We have been repeatedly warned against blindly believing everything we read or are forwarded. With that dash of sodium chloride, here is the gist of a message I was forwarded, not as a prospective job applicant, since I possess qualifications for neither.
We have been repeatedly warned against blindly believing everything we read or are forwarded. With that dash of sodium chloride, here is the gist of a message I was forwarded, not as a prospective job applicant, since I possess qualifications for neither. A restaurant 89 km from Ankamaly (Angamaly) requires a full-time “porotta maker”, at a monthly salary of Rs 18,000-20,000. A concern 60 km from Thrissur requires a full-time “civil engineering B.Tech or diploma holder”, at a monthly salary of Rs 6,000-7,000. These are two isolated advertisements from Kerala and don’t constitute a proper sample. However, some sample survey data are available on the Net, though sample sizes are small. For instance, salary for a cook (not a full-blown chef) is Rs 12,000 per month in Delhi and that for an engineering diploma (not degree) holder between Rs 10,000 and Rs 12,000 per month. That for a driver is Rs 14,000 per month. Therefore, correlation between education and salary isn’t quite what we might expect a priori. Let me throw in an anecdote from a colleague. His maid/cook is around 45 and has two sons, aged 18 and 20. These two exited school after Standard XII and sit at home, subsisting on their mother’s salary. When my colleague asked them, “Why don’t you work as a cook?” the response was, “That is work meant for girls.”
There is an anecdote that features in jokes about economists. I have seen it ascribed to many economists, in place of Kenneth Arrow. The only authentic source I know is attributed to Curt Monash, who studied in Harvard. “I was standing with Ken Arrow by a bank of elevators on the ground floor of William James Hall at Harvard. Three elevators passed us on our way to the basement. I foolishly said ‘I wonder why everybody in the basement wants to go upstairs.’ He responded, almost instantly: ‘You’re confusing supply with demand.’” The labour market is segmented, sectorally and geographically. However, regardless of sector and geography, principles of economics, supply and demand, do apply. There is a quote misattributed to Thomas Carlyle. “Teach a parrot the terms ‘supply and demand’ and you’ve got an economist.” It is misattributed in the sense there is no evidence Thomas Carlyle ever said or wrote anything like this. Parrot or not, prices of everything, labour included, are determined by intersection of supply and demand, unless institutional constraints get in the way of that clearing function. Let’s take the example of a cook’s wages being more than that of an engineering diploma holder.
What we have observed is a market clearing wage. Purely on this basis, it is impossible to ascribe it to either purely supply or demand, since the outcome happens to be a combination of both. Because NSS (National Sample Survey) data on unemployment are dated, a lot of people use the BSE-CMIE data, with a fairly decent sample size. This is based on household surveys, a better indicator in a country like India than enterprise surveys. There has been discussion in media about what this shows on the unemployment rate, for all-India, as well as for states. For example, in September 2017, the urban unemployment rate is very high (more than 15%) in Goa and Haryana. The rural unemployment rate is very high (more than 10%) in Haryana and Jammu & Kashmir. On October 3, the all-India rate was 5.83% for urban and 3.75% for rural. While the unemployment rate and its trend merits discussion, as does the question of creation jobs, what’s the definition of “unemployment rate”? Before that, the survey has four categories—‘currently employed’, ‘not employed, but is willing to work and is actively looking for a job’, ‘not employed, is willing to work, but is not actively looking for a job’, and ‘not employed, is not willing to work and is not looking for a job’. “The unemployment rate is computed as the sum of number of persons not employed but willing to work and actively looking for a job as a per cent of the total labour force, where the total labour force is the sum of all those who are employed and those who are not employed but are willing and looking for a job.”
We should certainly have a discussion on the unemployment rate. However, given the example I started with, there is an aspect that is missing from the customary discussion. This is highlighted in a document known as Unemployment in India: A Statistical Profile, a separate product from the same survey. This has the standard unemployment rate, but also has something known as greater unemployment rate, that is, including those who are unemployed and willing to work, but inactive in seeking jobs. The gap between the two rates is highest in the 15-19 age-group, followed by the 20-24 age-group for males, while it is uniform across all age-groups for females. Going back to supply and demand curves for labour and their intersection, everything else remaining the same, wages drop/increase when either supply or demand curves, or both, shift. I think there is an issue of correlation between education and skills, or its lack. Some educational attainment may help acquisition of skills, but the correlation isn’t strong. For females, the gap is uniform across age. However, for younger males, the job-seeker’s perception may be of a stronger correlation than warranted.