The data set that I selected reflects information from the GWIP (Global Work-Injury Policy Database) collected in 2020, pertaining to protections afforded to injured or otherwise incapacitated laborers. The primary variables relevant to this project are the ones pertaining to replacement rate (the percentage of an employee’s pre-tax salary with which they are compensated after being either temporarily or permanently incapacitated), duration of compensation (how long these benefits are offered), and overall work injury coverage (the percentage of the working population that benefits from these laws, as calculated by SIED). I specifically chose to use the replacement rates and durations for those who are permanently disabled, as I found this to be a better estimate of the extent to which these labor laws promote (or prohibit) social welfare. I believe that protection of labor rights and welfare is a valuable component of supporting human rights, and I was particularly interested in this data set as it provided historical and colonial context for the countries involved. It is no secret that the labor system under which the world currently operates has been heavily shaped by colonialism; Ashiagbor 2020 argues that dominant colonial countries such as the UK were only able to achieve the levels of social welfare that they presently offer because of the sheer amount of wealth that they derived from exploiting the global south. Other nuances that the author discusses, such as the mistreatment of migrant workers even within countries offering better labor protections, are unfortunately not within the scope of the data set, but are still valuable factors to keep in mind. Knowledge of the countries that have historically been exploited (or benefited from the exploitation of others) is valuable to incorporate when studying a topic that is influenced by so many globalized factors. Most of my cleaning involved getting rid of variables that either had too many NAs or were too similar to other variables (but provided less comprehensive data).
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library(ggplot2)library(viridis)
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library(plotly)
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insurance <-read_csv("gwip20.csv")
Rows: 189 Columns: 26
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Delimiter: ","
chr (8): country_name, ISO3c, labor_workinjury_firstnat_carriedover, labor_...
dbl (18): cow_code, independence, labor_workinjury_firstlaw, labor_workinjur...
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# Cleaning data set, filtering out columns with an excessive amount of NAs or that are repetitive/unnecessary, making variables more readable. insurance2 <- insurance |>select(!year &!RRgross_Sing &!RRgross_Fam &!labor_workinjury_first_fund &!cow_code &!workinjury_coverage_SIED_harmo &!workinjury_replacement_rate_single_SIED_harmo) |>mutate(duration_perm_sspw =str_replace_all(duration_perm_sspw, "months", ""), duration_temp_sspw =str_replace_all(duration_temp_sspw, "months", ""), global_south =str_replace_all(global_south, "1", "yes"), global_south =str_replace_all(global_south, "0", "no"), labor_workinjury_firstlaw_programtype =str_replace_all(labor_workinjury_firstlaw_programtype, "Employer-liability", "Employer liability"))
summary(insurance2)
country_name ISO3c independence labor_workinjury_firstlaw
Length:189 Length:189 Min. :1783 Min. :1854
Class :character Class :character 1st Qu.:1903 1st Qu.:1917
Mode :character Mode :character Median :1956 Median :1929
Mean :1929 Mean :1932
3rd Qu.:1967 3rd Qu.:1946
Max. :2011 Max. :2004
labor_workinjury_firstlaw_sspw labor_workinjury_firstins
Min. :1883 Min. :1854
1st Qu.:1916 1st Qu.:1924
Median :1938 Median :1951
Mean :1938 Mean :1948
3rd Qu.:1957 3rd Qu.:1969
Max. :2006 Max. :2006
NA's :15 NA's :6
labor_workinjury_firstnat_carriedover labor_workinjury_firstlaw_programtype
Length:189 Length:189
Class :character Class :character
Mode :character Mode :character
labor_workinjury_firstlaw_bluecollar_fullcoverage replacement_rate_perm_sspw
Min. :1880 Min. : 20.00
1st Qu.:1927 1st Qu.: 70.00
Median :1950 Median : 80.00
Mean :1950 Mean : 77.78
3rd Qu.:1970 3rd Qu.: 90.00
Max. :2017 Max. :150.00
NA's :22
duration_perm_sspw replacement_rate_temp_sspw duration_temp_sspw
Length:189 Min. : 0.00 Length:189
Class :character 1st Qu.: 66.70 Class :character
Mode :character Median : 75.00 Mode :character
Mean : 77.55
3rd Qu.:100.00
Max. :100.00
NA's :24
Region global_south colonial_history
Length:189 Length:189 Length:189
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
workinjury_coverage_ILO workinjury_coverage_SIED_harmo_full
Min. :0.0140 Min. :0.0270
1st Qu.:0.1960 1st Qu.:0.2400
Median :0.4780 Median :0.5040
Mean :0.4704 Mean :0.5227
3rd Qu.:0.7005 3rd Qu.:0.8050
Max. :1.0000 Max. :1.0000
NA's :22
workinjury_replacement_rate_single_SIED_harmo_full
Min. :0.100
1st Qu.:0.992
Median :1.042
Mean :1.037
3rd Qu.:1.158
Max. :1.500
# Testing the relationship between average replacement rate (the percentage of a worker's pre-tax income that they receive compensation for) for permanent injuries against the percentage of workers in the labor force covered by work injury lawsmodel1 <-lm(workinjury_coverage_SIED_harmo_full ~ replacement_rate_perm_sspw, data = insurance2, na.rm =TRUE)
Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
extra argument 'na.rm' will be disregarded
summary(model1)
Call:
lm(formula = workinjury_coverage_SIED_harmo_full ~ replacement_rate_perm_sspw,
data = insurance2, na.rm = TRUE)
Residuals:
Min 1Q Median 3Q Max
-0.47787 -0.29809 -0.02033 0.27434 0.47813
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.589976 0.104561 5.642 7.15e-08 ***
replacement_rate_perm_sspw -0.000681 0.001308 -0.521 0.603
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3121 on 165 degrees of freedom
(22 observations deleted due to missingness)
Multiple R-squared: 0.00164, Adjusted R-squared: -0.00441
F-statistic: 0.2711 on 1 and 165 DF, p-value: 0.6033
plot(model1$residuals)
The linear equation derived from this model is workinjurycoverage = 0.589976 + -0.000681(replacementrate), indicating that the percentage of workers covered when the replacement rate is equal to 0 is about 59%, decreasing by .07% with each percentage increase in replacement rate. Although the residuals plot shows no distinct pattern, indicating that the model is reasonable to use for the data, the p-value is quite high (at 0.603) and the adjusted R-squared is quite low (at -0.00441, implying that less than 1% of the variation in work injury coverage can be explained by replacement rate changes), suggesting that the correlation between the two variables is not statistically significant.
insurance2 |>ggplot(aes(x = independence, y = workinjury_coverage_ILO, col = colonial_history))+geom_point()+labs(x ="Year Country Gained Independence", y ="% of Workforce Covered for Injuries")
insurance2 |>ggplot(aes(x = labor_workinjury_firstlaw_programtype))+geom_bar(stat ="count")+labs(x ="Type of Program for First Work Injury Law")
plot1 <- insurance2 |>mutate(percentage = workinjury_coverage_SIED_harmo_full*100) |>ggplot(aes(x = colonial_history, y = percentage, col = replacement_rate_perm_sspw, text =paste0("Country: ", country_name, "\n Replacement Rate: ", replacement_rate_perm_sspw, "%", "\n Payment Duration: ", duration_perm_sspw, "\n Colonizing Country: ", colonial_history, "\n Proportion of Workforce Covered: ", percentage, "%")))+theme_minimal()+geom_jitter(alpha =0.65)+scale_color_viridis(option ="inferno")+theme(axis.text.x =element_text(angle =90), text =element_text(family ="serif"))+labs(x ="Country of Colonization", y ="% of Workforce Covered by Labor Laws", col ="% of Income Compensated \n for Permanent Injuries", title ="Labor Regulation Coverage and Generosity for Countries of the World", caption ="Source: Global Work-Injury Policy Database")plot2 <-ggplotly(plot1, tooltip ="text")plot2
This visualization yielded a lot of interesting information to consider. Although my previous linear model had indicated that labor law coverage and generosity did not have a very strong relationship, I was still surprised by the amount of countries with minimal coverage that offered high levels of compensation (such as Burundi, with a 100% replacement rate but only 4.4% of the workforce covered), or vice versa. At a future point in time, it might be interesting to compare the inequalities of wealth distribution within these countries to determine if that factor may be influential; is wealth inequality responsible for there being only a select few receiving reasonable payouts? Unsurprisingly, the countries that had not previously been subjected to colonization numbered among the highest in both coverage and generosity, supporting my initial belief that colonial history would be impactful on the data. I’d originally intended to create a map to display the information, but I was unable to find any CSVs containing latitude and longtiude information that would have joined cleanly with the ISO3 codes in my data set. I would certainly be interested in attempting another map at a later date, if I was able to find a better data set to join, or playing around with other historical factors given that the incorporation of colonial history proved fruitful.
