#Stayat home: Social distancing policies and mobility in Latin America and the Caribbean

Aromi, Daniel et al. Working Paper #Stayat home: Social distancing policies and mobility in Latin America and the Caribbean IDB Working Paper Series, No. IDB-WP-1147 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Aromi, Daniel et al. (2020) : #Stayat home: Social distancing policies and mobility in Latin America and the Caribbean, IDB Working Paper Series, No. IDB-WP-1147, Inter-American Development Bank (IDB), Washington, DC, https://doi.org/10.18235/0002685 This Version is available at: https://hdl.handle.net/10419/237447 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/igo/legalcode Abstract* This study examines the impact on human mobility of the national social distancing policies implemented in 18 Latin American and Caribbean countries in March 2020. To do so, it uses georeferenced data from cellular phones and variation between countries with regard to whether these measures were introduced and when. Mobility is measured as the percentage of people traveling more than 1 kilometer per day. Results indicate that lockdowns reduced mobility by an average of 10 percentage points during the 15 days following its implementation. This accounts for a third of the decline in mobility between the first week in March and the first week in April in countries that implemented lockdowns. However, this average effect hides an important heterogeneity. To start with, the impact during the second week of implementation is 28% lower compared to the effect documented during the first week. Also, while lockdowns reduced mobility by between 16 and 19 percentage points in Argentina, Bolivia, and Ecuador, in Paraguay and Venezuela, the reduction was only 3 percentage points. Additionally, we find that school closures reduced mobility by 4 percentage points. Finally, closures of bars and restaurants and cancellation of public events were found to have no impact on the mobility measurement analyzed. JEL classifications: C23, H12, I18 Keywords: Coronavirus, Social distancing, Mobility, Lockdowns * We would like to recognize the significant support and important comments provided by Ana María Ibañez, Alejandro Izquierdo, and Eric Parrado. We would also like to thank Roberto Araya, Javier Bartoli, Catalina Espinosa, María Frances Gaska, Gabriel Goette, Gastón Líberman, Andrés López, Valeria Lovaisa, Alejandra Mizala, Arturo Muente, Félix Quintero, Jimena Romero, Miguel Soldano, and Rosa Vidarte for their support. We would like to thank Juan Pablo Chauvín, Patricio Domínguez, and Diego Vera for their comments and suggestions, along with the participants of the seminar organized by the Inter-American Development Bank and the seminar on “Coronavirus and Big Data,” organized by the Instituto Interdisciplinario de Economía Polítca of Buenos Aires. The opinions expressed in this document are those of the authors and should not be attributed to the Inter-American Development Bank. Corresponding author: Julián Cristia (jcr[email protected]rg). 2 1. Introduction During the first half of 2020, the coronavirus wreaked havoc on health, the economy, and the overall well-being of the global population. The virus reached Latin America and the Caribbean in early March 2020, by which time its harmful effects were known because of the experiences of countries like China, Italy, and Spain. In turn, the region’s governments reacted quickly by implementing social distancing measures to reduce contact between people to slow the spread of the virus. To increase social distancing, governments implemented a series of obligatory measures restricting human mobility, including lockdowns, closing schools, closing bars and restaurants, and canceling public events. At the same time, governments used mass communications campaigns to persuade people to adopt social distancing. For their part, the media and social networks may have played a significant role in promoting social distancing. This collection of actions led to a drastic decline in human mobility in the region between March 13 and 25, 2020 (Aromi et al., 2020; Google, 2020). Against this background, a key question is: what was the impact of the mentioned social distancing national policies on human mobility? Because these measures are part of the basic arsenal of measures governments can use to quickly promote social distancing at new stages of the fight against the coronavirus, it is important to quantify their impacts. However, there is limited evidence as to the effect of these measures, and it mainly comes from developed countries (Dave et al., 2020a; Dave et al., 2020b; Cronin and Evans, 2020; Maloney and Taskin, 2020; Akim and Ayivodji, 2020). Also, simply analyzing the evolution of mobility in specific countries cannot determine for sure the impact of these measures because, as mentioned previously, changes in people’s behavior were also the result of other factors, including communications campaigns by governments and the roles played by the media and by social networks. This study evaluates the impact on human mobility of the national social distancing policies implemented in 18 Latin American and Caribbean countries during March 2020.1 Specifically, we study the impact of four social distancing policies implemented by national governments: 1 The analysis includes all countries of Latin America and the Caribbean with between 1 million and 50 million inhabitants. Brazil and Mexico were not included, as policies and movement within their borders were extremely heterogeneous. Cuba and Haiti were also not included, due to low rates of cellular phone use there. The countries included were: Argentina, Bolivia, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Jamaica, Nicaragua, Panama, Paraguay, Peru, Trinidad and Tobago, Uruguay, and Venezuela. 3 lockdowns, school closures, the closing of bars and restaurants, and the cancellation of public events. To estimate impacts, we used the variation between countries with regard to whether these measures were applied and when. The analysis focuses on March 1 through April 14, a critical period during which the majority of the countries analyzed implemented the measures mentioned above. During this period, the measures imposed to restrict mobility were mostly implemented by national governments, making it possible to analyze their impact at this level.2 The key outcome analyzed in this study is the percentage of people traveling more than 1 kilometer per day. This outcome is computed using georeferenced data from cellular phones provided by the company Veraset. In the first part of the analysis, we study the prevalence and implementation order of the social distancing policies under examination. We find clear patterns with regard to countries’ adoption of these measures. Specifically, we document that, of the 18 analyzed countries, all of them implemented public event cancellations and closure of schools, with the sole exception of Nicaragua, which did not implement any of the four measures. Additionally, 15 countries ordered the closure of restaurants and bars, while only 11 imposed lockdowns. This preference for implementing certain measures over others is also expressed in the order of implementation. Specifically, we found that the first measures were implemented on Tuesday, March 10, but following a particular sequence. As of Monday, March 16, 15 countries had implemented school closures, 14 had canceled public events, 8 had closed bars and restaurants, and only 2 countries had implemented lockdowns. In terms of mobility impacts, we found that the introduction of lockdowns produced an average reduction in the percentage of people traveling more than 1 kilometer of 10 percentage points during the 15 days following implementation. This large effect, however, diminished over time: while the average effect during the first week was 12 percentage points, the effects during the second week came to only 9 percentage points, a difference that is statistically significant. We also find that closing schools reduced mobility by 4 percentage points. For their part, no significant effects were detected from closing bars and restaurants or from canceling public events. However, it is important to consider that these measures may have a more significant effect on reducing 2 As described in Section 3, during this period, some local measures were also implemented to restrict human mobility within certain countries, including Argentina and Bolivia. However, considering the percentage of the population affected, it is clear that the national measures played the dominant role. 4 agglomeration, rather than reducing mobility in general. Therefore, these measures may be effective at slowing the spread of the virus, even without reducing mobility. The measures analyzed could have different effects in the different countries of the region as a result of the particular characteristics and enforcement efforts, as well as due to differing patterns of pre-coronavirus mobility. Hence, we quantify the effects of the lockdowns in each country using a synthetic control methodology (Abadie et al., 2010). Results indicate that while the lockdowns reduced mobility in Argentina, Bolivia, and Ecuador by between 16 and 19 percentage points, the reduction in Paraguay and Venezuela was only 3 percentage points. This study complements a growing body of literature seeking to document the impacts of social distancing policies on human mobility during the coronavirus crisis. Analyses performed thus far use as a measurement the percentage of people who stay home (Dave et al., 2020a; Dave et al., 2020b) and visits to certain locations, such as essential and non-essential businesses, entertainment, hotels, restaurants, and workplaces (Akim and Ayivodji, 2020; Bargain and Aminjonov, 2020; Cronin and Evans, 2020; Maloney and Taskin, 2020). For this study, however, the main measurement used is the percentage of people traveling more than one kilometer per day. This indicator makes it possible to capture more general mobility patterns that are not necessarily associated with visits to public places, but that still increase the risk of transmission of the coronavirus, like visits to friends or family members outside the home. Additionally, the majority of existing studies focus on the effects of social distancing policies in developed countries. Analyses in the United States have found that between 3% and 26% of the total reduction in mobility is the result of the implementation of lockdowns. They have also found that policies like closing schools, restaurants, and non-essential businesses have small but significant effects on mobility (Cronin and Evans, 2020; Maloney and Taskin, 2020). However, these conclusions may be different for lower-income countries, given that poverty rates and informality make it difficult for people to say home. Still, existing studies suggest that lockdowns reduce mobility in medium-income countries and African countries (Akim and Ayivodji, 2020; Maloney and Taskin, 2020). This paper sheds new light on the effects of other policies like school closures, closing restaurants and bars, and canceling public events in developing countries, and it is the first to document the effects of lockdowns on mobility in Latin America and the Caribbean. The paper also takes an in-depth look at the temporal dynamics of lockdowns and finds evidence indicating 5 the effects are reduced over time, in contrast to what has been found for Africa and the United States (Akim and Ayivodji, 2020; Cronin and Evans, 2020). Lastly, this study sheds light on the significant variation in the effects of lockdowns across Latin American and Caribbean countries. The paper is structured as follows. Section 2 describes the initial worldwide spread of the coronavirus and its arrival in Latin America and the Caribbean. Section 3 analyzes the process of adopting social distancing policies in this region, and Section 4 describes the data and methodology used to construct the mobility series. Finally, Section 5 presents the main findings of the study, and Section 6 concludes. 2. Context3 In the early months of 2020, the world was facing the rapid spread and mass infection of the coronavirus. The symptoms of the virus are typically moderate, and 80% of those infected recover without needing to be hospitalized (WHO, 2020a). However, the other 20% experience a range of symptoms—including difficulty breathing—and older patients and patients with preexisting conditions tend to be more likely to develop a severe illness. In this scenario, patients with moderate symptoms or asymptomatic patients become carriers and potential spreaders of the virus. They can infect the rest of the population, which, after becoming infected, may develop illnesses like pneumonia, acute respiratory distress syndrome, or renal insufficiency, in the worst cases leading to death. The coronavirus was first reported in Wuhan, China, on December 31, 2019, when the Municipal Health Commission reported a cluster of pneumonia cases. Subsequently, the World Health Organization (WHO) began publishing technical documents on what was known about the virus, offering countries recommendations on how to detect and manage potential cases. Then on January 13, the first case was reported outside of China, in Thailand. From that moment, the coronavirus spread to the rest of the world, reaching the United States on January 20 and then the European continent on January 24, when France reported its first case. On January 30, the WHO declared the novel coronavirus outbreak a Public Health Emergency of International Concern, with 7,818 cases reported in 19 countries. The next day, Italy 3 This section describes the basic characteristics of the coronavirus, its spread until March 2020, and the general policy recommendations issued by the World Health Organization during this initial period to help understand the context in which the governments of Latin America and the Caribbean applied measures during this initial stage. 12 that implemented lockdowns, either in all or part of their territory. For example, in the case of Chile, the series calculated at the national level do not include the mobility of individuals living in the metropolitan region.10 Table 4 gives descriptive statistics by country from the sample used for the study. The average number of observations during March 5-11 (pre-coronavirus) ranges between 10,000 for El Salvador, Jamaica, Nicaragua, Panama, Paraguay and 310,000 for Argentina. Dividing the number of observations by the total population of each country gives an approximation of the % coverage of the sample. In this case, the average coverage is 0.42%. Guatemala is the country with the least coverage, with 0.12%, and Trinidad and Tobago is the country with the greatest coverage, with 1.16%. The average of the main mobility indicator is also provided for the pre-coronavirus period—that is, the percentage of people who traveled more than 1 kilometer per day between March 5 and 11. As can be observed, on average for all countries, 66% of people traveled more than 1 kilometer per day during this period. Lastly, the percentage of people older than 15 with access to a cellular phone (not necessarily a smartphone) is shown. The table shows significant coverage of cellular phones in all the countries analyzed, averaging 82%. 5. The Impact of Social Distancing Policies on Mobility This section evaluates the impact that distancing policies had on human mobility in Latin America and the Caribbean at the start of the pandemic. The first subsection uses a difference in differences model to analyze the average impact of lockdowns, closing schools, closing bars and restaurants, and canceling public events. The second subsection goes into depth in analyzing lockdowns, showing the temporal dynamic of the impacts of this policy using an event study design. The third subsection presents further disaggregated results by analyzing the individual impact of lockdowns on each country using synthetic control methods (Abadie et al., 2010). 10 The following geographical areas were removed for each country: Argentina (Chaco, Jujuy, Mendoza, Misiones, Salta, Santa Fe, and Tierra del Fuego), Bolivia (Oruro), Chile (Araucanía, Aysen, Bio-Bio, Los Lagos, Magallanes and the Chilean Antarctic, Ñuble, Valparaiso, and the Santiago Metropolitan Region), and Colombia (Boyaca, Cundinamarca, Meta, and Santander). 13 5.1. The Average Impact of Social Distancing Policies To assess the average impact of the social distancing policies, we construct a balanced panel with one observation per country and day for the period of March 1 through April 14. In this subsection, we utilize the following difference in differences model: 𝑀𝑜𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡 = 𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑖+𝑑𝑎𝑦𝑡+𝛽1𝐿𝑜𝑐𝑘𝑑𝑜𝑤𝑛𝑖𝑡 +𝜀𝑖𝑡 (1) where 𝑀𝑜𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡 represents the percentage of people traveling more than 1 kilometer in country i and day t. 𝐿𝑜𝑐𝑘𝑑𝑜𝑤𝑛𝑖𝑡 is an indicator equal to 1 if this policy is in place in a particular country on a certain day (and zero if not). In turn, 𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑖𝑎𝑛𝑑 𝑑𝑎𝑦𝑡 are fixed effects per country and per day, respectively. The parameter of interest, 𝛽1, represents the average impact of implementing a lockdown in the sample of countries and period under analysis. Recent literature demonstrates that fixed effects models like the one presented in equation (1) can produce estimates that are biased when the units are treated at different times, even in the event of parallel trends between the treatment group and the comparison group in the absence of treatment (de Chaisemartin and D’Haultfoeuille, 2019; Goodman-Bacon, 2018; Sun and Abraham, 2020). However, de Chaisemartin and D’Haultfoeuille (2019) presents a new estimator that produces unbiased estimates, and that can be applied in cases in which the units entering the treatment group remain treated through the period under analysis. Given that this condition is met in our analysis, we estimate equation (1) following the methodology described in de Chaisemartin and D’Haultfoeuille (2019). To estimate the impact of school closures, closing bars and restaurants, and canceling public events, we used equations similar to (1) but replaced the lockdown indicator with the indicator of the policy analyzed. For school closings, we introduced two variations. First, we only included observations from weekdays in the sample (given that school closures should have no effect on weekends).11 Additionally, the policy indicator has a value of 1 for school closures only in countries where the school year had already begun by the time the closures were ordered.12 Table 5 presents the results of estimating equation (1) for each of the individual social distancing policies (odd columns). The results indicate that lockdowns had a significant impact on 11 One alternative would be to include weekends and set the “Schools closed” indicator to 0 on those days. However, this would make it infeasible to use the estimator described in de Chaisemartin and D’Haultfoeuille (2019), which requires any unit entering treatment to remain in that state for the duration of the period under analysis. 12 Closing schools in countries where the school year had not yet begun (Ecuador and Peru) should have no impact on mobility. 14 mobility. Specifically, the percentage of people traveling more than one kilometer declined by an average of 10 percentage points after the implementation of this policy. To benchmark this effect, we calculated the decline in mobility between the first week in March and the first week in April in the 11 countries that implemented lockdowns (34 percentage points). Therefore, the average impact of lockdowns accounts for close to a third of the average decline in mobility. Additionally, compared to pre-coronavirus levels (March 5-11), we found that lockdowns reduced the percentage of people traveling more than 1 kilometer by 15% (Table A.2). This finding was obtained by including the percentage change in mobility compared to the pre-coronavirus period in equation (1) as a dependent variable. Additionally, we estimate that school closures reduced mobility by 4 percentage points. The impact of lockdowns and school closures are significant at the 5% level. In contrast, the impact of closing bars and restaurants and canceling public events is close to zero and not statistically significant.13 The findings presented may be affected by different biases. To begin with, it is possible that the countries that implemented social distancing measures were reacting to certain bad news “shocks” (for example, reports of sharp increases in the number of cases), which may have directly reduced mobility aside from any policies implemented by the government. Additionally, it may be that countries decided to implement a package of measures (including the ones under examination here, as well as others), which may have separately impacted mobility. In either of these two cases, the estimate presented for a policy would be overestimating the real impacts. On the other hand, if governments tend to space out the measures implemented over time (for example, if when a country closes schools, it becomes less likely to implement other actions), then the findings presented could be underestimating the real impacts of the policies implemented. To analyze the robustness of the impacts presented, we conducted two complementary analyses. First, we used a model similar to the one presented in equation (1), but simultaneously controlling for the other three policies studied herein. Note that the methodology described in de Chaisemartin and D’Haultfoeuille (2019) does not permit using all four policies in a single equation. Rather, a separate model must be used for each coefficient of interest, adding the other policies as controls. This is why the even columns of Table 5 present the estimated coefficients for 13 The findings presented in Table 5 are similar to those obtained by using the traditional difference in differences estimator, with the exception of the effect of school closings, for which the effect was found to be close to 0 and not significant (see Table A.1) 15 each policy when the other policies are controlled for. The findings indicate that the estimated effects are similar to the baseline specification, with the exception of the coefficient for the impact of school closures, which increases slightly to 5 percentage points. Additionally, we explore whether there is evidence of parallel trends between treatment and comparison groups during the period prior to the implementation of each policy. For the four policies under analysis, we find consistent evidence of the existence of parallel trends, providing support for the identification strategy used in this study. 5.2. Dynamic Impacts of the Lockdowns The results from the previous subsection indicate that the lockdowns significantly reduced mobility. In this section, we delve further into this analysis, given the key role this policy can play in the fight against the coronavirus.14 We begin this analysis by comparing the average characteristics of the 11 countries that implemented lockdowns with those of the 7 countries that did not implement lockdowns.15 This analysis, presented in Table 6, suggests that both groups are balanced in terms of important indicators like the percentage of the population older than 65, the percentage of rural population, years of education, and per capita GDP. Likewise, the last two columns of this table show average mobility during the week of March 5-11 (when mobility still had not been affected by the coronavirus crisis), as well as for the day before the first lockdown declaration (March 15). Relevant to this analysis, the two groups are balanced in both pre-lockdown mobility indicators. The variable with significant differences across the two groups is total population, which averages 19 million for the countries that implemented lockdowns and 9 million for the countries that did not. Next, we analyzed the temporal dynamic of the impact of the lockdowns. For this, we used the balanced panel with day-country observations described in the previous subsection. Specifically, we conducted an event study estimating the following equation: 14 In principle, we can empirically analyze the dynamic impacts of school closures. However, because school closures have an immediate impact that is directly verifiable, it is difficult to believe they could have effects that change over time. Also, given that the vast majority of countries closed schools between Friday, March 13, and Monday, March 16, there is not enough variation to estimate impacts beyond the first day of school closures. 15 The countries that implemented lockdowns are Argentina, Bolivia, Colombia, Ecuador, El Salvador, Honduras, Panama, Paraguay, Peru, Trinidad and Tobago, and Venezuela. The countries that did not implement lockdowns are Chile, Costa Rica, the Dominican Republic, Guatemala, Jamaica, Nicaragua, and Uruguay. 16 𝑀𝑜𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡 =𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑖+𝑑𝑎𝑦𝑡+ ∑ 𝛿𝑟 𝑐𝑖𝑡 𝑟 15 𝑟=−15 +𝛿16 𝑎𝑛𝑑 𝑏𝑒𝑦𝑜𝑛𝑑 𝑐𝑖𝑡 16 𝑎𝑛𝑑 𝑏𝑒𝑦𝑜𝑛𝑑 +𝜂𝑖𝑡 (2) in which 𝑀𝑜𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡, 𝑑𝑎𝑦𝑡 and 𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑖 correspond to the same variables included in equation (1). The indicator 𝑐𝑖𝑡 15 has a value of 1 for day 15 after the introduction of the lockdown. More generally, the indicator 𝑐𝑖𝑡 𝑟 has a value of 1 for day r after the introduction of the lockdown.16 The coefficients of interest, 𝛿𝑟, capture the increase in mobility compared to the reference period (16 or more days before the introduction of the lockdown). This specification flexibly captures the dynamic of the lockdown’s daily effects. Specifically, the coefficients 𝛿0 to 𝛿15 make it possible to estimate the impacts of the lockdown for every day subsequent to the introduction of the lockdown. Additionally, analysis of the coefficients 𝛿−15 to 𝛿−1 enables exploring whether parallel trends existed prior to the introduction of the lockdown for countries that implemented it and countries that did not. As in Subsection 5.1, these effects are estimated using the methodology presented in de Chaisemartin and D’Haultfoeuille (2019).17 Figure 1 presents the findings of this event study. The coefficients for the days prior to the lockdown are close to 0 and never statistically significant. These findings indicate the existence of parallel trends prior to the introduction of a lockdown and provide evidence in favor of the identification strategy used. Analyzing the day-by-day impacts of the lockdowns, we note that mobility falls drastically by 10 percentage points on day 0 (when the lockdowns are introduced). The impact is even greater on days 1 through 3, reaching close to 13 percentage points. But over subsequent days, there is a distinct diminishment of these impacts, and by day 15 of the lockdown, the impact on mobility is only 7 percentage points. These findings contrast with existing evidence for Africa and the United States, which has generally revealed effects that are relatively stable over time (Akim and Ayivodji, 2020; Cronin and Evans, 2020; Dave et al., 2020a). To more systematically document the drop in the impact of lockdowns, we use the following event study, which has the same structure as the one presented in equation (2), with the difference that effects are estimated for periods of around one week, rather than one day: 16 The value of the coefficient 𝑐𝑖𝑡 16 𝑦 𝑏𝑒𝑦𝑜𝑛𝑑 is 1 for days 16 and beyond subsequent to the introduction of the lockdown. 17 The coefficients and standard errors associated with the equation (3) employed using the methodology described in de Chaisemartin and D’Haultfoeuille (2019) are very similar to those obtained using the traditional event study method. The coefficients and standard errors of both estimates are reported in Table A.3. 17 𝑀𝑜𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡 = 𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑖+𝑑𝑎𝑦𝑡+𝛿−8 𝑎−1 𝑐𝑖𝑡 −8 𝑎−1 +𝛿0 𝑐𝑖𝑡 0+𝛿1 𝑎 7 𝑐𝑖𝑡 1 𝑎 7 +𝛿8 𝑎 15 𝑐𝑖𝑡 8 𝑎 15 +𝛿16 𝑎𝑛𝑑 𝑏𝑒𝑦𝑜𝑛𝑑 𝑐𝑖𝑡 16 𝑎𝑛𝑑 𝑏𝑒𝑦𝑜𝑛𝑑 +𝜂𝑖𝑡 (3) where the variables 𝑀𝑜𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡, 𝑑𝑎𝑦𝑡 𝑎𝑛𝑑 𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑖 correspond to the same variables included in equation (2). The indicator 𝑐𝑖𝑡 −8 𝑎−1 has a value of 1 for the 7 days prior to the introduction in country i of the lockdown, and 𝑐𝑖𝑡 1 𝑎 7 is equal to 1 for the 7 days after its introduction. Likewise, the indicator 𝑐𝑖𝑡 8 𝑎 15 has a value of 1 for days 8 to 15 following the introduction of the lockdown, and 𝑐𝑖𝑡 16 𝑎𝑛𝑑 𝑏𝑒𝑦𝑜𝑛𝑑 is equal to 1 for the 16 or more days after the introduction of the lockdown. The coefficients 𝛿 associated with the indicators described reflect the increase in mobility compared to the period of 9 or more days before introduction of the lockdown.18 Results presented in Table 7 indicate the existence of parallel trends prior to the introduction of the lockdowns, as the coefficient for the period of -8 to -1 is not significant and close to zero. The table also shows an effect of about 11 percentage points for day 0, which increases in the first week following the introduction the lockdown (days 1 to 7), but declines over the following week (days 8 to 15). This decline in impact between the first week and the second week is 28% (8.92/12.40-1) and statistically significant. Why would the impacts of lockdowns decline over time? There are two possible explanations: people from the treatment group increased their mobility over time (or did so more than the comparison group), or the people in the comparison group reduced their mobility over time (or did so more than the treatment group). With regard to the first explanation, the people in the treatment group may have increased mobility during the second week compared to the first week post-lockdown because they were initially afraid of becoming infected, and this caused a significant and immediate reduction in mobility. However, with the passage of time, upon receiving more information on how to prevent contagion, people may have gained more confidence and begun to increase their mobility (Dave et al., 2020a). The increase in mobility for people subjected to the lockdown may also be the result of the levels of poverty and informality in the Latin American context that force people to go out after several days of lockdown because they need to generate income to cover essential living expenses. Concerning the latter explanation, 18 Because the de Chaisemartin and D’Haultfoeuille (2019) methodology does not make it possible to directly calculate effects by period, we employed the traditional event study estimator to estimate equation (3). These findings should be robust to the estimation method used, given that the results generated when analyzing effects by day estimating a traditional event study are very similar to those generated using the methodology described in de Chaisemartin and D’Haultfoeuille (2019). 18 those in the comparison group may have reduced their mobility during the second week because the process of disseminating information on the virus was slower, meaning that their reduction in mobility was not immediate. In that case, the lockdown not only would lead to a greater reduction in mobility, but would also make people to reduce their mobility more quickly. To explore these potential explanations, Figure 2 presents the difference in mobility between the first and second weeks following the implementation of the lockdown for each treatment country and its comparison group. Specifically, the countries are ordered according to when they implemented a lockdown and placed into two groups: countries that introduced this policy early (between March 16 and 17) and countries that implemented it later (between March 20 and 30). The figure shows that the countries that implemented a lockdown early saw mobility decline in the second week post lockdown compared to the first by around 6 percentage points. However, during that period, mobility was reduced by close to 12 percentage points in the countries in the comparison group. These results suggest that the lockdown initially accelerated the decline in mobility in countries where it was implemented, but that the decline is followed by a process of convergence. Meanwhile, different patterns are observed for countries that implemented a lockdown later. In this case, while mobility remains stable in the comparison countries, it increases slightly in the majority of countries that implemented a lockdown. Thus, for this group of countries that implemented lockdowns later, the findings suggest that the decline in impacts is the result of an increase in mobility among those subject to a lockdown. However, of the total effect of the lockdown on mobility, the increase in mobility in the treatment countries seems to be less important compared to the convergence of the comparison countries. 5.3. Dynamic Impacts of Lockdowns per Country How did the effects of the lockdowns vary by country? To answer this question, we analyze the mobility trend of each country that implemented a lockdown and compare it against the average mobility of the 7 countries that did not establish a lockdown. For each country that implemented a lockdown, we analyze the period covering the 15 days prior to and 15 days after the introduction of this measure. Figure 3 shows the mobility trend of each country that introduced a lockdown (black line) versus the comparison group (gray line). The horizontal axis marks days relative to the introduction of the lockdown (day 0 is when it was implemented), while the vertical axis charts the percentage 19 of people traveling more than 1 kilometer per day. For the case of Argentina, which implemented a lockdown on March 20, mobility is observed to decline during the days prior to the introduction of the lockdown, then decrease sharply during the days subsequent to the introduction of this measure. Also, we note that average mobility in the comparison countries followed a very similar trend in the days prior to the introduction of the lockdown in Argentina, but that the series diverges drastically when Argentina imposes its lockdown. Lastly, we observed that the mobility series of Argentina and the comparison countries tend to converge with the passage of days subsequent to the introduction of the lockdown, replicating the general finding that the effects of the lockdown lessen over time. These patterns documented for Argentina tend to be replicated in the majority of countries analyzed. To build the comparison series presented in Figure 3, a simple mobility average was calculated for the 7 countries that did not introduce lockdowns. To refine this analysis, we used a synthetic control methodology (Abadie et al., 2010) that involves producing comparison series by calculating a weighted average for the countries that did not implement the lockdown. The weights used for each comparison country are selected to minimize the root mean square error in the period prior to the intervention. Table A.4 in the Appendix shows the weights used for the comparison countries to generate the synthetic control for each country that implemented a lockdown. Table 8 shows the mobility difference for each country that introduced a lockdown compared to its synthetic control per day relative to the introduction of this measure. The lower row also presents the average effects estimated by country. The findings indicate significant country-by-country heterogeneity in the effects of the lockdowns. Countries with the greatest impact include the cases of Bolivia, Ecuador, and Argentina, with drops in mobility of 19, 17, and 16 percentage points, respectively, as a result of the lockdown. On the other hand, there are countries where the impacts were noticeably less, such as Paraguay and Venezuela, where mobility declined by only 3 percentage points. There are a variety of possible explanations for this heterogeneity of effects among countries, including different ways of communicating the lockdowns, different punishments for people violating them, varying enforcement efforts made by governments to ensure lockdowns were followed, and the socioeconomic characteristics of the population as far as the opportunity to telework and having sufficient financial resources to cover expenses during the lockdown period. Likewise, other policies could be interacting with the effects 20 of the lockdowns, including monetary transfers from governments to reduce the impact on the population of remaining at home without work.19 Lastly, we examine whether the effects observed from the lockdowns are statistically significant, or if they may have arisen simply from variability in the sample. In the case of the synthetic control methodology, the inferences are based on permutation tests called “placebo tests” (Abadie and Gardeazabal, 2003). These tests are performed by assigning each country from the comparison group to “treatment” and generating a distribution of placebo effects. The effect estimated using the synthetic control methodology is then compared with the placebo effect distribution (see the results in Table A.5). Generally, the p-values are relatively low and close to zero, except for countries where a lesser effect was documented, like Paraguay and Venezuela. 7. Conclusion This study evaluates the impact on mobility of national policies seeking to encourage social distancing. The sample includes mobility series from 18 Latin American and Caribbean countries for the period March 1 to April 14, constructed using georeferenced data from cellular telephones. The findings indicate that the lockdowns reduced the percentage of people traveling more than 1 kilometer per day by 10 percentage points. The effects are found to vary over time and among countries. Particularly, the effects on mobility are 28% less during the second week following the implementation of a lockdown, compared to the first week. Also, while lockdowns reduced mobility by between 16 and 19 percentage points in Argentina, Bolivia, and Ecuador, in Paraguay and Venezuela, the reduction was only 3 percentage points. We also find that school closures have a negative impact on mobility of 4 percentage points. Additionally, closing of bars and restaurants and cancellation of public events were found to have no impact on the mobility measurement analyzed. This analysis has its limitations. Given that the variation used in the study is not experimental, the estimates presented may have certain biases. With regard to external validity, 19 When comparing impacts among countries, it is important to recognize that the representativeness of the sample used in this study can fluctuate significantly among countries. In all countries, smartphone coverage is biased toward higher-income populations. However, this bias should be expected to be higher in countries with lower income levels. This is because the percentage of people of lower socioeconomic status with smartphones will vary substantially between countries with different income levels. For example, it should be expected that smartphone coverage among individuals in the lowest income quintile in Chile will be notably higher than such coverage in countries like Honduras or Nicaragua. 21 because the coverage of smartphones is greater in populations with higher incomes, estimated effects are more representative for this population than for the general population. With regard to this, the average effects found may hide important heterogeneous effects between high-income and low-income populations. Lastly, this study presents the results of a particular mobility measure, and it will be important to analyze the results when other alternative measures are used. Aside from these limitations, the results presented have important policy implications. Specifically, they suggest that lockdowns are a tool that can produce reductions in mobility quickly. This is important given the expectation that reduced mobility slows the spread of the coronavirus. This expected link between mobility and spread, based on the mechanisms by which the virus spreads, has been confirmed by recent empirical studies (Glaeser et al., 2020). However, it is important to consider the evidence presented with regard to variation in effects over time and among countries. These considerations suggest that the impacts of lockdowns on mobility cannot be assumed to be automatic and free from uncertainty. The study also indicates that closing schools also reduced mobility to a certain degree. Different research questions could be addressed by future studies. First, studies could analyze the causes of the changes in the effects of the lockdowns over time and among countries. Second, studies could explore how monetary transfer programs affect mobility and how they can interact with the social distancing measures presented herein (Akim and Ayivodji, 2020, analyze this phenomenon in Africa). Third, studies could analyze the impacts of policies implemented at different levels of geographic aggregation, such as country level, an initial subnational level (like state or province), or further subnational level (like a municipality or comuna). Fourth, studies could analyze the different effects of introducing and lifting lockdowns. Finally, it is crucial to delve further into the impacts of lockdowns on the spread of coronavirus and economic activity and how human mobility moderates these effects. Table 4. Sample Coverage and Mobility by Country Country Observations (millions) Population (millions) Coverage (%) Traveled more than 1 km, March 511 (%) Mobile phone access (% age 15+) (1) (2) (3)=(1)/(2) (4) (5) Argentina 0.31 44.49 0.69 65.03 81.60 Bolivia 0.04 11.35 0.39 63.73 87.91 Chile 0.03 18.73 0.18 67.57 90.22 Colombia 0.19 49.65 0.38 55.79 83.51 Costa Rica 0.05 5.00 1.01 70.16 91.55 Dominican Republic 0.05 10.63 0.48 63.61 81.38 Ecuador 0.06 17.08 0.34 65.54 76.63 El Salvador 0.01 6.42 0.22 66.71 74.05 Guatemala 0.02 17.25 0.12 68.51 75.77 Honduras 0.02 9.59 0.22 64.12 80.06 Jamaica 0.01 2.93 0.51 60.96 - Nicaragua 0.01 6.47 0.14 60.01 79.94 Panama 0.01 4.18 0.33 69.52 77.31 Paraguay 0.01 6.96 0.21 70.55 81.90 Peru 0.13 31.99 0.40 73.42 78.75 Trinidad and Tobago 0.02 1.39 1.16 68.81 90.96 Uruguay 0.02 3.45 0.63 74.71 91.49 Venezuela 0.04 28.87 0.14 67.35 73.70 Average 0.06 15.36 0.42 66.45 82.16 Notes: This table presents the descriptive statistics of the sample coverage and mobility by country. Column (1) reports the average observations between March 5 and 11. Column (2) reports the total population. Column (3) presents the coverage of the sample, which is calculated by dividing the number of observations (column 1) by the total population (column 2). Column (4) shows the average percentage of people who travel more than 1 kilometer between March 5 and 11. Column (5) shows the percentage of people over the age of 15 who has access to a mobile phone. The last row of the table presents the average of each of the columns for the 18 countries analyzed. Table 5. The Effects of Social Distancing Policies on Mobility (1) (2) (3) (4) (5) (6) (7) (8) Lockdowns -10.26** -10.09** (3.12) (3.09) School closings -3.74* -4.85** (1.62) (1.77) -2.53 -0.53 (1.39) (1.68) 0.08 0.01 (0.75) (0.78) N810 810 594 594 810 810 810 810 % of people who travel more than 1 km Cancellation of public events Bar and restaurants closings Notes: This table shows the average effects of social distancing policies on human mobility. The dependent variable is the percentage of people who travel more than one kilometer per day. The results are generated from a balanced panel of 18 Latin American and Caribbean countries covering the period from March 1 to April 14, 2020. Each column corresponds to a regression. The rows indicate the policy analyzed in each regression. The sample used to evaluate the impact of school closings, the results of which are presented in columns (3) and (4), do not include weekdays. In the odd columns, the calculation is made without controls, while in even columns controls for the other three distancing policies are included in the regression. Standard errors, presented in parentheses, are calculated by bootstrapping with 400 repetitions and clusters at the country level. Significance at one and five percent indicated by **, and *, respectively. Controls for other policies Dependent variable average (March 5-11) No Yes No Yes No Yes No Yes 66.45 66.45 66.4566.45 68.04 68.04 66.45 66.45 With lockdown Without lockdown Difference P-value (1) (2) (3) (4) Population (millions) 19.27 9.21 10.06 0.04 Population over the age of 65 (%) 7.96 8.83 -0.87 0.32 Rural population (%) 28.76 27.35 1.41 0.77 Average years of education (older than 25) 8.94 8.82 0.12 0.84 Life expectancy at birth 74.94 76.36 -1.42 0.10 GDP per capita, PPP (thousands of current dollars) 15.56 15.36 0.19 0.94 Poverty rate at US$5.50 per day (2011 PPP) (%) 24.68 17.22 7.46 0.14 Share of income of the highest decile (%) 34.00 34.98 -0.98 0.48 Unemployment (%) 5.45 7.26 -1.81 0.19 Self-employed (% of employed) 43.76 35.68 8.07 0.17 Mobile phone access (% age 15+) 80.58 85.06 -4.48 0.16 Internet access (% age 15+) 57.00 62.18 -5.18 0.42 Travels more than 1 km, March 5-11 average (%) 66.42 66.50 -0.09 0.93 Travels more than 1 km, March 15 (%) 60.19 61.16 -0.97 0.75 Notes: This table shows descriptive statistics of sociodemographic and mobility variables. Column (1) reports the average of the variables for the 11 countries that implemented the quarantine. Column (2) reports the average of the variables for the 7 comparison countries. Column (3) presents the difference in the averages between both groups. Column (4) shows the p-value of the difference from the average for each of the variables. Mobility is reported for March 15 because this is the day before the quarantine takes effect for the first countries that implemented it (Honduras and Peru). Table 6. Descriptive Statistics of Countries with and without Lockdowns Table 7. Dynamic Effects of Lockdowns on Mobility (1) (2) Trend from days -8 to -1 (pre-lockdown) 0.03 0.66 (1.23) (1.15) Effect of day 0 (post-lockdown) -10.85** -10.04** (2.78) (2.81) Effects of days 1 to 7 (post-lockdown) -12.40** -11.85** (2.16) (2.05) Effects of days 8 to 15 (post-lockdown) -8.92** -8.50** (2.31) (2.26) Effects of days 16 and beyond (post-lockdown) -7.60** -7.25** (2.53) (2.46) Controls for other policies No Yes N810 810 % of people who travel more than 1 km Notes: This table shows the average effect of distancing policies on human mobility for five time periods: prelockdown (days -8 to -1), post-lockdown effect for day 0, postlockdown effect for days 1 to 7, postlockdown effect for days 8 to 15, and postlockdown effect beyond 15 days. The dependent variable is the percentage of people who travel more than one kilometer per day. The sample includes the 18 Latin American and Caribbean countries analyzed in this study during the period from March 1 to April 14, 2020. Each column corresponds to a regression. In column (1), the estimate is made without controls that vary over time, while in the even columns controls for the other three social distancing policies analyzed are included (closing schools, closing bars and restaurants, and cancellations of public events). Standard errors, presented in parentheses, are calculated by bootstrapping with 400 repetitions and clusters at the country level. Significance at one and five percent indicated by **, and *, respectively. Table 8. The Dynamic Effects of Lockdowns on Mobility by Country Postlockdown days Argentina Bolivia Colombia Ecuador El Salvador Honduras 0 -19.02 -13.00 -6.15 -17.62 -13.04 -7.48 1 -18.02 -21.35 -12.15 -25.37 -15.39 -18.08 2 -15.49 -16.53 -11.66 -23.15 -13.86 -19.60 3 -23.21 -18.78 -14.17 -22.16 -11.65 -15.19 4 -20.28 -20.58 -4.95 -18.30 -12.19 -19.22 5 -17.58 -21.72 -0.35 -12.07 -10.10 -13.86 6 -16.78 -21.78 -12.44 -20.05 -8.91 -8.74 7 -16.58 -15.54 -10.30 -16.51 -6.80 -14.52 8 -14.19 -21.55 -7.43 -16.74 -9.59 -14.81 9 -12.48 -20.05 -7.98 -17.66 -9.24 -6.51 10 -17.25 -19.20 -9.66 -16.08 -8.79 -6.37 11 -17.52 -19.51 -2.09 -11.85 -9.24 -4.78 12 -13.68 -19.72 -1.02 -8.65 -10.27 -3.76 13 -13.07 -21.18 -10.28 -14.94 -7.65 -0.46 14 -13.95 -18.11 -3.32 -15.21 -8.22 -7.24 15 -9.50 -17.58 -9.40 -14.93 -8.51 -12.51 Average -16.16 -19.14 -7.71 -16.96 -10.22 -10.82 Notes: This table shows the daily effects of lockdowns on human mobility. The sample consists of each country analyzed that implemented a lockdown and countries included in the corresponding synthetic control. Table 8. The Dynamic Effects of Lockdowns on Mobility by Country (continued) Postlockdown days Panama Paraguay Peru Trinidad and Tobago Venezuela 0 -9.56 -3.99 -1.54 -19.00 -11.85 1 -9.29 -7.58 -13.23 -9.82 -11.58 2 -11.60 -8.01 -9.84 -8.43 -10.07 3 -8.29 -5.74 -13.66 -8.96 -9.23 4 -6.62 -4.21 -16.08 -6.65 -5.36 5 -11.66 -4.43 -10.94 -7.70 -4.00 6 -9.90 -2.62 -5.00 -6.72 -7.09 7 -13.21 -0.36 -11.15 -5.48 -6.74 8 -10.21 2.08 -10.79 -7.32 0.27 9 -14.26 1.13 -11.64 -6.58 1.60 10 -7.40 3.32 -9.51 -6.08 2.15 11 -15.40 -0.44 -9.86 -5.59 4.69 12 -12.66 -2.88 -6.47 -2.05 4.95 13 -9.16 -3.11 -5.10 -5.12 -0.72 14 -11.31 -0.71 -10.02 -15.23 0.89 15 -5.49 -3.24 -9.06 -4.91 0.07 Average -10.38 -2.55 -9.62 -7.85 -3.25 Notes: This table shows the daily effects of lockdowns on human mobility. The sample consists of each country analyzed that implemented a lockdown and countries included in the corresponding synthetic control. Figure 1. Event Study of the Effects of Lockdowns on Mobility Notes: This figure shows the average daily effects of lockdowns on human mobility. The results are generated following the methodology described in de Chaisemartin and D'haultfoeuille (2019). For each coefficient, a bar represents its respective 95% confidence interval. The horizontal axis represents days before and after the start of the lockdown in each country. Positive numbers represent days post-lockdown implementation and negative numbers pre-lockdown, with 0 being the first day of the lockdown. The vertical axis shows the effect on the % of people who travel more than 1 km. Figure 2. Change in Mobility between the First and Second Week Post-Lockdown Notes: This figure shows the change in mobility between the first and second week after a lockdown was implemented for the countries that implemented lockdowns and the average of this change for the group of comparison countries (that did not apply lockdowns). The countries are divided into two groups. The first is made up of the countries that implemented the lockdown early, that is, between March 16 and 17 (Ecuador, Honduras, Peru, and Venezuela). The second includes countries with late implementation, between March 20 and 30 (Argentina, Bolivia, Colombia, El Salvador, Panama, Paraguay, and Trinidad and Tobago). The dates of lockdown implementation by country are presented in Table 2. -15 -10 -5 0 5 10 15 Honduras Peru Ecuador Venezuela Argentina Paraguay Bolivia El Salvador Colombia Panama Trinidad and Tobago Comparison Treatment Early Lockdown (March 16-17) Late Lockdown (March 20-30) Figure 3. Mobility Trends in Countries with Lockdowns and in Comparison Countries Notes: These figures show pre-lockdown days (negative) and post-lockdown days on the horizontal axis. The 0 represents the first day of lockdown. The black line represents the percentage of people that travel more than 1 kilometer in a day for each country. The gray line represents the percentage of people that travel more than 1 kilometer in a day for the group of comparison countries. Figure 3. Mobility Trends in Countries with Lockdowns and in Comparison Countries (continued) Notes: These figures show pre-lockdown days (negative) and post-lockdown days on the horizontal axis. The 0 represents the first day of lockdown. The black line represents the percentage of people that travel more than 1 kilometer in a day for each country. The gray line represents the percentage of people that travel more than 1 kilometer in a day for the group of comparison countries. Table A.5. P-values of the Effects of Lockdowns on Mobility by Countries Time Argentina Bolivia Colombia Ecuador El Salvador Honduras 0 0.00 0.00 0.57 0.00 0.00 0.14 1 0.00 0.00 0.00 0.00 0.00 0.00 2 0.00 0.00 0.00 0.00 0.00 0.00 3 0.00 0.00 0.00 0.00 0.00 0.00 4 0.00 0.00 0.00 0.00 0.00 0.00 5 0.00 0.00 0.71 0.00 0.14 0.00 6 0.00 0.00 0.00 0.00 0.00 0.00 7 0.00 0.00 0.00 0.00 0.00 0.00 8 0.00 0.00 0.00 0.00 0.00 0.00 9 0.00 0.00 0.00 0.00 0.00 0.14 10 0.00 0.00 0.00 0.00 0.00 0.14 11 0.00 0.00 1.00 0.00 0.00 0.14 12 0.00 0.00 0.71 0.00 0.00 0.43 13 0.00 0.00 0.00 0.00 0.29 0.86 14 0.00 0.00 0.86 0.00 0.00 0.00 15 0.29 0.00 0.29 0.00 0.29 0.00 Notes: This table shows the p-values of the placebo tests for the synthetic control effect by country. In each column, the sample is made up of the treated country and the corresponding comparables. The p-value shows the proportion of countries where the effect is greater, in absolute value, than that of the treated country. Each column shows the p-value of the placebo test associated with the effect for each post-lockdown day. Table A.5. P-values of the Effects of Lockdowns on Mobility by Countries (continued) Time Panama Paraguay Peru Trinidad and Tobago Venezuela 0 0.14 0.71 0.71 0.00 0.00 1 0.29 0.00 0.00 0.00 0.00 2 0.00 0.29 0.29 0.00 0.00 3 0.00 0.57 0.00 0.00 0.14 4 0.14 0.29 0.00 0.00 0.14 5 0.00 0.29 0.00 0.14 0.71 6 0.00 0.57 0.29 0.14 0.14 7 0.00 1.00 0.00 0.57 0.14 8 0.00 0.43 0.00 0.14 0.57 9 0.00 0.71 0.00 0.14 0.29 10 0.29 0.43 0.00 0.29 0.29 11 0.00 1.00 0.00 0.29 0.29 12 0.00 0.57 0.00 0.71 0.57 13 0.29 0.57 0.57 0.29 0.71 14 0.00 1.00 0.00 0.00 1.00 15 0.29 0.57 0.14 0.14 1.00 Notes: This table shows the p-values of the placebo tests for the synthetic control effect by country. In each column, the sample is made up of the treated country and the corresponding comparables. The p-value shows the proportion of countries where the effect is greater, in absolute value, than that of the treated country. Each column shows the p-value of the placebo test associated with the effect for each post-lockdown day.