02_update.Rmd

---
title: "Impact of COVID-19 on refugees and migrants, Update 2"
author: "Mixed Migration Centre, 12 May 2020"
date: ''
output: github_document
 
---
<style>
body {
text-align: justify}
</style>

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tinytex)
```
\  

**This is the second update on the situation for refugees and migrants on mixed migration routes around the world in light of the COVID-19 pandemic based on data collected by the [Mixed Migration Centre](http://www.mixedmigration.org/) in North Africa, West Africa and Latin America. The objective of the global updates is to provide regular up-to-date findings on COVID-19 awareness, knowledge and risk perception, access to information, access to healthcare, assistance needs and the impact on refugees’ and migrants’ lives and migration journeys. Published once every two weeks, this series provides an aggregated overview; more detailed, thematic and response-oriented COVID-19 snapshots are also developed in each of the MMC regional offices and available [here](http://www.mixedmigration.org/resource-type/covid-19/).**


## Key Messages

•	Interviewed refugees and migrants across all three regions are well aware of the coronavirus. A large majority considers they know how to protect themselves and others, though this proportion is lower among respondents in West Africa (81%), and particularly Burkina Faso (75%), than in Latin America (98%) and North Africa (85%)

•	Protective measures taken by refugees and migrants vary widely across regions. Staying at home and isolating from others is much more frequent in Latin America (89%) than in North Africa (57%) and West Africa (11%). Similarly, in Latin America 90% of respondents think they can keep 1.5 metre distance from others, while in North Africa (47%) and West Africa (34%) these percentages are much lower

•	Sources of information on coronavirus for interviewed refugees and migrants differ between regions. In Latin America the main source is the national government (70%), in West Africa other migrants (65%) and in North Africa the online community (55%)

•	Across regions only 42% of respondents believe they would be able to access healthcare if they had coronavirus symptoms. The barriers to accessing healthcare vary widely: discrimination against foreigners is the most important barrier in North Africa (44%), while it is far less often mentioned in West Africa (8%). Lack of legal documentation is an important barrier in Latin America (49%), while seldom reported in West Africa (6%). Lack of money is an important barrier across all three regions, particularly in West Africa (47%)

•	Across regions, 86% of respondents stated that they are in need of extra help since the COVID-19 crisis began, but only 21% have received additional assistance, with a minimum of 7% in Libya. Specific needs differ across regions but cash and basic needs such as food, water and shelter are most commonly cited

•	The extent to which the increased difficulty of crossing borders affects refugees and migrants clearly differs across the three regions (19% in Latin America, 31% in North Africa and 65% in West Africa). This suggests that respondents in West Africa are the most mobile and are generally still in transit, but are now stuck


## Respondents

1,173 respondents were interviewed between 6 and 26 April 2020: 313 in Latin America, 516 in North Africa, and 344 in West Africa, in the following countries:

```{r table, echo=FALSE}
table_demographics <- data.frame(Region=c("Latin America", "", "North Africa", "", "West Africa", "", "", "Overall"), Country=c("Colombia", "Peru", "Libya", "Tunisia", "Burkina Faso", "Mali", "Niger", ""), n=c(250, 63, 211, 305, 98, 101, 145, 1173), `Percent women` = c(71, 51, 30, 33, 42, 24, 27, 41), `Mean age`=c(34, 32, 31, 29, 29, 27, 30, 30))
library(knitr)
kable(table_demographics, align = 'c', col.names = c("Region", "Country", "Number of respondents", "Percent women", "Mean age"))


```



## Methodology

A summary of the methodology can be found [here](http://www.mixedmigration.org/4mi/4mi_faq/). All figures are rounded to the nearest whole number. Figures for countries where the number of interviews is less than or around 100 should be interpreted with caution. Unless specified, the number of observations for all analyses and visualisations corresponds to those presented in the above table. Also note that for most items of the questionnaire, respondents can select several answer options. Finally, note that one inconsistency between the regions’ questionnaires meant that a few ‘none’ answer options could not be included in all analyses.

This second global update reports on Latin America, North Africa, and West Africa, which is where MMC first rolled out the adapted 4Mi COVID-19 survey. Data collection has also started in East Africa and Asia, and future updates will include data from these regions.

## Awareness, knowledge and risk perception

Knowledge of COVID-19 is high among respondents, but less than 2% (n = 22, with 28 refused answers) have been tested. All 1,173 respondents reported that they had heard of COVID-19, and across all regions, 93% reported seeing people acting more cautiously since the beginning of the crisis, with a minimum of 89% in Burkina Faso, and a maximum of 97% in Libya.

Similarly, across all regions, at least 9 respondents out of 10 agreed or strongly agreed that they are worried about catching coronavirus. More than two-thirds of all participants agreed or strongly agreed that they are worried about transmitting the coronavirus, with figures ranging from 69% (West Africa) to 78% (Latin America) between regions. Overall, 88% of respondents also stated that they know how to protect themselves and protect others, with a range of 81% (West Africa) to 98% (Latin America) across regions.

Respondents are well aware of the symptoms of coronavirus but less frequently indicated that the virus can also be asymptomatic (14% overall), especially in Colombia (5%) and Mali (8%). Respondents know which groups are more at risk, with older people cited more frequently (84% overall), followed by people who are already ill with another condition (57%), and health workers (37%). This order is the same across all regions and countries, with two exceptions: in Burkina Faso, adults (34%) and pregnant women (27%) are cited more frequently than health workers (22%), and in Mali, children under five (37%) are cited more frequently than health workers (33%). This suggests that some key messages about the specificity of COVID-19 compared to other common diseases, which affects predominantly children, have not yet reached some locations.

A vast majority of respondents take measures to protect themselves, with washing hands more regularly (80% overall), wearing a mask (63%) and staying at home or isolating from others (52%) being the most commonly cited. There are, however, notable differences between regions and countries. Wearing a mask is more frequent in Latin America (78%) than in West Africa (63%) and North Africa (54%). Likewise, staying at home and isolating from others is much more frequent in Latin America (89%) than in North Africa (57%) and West Africa (11%). In Mali and Burkina Faso, only 6% and 7% of respondents reported staying at home, respectively, compared to 90% in Colombia. Furthermore, respondents in West Africa are less likely to report avoiding crowded spaces (22%) and keeping distance from other people (20%) than respondents in North Africa (55% and 43%, respectively). Only 21% of respondents in Latin America report avoiding crowded spaces and only 29% report keeping distance from other people, but that might be explained by the fact that 89% of Latin America respondents reported staying at home or isolating from others.

Similarly, the number of respondents reporting that they are able to keep the recommended 1.5 metre distance differs across regions and is lower in West Africa (see Figure 1) and Tunisia (39%).

**_Figure 1: Do you think you are able to practice 1.5 metre distancing?_**

```{r 0, include=FALSE}
library(tidyverse)
load("rda/02_cleaned_data_20200430.rda")
```

```{r 1, echo=FALSE, fig.align='left', fig.height=7, fig.width=7}
fig1_data <- data %>%
  group_by(Region, c31) %>% 
  tally() %>% 
  mutate(Percent=round(n/sum(n)*100, digits = 1))
fig1  <- fig1_data %>% 
  mutate(c31=fct_relevel(c31, "Refused", "Don´t know", "No", "Yes")) %>% 
  ungroup() %>% 
  mutate(Region=fct_relevel(Region, "West Africa", "North Africa", "Latin America")) %>% 
  ggplot(aes(fill=c31, y=Percent))+
  geom_col(aes(x=Region, y=Percent), width = 0.40)+
  xlab("")+
  theme_bw()+
  coord_flip()+
  scale_y_continuous(breaks = seq(0, 100, by = 10))+
  theme(legend.title = element_blank())+
  theme(legend.position = c(0.70, 0.95), legend.direction = "horizontal", legend.title = element_blank())+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))
fig1

```
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\


Very few respondents (5% overall) reported not doing anything to protect themselves against coronavirus, although it is possible that respondents are reporting the measures that they are supposed to take, rather than the measures they are taking. There are also large differences between countries. In Burkina Faso and Niger, 22% and 10% reported not doing anything, respectively, whereas not a single participant reported so in Latin America. This figure was close to or below 5% for respondents in other countries.

\


## Access to information

There is a clear difference in terms of sources of information between regions, see Figure 2. In Latin America the national government is the most commonly used source of information (70%), while in North Africa and West Africa the figure is 49% and 39%, respectively. In West Africa, other migrants (65% in the whole region, and up to 73% in Niger) were cited more often, while in North Africa, the online community (55%) was also more often cited as the main source of information on coronavirus than the government. Furthermore, 36% of respondents in Mali and 26% of respondents in Niger cited smugglers, which is higher than in all other countries (range: 0 to 8%).  

**_Figure 2: Who did you receive information on COVID-19 from?_**

```{r 2, echo=FALSE, fig.align='left', fig.height=8, fig.width=8}
fig2_data <- data %>% 
  select(189, 190, 55:70) %>%
  pivot_longer(cols = 3:18, names_to = "Options", values_to = "Answer") %>% 
  filter(!is.na(Answer)) %>% 
  group_by(Region, N_region) %>% 
  count(Answer) %>% 
  mutate(Percent = round(n/N_region*100, digits = 1))
fig2  <- fig2_data %>%
  ungroup() %>% 
  mutate(Region=fct_relevel(Region, "West Africa", "North Africa", "Latin America")) %>% 
  mutate(Answer = reorder(Answer, Percent)) %>% 
  ggplot(aes(fill=Region))+
  geom_col(aes(x=Answer, y=Percent), width = 0.6, position = position_dodge2(preserve = "single", padding = 0))+
  ylab("Percent")+
  xlab("")+
  theme_bw()+
  scale_y_continuous(breaks = seq(0, 100, by = 5)) + 
  coord_flip()+
  theme(legend.title = element_blank())+
  theme(legend.position = c(0.80, 0.15), legend.direction = "vertical", legend.title = element_blank())+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))
fig2

```
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Across regions, however, the government is seen as the most trustworthy source of information (Latin America: 51%; North Africa: 53%; West Africa: 40%), followed by health professionals (range across regions: 36% to 49%). In West Africa, other refugees and migrants are also seen as trustworthy (34%), more so than among respondents in North Africa (11%) and Latin America (1%).

Most participants received information on the virus via the media (73% overall), social media (60%), or in-person (48%), with in-person communication seeming more important in West Africa (61%) than in the other regions (Latin America: 40%; North Africa: 45%), in line with the higher reliance on other migrants as a source of information. In West Africa, the media used were also more mixed than in other regions. For example, respondents in West Africa also used more phone calls (54%) and street advertising (35%) than respondents in other regions. Out of all participants who reported using social media (n=700), 86% and 80% reported using Facebook and WhatsApp, respectively.

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## Access to healthcare and prevention

Overall, only 42% of participants believe they would be able to access healthcare if they had coronavirus symptoms, with some differences between regions (West Africa: 50%; Latin America: 45%; North Africa: 35%), and more importantly between countries (63% in Niger, but 37% in Burkina Faso; 52% in Colombia, but only 16% in Peru, although note the number of interviews is low in Peru). It is also important to note that there is a high level of uncertainty, with 27% of participants across regions simply not knowing whether they would be able to access healthcare in case of need, with the higher percentages in Mali (39%) and Libya (34%).

When asked about the main barriers to accessing healthcare, respondents cited lack of money (41% overall), discrimination against foreigners (28%), and not knowing where to go for healthcare (23%), with again important differences between regions, see Figure 3. Discrimination against foreigners is the most frequently cited barrier in North Africa (44%, compared to 24% in Latin America and 8% in West Africa), and up to 55% in Tunisia (and 54% in Peru).

**_Figure 3: What are the barriers to accessing healthcare?_**

```{r 3, echo=FALSE, fig.align='left', fig.height=8, fig.width=8}
fig3_data  <- data %>% 
  select(189, 190, 113:125) %>%
  pivot_longer(cols = 3:15, names_to = "Options", values_to = "Answer") %>% 
  filter(!is.na(Answer)) %>% 
  group_by(Region, N_region) %>% 
  count(Answer) %>% 
  mutate(Percent = round(n/N_region*100, digits = 1)) 

fig3  <- fig3_data %>% 
  ungroup() %>% 
  mutate(Region=fct_relevel(Region, "West Africa", "North Africa", "Latin America")) %>% 
  mutate(Answer = recode(Answer, 
                          'Discrimination against foreigners limits access to services' = "Discrimination against foreigners",
                          'General insecurity and conflict prevent me from accessing healthcare' = "General insecurity",
                          'I am afraid of being reported to authorities, or arrest, or deportation' = "Afraid of being reported",
                          'I don\'t have the money to pay for health services' = "I don't have the money",
                          'I don\'t have the right or the legal documentation to access health services here' = "I don't have the right/documentation",
                          'The advice for testing and treating coronavirus is unclear here' = "The advice is unclear here",
                          'Services are overwhelmed and access is difficult for everyone' = "Services are overwhelmed",
                          'I don\'t know where to go for healthcare' = "I don't know where to go", 'Other (specify)'="Other")) %>% 
  mutate(Answer = reorder(Answer, Percent)) %>% 
  ggplot(aes(fill=Region))+
  geom_col(aes(x=Answer, y=Percent), width = 0.6, position = position_dodge2(preserve = "single", padding = 0))+
  ylab("Percent")+
  xlab("")+
  theme_bw()+
  scale_y_continuous(breaks = seq(0, 100, by = 5)) + 
  coord_flip()+
  theme(legend.title = element_blank())+
  theme(legend.position = c(0.80, 0.15), legend.direction = "vertical", legend.title = element_blank())+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))
fig3
```
\  
\

A lack of legal documentation is also an important barrier in Latin America (49%), but less reported in North Africa (20%), and much less in West Africa (6%), arguably because respondents in Burkina Faso, Mali, and Niger are primarily from countries that are also part of ECOWAS which means they have, in theory, regular status in the country of interview. Finally, although general insecurity and conflict is not mentioned as a barrier in Latin America and West Africa (both close to 0%), 10% of respondents in Libya mention general insecurity as a barrier to access healthcare. In contrast, 18% of respondents in North Africa mention not speaking the language as a barrier, while the figure is between 0 and 3% in other regions, where there is in most cases a common language spoken by both respondents and the host country.
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## Assistance needs

Out of all respondents, 86% stated that they are in need of extra help since the COVID-19 crisis began, with marginal differences between regions. Out of these 1,014 respondents, the most frequently needs cited were cash (74% overall) basic needs such as food, water and shelter (69%), and sanitary items such as sanitizer and masks (43%), but this time with important differences between regions. For example, 82% of respondents in West Africa mentioned cash, whereas the figure was 59% in Latin America. In contrast, basic goods were mentioned more frequently in Latin America (89%) than in North Africa (70%) and West Africa (38%).

Overall, 21% of respondents stated that they had received additional assistance since the coronavirus crisis began, with higher figures in Latin America (32%) than in West Africa (21%) and North Africa (15%, with a minimum of 7% in Libya). For these 249 participants who received assistance, it was predominantly basic needs (71%), followed by sanitary items, information about the virus, and cash, these last three all being cited by roughly one quarter of all participants who received assistance. There were differences between countries (for example, in West Africa, 39% of respondents received information about the virus, although the figure in Mali alone was 95%), however figures by country should be interpreted with caution, since the number of interviews, and especially of people who received assistance, is still very low. That being said, overall and across regions, a clear trend is emerging with a noticeable gap between extra assistance received (21%) and needed (86%), except for basic needs and information about the virus, as shown in Figure 4.

\
**_Figure 4: Kind of assistance received (bars) vs. assistance needed (lines)_**

```{r 4, echo=FALSE, fig.align='left', fig.height=8, fig.width=8}
fig4_received <- data %>% #N=249
  filter(c23 == "Yes") %>%#discard those that didn't receive help
  group_by(Region) %>% 
  mutate(N_region_part = length(Region)) %>% #get n by region
  select(189, 192, 128:140) %>%
  pivot_longer(cols = 3:15, names_to = "Options", values_to = "Answer") %>% 
  filter(!is.na(Answer)) %>% 
  group_by(Region, N_region_part) %>% 
  count(Answer) %>% 
  mutate(Percent = round(n/N_region_part*100, digits = 1))

fig4_needed  <- data %>% #N=1,014
  filter(c26 == "Yes") %>%#discard those that don't need extra help
  group_by(Region) %>% 
  mutate(N_region_part = length(Region)) %>% #get n by region
  select(189, 192, 154:166) %>%
  pivot_longer(cols = 3:15, names_to = "Options", values_to = "Answer") %>% 
  filter(!is.na(Answer)) %>% 
  group_by(Region, N_region_part) %>% 
  count(Answer) %>% 
  mutate(Percent = round(n/N_region_part*100, digits = 1))

fig4  <- fig4_received %>%
  ungroup() %>% 
  ggplot(aes(fill=Region))+
  geom_col(aes(x=Answer, y=Percent), width = 0.6, position = position_dodge2(preserve = "single", padding = 0))+
  ylab("Percent")+
  xlab("")+
  theme_bw()+
  labs(caption = "Number of respondents
  Latin America: received = 99, needed = 277
  North Africa: received = 78, needed = 441
       West Africa: received = 72, needed = 296")+
  scale_y_continuous(breaks = seq(0, 100, by = 10)) + 
  theme(legend.title = element_blank())+
  theme(legend.position = "none")+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))+
  theme(axis.text.x = element_text(angle = 40, hjust = 1))+
  facet_grid(rows = vars(Region))+
  geom_point(data = fig4_needed, aes(x=Answer, y=Percent))+
  geom_line(data = fig4_needed, aes(x=Answer, y=Percent, group=Region))+
  scale_x_discrete(labels=c("Access to health services"="Health services", "Distribution of sanitary items (sanitizer/ mask/ gloves/ etc)"="Sanitizer, masks, gloves", "Cash to pay for health services"="Cash for health services", "Documentation to access health services"="Documentation", "Information about the virus: symptoms/ what to do if I have symptoms/ how to protect myself"="Information", "Other basic needs: food, water, shelter"="Food, water, shelter", "Access to work and livelihoods"="Access to work", "Psychological assistance"="Psychological support", "Other (specify)"="Other"))
fig4
```
\  
\

Overall, the main providers of additional assistance were NGOs (41%, and up to 63% in West Africa), the local population (32%), and the government (27%), except in Libya, where family and friends were more frequently cited (64%), and assistance most frequently took the form of cash.


## Impact on refugees’ and migrants’ lives

Overall, the main impact of COVID-19 on refugees and migrants is reduced access to work (68%), followed by more stress (59%), and reduced availability of basic goods (51%), with increased racism (20%) and reduced access to asylum (13%) relatively less reported, see Figure 5. Reduced access to basic goods is more of an issue among respondents in Latin America (66%) than in North Africa (57%) and West Africa (27%), whereas the opposite is true for reduced access to asylum (Latin America 5%, West Africa 10%, North Africa 20%).


\

**_Figure 5: What impact has the crisis had on your day-to-day life?_**

```{r 5, echo=FALSE, fig.align='left', fig.height=8, fig.width=8}
fig5_data  <- data %>% 
  select(189, 190, 168:174) %>%
  pivot_longer(cols = 3:9, names_to = "Options", values_to = "Answer") %>% 
  filter(!is.na(Answer)) %>% 
  group_by(Region, N_region) %>% 
  count(Answer) %>% 
  mutate(Percent = round(n/N_region*100, digits = 1))
fig5 <- fig5_data %>% 
  ungroup() %>% 
  mutate(Answer = recode(Answer, 'Other (specify)' = "Other")) %>% 
  mutate(Region=fct_relevel(Region, "West Africa", "North Africa", "Latin America")) %>% 
  mutate(Answer = reorder(Answer, Percent)) %>% 
  ggplot(aes(fill=Region))+
  geom_col(aes(x=Answer, y=Percent), width = 0.6, position = position_dodge2(preserve = "single", padding = 0))+
  ylab("Percent")+
  xlab("")+
  theme_bw()+
  scale_y_continuous(breaks = seq(0, 100, by = 5)) + 
  coord_flip()+
  theme(legend.title = element_blank())+
  theme(legend.position = c(0.80, 0.15), legend.direction = "vertical", legend.title = element_blank())+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))
fig5

```
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Furthermore, just about two-thirds of all respondents reported that they had lost income due to coronavirus restrictions, with higher figures in Latin America (89%) than in North Africa (60%) and West Africa (51%). Across all countries, the lowest percentage observed was in Mali, with 37% stating they had lost income. This could be linked with refugees and migrants in Latin America being perhaps more likely to have had some sort of income, while perhaps people in North Africa and West Africa were less likely to be working before the crisis.


## Impact on migration journeys

Unsurprisingly, the COVID-19 crisis has had an impact on refugees’ and migrants’ journeys, but with important differences across regions and countries, see Figure 6. Among those who stated that the crisis had no impact on their journey, there was a much higher percentage in Latin America (48%) than in North Africa (13%) and West Africa (6%). Overall, 20% said they were simply too afraid to either continue their journey or return home. Across countries, increased risk of detention and deportation seem higher in Libya (26%) and Niger (18%) than in other countries.

The extent to which the increased difficulty of crossing borders affects refugees and migrants clearly differs across the three regions, with 19% citing this in Latin America, 31% in North Africa and 65% in West Africa. This probably reflects the higher mobility of the respondents in West Africa, who are generally still in transit, but now stuck. This matches with the finding that there are notable differences between the percentages saying they had not reached the end of their journey (11% in Latin America, 74% in North Africa and 83% in West Africa).  

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**_Figure 6: What impact has the coronavirus crisis had on your migration journey?_**

```{r 6, echo=FALSE, fig.align='left', fig.height=8, fig.width=8}
fig6_data <- data %>% 
  select(189, 190, 177:187) %>%
  pivot_longer(cols = 3:13, names_to = "Options", values_to = "Answer") %>% 
  filter(!is.na(Answer)) %>% 
  group_by(Region, N_region) %>% 
  count(Answer) %>% 
  mutate(Percent = round(n/N_region*100, digits = 1))
fig6 <- fig6_data %>% 
  ungroup() %>% 
  mutate(Answer = recode(Answer, 'Other (specify)' = "Other", 'I\'ve been delayed because I was sick, or because I had to stop and take care of people who got sick' = "Delayed because I or other people were sick",
                         'I feel too afraid to move (to continue my journey or return)' = "I feel to afraid to move",
                         'Increased difficulty moving around inside countries'="Increased difficulty moving around",
                         'I was going to be resettled, but this is now delayed'="About to be resettled, but now delayed",
                         'Disembarked / deported back to previous country'="Deported back to previous country")) %>% 
  mutate(Region=fct_relevel(Region, "West Africa", "North Africa", "Latin America")) %>% 
  mutate(Answer = reorder(Answer, Percent)) %>% 
  ggplot(aes(fill=Region))+
  geom_col(aes(x=Answer, y=Percent), width = 0.6, position = position_dodge2(preserve = "single", padding = 0))+
  ylab("Percent")+
  xlab("")+
  theme_bw()+
  scale_y_continuous(breaks = seq(0, 100, by = 5)) + 
  coord_flip()+
  theme(legend.title = element_blank())+
  theme(legend.position = c(0.80, 0.15), legend.direction = "vertical", legend.title = element_blank())+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))
fig6

```

\  
\

Furthermore, just over half of all respondents (52%) stated that they had not changed their plans as a result of the coronavirus outbreak; this percentage is much higher in Latin America (73%) than in North Africa and West Africa (both 44%). Across countries, there is also a sharp contrast between respondents who would like to continue their journey but are stuck, and those who would like to return home. In Colombia and Peru, less than 6% said they were stuck and in Peru 24% would like to return home, whereas in Libya and Niger, up to 43% are stuck and less than 7% would like to return home, again confirming the transit nature of refugees and migrants in West Africa and, to a slightly lesser extent in North Africa, compared with the respondents in Colombia and Peru. Overall, only 2% and 9% of respondents had changed their intended destination or planned route, respectively, with relatively small differences between countries.

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### Acknowledgement 

The owner and source of the data used for this study is the [Mixed Migration Centre](http://www.mixedmigration.org/), and this study has been published first [here](http://www.mixedmigration.org/resource-type/covid-19/) on 12 May 2020. Data analyst: Jean-Luc Jucker, PhD  
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