# _2.3. Employment outcomes across sources_

Given the data disparities across countries and over time, it is not surprising that employment outcomes depend very much on the data source and do not necessarily coincide with those reported by the ILO. Figure 2 compares observations in our dataset with both national estimates and model-based estimates publicly available from the ILO. The focus is on employment rates, defined as the ratio of total employment to the working-age population, defined as all individuals aged 15 and above.

In Bangladesh, employment rates from the HIES are much lower than those from the LFS. This is in part because the HIES does not fully account for unpaid work while the LFS does. Another difference between the LFS and the HIES that may explain the disparity is the procedure for employment data collection. While the HIES asks about employment directly, in the LFS the employment variable is constructed from activity descriptions. There are also differences with the ILO data. The national estimate reported by ILO shows a decline of the employment rate from 2010 to 2013 but an increase from 2013 to 2016. The LFS, on the other hand, shows a steady decrease. For the last observation, the national estimate reported by the ILO is 3 percentage points above ours.

In India, the census delivers much lower employment rates than the LFS, a disparity that has been extensively discussed (e.g. Hirway 2012, Bardhan 2013, Shaw 2013, Papola 2014, Kasturi 2015). A first explanation for the gap is that the census classifies the population binarily into workers and non-workers while in the NSS a person’s ‘activity status’ can be either employed, unemployed, or not being in the labor force. Second, the NSS underestimates the total population compared to the Census. Third, there are differences in the definition of economic activity. And fourth, there are differences in the minimum time a person has to spend working before being considered employed.

In Sri Lanka, Nepal and Pakistan, the ILO model-based estimates are generally above both the national estimates and our own data points. There are also differences among the latter. In the case of Sri Lanka, the main difference between data sources concerns the recall period, as discussed above. In Nepal and Pakistan, it relates to the treatment of activities such as fetching water or collecting firewood.

Our employment breakdowns by type of job and sector of activity are not identical to those reported by the ILO either. To begin with, our breakdowns are more disaggregated as we differentiate between regular and casual wage employment, as well as between manufacturing and construction. A comparison between our dataset and ILO estimates thus requires some prior aggregation. But even then, some differences emerge for specific countries, as shown in Table A3 in the Appendix. For example, in Bangladesh, India, Nepal and Pakistan, self-employment accounts for a slightly lower share of total employment in our dataset than in the ILO estimates. Conversely, our estimates for wage employment are slightly higher than those of the ILO. But differences can be wide in some cases. In Bhutan, wage and salaried employment is 7 percent of the total in our data, compared to 20 percent for the ILO. The biggest gaps concern the breakdown of employment by sector of activity. In Nepal, for instance, agriculture accounts for 41 percent of total employment in our dataset, but for 60 percent according to the ILO. In Afghanistan, the industry sector accounts for 10 percent of employment in our case, but for only 3 percent in the ILO estimates.

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