Reemployment during the Covid-19 pandemic in Indonesia: What kinds of skill sets are needed?

Dartanto, Teguh; Susanti, Hera; Augustin, Eldest; Fitriani, Kania Article Reemployment during the Covid-19 pandemic in Indonesia: What kinds of skill sets are needed? Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Dartanto, Teguh; Susanti, Hera; Augustin, Eldest; Fitriani, Kania (2023) : Reemployment during the Covid-19 pandemic in Indonesia: What kinds of skill sets are needed?, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 11, Iss. 2, pp. 1-31, https://doi.org/10.1080/23322039.2023.2210382 This Version is available at: https://hdl.handle.net/10419/304080 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Teguh Dartanto, Hera Susanti, Eldest Augustin & Kania Fitriani To cite this article: Teguh Dartanto, Hera Susanti, Eldest Augustin & Kania Fitriani (2023) Reemployment during the Covid-19 pandemic in Indonesia: What kinds of skill sets are needed?, Cogent Economics & Finance, 11:2, 2210382, DOI: 10.1080/23322039.2023.2210382 To link to this article: https://doi.org/10.1080/23322039.2023.2210382 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 27 Jun 2023. Submit your article to this journal Article views: 1608 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Reemployment during the Covid-19 pandemic in Indonesia: What kinds of skill sets are needed? Teguh Dartanto 1 *, Hera Susanti 1 , Eldest Augustin 2 and Kania Fitriani 2 Abstract: The COVID-19 pandemic has disrupted the labor market leading to significant unemployment. This study explores the 2019, 2020, and 2021 National Labor Force Survey (Sakernas) to examine the labor market changes and the relationship between workers’ skill sets, such as hard skills (vocational education), soft skills, and digital literacy and reemployment during the COVID-19 pandemic. Our descriptive statistics analysis confirms that the scarring effect exists as the share of the informal sector increases by around 4.5 percentage points during the COVID-19 pandemic. Moreover, our estimations using the Bivariate Probit Model show that social skills and digital literacy are important determinants for reemployment at the national level. In contrast, vocational education and problemsolving skills are statistically insignificant. Workers with social skills tend to have a higher probability of being reemployed, by 41% in 2020 and 27% in 2021, compared to workers without any. Our study also finds a heterogenous relationship between skill sets and reemployment. Social skill is significantly correlated with reemployment in an urban area, Java-Bali, and young workers in the 15–24 age group. In addition, vocational education is crucial for reemployment, especially among young workers during the economic recovery period in 2021. Our study suggests that the government should focus on preparing the correct and relevant skill sets for young workers aged 15–24 to respond to the significant demand changes in the postpandemic labor market. Subjects: Economics and Development; Population & Development; Economics Keywords: Covid-19; reemployment; hard skill; soft skill; digital literacy; bivariate probit 1. Introduction Since the COVID-19 pandemic was declared in early 2020, most governments worldwide implemented restrictions on economic activities and people mobility, resulting in significant disruptions to the labor market, leading to job losses and unemployment as many workers struggled to find new employment opportunities. The International Labour Organization (2020) reported that confinement measures impacted nearly 81% of the global workforce, and 38% remain at high risk of experiencing adverse employment outcomes such as unemployment and reduced working hours. Evidence from Germany, the UK, and the US also show that labor market shocks magnify disparities within the workforce, mainly among women and young workers with lower skills (Adams- Prassl et al., 2020). While recent studies have seen a return in rural to urban migrations, the COVID-19 pandemic in India saw a mass exodus of migrant workers from major urban centers to Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 1 of 31 Received: 15 April 2022 Accepted: 02 May 2023 *Corresponding author: Teguh Dartanto, Research Cluster on Poverty, Social Protection and Development Economics, Department of Economics, Faculty of Economics and Business, Universitas Indonesia, Campus UI Depok 16424, Depok, Indonesia E-mail: [email protected] Reviewing editor: Muhammad Shafiullah, Department of Economics and Social Science, BRAC University, Dhaka, Bangladesh Additional information is available at the end of the article © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. their native villages (Misra, P & Gupta, J, 2021). As mobility restrictions have eased, recovery has begun. Still, historical experience warns for caution: the unemployment rate following the 1980 recession in the US remained high even almost a decade after the recession (Elsby et al., 2011). Indonesia’s own National Labor Force Survey (Sakernas) shows that the unemployment rate was 7.07% in August 2020 (2.56 million), increasing by 2.13 percentage points from 4.94% in February 2020. Around 24 million workers experienced a decline in working hours, while the share of those working in the informal sector increased from 56% (August 2019) to 60.5% (August 2020) (Statistics Indonesia, 2020, 2021). This unprecedented spike in unemployment was accompanied by an increase in the poverty rate, which saw 1.63 million people living in poverty and 5 million people losing their health protection as of March 2020 (Sparrow et al., 2020). Data from the Social Security Agency for Employment (BPJS Ketenagakerjaan-SSAE) also showed a 3% decrease in its Old-Age Program’s membership numbers. 2021 saw gradual economic recovery as the Sakernas showed a decline in the national unemployment rate from 7.07% (August 2020) to 6.49% (August 2021), and a decrease in the share of those working in the informal sector by around one percentage point (which remains higher than pre-pandemic levels). These two conditions indicate reemployment and an early indication of a scarring effect in the labor market. As reemployment is an essential factor in leveraging household welfare toward the nation’s economic recovery, reemployment has become a central concern for governments worldwide, and numerous policy packages have been implemented to encourage reemployment. These policies have emphasized the need for workers to have a range of skill sets to meet the changing demands of the job market during the pandemic. The OECD (2021) and European Commission (2020) highlight that those workers who can adapt quickly to new situations, acquire new skills, and embrace remote work and digital tools are in high demand. They note that the fundamental skills necessary for reemployment during the pandemic include digital literacy, technical skills, soft skills, language skills, creative skills, and health and safety skills. International consensus underlined that education (hard skills) positively and significantly impacts labor market outcomes during economic shocks. However, Deming (2017) argued that soft and social skills also contribute to the probability of employment and higher incomes, complementing existing hard skills. Furthermore, problem-solving skills are also argued as soft skills that should be possessed in the twenty-first-century workplace (OECD, 2013a, 2013b), alongside digital skills, which have become prominent as social distancing due to the pandemic, have ushered drastic changes in the work landscape. In the time of Covid-19, social distancing drives the contactless economy, and those businesses pursuing digital technology are most able to thrive and recover. Therefore, there is an urgent need to explore the relationships between the role of skill sets and reemployment during the pandemic, especially in developing countries such as Indonesia, where evidence is less available for policy formulation. This evidence is urgently required to help workers navigate the current job market and prepare for the future. By identifying the skills in demand and the most effective training programs, policymakers and employers can help ensure that workers have the skills they need to succeed in the post-pandemic job market. This study will then empirically test three hypotheses using Indonesia’s National Labor Force Survey. First, workers with higher hard skills (vocational education) should be more likely to be reemployed than workers with lower hard skills. This study investigates whether workers with vocational education are more likely to reemployment. Second, workers with soft skills, including social and problem-solving skills, should be more likely to be reemployed than workers who do not have soft skills. Third, workers with digital skills should have a higher probability of being reemployed than workers who do not have digital skills. The rest of this paper is structured as follows. Section 2 depicts the economic and labor market condition in Indonesia. Section 3 reviews the literature on Covid-19, reemployment, hard skills, soft Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 2 of 31 skills, and digital literacy. Section 4 explains our data and econometric method. Section 5 describes our analysis and discusses the result. Last, section 6 concludes the paper. 2. Labor market condition in Indonesia Economic transformation can lead to changes in the types of jobs available in the labor market, as well as the skills in demand. For example, as economies transition from agricultural to manufacturing or service-based economies, the demand for workers with technical, digital, or soft skills may increase. This can lead to a shift in the training and education programs needed to prepare workers for these jobs. Dartanto et al. (2018) confirm that Indonesia experienced an agriculture—service transition before the industrial sector matured. The labor market has also undergone a fundamental transition, with the growth of employment occurring in the services sector while agricultural employment is on a decline. Yet, although the country’s economic structure has become more serviceoriented (see Table 1), the labor market remains dominated by workers in agricultural sectors: although the services sector contributes the highest share of GDP, almost one-third of Indonesia’s workforce is employed in agriculture. Like many developing economies, informality is high in Indonesia: almost 63% of all workers in Indonesia belong to the informal sector (see Table 2). However, Suryahadi et al. (2018) noted that formal sector employment in Indonesia is on the rise, particularly in urban, industry, and services, supported by the employment of younger, more educated workers, primarily new entrants to the labor market. On the labor supply side, the size and quality of Indonesia’s workforce are influenced by moderately fast growth in the working-age population, the “demographic bonus” which is expected to last until around 2030, urbanization that has been quite rapid, a relatively high female participation rates in the workforce, and low in average years of schooling (Manning & Pratomo, 2018). Hence, the transformation of the country’s economic structure, the evolution of labor market conditions, and the ramifications of the COVID-19 pandemic are poised to shift the skills required for job demand and reemployment in Indonesia. 3. Literature review 3.1. Covid-19 and reemployment The impact of the COVID-19 pandemic on the labor market has been severe and fast-paced. Due to social distancing and mobility restrictions, governments, private offices, and companies have partially or fully implemented work from home (WFH) policies. This has led some companies to reduce their activities, forcing many to close. The resulting unemployment has created scarring and habituation effects (Clark et al., 2001). The scarring effect in the labor market refers to the negative long-term impact that a person’s early experiences of unemployment and/or underemployment in the job market can have on their future career prospects and earning potential. This includes difficulties in finding stable, well-paying jobs later in life. Unemployed workers may enter the informal sector or lowpaid jobs after losing a career in the formal sector (Cruces et al., 2012). If an individual has been unemployed for some time, they become accustomed to the situation: a condition called “habituation”. This habituation can lead to lower incentives to change one’s labor force status, including the duration of their unemployment. Thus, the worker has difficulties returning to work, then, in the end, they should be released from the labor market (Cockx, 2000; OECD, 2002). The impact of job loss varies according to income levels, job skills, education, and age. Belotti et al. (2021), reviewing the various COVID-19 and work-related aspects studies, found that the most affected were low-income, low-skill jobs, and temporary workers. Still, they are also able to get back to work quickly. Fewer older workers lose their jobs, but it is more difficult to find a new job (Belotti et al., 2021). In Indonesia, where there is no unemployment insurance, people who have lost their jobs are forced to be reemployed as soon as possible. However, the reemployment process during the pandemic is challenging. Therefore, this calls for a holistic exploration of skills Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 3 of 31 Table 1. Sectoral Gross Domestic Product (GDP) and Labor productivity No Sectoral/Industry GDP (billion rupiah) Productivity (GDP/Number of Labor) 2019 2020 2021 2019 2020 2021 1 Agriculture, Forestry & Fisheries 1,354,399.10 1,378,331.40 1,403,710.00 38,205,585 36,058,969 37,804,590 2 Mining 806,206.20 790,475.20 822,099.50 564,350,435 584,568,966 569,548,961 3 Manufacturing 2,276,667.80 2,209,920.30 2,284,821.70 118,589,326 126,405,044 122,219,167 4 Electricity and Gas 111,436.70 108,826.40 114,861.10 306,452,074 358,511,090 403,704,159 5 Water supply, sewerage, Waste management and Recycling 9,004.90 9,449.30 9,919.20 17,927,941 19,245,637 17,627,051 6 Construction 1,108,425.00 1,072,334.80 1,102,517.70 127,765,721 132,936,862 132,933,254 7 Trading 1,440,185.70 1,385,747.40 1,450,226.30 59,600,638 56,097,013 56,349,864 8 Transportation 463,125.90 393,437.90 406,187.60 81,877,686 70,358,021 74,616,719 9 Accommodation & Food-Beverages Services 333,304.60 299,122.40 310,754.70 38,927,330 35,010,488 33,850,021 10 Information & Comunication 589,536.10 652,062.90 696,460.40 639,971,624 698,683,986 697,716,988 11 Financial Services and insurance 443,093.10 457,482.90 464,638.60 249,589,278 293,648,483 290,798,064 12 Real Estate 316,901.10 324,259.40 333,282.90 784,591,217 823,693,750 936,306,275 13 Company/Business Services 206,936.20 195,671.10 197,106.70 106,498,570 108,902,494 97,719,267 14 Government Administration 365,538.80 365,439.30 364,233.40 73,877,967 79,965,781 75,115,468 15 Education Services 341,349.90 350,264.60 350,655.30 53,200,245 58,100,391 54,016,543 16 Health Services and Social Activities 127,487.90 142,228.40 157,104.70 64,299,854 70,918,394 71,498,065 17 Other services 205,011.40 196,608.70 200,772.90 32,212,758 30,674,251 34,772,620 Total 10,498,610 10,331,662 10,669,353 81,539,267 80,430,719 81,414,041 Source: Statistics Indonesia (2022) Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 4 of 31 Table 2. Labor status (formal and informal) by sector No Sectoral/ Industry Labor 2019 2020 2021 Formal Informal Total Formal Informal Total Formal Informal Total 1 Agriculture, Forestry & Fisheries 4,400,819 31,049,472 35,450,291 4,368,059 33,856,312 38,224,371 4,295,790 32,834,886 37,130,676 2 Mining 912,841 515,715 1,428,556 789,125 563,111 1,352,236 846,874 596,548 1,443,422 3 Manufacturing 12,473,585 6,724,330 19,197,915 10,698,254 6,784,595 17,482,849 11,667,186 7,027,277 18,694,463 4 Electricity and Gas 326,546 37,089 363,635 262,181 41,370 303,551 253,843 30,675 284,518 5 Water Supply, Sewerage, Waste Management and Recycling 248,216 254,067 502,283 237,259 253,725 490,984 253,168 309,558 562,726 6 Construction 4,451,736 4,223,713 8,675,449 3,438,172 4,628,325 8,066,497 3,366,161 4,927,608 8,293,769 7 Trading 8,276,988 15,886,943 24,163,931 7,387,081 17,315,614 24,702,695 7,909,540 17,826,570 25,736,110 8 Transportation 2,303,309 3,353,005 5,656,314 2,400,088 3,191,853 5,591,941 2,365,404 3,078,250 5,443,654 9 Accomodation & Food- Beverages Services 2,763,773 5,798,453 8,562,226 2,232,434 6,311,360 8,543,794 2,300,628 6,879,712 9,180,340 10 Information & Comunication 643,676 277,515 921,191 641,139 292,134 933,273 675,289 322,910 998,199 11 Financial Services and Insurance 1,726,042 49,247 1,775,289 1,502,637 55,290 1,557,927 1,535,031 62,774 1,597,805 (Continued) Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 5 of 31 Table 2. (Continued) No Sectoral/ Industry Labor 2019 2020 2021 Formal Informal Total Formal Informal Total Formal Informal Total 12 Real Estate 312,612 91,294 403,906 238,356 155,309 393,665 204,062 151,893 355,955 13 Company/ Business Services 1,514,533 428,556 1,943,089 1,374,076 422,679 1,796,755 1,584,801 432,270 2,017,071 14 Government Administration 4,947,873 - 4,947,873 4,569,946 - 4,569,946 4,848,980 - 4,848,980 15 Education Services 6,206,669 209,653 6,416,322 5,720,359 308,251 6,028,610 6,193,224 298,404 6,491,628 16 Health Services and Social Activities 1,813,792 168,917 1,982,709 1,812,799 192,723 2,005,522 1,969,734 227,594 2,197,328 17 Other Services 3,481,297 2882,995 6,364,292 3,099,884 3,309,684 6,409,568 2,872,363 2,901,516 5,773,879 Total 56,804,307 71,950,964 128,755,271 50,771,849 77,682,335 128,454,184 53,142,078 77,908,445 131,050,523 Source: Statistics Indonesia, 2022 Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 6 of 31 among job seekers as structural changes in working conditions demand improving their skills (Wanberg et al., 2002). Once job seekers have the skills preferred by work environments, their employment probability will also increase (Voogt & Roblin, 2012). 3.2. Worker’s skill sets The pandemic has created a need for workers to have diverse skills, including hard skills, soft skills, complex problem-solving skills, and digital literacy, to be reemployed in the current job market. Heckman et al. (2006) showed that a higher level of hard skills has a positive relationship with productivity, while OECD (2020) showed that cognitive skills had become an essential skill in the current era of automation. Education, often characterized as a tool for assessing hard skills, remains critical. The positive impacts of higher education levels on labor market outcomes are well-documented and include a lower unemployment rate and a shorter length of unemployment (Grossman, 2005; Hanushek & Woessmann, 2008). During the pandemic and its resulting recession, workers with lower education levels have generally experienced the brunt of the impact of the economic downturn. However, evidence shows that the demand for higher education has increased following the recession. This is echoed in history: during the recovery from the Great Recession, the requirement for bachelor’s degrees and college degrees in the job demand reached almost 67%, and the need for those with a highschool degree or lower fell to only 1%. (Carnevale et al., 2016). Despite solid evidence that the relationship between education and reemployment is positive and significant, this study posits that soft skills, defined as a cluster of capabilities that enable workers to work productively (Eyster et al., 2013), also play an essential role in the transition process of reemployment following the pandemic. In addition, this study posits that problem-solving skills, defined as a process of acquiring new knowledge and a set of acts to investigate the problem, identify the possible alternative solutions, and provide an action to be taken (Funke, 2010; Gonzalez et al., 2005; Greiff et al., 2013), have become crucial in affecting the dynamics of reemployment despite low-skill jobs not taking advantage from problem-solving skills (Athanasou, 2012) Meanwhile, social skills have risen in importance in the workplace (Lonnides & Datcher-Loury, 2004). Social skills are described as the ability to conduct networking to gain job opportunities (Van Hoye et al., 2009; Wanberg et al., 2000). They have been shown to positively and significantly affect labor market success (Beaman, 2012; Hulshof et al., 2020; Van Hoye et al., 2009). Information gathered from social networks, and colleagues play an essential role in reemployment (Giles et al., 2006), although some studies have found a negative relationship between social skills and the job-finding rate (McArdle et al., 2007; Saks, 2006). One final skill to consider as global trends accelerated by the pandemic continue to transform the workplace is digital literacy. Digital technology has become a necessity of the workplace (Coibion et al., 2020; Van Laar et al., 2017, Shkalenko & Fadeeva, 2020), disadvantaging most of those with the least education (Dingel & Neiman, 2020; Espinoza & Reznikova, 2020; Sostero et al., 2020). Their greater difficulties in utilizing technologies threaten to worsen inequalities in the labor market, especially as recent trends have pushed businesses of all sizes to be increasingly dependent on digital technologies. However, as digital technologies help companies to survive, Lane and Conlon (2016) have concluded that digital technologies can benefit even low-educated workers so long as they are effectively managed. 4. Data and methodology 4.1. Data dan measurement We employ the Sakernas to explore the relationship between skill sets, including hard skills, problemsolving skills, social skills, digital skills, and reemployment during the COVID-19 pandemic in Indonesia. The Sakernas records employment characteristics, unemployment, and underemployment, and Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 7 of 31 Table 4. (Continued) Characteristics Reemployed No Reemployed Total Formal Informal 2019 2020 20212019 2020 2021 2019 2020 2021 2019 2020 2021 Diploma 2.8% 2.2% 2.6% 3.6% 2.9% 3.8% 1.8% 1.8% 1.9% 2.8% 3.0% 2.6% >= Bachelor 8.1% 5.9% 6.3% 11.2% 9.6% 10.7% 4.5% 4.0% 3.7% 6.7% 7.4% 5.8% Age 4,892,316 8,664,646 9,212,851 2,659,153 3,005,649 3,485,664 2,233,163 5,658,997 5,727,187 5,283,780 6,761,845 7,279,960 15-24 28.2% 18.6% 18.9% 35.3% 25.9% 29.2% 19.8% 14.8% 12.6% 31.8% 28.5% 24.5% 25-40 47.1% 43.6% 42.7% 47.8% 47.9% 47.6% 46.3% 41.3% 39.6% 34.3% 36.9% 32.6% >40 24.6% 37.8% 38.4% 16.8% 26.1% 23.2% 33.9% 44.0% 47.7% 33.9% 34.7% 42.9% Source: Authors’ estimation based on Sakernas 2019, 2020 and 2021 Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 14 of 31 Surprisingly, the number of workers not reemployed who are “still in the workforce” category in 2020 (54%) is higher than those in 2019 (37%). This is likely due to the early pandemic stage, where people still hope that the pandemic will pass soon and hold out for job opportunities. Indeed, in 2021, 70% of those who have not been reemployed shifted out of the workforce. This reflects the striking habituation effects a year into the pandemic. Indonesia’s economy has not fully recovered: scarce job opportunities have pushed the unemployed out of the workforce. 5.1.2. Heterogeneous characteristics of reemployment Table 4 describes reemployment by gender, rural-urban, regional, education, and age. Before the pandemic, in 2019, youth workers (15–24 years) dominated almost 30% of the reemployed workers, but during the pandemic years of 2020 and 2021, youth workers had the lowest reemployment rate, 18.6%, and 18.9%, respectively. Employers need workers who have more experience in hard times. In 2020 and 2021, workers aged 25–40 years had the highest reemployment rate at roughly 42%. Thus, the limited experience of youth workers has become a barrier to having decent work with good incomes. Disruptions in education and training further compound the issue as those who recently graduated during the pandemic experienced learning loss. Vocational education has played a role in the reemployment process: it is the education category with the highest proportion of workers reemployed in 2019. Yet, this has changed during the pandemic: the highest proportion of reemployed workers is entitled to only those with a primary school background, 25.2% in 2019 and 24.8% in 2020. During the pandemic, having a vocational education background did not significantly affect reemployment outcomes, and people with lower education were more easily reemployed. In this situation, workers are forced to work in the more flexible sector, or informal sector or are self-employed, so vocational education is no longer valued as highly. The pandemic has also affected the proportion of people who stopped working and are reemployed based on their geographic location. In 2019, the urban-rural proportion of reemployed workers was 62.5% and 37.5%. Following the pandemic, the proportion of those reemployed in urban areas remains higher than those in rural areas, 56.8% in 2019 and 55.4% in 2021. Indeed, the impact of the pandemic is more pronounced in rural areas. This is aligned with Mueller’s (2020) results that rural areas are dominated mainly by a single industry, such as agriculture, making them more vulnerable and disproportionately impacted by the pandemic. The low rate of reemployment in rural areas may also be either directly due to a decrease in the purchasing power of the local community or indirectly due to the decline in demand from other regions, including urban areas. However, the rural-urban proportion trend has seen the share of rural reemployment rise from 43.2% in 2020 to 44.6% in 2021. This reflects a negative aspect of rural employment: employment in rural areas is generally more flexible, causing migration back into villages to seek informal reemployment. 5.1.3. Reemployment by skills As explained in the literature review section, digital technology has become necessary in the workplace. However, Table 5 shows that, among the total who are reemployed, the share of those without digital literacy skills is higher than those with digital literacy skills. This is reasonable as higher reemployment occurs in the informal sector, which tends to be more traditional and thus does not require digital skills (La Porta & Shleifer, 2014). Regarding problem-solving and social skills, our respondents already have a job but are still looking for another job. The table shows that, in the reemployment process, the share of people with problem-solving skills is lower than those who don’t have those skills. The same reasoning as digital literacy is applied here; more reemployment occurs in the informal sector, which tends to absorb low-skill workers who do not require problem-solving skill qualifications. Different findings are found in social skills. People who have social skills and are still looking for another job take up a higher share of those reemployed than people who do not have those skills. Even before the pandemic in 2020, social skills were already needed in the reemployment process. Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 15 of 31 Table 5. Reemployment by Skill Characteristics Reemployed No Reemployed Total Formal Informal 2019 2020 2021 2019 2020 2021 2019 2020 2021 2019 2020 2021 Digital Literacy 4,892,316 8,664,646 * 2,659,153 3,005,649 * 2,233,163 5,658,997 * 5,283,780 6,761,845 * Have Digital Literacy 41.7% 40.1% * 50.4% 52.0% * 31.3% 33.8% * 26.9% 30.5% * Does not have 58.3% 59.9% * 49.6% 48.0% * 68.7% 66.2% * 73.1% 69.5% * Vocational Education 4,892,316 8,664,646 9,212,851 2,659,153 3,005,649 3,485,664 2,233,163 5,658,997 5,727,187 5,283,780 6,761,845 7,279,960 Vocational Education 24.0% 19.4% 19.7% 29.2% 25.7% 28.5% 17.8% 16.0% 14.4% 17.2% 23.0% 20.1% No Vocational Education 76.0% 80.6% 80.3% 70.8% 74.3% 71.5% 82.2% 84.0% 85.6% 82.8% 77.0% 79.9% Problem Solving 640,667 1,564,562 893,277 229,949 376,943 222,492 410,718 1,187,619 670,785 1,933,354 3,644,459 2,,219,541 Has Problem solving skill 43.2% 35.6% 36.6% 45.1% 44.7% 46.7% 42.1% 32.8% 33.3% 49.6% 44.3% 37.7% Does not have 56.8% 64.4% 63.4% 54.9% 55.3% 53.3% 57.9% 67.2% 66.7% 50.4% 55.7% 62.3% Social Skill 640,667 1,564,562 893,277 229,949 376,943 222,492 410,718 1,187,619 670,785 1,933,354 3,644,459 2,219,541 Has Sosial skill 87.2% 92.4% 91.6% 83.1% 90.8% 89.0% 89.5% 92.9% 92.5% 82.4% 77.2% 73.2% Does not have 12.8% 7.6% 8.4% 16.9% 9.2% 11.0% 10.5% 7.1% 7.5% 17.6% 22.8% 26.8% *due to the change of questions about internet utilization, the 2021 data is not comparable. Source: Authors’ estimation based on Sakernas 2019 –2021 Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 16 of 31 These skills have become more critical during the pandemic: the share of people among the reemployed with social skills rose from 87,2% in 2019 to 92,4% in 2020 and 91,6% in 2021. Networking is essential; people prefer to hire someone they know or have good references. According to a LinkedIn global survey, almost 80% of professionals consider professional networking important to career success. Career networking involves personal, familial, or professional contact to assist with a job search. This is consistent with the increase in the number of reemployed people with social skills in both the formal and informal sectors. 5.2. Estimation results of the bivariate probit model Table 6 shows the marginal effect of the different skills on reemployment. In 2019, hard skills, proxied by vocational education, positively correlated with reemployment outcomes, but not in 2020 and 2021. While previous research show education is an important predictor during the recession (Isengard, 2003), this study provides the opposite result. This potentially hints that vocational education does not impact the Indonesian labor force during crisis periods as the highest reemployment occurs in the informal sector, which tends to employ less educated workers (Ginting et al., 2018). In 2020, the effect of vocational education turned negative in rural areas: having vocational education reduces the chances of reemployment. This is likely due to reemployment being dominated by the informal sector and by those with low levels of education. Another possible explanation is the competencies of graduates of vocational education, which focus on occupations (OECD/ADB, 2020). The OECD stated that the Indonesian National Work Competency Standards (INWCS) and the Indonesian National Qualification Framework (INQF) are the standards that should be fulfilled to ensure harmonization between vocational education and each employment outcome. However, during the pandemic, the demand for competencies flexibility is high as people must work only based on limited job availability. Furthermore, nearly 30% of graduates with vocational education backgrounds are absorbed in manufacturing industries (Khurniawan & Erda, 2019). The growth of the manufacturing sector turned negative in the third quarter of 2020, resulting in many vocational education graduates becoming unemployed (Miftahudin, 2021). However, in 2021, vocational education again had a positive and significant correlation with reemployment outcomes, specifically for those in the 15–24 age group. Having vocational education background increased reemployed chances in the second pandemic year by 8.9%, with a 99% confidence level. This suggests that the Indonesian economy’s manufacturing sector has gradually recovered. Furthermore, this study suggests that digital literacy has significant and positive associations with reemployment outcomes in 2020. This implies that having digital literacy increases the probability of worker reemployment during the pandemic, but with a lower in 2020 (2.2%) than in 2019 (8%). It is plausible as almost all non-essential industries must be closed during the pandemic, so having digital literacy might not positively influence the reemployment outcome. This finding is consistent with prior expectations., and Zarska (2020)’s findings However, this effect varies by age: digital literacy is significantly and positively associated with reemployment among youth workers (those aged 15–24). This indicates that youth workers are more aware of the demand for IT in employment. Following the COVID-19 pandemic, business closures dampened the need for IT expertise, but as recovery continues, the demand for workers in the IT sector continues to increase even as the shortage of IT professionals continues. This higher demand for IT thus increases the need for digital literacy (Vukmirović et al., 2021). This study also provides an exciting finding that digital literacy in 2020 significantly predicts the reemployment outcome in rural areas. In addition to various government policies related to digital literacy in rural areas, the higher number of workers in the informal sector might explain a shift of workers from the formal sector in the urban area to the informal sector in the rural area. Workers from urban areas who tend to be more digitally literate bring their skills to the rural area. Field observations show several innovations in rural areas carried out by workers who previously worked Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 17 of 31 Table 6. Summary of estimation results Classification Vocational Education Digital Literacy 2019 2020 2021 2019 2020 Coef SE Coef SE Coef SE Coef SE Coef SE All 0.033*** 0.007 0.01 0.011 -0.012* 0.007 0.080*** 0.006 0.022** 0.009 Rural-Urban Urban 0.003 0.023 -0.009 0.021 -0.009 0.016 0.127*** 0.015 0.025 0.023 Rural -0.012 0.015 -0.025*** 0.001 -0.004 0.012 0.051*** 0.007 0.061*** 0.014 Regional Sumatera 0.017 0.026 0.015 0.047 -0.015 0.013 0.052*** 0.015 0.02 0.022 Jawa-Bali 0.030** 0.014 -0.011 0.016 -0.013 0.011 0.110*** 0.011 -0.062* 0.014 Kalimantan 0.028** 0.013 0.026* 0.015 -0.02 0.012 0.075*** 0.265 0.074** 0.030 Sulawesi -0.046 0.020 -0.023 0.022 0.002 0.024 -0.023 0.230 -0.001 0.023 Others -0.031*** 0.005 -0.011 0.022 -0.019*** 0.002 0.041** 0.185 0.009 0.029 Age 15-24 0.075*** 0.021 0.029 0.036 0.089*** 0.022 0.131*** 0.005 0.073*** 0.019 25-40 0.033** 0.016 0.006 0.019 -0.014 0.012 0.800*** 0.013 0.024 0.015 >40 0.016*** 0.002 -0.005 0.013 -0.019*** 0.001 0.035*** 0.101 0.002 0.013 (Continued) Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 18 of 31 Table 6. (Continued) Classification Problem Solving Social Skill 2019 2020 2021 2019 2020 2021 Coef SE Coef SE Coef SE Coef SE Coef SE Coef SE All -0.122*** 0.041 -0.082** 0.039 -0.030 0.038 -0.155 0.163 0.412*** 0.085 0.268*** 0.086 Rural-Urban Urban -0.190** 0.046 -0.041 0.053 0.015 0.047 -0.393*** 0.077 0.598*** 0.012 0.258*** 0.084 Rural -0.125*** 0.031 -0.115*** 0.011 -0.071 0.048 0.112 0.338 0.520*** 0.110 0.325* 0.177 Regional Sumatera -0.067 0.071 -0.048 0.066 -0.098*** 0.030 -0.02 0.248 0.380** 0.151 0.285 0.270 Jawa-Bali -0.207*** 0.039 0.018 0.041 -0.034 0.053 -0.076 0.535 0.206 0.151 0.284** 0.142 Kalimantan -0.088 0.092 -0.011 0.122 0.034 0.100 -0.43*** 0.083 -0.012 0.336 0.323 0.201 Sulawesi 0.0738*** 0.018 -0.025 0.115 0.058 0.039 0.083 1.025 -0.314*** 0.076 0.012 0.219 Others 0.035 0.073 0.022 0.087 0.049 0.105 -0.458 2.966 0.208 0.287 0.572*** 0.264 Age 15-24 -0.086 0.079 -0.042 0.057 0.013 0.063 -0.287 0.175 0.230 0.151 0.340*** 0.130 25-40 -0.126*** 0.045 -0.121*** 0.011 -0.054 0.054 -0.217 0.210 0.636*** 0.032 0.204 0.213 >40 -0.019 0.077 -0.071 0.061 -0.034 0.040 0.536*** 0.054 0.356 0.298 0.215* 0.127 Statistical significance: * at 10 percent, * at 5 percent, and *** at 1 percent level Outcome variable is reemployment worker. Bivariate probit regression-based estimate the marginal effect of variables. Control variables applied in the biprobit model are gender, age, unemployment rate, poverty rate, GRDP, and covid case. Digital skill in 2021 is not estimated due to changes in the questionnaire in Sakernas 2021. Source: Authors’ estimation based on Sakernas 2019, 2020 and 2021 Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 19 of 31 in the city in marketing their businesses. Among these innovations is creating a WhatsApp group between sellers to buy each other’s products or offer their products online. We also found an offer to become a member of the online marketplace. Several food stall owners and sellers were contacted to market their products via the marketplace. This is supported by previous studies on internet utilization in rural areas, which find that personal networks for internet use are significantly associated with adopting the internet in rural areas (Boase, 2010). In terms of social and problem-solving skills, this study investigates both people who do not have jobs and already have a job, but are still struggling to find another job. This study reveals that workers with social skills have a higher probability of being reemployed during the pandemic than workers without social skills. In 2019, social skills did not affect reemployment outcomes, but in 2020 and 2021, social skills are valued as essential in determining reemployment outcomes. This finding resonates with previous studies which argue the importance of social skills during the job search process (Pierson, 2009). Moreover, Montgomery (1991) indicates in his study that 50% of new job seekers have access to work based on their social networking. This implies that social skills might be considered necessary in their role in increasing reemployment. Sub-analysis based on rural-urban and age categories also shows that social skills increase the chance of being reemployed in rural and urban areas. Social skills will increase the opportunity of being reemployed by almost 60% in urban areas and 52% in rural areas. Regarding age group, social skills are only significant in increasing reemployed chances for workers in the 25–40 age group in 2020 (by 64%). However, in 2021, social skills were no longer significant for those aged 25–40 but became significant and positive (34%) for those aged 15– 24. This is likely due to those in the 25–40 age group category being considered more experienced and more productive, and thus are more required during the first wave of the pandemic. However, in the second year of the pandemic, employers may start to look for those among the younger group of 15–24. However, contrary to our hypothesis, problem-solving skills have a negative and significant relationship with reemployment outcomes in 2019 and 2020 and no effect in 2021. This implies that workers with high problem-solving skills are unlikely of being reemployed during the pandemic. This is likely due to informal sector workers dominating Indonesia’s labor force, with their numbers rising further during the pandemic. This is in line with Singh, M (1998) results which find that workers in the informal sector have a shortage of problem-solving skills. We conduct several robustness tests to check whether our Bivariate Probit estimations are appropriate to quantitatively measure the relationship between skill sets and reemployment in Indonesia. Appendix 2 and 3 show the results of OLS estimations for our models. The magnitude of OLS estimations is consistent with those of our Bivariate Probit estimations, except for vocational education and problem-solving in 2020. To confirm the validity of instrumental variables used in the Bivariate Probit, we test the weakness of instrumental variables using the Sandersons-Windmeijer, Stock Yogo, and Wald F tests. Keane & Neal (2022) notes that an F statistic over 10 is generally required to argue that instruments are sufficiently strong. Except for the Stock-Yogo F test, all tests show that our instrumental variables are adequately strong except for those of social skills (Appendix 4). In addition, we also offer the first regression using the ivprobit syntax in Stata (Appendix 5). As our primary purpose is not estimating a causal inference of skill sets’ effect on reemployment, the application of the Bivariate Probit model to resolve issues of endogeneity and sample selection biases is an appropriate approach for estimating the relationship between skill sets and reemployment in Indonesia before, during, and during the recovery process in Indonesia. Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 20 of 31 6. Concluding remarks The decrease in economic growth due to COVID-19 has resulted in a surge in unemployed workers. Reemployment becomes strategic to be investigated as previous studies have shown a positive association between reemployment and economic recovery during crises. Understanding the importance of predictors for reemployment during the pandemic is expected to mitigate longterm unemployment that historically persists following economic crises. Our findings show that both the scarring and habituation effects were observed during the COVID-19 pandemic: more workers shifted their occupation from the formal to the informal sector, and many workers quit the workforce because of the pandemic. The reemployment process occurred mainly in the informal sector, in urban areas, among those with an elementary school background, in the 25–40 age group, and in the Java-Bali region. This potentially hints that workers with those backgrounds do not have many alternatives for livelihoods and have chosen to work even in jobs with lower occupational earnings. The estimations of the Bivariate Probit model confirm that social skills and digital literacy are consistent determinants for reemployment during the pandemic. However, having digital literacy is valued less for reemployment during the pandemic compared to normal economic times. During the pandemic, workers with social skills tend to have a higher probability of being reemployed, 41% in 2020 and 27% in 2021 higher compared to workers without any social skills, but social skills were not a significant predictor for reemployment in 2019. Hence, having a network of friends and relatives, as a proxy of social skills, was extremely important for being reemployed during the pandemic, but not during normal periods. In addition, this study finds no evidence that vocational education and problem-solving are significant predictor for reemployment during the pandemic. High unemployment and an absence of unemployment insurance force unemployed workers to find any type of job for survival, so vocational education and problem-solving skills will be valued less for reemployment. Our estimations also show heterogeneous relationships between skill sets and reemployment during the COVID-19 pandemic. For example, social skill is significantly correlated with reemployment in urban areas, the Java-Bali, and among young workers aged 15–24 in 2021; however, this pattern varies from 2020 to 2019. Surprisingly, digital skills are significantly and positively correlated with reemployment in a rural area, Kalimantan, and the age group of 15–24 years old. Moreover, during the economic recovery in 2021, vocational education was crucial for reemployment, especially among young workers. Our study thus suggests that the government should equip young workers aged 15–24 years old with the correct and relevant skill sets for after the COVID-19 pandemic. As the economy recovers, skill sets will also evolve. Improving digital literacy, social skills, and vocational education should accelerate the reemployment of youth workers. Moreover, as social skills significantly predict the reemployment process, it should be critical in the optimization of job seeker ecosystems which enables information exchange between workers who are in the job search process. Finally, this study, using the Sakernas 2019, 2020 and 2021 should be interpreted as an initial study and a rapid comparative assessment of the relationship between skill sets and reemployment before, during, and during recovery from the COVID-19 pandemic. The limitations of this study suggest several areas for improvement, including 1) a more extended study period, 2) a longitudinal study of workers, 3) causal inference methodologies for exact estimation of the relationship between skill sets and reemployment, and 4) a specific data set to avoid sample selection bias. Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 21 of 31 Acknowledgments The authors thank Bank Indonesia for providing generous funding through the 2021 Research Grant of Bank Indonesia. We thank Dr. Asep Suryahadi, Dr. Maxensius Sambodo, Dr. Wahyoe Soedarmono, and two anonymous referees for valuable and insightful comments and feedback for improving this article. We especially thank Muhammad Abdul Rohman for his dedication as a research assistant during the completion of this study. The first author gratefully thanks ChatGPT (https://chat. openai.com/chat) for fruitful and insightful conversation and discussion while revising the manuscript. Funding The work was supported by the the 2021 Research Grant of Bank Indonesia [No.23/23/PKS/BINS/2021]. Author details Teguh Dartanto 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-1737-3650 Hera Susanti 1 Eldest Augustin 2 Kania Fitriani 2 Muhammad Shafiullah 1 Research Cluster on Poverty, Social Protection and Development Economics, Department of Economics, Faculty of Economics and Business, Universitas Indonesia, Campus UI Depok, Depok, Indonesia. 2 Department of Research & Development for Social Security, Social Security Agency for Employment, Jakarta, Indonesia. Disclosure statement No potential conflict of interest was reported by the authors. Citation information Cite this article as: Reemployment during the Covid-19 pandemic in Indonesia: What kinds of skill sets are needed?, Teguh Dartanto, Hera Susanti, Eldest Augustin & Kania Fitriani, Cogent Economics & Finance (2023), 11: 2210382. Note 1. This study applies different exogenous variables or instrumental variables for predicting skill sets. 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Journal of Political Economy, 125(2), 527–561. https://doi.org/10.1086/690828 Shkalenko, A., & Fadeeva, E. (2020), “Analysis of the impact of digitalization on the development of foreign economic activity during COVID-19 pandemic”, in 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth” (MTDE 2020), Atlantis Press, Paris, pp. 1190–1195 Singh, M. (1998). Curricular implications of competency requirement and utilization among small producers in New Delhi. In L. Buchert (Ed.), Education reform in the South in the 1990s (pp. 245–267). UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/ pf0000114248?posInSet=1&queryId=00b0794c- 7370-48f3-836a-cb6b27ece360 Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 23 of 31 Appendix 4. Weakness Instrument Test Appendix 5. Test IV First Regression Test Category Vocational Education Digital Literacy Problem Solving Social Skill F testsandersons -windmeijer 30.73 223.51 11.4 6.27 F teststock yogo 9.08 9.08 9.08 9.08 F test- Wald 30.73 223.51 11.4 6.27 Number of observations 51,250 51,250 14,790 14,790 Source: Authors’ estimation based on Sakernas 2020 Variables Vocational Education Digital Literacy Problem Solving Social Rainfall −2.31e-05*** −2.63e-05*** 1.92e-05*** (3.43e-06) (4.35e-06) (4.73e-06) Mutual cooperation 0.0004 0.0023 −0.0008 (0.0012) (0.0015) (0.0016) Rugedness −5.53e-05*** −2.99e-05* −5.15e-05*** (9.74e-06) (1.63e-05) (1.83e-05) BTS 0.0302*** (0.00217) Operator 0.0382*** (0.00238) Elevation 9.83e-06 (7.76e-06) Age −0.0061*** −0.0026*** −0.0062*** −0.0083*** (0.00012) (0.00014) (0.00016) (0.00019) Gender 0.0103*** −0.0159*** 0.0843*** 0.323*** (0.0033) (0.0041) (0.0048) (0.0056) Unemployment Rate 0.0068*** 0.0027*** 0.0121*** 0.0123*** (0.00071) (0.0009) (0.0010) (0.0012) Poverty Rate −0.0025*** −0.0035*** −0.0017*** −0.0012*** (0.00023) (0.0003) (0.00036) (0.0004) GRDP 2020 0.0108*** −0.0011 0.0243*** 0.0225*** (0.0018) (0.0024) (0.0027) (0.0031) Covid case 0.0116*** 0.0058*** 0.0039** −0.0003 (0.0013) (0.0017) (0.0019) (0.0022) Constant −0.0216 0.267*** −0.617*** −0.109 (Continued) Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 30 of 31 Variables Vocational Education Digital Literacy Problem Solving Social (0.131) (0.0662) (0.167) (0.183) Observations 51,250 51,250 26,027 26,027 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Statistical significance: * at 10 percent, * at 5 percent, and *** at 1 percent level Note #1: Outcome variables are vocational education, digital literacy, problem-solving and social. Note #2: Rainfall has a significant negative impact on vocational education and problem-solving. As mentioned earlier, higher rainfall can cause school dropout rates, especially in rural areas. Thus, higher rainfall could also decrease the participation in vocational education and the intensity of social networking. Ruggedness has a significant negative impact on vocational education, problem-solving and social. Geographical ruggedness makes it expensive to build infrastructures; thus, it is hard to build education facilities. People are also less to socialize with each other in the area with increased ruggedness. Operators and BTS have significant positive effects on Digital Literacy. The more operators and BTS in a room, the easier it will use the internet. Dartanto et al., Cogent Economics & Finance (2023), 11: 2210382 https://doi.org/10.1080/23322039.2023.2210382 Page 31 of 31