Dignified Jobs: What goes into creating them?

Upaya looks at much more than the financial viability of the promising small and growing businesses we consider for investment. One of the fundamental criteria for Upaya from the impact lens is whether the business has the potential to create dignified jobs for the poorest of the poor. Creating dignified jobs is our North Star. Businesses are evaluated on the basis of their focus on extreme poverty, the depth of their impact, and the scale of their impact. Whether a company’s jobholders are earning consistent and stable income at par or more than their next best alternatives is a make or break for Upaya’s decision making.

With our objective of creating dignified jobs clearly defined, our Impact Management strategy thus leads us into understanding and measuring what, how, and how much impact the jobholders are experiencing with their jobs and the resultant income. The basic method has been to test if the job is leading to an income increase for the jobholder’s household. The resultant data loops back into our conversation with the respective partner entrepreneurs to discuss how their jobholders are responding to the questions and the potential scope of improvement, if any.

While conventional survey questions target our jobholders’ income earned and income increase experienced, we can also use our findings to dig into broader socio-economic trends. When we apply a gender lens to our data, for instance, the results paint a colorful picture that has led our team to do some soul-searching.

Definition of wage equity and minimum wages in India

Theoretically, we must map back our jobholders’ income with the minimum wages in India to set a benchmark. India remains one of the first developing countries to introduce a minimum wage policy followed by the Minimum Wages Act in 1948. However the minimum wage is rather complex, tabled arbitrarily by the state governments for employees working in “scheduled employment,” and resulting in 1,709 different rates across the country. Furthermore, the minimum wage policy is applicable to only 60% of the wage workers covered under “scheduled employment.” In 40% of the states, the rates legally cover starkly less than 50% of the wage workers.

Thus, to eliminate the above caveat, Upaya instead monitors the increase in income relative to the jobholders’ previous income. 

Uncovering hidden inequity

Our recent initiative to draw parallels between our data evidence and the industry standards made us drill down into some untapped and unanswered questions. The whole exercise made us analyze what the income paid looks like through the gender lens — cutting across states, sectors, and location (urban/rural) slices. 

While our jobholders, when surveyed, reported on average a 90% increase in income from their previous jobs, a deeper drill down showed stark disparities between wages paid to women and male jobholders. Women jobholders, on an average, earned less than their male counterparts, irrespective of their state, sector, or urban/rural segregation. As per our findings:

1. Gender wage disparity between urban and rural areas across India: On an average, in rural areas, male jobholders earned 157% more than their female counterparts, while in urban areas the differential observed was 25%.

2. Gender wage disparity across sectors: Agriculture reported the largest gender wage disparity at 250%, followed by skill development and employability (62%), waste management and sanitation (61%), rural manufacturing (41%), textiles and handicrafts (22%). See the chart at the bottom of this page.

3. Gender wage disparity across states: Out of our entire portfolio, Tamil Nadu and West Bengal were the only states with a gender wage equity in favour of women, wherein women in Tamil Nadu earned 37% higher wages than men, while in West Bengal the differential is 77%. All other states saw huge disparities where male workers’ wages fared substantially higher with Uttar Pradesh at 325%, Karnataka at 77%, and Odisha at 262%. More detail is available in the chart at the bottom of this page.

Here, one must note, the disparity at any level is the highest amongst the lower earning workers, implying a multi-fold effect of lower wage and gender wage disparity for women.

Stepping into interpretation

Such daunting data findings as above made Upaya go into a retrospective mode. 

Here, it is necessary to underscore an important caveat. Upaya collects jobholder and wage data irrespective of job type and level. Hence our resulting numbers above calculate an average metric across all roles, regardless of job rank and category. A study built specifically to assess the gender wage gap would have to control for job type/level, education level, skill set, and other job factors. 

This may explain Upaya’s data findings to an extent. However, the fact that a woman jobholder is more likely to enter the workforce at a low skill level, part-time job, given her education and other factors cannot be denied. In fact, it critically reinforces how much sensitivity and weightage gender inequality deserves. It cannot be ignored when discussing wage inequality as a whole. 

Though the exact quantum of the gender wage gap can only be identified through a precise data collection methodology targeted to address this specific question and not any other proxy, the problem statement remains real, irrespective of geography, location, or sector.

Research holds a set of interpretation and reasoning attributing the gender wage gap to a combination of parameters, including, but not limited to:

1. An undervaluation of women’s work and discrimination in pay;

2. Workplace characteristics - i.e. skills and experience needed;

3. Gender segregation - channeling women into part-time or low value added jobs;

4. Wage structure in the country - and the possibility of wage setting with a focus on male dominated sectors;

5. The view of women being economically dependent (despite contributing equally as in agricultural households);

6. Likelihood of women being in unorganised sectors with less bargaining power

While analysing and understanding the wage gaps, the data might point to the “endowment effect” that can be mapped back to the different education, experience, skill set, and employment opportunities between female and male jobholders. However, there still remains an unexplained gap, which draws us back to the “discrimination effect” prevalent in the context of Indian workforce.

Working towards equity

Upaya’s constant drive has been to create jobs for the poor in the most underserved segments of the country. We also strive to make sure that the jobs created across sectors, states, and partners are equally distributed for men and women. With 60% of our surveyed jobholders being women, our above findings suggest there is still much work to be done to make gender equity a reality.

Shrishti Puri has a strong drive to create impact using data and believes in efficient decision making through powerful data and analytics. She is currently working as a Senior Data Associate under the Impact Management and Portfolio Management teams at Upaya.

Photo: Saahas Zero Waste