Working conditions/Access to work Equality Index

Center (ownership): 
Primary contact: 
Tool typology: 
Gender in value chains
Description: 

The objective of this index is assessing key variables that characterize access to work and working conditions. The index is based on three premises: 1) Measurement of gender gaps; 2) Easy to compute; and 3) The final value is bound between 0 (inequality) and 1 (equality) to facilitate comparisons and interpretation. It has two categories: 1) variables that characterize working conditions and 2) variables that describe access to work. 

Objective: 
The objective of this index is assessing key variables that characterize access to work and working conditions. The index is based on three premises: 1) Measurement of gender gaps; 2) Easy to compute; and 3) The final value is bound between 0 (inequality) and 1 (equality) to facilitate comparisons and interpretation. It has two categories: 1) variables that characterize working conditions and 2) variables that describe access to work. Analyzing working conditions and access to work gender equality is useful to understand specific barriers to growth within a value chain. Growing potential could not be successfully and sustainably realized without effective strategies and policies to close gender gaps in working conditions, and access to economic opportunities.
Type of tool: 
Quantitative
Methodology: 

This index follows the empirical methodology used by Hausmann et al. 2012 as presented below.

1 step: Calculate ratios by gender for each variable i in each observation. For example, if working with production node (segment) and have information about 100 farmers, a ratio needs to be calculated for each variable i in each farm f.

2 step: Truncate at equality (1) when necessary. This is to have bounds between 0 and 1 where 1 means equal number of women and men.

3 step: Calculate sub-index scores (for each category of variables) For this, is necessary to calculate weighted average of the variables within each category to create two sub-indexes (one for working conditions, and other for access to work). As mentioned by Hausmann 2012, a simple average would implicitly give more weight to the measure that has more variability. So, he proposes to normalize the variables by equalizing their standard deviations. So, first, standard deviations for each variable need to be calculated and then, a 1% point change of that would be calculated as below:

Then, sum var_sd over each category j. 

And then to construct the weight, dividing each var_sd over sumj.

Then, these values would be used as weights to calculate the weighted average of the four variables. In this way, a variable with a small variability of standard deviation, gets a larger weight, therefore when there is great gender gap in that variable it would heavily penalized.

4 step: Calculate final score An un-weighted average for each sub-index is taken to create the overall Working conditions/Access to work Equality Index. Sub-indexes are for: i) variables that characterize working conditions, and ii) variables that describe access to work. Indexes calculated could be roughly interpreted as a percentage value that reveals how much of the gender gap has closed. This tool could be applied to each node (segment) or to the entire value chain. It allows for comparisons between nodes and between value chains since it is based on ratios in contrast to levels, and in this way is independent from different levels of value chain development.

Variables needed: 
Minumum data needed: 1) Working conditions Wage (hourly/weekly) 2) Access to work Participation (employment by gender), Literacy or education level Desirable for further analysis: 1) Working conditions Skilled, semi-skilled, non-skilled, Occupation (job activity), Category (owner, worker, family worker), Tenure, Temporary/Permanent, Contract, Physical Safety/risk of task performed 2) Access to work Education level, Requirements for job (experience, abilities, etc), Job-training
Implementation procedure: 
Implementation requires three steps. First, the application of a questionnaire following the module created for this purpose; Second, processing the information and generating the desirable indicators which are presented in a Stata do-file; Third, using excel to create a table and graphs. 1.      Questionnaire module on employment and human resources. Additionally, a module of unit identification is also available.  Please be aware that most information in both modules are necessary to calculate wage gaps, so please make sure that all questions needed are adapted. Furthermore, there are two types of modules recommended: one for the producer node and other for the commercialization node within the value chain. This is a general instrument that needs that the questions are adapted depending on the value chain type and context.  Producer Node: Module in English Module in Spanish Coomercialization Node: Module in English Module in Spanish The following excel file provides a list of activities that could be used as a guide to modify and improve the list of relevant job activities (question Q2.3_L) in the labor module according to each value chain node and commodity. It includes primary, secondary and trade activities.  Time use activity classification 2.     Do-file contains detailed steps to create variables and estimate indicators using the variables suggested in the questionnaire. The indicators created could be used as a base for further analysis, as mentioned before, the examples provided here are not limiting but rather are meant to provide a first basic idea of what the tool can do. Additionally, there is a raw dataset to perform the example described in do-file and an output dataset which includes the estimated indicators. This dataset was created with the purpose of exemplify how the related tool works. It is in Stata format. Additionally, there is a companion Stata do-file that contains detailed steps to create variables and estimate indicators using the variables suggested in the questionnaires. The raw dataset can be used to perform the example described in the do-file. The indicators created could be used as a base for further analysis, the examples provided here are not limiting but rather are meant to provide a first basic idea of what the tool can do. Stata code Stata data (raw)
Output examples: 

This tool will produce tables like this one:


Suggested interpretation: In the production node, the Working Conditions/Access to Work Equality Index is 31% suggesting a large gap between females and males in working conditions and access to work. This implies that there is a gap of 69%. 
Spatial coverage: 
Dataset: 
Commodity: 
Domesticated Animal: 
Version: 
2.0
First released on: 
Monday, August 12, 2013
Last version on: 
Monday, August 12, 2013
Format: 
PDF, Excel, Stata
Source codes: 
Target audience: 
Researcher