During the first phase of the Banana Retail Commitment on Living Wage, the private sector parties involved provide insight into the gap between current wages and living wage benchmarks.
After a third year of data collection, covering wages for the calendar year 2021, the identified living wage gap is 14,96%, covering 86% of the volumes purchased by the Dutch retailers. The first year showed an average living wage gap of 9% for 42% of the volumes purchased by Dutch retailers. The second year showed an average living wage gap of 10,7% for 75% of the volumes purchased by Dutch retailers.
During the third year of this project, there was a substantial increase in the number of farms submitting a Salary Matrix compared to last year (332 vs. 217 for the second year and 117 for the first year). With this, the diversity of farms is also increasing, for example, in terms of location, structure, and size. All these variables mean that a diverse range of solutions will be necessary to close the living wage gap. There is no one-size-fits-all approach for the different farms.
The retailers can use the Salary Matrix, made available by IDH, to calculate the difference between the current wages and the living wage. By using the tool, they can analyze prevailing wages in the supplying locations of their total banana assortment.
1. How was the data checked (2019-2021)?
According to the project’s Monitoring Protocol, an independent consultant was hired for the following:
A banana sourcing information sheet was completed by each participating retailer indicating 100% of the volume purchased per supplier over a specific period. The same was done by each supplier indicating their volumes sold to respective retailers. The figures matched with acceptable differences of minor percentages.
The next step was to check the data entered into Salary Matrices. For the first and second years, field verification was made impossible due to the Covid-19 pandemic, and thus a simplified online approach was developed. A maximum of 5 farms per supplier per country were interviewed and asked to provide underlying evidence during individual video conferences. Production volumes indicated in the matrices were also checked against the volumes reported in the sourcing information sheets. Discrepancies were discussed and resolved with suppliers.
In addition to providing the intended data checks, the process provided deeper insight into the variety of human resource management systems used in the banana sector. It also highlighted the importance of field-level verification to collect additional evidence and confirm results through interviews with workers and worker representatives.
Coinciding with the third year of this project, the new Rainforest Alliance certification program started requiring farms to make an annual comparison of the remuneration of all workers against the living wage benchmark using the Salary Matrix. Whilst capacity to audit the Salary Matrix will increase over time, the organization has already reported that from the 272 farms for which Rainforest Alliance certificate holder numbers were made available, 120 (44.1%) are certified. That is, they have been granted a certification license after undergoing their first transition audit to the new Standard. It is important to note that some of the farms with a license after their first transition audit would have no non-conformities with their Salary Matrix, while others would be working to address non-conformities based on the plan they developed to close them.
Rainforest Alliance grants a certification license after the farms has undergone their first transition audit even if there are non-conformities with their Salary Matrix, provided the farm has a plan how to address the non-conformities within 1 year. Grounds for non-conformities include systematic lack of evidence or deliberate misrepresentation of data, to support the validity of the remuneration of workers as per filled in the Salary Matrix (see rule 37, page 139 of Rainforest Alliance’s Certification and Auditing Rules for more information). What this entails in practice is that some of the farms with a license in the first transition audit would have no non-conformities, while others would be working to address them based on the plan they developed. Moreover, the Salary Matrix requirements do not apply to 18 (6.6%) of the certified farms in Ecuador, as per the exception policy on living wage in Ecuador, or to any “small” farms within the scope of these certificates. Lastly, Rainforest Alliance auditing rules mean that all farms that are part of a certified group are audited at least once per every 3-year certification cycle; if a “large” farm is part of a group, it does not automatically mean they were audited last year.
An additional 144 (52.9%) farms have already been audited but are yet to complete the certification process and receive a final certification decision. Hence, 97.1% of the farms for which Rainforest Alliance certificate holder numbers have been provided, have been audited under the new Standard but the certification process for audits conducted in 2022 would not be finalized for all farms until April 2023.
The remaining 8 (2.9%) have not been audited against the new Standard. 60 farms did not submit a Rainforest Alliance certificate holder number and therefore it was not possible to check their audit status for 2022.
Farms must complete the Salary Matrix by the second transition audit (the timeline for which varies from producer to producer, starting from now onwards and no later than December 31st 2023 for all farms). The verification process would be finalized no later than 14 weeks after the audit, no later than April 2024 for all farms. To be granted a license after their second transition audit, farm certificate holders must close all non-conformities related to the Salary Matrix (as per Policy on changes to certification and auditing rules for audits in the transition year, section F49, pg.8).
2. What methodological approach was followed?
This project utilized the IDH Roadmap on Living Wages to ensure the adoption of a uniform approach regarding living wages. The Roadmap is a platform with leading organizations developing solutions for measuring and closing living wage gaps. It aims to strengthen international alignment and develop consensus on the definitions, tools, and approaches for companies to work on living wages.
The Roadmap includes 5 steps which are inspired by the needs of companies as they move forward to close living wage gaps:
- Identify reliable living wage benchmarks
- Measure current gaps
- Verify calculations of gaps
- Close living wage gaps
- Share learnings
There are several organizations that provide support for the calculation of costs associated with a decent standard of living in specific banana producing regions. To establish the estimated value of a living wage (namely living wage benchmark), these organizations gather data on local costs of, among others, food, housing, education, healthcare, transportation, and more. To understand the wage that is needed for one worker to afford such costs of living, these costs are divided by the typical number of wage earners in a family and the mandatory deductions (such as social security or taxes) are accounted for.
Benchmarks utilized in this project are developed by independent researchers, not by the companies involved in the project. When available, the Roadmap on Living Wages recommends the use of benchmarks supported by the Global Living Wage Coalition (GLWC) and developed using the Anker methodology. All banana producing regions participating in this first year, except for Peru, have a benchmark developed using such methodology and these are publicly available on the GLWC’s website and IDH’s living wage benchmark finder. In the case of Peru, a benchmark using the methodology called Anker Reference Values was utilized, which is also available on the GLWC’s website.
There are other methodologies available to calculate living wage benchmarks. IDH has developed, for the Roadmap on Living Wages, a recognition process to evaluate the reliability of the methodologies available in the market.
IDH’s Salary Matrix enables companies to evaluate how the total remuneration (including cash and in-kind benefits) compares to the relevant living wage benchmark under strict non-disclosure agreements that need to get acknowledged every time a Salary Matrix is developed. By understanding current wages and how these compare with living wage benchmarks, participating companies in this project are able to plan strategies for closing those gaps.
The Salary Matrix has been tested in over a dozen countries and seven agricultural sectors. It is a tool that has proven useful for:
- Calculating current remuneration including bonuses and in-kind benefits.
- Comparing against living wage benchmarks.
- Unveiling barriers and find solutions together with suppliers and retailers.
- Tracking yearly progress over time.
- Supporting work with certification programs that are introducing requirements on living wages.
- Beginning to understand gendered aspects of work.
- Raising awareness in the entire sector.
For this first year, participating retailers aimed at working with their suppliers and producers to implement the Salary Matrix in individual banana farms and cooperatives with hired labor that produce at least 33% of the total volume purchased by each participating retailer. In some sourcing countries, greater coverage was achieved than in others. The objective is to get to 100% coverage of the total volume purchased in a maximum of three years.
Several training webinars were held with suppliers and producer groups on the utilization of the Salary Matrix. These matrices were to cover:
- All farm or cooperative workers handling bananas including field personnel and packing plants.
- Workers providing transportation, security and cleaning services if they are part of the same company/farm.
- Seasonal workers, even when hired through other agencies.
- Workers/workers in the administrative positions.
- Managerial positions were optional.
The project encourages all farms, regardless of size and number of workers, to develop their own Salary Matrix. However, small farms with less than 5 workers (permanent or temporary) certified under group certifications by sustainability schemes that have explicit requirements on living wages, do not need to develop their own Salary Matrix. Nonetheless, the organization managing the group (cooperative, association, etc.) needs to have a Salary Matrix for the workers directly hired by such organization. This definition of smallholders is aligned with those of key certification schemes relevant in the banana sector.
Salary Matrices were filled in by participating producers as a self-assessment exercise. Ultimately, the desired scenario is that the utilization of the tool and the calculations of remuneration and how it compares to a living wage benchmark can be audited by certification schemes that have living wage requirements in their standards and are active in the banana sector. Currently, several programs are strengthening their certification standards and auditing procedures to include living wage related requirements. To support that process, IDH developed recommended guidelines for verifying living wage gaps through the appropriate use of the Salary Matrix. In future years, it is expected that verification can be performed by auditing schemes that adhere to these guideless.
For this year, IDH supported basic data validation as described in section 6.
The 4th step of the Roadmap is to find solutions that help reduce living wage gaps. The information in this report can assist participating retailers to engage in dialogue with suppliers, producers, and other relevant local players (unions, workers’ representatives, government agencies, etc.), as well as other international buyers, to find solutions for closing living wage gaps over time. These can include co-investment in supply chain projects to increase farm efficiency, productivity, social dialogue, and the adoption of sustainable procurement and trading practices.
The first three steps ensure a uniform approach on measurements, and the fourth step is to support participating retailers, suppliers, producers, and other relevant local actors in taking effective action to address living wage gaps in their supply chain. This last step entails sharing findings, best practices, and learnings on how the gaps can be reduced over time. CBL and IDH will support this process in the upcoming years by continuously incorporating lessons learned through the work done and by also researching best practices. See section 9 at the end of this report with initial recommendations drawn from this years’ experience.
3. Are there any considerations when using the Salary Matrix?
The Salary Matrix is a tool to measure and compare current remuneration with living wage benchmarks. To make this comparison, the tool adjusts wages to a living wage-comparable unit. Through this process, the tool therefore does not necessarily display current wages as they are paid.
For a more detailed description of how the tool functions, contact IDH at firstname.lastname@example.org.
The quality of the results is significantly influenced by the quality of the data entered by the user. If users do not correctly follow instructions, or if instructions are not well understood, this may result in incorrect data when generating the respective reports. If users choose to exclude workers or job categories from the Salary Matrix, this will result in incorrect data. If users incorrectly indicate values like country, currency, or the region, this will also influence the results. The tool has been designed to be as simple and clear as possible and to reduce unnecessary time needed from staff at facilities. In this way, the results are only as complete as the efforts of the user.
If users have questions about the instructions of the Salary Matrix, contact IDH at email@example.com.
The Salary Matrix relies on data organized by job category.
Definition: A Job Category may only consist of workers who do the same task, are paid the same way, and receive the same in-kind benefits. New job categories may be created if people are paid in different ways or receive different in-kind benefits or eligible bonuses. All temporary and informal workers must be included.
The creation of job categories helps simplify and reduce the work farms/facilities need to do to enter the information for large amounts of workers, particularly when they do not maintain specific and detailed human resources (HR) records. In the case that detailed payroll information is maintained per individual, job categories also provide the flexibility to accept data at the individual level while keeping it anonymous.
When data at the individual level is entered, the results are highly specific. When such workers are grouped into larger job categories, the average wage entered in the tool may hide the lower and higher paid workers among that group.
The Salary Matrix is a dynamic tool which allows users to enter in a number of wage payment types. Wages for people paid in time-related units such as day, week or month, can be easily entered by selecting the appropriate unit and entering the number of hours worked per day and per week. When people are paid in other units, such as hectares sprayed, miles driven, or even banana boxes packed, the appropriate unit can be entered into the tool along with the average number of units completed per day, hours worked per day and hours worked per week.
From the data entered, the tool calculates a representative wage for the day, week and month so that wages may be compared between job categories and against monthly living wage benchmarks. Although the tool allows different wages to be entered for different seasons, it may still be the case that entering the average estimated units completed per day does not result in the exact same monthly wages of workers.
The Salary Matrix follows key guidance from the expert literature on calculating wages to compare with living wage benchmarks by Martha and Richard Anker (Living Wage Around the World, 2017). This means accounting for standard working hours, pro-rating yearly bonuses to monthly values, and limiting in-kind benefits to certain thresholds.
To account only for wages earned during a standard work week, the tool relies on a database of standard working weeks. This is defined as the legal maximum hours per week that can be worked before the payment of overtime is mandatory. The tool also applies the ILO maximum standard work week of 48 hours as the maximum limit, although when national law is lower, the lower value is applied. In some cases, what is legally defined as the standard work week for the facility is not accurately represented in the tool. This may be due to recent changes in labour law, or additional specific working hours related to a sector or region. If the standard work week indicated in the results of the Salary Matrix do not reflect the local standard work week, users can contact IDH at firstname.lastname@example.org.
To calculate the wages representative of a standard work week, the Salary Matrix gathers the information on all wages paid as well as the hours worked for which that wage is paid. Then, the tool simply takes a portion of the wage equivalent to the portion of hours that is a standard work week. The tool does not account for overtime pay rates. For example, if the standard work week is 40 hours but workers are indicated to have worked 80 and earned 100 dollars, this will result in a value of 50 dollars, regardless of what they earned for the first 40 hours and during overtime. In this way, when high amounts of overtime are worked, average wages may be inflated. IDH is currently considering various approaches to further improve this calculation.
The Salary Matrix gathers information on all bonuses earned by workers throughout the course of the year that meet the criteria for being valuable to workers. The tool then divides this yearly bonus by the number of months worked and adds the portion to the monthly wages and in-kind benefits. In this way, the results of the tool are only representative of remuneration comparable with living wage and do not indicate the monthly amount workers earn.
Limited In-kind benefits
In accordance with leading living wage guidance that addresses the valuation of in-kind benefits, Living Wage Around the World (2017), only those in-kind benefits which meet specific criteria are included in the Salary Matrix. Also, each in-kind benefit is limited to a certain percentage of total remuneration.
All in-kind benefits must be accepted by the workers as being valuable, directly reduce the cost of basic living for a worker, are provided during regular working hours, are regularly provided, are expected in advance, and are not mandatory by law. If workers contribute or pay a small amount for the benefit, subtract this amount from the cost for providing the in-kind benefit.
In most cases, the total value of in-kind benefits is limited to 30% of workers’ total remuneration. Individual in-kind benefits are also limited to 10% of workers’ total remuneration, aside from housing which is limited at 15%. In-kind benefits above these thresholds are not displayed in the results and in this way may not represent actual total value of in-kind benefits for workers.
Without further on-the-ground verification, the self-assessment tool alone cannot ensure all criteria on in-kind benefits are met.
The definition of living wage does not include specifications for vacation—paid or unpaid. In the case that workers’ wages include paid leave, but hours worked exclude paid leave time, this may artificially inflate the results.
Over the lifetime of the tool, other minor elements and additional data points that would more accurately calculate living wage gaps have been collected. The following are considerations for future iterations of the tool:
- Wage/piece rates that vary by buyer;
- The use of outside hiring agencies to hire workers, and any associated fees for workers;
- Facilities where collective bargaining agreements indicate wages and, if so, which workers they cover; and
- The use of in-benefits by others in addition to workers (e.g., public medical centers, schools, etc.).