Social Science Research/Statistics on Women in Low-Middle-Income Countries

Social Science Research Statistics on Women in Low-Middle-Income Countries Predicting Violence Against Women International Migration Trends The study https://pmc.ncbi.nlm.nih.gov/articles/PMC9684414, titled "LivWell: a sub-national dataset on the living conditions of women and their well-being for 52 countries - PMC," describes LivWell, a global, longitudinal dataset examining women's living conditions and well-being at the subnational level in 52 low- and middle-income countries from 1990 to 2019. The dataset combines data from the Demographic and Health Surveys (DHS) with information on regional climate and socioeconomic conditions. The dataset can be downloaded from Zenodo here: https://zenodo.org/records/7277104. Belmin C, Hoffmann R, Elkasabi M, Pichler PP. LivWell: a sub-national Dataset on the Living Conditions of Women and their Well-being for 52 Countries. Ski Data. 2022 Nov 22;9(1):719. doi: 10.1038/s41597-022-01824-2. PMID: 36418857; PMCID: PMC9684414. Data Loading Data is analyzed based on the latest year available per country: library(dplyr) livwell <- read.csv("livwell.csv") df<-livwell %>% group_by(country_name,country_code) %>% summary(year=max(year)) df <- merge(df,livwell, by.x=c("country_name","country_code","year")) Women's Work Countries have a high prevalence of unpaid female workers. In contexts of poverty and economic insecurity, women may be more vulnerable to unpaid work to contribute to family support. In low-income countries, a significant portion of the female population works in the agricultural sector, often in family farms where work is not formally paid. df1<-df %>% group_by(country_name) %>% summarise(female workers=mean(WK_working12_p),paid_workers=female workers*mean(WK_working_paid_p, na.rm=T)/100, unpaid_workers=female workers-paid_workers) %>% arrange(desc(female workers)) df1 <- df1[-which(is.na(df1$paid_workers)),] df2<-df1 %>% select(country_name,unpaid_workers) %>% arrange(desc(unpaid_workers)) df2 <- data.frame(country_name=df2$country_name,unpaid_workers=paste(round(df2$unpaid_workers,2),"%")) df2 country_name unpaid_workers 1 Nepal 73.21 % 2 Madagascar 64.63 % 3 Rwanda 57.09 % 4 Burundi 53.88 % 5 Burkina Faso 52.89 % 6 Malawi 43.69 % 7 Congo Democratic Republic 41.24 % 8 Uganda 40.43 % 9 Sierra Leone 40.41 % 10 Guinea 37.84 % 11 Cote d'Ivoire 36.92 % 12 Ghana 34.09 % 13 Cameroon 32.29 % 14 Tanzania 30.23 % 15 Liberia 30.06 % 16 Haiti 29.02 % 17 Benin 28.31 % 18 Mozambique 25.09 % 19 Peru 23.64 % 20 Togo 23.49 % 21 Ethiopia 22.66 % 22 Nigeria 21.75 % 23 Zambia 21.14 % 24 Timor-Leste 20.5 % 25 Mali 19.15 % 26 Senegal 19.03 % 27 Indonesia 18.2 % 28 India 17.92 % 29 Bolivia 16.46 % 30 Philippines 14.8 % 31 Cambodia 13.62 % 32 Bangladesh 13.56 % 33 Gabon 11.94 % 34 Honduras 11.22 % 35 Zimbabwe 11.08 % 36 Armenia 10.84 % 37 Lesotho 8.39 % 38 Niger 6.27 % 39 Colombia 5.92 % 40 Nicaragua 5.89 % 41 Morocco 5.39 % 42 Namibia 5.36 % 43 Tajikistan 4.94 % 44 Guatemala 4.32 % 45 Pakistan 3.76 % 46 Egypt 2.59 % 47 South Africa 2.08 % 48 Maldives 1.44 % Education df1 <- df[-which(is.na(df$ED_attainment_no_educ_p)),] df2<-df1 %>% group_by(country_name) %>% summary(women_without_education=mean(ED_attainment_no_educ_p)) %>% arrange(desc(women_without_education)) df2 <- data.frame(country_name=df2$country_name,women_without_education=paste(round(df2$women_without_education,2),"%")) df2 country_name women_without_education 1 Burkina Faso 77.49 % 2 Niger 75.2 % 3 Guinea 71.14 % 4 Mali 68.66 % 5 Cote d'Ivoire 60.28 % 6 Benin 54.2 % 7 Nepal 53.7 % 8 Morocco 51.35 % 9 Senegal 48.24 % 10 Sierra Leone 44.6 % 11 Ethiopia 44.32 % 12 Pakistan 39.61 % 13 Liberia 38.92 % 14 Togo 35.88 % 15 India 31.6 % 16 Burundi 31.09 % 17 Mozambique 29.75 % 18 Nigeria 28.58 % 19 Ghana 25.28 % 20 Timor-Leste 24.62 % 21 Madagascar 21.4 % 22 Nicaragua 18.1 % 23 Congo Democratic Republic 16.03 % 24 Egypt 15.9 % 25 Guatemala 15.3 % 26 Cambodia 14.87 % 27 Tanzania 14.83 % 28 Haiti 14.05 % 29 Cameroon 13.7 % 30 Bangladesh 12.88 % 31 Rwanda 11.58 % 32 Uganda 10.3 % 33 Malawi 10.11 % 34 Turkey 9.19 % 35 Zambia 7.89 % 36 Namibia 6.42 % 37 Vietnam 5.67 % 38 Bolivia 5.12 % 39 Maldives 5 % 40 Honduras 4.98 % 41 Gabon 4.03 % 42 Peru 3.05 % 43 Indonesia 2.17 % 44 Jordan 1.89 % 45 South Africa 1.73 % 46 Tajikistan 1.57 % 47 Colombia 1.41 % 48 Lesotho 1.37 % 49 Zimbabwe 1.25 % 50 Philippines 1.16 % 51 Armenia 0.1 % Physical or sexual violence by a partner df1 <- df[-which(is.na(df$DV_phys_or_sex_partner_p)),] df2<-df1 %>% group_by(country_name) %>% summarise(physical_or_sexual_violence_by_partner=mean(DV_phys_or_sex_partner_p)) %>% arrange(desc(physical_or_sexual_violence_by_partner)) df2 <- data.frame(country_name=df2$country_name,partner_sexual_or_physical_violence=paste(round(df2$partner_sexual_violence,2),"%")) df2 country_name partner_sexual_or_physical_violence 1 Gabon 50.3 % 2 Sierra Leone 49.8 % 3 Congo Democratic Republic 49.78 % 4 Uganda 47.12 % 5 Burundi 42.98 % 6 Timor-Leste 39.88 % 7 Peru 38.83 % 8 Zambia 38.58 % 9 Malawi 35.34 % 10 Rwanda 34.43 % 11 Colombia 34.06 % 12 Zimbabwe 33.54 % 13 Mali 33.42 % 14 Tanzania 33.31 % 15 Cameroon 33.03 % 16 Mozambique 33 % 17 India 30.28 % 18 Nigeria 24.58 % 19 Namibia 24.56 % 20 Cote d'Ivoire 24.03 % 21 Togo 23.83 % 22 Ethiopia 22.35 % 23 Benin 22.21 % 24 Tajikistan 22 % 25 Haiti 20.44 % 26 Honduras 20.21 % 27 Pakistan 20.02 % 28 South Africa 18.22 % 29 Egypt 17.91 % 30 Cambodia 17.75 % 31 Guatemala 17.11 % 32 Senegal 14.49 % 33 Maldives 13.72 % 34 Philippines 13.3 % 35 Burkina Faso 11.68 % 36 Jordan 9.41 % 37 Armenia 9.18 %