||-0.011<sup>∗∗∗</sup><br>(0.004)|||||
|Post * June||1.14<sup>∗∗∗</sup><br>(0.133)|||||
|Red zone district*July||-0.026<sup>∗∗</sup><br>(0.012)|||||
|Orange zone district*July||-0.0066<br>(0.005)|||||
|Post * July||1.15<sup>∗∗∗</sup><br>(0.132)|||||
|Shuffled Red zones*Post||||||-0.0027<br>(0.003)|
|Shuffled Orange zones*Post||||||-0.00041<br>(0.005)|
|Per-capita COVID Infections|-0.067|-0.071|-0.073|-0.024<sup>∗∗∗</sup>|-0.065|-0.076|
||(0.051)|(0.052)|(0.060)|(0.009)|(0.052)|(0.050)|
|_Standard Controls:_|||||||
|Previous year nightlights|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Months (2020) fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean of dependent variable|6.76|6.76|6.76|6.76|6.76|6.76|
|Std. dev. of dependent variable|26.32|26.32|26.32|26.32|26.32|26.32|
|R-squared|0.997|0.996|0.996|0.992|0.996|0.992|
|Number of observations|3632|3632|2972|3552|3557|3632|

Notes: The unit of observation is a district-month. Asterisks denote significance: ∗ _p_ < 0.10, ∗∗ _p_ < 0.05, ∗∗∗ _p_ < 0.01. Standard errors clustered at state level.

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Our results can be viewed in context of other recent economic shocks in India. For instance, ChodorowReich et al. (2020) estimate the impact of India’s demonetization shock as a 11.7% contraction in nighttime light intensity, very similar to the gap between Red and Green zone districts in May.

# **Robustness**

Figure 2 shows month by month coefficients from estimation of equation (2). Most of the coefficients are close to and statistically indistinguishable from the null, with no discernible pattern in the pre-period. This finding offers greater confidence that the main results are not driven by differential pre-existing trends in nighttime light intensity.

Column (3) in Table 1 reports the results with a restricted sample of districts that border one with a different zone classification. We find that the main coefficients are qualitatively consistent with those in Column (1) of Table 1, suggesting that the differences across district classifications are not driven by omitted factors. Column (4) reports the results from a sample that omits large metropolitan cities that disproportionately drive nighttime light intensity in India. The reported coefficients (-0.031 σ, _p_ < 0.01 for Red zone districts and -0.009 σ, _p_ < 0.01 for Orange zone districts) are qualitatively consistent with those from the main sample. Column (5) in Table 1 reports the results after dropping the 15 districts where zone classifications changed during the second half of the differential lockdown, and finds no change from the main findings.

Finally, Column (6) report the results after randomly scrambling the zone classifications. The new coefficients are small and statistically indistinguishable from zero, which suggests that the results in Column (1) of the table are not driven by spurious correlations. As a result of these analyses, we have great confidence in the robustness of the main findings.
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# **5.2  Channels**

Column (1) in Table 2 shows that mobility was 0.15 σ lower in Red zone districts in May compared to Green zone districts ( _p_ < 0.01), consistent with the most severe mobility restrictions in those districts. Orange zone mobility was also lower than Green zone districts, but the impact was not statistically significant even at the 10 percent level (-0.004 σ, _p_ > 0.10). During ‘Unlock 1.0’ in June, mobility rebounded in both Red and Orange zone districts, consistent with removal of the zonal classifications. These findings corroborate the importance of mobility restrictions in the differential containment policies, while suggesting an important channel through which containment policies led to relatively lower economic activity in May.

Consistent with lower mobility in Red zone districts, income was lower by 0.065 σ ( _p_ < 0.01) in May, though this did not translate into consumption differences -0.011 σ ( _p_ > 0.10). Orange zone districts experienced both relatively lower incomes (-0.147 σ, _p_ < 0.01) as well as consumption (-0.027σ, _p_ < 0.01). The pattern of these results diverged in June as Unlock 1.0 commenced. Income gaps persisted in June (- 0.085 σ, _p_ < 0.01 for Red, and -0.068 σ, _p_ < 0.01 for Orange zone districts) but consumption rebounded sharply (0.074 σ, _p_ < 0.01 for Red, and 0.071 σ, _p_ < 0.01 for Orange zone districts). One possible reason for this rebound is that households in Red zone districts could not reduce or delay consumption of nondurable goods, and used savings to finance these expenditures. This suggests that widespread job and income losses are potential reasons, but less so consumption, as macro channels through which the zone containment policies yielded lower aggregate economic activity.

# **5.3 Heterogeneity analysis**

This section discusses how the role of COVID-19 containment policies on economic activity differs by preexisting district characteristics such as population density, the share of services employment share, average age and per-capita bank credit. Table 3 reports that Red zone districts with above median population density had 0.088 σ ( _p_ < 0.01) lower nighttime light intensity compared to Green zone districts, while Orange zone districts had 0.024 σ ( _p_ < 0.01) lower nighttime light intensity. In contrast, districts with below median population density did not differ significantly from Green zone districts (Red Zone: -0.016 σ; Orange zone: -0.002 σ, _p_ > 0.10 for both). Our aggregate results are hence driven by the more densely populated areas.

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Table 2: Channels

||Mobility<br>(1)|Income<br>(2)|Consumption<br>(3)|
|---|---|---|---|
|Red zone district*May|-0.15<sup>∗∗∗</sup><br>|-0.065<sup>∗∗∗</sup><br>|-0.011<br>|
||(0.042)|(0.019)|(0.012)|
|Orange zone district*May|-0.0041|-0.147<sup>∗∗∗</sup>|-0.027<sup>∗∗∗</sup>|
||(0.024)|(0.017)|(0.010)|
|Post * May|0.11|0.053<sup>∗∗∗</sup>|0.045<sup>∗∗∗</sup>|
||(0.111)|(0.015)|(0.015)|
|Red zone district*June|0.23<sup>∗∗∗</sup>|-0.085<sup>∗∗∗</sup>|0.074<sup>∗∗∗</sup>|
||(0.037)|(0.017)|(0.012)|
|Orange zone district*June|0.11<sup>∗∗∗</sup>|-0.068<sup>∗∗∗</sup>|0.071<sup>∗∗∗</sup>|
||(0.022)|(0.016)|(0.010)|
|Post * June|0.88<sup>∗∗∗</sup>|0.201<sup>∗∗∗</sup>|0.274<sup>∗∗∗</sup>|
||(0.112)|(0.014)|(0.019)|
|Red zone district*July|0.34<sup>∗∗∗</sup><br>(0.043)|||
|Orange zone district*July|0.091<sup>∗∗∗</sup><br>(0.027)|||
|Post * July|0.69<sup>∗∗∗</sup><br>(0.118)|||
|Per-capita COVID Infections|-0.37<sup>∗∗∗</sup>|0.036<sup>∗∗</sup>|-0.215<sup>∗∗∗</sup>|
||(0.068)|(0.014)|(0.012)|
|_Controls:_||||
|Previous year nightlights|Yes|No|No|
|District fixed effects|Yes|No|No|
|State*Months (2020) fixed effects|Yes|Yes|Yes|
|Household fixed effects|N/A|Yes|Yes|
|R-squared|0.962|0.717|0.475|
|Number of observations|3223|299,781|299,254|

Notes: The unit of observation is a district-month. Asterisks denote significance: ∗ _p_ < 0.10, ∗∗ _p_ < 0.05, ∗∗∗ _p_ < 0.01. Standard errors clustered at state level.

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Table 3: Heterogeneity Analysis

||Populatio<br>|n Density<br>|Services e<br>|mployment<br>|Mea<br>|n Age<br>|Bank<br>|Deposits<br>|
|---|---|---|---|---|---|---|---|---|
||Above<br>median<br>(1)|Below<br>median<br>(2)|Above<br>median<br>(3)|Below<br>median<br>(4)|Above<br>median<br>(5)|Below<br>median<br>(6)|Above<br>median<br>(7)|Below<br>median<br>(8)|
|Post Period|1.24<sup>∗∗∗</sup>|-0.037<sup>∗∗∗</sup>|0.27<sup>∗∗∗</sup>|0.20<sup>∗∗∗</sup>|1.16<sup>∗∗∗</sup>|0.0073<sup>∗</sup>|1.16<sup>∗∗∗</sup>|0.0043|
||(0.136)|(0.009)|(0.060)|(0.010)|(0.136)|(0.004)|(0.143)|(0.006)|
|Red zone district*Post period|-0.088<sup>∗∗∗</sup>|-0.016|-0.086<sup>∗∗∗</sup>|-0.016<sup>∗∗∗</sup>|-0.057<sup>∗∗∗</sup>|-0.031<sup>∗∗∗</sup>|-0.091<sup>∗∗∗</sup>|-0.016<sup>∗∗∗</sup>|
||(0.020)|(0.010)|(0.022)|(0.003)|(0.019)|(0.005)|(0.025)|(0.003)|
|Orange zone district*Post period|-0.024<sup>∗∗∗</sup>|-0.002|-0.017<sup>∗∗</sup>|-0.0014|-0.013<sup>∗∗</sup>|-0.0075<sup>∗∗∗</sup>|-0.028<sup>∗∗∗</sup>|-0.0055<sup>∗∗∗</sup>|
||(0.006)|(0.003)|(0.007)|(0.001)|(0.006)|(0.002)|(0.010)|(0.001)|
|Per-capita COVID Infections|-0.073<sup>∗</sup>|-0.038|-0.071|-0.029<sup>∗∗</sup>|-0.059|-0.037<sup>∗∗∗</sup>|-0.059|-0.0081<sup>∗</sup>|
||(0.038)|(0.070)|(0.049)|(0.013)|(0.057)|(0.013)|(0.057)|(0.005)|
|Red*Post (Above) = Red*Post (Below)|-0.0|72<sup>∗</sup>|-0|.07|-0|.026|-0.|075<sup>∗</sup>|
|Orange*Post (Above) = Orange*Post (Below)|-0.02|22<sup>∗∗</sup>|-0.0|156|-0.|0055|-0.|0225|
|Standard controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.997|0.993|0.996|0.993|0.996|0.996|0.993|0.994|
|Number of observations|2,579|2,542|2,217|2,245|2,772|2,455|2,487|2,495|

Notes: The unit of observation is a district-month. Above median category includes observations with value equal to median value. Asterisks denote significance: ∗ _p_ < 0.10, ∗ ∗ _p_ < 0.05, ∗ ∗ ∗ _p_ < 0.01. Standard errors clustered at state level.
We report that Red zone districts with an above median share of services wage employment experienced a 0.086 σ ( _p_ < 0.01) lower and Orange zones experienced a 0.017 σ lower ( _p_ < 0.05) nighttime light intensity compared to Green zones. In contrast, differences in nighttime light intensity in districts with below median services wage employment were quantitatively smaller both for Red zone districts (0.016 σ, _p_ < 0.1) as well as for Orange zone districts (0.001 σ, _p_ > 0.10).

Table 3 also examines the influence of the population’s age structure on the impact of COVID-19 containment policies. Nighttime light intensity in the sub-sample of older districts was 0.057 σ ( _p_ < 0.01) lower in Red zone districts, and 0.013 σ ( _p_ < 0.05) lower in Orange zone districts than Green zone districts. The nighttime light intensity gaps in the relatively younger sub-sample was lower in both Red zone (-0.031 σ, _p_ < 0.01) and Orange zone districts (-0.0075 σ, _p_ < 0.01). This indicates that the marginal impact of the restrictions was relatively greater in districts with above median average age.

Finally, nighttime light intensity gaps were greater (Red zone: -0.091 σ; Orange zone: -0.028 σ, _p_ < 0.01 for both) in the sub-sample of districts with above median bank credit per capita, compared to the sub sample of districts with below median per capita credit (Red zone: -0.016 σ; Orange zone: -0.005 σ, _p_ < 0.01 for both). These results point to greater economic contraction when districts with higher access to finance faced disruption in business activity.

Taken together, we find that the Red and Orange zone districts in more developed areas (greater population density, with older residents, more services employment and bank credit) experienced relatively greater nightlight intensity gaps during the differential lockdown period.

# **6 Conclusion**

Government intervention is critical to mitigate the impact of pandemics. We focus on the economic cost of containment measures by examining the impact of spatially heterogeneous policies on nighttime light intensity. Districts with the most severe restrictions witnessed 12.4% (0.060 standard deviation) lower nighttime light intensity compared to those with the least restrictions in May 2020. The decrease in districts with intermediate restrictions was 1.7% (0.010 standard deviation) lower compared to those with the least restrictions. Restricted mobility and lower household income, but not consumption, are potential channels for these findings. These estimates point to large short-run costs of containment policies and especially of the mobility restrictions that differentiated Red zone and Orange zone districts.

Our findings should be read with a few caveats. First, the estimates of the impact of government containment policies on economic activity in this context might be different in other economic and social 18
contexts. Our heterogeneity analysis suggests that the ability to respond to government restrictions varies, which enables economic activity despite mobility restrictions. The nature of government containment policies could also vary, and different types or intensity of policies could produce qualitatively different aggregate responses. Second, the main results reported in nighttime light intensity cannot be directly converted to GDP, and we can at best report a range of estimates based on past studies. Using elasticities from a cross-country study accounting for a country’s level of development (Hu and Yao 2019) suggests that Red zone districts had between 12.4 and 18.6 percent lower GDP than Green zone districts in May and on average between 8.9 and 13.4 percent lower GDP from May to July. However, the elasticity of nighttime lights to GDP might have changed during the COVID-19 pandemic and, moreover, may not be equal across districts. Third, we do not develop a comprehensive model of epidemics and pandemics, associated containment policies and the corresponding economic response. Absent such a model, policy counterfactuals such as more targeted containment or subsidies for private initiatives are difficult to estimate.

Nonetheless, our work accounts for the role of government containment policies, which has been much debated around the world for the COVID-19 pandemic, on aggregate economic outcomes. We hope that the results will guide future research and policy analysis.

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