Table 2.2: Summary of key concepts and variables

• Shifts in the employment status of the total factory workforce

• Working conditions and facilities in the factory

• Impact of Covid-19 pandemic and resultant actions of global apparel brands on the operations of the factory. 3. In-depth qualitative interviews and Focus Group Discussions (FGDs) were conducted with selected workers to capture anecdotal information and further substantiate and elaborate on the objective data collected through the structured interview and factory profile schedules.

The names of interviewed workers have been replaced with pseudonyms to preserve their anonymity and privacy.

Summary Of Key Concepts And Variables

Table 2.2: Summary of key concepts and variables

Concept/ Variables Description of Variables and Calculations

Wages paid to workers on a monthly basis.

Wages It includes the total earnings of worker on a monthly basis, including overtime payment and incentives, and is not restricted to basic pay.

Average or median monthly wages have been calculated based on the requirements of each chapter.

% Wage Theft The difference in wages calculated for each month in the pandemicinduced recession period (March to December 2020) from the prerecession wages (January to February 2020).

Actual Wage Theft

Bonus Actual Wage Theft is the cumulative figure of difference in wages for each month in the pandemic-induced recession period from the prerecession monthly wages.

Annual or festive bonus payment made to the worker based on the legislations or customary practices of the respective country.

Actual Bonus Theft

Wage Theft Estimates A simple sample weight driven estimation process for wage theft has been followed to extrapolate wage theft to the total workforce across surveyed factories. The estimation process for the wage theft measure involves using sampling weights at each factory which is adjusted for mean sampling errors and other data values. The sampling weights for factories were adjusted to the lowest figure to account for estimation bias. The drawback of the estimation method used here is underestimation, by virtue of the method, sampling weights, and nature of the sample.

Wage Theft Estimates per Factory

Hourly Wages Actual Bonus Theft is the cumulative figure of difference in bonus due to each worker based on the respective country’s customary practice or legislations and actual bonus received.

Note: Bonus owed to workers has not been calculated for Pakistan and Cambodia as there is no customary practice of paying a specific amount annually to workers bonus payment. Rather it is dependent on several other factors due to which workers receive varied bonus amounts. It has not been calculated for Bangladesh due to limited data collection.

Note: Wage theft estimates have not been calculated for Bangladesh due to limited data collection. Wage theft per factory is calculated as the weighted average of the Wage theft estimates, weighted to the size of workforce.

- Wage theft estimates per factory

- Weight for nth term

- Value of nth term

- Sum of all weights Average hourly wages of the workers calculated as follows

Hourly wages = ω/(d*h)

% Loss in Work Days The difference in days of work calculated for each month in the pandemicinduced recession period (March to December 2020) from the days of work in the pre-recession period (January to February 2020)

% of Work Days Lost =

Trend in Number of Work Days

Total Consumption Expenditure

Debt

Trend in Debt

% Increase in Debt Average number of work days available to workers on a monthly basis.

Monthly expenditure on a basic consumption requirement of Food, Accommodation, Education, Health Care, Travel, Leisure, Socio-cultural aspects (including expenditure on festivals, weddings and other social events).

Note: In the case of single migrant workers living away from their families, the total consumption includes monthly consumption expenditure incurred by the single migrant worker and the monthly remittances sent by the worker to their family. Debt is captured as the monthly debt incurred by the worker.

Trend in Debt is represented as cumulative sum across each month for the worker The difference in cumulative debt calculated for the year 2020 from the debt in pre-recession period (January to February 2020). The cumulative debt in 2020 includes all 12 months of 2020. Debt in pre-recession months include the cumulative debt in January and February 2020.

% Increase in Debt =

Share of Wage in Consumption Ratio of Monthly Wage to the Total Monthly Consumption

Share of Wages in Consumption =

Share of Debt in Consumption Ratio of Monthly Debt incurred to the Total Monthly Consumption

Share of Debt in Consumption =

Household Income The household income is the cumulative income across each worker household comprising of income of family members and any other subsidiary income.

Poverty Line

% Workers pushed below International Poverty Line

AFWA Living Wage AFWA living wage in USD is calculated based on the AFWA living wage figures for 2020

Poverty line is calculated as monthly household level poverty line based on the poverty line figures reported by World bank based on different income levels of countries in PPP USD.

The percentage of workers pushed below international poverty line is calculated for the peak Covid-19 period for each country or apparel brand:

% Workers pushed below International Poverty Line =

Structure Of Analysis

The data on extent and forms of wage theft, and its impact on consumption levels and indebtedness of workers’ households has been presented through:

• Country level analysis: Extent and forms of wage theft and its impact on garment workers and their households in different countries has been analysed.

• Inter-country and Asia-regional analysis: The findings at the country level are compared, and the different forms of wage theft prevalent across countries have been used to formulate a typology of managerial power and resultant wage theft at the regional level.

• Brand level analysis: The extent and forms of wage theft experienced by garment workers in the supplier factories of major global apparel brands at the Asia-regional and country levels has been analysed and placed in context of the revenues of the brands.