POLICY BRIEF NOVEMBER, 2021 Impacts of covid-19 on work and wages in 2020 N E PA L P O L I C Y B R I E F : I M PA CTS O F CO V I D - 1 9 O N W O R K A N D W A G E S I N 2 0 2 0 – N E PA L 2 Data and Survey Description1 The data used in this brief come from the first round job type, and labor earnings. Job- and earnings-related of the World Bank’s South Asia COVID-19 Phone outcomes are tracked in three periods: (a) in January Monitoring Survey, which surveyed individuals in 2020; (b) at the time of the first lockdowns (March–April the eight countries of the South Asia Region (SAR) 2020), through a series of retrospective questions; and (box 1). The survey assessed the short-run impacts of (c) in the previous seven days, through standard ques- COVID-19 on the labor market, the impacts of income tions about current work status. This retrospective panel shocks on the ability to meet basic needs, and the cop- design allows for assessing changes in labor market out- ing strategies available to households. comes in people who were economically active in 2020. A complementary brief examines the impacts of income This brief describes the results of the labor market shocks on the ability to meet basic needs and the coping module, which tracks changes in employment status, strategies available to households. Main Messages 1 Stringent lockdowns in response to the health risks posed by COVID-19, and the associated contrac- tions in economic activity, produced a significant shock to labor income in Nepal. More than two of every five economically active workers reported either a job loss or a prolonged work absence (a loss in “effective employment”) as a result of COVID-19 in 2020. Among people who remained employed, 46 per- cent reported earnings losses. 2 Effective employment losses varied significantly by sector, but they were large across sectors (60 percent in services, 50 percent in manufacturing, and 32 percent in agriculture). These losses were accompanied by a change in the sectoral composition of employment relative to January 2020, with a 7-percentage point shift toward agriculture by late 2020. 3 The effects of COVID–induced labor income shocks fell hardest on women, young people, the less edu- cated, and small and microenterprises. Men and women experienced similar losses in effective employ- ment, but more women reported permanently losing a job. Younger workers, especially new entrants to the labor force ages 15–25, suffered the largest job losses of any age group. The self-employed—who overwhelm- ingly report working in or owning small and microenterprises—were far more likely than wage workers to report high income losses in 2020 (60 percent versus 23 percent). 1 The SAR COVID-19 survey is supported by the Program for Asia Connectivity and Trade (PACT), a South Asia regional trust fund administered by the World Bank and funded by the UK Foreign, Commonwealth, and Development Office (FCDO). Additional support from the Evidence for Development initiative funded by the UK Foreign, Commonwealth, and Development Office (FCDO) is gratefully acknowledged. P O L I C Y B R I E F : I M PA CTS O F CO V I D - 1 9 O N W O R K A N D W A G E S I N 2 0 2 0 – N E PA L 3 Box 1. The SAR COVID-19 Phone Monitoring Survey The SAR COVID-19 Phone Monitoring Survey surveyed 43,000 individuals, including 6,389 in Nepal, over their mobile phones. Roughly half of the sample in Nepal was reached through random digit dialing (RDD); the other half came from a follow-up to the Household Risk and Vulnerability Survey (HRVS).2 This brief reports the results from the nationwide RDD sample of 3,267 Nepali individuals 15 and older who were interviewed between August and October 2020. RDD is a method by which plausible phone numbers are generated using existing mobile phone prefixes. It was used in this survey because there were no recent representative household surveys with phone num- bers of respondents to use as benchmark data. When implemented well, RDD provides a means of collecting data that are representative of the population owning a mobile phone and allow for broad inferences about the impacts of COVID-19. Mobile phone ownership can be biased toward wealthier individuals and better-con- nected regions; patterns of non-response create additional potential sources of bias (if, for example, better-off people are less likely to pick up a number they do not recognize or agree to be surveyed if they do).3 Mobile phone ownership at the household level is high in Nepal (96.2 percent), mitigating the bias associated with RDD. However, in 2019, only 88.3 percent of households in the bottom wealth quintile owned mobile phones, and women 15–49 were less likely to own a mobile phone than men 15–49 (79.3 percent versus 91.4 percent), according to the Multiple Indictors Cluster Survey. Three steps were taken to increase the representativeness of the data and reduce bias: • For a subset of randomly selected calls with male respondents, the interviewer asked to speak to an adult woman in the household. • Caps were applied to the largest subnational administrative units (provinces) in terms of population size, in order to expand the geographic coverage of the sample. • Weights were computed to adjust estimates for different selection probabilities, including phone ownership. In addition, all of the interviewers were women. 2 The background note on the impacts of COVID-19 provides details on these surveys and their sampling. See “The Implications of COVID-19 for Welfare and Vulnerability in Nepal” (http://documents.worldbank.org/curated/en/233291624285050426/ The-Implications-of-COVID-19-for-Welfare-and-Vulnerability-in-Nepal). 3 See “Mobile Phone Surveys for Understanding COVID-19 Impacts: Part I Sampling and Mode” (https://blogs.worldbank.org/impactevaluations/ mobile-phone-surveys-understanding-covid-19-impacts-part-i-sampling-and-mode). P O L I C Y B R I E F : I M PA CTS O F CO V I D - 1 9 O N W O R K A N D W A G E S I N 2 0 2 0 – N E PA L 4 Figure 1. Changes in mobility, February 20, 2020–February 1, 2021 30 20 10 Average percent change 0 -10 -20 -30 -40 -50 -60 -70 -80 20 20 20 20 0 0 21 02 02 20 20 20 20 20 ,2 ,2 1, 1, 1, 1, 1, r1 r1 ry ril ne st ry be be gu ua ua Ap Ju to m Au br br ce Oc Fe Fe De Grocery and pharmacy Residential Transit stations Parks Retail and recreation Workplaces Note: Baseline data are for January 3–February 6, 2020. The first wave of the COVID-19 crisis—and the eco- Labor market exposures to the COVID crisis in Nepal nomic lockdown imposed to contain it—caused large were wide and deep. Restrictions in economic activ- contractions in economic activity across all sectors ity were accompanied by large losses in employ- in Nepal. National and local lockdowns were continu- ment. More than two out of every five economically ously in place for six months, from March until mid-Sep- active workers reported a loss in effective employment tember, with a gradual easing of restrictions between (either a job loss or a prolonged work absence) (figure October and December 2020. 2). A quarter of these jobs had not been recovered in late 2020, and 19 percent of workers continued to report a Google mobility trend reports reveal the impacts of prolonged absence, with an average absence of more this prolonged lockdown (figure 1). Day-to-day mobil- than four months. The gap in the payment of wages is ity in Nepal remained consistently below the pre-COVID similar, with the average respondent experiencing a levels for at least six months. On all four dimensions of prolonged work absence not having been paid in the economic activity measured, mobility remained below four months before the survey. pre-COVID levels until mid-October. Employment losses were greatest among women Nepal’s GDP is expected to grow at an average rate and younger workers. Although men and women expe- of just 0.4 percent in 2020 and 2021, down from an rienced similar losses in effective employment, more average annual growth rate of 6 percent in 2017–19. female workers (30 percent) reported permanently losing Manufacturing and services are expected to contract. a job than male workers (23 percent). Younger workers suffered the largest job losses of any age group, with 59 percent of workers 15–25 reporting effective employment losses, compared with 33 percent among adults 26–35. P O L I C Y B R I E F : I M PA CTS O F CO V I D - 1 9 O N W O R K A N D W A G E S I N 2 0 2 0 – N E PA L 5 Manufacturing and service jobs were harder hit than percent in the manufacturing sector and 66 percent in agriculture, but the number of hired laborers in agri- the services sector reported a loss of earnings, suggest- culture fell by half. Although the agricultural sector ing that further exits may be likely unless the economy was the least exposed to the effects of the crisis, one returns to normal quickly. More women (51 percent) than in three workers in agriculture reported a job loss (see men (44 percent) reported earnings losses. Workers with figure 2). Farmers reduced hired labor more than own all education levels lost earnings, but workers with the labor on farm: Almost half of wage workers in agricul- lowest education levels were most affected. Even among ture lost employment. Job losses were accompanied workers with secondary or higher education, one in three by a change in the sectoral composition of employment reported earnings losses. relative to January 2020, with a 7-percentage point shift toward agriculture by late 2020. Among workers who remained employed, 40 per- cent in the agriculture sector, 47 percent in services, Nearly half (46 percent) of survey respondents and 52 percent in manufacturing reported earnings reported earnings losses (figure 3). Self-employed losses. Among the self-employed, people in the manu- workers were far more likely than wage workers to report facturing and services were particularly hard hit, with 82 income losses in 2020 (60 percent versus 23 percent), high- percent and 66 percent, respectively, reporting a loss. lighting the risks of permanent losses in entrepreneurial Among wage workers, in contrast, earnings losses were capital. Among the self-employed who were working, 82 more common in agriculture than in other sectors. Figure 2. Losses in effective employment by people economically active in 2020 a. By gender b. By sector 60 44 45 49 43 30 33 32 Percent Percent 25 27 27 23 22 19 22 23 14 10 Total Men Women Agriculture Manufacturing Services Job losses Temporary absence Job losses and Temporary absence P O L I C Y B R I E F : I M PA CTS O F CO V I D - 1 9 O N W O R K A N D W A G E S I N 2 0 2 0 – N E PA L 6 Figure 3. Changes in effective employment and wages/earnings in 2020, by labor market characteristic Monthly 62 Monthly 15 contract contract wage worker wage worker Wage Wage Daily/Weekly 61 Daily/Weekly 35 wage worker wage worker Wage Worker 64 Wage Worker 23 Job type Job type Self-Employed Worker 32 Self-Employed Worker 60 Services 49 Services 47 Sector Sector Manufacturing 60 Manufacturing 52 Agriculture 32 Agriculture 40 46 and older 38 46 and older 50 Age (years) Age (years) 36–45 45 36–45 53 26–35 33 26–35 47 15–25 59 15–25 34 Secondary complete 44 Secondary complete and above and above 32 Education Education Primary incomplete Primary incomplete /complete 46 /complete 48 No schooling 42 No schooling 47 Female 43 Female 51 Overall Gender Overall Gender Male 45 Male 44 Total 44 Total 46 Percent of economically active population Percent of currently employed population Job losses and temporary absences Earnings/wage losses