A key foundation of containment policies to restrict the spread of COVID-19 is encouraging people to #StayAtHome. Social distancing, lockdowns, and quarantine related policies have become essential in the fight to “flatten the curve” of this disease. However, as a recent article showed using cell phone location data from approximately 15 million users in the U.S.—the ability to stay at home during coronavirus may be a luxury that many are unable to afford.

The data showed that in the U.S., while people in all income groups were moving less than they were before, richer people were staying at home the most and began doing so several days before those with lower incomes. This type of data points to the inequalities present in the ability to protect ourselves and our communities from the virus. Indeed, it is the “few” who have the privilege to stay put and work remotely from the safety of their own home.

In Latin America and the Caribbean, a region with higher poverty rates and over half the workforce employed in the informal sector—staying at home presents an even starker challenge for many. Some people simply cannot afford to stay at home. Thus, in order for COVID-19 infection containment strategies to succeed, vulnerable groups need to be compensated economically for staying at home, not only the poor. It is not a time to be concerned about inclusion errors.

With data released by Google in their COVID-19 Community Mobility Reports, we are able to see how changes in mobility patterns are actually taking shape in LAC. The current mobility data they released are for March 29, 2020 and show how visits and length of stay at different places change compared to a baseline value. The baseline is the median value, for the corresponding day of the week, during the 5- week period Jan 3–Feb 6, 2020. The data include changes in mobility to destinations that are relevant to social distancing efforts as well as access to essential services. Specifically, the reports include data for the follow types of destinations: retail and recreation (places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters); grocery & pharmacy (places like grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies); parks (places like national parks, public beaches, marinas, dog parks, plazas, and public gardens); transit stations (places like public transport hubs such as subway, bus, and train stations); places of work; and places of residence.

In order to have a better idea of inequalities in mobility patterns, we would need to consider how these trends differ by factors such as gender, ethnicity, or socioeconomic status. Unfortunately, the data is not currently available in such a disaggregated format. However, by comparing changes in mobility patterns with the share of population living in poverty in a country—we begin to see roughly how economic inequalities in mobility may be taking shape. Specifically, this #GraphForThought shows that in the wake of COVID-19, LAC countries with a higher share of people living in poverty have seen smaller reductions in mobility trends outside the home. This association between mobility and poverty is true for all destination types included in the dataset and all international poverty lines ($1.90 a day, $3.20 a day, and $5.50 a day).

At the policy level, this suggests that for containment measures to be successful, compensation to those unable to generate income needs to be put in place, particularly in contexts of high poverty and high informality. For example, social safety net approaches to provide informal workers with alternative sources of income may help to enable this population to reduce their travel outside the home. In the absence of these policies, isolation might simply not be an option for some, and COVID-19 may continue to be a virus that discriminates against the poor.