Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months by age
Global Development Indicators - Grado en Global Studies
Ainhoa Barrera Molinuevo, Cristina Fernández Fernández and Silvia Gallego Peláez
29/05/2024
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Since according to the United Nations, one in three women has experienced physical or sexual violence by an intimate partner at some point in their lives, we will analyse Sustainable Development Goal (SDG) 5.2.1, which addresses the “Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age”. Nevertheless, as we will see throughout this work, we have faced considerable challenges, from the scarcity of data to the difficulty in obtaining continuous monitoring of this indicator. In other words, we have faced factors that have raised questions about trends over time and obstacles in collecting accurate and timely information on gender-based violence. Indeed, these events have led us, as we will explain below, to consult various databases, among which we have researched the World Health Organization’s data on the same indicator, which establishes that in the current situation, there is very limited agreement on standardised measures, so we didn’t end up using their data. This of course poses a problem to properly analyze the matter in depth. However, our findings offer insight into the current situation and highlight the importance of making further progress in the fight against gender-based violence in all its forms.
DATA ANALYISIS
We aimed to investigate the availability of values and trends in 2018 data, broken down by both regions and nations, according to SDG 5.2.1 ‘VC_VAW_MARR’ among women aged 15 to 49. Nonetheless, even though data for 180 nations was available for 2018, we were unable to monitor the indicator itself because of the lack of factors other than “female sex,” “the year 2018,” and “15-49 years.” As a result, data and trends could not be analyzed. For this reason, after examining databases like the WHO and running into the same issue of not having enough data to complete the analysis we had originally planned, we discovered that the DHS Programme’s STATcompiler, which uses surveys to gather data from more than 90 countries, provided us with more access to the data we required. In fact, the data from SDG 5.2.1 and the STATcompiler were combined to create the data that we replicated and utilized in our research. We have actually been able to collect data for eight nations for the years 2021 and 2022 by using this technique, which involves looking at the STATcompailer data regarding this particular statistic.
ANALYSIS
As previously stated, while our primary goal was to analyze the SDG 5.2.1 indicator, we ran into a number of problems and roadblocks along the way, which resulted in a shortage of data for a thorough study. Put another way, with data limited to the year 2018, female sex, age 15-49, and with the only variable being the countries and consequently the values, we were unable to analyze any kind of data because the trends over the years could not be observed, leaving us with no choice but to draw a cursory comparison between the values of each country. This led us to realize that we set out to investigate an indicator for which there is no monitoring. However, we wanted to check what this phenomenon was due to, and we came across various information and research papers such as The Lancet (which publishes peer-reviewed research and reviews on a wide range of medical topics), that explained how the data collected in the SDG itself came from a conglomerate of results obtained after various analyses in different databases and that data were scarce. In fact, The Lancet in 2022 stated that “collecting robust data on the magnitude and nature of the problem is a necessary first step in recognising and understanding the problem and initiating discussions on policies and strategies to address it”.
Nevertheless, as we did not want to be left with this scarce information and data collection, we wondered if we could go a little further, and that is why we started a search of different databases, in this case, the WHO which didn’t allow us to get more information that the one we had previously obtained from the SDG itself, and the STATcompailer of the DHS Programme, where we saw data which we didn’t have. After choosing the indicator that matched our baseline SDG 5.2.1, we managed to obtain information from only 8 countries on the rate of physical or sexual violence suffered by women aged 15-49. Although this is not a wide range of information, analysing these eight countries, two of which correspond to 2021 and its equivalent values and the rest to 2022, has allowed us to analyse certain trends. Therefore, we have created a flextable by choosing only those eight countries provided by the STATcompiler and we have compared them with those collected previously from the SDG, thus creating the following table:
## Time Series:
## Start = 2021
## End = 2028
## Frequency = 1
## Survey Year Value
## 2021 2021 9.8
## 2022 2021 5.4
## 2023 2022 13.0
## 2024 2022 18.8
## 2025 2022 16.9
## 2026 2022 12.9
## 2027 2022 3.8
## 2028 2022 26.4## tibble [8 × 11] (S3: tbl_df/tbl/data.frame)
## $ Country Code : chr [1:8] "BF" "KH" "GH" "KE" ...
## $ Country Name : chr [1:8] "Burkina Faso" "Cambodia" "Ghana" "Kenya" ...
## $ Survey Year : num [1:8] 2021 2021 2022 2022 2022 ...
## $ Survey Name : chr [1:8] "2021 DHS" "2021-22 DHS" "2022 DHS" "2022 DHS" ...
## $ Indicator : chr [1:8] "Physical or sexual violence committed by husband/partner in last 12 months" "Physical or sexual violence committed by husband/partner in last 12 months" "Physical or sexual violence committed by husband/partner in last 12 months" "Physical or sexual violence committed by husband/partner in last 12 months" ...
## $ By Variable : chr [1:8] "Ever-married or ever had intimate partner" "Ever-married or ever had intimate partner" "Ever-married or ever had intimate partner" "Ever-married or ever had intimate partner" ...
## $ Characteristic Category: chr [1:8] "Total 15-49" "Total 15-49" "Total 15-49" "Total 15-49" ...
## $ Characteristic Label : chr [1:8] "Total 15-49" "Total 15-49" "Total 15-49" "Total 15-49" ...
## $ Value : num [1:8] 9.8 5.4 13 18.8 16.9 12.9 3.8 26.4
## $ CI High : logi [1:8] NA NA NA NA NA NA ...
## $ CI Low : logi [1:8] NA NA NA NA NA NA ...## [1] "Goal" "Target" "Indicator"
## [4] "SeriesCode" "SeriesDescription" "GeoAreaCode"
## [7] "Country Name" "TimePeriod" "SDG Value"
## [10] "Time_Detail" "TimeCoverage" "UpperBound"
## [13] "LowerBound" "BasePeriod" "Source"
## [16] "GeoInfoUrl" "FootNote" "Characteristic"
## [19] "Nature" "Reporting_Type" "Sex"
## [22] "Units"## [1] "Country Code" "Country Name"
## [3] "Survey Year" "Survey Name"
## [5] "Indicator" "By Variable"
## [7] "Characteristic Category" "Characteristic Label"
## [9] "Value" "CI High"
## [11] "CI Low"## [1] "Goal" "Target" "Indicator"
## [4] "SeriesCode" "SeriesDescription" "GeoAreaCode"
## [7] "GeoAreaName" "TimePeriod" "Value"
## [10] "Time_Detail" "TimeCoverage" "UpperBound"
## [13] "LowerBound" "BasePeriod" "Source"
## [16] "GeoInfoUrl" "FootNote" "Age"
## [19] "Nature" "Reporting_Type" "Sex"
## [22] "Units"## [1] "Goal" "Target"
## [3] "Indicator.x" "SeriesCode"
## [5] "SeriesDescription" "GeoAreaCode"
## [7] "Country Name" "TimePeriod"
## [9] "SDG Value" "Time_Detail"
## [11] "TimeCoverage" "UpperBound"
## [13] "LowerBound" "BasePeriod"
## [15] "Source" "GeoInfoUrl"
## [17] "FootNote" "Characteristic"
## [19] "Nature" "Reporting_Type"
## [21] "Sex" "Units"
## [23] "Country Code" "Survey Year"
## [25] "Survey Name" "Indicator.y"
## [27] "By Variable" "Characteristic Category"
## [29] "Characteristic Label" "Value"
## [31] "CI High" "CI Low"Country Name | TimePeriod | SDG Value | Characteristic | Survey Year | Stat Value |
|---|---|---|---|---|---|
Cambodia | 2,018 | 9.1 | 15-49 | 2,021 | 5.4 |
Ghana | 2,018 | 10.2 | 15-49 | 2,022 | 13.0 |
Kenya | 2,018 | 22.8 | 15-49 | 2,022 | 18.8 |
Mozambique | 2,018 | 16.4 | 15-49 | 2,022 | 16.9 |
Nepal | 2,018 | 11.4 | 15-49 | 2,022 | 12.9 |
Philippines | 2,018 | 5.9 | 15-49 | 2,022 | 3.8 |
Tanzania | 2,018 | 24.3 | 15-49 | 2,022 | 26.4 |
Burkina Faso | 2,018 | 11.2 | 15-49 | 2,021 | 9.8 |
As we can see, we have on the one hand the information collected from one database and on the other hand the information from the other, with the corresponding years and the values of physical or sexual violence suffered by women between 15-49 years of age and the corresponding year. Therefore, we found that in these few countries, eight precisely, we could conduct a small study on trends between the different values and years, seeing that according to the data, there is a fall in the rate of violence among women as time goes on. Nevertheless, there still is a clear limitation to analysing more data, which means that if we would like to obtain more comprehensive answers or trends (which was our initial idea), we would need to check more data, which is not currently available in any database.
As a more generalised point of view and linking all the information we have mentioned, we can see that this problem is a limitation that not only existed in 2018 but that it seems that it is not being resolved, given that the data collected for 2021 and 2022 that we have available shows the same shortages. It should be noted, however, that there is a drop in the rate of violence in 2021 and 2022, which shows that the situation, even though it is far from being eradicated, is undergoing positive progress, or at least that’s what we can see in those eight countries. In the same way, the diversity of phenomena involved in gender violence, divided by physical, sexual, and psychological/emotional makes the results vary profoundly between them, where the highest values of violence are those referring to psychological violence, despite the fact that we have focused only on physical or sexual violence because we started from the data collected in SDG 5.2.1, which does not provide information for that variable, just physical or emotional. This information has been obtained after looking for information on numerous research papers and databases. On the other hand, we can also see that there is more data for ages 15-49 than the rest of the female population because the DHS surveys are focused largely on women of reproductive age. This is because one of the main objectives of these surveys is to collect detailed information on fertility, and maternal and child health, among others.
Finally, in an effort to present limited information in a more illustrative manner, we have, on the one hand, imported the maps from The Lancet, figure 3, which depict the prevalence estimates of lifetime physical, sexual, or both forms of intimate partner violence among women who have ever been in a relationship and are between the ages of 15 and 49 in 2018 across all nations for which data is available.
On the other hand, in order to see the disparities in the figures, we have produced two heat maps. The first one displays the values for each nation for 2018 as gathered by the SDG, while the second one shows the values for each country for 2021 and 2022 as gathered by the STATcompailer.
## [1] "featurecla" "scalerank" "labelrank" "sovereignt"
## [5] "sov_a3" "adm0_dif" "level" "type"
## [9] "tlc" "admin" "adm0_a3" "geou_dif"
## [13] "geounit" "gu_a3" "su_dif" "subunit"
## [17] "su_a3" "brk_diff" "name" "name_long"
## [21] "brk_a3" "brk_name" "brk_group" "abbrev"
## [25] "postal" "formal_en" "formal_fr" "name_ciawf"
## [29] "note_adm0" "note_brk" "name_sort" "name_alt"
## [33] "mapcolor7" "mapcolor8" "mapcolor9" "mapcolor13"
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## [41] "gdp_year" "economy" "income_grp" "fips_10"
## [45] "iso_a2" "iso_a2_eh" "iso_a3" "iso_a3_eh"
## [49] "iso_n3" "iso_n3_eh" "un_a3" "wb_a2"
## [53] "wb_a3" "woe_id" "woe_id_eh" "woe_note"
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## [69] "adm0_a3_de" "adm0_a3_gb" "adm0_a3_br" "adm0_a3_il"
## [73] "adm0_a3_ps" "adm0_a3_sa" "adm0_a3_eg" "adm0_a3_ma"
## [77] "adm0_a3_pt" "adm0_a3_ar" "adm0_a3_jp" "adm0_a3_ko"
## [81] "adm0_a3_vn" "adm0_a3_tr" "adm0_a3_id" "adm0_a3_pl"
## [85] "adm0_a3_gr" "adm0_a3_it" "adm0_a3_nl" "adm0_a3_se"
## [89] "adm0_a3_bd" "adm0_a3_ua" "adm0_a3_un" "adm0_a3_wb"
## [93] "continent" "region_un" "subregion" "region_wb"
## [97] "name_len" "long_len" "abbrev_len" "tiny"
## [101] "homepart" "min_zoom" "min_label" "max_label"
## [105] "label_x" "label_y" "ne_id" "wikidataid"
## [109] "name_ar" "name_bn" "name_de" "name_en"
## [113] "name_es" "name_fa" "name_fr" "name_el"
## [117] "name_he" "name_hi" "name_hu" "name_id"
## [121] "name_it" "name_ja" "name_ko" "name_nl"
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## [153] "fclass_eg" "fclass_ma" "fclass_pt" "fclass_ar"
## [157] "fclass_jp" "fclass_ko" "fclass_vn" "fclass_tr"
## [161] "fclass_id" "fclass_pl" "fclass_gr" "fclass_it"
## [165] "fclass_nl" "fclass_se" "fclass_bd" "fclass_ua"
## [169] "Country Name" "TimePeriod" "SDG Value" "Characteristic"
## [173] "Survey Year" "Stat Value" "geometry"## [1] "featurecla" "scalerank" "labelrank" "sovereignt"
## [5] "sov_a3" "adm0_dif" "level" "type"
## [9] "tlc" "admin" "adm0_a3" "geou_dif"
## [13] "geounit" "gu_a3" "su_dif" "subunit"
## [17] "su_a3" "brk_diff" "name" "name_long"
## [21] "brk_a3" "brk_name" "brk_group" "abbrev"
## [25] "postal" "formal_en" "formal_fr" "name_ciawf"
## [29] "note_adm0" "note_brk" "name_sort" "name_alt"
## [33] "mapcolor7" "mapcolor8" "mapcolor9" "mapcolor13"
## [37] "pop_est" "pop_rank" "pop_year" "gdp_md"
## [41] "gdp_year" "economy" "income_grp" "fips_10"
## [45] "iso_a2" "iso_a2_eh" "iso_a3" "iso_a3_eh"
## [49] "iso_n3" "iso_n3_eh" "un_a3" "wb_a2"
## [53] "wb_a3" "woe_id" "woe_id_eh" "woe_note"
## [57] "adm0_iso" "adm0_diff" "adm0_tlc" "adm0_a3_us"
## [61] "adm0_a3_fr" "adm0_a3_ru" "adm0_a3_es" "adm0_a3_cn"
## [65] "adm0_a3_tw" "adm0_a3_in" "adm0_a3_np" "adm0_a3_pk"
## [69] "adm0_a3_de" "adm0_a3_gb" "adm0_a3_br" "adm0_a3_il"
## [73] "adm0_a3_ps" "adm0_a3_sa" "adm0_a3_eg" "adm0_a3_ma"
## [77] "adm0_a3_pt" "adm0_a3_ar" "adm0_a3_jp" "adm0_a3_ko"
## [81] "adm0_a3_vn" "adm0_a3_tr" "adm0_a3_id" "adm0_a3_pl"
## [85] "adm0_a3_gr" "adm0_a3_it" "adm0_a3_nl" "adm0_a3_se"
## [89] "adm0_a3_bd" "adm0_a3_ua" "adm0_a3_un" "adm0_a3_wb"
## [93] "continent" "region_un" "subregion" "region_wb"
## [97] "name_len" "long_len" "abbrev_len" "tiny"
## [101] "homepart" "min_zoom" "min_label" "max_label"
## [105] "label_x" "label_y" "ne_id" "wikidataid"
## [109] "name_ar" "name_bn" "name_de" "name_en"
## [113] "name_es" "name_fa" "name_fr" "name_el"
## [117] "name_he" "name_hi" "name_hu" "name_id"
## [121] "name_it" "name_ja" "name_ko" "name_nl"
## [125] "name_pl" "name_pt" "name_ru" "name_sv"
## [129] "name_tr" "name_uk" "name_ur" "name_vi"
## [133] "name_zh" "name_zht" "fclass_iso" "tlc_diff"
## [137] "fclass_tlc" "fclass_us" "fclass_fr" "fclass_ru"
## [141] "fclass_es" "fclass_cn" "fclass_tw" "fclass_in"
## [145] "fclass_np" "fclass_pk" "fclass_de" "fclass_gb"
## [149] "fclass_br" "fclass_il" "fclass_ps" "fclass_sa"
## [153] "fclass_eg" "fclass_ma" "fclass_pt" "fclass_ar"
## [157] "fclass_jp" "fclass_ko" "fclass_vn" "fclass_tr"
## [161] "fclass_id" "fclass_pl" "fclass_gr" "fclass_it"
## [165] "fclass_nl" "fclass_se" "fclass_bd" "fclass_ua"
## [169] "Country Name" "TimePeriod" "SDG Value" "Characteristic"
## [173] "Survey Year" "Stat Value" "geometry"
CONCLUSION
We have been faced with a very particular and interesting SDG indicator case, not only because the data mainly collected by the SDGs is a statistical model where the data is for 2018, but also because the monitoring of the indicator has been waived for the time being. Limited adequate resources, a lack of strong international cooperation and consequently a number of complexities, have made data collection and management a more complicated process, putting the monitoring of the indicator itself on temporary waiver. Indeed, The Lancet in 2022 said that “Some countries or regions might not be adequately represented due to a lack of comprehensive surveys or data collection efforts”. Therefore, with the data provided, we have only been able to see the difference between eight countries in different years and see trends in them, but the lack of information has hindered further exploration. On the positive side, however, we can say that the only trend that we can see is a drop in the values of gender-based violence, but research on the topic is needed, due to its relevance and because it affects thousands of women globally.
BIBLIOGRAPHY
Sardinha, L., Maheu-Giroux, M., Stöckl, H., Meyer, S. R., & García-Moreno, C. (2022). Global, regional, and national prevalence estimates of physical or sexual, or both, intimate partner violence against women in 2018. Lancet, 399(10327), 803–813. https://doi.org/10.1016/s0140-6736(21)02664-7 Violence against women prevalence estimates, 2018 – WHO African Region. (2021, March 3). Who.int; World Health Organization. https://www.who.int/publications/i/item/WHO-SRH-21.7