I recently attended a workshop on jobless growth in South Asia. It was a good forum which brought to light the little evidence on the subject and the need for more digging around. The scarcity of employment data in India is acute. The Labour Bureau conducts Quarterly Employment Surveys of selected sectors, covering factory units that employ 10 persons or more in the manufacturing, construction, trade, transport, hotels and restaurants, IT/BPO, eduction and health industries. Another dataset BSE-CMIE household survey, started in January 2016, covers all kinds of employment including unorganised sector and even self-employed persons. In terms of greater coverage and independence from government intervention, BSE-CMIE survey seems more reliable, however the dataset is quite recent and is costly. The CEO of CMIE's recent opinion piece on data quality suggests many caveats on data interpretation. Not only is the sectoral coverage an issue, one has to be careful in choice of time periods before coming to a conclusion on patterns of employment growth.
In simplest terms, jobless growth is a phenomenon where the growth of output outpaces the growth of employment creation. Is this a developing economy phenomenon? A recent paper by Vu (SCED 2009) finds that in almost 20 Asian economies in the period 1970-2012, growth rate in GDP per capita was higher than growth rate of employment. To a great extent, this is an effect of structural change, a process in economic development where workers move across sectors. McMillan and Rodrik (2011) document that in Asian and other high income countries structural change is accompanied with movement of workers from low productive sectors to high productive sectors. Countries in Latin America witness reverse trend where structural change contributes to lower economic growth. The authors find that in India workers are moving out of agriculture to more productive manufacturing and services sectors. The question is for every worker exiting agriculture, how many are joining manufacturing or services sectors?
To answer this, job classification and worker skills data information becomes important. Using ASI manufacturing firm level data, Kapoor and Krishnapriya (2018) find that in the period 2001-13 it is the contract worker jobs which have grown across all Indian states, with high or low labor regulation norms. It suggests that labor regulations play an important role in creation of permanent jobs. However, there is a set of tasks which are substitutable across firms in an industry. These set of tasks are outsourced on contractual basis. Any dataset which discounts the creation of these informal jobs would not yield the correct numbers for employment creation. The wage differentials suggest that permanent workers are more productive than contractual workers. But are contractual workers more productive in these tasks than in agriculture related jobs? Is structural transformation more pronounced in regions that are manufacturing hubs? Is access to capital or skill a constraint in structural transformation or employment generation? What policy intervention is required so as to enable these workers transition to permanent jobs? How does an economy increase its employment elasticity? Is low employment elasticity a manifestation of development process as a symptom of greater macroeconomic problem? How would one differentiate?
If only we had Modi's pakora-nomics to come up with some optimistic answers. We ended the conference with clearer questions.
In simplest terms, jobless growth is a phenomenon where the growth of output outpaces the growth of employment creation. Is this a developing economy phenomenon? A recent paper by Vu (SCED 2009) finds that in almost 20 Asian economies in the period 1970-2012, growth rate in GDP per capita was higher than growth rate of employment. To a great extent, this is an effect of structural change, a process in economic development where workers move across sectors. McMillan and Rodrik (2011) document that in Asian and other high income countries structural change is accompanied with movement of workers from low productive sectors to high productive sectors. Countries in Latin America witness reverse trend where structural change contributes to lower economic growth. The authors find that in India workers are moving out of agriculture to more productive manufacturing and services sectors. The question is for every worker exiting agriculture, how many are joining manufacturing or services sectors?
To answer this, job classification and worker skills data information becomes important. Using ASI manufacturing firm level data, Kapoor and Krishnapriya (2018) find that in the period 2001-13 it is the contract worker jobs which have grown across all Indian states, with high or low labor regulation norms. It suggests that labor regulations play an important role in creation of permanent jobs. However, there is a set of tasks which are substitutable across firms in an industry. These set of tasks are outsourced on contractual basis. Any dataset which discounts the creation of these informal jobs would not yield the correct numbers for employment creation. The wage differentials suggest that permanent workers are more productive than contractual workers. But are contractual workers more productive in these tasks than in agriculture related jobs? Is structural transformation more pronounced in regions that are manufacturing hubs? Is access to capital or skill a constraint in structural transformation or employment generation? What policy intervention is required so as to enable these workers transition to permanent jobs? How does an economy increase its employment elasticity? Is low employment elasticity a manifestation of development process as a symptom of greater macroeconomic problem? How would one differentiate?
If only we had Modi's pakora-nomics to come up with some optimistic answers. We ended the conference with clearer questions.