Introduction

The O.S. strives for a sustainable economy by means of a free market, functioning within moral and convention-based boundaries. Poverty is one of the major sustainability aspects and Sustainable Development nr. 1., but also the one most affecting other aspects. Global population growth, climate, conflict, health and safety conditions, erosion, forest clearing, pollution and many other conditions, all are strongly (positively or negatively) interrelated to poverty. Therefore, the O.S. includes criteria on fair pay of both fair wages (this section) and fair transactions (see “economic”).
The most commonly applied indicator for a “Fair minimum wage” is the living wage. However, for our purpose of what is “Fair minimum wage”, the living wage has the following limitations:

1. It was developed as an indicator of poverty (Anker, 2005); not as an indicator for what is fair.
2. It is insufficiently suited to be applied in middle and high income countries.
3. It insufficiently accounts for a sustainable family size of 2 children per woman.
4. It insufficiently takes into account the full life time of people.
5. It requires thorough research and continuous maintenance in every country in the world.

Therefore, for the Oiconomy Pricing system a new indicator, suited and easy to calculate for all countries was developed at the Utrecht university (Croes & Vermeulen, 2016b).

The Fair Minimum Wage (FMW) is the maximum of the legal minimum wage and a calculated minimum (which is also called “FMW”). The FMW is determined as a percentage of the Gross National income per capita (GNI/cap), bottom truncated by an absolute minimum for the lowest income countries. The percentage of the GNI/cap is determined by the average in the 20% most sustainable countries, determined by the Sustainable Development Index – Human Development (SSI -HW) (Croes & Vermeulen, 2016a, 2016b). Because this index is not maintained any more and the original papers also showed that the Human Development Index (UNDP, 2010) gives the same result, currently the HDI is currently used as indicator of the top 20% countries. The percentage was, based on 2011 data and the 2011 group of best performing countries, determined at 44,89% of the GNI/cap.
The absolute minimum is determined by application of various ILO conventions on workhours, and holidays, resulting in a work-year of 1864 hours, a sustainable fertility rate of 2 children per woman, a thereof derived labour working life and labour partition rate, average life expectancy in the top 20% performing countries, and the WorldBank moderate poverty line of $3,20 /day.capita.
In October 2022 the moderate poverty line was raised to $ 3,65 and integrated in the O.P.T. in may 2023.
Calculation formulas and a precise description can be found in (Croes & Vermeulen, 2016b). The method includes pensions (accounting for a lifetime income. (Either institutionalized by means of countries or lifetime parental and children’s care for each other). The absolute FMW anno 2022 is € 2.181,71/ year and € 1,17/hour.

Indicator

The impact category is unfair wages and the indicator the distance of paid wages from the fair minimum wage. See above for a short explanation.

Targets

A fair minimum wage (FMW) for every worker as defined by (Croes & Vermeulen, 2016b).
Fair overtime work payment (the maximum of 50% and the local legal premium).
All hours > Min(legal standard workweek hours; 40) are considered overtime.

Background calculations

Background ESCU’s are calculated as the difference between the lowest wage that can be found and the FMW, applied to the low-paid labour percentage of the product price and accounting for a default percentage of overtime. To determine this, background values need to be found for both the amount of work-hours per unit of product, and the lowest (worst case) wage paid in the country.
This results in the following calculation formula:

ESCU’s = LWL% x PP x å(LWp – FMW), where:
LWL% is the low-skilled labour-hour % of the product price, to be found per sector in WIOD social accounts 2014 (https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release).
PP is the Product’s Price.
LWp is the Lowest Wage Paid to adults in the country as found in Wageindicator.org.
In addition a background standard 33% of the workhours is considered overtime.

Foreground calculations

Foreground ESCU’s are calculated as the difference between the demonstrable actual wage and the FMW aggregated for all workers paid below the FMW, as follows:
ESCU’s = FWh x å(PWp – FMW), where:
FWh is the Foreground amount of Workhours spent per unit of product.
PWp is the Paid Wage per Person per unit or product.
In addition, added shall be the cost distance to overtime payment, where overtime is defined as:
Overtime ESCU’s = Min (LSWW ;40), where:
LSWW is the Legal Standard workweek.

Introduction

According to ILO Conventions No. 1 and No. 30, the rate of pay for overtime shall be not less than one-and-one-quarter times the regular rate (ILO, 2004). Overtime premiums of 50 per cent above the regular wage and higher for weekends are standard in many countries.
Legislation and Practice show a great variation of overtime systems, but in the EU 50% extra payment above standard compensation is the most common legal compensation (Eurofound, 2022).
However, practice is different. Much overtime is not paid at all (Perinelli & Baker, 2010). Preliminarily, for the Oiconomy, +50% is taken as a fair extra compensation for overtime.
In the Netherlands there is no mandatory premium for shiftwork, but such premium is commonly paid.

Indicator

The distance between actual overtime payment and 50% premium.

Target

Zero overwork without at least 50% premium for overtime.
Premiums shall be equal for all workers with incomes < 4 times the FMW. Workers with an income > 3 times the FMW are considered to be able to negotiate their income themselves. (without justification).

Background calculations

For background ESCU’s (allocated if no proper overtime registration can be demonstrated, it is assumed that 50% of the worked hours are overtime, based on (Common Objective, 2018; Kuddo, 2009), which results in the following ESCU calculation:

ESCU’s = Max(BWh x BOW% x FMW x BOW% x (OWP-AWP);0), where:

BWH = the background workhours on the product, set to 50% of the product price
FMW is the fair minimum wage.
BOW% is the background amount of overtime hours spent on the product, which is set to 50% (60 hours per week instead of 40).
BOWP is the target minimum overtime percentage, which is set to 50%
BAWP is the actually used overtime percentage, which is set to zero.

Foreground calculations

Foreground ESCU’s are calculated equal to the sum required to raise all overtime remunerations of workers with an income > 3 times the FMW to the fair overtime compensation.
For the workers with an income < 3 x FMW applies:

ESCU’s = Max (å (OWPp-AWPp);0), where:
OW%p is the actually amount of overtime hours for each worker spent on the product.
OWPp is the target minimum overtime percentage for workers.
AWPp is the actually used overtime percentage.

Introduction

Not for all child labour ESCU’s are allocated. In short, physically and mentally harmless work answering to defined criteria and not limiting schooltime, by children belonging to the family is not considered a type of child labour that needs ESCU allocation.
Considered conditions, depending on children’s age were included:

Maximum workhours/week: For the maximum workhours per week, the Netherlands legislation is taken as target. The maximum workhours depend on age and type of day (schoolday, weekend, holiday. Maxima are set per day, per schoolweek and holiday-week. In the tool, only the maxima per schoolweek are included). Source: (Netherlands Labour Organization, 2022).

Minimum wage, as a percentage of the adult minimum wage, for which the FMW is taken and for the percentages the slightly rounded legal Dutch percentages: (www.uwv.nl/particulieren/bedragen/detail/minimum-jeugd-loon). Maximum weight to be lifted: The maximum weight to be lifted is complex. It depends on the age, gender, person’s posture, the frequency of lifting, the height of lifting ,the shape of the load and the distance to the body. The given weights are maxima under ideal conditions. Literature on the diverse conditions can be found in (Charoenporn et al., 2019).

Maximum distance from home: No legislation or standards could be found on the maximum travel distance for children to work. Some could be found on the distance to school, like max. 3 km in India (The Indian Express, 2022), for children up to 14 years. However, in the case of preventing child labour, any organized long distance transport should be prevented, which is a different cause than school commuting. Therefore, a maximum of 3 km was chosen for children up to 14 years old; 10 km for children from 15 – 17 years and unlimited for 18+.

Indicator

Target wage – Paid wage, where in case of the worst form of child labour, the target wage is the FMW for adults.

Targets

Demonstrable Zero Child Labour according to the above definition

Background calculations

Background ESCU’s are allocated for child labour, where no absence of child labour can be demonstrated and no foreground data are available. In countries without child labour according to the Unicef statistics (Unicef, 2018), zero ESCU’s are allocated.
Because the Unicef child labour statistics on the % of children involved in child labour is not complete for all countries, this statistic could not be used for the development of an governance dependent reducing indicator. Therefore, the Human Development index was taken for the purpose.

ESCU’s = HP x (1-HDI) x (FMW – ACW), where:
HP is the most likely but demonstrable amount of Hours (HP) of low skilled labour, worked on one unit of Product. Sources are IO databases, the internet and information and tests at accessible suppliers.
HDI is the Human Development country of the most likely country of the work.
FMW is the Fair Minimum Wage for adults.
ACW is the most likely Average Childrens’ hourly Wage (ACW) paid to children in the default country, without other demonstrable data determined as 30% of the lowest paid hourly wage for adults that can be found at Wageindicator.org for the country.

Foreground calculations

Foreground ESCU’s for Child Labour are calculated as the extra costs to improve the conditions to the minimum conditions, or to replace the child by an adult, paid the FMW.
First, a series of conditions is checked that lead to zero ESCU’s:

• A relevant certificate.
• No child Labour is known in the country.
• The organization declares that it does not use child labour as detailed defined in the tool and agrees to obtain unannounced audits.

Another considered condition is that the organization uses child labour but the only violation against the defined criteria is underpayment. In that case:
ESCU’s = å(TWc-AWc), where
TWc is the target wage per child.
AWc is the actual wage per child.

Other cases are considered as “worst form on child labour”, for which background ESCU’s are allocated as described above:

Introduction

Remuneration differences are necessary because they belong to the free market, providing motivation and opportunities. Some rewards however, are harmful because they stimulate decision takers to prevalent their own interests and the financial interest of their shareholders over the interest of sustainability. Reduction of Inequality is Sustainability Development Goal nr. 10. Therefore, the O.S. includes criteria on inequality and ESCU’s are allocated for unfair inequality. For this purpose a definition was developed for “Fair Inequality” in (Croes & Vermeulen, 2016a), based on the following principles:

The average ratio between the highest and lowest parliamentary determined wages in the top 20% countries determined by the Sustainable Development Index – Human Development (SSI -HW).
For the highest wage, the wage of the president or prime minister was taken and for the lowest, the legal minimum wage. The obtained ratio (14,1) is applied to governmental organizations. For semi governmental organization, this ratio is augmented with 20%, (following Dutch regulations), resulting in a ratio of 18,3 and for other organizations with another 20%, resulting in a ratio of 23,8, reasonably in accordance with recent developments in various countries (Croes & Vermeulen, 2016a).
This means that (for industry) for any wage lower than 4,2% of the highest wage in the organization, ESCU’s are allocated, equal to the amount needed to raise the wage to 4,2% of the highest wage.

Indicator

The impact category is inequality and the indicator the ratio between the highest and lowest income in the organization.

Targets

A maximum inequality ratio than the Fair Inequality Ratio (determined as a factor 23,8 by (Croes & Vermeulen, 2016a), based on the income ratios between of presidents and the minimum wage in the 20% best performing countries, augmented with 2 times 20% via semi governmental organizations to industry.

Background calculations

For Background ESCU’s, all low paid wages are assumed equal to the lowest wage found for the country in Wageindicator.org. The highest wage is assumed equal to the highest CEO salary (including bonusses) found on the internet (e.g. year reports) for industry leaders in the sector. A default overtime of 50% is assumed for the highest paid person, based on internet research showing that 50% is the most common %.
In the USA, the median CEO income of companies with $25 – $50 mio revenue is $355.000 (W. Cooper, 2019), which is about $190/hour. The FMW for the USA is $ 13,94. Paying a lowest wage of the FMW, the maximum fair total CEO income with a 40 hour workweek would be 23,8 x US$ 13,94 = $331.772, but with a 60 hour workweek $663.554. Based on these data, the risk of irresponsible income inequality in organizations with a revenue < $ 25 mio is small and are therefore exempted from assessment. For organizations with a revenue > $ 25 mio, ESCU’s are calculated as follows:

ESCU’s = å (BHI –  (WH x FMW)), where:
BHI is the highest hourly total income in the organization, to be determined by searching the internet for the highest CEO income in the sector. For the calculation of the hourly CEO income, for the CEO a background 60 hour workweek is assumed.
WH is the background amount of workhours per unit, to be found per sector in WIOD social accounts 2014 (https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release).
FMW is the fair minimum wage. (Extra ESCU’s for incomes below the FMW were already allocated in the subcategory of fair wages)

Foreground calculations

Foreground ESCU’s are calculated as the amount needed to raise the wages of all workers to  4,2% of the highest wage (including bonusses). Overtime income of both highest paid person and other workers is preliminarily calculated as 1,5 hour for all types of overtime.
The ESCU’s are calculated as follows:
ESCU’s = FWH x å (FBHI – Wp), where:
FWH is the foreground amount of workhours per unit of product.
FBHI  is the foreground highest total income in the organization.
Wp is the wage of each worker in the organization.

Introduction

Considered are 22 criteria/issues on labour conditions (mostly from Dreyer, Hauschild, & Schierbeck, 2010), and collected from ILO conventions, the GRI and ISO 22000).
Assumed is a maximum preventative costs of 8,33% (one month’s pay/year) of the product’s labour costs. Currently, there is no justification for this percentage, but given the percentages for other aspects, OSH and the gender wage gap, we trust this percentage is in the right order of magnitude.
There is neither a characterization factor for the impact of the 22 different issues, nor do we have preventative costs for the individual issues, which is the reason to use the method of the governance level reducing multiplication factor.

Indicator

The maximum preventative costs, multiplied by a governance level determined reducing multiplication factor (RMF).

Targets

Full compliance to the 22 criteria on Labour Conditions.

Background calculations

The ESCU’s are calculated as The maximum preventative costs, multiplied by a reducing factor for the quality of governance on the aspect.
ESCU’s = LCMax% x BLC% x PP, where:
LCMax% is the maximum preventative costs for optimal labour conditions where non conformances occur, preliminarily set on one month pay per year (= 8,33% of the labour costs).
BLC% is the Background Labour Costs % of the product price, obtained from IO databases for the sector.
PP is the Product Price, estimated based on company-own research.

Foreground calculations

The organization is challenged to determine its own foreground costs to close the distance to optimal labour conditions by means of good governance. ESCU’s are calculated as:
ESCU’s = LCMax% x RMF x FLC% x PP, where:
LCMax% is the maximum preventative costs for optimal labour conditions.
FLC is the Foreground Labour Cost % x PP.
RMF is the foreground governance level determined reducing multiplication factor.
PP is the Product Price.

Introduction

All workers deserve to be able to develop themselves to their abilities, even when that could mean that these workers may use their learnt abilities elsewhere.

As target of expenditures on personnel development, required are the average company expenditures in the group or 20% best performing countries. Preliminary ESCU’s are based on USA  data. The USA average wage (2013) was $ 44888 (https://www.ssa.gov/oact/cola/AWI.html); The USA average industry direct learning expenditures (2013) were about $1208 (Bouchrika, 2020; Miller, 2014), which is 2,7% of the average wage.

Indicator

Cost distance for personnel development to 2,7% of the wage sum.

Targets

Minimum employer contribution to the development of workers of 2,7% of the wage sum.

Background calculations

Usually, organizations can demonstrate their own contribution to workers’ development, but sometimes not from not cooperating first or further suppliers. For those, background ESCU’s for personnel development contribution are calculated as follows:
ESCU’s = (TPDC-BPDC) x WS% x PP, where:
TPDC is the Target Personnel Development Contribution %.
BPDC is the Background Personnel Development Contribution %, set to zero.
WS% is the Wage Sum % (from sheet preparation).
PP is the Product Price, derived from price research.

Foreground calculations

Preliminarily, ESCU’s are calculated as the cost distance to spent expenditures on personnel development to 2,7 % of the wage sum, as follows:
ESCU’s = (TPDC-PPDC) x WS% x PP, where:
TPDC is the Target Personnel Development Contribution %.
PPDC is the actually Paid Personnel Development Contribution %.
WS% is the actual Wage Sum % (from sheet preparation).
PP is the actual Product Price.

Sometimes customers or governments provide or pay worker’s training instead of the organization itself. If the costs spent by these external organizations can be demonstrated, these may be entered.
However, if these costs are entered by the organization, the involved customer may not enter these costs as bonus, because that would be double counting.

Introduction

Considered are 7 of worker’s contract and payment related issues, such as lack of timely payment, a gender wage gap and paid parental leave:

• Contracts and notification before start and termination.
  See (Bakina, 2021)
• Wages paid at regular intervals of not longer than one month.
• No money, hiring fees taken from workers.
• No parts of remuneration withhold other than legally required.
• Equal pay for equal work and no gender remuneration gap.
  (Eurostat, 2022; ILO, 2020)
• No costs enforced on workers.
  See (D. Cooper & May, 2017)
• Parental leave.
  Maternity rights are set out in the 1992 Pregnant Workers Directive. This EU legislation sets the minimum period for maternity leave at 14 weeks, with 2 weeks’ compulsory leave before and/or after confinement and an adequate allowance subject to national legislation (Giulio Sabbati; Martina Prpic; Ulla Jurviste, 2019). Legislations in the USA and OECD countries can be found in (Bipartisan Policy Center, 2020; OECD, 2021).

Because no data are available on the relative impact of these 7 issues and for most of these no costs of prevention could be found, preliminarily, the ESCU’s are based on a scoring method. (2 points for perfect governance; 1 point for good governance but incidental violations and 0 points for worse.
The ESCU’s are calculated as follows:
ESCU’s = (TVLS/14) * MC where:
TVLS is the Total Various Labour aspect Score
14 is the maximum score at perfect governance for all 7 issues.
MC is the maximum Costs. Because background costs could be derived for fixing the gender wage gap, preliminarily, fixing the Gender Wage Gap (GWP) was assumed being the worst case. The global average factor weighted GWP is 18,8% (ILO, 2017, 2019). At a 50% work share between genders, a  raising of the average womans’ wage would cost 9,4% of the product’s labour costs.

Indicator

Simple governance scoring from 0 to 2 points per issue.

Targets

Zero violations with the above issues, resulting in a maximum governance score of 7 x 2 = 14.

Background calculations
For the background costs the maximum number of violations is assumed, resulting in the Formula: ESCU’s = (TVLS/14) x MC. (See above for the explanation of the variables).

Foreground calculations
Preliminarily, the ESCU’s are calculated based on the number of violations in the organization, as follows.

Introduction

In most high performance countries, employers contribute to employees’ and their families’ health insurance premiums. According to the O.S. system, ESCU’s should be calculated as the cost distance to the average employers’ a average contribution to the health care insurance premiums in group of the top 20% performing countries. Required data by country are the average health care costs per family and the average employer’ contribution.

Countries have very different systems, which makes it very difficult to obtain these data. Some countries have full governmental coverage of health care, paid from the taxes. Because taxes are partly paid by industry, companies still contribute via that tax. Other countries have no or little governmental contribution to the costs and leave that to private insurances. In some of these countries without a proper governmental health care system, employers pay most of the premiums, but leave the unemployed without health insurance, but in other (mostly low income countries), also workers have no or little health insurance.  Several countries have, next to their taxation, a separate tariff for health insurance, an average of around 60%  is paid by the employer (Deloitte, 2017). In some countries health care contribution is specifically specified, in others part of the greater category of social security (Deloitte, 2017).

Indicator

Cost distance for health insurance contribution to 6% of the wage sum.(see below under background calculations for the justification of 6%).

Targets

Minimum employer contribution of health insurance cost of workers of 6% of the wage sum.

Background calculations

Usually, organizations can demonstrate their own contribution to workers’ health insurance, but sometimes not from not cooperating first or further suppliers. For those, background ESCU’s for health insurance contribution are allocated.
Total health care expenditures in the top 20% group of countries is around 10% of the GDP/capita (The World Bank, 2017), of which industry pays 60%. If we assume that the added value of products’ represents their contribution to the GDP, equal to [1-purchased value ratio], as determined in Pr 21 (Sheet preparation), 6% of the wage sum represents the average health insurance costs, as wages represent the greater part of the added value. The Background ESCU’s are calculated as follows:

ESCU’s = (THIC-BHIC) x WS% x PP, where:
THIC is the Target Health Insurance Contribution %.
BHIC is the Background Health Insurance Contribution %, set to zero.
WS% is the Wage Sum % (from sheet preparation).
PP is the Product Price, derived from price research.

The assumptions are not completely accurate, because the GDP only includes the value produced inside the country, where preliminarily in the O.S., the purchased value is based on the total of residential and imported items.

Foreground calculations

The organization is demonstrating its foreground workers’ health insurance contribution.
ESCU’s = (THIC-PHIC) x WS% x PP, where:
THIC is the Target Health Insurance Contribution %.
PHIC is the actually Paid Health Insurance Contribution %.
WS% is the actual Wage Sum % (from sheet preparation).
PP is the actual Product Price.