INTRODUCTION
The Covid-19 pandemic has impacted health, economic and
social crises (Milani, 2021). The recession
caused global economic growth to decline and even experienced negative growth (Gallant, Kroft, Lange, & Notowidigdo, 2020). Economic activity
has contracted, and even many companies have stopped producing, increasing
unemployment and the number of poor people (Goma, 2021). The Covid-19 pandemic
situation has further widened economic and social inequality in all countries
of the world. The State of Indonesia at the ASEAN level is ranked 4th with a
global ranking of 93 (Ahmed et al., 2022; Goma, 2021).
Research that
examines the impact of Covid-19 among others in the United States, many
residents initially have multiple jobs. Still, after Covid-19, they lost one of
their jobs and got cut hours in other positions (Horwitz & Lascar, 2021). Covid-19 mainly
affects low-income adults. Covid-19 attacks more vulnerable individuals in the
labor market, less educated field workers, and service providers with low
incomes (Galasso, 2020). In South Africa,
the reduction in working hours by workers with primary school education is at
most more than 40 per cent. In comparison, highly educated workers suffer much
smaller losses but are still substantially reduced by around 26 per cent (Arndt et al., 2020).
In research conducted by Ozili and Arun (2020), almost all formal
sectors are affected by the Covid-19 pandemic, namely the industrial sector
including industry, such as the travel industry, hospitality, sports, events,
and entertainment; export-import sector; financial sector; money market sector;
health sector; even the education sector. The impacts include macroeconomic
impacts in the form of shocks to production factors such as labor supply,
production costs, and consumption demand (McKibbin & Fernando, 2020).
In the eight countries studied, refugees, are 60% more
likely to lose their job or income due to Covid-19 than residents (Brickhill-Atkinson & Hauck, 2021).
Globally working hours were reduced by 6.7 per cent in the second quarter of
2020 and impacted 195 million workers, mainly in the agricultural and informal
sectors, especially in the case of women (Kaur, Goyal, & Goyal, 2020).
Indayani and Hartono (2020)
state the rise and fall of the gross domestic product (GDP) produced by a
country is an indicator of Indonesia's economic growth because it is related to
the number of unemployed in GDP. Every year during the Covid-19 pandemic,
Indonesia's economic growth slows down. 212,394 workers lost their jobs due to
layoffs from the companies that employed them due to declining economic growth.
Kasnelly (2020)
shows Covid-19 pandemic also has an impact on increasing unemployment. Agustiana (2020)
also found that the effects of the Covid-19 pandemic impacted 2.8 million
workers, 1.7 million, and 749.4 thousand workers lost their jobs due to
layoffs.
The Covid-19 pandemic has had an impact on labor
conditions in Indonesia. According to the Statistic (2021), the working-age
population affected by Covid-19 based on the results of the Sakernas
are grouped into four components, namely: (1) Unemployment due to Covid-19; (2)
Not the Work Force (BAK) due to Covid-19; (3) Temporarily not working due to
Covid-19; and (4) working residents who experience reduced working hours due to
Covid-19. Conditions (1) and (2) are the impact of the Covid-19 pandemic on
those who stop working, while conditions (3) and (4) are the impact of the
Covid-19 pandemic felt by those who are still working. Unemployment as of
February 2021 increased by 1.82 million compared to February 2020. The number
of unemployed people in February 2021 was 8.75 million people. The Covid-19
pandemic has caused some residents to lose or stop working and become
unemployed or not in the labor force (BAK). The Covid-19 pandemic has also made
some residents temporarily out of work or experience reduced working hours.
The impact of the economic slowdown in Indonesia was
followed by a downturn in the economy in each region. Thousands of small
businesses were seriously affected by Covid-19 56% experienced a decline in
sales, 22% had difficulty with capital, 15% had difficulty distributing
products, and 4% had trouble finding raw materials (Tambunan, 2021). By sector, 96.02%
of the transportation and warehousing business experienced a decline in revenue
due to the pandemic. Meanwhile, 16.24% of other companies received more than a
75% revenue increase. The workforce in Bali Province at most stopped working in
the first two months of the pandemic, namely March and April 2020 (Ningsih & Dokhi, 2022). The Large-Scale
Social Restrictions (PSBB) policy in South Sulawesi reduced the number of
working hours and even caused commuter workers not to work for a while (Triany, 2021).
Statistic (2021)
reported that the Covid-19 pandemic that had occurred since mid-March 2020
brought significant changes to all aspects of the life of the residents of DIY.
The DIY economy in aggregate contracted by 2.69 per cent until the end of 2020,
and it is uncertain how long it will end. The deteriorating macroeconomic
conditions were also accompanied by increased open unemployment and poverty
rates. The policy of limiting social mobility to anticipate the spread of the
virus in various forms also impacts changes in social and economic conditions.
The Special Region of Yogyakarta is one of the areas affected by the COVID 19
pandemic.
Bank Indonesia (2022)
reported that the number of workers in the informal sector has decreased. The
informal sector is a sector that generally accommodates workers with low levels
of skills and education and is often the focus of people with low incomes. A
total of 1,233.61 thousand people (55.36%) work in the informal sector. Three
groups forming informal workers experienced a decline, namely workers assisted
by temporary workers/unpaid workers, casual workers, and family workers/unpaid
workers.
There is a lot of research on labor income,
but there is still little research on labor income during the COVID-19
pandemic. This paper complements the literature that studies the impact of
COVID-19 on labor income using survey micro data. Bong et al. (2020)
researched on covid 19 pandemic effect on low- and middle-income countries
(LMICs). Arndt et al. (2020)
researched on effect of covid 19 pandemic on labor income based on their
education. In this study, the control variable is used whether the workforce
uses digital equipment in their work.
This study aims to see the economic impact of the Covid-19 pandemic at the
local level, namely in the Special Region of Yogyakarta (DIY). The economic
impact indicator used is labor income. Labor income is an easy way to measure
the impact of the Covid-19 pandemic. This study uses secondary data from the
2021 Employment Survey (Sakernas) from data from the
Central Statistics Agency for the Special Region of Yogyakarta.
METHOD
This research data uses
data from the National Employment Survey (Sakernas) conducted by the
Central Bureau of Statistics of the Special Region of Yogyakarta in August
2021. The sample criteria are individuals who have worked activities for the
past week. The working status of Wira is defined as
an individual with the status of self-employed, working with the help of
non-permanent workers, and working alone with the use of permanent workers.
Employee status is defined as employee/permanent worker/employee. Precarious
labor is the basis for the dummy. Family workers/unpaid workers in this study
were omitted. The sample selection begins with the respondents' answers to the
R9A questionnaire. In the past week, did you work? (Work is doing activities to
earn income at least 1 hour a week). From the sample criteria obtained, as many
as 4,029 workers in the Special Region of Yogyakarta as the sample of this
study.
This research model uses
the Mincer Earning Function by adding the variables of gender, marital status,
participation in training, working hours, use of digital devices, and working
status. The left is equal to the dependent variable, while the right is equal
to the independent variable.
Log(Income)i = β0+ β1Educi
+ β2Experi + β3Expersqi + β4Malei
+ β5Marriedi + β6Coursei
+ β7Houri +
β8Digitali + β9Wirai + β10Employeei + εi
The dependent variable in
this model is income. Income is defined as income/income/salary/net wages that
individuals receive during the last month from this work or business activity,
both in the form of money and goods in rupiah. The independent variables in
return to education include education, experience, experience square, gender,
marital status (married), course (training), working hours (hours), digital,
entrepreneurship, and employees. Education is defined as the level of education
that an individual has successfully taken. 1 means no school/not finished
elementary school, 2 means elementary school graduate, 3 means junior high
school graduate, 4 means high school/ vocational/ Islamic High School graduate,
5 means Diploma graduate, 6 means Bachelor/Diploma IV graduate, 7 masters
graduates, and 8 doctoral graduates. School is the length of education which is
calculated in years of completion of education. Experience is individual work
experience in units of years obtained from 2021-the first year of graduating
from the highest education. Experience Square is obtained from Experience
squared to determine whether there is diminishing. Male is 1 for boys and 0 for
girls. Married is worth 1 for married individuals and 0 for unmarried or
divorced individuals. The course is worth 1 for individuals who have attended
training/ courses while 0 has never attended training/courses. An hour means
the number of hours worked during one week. Digital is worth 1 if the work the
individual does requires a digital device, while 0 means that he does not use a
digital device.
RESULTS AND DISCUSSION
A. Results
Results The
results of this study are presented in the form of descriptive analysis and
analysis of regression results. The descriptive analysis is in the form of an
illustrative statistical table. In contrast, the analysis of the regression
results aims to determine the factors that affect labour income during the
Covid-19 pandemic. Table 1 presents the results of the study in the form of
descriptive statistics of the variables contained in the Mincer Earning Function, namely
income (Income, Log income),
education (Educ, School), experience (Exper, Expersq),
gender (Male), marital status (Married), job training (Course), number of working hours (Hour), digital work equipment (Digital), dummy working status (Entrepreneur or Employee).
Based on table
2, it can be explained that all independent variables in the Mincer Earning
Function together have a significant effect on labour income. The variables
of Education, experience, gender, marital status, training, working hours,
digital devices, entrepreneurial status, and employees have a positive and
significant effect on income. The exceptions are the experience square variable
and self-employed dummy, which harms income. All variables are at a
significance level of <0.01. This finding shows that all independent
variables significantly affect labour income.
The regression
results show the R-squared value of 0.405. This means that all
independent variables (education, experience, gender, marital status, training,
working hours, digital devices, entrepreneurial status, and employees) can
simultaneously explain changes in the dependent variable (income) of 40.5%. The
remaining 58.5% is explained by other variables outside the regression model
used in this study.
B. Discussion
The education
variable has a positive and significant effect on the income variable. Workers
with higher levels of education had better incomes during the pandemic. These
results are the following (Aisyah & Rahman, 2022). Simanjuntak's (1998)
relationship between income level and education level is positive. This is
because higher education will increase work productivity and eventually make
the income level higher as well. A person with higher education can also enter
the level of specialist work with certain qualifications. These jobs can
generate higher income than menial jobs.
Work experience
has a positive influence on labour income. It is rational if someone who has
worked for a long time has more skills and understanding than the new
workforce. As a result, productivity will increase and make the income higher.
The results of this study follow research conducted by Sicherman and Galor (1990),
which states that individuals gain job experience and skills in one job to
increase higher income from a job. At a certain age, this experience has a
negative effect, indicated by the experience square variable, which has
a negative coefficient or is diminishing. This is in line with Takasaki (2017)
that the more extended work experience the workforce can potentially increase
their income to a certain point.
The gender
dummy variable (1=male, 0=female) has a positive regression coefficient,
meaning that male workers have a better income than female workers. These
results are the following (Pirmana, 2006). The dual role
played by women in carrying out domestic activities reduces women's hours to be
able to do activities outside the home (Sohn, 2015). This causes
women's income to be lower. The difference in pay between men and women is
generally still significant in Asian and African countries but is getting
smaller in European and American countries (World Economic Forum, 2018). The average wage
for male workers is 2.96 million rupiahs, and the average salary for female
workers is 2.35 million (Badan Pusat Statistik, 2021).
Workers who
have married status have a higher income than those who are not/unmarried. This
is because married individuals feel they have more responsibility and are
motivated to be more productive, ultimately positively affecting their income.
This is in line with the results of Prayogo and Suprayogi (2019), who found that
the average income of men after marriage was higher than that of men before
marriage.
Workers who are
equipped with job training have higher incomes. The existence of job training
certainly adds to the provision of skills possessed by workers. A skilled
worker will have higher productivity compared to a less experienced worker. In
the end, the worker will earn a higher income. Denning, Jacob, Lefgren, and Vom Lehn (2019)
found that training attended by workers is one of the essential factors that
can increase their income compared to workers who have never participated in
the activity. These results follow the ILO (2017),
which states that the workforce needs on-the-job training to increase
productivity to achieve more significant economic benefits, namely income.
The number of
hours affects income positively and significantly. The length of time working
will increase productivity to increase revenue. For labourers, each additional working hour will increase their
income because they get wages according to the hours worked. For entrepreneurs,
every increase in working hours will create the possibility of increasing sales
turnover, which will eventually increase their income as well. This result is
different from the research of
Del
Rey, Naval, and Silva (2022), who found that
the number of hours worked per week was not strongly associated with increased
wages in employment.
Information and
communication technology development encourages various changes, including in
the world of work. Using digital devices will make it easier for workers to
increase productivity and income. Jobs that use digital devices have higher
incomes. This finding is in line with the results of the study by Graetz and Michaels (2018) about the use
of robot technology in the workforce which has a positive and significant
effect on increasing average income.
Entrepreneurship
has a negative influence on income. This is because the turnover of
entrepreneurs during the pandemic tends to decrease, causing their income to
fall in contrast to workers who rely on a fixed income every month. This result
is different from the findings of Hendajany and Rizal (2020), who found that
the average income of self-employed people assisted by permanent workers had
the highest revenue.
CONCLUSION
The income of workers in the
Special Region of Yogyakarta during the Covid-19 period was influenced by
education level, work experience, gender, marital status, job training, number
of hours worked, digital devices, and work status, with a total effect of
40.5%. The education variable has a positive and significant impact on the
income variable. Workers with higher levels of education had better incomes
during the pandemic. Work experience positively affects the payment of workers
up to a certain age and then experiences a diminishing. Male workers have a
better income than female workers. Workers who have married status have a
higher income than those who are not/unmarried. Workers who are equipped with
job training have higher incomes. The number of hours affects income positively
and significantly. Jobs that use digital devices have higher incomes.
Self-employment has a negative influence on income during a pandemic. This
study's results indicate that using digital devices at work can increase
revenue. Therefore, the workforce also needs to be equipped with the ability to
transform their work from manual to digital.
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