Abstract
In this chapter, Borooah discusses labour market risk. Every time a job-seeker applies for a job he/she runs the risk of not getting it. However, these risks may not be uniformly distributed across job-seekers: some have a better chance of negotiating obstacles to employment; others have a higher chance of stumbling. The important question relates to the determinants of such risk. In particular, does this risk differ significantly between job-seekers from different groups: gender, religion, or caste? The chapter uses a famous result in statistics, Bayes’ theorem, to make explicit the concept of risk and to explain why, under this theorem, different groups might have different rates of success of securing employment. The theoretical results are buttressed by data from two rounds of the NSS of Employment: the 68th round (July 2011–June 2012) and the 55th round (July 1999–June 2000). These data are used, in subsequent sections, to quantify the concept of risk set out in the earlier part of the chapter.
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Notes
- 1.
See The Economist, “In Praise of Bayes”, 28 September 2000, https://www.economist.com/science-and-technology/2000/09/28/in-praise-of-bayes
- 2.
The updating factor is the ratio of the probability of observing the data when the theory is true to that of observing the data regardless of whether the theory is true or false: \( P(A)=P\left(A|T\right)P(T)+P\left(A|\overline{T}\right)P\left(\overline{T}\right) \), \( \overline{T} \) being the event that the theory is false.
- 3.
P in this term represents probability and the symbol | denotes that the event following | has already occurred.
- 4.
“False positive”: an unemployable candidate (false) passes the test (positive).
- 5.
“True negative”: an “employable” candidate (true) fails the test (negative).
- 6.
Similarly, the question of interest is not what is the likelihood of an “unemployable” candidate failing the test, \( P\left(\tilde{A}|\tilde{T}\right) \), but, rather, what is the likelihood that a candidate who fails the test will be “unemployable”, \( P\left(\tilde{T}|\tilde{A}\right) \).
- 7.
\( P\left(T|A\right)=P\left(A|T\right)=0.95\Rightarrow 0.95=\frac{P(T)\times 0.95}{P(T)\times 0.95+\left(1-P(T)\right)\times 0.15}\Rightarrow P(T)=\frac{0.15}{0.2}=0.75. \)
- 8.
\( P\left({\tilde{A}}_S|{\tilde{T}}_S\right)=\frac{0.85-0.75\times 0.82}{0.25}=0.94. \)
- 9.
\( P\left({\tilde{A}}_S|{\tilde{T}}_S\right)=\frac{0.85-0.75\times 0.80}{0.25}=1. \)
- 10.
See Matthews (2000).
- 11.
Consequently, as Tendulkar (2007) observes, “the availability of well-trained and motivated staff poses the biggest constraint given the budget provided by the government, their procedures of recruitment, and their distribution in a multilingual country” (p. 368).
- 12.
Excluded from this chapter’s analysis were pensioners, rentiers, and remittance receivers and also those who earned their living from begging or prostitution.
- 13.
The Sachar Committee (2006) refers to the caste system applying also to Muslims, with the ashraf (meaning “noble”) referring to high-born Muslims and converts to Islam from Hindus of the higher castes, and the ajlaf (meaning “degraded” or “unholy”) referring to converts to Islam from the lower castes. So, among Muslims too there are “backward classes” (like Teli, Ansari, Qureshi, Banjara, Nai) and forward classes.
- 14.
It is important, at the very outset, to draw attention to the fact that all the results reported in this book are obtained after grossing up the survey data using the observation-specific weights provided by the NSS for the 68th and 55th rounds.
- 15.
These figures were calculated as follows: after grossing up, there were 2,538,594 men from the ST in RSWE and 18,619,663 (= 21,158,257 − 2,538,594) men from the ST not in RSWE, yielding a BF = 0.14 (= 2,538,594/18,619,663). Similarly, after grossing up, there were 21,940,189 men from the SC in CWE and 23,976,299 (= 45,916,488 − 21,940,189) men from the SC not in CWE, yielding a BF = 0.92 (= 21,940,189/23,976,299).
- 16.
These figures were calculated as follows: after grossing up, there were 8,345,334 men from the SC and 18,747,396 men from the FC in RSWE, yielding, for SC men, an IBF = 0.45 (= 8,345,334/18,747,396). Similarly, after grossing up, there were 5,461,887 Muslim men in RSWE, yielding, for Muslim men, an IBF = 0.29 (= 5,461,887/18,747,396).
- 17.
In the language of inequality analysis this transfer from an “access-rich” group to an “access-poor” group constitutes a progressive transfer and, by virtue of this, is inequality reducing.
- 18.
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Borooah, V.K. (2019). Labour Market Inequality. In: Disparity and Discrimination in Labour Market Outcomes in India. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-16264-1_2
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