ECONOMIC IMPLICATIONS OF VIOLENCE

AGAINST WOMEN IN THE LABOR MARKET

INTRODUCTION

Topic:

Violence against women is a broad term that represents a growing concern in today’s society. Studies show that many acts of violence against women are committed by an intimate partner. Over one third of women have experienced some type of physical violence over the course of their life. From sex trafficking to domestic violence, woman continue to feel the effects of a world that has historically believed them to be inferior. While examples of this can be seen in many different areas, some of the clearest examples, and the ones I’ll be focusing on in this paper, can be found in the work force. Data from UN Women suggests that in countries like India, a woman will miss an average of five days paid work per incident, which significantly affects income earning potential.

For this assignment, I wanted to further explore this relationship between labor participation and domestic violence on a global scale. I predict domestic violence variables will be inversely related to selected proxy measures for labor across all sampled countries (grouped by income parameters).

Hypothesis:

Does domestic violence influence female labor force participation within global income groups?

H0: There is no significant relationship between domestic violence and female labor force participation within global income groups.
HA: There is a significant relationship between domestic violence and female labor force participation within global income groups.

Variables:

Indicators of Domestic Violence.

  • SG.VAW.1549.ZS: Numerical variable that measures the proportion of women subjected to physical and/or sexual violence by a current or former partner in the past 12 months.
  • SG.LEG.DVAW: Ordinal variable that indicates the existance of legislation on domestic violence. “Yes” values equal 1, while “no” values equal 0.

Indicators of Economic Gender Equity.

  • SL.TLF.TOTL.FE.ZS: Numerical variable that measures female labor force participation as a percentage of the total labor force.
  • IC.FRM.FEMM.ZS: Numerical variable that measures of firms with female top manager (% of firms).

METHODOLOGY

Data Preparation:

Data was queried for each variables and stored as a dataframe. I used data transformations to join the individual frames into the sample frame, shown in Appendex A. Further documentation of this process can be found in the code.R file.

Dependencies:

source("code.R")
library(wbstats)
library(dplyr)
library(stringr)
library(DATA606)
library(tidyr)
library(knitr)
try(setwd("/project"))

Sample design:

World Bank Economies List
Income.group n prop
1.high 81 0.37
2.upper.mid 56 0.26
3.lower.mid 47 0.22
4.low 34 0.16

I created an observational study using a stratified random sample of countries by income group and indicators obtained from The World Bank API. Data was not consistently reported and/or updated within the same year for all variables. Thus, I limited the scope of the study to the most recent year, within the past 5 years, that the data was available.

These variables were transformed and joined to create the sample frame consisting of 33 cases (countries) within 4 distinct income groups. The sample countries were evaluated within four stratas for income group from the World Bank Economy List. The chart on the right shows the the true Population of all countries reported by the World Bank within each strata.

Data published by the World Bank must meet data quality standards and adhere to proper statistical methodology. Thus, I have assumed all conditions for inference have been met.

ANALYSIS

Domestic Violence Proportion and Legislation Variables:

Domestic Violence Proportion Variable:

On average, domestic violence was reported to affect 14.5% of women within the observations of this study. The summary statistics for v.prop are shown below:

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     2.0     7.0    11.0    14.5    19.9    46.1
## Warning: Ignoring unknown parameters: binwidth, bins, pad

Review of the histogram and box plot shows that the aggregate variable is multimodal and skewed to the right. However, high and upper middle income countries are unimodal and have a smaller spread between 0 and 20 indicating lower domestic violence rates. Higher and more variable rates of domestic violence can be seen in low and lower middle income countries.

Domestic Violence Legislation Variable:

The data for this variable is binary and coded as “Yes” values equal 1 and “no” values equal 0. The proportion table for the v.law variable shows that nearly 80% of countries have legilation on domestic violence.

Evaluation of a bar chart or this variable further this distribution. Nearly all high and upper middle income countries have laws on domestic violence. Noteably, there was one instance of a high income country (Estonia) which did not this law, which is an outlier within these observations.

Relationship between Domestic Violence Variables

The relationship between v.law and v.prop is depicted in the below histogram. Countries without a domestic violence law show a much wider spread of reported proportion of domestic violence incidents. This distribution is unimodal, skewed right, and centered around 10%. The distribution for countries with laws is more narrow and is also unimodal and centered around 10%.

Female Labor and Managerial Participation Variables:

Female Labor Force Participation Variable:

The summary statistics for f.labor are shown below:

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   17.34   41.12   45.76   43.12   48.88   50.62

Note that this variable measures participation as a percentage of the total labor force within a country. Values over 50 indicate more females than males participate in the labor force.

Review of the histogram and box plot shows that the aggregate variable is multimodal and skewed to the left. High income countries have the narrowest spread and highest rates of labor participation. The other groups show larger spreads and some potential outliers.

Female Participation in Management Variable:

The summary statistics for f.mgr are shown below:

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2.40   12.50   18.70   18.54   23.80   57.30

Review of the histogram and box plot shows that the aggregate variable is multimodal and skewed to the left. There are a few potential outlier countries in this variable. The following two countries particularly stood out in their individual income groups: - Cambodia, a lower middle income country, reports the highest level (57.3%) of female managerial participation. - Jordan, an upper middle country, reports the lowest amount (2.4%) of participation in both the country and income grouping.

Again, I found that high income countries have the narrowest spread. Participation is much more variable among the other income groupings.

Relationship between Labor Variables

The relationship between the labor variables appears positive and exponential. This pattern indicates that female labor participation rates have a proportional affect on female managerial participation.

APPENDIX

A. Sample Frame of Most Recently Published Data from 2013-2018
Code Economy Region Income.group v.prop v.law f.labor f.mgr
AFG Afghanistan South Asia 4.low 46.1 0 17.33995 4.7
BGR Bulgaria Europe & Central Asia 2.upper.mid 9.0 1 46.35027 23.9
KHM Cambodia East Asia & Pacific 3.lower.mid 10.9 1 49.89908 57.3
COD Congo, Dem. Rep. Sub-Saharan Africa 4.low 36.8 0 49.83135 10.8
CIV Côte d’Ivoire Sub-Saharan Africa 3.lower.mid 22.0 0 41.12111 14.3
HRV Croatia Europe & Central Asia 1.high 4.0 1 46.41201 18.7
CZE Czech Republic Europe & Central Asia 1.high 6.0 1 44.42768 11.6
DOM Dominican Republic Latin America & Caribbean 2.upper.mid 16.0 1 41.28231 21.2
EGY Egypt, Arab Rep. Middle East & North Africa 3.lower.mid 14.0 0 23.07036 4.9
EST Estonia Europe & Central Asia 1.high 4.0 0 48.52923 25.3
HND Honduras Latin America & Caribbean 3.lower.mid 11.0 1 37.83873 28.0
HUN Hungary Europe & Central Asia 1.high 8.0 1 45.75801 20.4
JOR Jordan Middle East & North Africa 2.upper.mid 14.1 1 17.65568 2.4
KEN Kenya Sub-Saharan Africa 3.lower.mid 25.5 1 48.49929 13.4
KGZ Kyrgyz Republic Europe & Central Asia 3.lower.mid 17.1 1 39.99600 28.8
LVA Latvia Europe & Central Asia 1.high 7.0 1 50.13107 31.5
LTU Lithuania Europe & Central Asia 1.high 6.0 1 50.61934 21.0
NAM Namibia Sub-Saharan Africa 2.upper.mid 20.2 1 49.54208 27.4
NGA Nigeria Sub-Saharan Africa 3.lower.mid 11.0 1 45.43899 13.9
PER Peru Latin America & Caribbean 2.upper.mid 12.9 1 45.42174 19.9
PHL Philippines East Asia & Pacific 3.lower.mid 7.1 1 39.89898 29.9
POL Poland Europe & Central Asia 1.high 3.0 1 45.03609 20.6
ROU Romania Europe & Central Asia 2.upper.mid 7.0 1 43.14949 20.1
SLE Sierra Leone Sub-Saharan Africa 4.low 28.7 1 50.14706 15.9
SVK Slovak Republic Europe & Central Asia 1.high 8.0 1 45.48798 14.0
SVN Slovenia Europe & Central Asia 1.high 2.0 1 46.57443 18.8
SWE Sweden Europe & Central Asia 1.high 6.0 1 47.65477 12.5
TJK Tajikistan Europe & Central Asia 4.low 15.2 1 38.64777 9.6
TZA Tanzania Sub-Saharan Africa 4.low 29.6 0 48.87651 14.0
TGO Togo Sub-Saharan Africa 4.low 12.7 0 49.22675 11.4
TUR Turkey Europe & Central Asia 2.upper.mid 11.0 1 32.21361 5.4
ZMB Zambia Sub-Saharan Africa 3.lower.mid 26.7 1 47.80405 23.8
ZWE Zimbabwe Sub-Saharan Africa 4.low 19.9 1 49.16097 16.3