\begin{center}
{\LARGE Online Appendix}
\end{center}

\section{Policy Timeline and Econometric Interpretation}\label{app:policy}
\label{policy_timelines}

The main results use data on migration patterns to predict patterns of confirmed COVID-19 cases near the start of its spread in Bangladesh, India, and Pakistan. A potential concern is that correlations between migration and confirmed COVID-19 cases could be a mechanical result of testing policy---since confirming a case requires a test and not everyone was eligible for testing. A mechanical bias could arise through at least three sources. 

First, especially at the start of the pandemic, countries were selective in who could be tested. Asymptomatic people were generally not tested, nor were all people with symptoms tested. Instead, at least early on in Bangladesh and India, people with symptoms were more likely to be tested if they were a migrant or had been in close contact with a migrant. These screening criteria could mechanically create a strong correlation between migration and confirmed COVID-19 cases.

To address the concern, we describe shifts in policy below. Robustness checks in Appendix \ref{app:windows} show that as testing criteria became more inclusive (dropping the early screening criteria that had been based on migration status), correlations between migration and contagion in fact strengthened. 

Second, testing centers might be more likely to be placed in areas with higher migrant populations. This again could drive a mechanical correlation between the incidence of migration and the incidence of COVID-19. Again, robustness checks show that this does not drive the results. In regressions reported in Appendix section \ref{section_robustness_labs}, we control for the number of testing centers in each district, and the main results are unchanged. This suggests that neither testing criteria nor testing availability drive the correlation between migration and contagion. 

Third, the correlations could be created by a steady and growing influx of international migrants (rather than through community spread to non-migrants). However, this was not the case. All three countries restricted their international borders early in the sample periods, and international flights had not resumed by the end of the sample periods. 

For example, the sample in India begins on March 15 with the 100th COVID case, and  India closed its international borders by March 22 (8 days later). Similarly, the Pakistan sample starts on March 16 and the last international flight to Pakistan was on March 21 (5 days later). In Bangladesh, the sample starts on April 6 and flights from continental Europe, North America, and the Gulf had already ended on March 14.

Borders were not entirely tight after these restrictions. There were still some flights from China to Bangladesh and some from the UK, and there were a few charter flights that arrived in India and Pakistan after the travel ban. But the numbers of people coming in at that point were relatively small—and much smaller than can explain COVID incidence. Again, in India and Pakistan, the correlations in Appendix section \ref{section_robustness_labs} continued to grow during the sample period, which is consistent with community spread.


\subsection{Policy timeline in Bangladesh (Sample: April 6-May 20)} 

\vspace{1em}
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      \draw (\x cm,3pt) -- (\x cm,-3pt);

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    \draw (0,0) node[below=3pt] {$  $} node[below=5pt] {{March 8}};
    \draw (0,0) node[below=19pt] {$  $} node[below=19pt] {First COVID-19 case};
    \draw (3,0) node[below=3pt] {$ $} node[above=5pt] {March 26: General };
    \draw (3,0) node[below=19pt] {$ $} node[above=19pt] {Holiday (lockdown) imposed};
    \draw (5.5,0) node[below=3pt] {$  $} node[below=5pt] {March 31: International};
    \draw (5.5,0) node[below=19pt] {$  $} node[below=19pt] { borders closed};
    \draw (8,0) node[above=13pt] {\textbf{Day 1: April 6}} node[above=13pt] {$  $};
    \draw (8,0) node[above=3pt] {100th COVID-19 case} node[above=3pt] {$  $};
    \draw (12,0) node[below=3pt] {$ $} node[below=5pt] {\textbf{Day 45: May 20}};
    \draw (12,0) node[below=3pt] {$ $} node[below=19pt] {End of study period};
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\vspace{1em}

In Bangladesh, testing requirements were such that all “suspect cases” were to be tested, defined as all symptomatic individuals with travel history or contact with confirmed cases, as well as all symptomatic individuals with respiratory issues that required hospitalization. 
Specifically: (a) the patient had acute respiratory illness (fever and at least one sign or symptom of respiratory disease, e.g., cough, shortness of breath) and (b) either had very recently (i) lived in or traveled to a region reporting community transmission of COVID-19 or (ii) had been in contact with someone who had been abroad who had a confirmed or probable COVID-19 case. Alternatively, they could be showing clear symptoms of COVID-19 infection and requiring hospitalization. People with less severe symptoms were told to stay home and were not tested.

The criteria exclude symptomatic individuals who were (a) not sick enough to go to the hospital and (b) who had not been in clear contact with a confirmed case or international traveler. To confirm what was happening on the ground (rather than just in documents), we spoke with a first-responder in charge of COVID patients in a government hospital in Dhaka. His response was that by the start of our sample period, they were testing everyone who was symptomatic, irrespective of whether they had been in contact with travelers.

A second problem for analysis in Bangladesh is that the overall testing rate was very low relative to India and Pakistan. On a population-adjusted basis, India’s testing rate is about 3 times that in Bangladesh. See, for example, the data on testing and cases at Our World in Data (``COVID-19: Daily tests vs. Daily new confirmed cases per million''), https://ourworldindata.org/grapher/covid-19-daily-tests-vs-daily-new-confirmed-cases-
per-million?country=BGD~IND~PAK (accessed September 7, 2020).

The results from Bangladesh are thus noisy and we do not in fact find strong evidence of an international-migration-COVID link in Bangladesh. 


\subsection{Policy timeline in India (Sample: March 15-April 28)}

\vspace{1em}
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    \draw (0,0) node[below=3pt] {$  $} node[below=5pt] {\textbf{Day 1: March 15}};
    \draw (0,0) node[below=19pt] {$  $} node[below=19pt] {100th COVID-19 case};
    \draw (3,0) node[below=3pt] {$ $} node[above=13pt] {Day 6: All symptomatic};
    \draw (3,0) node[below=3pt] {$ $} node[above=3pt] {individuals to be tested};
    \draw (5.5,0) node[below=3pt] {$  $} node[below=5pt] {Day 8: International};
    \draw (5.5,0) node[below=19pt] {$  $} node[below=19pt] {borders closed};
    \draw (8,0) node[above=13pt] {Day 11: Nationwide} node[above=13pt] {$  $};
    \draw (8,0) node[above=3pt] {lockdown imposed} node[above=3pt] {$  $};
    \draw (12,0) node[below=3pt] {$ $} node[below=5pt] {\textbf{Day 45: April 28}};
    \draw (12,0) node[below=3pt] {$ $} node[below=19pt] {End of study period};
\end{tikzpicture}
\vspace{1em}

The sample in India begins on March 15 with the 100th COVID case. At the start of the sample (i.e., March 15), testing protocols had travel history as a criterion, but testing was not exclusive to international travelers or their contacts. 

The Indian Council of Medical Research expanded its criteria for testing over time. At the start of the sample (i.e., March 15, 2020), travel history was a criterion used to determine whether or not to administer COVID tests. On March 17, guidance was issued recommending testing for (i) all asymptomatic individuals who had undertaken international travel, (ii) all symptomatic contacts of laboratory confirmed positive cases, and (iii) healthcare workers managing respiratory distress and/or severe acute respiratory illness (fever, cough and/or shortness of breath). On March 20 (day 6 of our sample), this was expanded to include all hospitalized patients with severe acute respiratory illness, as well as asymptomatic direct and high-risk contacts of confirmed cases. On April 9, this was further revised to include all symptomatic individuals in hot spots or clusters (as defined by the Ministry of Health and Family Welfare) and large migration gatherings and evacuee centers.

Thus, by the middle of the sample window, testing had substantially widened, although people who were not sick enough to be hospitalized were generally not tested unless they met the travel or contact criteria. 

After the study period ended on April 28, 2020, the government launched a program to repatriate migrants in other countries, beginning on May 7, 2020 with flights from Abu Dhabi and Dubai ( \url{https://mea.gov.in/vande-bharat-mission-list-of-flights.htm}). The repatriation does not affect the results here.


\subsection{Policy timeline in Pakistan (Sample: March 16-April 27)}
\vspace{1em}
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    \draw (0,0) node[below=19pt] {$  $} node[below=19pt] {100th COVID-19 case};
     \draw (5.5,0) node[below=3pt] {$  $} node[below=5pt] {Day 7: International};
    \draw (5.5,0) node[below=19pt] {$  $} node[below=19pt] {borders closed};
    \draw (8,0) node[above=13pt] {Day 8: Lockdown} node[above=13pt] {$  $};
    \draw (8,0) node[above=3pt] {imposed} node[above=3pt] {$  $};
    \draw (12,0) node[below=3pt] {$ $} node[below=5pt] {\textbf{Day 43: April 27}};
    \draw (12,0) node[below=3pt] {$ $} node[below=19pt] {End of study period};
\end{tikzpicture}
\vspace{1em}


The 100th case in Pakistan was confirmed on March 16. In Pakistan, all patients showing symptoms were tested, and contact with someone who had traveled internationally was not a screen for testing. 

The initial official protocol was that all patients showing symptoms are tested, but Pakistan had only 15 testing labs at the start of April and only 60 by the start of May.\footnote{Sources include: https://www.dawn.com/news/1538338, http://covid.gov.pk/facilities/List of Province-wise COVID-19 Testing Facilities Pakistan.pdf, http://covid.gov.pk/facilities/30 Apr Current Laboratory Testing Capacity for COVID.pdf.} Random testing started in Lahore and then extended to Karachi \citep{dailypakistan_tests}.\footnote{At the end of May 2020, the testing capacity was about 14,000 tests a day with an aim to reach 20,000 a day (\url{https://www.dawn.com/news/1538338}).}

The tables in Appendix section \ref{app:windows} show that correlations are stronger at the end of the period, consistent with community-spread.


\section{Data Appendix}\label{app:data}

\subsection{Bangladesh}

\noindent\textbf{Bangladesh COVID-19 Cases:}

\begin{itemize}
  \item \textbf{Any Cases:} Indicator variable equal to 1 if a district has at least 1 case of COVID-19, and 0 otherwise. Source: \url{www.iedcr.gov.bd}, May 20, 2020.

  \item \textbf{Cases per 1 Million People:} Cumulative number of COVID-19 cases in the district as of date $t$ divided by the  district population in 2011 (see population below). Source: \url{www.iedcr.gov.bd}, May 20, 2020 and Population and Household Census, 2011.
\end{itemize}

\noindent\textbf{Bangladesh Outmigration Variables:}

\begin{itemize}
  \item \textbf{International Outmigration:} We used nationally representative household survey that asked specific questions about having a migrating member in the household and the location of the migrating member. We calculated the number of international migrants within the district per 100 people. Source: Household Income and Expenditure Survey (HIES 2016). Calculated district-wise as the number of migrants surveyed divided by the number of individuals surveyed (in hundreds). Out-migration rates adjusted using survey weights.

  \item \textbf{Domestic Outmigration:} Number of international migrants within the district per 100 people. Source: Household Income and Expenditure Survey (HIES 2016). Calculated district-wise as the number of migrants surveyed divided by the number of individuals surveyed (in hundreds). Out-migration rates adjusted using survey weights.
\end{itemize}

\noindent\textbf{Bangladesh Controls:}

\begin{itemize}
  \item \textbf{Population:} District level population, in millions, based on Population Census of 2011, reported by the Bangladesh Bureau of Statistics (BBS). Source: Population and Household Census, 2011, BBS. 

  \item \textbf{Population Density:} Number of individuals per square kilo-meter. Source: Bangladesh Bureau of Statistics, Population Census report, 2011. Calculated as the total population of the district divided by the district area (measured in squared kilometer). 

  \item \textbf{Fraction Urban Population:} Number of individuals residing in urban areas of the district divided by the total number of individuals surveyed from that district. Source: Household Income and Expenditure Survey (HIES 2016). Fraction below urbanization adjusted using survey weights.

  \item \textbf{Fraction Below Poverty Line:} Number of households below the poverty line in the district reported in the interactive poverty map of Bangladesh. Source: \url{www.worldbank.org/en/data/interactive/2016/11/10/bangladesh-poverty-maps}

  \item \textbf{Number of Testing Labs:} Total number of government laboratories, private laboratories, and collection sites in the district. Source: compiled from various newspaper articles, as of May 20, 2020.
  
   \item \textbf{Number of Hospital Beds:} Total number of hospital beds at public hospitals within the district, as reported in 2016 - 2017. Source: Management Information System (MIS) of DGHS (Directorate General of Health Services) of Ministry of Health \& Family Welfare of Bangladesh. \url{https://dghs.gov.bd/index.php/en/home}
  
%   \item \textbf{Number of Ambulances:} Total number of functional ambulances at public hospitals within the district, as reported in 2016 - 2017. Source: Management Information System (MIS) of DGHS (Directorate General of Health Services) of Ministry of Health \& Family Welfare of Bangladesh. \url{https://dghs.gov.bd/index.php/en/home}
   
%   \item \textbf{Hospital Bed Occupancy Rate:} Bed occupancy rate at public hospitals within the district, as reported in 2016 - 2017. Source: Management Information System (MIS) of DGHS (Directorate General of Health Services) of Ministry of Health \& Family Welfare of Bangladesh. \url{https://dghs.gov.bd/index.php/en/home}
   
   
\end{itemize}

\noindent\textbf{Bangladesh Data Cleaning Notes:}

\begin{itemize}
  \item  The capital city of Bangladesh (Dhaka) has been dropped from the analysis.
  \item The top 1\% of each variable (except the number of testing labs) has been winsorized to remove outliers.
  \item In the IEDCR daily reports, Dhaka is reported twice: Dhaka City and Dhaka (District). We aggregate both this under Dhaka district. We noticed discrepancy between what is reported as a total of the day (T) and district-wise aggregation (D), where T is always greater than D. As a rule of thumb, T-D is added with Dhaka for consistency with the daily new reported cases. Also, we notice discrepancy at the district level reporting, which sometimes goes down than the past reported numbers. In such cases, we kept the higher numbers.
\end{itemize}

%\pagebreak
\subsection{India}

\noindent\textbf{India COVID-19 Cases:}

\begin{itemize}
  \item \textbf{Any Cases:} Indicator variable equal to 1 if a district has at least 1 case of COVID-19, and 0 otherwise. Source: \url{www.covindia.com}, April 28, 2020.

  \item \textbf{Cases per 1 Million People:} Cumulative number of COVID-19 cases in the district as of date $t$ divided by the projected district population in 2020 (see population below). Source: \url{www.covindia.com}, April 28, 2020 and Primary Census Abstract, 2001 - 2011.

  \item Note: High correlation between data from \url{www.covindia.com} on April 28, 2020 and data from the Ministry of Health and Family Welfare on April 28, 2020: Correlation = 0.901. Using data from \url{www.covindia.com} as the government data is not available at the district-day level. 
\end{itemize}

\noindent\textbf{India Outmigration Variables:}

\begin{itemize}
  \item \textbf{International Outmigration:} Number of migrants who left the district within the past 5 years for another country, per 100 people. Source: National Sample Survey, 2007-08. Calculated district-wise as the number of migrants surveyed divided by the number of individuals surveyed (in hundreds). Out-migration rates adjusted using survey weights.

  \item \textbf{Domestic Outmigration:} Number of migrants who left the district within the past 5 years for another district in India, per 100 people. Source: National Sample Survey, 2007-08. Calculated district-wise as the number of migrants surveyed divided by the number of individuals surveyed (in hundreds). Out-migration rates adjusted using survey weights.
\end{itemize}

\noindent\textbf{India Controls:}

\begin{itemize}
  \item \textbf{Population:} Projected number of individuals in 2020, in millions. Source: Primary Census Abstract, 2001 - 2011. Calculated as a projection based on district-wise population growth rates between 2001 and 2011. 

  \item \textbf{Population Density:} Number of individuals (thousands) per square kilometer. Source: Primary Census Abstract, 2001 - 2011 and 2011 district shapefiles from the World Bank. Calculated as the projected number of individuals in 2020 divided by the land area of the district. 

  \item \textbf{Fraction Urban Population:} Number of individuals residing in urban areas of the district in 2011 divided by the total number of individuals residing in the district in 2011. Source: Primary Census Abstract, 2011. 

  \item \textbf{Fraction Below Poverty Line:} Number of households below the poverty line in the district divided by the total number of households in the district. Source: National Sample Survey, 2007-08. A household in a rural area is defined to be below the rural poverty line if the daily per capita household expenditure was less than 22.42 rupees (2012 rural poverty line). A household in an urban area is defined to be below the urban poverty line if the daily per capita household expenditure was less than 28.65 rupees (2012 urban poverty line). Poverty rates adjusted using survey weights.
  
  \item \textbf{Primary Health Centers per Capita:} The number of Primary Health Centers (PHCs) in the district divided by projected population (millions) in the district in 2017. Source: ``District-wise availability of health centres in India as on 31st March, 2017'': Open Government Data (OGD) Platform India and Primary Census Abstract, 2001 - 2011. 

  \item \textbf{Number of Testing Labs:} Total number of government laboratories, private laboratories, and collection sites in the district. Source: Indian Council of Medical Research, April 28, 2020.  
\end{itemize}

\noindent\textbf{India Data Cleaning Notes:}

\begin{itemize}
  \item All districts with state capitals have been dropped for the analysis (for example, Delhi, Mumbai, Chennai, Kolkata, etc.)
  \item The top 1\% of each variable (except the number of testing labs) has been winsorized to remove outliers.
\end{itemize}



\subsection{Pakistan}
Pakistan has a total of 154 districts at present. Of these due to survey difficulties and data issues we do not include the 14 in Gilgit–Baltistan, 7 which were formerly in Federally Administered Tribal Areas (FATA)\footnote{On 28 May, 2018 an amendment was passed that merged all seven agencies of Federally Administered Tribal Areas (FATA) into  Khyber Pakhtunkhwa province. May 2018. \url{https://tribune.com.pk/story/1762047/1-notification-issued-composition-new-administrative-divisions-mohmand-khyber/}} and 10 in Azad Jammu and Kashmir. We do not separately consider Karachi East, West, Malir, Central and Korangi but rather only Karachi as a whole and also do not separately consider upper and lower Kohistan but rather only Kohistan as a whole. Additionally, for two districts in Balochistan, Kech and Pangaur, we do not have data for international migration rates and district controls so these have been dropped for all analysis reported. 

\begin{enumerate}
\item  \textbf{Internal Migration: }From the Labour Force Survey 2007-08. 
\begin{itemize}
    \item \textbf{Domestic migrants per 100 people:} number of internal migrants from a district per 100 people surveyed from the district. 
    \item \textbf{Rural-urban migrants per 100 people:} number of internal migrants from a rural area who are currently in an urban area per 100 people surveyed from the sending district.
\end{itemize}

\item \textbf{International migrants per 100 people: }From the Pakistan Social and Living Standards Measurement (PSLM) 2014-15 survey. Total number of international migrants (assuming 1 international migrant in every household reporting receiving foreign remittances) in a district per 100 people surveyed in the district.
\item \textbf{Shapefiles: }These data were extracted from the GADM database (\url{www.gadm.org}), version 2.5, July 2015. 
\item \textbf{Covid cases:} Data obtained from the Pakistan Institute of Development Economics Covid-19 dashboard \url{https://pide.org.pk/index.php?option=com_content&view=article&id=695} OR data obtained from the Government of Pakistan Covid-19 Dashboard \url{http://covid.gov.pk/stats/pakistan}. The first recorded cases in Pakistan are on 10th March 2020 (cases jumped from 53 on 15th March to 187 on 16th March)
\item \textbf{District level controls} obtained from: \url{http://www.data4pakistan.com/}. Glossary of indicators: \url{http://www.data4pakistan.com/download/Glossary-of-Indicators.pdf}

\begin{itemize}
    \item \textbf{Poverty rate:} The percentage of population living below the official national poverty line. The district poverty headcount rate is calculated using a small area estimation approach based on successive pairs of HIES and PSLM survey rounds. The HIES and PSLM are fielded in alternate years, and the following HIES–PSLM pairs were used to construct district poverty rates 2014/15: HIES 2013/14 and PSLM 2014/15. The software used for the small area estimation is the sae command in Stata. sae is a user-written program that is freely distributed to the research community. [Source: World Bank. (2017). “Small Area Estimation: An extended ELL approach.”] 
      \item \textbf{Accessibility:} Percentage of population which has access to a health clinic or hospital within 15 minutes of their dwelling. 
\item \textbf{Urban pop share:} Percentage of population in the district who lives in urban areas. This is obtained from the PSLM.
\item \textbf{Population:} District population figures based on the provisional results of the 2017 Population Census. 
\item \textbf{Population density:} Population density is the population from the census divided by the area of the district. fieldarea, a user-written Stata program is used to calculate the area of each district.
\end{itemize}
\item \textbf{Number of Testing Labs: }Total number of government laboratories, private laboratories, and collection sites in the district.  Source: National Institute of Health. April 28, 2020.
\end{enumerate}

\section{Robustness Checks}\label{app:robustnes}

In this section, we assess robustness of our results to controlling for the number of testing labs in section \ref{section_robustness_labs} and the inclusion of state, provincial, and country capitals in section \ref{section_robustness_capitals}. 

\subsection{Control for Testing Capacity}
\label{section_robustness_labs}

A potential concern with the analysis could be that the number of cases reported is constrained by the testing capacity of each district. To assess robustness of our results to testing capacity constraints, we additionally control for the number of COVID-19 testing labs in each district, thereby comparing the relationship between rates of out-migration and COVID-19 cases across districts with the same testing capacity. 

Table \ref{table_labs} presents these results. Columns (1) - (2) present the relationship between domestic out-migration and cases per capita, unconditional of the rate of international out-migration. Columns (3) - (4) present the relationship between international out-migration and cases per capita, unconditional of the rate of domestic out-migration. Columns (5) - (6) present the relationship between domestic and international out-migration and cases per capita. The odd numbered columns control for the set of five control variables (population, population density, the fraction of urban population, the fraction of population below the poverty line, and a measure of health access). The even numbered columns additionally control for the number of testing labs in the district.   

Across the three countries, an increase in the number of testing labs is associated with an increase in the number of cases per capita. In India, an additional testing lab in the district is associated with an increase of 1.12 - 1.14 cases per capita in the district. The relationship between the number of testing labs and cases per capita is positive but not statistically significant at conventional levels for Bangladesh and Pakistan.

Overall, the relationships between out-migration and cases per capita are robust to controlling for the number of COVID-19 testing labs in each district. For India, the international out-migration coefficient unconditional of domestic out-migration increases slightly from 1.253 in column (3) to 1.283 in column (4). The international out-migration coefficient conditional on the rate of domestic out-migration increases slightly from 1.235 in column (5) to 1.266 in column (6). These results are statistically significant at the 1\% level.

For Pakistan, the international out-migration coefficient unconditional of domestic out-migration increases slightly from 5.895 in column (3) to 6.023 in column (4) ($p <$ 0.05). The international out-migration coefficient conditional on the rate of domestic out-migration decreases slightly from 5.561 in column (5) to 5.659 in column (6) ($p <$ 0.05). 

\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{COVID-19 and Out-migration - Control for Number of Testing Labs}
\label{table_labs}
\resizebox{1\textwidth}{!}{
\begin{tabular}{l*{6}{c}}
\hline\hline
                    &\multicolumn{6}{c}{Dependent Variable: Cases per 1 Million People}\\ \cmidrule(lr){2-7} 
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}&\multicolumn{1}{c}{(5)}&\multicolumn{1}{c}{(6)}\\
\hline
\multicolumn{5}{l}{\emph{Panel A: Bangladesh (April 6 - May 20)}} \\
\hline
Number of Domestic Migrants&      -0.622         &      -0.846         &                     &                     &       0.629         &       0.386         \\
per 100 People (z-score)                    &     (1.770)         &     (1.839)         &                     &                     &     (2.147)         &     (2.179)         \\
[0.5em]
Number of International Migrants &                     &                     &      -4.122         &      -4.102         &      -4.319         &      -4.222         \\
per 100 People (z-score)                    &                     &                     &     (2.968)         &     (2.768)         &     (3.238)         &     (3.067)         \\
[0.5em]
Number of Testing Labs&                     &       7.510         &                     &       7.296         &                     &       7.225         \\
                    &                     &     (5.454)         &                     &     (4.831)         &                     &     (4.864)         \\
\hline
\(R^{2}\)           &       0.561         &       0.566         &       0.569         &       0.573         &       0.569         &       0.573         \\
Dependent Variable Mean &       19.17         &       19.17         &       19.17         &       19.17         &       19.17         &       19.17         \\
Observations        &        2,835         &        2,835         &        2,835         &        2,835         &        2,835         &        2,835         \\
\hline
\multicolumn{5}{l}{\emph{Panel B: India (March 15 - April 28)}} \\
\hline
Number of Domestic Migrants&       0.316         &       0.310         &                     &                     &       0.119         &       0.108         \\
per 100 People (z-score)                    &     (0.240)         &     (0.237)         &                     &                     &     (0.212)         &     (0.211)         \\
[0.5em]
Number of International Migrants&                     &                     &       1.253\sym{***}&       1.283\sym{***}&       1.235\sym{***}&       1.266\sym{***}\\
per 100 People (z-score)                    &                     &                     &     (0.460)         &     (0.446)         &     (0.458)         &     (0.446)         \\
[0.5em]
Number of Testing Labs&                     &       1.120\sym{**} &                     &       1.143\sym{**} &                     &       1.142\sym{**} \\
                    &                     &     (0.424)         &                     &     (0.441)         &                     &     (0.438)         \\
\hline
\(R^{2}\)           &       0.164         &       0.196         &       0.188         &       0.221         &       0.188         &       0.221         \\
Dependent Variable Mean &        2.56         &        2.56         &        2.56         &        2.56         &        2.56         &        2.56         \\
Observations        &       26,145         &       26,145         &       26,145         &       26,145         &       26,145         &       26,145         \\

\hline
\multicolumn{5}{l}{\emph{Panel C: Pakistan (March 16 - April 27)}} \\
\hline
Number of Domestic Migrants &      -3.316\sym{**} &      -3.470\sym{**} &                     &                     &      -2.635\sym{**} &      -2.744\sym{**} \\
    per 100 People (Z-score)                 &     (1.396)         &     (1.378)         &                     &                     &     (1.268)         &     (1.255)         \\
[0.5em]
Number of International Migrants &                     &                     &       5.895\sym{***}&       6.023\sym{***}&       5.561\sym{**} &       5.659\sym{**} \\
                 per 100 People (Z-score)     &                     &                     &    (2.173)         &     (2.174)         &     (2.127)         &     (2.130)         \\
[0.5em]
Number of Testing Labs &     &  3.098         &                     &       2.809         &                     &       2.462                             \\
                &    &     (2.226)         &                     &     (2.154)         &  &     (2.098) \\  \hline
\(R^{2}\)           &       0.297         &       0.293         &       0.327         &       0.323         &       0.336         &       0.333          \\

Dependent Variable Mean &       11.81         &       11.81         &       11.81         &       11.81         &       11.81         &       11.81         \\
Observations        &        4,773         &        4,773         &        4,773         &        4,773         &        4,773         &        4,773         \\
\hline
Controls            &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         \\
Day Fixed Effects   &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         \\
\hline\hline
\vspace{-1.5em}
\floatfoot{\footnotesize{Notes: State/Provincial/Country capitals have been omitted. The sample includes all days for which we have available data since the 100th COVID-19 case was reported in each country. All regressions include day fixed effects and the following district-level controls: population, population density, the fraction of urban population, the fraction of population below the poverty line, and a measure of health access (Bangladesh: number of hospital beds per capita, India: number of primary health centers per capita, Pakistan: percentage of population which has access to a health clinic or hospital within 15 minutes of their dwelling). Standard errors in parentheses and double-clustered by day and district. \sym{*}\(p<0.10\), \sym{**}\(p<0.05\), \sym{***}\(p<0.01\).}}
\end{tabular}
}
\end{table}
\FloatBarrier

\subsection{Inclusion of State Capitals}
\label{section_robustness_capitals}

Table \ref{table_capitals} presents the relationships between domestic and international out-migration and COVID-19 cases per capita, including in the sample all state, provincial, and country capitals for which data is available for all dependent and independent variables. Columns (1) - (2) present the relationship between domestic out-migration and cases per capita, unconditional of the rate of international out-migration. Similarly, columns (3) - (4) present the relationship between international out-migration and cases per capita, unconditional of the rate of domestic out-migration. Columns (5) - (6) present the relationship between domestic and international out-migration and cases per capita. 

Overall, the results on the relationship between international out-migration and cases per capita are robust to the inclusion of the state, provincial, and country capitals. For India, the international out-migration coefficient unconditional of domestic out-migration decreases slightly from 1.253 in column (2) of table \ref{table_intensive_extensive} to 1.211 in column (4) of table \ref{table_capitals}. The international out-migration coefficient conditional on the rate of domestic out-migration decreases slightly from 1.235 in column (4) of table \ref{table_intensive_extensive} to 1.190 in column (6) of table \ref{table_capitals}. These results are statistically significant at the 5\% level. 

For Pakistan, the international out-migration coefficient unconditional of domestic out-migration increases slightly from 6.023 in column (2) of table \ref{table_intensive_extensive} to 6.308 in column (4) of table \ref{table_capitals}. The international out-migration coefficient conditional on the rate of domestic out-migration increases slightly from 5.659 in column (4) of table \ref{table_intensive_extensive} to 5.924 in column (6) of table \ref{table_capitals}. These results are statistically significant at the 1\% level. 

\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{COVID-19 and Out-migration - Including Capitals}
\label{table_capitals}
\resizebox{1\textwidth}{!}{
\begin{tabular}{l*{6}{c}}
\hline\hline
                    &\multicolumn{6}{c}{Dependent Variable: Cases per 1 Million People}\\ \cmidrule(lr){2-7} 
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}&\multicolumn{1}{c}{(5)}&\multicolumn{1}{c}{(6)}\\
\hline
\multicolumn{5}{l}{\emph{Panel A: Bangladesh (April 6 - May 20)}} \\
\hline
Number of Domestic Migrants    &      -8.149         &       -0.686        &                     &                     &      -9.009         &       0.320        \\
per 100 People (z-score)       &     (5.742)         &     (1.776)         &                    &                    &     (5.866)         &     (2.119)         \\
[0.5em]
Number of International Migrants&                     &                     &       1.814         &      -3.405      &       3.835         &      -3.502         \\
per 100 People (z-score)        &                     &                     &     (3.736)         &    (2.903)      &     (3.698)         &     (3.166)         \\
\hline
\(R^{2}\)                       &       0.114         &       0.629         &      0.093         &       0.632       &       0.118         &       0.632         \\
Dependent Variable Mean       &       22.96         &       22.96         &       22.96         &       22.96           &       22.96         &       22.96          \\
Observations                   &        2,880         &        2,880         &        2,880         &        2,880         &        2,880         &        2,880         \\ 
\hline
\multicolumn{5}{l}{\emph{Panel B: India (March 15 - April 28)}} \\
\hline
Number of Domestic Migrants&     -0.0744         &       0.328         &                     &                     &      -0.257         &       0.141         \\
per 100 People (z-score)        &     (0.232)         &     (0.244)         &                     &                     &     (0.215)         &     (0.224)         \\
[0.5em]
Number of International Migrants&                     &                     &       1.666\sym{***}&       1.211\sym{**} &       1.694\sym{***}&       1.190\sym{**} \\
per 100 People (z-score)   &                     &                     &     (0.453)         &     (0.456)         &     (0.452)         &     (0.457)         \\
\hline
\(R^{2}\)           &       0.093         &       0.187         &       0.132         &       0.204         &       0.133         &       0.205         \\
Dependent Variable Mean  &        3.18         &        3.18         &        3.18         &        3.18         &        3.18         &        3.18         \\
Observations       &       27,540         &       27,540         &       27,540         &       27,540         &       27,540         &       27,540         \\
\hline
\multicolumn{5}{l}{\emph{Panel C: Pakistan (March 16 - April 27)}} \\
\hline
Number of Domestic Migrants &       0.563         &      -4.047\sym{***}&                     &                     &      -1.162         &      -3.384\sym{***}\\
           per 100 People (Z-score)         &     (1.192)         &     (1.359)         &                     &                     &     (0.952)         &     (1.247)         \\
[0.5em]
Number of International Migrants &                     &                     &       6.356\sym{***}&       6.308\sym{***}&       6.660\sym{***}&       5.924\sym{***}\\
      per 100 People (Z-score)              &                     &                     &     (1.798)         &     (2.070)         &     (1.852)         &     (2.005)         \\
\hline
\(R^{2}\)            &       0.187         &       0.342         &       0.250         &       0.365         &       0.252         &       0.376         \\   
Dependent Variable Mean &       13.29         &       13.29         &       13.29         &       13.29         &       13.29         &       13.29         \\
Observations        &        4,988         &        4,988         &        4,988         &        4,988         &        4,988         &        4,988         \\

\hline
Controls            &           &  \checkmark         &           &  \checkmark         &      &  \checkmark         \\
Day Fixed Effects   &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         \\
\hline\hline
\vspace{-1.5em}
\floatfoot{\footnotesize{Notes: State/Provincial/Country capitals have been included. The sample includes all days for which we have available data since the 100th COVID-19 case was reported in each country. All regressions include day fixed effects and the following district-level controls: population, population density, the fraction of urban population, the fraction of population below the poverty line, and a measure of health access (Bangladesh: number of hospital beds per capita, India: number of primary health centers per capita, Pakistan: percentage of population which has access to a health clinic or hospital within 15 minutes of their dwelling). Standard errors in parentheses and double-clustered by day and district. \sym{*}\(p<0.10\), \sym{**}\(p<0.05\), \sym{***}\(p<0.01\).}}
\end{tabular}
}
\end{table}
\FloatBarrier

\section{Out-migration and COVID-19: Results by 14-Day Periods}\label{app:windows}

\subsection{International Out-migration \& COVID-19}

\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{COVID-19 Cases and International Out-migration: Day 1 to 14 since 100\textsuperscript{th} Case}
\label{table_int1_migration}
\begin{tabularx}{\textwidth}{l*{4}{Y}}
\hline\hline
                    &\multicolumn{2}{c}{Any Cases}&\multicolumn{2}{c}{Cases per Million People}\\ \cmidrule(lr){2-3} \cmidrule(lr){4-5} 
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}\\

\hline
\multicolumn{5}{l}{\emph{Panel A: Bangladesh (April 6 - 19)}} \\
\hline
Number of International Migrants &       0.109\sym{**} &      0.0750         &       0.879         &      -0.999         \\
per 100 People (z-score)                   &    (0.0469)         &    (0.0475)         &     (0.560)         &     (0.608)         \\
\hline
\(R^{2}\)           &       0.194         &       0.294         &       0.067         &       0.506         \\
Dependent Variable Mean &         0.55         &         0.55         &        2.84         &        2.84         \\
Observations        &         882         &         882         &         882         &         882         \\
\hline
\multicolumn{5}{l}{\emph{Panel B: India (March 15 - 28)}} \\
\hline
Number of International Migrants&      0.0856\sym{***}&      0.0672\sym{***}&       0.634\sym{**} &       0.607\sym{**} \\
per 100 People (z-score)                    &    (0.0161)         &    (0.0146)         &     (0.233)         &     (0.227)         \\
\hline
\(R^{2}\)           &       0.111         &       0.223         &       0.167         &       0.173         \\
Dependent Variable Mean &         0.11         &         0.11         &         0.21         &         0.21         \\
Observations        &        8,134         &        8,134         &        8,134         &        8,134         \\

\hline
\multicolumn{5}{l}{\emph{Panel C: Pakistan (March 16 - 29)}} \\
\hline
Number of International Migrants &      0.0909\sym{**} &      0.0560         &     -0.0461         &       0.160         \\
           per 100 People (z-score)         &    (0.0318)         &    (0.0416)         &     (0.479)         &     (0.661)         \\
\hline
\(R^{2}\)           &       0.170         &       0.267         &       0.030         &       0.076         \\
Dependent Variable Mean &         0.45         &         0.45         &        2.66         &        2.66         \\
Observations        &        1,554         &        1,554         &        1,554         &        1,554         \\


\hline \hline
Controls &          &  \checkmark         &        &  \checkmark         \\
Day Fixed Effects   &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         \\
\hline\hline
\vspace{-1.5em}
\floatfoot{\footnotesize{Notes: State/Provincial/Country capitals have been omitted. The sample includes the first 14 days since the 100th COVID-19 case was reported in each country. All regressions include day fixed effects, while columns (2) and (4) additionally include the following district-level controls: population, population density, the fraction of urban population, the fraction of population below the poverty line, and a measure of health access (Bangladesh: number of hospital beds per capita, India: number of primary health centers per capita, Pakistan: percentage of population which has access to a health clinic or hospital within 15 minutes of their dwelling). Standard errors in parentheses and double-clustered by day and district. \sym{*}\(p<0.10\), \sym{**}\(p<0.05\), \sym{***}\(p<0.01\).}}
\end{tabularx}
\end{table}
\FloatBarrier


\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{COVID-19 Cases and International Out-migration: Day 15 to 28 since 100\textsuperscript{th} Case}
\label{table_int2_migration}
\begin{tabularx}{\textwidth}{l*{4}{Y}}
\hline\hline
                    &\multicolumn{2}{c}{Any Cases}&\multicolumn{2}{c}{Cases per Million People}\\ \cmidrule(lr){2-3} \cmidrule(lr){4-5} 
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}\\

\hline
\multicolumn{5}{l}{\emph{Panel A: Bangladesh (April 20 - May 3)}} \\
\hline
Number of International Migrants &      0.0319\sym{**} &      0.0105         &       3.020         &      -5.799\sym{*}  \\
per 100 People (z-score)                   &    (0.0144)         &    (0.0137)         &     (2.529)         &     (2.756)         \\
\hline
\(R^{2}\)           &       0.053         &       0.200         &       0.025         &       0.690         \\
Dependent Variable Mean &        0.94          &   0.94              &       16.17         &       16.17         \\
Observations        &         882         &         882         &         882         &         882         \\
\hline
\multicolumn{5}{l}{\emph{Panel B: India (March 29 - April 11)}} \\
\hline
Number of International Migrants&       0.132\sym{***}&       0.102\sym{***}&       1.745\sym{***}&       1.520\sym{**} \\
per 100 People (z-score)                    &    (0.0109)         &    (0.0105)         &     (0.541)         &     (0.553)         \\
\hline
\(R^{2}\)           &       0.116         &       0.272         &       0.152         &       0.191         \\
Dependent Variable Mean &         0.36         &         0.36         &        1.71         &        1.71         \\
Observations        &        8,134         &        8,134         &        8,134         &        8,134         \\

\hline
\multicolumn{5}{l}{\emph{Panel C: Pakistan (March 30 - April 12)}} \\
\hline
Number of International Migrants &       0.147\sym{***}&     0.003         &       4.447\sym{***}&       4.664\sym{**} \\
         per 100 People (z-score)           &    (0.031)         &    (0.039)         &     (1.451)         &     (1.867)         \\
\hline
\(R^{2}\)           &       0.102         &       0.428         &       0.113         &       0.205         \\
Dependent Variable Mean &0.68        &0.68     &9.30        &9.30        \\
Observations        &        1,554         &        1,554         &        1,554         &        1,554         \\


\hline \hline
Controls &          &  \checkmark         &        &  \checkmark         \\
Day Fixed Effects   &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         \\
\hline\hline
\vspace{-1.5em}
\floatfoot{\footnotesize{Notes: State/Provincial/Country capitals have been omitted. The sample includes days 15 - 28 since the 100th COVID-19 case was reported in each country. All regressions include day fixed effects, while columns (2) and (4) additionally include the following district-level controls: population, population density, the fraction of urban population, the fraction of population below the poverty line, and a measure of health access (Bangladesh: number of hospital beds per capita, India: number of primary health centers per capita, Pakistan: percentage of population which has access to a health clinic or hospital within 15 minutes of their dwelling). Standard errors in parentheses and double-clustered by day and district. \sym{*}\(p<0.10\), \sym{**}\(p<0.05\), \sym{***}\(p<0.01\).}}
\end{tabularx}
\end{table}
\FloatBarrier



\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{COVID-19 Cases and International Out-migration: Day 29 to 42 since 100\textsuperscript{th} Case}
\label{table_int3_migration}
\begin{tabularx}{\textwidth}{l*{4}{Y}}
\hline\hline
                    &\multicolumn{2}{c}{Any Cases}&\multicolumn{2}{c}{Cases per Million People}\\ \cmidrule(lr){2-3} \cmidrule(lr){4-5} 
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}\\
\hline
\multicolumn{5}{l}{\emph{Panel A: Bangladesh (May 4 - May 17)}} \\
\hline
Number of International Migrants &     0.00347         &   -0.000498         &       7.806         &      -6.043         \\
per 100 People (z-score)                   &   (0.002)         &   (0.0015)         &     (4.678)         &     (5.258)         \\
\hline
\(R^{2}\)           &       0.019         &       0.036         &       0.033         &       0.719         \\
Dependent Variable Mean &         0.99         &         0.99         &       32.14         &       32.14         \\
Observations        &         882         &         882         &         882         &         882         \\
\hline
\multicolumn{5}{l}{\emph{Panel B: India (April 12 - 25)}} \\
\hline
Number of International Migrants&       0.117\sym{***}&      0.0916\sym{***}&       2.236\sym{***}&       1.587\sym{**} \\
per 100 People (z-score)                    &    (0.0112)         &    (0.0117)         &     (0.628)         &     (0.658)         \\
\hline
\(R^{2}\)           &       0.060         &       0.199         &       0.058         &       0.167         \\
Dependent Variable Mean &         0.54         &         0.54         &        5.11         &        5.11         \\
Observations        &        8,134         &        8,134         &        8,134         &        8,134         \\

\hline
\multicolumn{5}{l}{\emph{Panel C: Pakistan (April 13 - 26)}} \\
\hline
Number of International Migrants &       0.172\sym{***}&     -0.018         &       14.41\sym{***}&       12.29\sym{***}\\
            per 100 People (z-score)        &    (0.031)         &    (0.028)         &     (3.155)         &     (4.043)         \\
\hline
\(R^{2}\)           &       0.140         &       0.596         &       0.301         &       0.374         \\
Dependent Variable Mean &0.68       &0.68         &          22         &          22         \\
Observations        &        1,554         &        1,554         &        1,554         &        1,554         \\


\hline \hline
Controls &          &  \checkmark         &        &  \checkmark         \\
Day Fixed Effects   &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark         \\
\hline\hline
\vspace{-1.5em}
\floatfoot{\footnotesize{Notes: State/Provincial/Country capitals have been omitted. The sample includes days 29 - 42 since the 100th COVID-19 case was reported in each country. All regressions include day fixed effects, while columns (2) and (4) additionally include the following district-level controls: population, population density, the fraction of urban population, the fraction of population below the poverty line, and a measure of health access (Bangladesh: number of hospital beds per capita, India: number of primary health centers per capita, Pakistan: percentage of population which has access to a health clinic or hospital within 15 minutes of their dwelling). Standard errors in parentheses and double-clustered by day and district. \sym{*}\(p<0.10\), \sym{**}\(p<0.05\), \sym{***}\(p<0.01\).}}
\end{tabularx}
\end{table}
\FloatBarrier

\pagebreak

\subsection{Domestic Out-migration \& COVID-19}

\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}

\caption{COVID-19 Cases and Domestic Out-migration: Day 1 to 14 since 100\textsuperscript{th} Case}
\label{table_dom1_migration}
\resizebox{1\textwidth}{!}{
\begin{tabular}{l*{8}{c}}

\hline\hline
                    &\multicolumn{4}{c}{Any Cases}&\multicolumn{4}{c}{Cases per Million People}\\ \cmidrule(lr){2-5} \cmidrule(lr){6-9} 
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}
                    &\multicolumn{1}{c}{(5)}&\multicolumn{1}{c}{(6)}&\multicolumn{1}{c}{(7)}&\multicolumn{1}{c}{(8)}\\

\hline
\multicolumn{5}{l}{\emph{Panel A: Bangladesh (April 6 - 19)}} \\
\hline
Number of Domestic Migrants&      0.0694         &      0.0836\sym{**} &      0.0480         &      0.0680\sym{*}  &      -0.487         &       0.257         &      -0.710         &       0.601         \\
per 100 People (z-score)                    &    (0.0395)         &    (0.0359)         &    (0.0379)         &    (0.0369)         &     (0.881)         &     (0.321)         &     (0.972)         &     (0.392)         \\
[0.5em]
Number of International Migrants &                     &                     &      0.0986\sym{*}  &      0.0538         &                     &                     &       1.031         &      -1.187\sym{*}  \\
per 100 People (z-score)                    &                     &                     &    (0.0469)         &    (0.0472)         &                     &                     &     (0.667)         &     (0.631)         \\
\hline
\(R^{2}\)           &       0.166         &       0.300         &       0.203         &       0.309         &       0.061         &       0.497         &       0.072         &       0.510         \\
Dependent Variable Mean &         0.55         &         0.55         &         0.55         &         0.55         &        2.84         &        2.84         &        2.84         &        2.84         \\
Observations        &         882         &         882         &         882         &         882         &         882         &         882         &         882         &         882         \\
\hline
\multicolumn{5}{l}{\emph{Panel B: India (March 15 - 28)}} \\
\hline
Number of Domestic Migrants       &      0.0280\sym{**} &      0.0329\sym{***}&      0.0182\sym{*}  &      0.0227\sym{**} &      0.0657         &       0.123         &    -0.00889         &      0.0266         \\
per 100 People (z-score)     &    (0.0108)         &    (0.0104)         &   (0.00958)         &   (0.00919)         &    (0.0475)         &    (0.0791)         &    (0.0361)         &    (0.0530)         \\
[0.5em]
Number of International Migrants&                     &                     &      0.0834\sym{***}&      0.0638\sym{***}&                     &                     &       0.635\sym{**} &       0.603\sym{**} \\
per 100 People (z-score)       &                     &                     &    (0.0159)         &    (0.0145)         &                     &                     &     (0.234)         &     (0.226)         \\
\hline
\(R^{2}\)           &       0.040         &       0.188         &       0.115         &       0.228         &       0.010         &       0.044         &       0.167         &       0.174         \\
Dependent Variable Mean &         0.11         &         0.11         &         0.11         &         0.11         &         0.21         &         0.21         &         0.21         &         0.21         \\
Observations        &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         \\

\hline
\multicolumn{5}{l}{\emph{Panel C: Pakistan (March 16 - 29)}} \\
\hline

Number of Domestic Migrants &      0.0286         &     -0.085\sym{**} &     0.006         &     -0.079\sym{**} &      -0.434         &      -1.057         &      -0.452         &      -1.055         \\
         per 100 People (z-score)           &    (0.038)         &    (0.036)         &    (0.034)         &    (0.036)         &     (0.360)         &     (0.633)         &     (0.346)         &     (0.668)         \\
[1em]
Number of International Migrants&                     &                     &      0.0895\sym{***}&      0.0456         &                     &                     &      0.0705         &      0.0196         \\
      per 100 People (z-score)               &                     &                     &    (0.029)         &    (0.041)         &                     &                     &     (0.477)         &     (0.682)         \\
\hline
\(R^{2}\)           &       0.139         &       0.277         &       0.170         &       0.282         &       0.034         &       0.091         &       0.034         &       0.091         \\
Dependent Variable Mean &         0.45         &         0.45         &         0.45         &         0.45         &        2.66         &        2.66         &        2.66         &        2.66         \\
Observations        &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         \\
\hline\hline

Controls &          &  \checkmark         &        &  \checkmark   &        &  \checkmark  &  & \checkmark           \\
Day Fixed Effects   &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark  &  \checkmark         &  \checkmark      &  \checkmark         &  \checkmark         \\
\hline\hline
\vspace{-1.5em}
\floatfoot{\footnotesize{Notes: State/Provincial/Country capitals have been omitted. The sample includes the first 14 days since the 100th COVID-19 case was reported in each country. All regressions include day fixed effects, while columns (2), (4), (6) and (8) additionally include the following district-level controls: population, population density, the fraction of urban population, the fraction of population below the poverty line, and a measure of health access (Bangladesh: number of hospital beds per capita, India: number of primary health centers per capita, Pakistan: percentage of population which has access to a health clinic or hospital within 15 minutes of their dwelling). Standard errors in parentheses and double-clustered by day and district. \sym{*}\(p<0.10\), \sym{**}\(p<0.05\), \sym{***}\(p<0.01\).}}
\end{tabular}
}
\end{table}

\FloatBarrier

\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}

\caption{COVID-19 Cases and Domestic Out-migration: Day 15 to 28 since 100\textsuperscript{th} Case}
\label{table_dom2_migration}
\resizebox{1\textwidth}{!}{
\begin{tabular}{l*{8}{c}}

\hline\hline
                    &\multicolumn{4}{c}{Any Cases}&\multicolumn{4}{c}{Cases per Million People}\\ \cmidrule(lr){2-5} \cmidrule(lr){6-9} 
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}
                    &\multicolumn{1}{c}{(5)}&\multicolumn{1}{c}{(6)}&\multicolumn{1}{c}{(7)}&\multicolumn{1}{c}{(8)}\\

\hline
\multicolumn{5}{l}{\emph{Panel A: Bangladesh (April 20 - May 3)}} \\
\hline
Number of Domestic Migrants &      0.0216         &      0.0134         &      0.0155         &      0.0114         &      -3.411         &      0.0250         &      -4.262         &       1.874         \\
per 100 People (z-score)                    &    (0.0128)         &    (0.0145)         &    (0.0119)         &    (0.0143)         &     (4.158)         &     (2.035)         &     (4.540)         &     (2.345)         \\
[0.5em]
Number of International Migrants&                     &                     &      0.0286\sym{*}  &     0.00699         &                     &                     &       3.932         &      -6.383\sym{**} \\
 per 100 People (z-score)                   &                     &                     &    (0.0136)         &    (0.0132)         &                     &                     &     (2.987)         &     (2.923)         \\
\hline
\(R^{2}\)           &       0.043         &       0.201         &       0.057         &       0.201         &       0.027         &       0.666         &       0.040         &       0.692         \\
Dependent Variable Mean &0.94       &0.94         &0.94         &0.94         &       16.17         &       16.17         &       16.17         &       16.17         \\
Observations        &         882         &         882         &         882         &         882         &         882         &         882         &         882         &         882         \\

\hline
\multicolumn{5}{l}{\emph{Panel B: India (March 29 - April 11)}} \\
\hline
Number of Domestic Migrants&      0.0498\sym{**} &      0.0561\sym{***}&      0.0347\sym{*}  &      0.0408\sym{**} &       0.183         &       0.401\sym{*}  &     -0.0224         &       0.162         \\
per 100 People (z-score)      &    (0.0172)         &    (0.0158)         &    (0.0163)         &    (0.0156)         &     (0.184)         &     (0.223)         &     (0.141)         &     (0.162)         \\
[0.5em]
Number of International Migrants&                     &                     &       0.128\sym{***}&      0.0957\sym{***}&                     &                     &       1.748\sym{***}&       1.495\sym{**} \\
per 100 People (z-score)       &                     &                     &    (0.0109)         &    (0.0111)         &                     &                     &     (0.537)         &     (0.547)         \\
\hline
\(R^{2}\)           &       0.049         &       0.241         &       0.121         &       0.279         &       0.027         &       0.108         &       0.152         &       0.192         \\
Dependent Variable Mean &         0.36         &         0.36         &         0.36         &         0.36         &        1.71         &        1.71         &        1.71         &        1.71         \\
Observations        &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         \\

\hline
\multicolumn{5}{l}{\emph{Panel C: Pakistan (March 30 - April 12)}} \\
\hline

Number of Domestic Migrants&       0.116\sym{**} &     -0.0741\sym{*}  &      0.0839\sym{**} &     -0.0750\sym{*}  &       0.516         &      -2.745\sym{*}  &      -0.663         &      -2.184\sym{*}  \\
            per 100 People (z-score)         &    (0.0391)         &    (0.0376)         &    (0.0360)         &    (0.0390)         &     (1.081)         &     (1.304)         &     (0.912)         &     (1.225)         \\
[0.5em]
Number of International Migrants &                     &                     &       0.125\sym{***}&    -0.00714         &                     &                     &       4.618\sym{***}&       4.375\sym{**} \\
          per 100 People (z-score)          &                     &                     &    (0.0297)         &    (0.0398)         &                     &                     &     (1.455)         &     (1.826)         \\
\hline
\(R^{2}\)           &       0.064         &       0.444         &       0.134         &       0.444         &       0.032         &       0.170         &       0.115         &       0.217         \\
Dependent Variable Mean &0.68         &0.68        &0.68        &0.68         &9.30         &9.30         &9.30        &9.30        \\
Observations        &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         \\
\hline\hline

Controls &          &  \checkmark         &        &  \checkmark   &        &  \checkmark  &  & \checkmark           \\
Day Fixed Effects   &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark  &  \checkmark         &  \checkmark      &  \checkmark         &  \checkmark         \\
\hline\hline
\vspace{-1.5em}
\floatfoot{\footnotesize{Notes: State/Provincial/Country capitals have been omitted. The sample includes days 15 - 28 since the 100th COVID-19 case was reported in each country. All regressions include day fixed effects, while columns (2), (4), (6) and (8) additionally include the following district-level controls: population, population density, the fraction of urban population, the fraction of population below the poverty line, and a measure of health access (Bangladesh: number of hospital beds per capita, India: number of primary health centers per capita, Pakistan: percentage of population which has access to a health clinic or hospital within 15 minutes of their dwelling). Standard errors in parentheses and double-clustered by day and district. \sym{*}\(p<0.10\), \sym{**}\(p<0.05\), \sym{***}\(p<0.01\).}}
\end{tabular}
}
\end{table}

\FloatBarrier


\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}

\caption{COVID-19 Cases and Domestic Out-migration: Day 29 to 42 since 100\textsuperscript{th} Case}
\label{table_dom3_migration}

\resizebox{1\textwidth}{!}{
\begin{tabular}{l*{8}{c}}
\hline\hline
                    &\multicolumn{4}{c}{Any Cases}&\multicolumn{4}{c}{Cases per Million People}\\ \cmidrule(lr){2-5} \cmidrule(lr){6-9} 
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}
                    &\multicolumn{1}{c}{(5)}&\multicolumn{1}{c}{(6)}&\multicolumn{1}{c}{(7)}&\multicolumn{1}{c}{(8)}\\
\hline
\multicolumn{5}{l}{\emph{Panel A: Bangladesh (May 4 - May 17)}} \\
\hline
Number of Domestic Migrants &    -0.00691         &    -0.00805         &    -0.00803         &    -0.00869         &      -6.912         &      -1.636         &      -9.019         &       0.126         \\
per 100 People (z-score)                    &   (0.00678)         &   (0.00714)         &   (0.00756)         &   (0.00784)         &     (6.625)         &     (2.887)         &     (7.344)         &     (3.578)         \\
[0.5em]
Number of International Migrants &                     &                     &     0.00519         &     0.00221         &                     &                     &       9.734         &      -6.082         \\
per 100 People (z-score)                   &                     &                     &   (0.00398)         &   (0.00278)         &                     &                     &     (5.548)         &     (5.794)         \\
\hline
\(R^{2}\)           &       0.023         &       0.044         &       0.026         &       0.044         &       0.029         &       0.709         &       0.060         &       0.719         \\
Dependent Variable Mean &         0.99         &         0.99         &         0.99         &         0.99         &       32.14         &       32.14         &       32.14         &       32.14         \\
Observations        &         882         &         882         &         882         &         882         &         882         &         882         &         882         &         882         \\
\hline
\multicolumn{5}{l}{\emph{Panel B: India (April 12 - 25)}} \\
\hline
Number of Domestic Migrants&      0.0592\sym{**} &      0.0659\sym{***}&      0.0461\sym{**} &      0.0525\sym{**} &      0.0271         &       0.388         &      -0.239         &       0.138         \\
per 100 People (z-score)  &    (0.0198)         &    (0.0193)         &    (0.0193)         &    (0.0194)         &     (0.422)         &     (0.439)         &     (0.405)         &     (0.426)         \\
[0.5em]
Number of International Migrants&                     &                     &       0.112\sym{***}&      0.0835\sym{***}&                     &                     &       2.264\sym{***}&       1.565\sym{**} \\
per 100 People (z-score)     &                     &                     &    (0.0112)         &    (0.0123)         &                     &                     &     (0.627)         &     (0.662)         \\
\hline
\(R^{2}\)           &       0.018         &       0.182         &       0.069         &       0.209         &       0.011         &       0.146         &       0.059         &       0.167         \\
Dependent Variable Mean &         0.54         &         0.54         &         0.54         &         0.54         &        5.11         &        5.11         &        5.11         &        5.11         \\
Observations        &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         &        8,134         \\

\hline
\multicolumn{5}{l}{\emph{Panel C: Pakistan (April 13 - 26)}} \\
\hline
Number of Domestic Migrants &       0.183\sym{***}&     -0.0215         &       0.150\sym{***}&     -0.0243         &       3.052         &      -6.149\sym{**} &      -0.673         &      -4.653\sym{**} \\
            per 100 People (z-score)        &    (0.0338)         &    (0.0231)         &    (0.0281)         &    (0.0243)         &     (2.232)         &     (2.433)         &     (1.698)         &     (2.120)         \\
[0.5em]
Number of International Migrants &                     &                     &       0.133\sym{***}&     -0.0215         &                     &                     &       14.58\sym{***}&       11.68\sym{**} \\
          per 100 People (z-score)          &                     &                     &    (0.0263)         &    (0.0282)         &                     &                     &     (3.299)         &     (3.972)         \\
\hline
\(R^{2}\)           &       0.161         &       0.596         &       0.240         &       0.597         &       0.051         &       0.290         &       0.301         &       0.390         \\
Dependent Variable Mean &0.68        &0.68        &0.68         &0.68         &          22         &          22         &          22         &          22         \\
Observations        &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         &        1,554         \\

\hline\hline

Controls &          &  \checkmark         &        &  \checkmark   &        &  \checkmark  &  & \checkmark           \\
Day Fixed Effects   &  \checkmark         &  \checkmark         &  \checkmark         &  \checkmark  &  \checkmark         &  \checkmark      &  \checkmark         &  \checkmark         \\
\hline\hline
\vspace{-1.5em}
\floatfoot{\footnotesize{Notes: State/Provincial/Country capitals have been omitted. The sample includes days 29 - 42 since the 100th COVID-19 case was reported in each country. All regressions include day fixed effects, while columns (2), (4), (6) and (8) additionally include the following district-level controls: population, population density, the fraction of urban population, the fraction of population below the poverty line, and a measure of health access (Bangladesh: number of hospital beds per capita, India: number of primary health centers per capita, Pakistan: percentage of population which has access to a health clinic or hospital within 15 minutes of their dwelling). Standard errors in parentheses and double-clustered by day and district. \sym{*}\(p<0.10\), \sym{**}\(p<0.05\), \sym{***}\(p<0.01\).}}
\end{tabular}
}
\end{table}

\FloatBarrier

\pagebreak

\section{Heatmaps}
Figure \ref{heatmaps} shows heatmaps of COVID-19 cases per million people and international and domestic migration per 100 people in Bangladesh, India and Pakistan. Visual inspection shows a broad similarity of patterns.  

In India, the raw data show a correlation of 0.14 between international out-migration from each district and cases per capita (as of April 28, 2020). The raw correlation between domestic out-migration and cases per capita is small and negative (-0.02).  In Pakistan, the raw correlation between international out-migration and cases per capita is 0.24 (as of April 27, 2020), and again the correlation between domestic out-migration and cases per capita (= -0.04) is small and negative. In Bangladesh, the data show a positive, low correlation between international out-migration and cases per capita (= 0.03) as of May 20, 2020. The correlation between domestic out-migration and cases per 1 million people is larger and negative (= -0.14). 

Analysis in the main text corroborates these broad patterns: consistent, relatively large correlations between international migration and COVID-19 cases in India and Pakistan, and weaker correlations with domestic migration. In Bangladesh, the results are sensitive to the context and comparison. 

%\begin{landscape}
\begin{figure}[htbp]
\centering
%\resizebox{0.75\textwidth}{!}{
\begin{subfigure}{.33\textwidth}
  \centering
     \includegraphics[width=5cm,trim=9 0 9 0,clip]{figures_tables/Bangladesh/Covid19 cases Bangladesh -Heat Map - Base Num Surveyed.png}
     \includegraphics[width=5.55cm,trim=9 0 9 0,clip]{figures_tables/India/Districtwise Cases per Capita India - Heat Map - April 28.png}
     \includegraphics[width=5.55cm,trim=9 0 9 0,clip]{figures_tables/Pakistan/Districtwise Cases per Capita Pakistan - Heat Map.png}
     \end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
      \includegraphics[width=5cm,trim=9 0 9 0,clip]{figures_tables/Bangladesh/Districtwise International Migration Bangladesh -Heat Map - Base Num Surveyed.png}
     \includegraphics[width=5.55cm,trim=9 0 9 0,clip]{figures_tables/India/Districtwise International Outmigration India - Heat Map - Base Num Surveyed - Weights2.png}
     \includegraphics[width=5.55cm,trim=9 0 9 0,clip]{figures_tables/Pakistan/International outmigration per 100 people Pakistan - Heat Map.png}
     \end{subfigure}
\begin{subfigure}{.33\textwidth}
  \centering
     \includegraphics[width=5cm,trim=9 0 9 0,clip]{figures_tables/Bangladesh/Districtwise Domestic Migration Bangladesh -Heat Map - Base Num Surveyed.png}
     \includegraphics[width=5.55cm,trim=9 0 9 0,clip]{figures_tables/India/Districtwise Outmigration India - Heat Map - Base Num Surveyed - Weights2.png}
     \includegraphics[width=5.55cm,trim=9 0 9 0,clip]{figures_tables/Pakistan/Domestic outmigration per 100 people Pakistan - Heat Map.png}
     \end{subfigure}
\caption{COVID-19 and Out-migration - Heatmaps}
\label{heatmaps}
%}
\end{figure}
\FloatBarrier
%\end{landscape}