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Risks for Women

Indicator Definition

Assessment of how women, specifically foreign businesswomen, are treated locally and how safe it is for women to travel and conduct business.


Coding Methodology

The Hassle Factor Risk to Female Executives indicator is a composite measure that combines two sub-indicators; risks unique to females and travel safety. For the risks unique to females sub-indicator, we used “Scale #1: Physical Security of Women” (PSW) data from the WomanStat project at Brigham Young University. For the safety sub-indicator, we used the “Risk Map” from Control Risk. These data sets were selected as they provide data for more countries than any other available source. We checked for the validity of the measure by asking selected experts about their perception of the scores.

The metric combines the sub-indicators and converts them to a 1 to 7 scale using the following formulas: 

  • Risk to Female Executives 2008 = PSW2007 x Control Risk2008/3
  • Risk to Female Executives 2010 = PSW2009 x Control Risk2010/3
  • Risk to Female Executives 2012 = [(PSW2007+PWS2009+PWS2014)/3] x Control Risk2012/3*
  • Risk to Female Executives 2014 = PSW2014 x Control Risk2014/3
  • Risk to Female Executives 2016 = [(PSW2007+PWS2009+PWS2014)/3] x Control Risk2016/3*

* WomanStat data for PSW is published in three waves: 2007, 2009 and 2014. The Hassle Factor formulas for 2012 and 2016 use averages of the available data from 2008, 2010 and 2014.

Formula Description: Data for the Physical Security of Women sub-indicators was expressed on a one to four scale, one being the lowest risk measure and four the highest. Similarly, Control Risk scores travel risks on a one to five scale with one being the lowest risk measure and five the highest. The scales were combined by multiplying the sub-indicator scores for each country, resulting in a one to 20 scale. This was converted to a one to seven scale by dividing results scores by three. Finally, the scores were rounded to the nearest whole number and scores of less than one-half were rounded to one. The final scores were again checked for consistency and validity.


Data Imputation

The 2008 WomanStat data set was missing scores for four countries that were included in subsequent data sets. Similarly, one value was missing for 2010. In these cases, we estimated the missing scores by calculating the average using the data available for that country in preceding or subsequent years. The following table summarizes the estimated scores.


Estimated value 2008

Estimated value 2010

Brunei Darussalam



Korea, North (Dem.)






In addition, 13 countries included in the Hassle Factor are not included in the WomanStat data set for all the years we measured. For these countries, we estimated the physical security of women sub-indicator using the average values from countries with similar World Bank Economic classifications. To calculate these averages, we used the same approach we used to estimate the missing values in the business facilitation indicator. The following tables summarize the estimated values for the physical security of women sub-indicator for each year measured.

Table of Estimated Sub-Indicator Values by Country
  2008 2010 2012 2014 2016
Country EC Est. EC Est. EC Est. EC Est. EC Est.
Value Value Value Value Value
Bermuda HI 2 HI 2 HI 2 HI 2 HI 2
Greenland HI 2 HI 2 HI 2 HI 2 HI 2
Hong Kong, China HI 2 HI 2 HI 2 HI 2 HI 2
Macao HI 2 HI 2 HI 2 HI 2 HI 2
New Caledonia HI 2 HI 2 HI 2 HI 2 HI 2
Macao HI 2 HI 2 HI 2 HI 2 HI 2
New Caledonia HI 2 HI 2 HI 2 HI 2 HI 2
Puerto Rico HI 2 HI 2 HI 2 HI 2 HI 2
Palau Islands UMI 3 UMI 3 UMI 3 UMI 3 HI 2
Samoa LMI 3 LMI 3 LMI 3 LMI 3 LMI 3
Timor-Leste LMI 3 LMI 3 UMI 3 UMI 3 UMI 3
Tonga LMI 3 LMI 3 UMI 3 UMI 3 UMI 3
Vanuatu LMI 3 LMI 3 LMI 3 LMI 3 LMI 3


Global Road Warrior

World Bank, Classifying Countries by Income

Womanstats Project


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