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A Crisis of Long-Term Unemployment Is Looming in the U.S.

• Ofer Sharone

How biases trap qualified job seekers in a cycle of rejection — and how to help them break free.

The stigma of long-term unemployment can be profound and long-lasting. As the United States eases out of the Covid-19 pandemic, it needs better approaches to LTU compared to the Great Recession. But research shows that stubborn biases among hiring managers can make the lived experiences of jobseekers distressing, leading to a vicious cycle of diminished emotional well-being that can make it all but impossible to land a role. Instead of sticking with the standard ways of helping the LTU, however, a pilot program that uses a wider, sociologically-oriented lens can help jobseekers understand that their inability to land a gig isn’t their fault. This can help people go easier on themselves which, ultimately, can make it more likely that they’ll find a new position.

Covid-19 has ravaged employment in the United States, from temporary furloughs to outright layoffs. Currently, over 4 million Americans have been out of work for six months or more , including an estimated 1.5 million workers in white-collar occupations, according to my calculations. Though the overall unemployment rate is down from its peak last spring, the percent of the unemployed who are long-term unemployed (LTU) keeps increasing and is currently at over 40%, a level of LTU comparable to the Great Recession but otherwise unseen in the U.S. in over 60 years.

• Ofer Sharone is an expert on long-term unemployment and the author of the book Flawed System/Flawed Self: Job Searching and Unemployment Experiences (University of Chicago Press). Sharone received his PhD in sociology from the University of California Berkeley, his JD from Harvard Law School, and is currently an associate professor of sociology at the University of Massachusetts Amherst.

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Interest Rates and Unemployment: An Underwhelming Relation

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For the last few months, I’ve been studying the distributional effects of interest-rate hikes. There’s been no shortage of surprising results.

In this post, I’ll discuss an effect that is surprising because it’s underwhelming. Many economist claim that when interest rates rise, unemployment will increase. The idea is that higher rates make businesses tighten their belts, leading to less hiring and greater unemployment.

Looking at the evidence, I find that this claim is not particularly compelling.

Interest rates and unemployment in the United States

Although economists love to dazzle with fancy econometric models, I’m more of a here-is-the-data-let’s-look-at-it person. If a simple scatter plot fails to show something interesting — and by ‘interesting’, I mean a relation that slaps you in the face — then I’m usually apathetic about further analysis. In other words, doing econometrics with a muddy scatter plot feels to me like polishing a turd.

To that end, when economists claim that higher interest rates worsen unemployment, my first impulse is to simply plot the data. And since the United States has the best historical data, I usually start there.

On that front, Figure  1 shows the long-term relation between the US unemployment rate and the US bond yield. (If you’re unfamiliar, the bond yield is a common proxy for the rate of interest.) Staring at the two time series, you can tell that there’s not much of a connection. This intuition is confirmed by the inset scatter plot, which is a textbook example of statistical mud. Based on the R 2 value, we can say that the movement of bond yields explains about 1% of the rise and fall of unemployment. Impressive that is not.

A nexus is born

Since so much of economic theory is built on US observations, you have to wonder why economists insist that higher interest rates worsen unemployment. After all, a simple scatter plot tells us that this claim is underwhelming. Am I missing something?

First, let’s make sure that I’m not making a straw man argument. Do economists actually claim that higher interest rates worsen unemployment? A quick internet search says yes. Here’s a sample of such claims:

[H]igher interest rates tend to lower consumer spending and business investments, leading to a reduction in hiring and an increase in unemployment. ( CUPE )

When interest rates go up, it can have an negative impact on employment. Increased borrowing costs will likely lead to higher unemployment rates. ( Kathryn Underwood )

With interest rates going up, unemployment numbers also tend to rise. ( Eric Rosenberg )

So today, many economists think that interest-rate hikes lead to more unemployment. But have they always thought so? The answer seems to be no.

Now, if I was a judicious scholar, I’d read hundreds of papers and trace the evolution of the interest-rate-unemployment thesis. But to be honest, the prospect bores me. I’d rather let a bot do the work for me. And to be fair (to me), Google’s text-scraping bots are far more thorough than I could ever be.

When we look at the text that Google has scraped, we find that the interest-rate-unemployment thesis seems to have emerged in the late 1970s. Figure  2 A runs the numbers. Here, I’ve headed to the Google English corpus and measured the frequency of the phrase ‘interest rates and unemployment’. Prior to the 1970s, it seems that virtually no one thought to connect these two phenomena. But by the mid-1980s, loads of people were making the connection.

So if our modern internet writers were being forthright, they’d say that today , economist think that higher interest rates create more unemployment. But before 1970, hardly anyone made this connection.

Putting on our sleuth hat, we have to wonder why the theoretical zeitgeist changed.

One possibility is that in the 1970s, the ‘truth’ was discovered. In other words, higher interest rates had always led to greater unemployment, but this fact wasn’t discovered until 1970(ish). While this scenario is conceivable, the statistical mud shown in Figure  1 makes me discount it.

Another possibility is that there is no general relation between interest rates and unemployment. Instead, there are periods when interest rates tend to move with unemployment, making it look like rate hikes cause unemployment. Conversely, there are periods where interest rates have nothing to do with unemployment (and so no one thinks to connect the two phenomena).

In Figure  2 B I test this second possibility. Here’s what I’ve done. The blue line shows the slope of a trailing 30-year regression between US unemployment and the US bond yield. In simple terms, this slope tells us how bond yields relate to unemployment over the preceding thirty years. When the slope is zero , bond yields don’t respond to changes in unemployment. But when the slope is one , bond yields have a one-to-one reaction to unemployment.

Looking at Figure  2 B, we can see that the slope of our trailing regression bears an eerie resemblance to the word frequency data plotted above. Prior to 1970, there was essentially no connection between unemployment and bond yields. But from 1980 to the late 2000s, there was a one-to-one connection. In other words, just as bond yields started to move with unemployment, economists began to connect unemployment with the rate of interest.

Given economists’ penchant for reactionary fads, it seems plausible that the interest-rate-unemployment nexus is not a general truth. Instead, it may have been a theoretical reaction to a transient period in US history.

Interest rates and unemployment across countries

If my ‘reaction thesis’ is correct, then when we look at the broadest scale, there should be no pattern between interest rates and unemployment. Speaking of broad scales, the World Bank has extensive cross-country data for both the rate of interest and the rate of unemployment.

The nice thing about this international data is that it reaches extremes that are unheard of in the US. For example, in the World Bank database, lending interest rates cover a range of over 100 percentage points. And the spread in unemployment is similarly large, ranging from a low of 0.1% in Qatar (in 2019) to a high of 38% in Lesotho (in 1997). In short, if higher interest rates create greater unemployment, this database ought to reveal the effect.

Yet when we stare at the cross-country data, it is shockingly unimpressive. Figure  3 tells the story. Here, I’ve plotted the cross-country trend between unemployment and the lending rate of interest, compiled using data from 132 countries observed over the last thirty years. Looking at the blue line, you can see a slight uptick in unemployment as interest rates climb from 0.5% to 100%(!). But this uptick is statistical mud. 1

A lagged effect?

At this point, I’m ready to quit. But from experience, I know that if I stop here, I’ll regret it.

You see, when it comes to monetary policy, economists have been taught that the effects come with lags that are ‘long and variable’. So if I don’t do a lag analysis, I’ll get an endless stream of requests to ‘lag the data’. Let me preempt that torture.

In Figure  4 , I’ve returned to the US data and done a lagged analysis of bond yields and unemployment. The horizontal axis shows the annual change in the US bond yield. The vertical axis shows the annual change in unemployment in the following year. Surprisingly, we get a positive trend. In other words, today’s interest-rate hikes seem to increase next-year’s unemployment.

The caveat here is that the lagged trend is produced in large part by a few outlier years, all of which are in the late 1970s and the early 1980s. Of course, what constitutes an ‘outlier’ is mostly a matter of taste. Still, from Figure  2 , we know that the 1980s standout as a unique period when US interest rates moved consistently with unemployment. In other words, it’s dubious to take a pattern from that decade and pronounce it a ‘general tendency’.

Things get worse when we realize that a lagged effect doesn’t mean much on its own. That’s because when we’re dealing with cyclical data, we’ll inevitably find that an observation today predicts an observation later. For example, tonight’s sunset predicts tomorrow’s dawn. Why? Because the Earth has a reliable rotation.

Matters are similar (although less reliable) with unemployment. Looking at Figure  1 , you can see that US unemployment rises and falls with a fairly regular frequency, corresponding to what economists call ‘business cycles’. Because of these cycles, today’s unemployment will predict unemployment in the future.

In the case of a one year lag, it’s fairly easy to understand what we’ll observe. In the US, unemployment oscillates with a roughly 8-year cycle. In that context, a one year lag represents an eighth of a cycle. If we do the math, we find that an uptick in this year’s unemployment should be followed by another uptick next year. In other words, changes in unemployment this year ought to correlate positively with unemployment changes next year. And indeed they do.

Figure  5 shows the pattern. Here, the blue curve shows the self-correlation between US unemployment changes today and those same changes next year. As expected, the pattern is positive — a fact that doesn’t bode well for the idea that interest-rate hikes worsen unemployment. As the red curve shows, the bond-yield ‘treatment effect’ is pretty much the same as the relation between unemployment and itself. 2

(For a longer explanation of why lagged effects should exceed self-correlation, see this post .)

When we leave the US behind and look at the lagged pattern found across countries, the picture grows even muddier. Figure  6 shows what happens.

Here, the red curve shows the effect that a rise in interest rates has on next-year’s unemployment. Even on its own terms, the effect is lack-luster. And when we compare this ‘treatment effect’ to the self-correlation effect — the relation between changes in unemployment today and the same changes next year — things really fall apart. The self-correlation is significantly stronger than the ‘treatment’ effect. In short, when we look at the lagged trend across countries, there’s no evidence that interest-rate hikes worsen unemployment.

Is full employment always good?

I have to admit that I find these results disappointing. Although I’ve learned to take economists’ pontifications with a boulder of salt, my intuition was that interest rates would connect with unemployment And yet the evidence suggests otherwise.

That said, there are ways to connect interest income to unemployment — ways that are better supported by evidence. In my next post, I’ll discuss Jonathan Nitzan and Shimshon Bichler’s concept of the ‘maturity of capitalism’ , which pits interest income against profit income. It turns out that unlike interest rates , the interest-to-profit income ratio is related to unemployment.

But for now, back to interest rates and unemployment.

Karl Marx famously argued that unemployment is a tool of class warfare. The unemployed, Marx noted, are a ‘reserve army’ that bids down the price of labor, to the obvious benefit of capitalists. Viewed in this light, it seems like the unemployment rate should relate to the distribution of income.

On that front, my last post made a big deal about how interest rates are a ‘distributional variable’ that ratchets up or down the income shares of different groups. If unemployment is also a ‘distributional variable’, then it seems plausible that interest rates might related to unemployment. And yet they seemingly don’t.

So what went wrong?

To some extent, I think the problem lies with the concept of unemployment itself. Obviously, mass unemployment is bad (as during the Great Depression). But the flip side is that low unemployment isn’t necessarily ‘good’.

Take the example of Qatar. In 2019, it had an unemployment rate of just 0.1% — a rate so low that there was effectively ‘full employment’. In other words, everyone who wanted a job had one. So Qatar sounds like a good place to work, right?

In 2019, Qatari workers took home just 25 % of the income pie. 3 (The rest presumably went to property owners.) So despite having full employment, Qatar had an extremely despotic distribution of (class-based) income. Why?

The answer is found not in the unemployment rate, but in the nature of Qatari ‘employment’. You see, Qatar is a nation built on indentured servitude. Exceedingly rich from oil money, few Qatari citizens work for a living. Instead, they import an army of foreign laborers to do the dirty work. (Migrant workers constitute about 95% of the Qatari labor force.)

The effect of this arrangement is that unemployment is basically impossible. If a foreign worker loses their job, they lose the right to stay in Qatar. So yes, Qatar has ‘full employment’. But by that, we mean that Qatar has a workforce that cannot be unemployed.

The lesson here is that the scale of ‘unemployment’ doesn’t necessarily tell us anything about the welfare of the working class. Hell, if you’re willing to bend categories, you could say that slave states have ‘full employment’. But that’s because ‘unemployment’ is impossible. In a slave state, you can either be a slaver , a slave , or a fugitive . There is no unemployment.

The point is that measurements must take into account the social order that they are quantifying. On that front, if we divide society into the ‘employed’ and the ‘unemployed’, we’re excluding a third category: the shittily employed. As big corporations increasingly turn to ‘flexible’ labor to do their bidding, we’d best pay attention to this third category.

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Sources and methods

Data for us bond yields.

• Bond yields from 1798 to 1959: Historical Statistics of the United States, Table Cj1192-1197 (long-term bond yields). This table contains several series for bond yields, each of which covers a different period of time. To construct the long-term index, I calculate the average (the unweighted mean) of the reported data in each year.
• Bond yields from 1960 to 2022: FRED series IRLTLT01USM156N , long-term government bond yields, 10-year.

Data for US unemployment

• Unemployment from 1890 to 1946: Historical Statistics of the United States, Table Ba475 .
• Unemployment from 1947 to 2020: Bureau of Labor Statistics, series LNU04000000

Text frequency

Word frequency data is from the 2019 Google English corpus, downloaded with the excellent R package ngramr .

International interest rates

International interest rate data comes from two sources:

• World Bank, series FR.INR.LEND , lending interest rate
• OECD, series LTINT , long-term interest rates

Note: when merging the World Bank and OECD data, if/when I found duplicate country-year observations, I used the World Bank data.

International unemployment

• International unemployment data is from the World Bank, series SL.UEM.TOTL.ZS

Interestingly, when describing this unemployment data, the World Bank warns that low unemployment isn’t necessarily ‘good’:

Paradoxically, low unemployment rates can disguise substantial poverty in a country, while high unemployment rates can occur in countries with a high level of economic development and low rates of poverty. In countries without unemployment or welfare benefits people eke out a living in vulnerable employment.

Further reading

Standing, G. (2011). The precariat: The new dangerous class . New York: Bloomsbury.

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Interest rates are a response to inflation and in a globalized economy what matters is global slack not domestic slack. This is known as the Global Slack Hypothesis. Here’s a quote:

[quote] The competing explanation is the Global Slack Hypothesis which says that due to the integration of global markets, what now drives inflation is not domestic slack but rather global slack. Due to competition from global rivals, domestic producers in the tradable sector cannot raise prices when the domestic labor market tightens and wage pressures build. Instead, they either rebalance their global supply chains and off-shore production; or they lose business to their foreign rivals. In either case, domestic inflation is determined as much by global slack as by domestic slack.

The evidence is mounting that the second explanation is the right one. What is especially compelling is the evidence that global slack is statistically significant in ALL countries for which data is available while domestic slack is significant is NONE since 2000. See Figure 3. The last column corresponds to global slack (“foreign gap”); no stars means the variable is not significant; three means it is significant at the 1 percent level. [end quote]

source with charts: https://policytensor.com/2016/12/17/global-slack-us-inflation-and-the-feds-policy-error/

Here’s an article from the Fed titled “The Global Slack Hypothesis” (opens a pdf file): https://www.federalreserve.gov/monetarypolicy/files/FOMC20091208memo03.pdf

[…] my last post, I discussed the underwhelming relation between interest rates and unemployment. In this post, I’ll look at a better way to connect unemployment to interest […]

[…] my last post, I discussed the underwhelming relation between interest rates and unemployment. In this post, I’ll look at a better way to connect unemployment to interest […]

It seems to me that Economists (and this the Fed) take thw argument that interest rates CAN impact unemployment and inflation, and then conclude that interested WILL ALWAYS impact unemployment and inflation, and always in the direction they prefer. Just like how they look at the equation MV = PQ and completely ignore the Q and V.

The Phillips curve was never about inflation versus unemployment but wage inflation versus unemployment. It’s right in the original paper. See John Hussman for some interesting charts about that.

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Analysis of the COVID-19 impacts on employment and unemployment across the multi-dimensional social disadvantaged areas

This is the study of economic impacts in the context of social disadvantage. It specifically considers economic conditions in regions with pre-existing inequalities and examines labor market outcomes in already socially vulnerable areas. The economic outcomes remain relatively unexplored by the studies on the COVID-19 impacts. To fill the gap, we study the relationship between the pandemic-caused economic recession and vulnerable communities in the unprecedented times. More marginalized regions may have broader economic damages related to the pandemic. First, based on a literature review, we delineate areas with high social disadvantage. These areas have multiple factors associated with various dimensions of vulnerability which existed pre-COVID-19. We term these places “ multi-dimensional social disadvantaged areas ”. Second, we compare employment and unemployment rates between areas with high and low disadvantage. We integrate geospatial science with the exploration of social factors associated with disadvantage across counties in Tennessee which is part of coronavirus “red zone” states of the US southern Sunbelt region. We disagree with a misleading label of COVID-19 as the “great equalizer”. During COVID-19, marginalized regions experience disproportionate economic impacts. The negative effect of social disadvantage on pandemic-caused economic outcomes is supported by several lines of evidence. We find that both urban and rural areas may be vulnerable to the broad social and economic damages. The study contributes to current research on economic impacts of the COVID-19 outbreak and social distributions of economic vulnerability. The results can help inform post-COVID recovery interventions strategies to reduce COVID-19-related economic vulnerability burdens.

1. Introduction: social disadvantage

Pandemics create severe disruptions to a functioning society. The economic and social disruptions intersect in complex ways and affect physical and mental health and illness ( Wu et al, 2020 ). Additionally, loss of jobs, wages, housing, or health insurance, as well as disruption to health care, hospital avoidance, postponement of planned medical treatment increase mortality, e.g., premature deaths ( Kiang et al., 2020 ; Petterson et al., 2020 ). The COVID-19, misleadingly labelled the “great equalizer” implies everyone is equally vulnerable to the virus, and that the economic activity of almost everyone is similarly impacted regardless of social status ( Jones & Jones, 2020 ). We set out to answer whether economic vulnerability is equally distributed during the COVID-19-caused economic recession or whether is it based on structural disadvantages? Is the social distribution of economic vulnerability magnified in regions with pre-existing social disparities, thus, creating new forms of inequalities? Knowledge of what areas experience the greater economic burden will help identify the most economically vulnerable communities relevant to post-COVID recovery interventions ( Qian and Fan, 2020 ).

Current studies on the impacts of COVID-19 largely focus on medical aspects including the COVID diagnosis and treatment ( Cai et al., 2020 ; Kass et al., 2020 ; O’Hearn et al., 2021 ; Price-Haywood et al., 2020 ). Non-medical urban research primarily concentrates on the impact of COVID on cities by studying factors related to environmental quality including meteorological parameters, and air and water quality ( Sharifi and Khavarian-Garmsir, 2020 ). COVID-related socio-economic impacts on cities are relatively less well studied, especially during the later stages of the recession.

Many pre-pandemic disparities unfold during COVID-19. To illustrate, residents of Black and Latino communities are suffering disproportionately higher unemployment rates, greater mortality due to the COVID-19 ( Thebault, Tran, & Williams, 2020 ; Wade, 2020 ), higher hospitalizations ( O’Hearn et al., 2021 ) and financial troubles. In contrast, some attributes make persons and communities more resilient. In China’s context, these include higher worker education and family economic status, membership in Communist Party, state-sector employment, and other traditional markers. These factors protect people from the pandemic-related financial stress and diminish its adverse economic effects ( Qian and Fan, 2020 ). Building on these recent studies on economic impacts, this social justice research focuses on areas with pre-existing social disadvantages. We study the role of social disadvantage and its impact on labor market during the COVID.

The distribution of economic vulnerability may potentially be related to COVID-19 conditions including those of economic burdens for people living in the pandemic epicenters ( Creţan and Light, 2020 ). Similarly, socio-economic disruptions create “a characteristic mosaic pattern in the region” ( Krzysztofik et al., 2020 , p. 583). The disruptions are strongly correlated with the spatial distribution of the COVID-19-related health effects. This study is set in Tennessee which is part of coronavirus “red zone” states of the US southern Sunbelt region. It is among the U.S. states with the highest rates of cases per capita, with 137,829 cases per 1 million people, or the 6th highest as of August 13, 2021 ( Worldometers, 2020 ; https://www.worldometers.info/coronavirus/country/us/ ). The study seeks to explore the impacts of social disadvantage on economy. The impact is measured by employment and unemployment in unprecedented times in the US context of prolonged disruptions to the health system, society, and economy intersecting in complex ways ( Kiang et al., 2020 ). We answer the following questions: (1) Do communities with high social disadvantage already burdened pre-COVID-19 by the lack of income, healthcare access, lacking resources, have less jobs available during the COVID-19 pandemic? (2) Do these areas simultaneously experience higher unemployment compared with other areas in the context of the pandemic?

The paper is organized as follows: Section 1 introduces the topic, provides the background information on social disadvantage and a brief description of the study implementation. It further discusses the links between employment and unemployment, and coronavirus, respectively, and introduces the study area. Section 2 describes in detail materials and methods used in the study. Section 3 provides the theory and calculations. Section 4 reports the results, and Section 5 offers a discussion. Finally, the paper concludes with conclusions found in Section 6 .

1.1. Background

Certain socio-economic and demographic conditions burden some communities more than others including racial and ethnic minorities, lower-income groups, and rural residents. The conditions include lacking economic opportunities and other inequalities ( Petterson et al., 2020 ) caused by social environment. Prior to the pandemic, it was challenging to live in areas with high social disadvantage where residents already have increased vulnerability to poor health due to greater psychosocial stress such as discrimination, unhealthy behaviors, and poorer health status ( Hajat et al., 2015 ). This is true for poor, marginalized communities elsewhere as spatial segregation of disadvantaged and marginalized communities decreases life opportunities for their members who have limited relationships with broader communities ( Méreiné-Berki et al., 2021 ). Within the context of studying disadvantaged urban communities, a recent work by Creţan et al. (2020) focused on the everyday manifestations of contemporary stigmatization of the urban poor using the case study of the Roma people who have been historically subject to state discrimination, ghettoization, inadequate access to education, housing, and the labor market for many decades in the past in multicultural urban societies of Central and Eastern Europe. The inequalities may persist and even increase if left unaddressed during pandemics ( Wade, 2020 ) leading to stark COVID-19-related health and economic disparities. Indeed, during the COVID-19, economic impacts of the pandemic disproportionately affect marginalized groups. The impact of coronavirus was harsh for those people as many of the already existing disparities unfold during COVID-19: black communities in the United States are disproportionately affected by higher death rates due to the COVID-19 virus ( Thebault et al., 2020 ), unemployment, and financial stress. Other growing COVID-19 research similarly suggests that elsewhere outside of the United States, areas that were disadvantaged prior to the pandemic with high rates of poverty and unemployment tended to be affected the strongest by the COVID-19 with the largest concentration of cases, while other spatially segregated ethnicity-based communities (e.g., the Roma) that have been vulnerable decades prior to COVID-19, saw an increase in the existing discrimination and stigmatization experiencing greater marginalization even during the current COVID-19 pandemic period ( Crețan & Light, 2020 ).

To achieve greater economic stability, and secure a dynamic labor market, countries in the global north and south for several decades have been increasing service employment much of which is low wage. The recent book Corona and Work around the Globe ( Eckert and Hentschke, 2020 ) describes the tremendous impact of the pandemic on human life and livelihoods as it sheds light on various experiences of workers during COVID-19 in various countries. Among the dramatically different cases worldwide, Germany which for decades has been promoting the low-wage sector to combat unemployment, provides a good example. The official approach to handling a disease differed substantially depending on whether the infected individuals were working people from the low- or upper-wage sector of the economy: applying a strict lockdown to the entire high-rise building where ethnic workers lived and preventing them from going to work in the former case and granting permission to work from home in the latter ( Mayer-Ahuja, 2020 ). The plight of the agricultural migrant workers who come to Germany from Eastern and Southeastern Europe, subjected during the pandemic to low wages or no payments and poor working and living conditions, however, is shared among the workers of low-wage sector across all countries who are more likely to get infected due to higher exposure and direct contact, but often experience unfair treatment based on ethnicity, migration and class status.

In yet another case set in the U.K., disadvantaged households have experienced intensified disadvantage during the COVID-19 as they could not access vital necessities, already stretched for resources pre-COVID-19. As provision of services or employment was discontinued due to their closure, disadvantaged households had significant impacts on their income level, mental health and wellbeing, education, nutrition, and domestic violence. In the absence of the key support of public institutions including schools, community centers, and social services, care for the most vulnerable members such as elderly, children, the disabled, have been absorbed by households ( Bear et al., 2020 ).

Another aspect experienced by workers during the pandemic is the total loss of earnings which is especially harsh in places with precarious employment even under normal circumstances. Informal workers in India who represent the vast majority of working population (over 93%), with no social security benefits and absent job security, experienced prolonged periods of time of no work due to lockdown and suspended transport services preventing them from getting to their workplaces, many on the verge of starvation ( Banerjee, 2020 ). This study looks into this aspect of COVID-19 economic impacts and confirms the findings of the growing COVID-19 research.

However, not only the poorest and marginalized people, but also marginalized regions are more likely to suffer from broader social and economic damages related to the pandemic compared with more privileged areas ( Creţan and Light, 2020 ; Krzysztofik et al., 2020 ). When disadvantages combine, it may lead to environment-driven COVID-19-related disparities in health. Besides a direct health effect, disadvantaged communities are disproportionally experiencing other side effects of COVID-19 such as negative labor market outcomes including forced unemployment, loss of income and social isolation. Studies found the extreme vulnerability of cities and urban areas exposed during the global pandemic ( Batty, 2020 ; Gössling et al., 2020 ). We argue that rural areas may be equally vulnerable to the broad range of social and economic damages if there is a spatial concentration of factors related to various dimensions of vulnerability.

This study is situated in the context of social disadvantage. Prior studies developed the methodology of the delineation of disadvantaged residential communities proxied by low-income workers ( Antipova, 2020 ). Disadvantaged low-income workers can be defined as those with inadequate access to material and social resources in the study area. However, this is a narrow approach which uses only a single dimension of a disadvantage, that of worker low earnings and misses other social inequality indicators. Accordingly, an approach adopted in this study identifies areas where socio-economic and demographic attributes each associated with multiple dimensions of social disadvantage are spatially co-locating. Spatial segregation of disadvantaged and marginalized communities decreases life opportunities for their members who have limited relationships with wider communities ( Méreiné-Berki et al., 2021 ). We identify these attributes based on a thorough literature review. Thus, we simultaneously consider multiple factors associated with disadvantage capturing a multi-dimensional social disadvantage. To meet the objective, we integrate geospatial science with the exploration of predictive geographic and social factors associated with disadvantage across counties in TN. The geospatial analysis includes point interpolation within the Geographic Information System (GIS) environment for the generation of a surface from a sample of social disadvantage values. This allowed us to visualize the spatial extent of disadvantaged communities. The focus is on labor market outcomes which are important indicators of society well-being. We study the association between pre-existing inequalities and COVID-19-related employment and unemployment rates. Thus, we identify the role of social disadvantage on labor market conditions in the context of the ongoing pandemic-caused economic recession.

Prior research determined the key metrics of social disadvantage. Conditions contributing to various aspects of disadvantage include poverty, occupations with low earnings, low rent, segregation and discrimination-related residential concentrations of minorities, and exposure to poor air quality ( Bullard, 2000 ). The recent COVID-19-related literature focuses on the separate effect of minorities, Hispanics, crowded households, dense areas, obesity, poverty, air pollution exposure and identifies those as important COVID-19 health risk factors ( Finch & Hernández Finch, 2020 ; Golestaneh et al., 2020 ; Han et al., 2020 ; Millett et al., 2020 ). These community-level variables result in neighborhood disadvantage comprising sub-standard housing quality, crowded conditions, poverty- and violence-caused stress which combined increase the risk of disease and other negative outcomes in life among socially disadvantaged groups ( Malhotra et al., 2014 ). The demographic and socio-economic attributes selected to represent the various aspects of social disadvantage in this research include minorities and ethnicities, poverty, housing crowdedness, educational attainment, underlying population health conditions, and pre-COVID-19 unemployment which may collectively drive a greater vulnerability to the COVID-19 infection and mortality as well as loss in employment and higher unemployment. It is challenging to isolate the separate effects of the multiple risk factors. By “critically analyzing the theoretically intended meaning of a concept” ( Song et al., 2013 ), a composite variable can be created to logically represent a multi-dimensional social disadvantage .

The following subsection briefly describes study implementation. First, we locate areas of disadvantage where multiple factors associated with various aspects of disadvantage co-locate spatially and term these places “multi-dimensional social disadvantaged areas”. Then, we examine how employment and unemployment were impacted in these already socially vulnerable areas. We map geographical inequalities in employment and unemployment rates during the period of COVID-19-related economic recession. For the first objective, we identify socially disadvantaged counties within TN which is part of coronavirus “red zone” states of the US southern Sunbelt region applying consistent criteria. For the second objective, we compare employment and unemployment outcomes between areas with high and low disadvantage.

1.1.1. Employment and coronavirus

This subsection discusses the role of employment and how it was impacted by the COVID-19-caused economic recession. The literature recognizes the complex interrelationship between employment and overall health and well-being. Negative COVID-19 impacts on urban economy include loss of citizens' income, while movement restrictions and ‘stay home’ measures adversely impacted tourism and hospitality and small- and medium sized businesses due to the closure of markets, food outlets and social spaces ( Wilkinson et al., 2020 ).

Millions of essential or blue-collar workers are still doing their jobs out of necessity and because they cannot telecommute and work jobs that cannot be done from home and have higher exposure to the virus. Some racial groups disproportionally have jobs that do not allow them to work from home and where social distancing is a challenge. Prior studies find that workplaces of low-income individuals tend to be close to their residential spaces, and disproportionately concentrated in lower-wage industries such as hospitality and retail services ( Antipova, 2020 ). These industries commonly represent essential services experiencing higher exposure to the COVID virus through workplaces. At the same time, minorities and lower-income groups often live in inner-ring suburbs with older housing and aging infrastructure ( Antipova, 2020 ) in multiunit structures and in multigenerational households which inhibit the ability to practice social distancing increasing the risks of disease occurrence and deaths ( Qualls et al., 2017 ). In addition, minorities and lower-income groups have fewer options for protecting both their health and economic well-being ( Gould and Wilson, 2020 ). Nearly two-thirds of Hispanic people (64.5%) considered at high risk for coronavirus live with at least one person who is unable to work from home, compared to 56.5% of black and less than half (47%) of white Americans, according to a recent study ( Selden and Berdahl, 2020 ).

Despite the pandemic-induced layoffs, job hires have occurred by major retailers such as Walmart and e-commerce giant Amazon, and takeout and delivery-based services such as Domino’s Pizza and Papa John’s which may become permanent positions. These workplaces may match the job skill sets of low-income residents of vulnerable communities. However, oftentimes many low-income workers benefitted less, even when jobs were created during the COVID-19. To illustrate, big technology companies (i.e., communication services: Netflix, Tencent, Facebook, T-Mobile; information technology: Microsoft, Nvidia, Apple, Zoom Video, PayPal, Shopify; consumer discretionary: Amazon, Tesla, Alibaba, etc.) prospered in the pandemic with the financial success measured by equity value added ( Financial Times, 2020 ). Workers who lost jobs in low-income segment such as hospitality sector may be hired by retailers such as Kroger or CVS. However, many others from the communities with high social disadvantage may not have a skill set needed at technology firms that benefit from the working from home trend and hire skilled workers including software engineers and product designers. Cross-industry employment shifts plays a minor role in total job creation, while employer-specific factors primarily account for job reallocation ( Barrero et al., 2020 ).

1.1.2. Unemployment and coronavirus

This subsection discusses how unemployment was impacted by the COVID-19-caused economic recession. An economic recession occurs when there is a substantial drop in overall economic activity diffused throughout the economy for longer than a few months. While past recessions were driven by an inherently economic or financial shock, the current recession is caused by a public health crisis ( Weinstock, 2020 ). COVID-19 caused a drop in consumer demand across all industrial sectors resulting in economic recession and massive unemployment where not only hourly workers but salaried professionals lost their jobs ( Petterson et al., 2020 ). A range of factors contributed to the spatial variation in economic damage including the share of jobs in industries delivering non-essential services to in-person customers ( Dey and Loewenstein, 2020 ), declines in personal consumption caused by individual fears of contracting COVID-19 ( Goolsbee and Syverson, 2020 ), and the implementation of social policies including stay-at-home orders and business shutdowns ( Gupta et al., 2020 ).

Unemployment rate is defined as a percentage of unemployed workers in the total labor force. The rate is published monthly by the Bureau of Labor Statistics (BLS) which uses both the establishment data (captured by the Current Employment Statistics program) and household surveys (Current Population Survey) to generate the labor market data ( Bureau of Labor Statistics (BLS), 2020b ). A person is unemployed if they were not employed during the survey’s reference week and who had actively searched for a job in the 4-week period ending with the reference week, and were presently available for work ( BLS, 2020b ).

Caused by the COVID-19, the unemployment rate reached a peak in April 2020 at 14.7% nationwide, an unprecedented joblessness amount since employment data collection started in 1948. It exceeded the previous peaks during the Great Recession and after ( Falk et al., 2020 ). The official unemployment rate may have been over 20%, since the actual level of joblessness could have been understated due to local unemployment rate measurement errors ( Coibion et al., 2020 ). In addition, the unemployment rate was understated due to a geographically widespread misclassification of those who was not at work but considered employed and non-inclusion of labor force non-participants who still counted as employed ( Bureau of Labor Statistics (BLS), 2020a ). Further, the COVID-19 caused the rapid rate of change in unemployment at the national level challenging accurate forecast of the monthly unemployment rate ( Weinstock, 2020 ).

Overall, current unemployment (using the most recently available county-level data at the time of writing for December 2020) is still elevated and is almost twice as high as it was back in February 2020 which represented the business cycle peak with the peak of payroll employment. March 2020 was the first month of the subsequent current economic recession as declared by The National Bureau of Economic Research (NBER, 2020) caused by the COVID-19 pandemic which turned out the worst downturn after the Great Recession. As Fig. 1 shows using the Current Population Survey data (Series ID: LNS14000000) from the BLS, during the prior recessions the unemployment rate rose gradually reaching its peak, and in the pandemic-caused recession it increased unprecedentedly to its peak over one month, from March 2020 to April 2020 by 10.3% (from 3.5% in February 2020 to 4.4% in March 2020 to 14.7% in April). After that, the rate declined as workers continued to return to work to 6.3% in December 2020.

U.S. Historical unemployment rate for workers 16 years and over, January 1948 to December 2020, % (seasonally adjusted).

Some communities can absorb the impact of economic downturns due to more favorable economic and social factors protecting residents from adversity. Yet other communities are witnessing the effect of rising unemployment in the time of COVID-19. Loss of income and livelihood has further effects: as wages drop, more people are forced into poverty while simultaneously people's health is impacted. Unemployment impacts all-cause mortality. Fig. 2 presents the dynamics of unemployment distribution across counties in TN for the selected months. Shown are pre-COVID-19 unemployment rates as of August 2019 ( Fig. 2 a), followed by May 2020 ( Fig. 2 b) where even the lowest levels of unemployment exceed the highest rates of the pre-pandemic period even in wealthy counties around Nashville (seen in the legend entries), August 2020 ( Fig. 2 c), and September 2020 ( Fig. 2 d). The overall unemployment abates somewhat during the later stage, and the general spatial pattern resembles that of the pre-COVID-19 period with higher unemployment concentrated in the southwestern corner of the state around Memphis.

Dynamics of unemployment rate across counties in TN for selected months: (a) August 2019, (b) May 2020; (c) August 2020; (d) September 2020.

1.1.3. Study area

Tennessee is home to large cities including Nashville (the county seat), Memphis, Knoxville and Chattanooga. Despite urban diversified economy, there was a steep decline in the number of international and domestic tourists impacting urban economy. Among cities listed above, Memphis, located in Shelby County, is a shrinking city with a declining population base. Urban shrinkage makes cities more vulnerable due to very negative impacts on urban economy. Shrinking cities are characterized by higher unemployment rates, depopulation (as people with higher economic and social status leave elsewhere), and a higher share of older people (increasing a share of individuals with underlying health conditions) ( Haase et al., 2014 ; Hartt 2019 ; Hoekveld 2012 ; Krzysztofik et al., 2020 ). The shrinking cities have higher exposure to extreme socioeconomic phenomena, including financial stress due to the decreases in the city’s budget. Decreasing budget in its turn has further urban development implications since implementation of some plans deemed of lesser priority such as environmental and cultural may be delayed and cancelled altogether ( Kunzmann, 2020 ; Sharifi and Khavarian-Garmsir, 2020 ).

Tennessee is one of the US southern Sunbelt states which had infection surges since summer 2020 due to the aggressive push for economy opening by then-President Trump administration. The pandemic has affected unemployment for every state in the United States ( Falk et al., 2020 ). Fig. 3 portrays selected industries impacted by the economic recession in Tennessee using seasonally adjusted data on employees on nonfarm payrolls for November 2019 (as a base period), September–November 2020. Unemployment rates concentrate disproportionately in sectors providing in-person non-essential services where some demographic groups are overrepresented. This results in substantially higher unemployment rates for those workers ( Cortes and Forsythe, 2020 ; Fairlie, 2020 ). Accordingly, it can be seen in Fig. 3 that in Tennessee, among the reported industries, leisure and hospitality has suffered the most, followed by jobs in government, education and health services, professional and business services, and trade, transportation, utilities. There was a slight increase in jobs in financial activities from 2019 to 2020 ( Bureau of Labor Statistics (BLS), 2020a ). The hardest hit industries tend to employ demographic groups such as women, minorities, low-income workers, and younger workers who have experienced greater job losses ( Murray and Olivares, 2020 ).

Employees on nonfarm payrolls by selected industry sector, seasonally adjusted, in TN.

2. Materials and methods

In the absence of fine-scale monthly data on employment and unemployment, we sourced county-level data from the Bureau of Labor Statistics (BLS) to track monthly changes in employment and unemployment in Tennessee (retrieved from https://www.bls.gov/lau/ ). Labor force data were extracted from this official primary source.

We used a comparative assessment approach to analyze the COVID-19-based labor market outcomes including the rates of COVID-19-related employment and unemployment attributable to social disadvantage conditions. For this, we stratify data based on community disadvantage status, and combine data in a comparative assessment framework. We proceed and identify disadvantaged communities using the methodology described below. Next, we test the hypothesis that in areas with high social disadvantage where more essential workers are more likely to reside, the unemployment is higher while employment opportunities are lower by comparing unemployment and employment rates within these communities to those of more privileged communities.

3. Theory/calculation

We focus on the areas where the multiple risk factors identified in the recent literature co-locate spatially and term these places “ multi-dimensional social disadvantaged areas ”. We carried out a rigorous literature review of the variables to stand in for social disadvantage in this research. The following demographic and socio-economic factors have been selected to represent community’s vulnerability: (1) Minorities and ethnicity; (2) Crowded households; (3) Poverty; (4) Education; (5) Underlying medical conditions (obesity); and (6) Unemployment. For the 1st variable, minorities and ethnicity , we used percent minority population and Hispanic ethnicity as studies commonly use race and ethnicity as vulnerability metrics (as explained in Section 2 Background information). For the 2nd variable, crowded households , we used percent households that are multigenerational as an indicator of crowdedness, and thus, indicating area’s disadvantage with a high share of such households. For the 3rd variable, poverty , we chose percent of households below 100% of federal poverty level which is also known as the poverty line. It is an economic measure of income. The poverty guidelines are updated annually by the US Department of Health and Human Services to indicate the minimum income needed by a family for housing, food, clothing, transportation, and other basic necessities and to determine eligibility for certain welfare benefits. This measure was used because less affluent and less privileged households have fewer means and less access to various resources to cope with the effects of financial crises ( Pfeffer et al., 2013 ). Low-income households may be especially vulnerable to wage losses during the outbreak ( Qian and Fan, 2020 ). For the 4th variable, education , we used percent of population with less than high school diploma since lower educational attainment is an indicator of poverty and thus captures social disadvantage, while workers with better education have higher economic resilience when challenged with a large-scaled social shock ( Cutler et al., 2015 ; Kalleberg, 2011 ). For the 5th variable, underlying medical conditions , we used percent population with obesity as the top risk for COVID-19-related hospitalization. Supported by several lines of evidence, both domestically and internationally, obesity may predispose to more severe COVID-19 outcomes ( O’Hearn et al., 2021 ). Finally, for the 6th variable, unemployment , unemployment rate (averaged from August 2019 to January 2020 to adjust for seasonality) was used as a marker of overall vulnerability as it is linked to overall mortality. Further, regions with higher unemployment are more susceptible to business-cycle fluctuations, and thus, are more socially and economically vulnerable.

These socio-economic and demographic attributes (minority population, Hispanic ethnicity, federal poverty level, crowded households, adult obesity, lower educational attainment, and unemployment) have been used in this research to create a composite variable to represent a multi-dimensional social disadvantage (also referred to as vulnerability). Due to different variances in the original variables, we standardized them to prevent a disproportionate impact which may be caused by any one original variable with a large variance. The z-score transformation was applied by averaging the original variables and computing z scores with a mean of 0 and values ranging from negative to positive numbers ( Song et al., 2013 ).

Thus, the original variables were converted to z-scores to preserve the distribution of the raw scores and to ensure the equal contributions of the original variables. Next, we created a composite variable capturing a multi-dimensional social disadvantage. It was calculated by summing standardized z-scores of the original risk factors. The higher value can be interpreted as higher disadvantage while the lower value means more privileged communities. Based on the frequency distribution of values of the composite variable, we established a cut-off value for the composite variable to designate communities with high or low exposure to social disadvantage. We used the following method to determine the cut-off value of the composite variable. The values greater than 3.38 correspond to 1 standard deviation above the mean (or, the 88th percentile in the value distribution) indicating communities in the top 12 percent of social disadvantage and therefore, a higher share of factors contributing to disadvantage. This value was used to differentiate communities according to their disadvantage status. We identified twelve counties with high social disadvantage (N high  = 12), and other counties represent more privileged communities (N low  = 83). To test whether the taken approach correctly identifies disadvantaged communities, we conducted a Wilcoxon two-sample test for the variables of interest ( Table 1 ). We report the results of the estimates in the following section. The above socio-economic and demographic population characteristics come from the 2018 American Community Survey (ACS) 5-year data, an annual nationwide survey conducted by the US Census Bureau, available for various geographic units and applied for areal units within the study area ( U. S. Census Bureau, 2020 ).

Descriptive statistics.

The basic descriptive demographic and socio-economic characteristics of the TN population are shown in Table 1 . It includes the summaries for communities with high and low social disadvantage allowing to compare the variables of interest between these communities. The following variables are reported: percent African American, percent Hispanic, median income, percent of people over 25 years who are less than high school graduates, estimated percent of obese adults, percent households below 100% of federal poverty level, and percent of multi-generation households. The factors comprising social disadvantage were statistically significantly different than those extant in more privileged counties. Compared with the general TN population, the disadvantaged cohort was generally more likely to be of non-Hispanic Black race; more impoverished; with less educational attainment, more obese, and had more households with crowded conditions.

To visualize social disadvantage and show how it varies across the space, we used our sample of social disadvantage measurements and created a surface of social disadvantage within the study area using the Geographic Information System (GIS). The interpolated surface was derived from an Inverse Distance Weighted technique ( Watson and Philip, 1985 ). Fig. 4 presents the surface illustrating that both urban and rural counties in Tennessee are subject to social disadvantage.

Social disadvantage within the study area.

We examined how unemployment changed from August 2019 to December 2020. Currently, all counties have substantially higher unemployment compared with that prior to COVID. Fig. 5 presents the results of the Nonparametric One-Way ANOVA test showing the distribution of Wilcoxon scores for unemployment rate for all counties in Tennessee combined, regardless of social disadvantage status, for 17 months. A statistically significant difference is found for unemployment rates between the pre-COVID period and the period since April 2020, with current unemployment rates although decreased but still significantly higher compared with those prior to the recession.

Nonparametric One-Way ANOVA and distribution of Wilcoxon scores for unemployment rate for all counties combined for 17 months (August 2019–October 2020), regardless of social disadvantage status.

We compared employment and unemployment rates for Tennessee counties stratified by the type of social disadvantage separately for each month. Fig. 6 presents the average employment and unemployment rates by community disadvantage from August 2019 to December 2020 in a graphical form. The results of the non-parametric Wilcoxon test for employment and unemployment rates are presented in Table 2 . Pre-COVID and before the unemployment peak in April 2020, communities with high social disadvantage consistently had less jobs and greater unemployment, which we tested statistically and found a significant difference for both outcomes of the labor market between communities by their disadvantage status ( Table 2 ). Shown in Table 2 , in April and May 2020, during the peak of unemployment and immediately after, unemployment rates observed in both types of communities were high with no statistical difference. In June, the differences again became prominent, when there were more jobs available in more advantaged areas and employment rate remained consistently greater in areas with less disadvantage. Also in June, unemployment rate remained consistently greater in areas with higher disadvantage. This month saw the greater difference in both outcomes since the COVID-19 than pre-pandemic (supported by higher p-values). Compared with all TN population, residents of disadvantaged counties had less jobs available and were more likely to be unemployed during all periods except for April and May.

Mean employment and unemployment stratified by community disadvantage status.

Wilcoxon Two-Sample Test: Distribution of Wilcoxon scores in employment and unemployment rates by community disadvantage status by month (August 2019–December 2020).

We examined the percent change in both labor market outcomes. Fig. 7 presents the percent change in mean employment ( Fig. 7 a), and mean unemployment by community disadvantage ( Fig. 7 b). The percent change in employment and unemployment was relatively small in both types of community during the pre-COVID period. However, the overall fluctuations in both conditions were greater in communities with high social disadvantage (evidenced by a greater range between ups and downs for disadvantaged communities shown with the black-colored symbols). On the other hand, employment and unemployment were more stable in more privileged communities (shown with the grey-colored symbols in the Fig. 7 ). During the unemployment peak in April 2020, the change in percent employment was −11.5 points from the previous month even in more advantaged counties, while the unemployment in April increased by 10.42 percentage points in disadvantaged counties.

Percent change in (a) mean employment; (b) mean unemployment by community disadvantage.

We show how various factors of social disadvantage intersect and combined impact economic vulnerability measured by unemployment rate. Fig. 8 reports the link between unemployment and social disadvantage pre-COVID (unemployment rate was averaged over August 2019–January 2020 in Fig. 8 a), and during COVID (unemployment rate for November 2020 is shown in Fig. 8 b). During the COVID pandemic, its impact is even stronger as evidenced by a greater slope of the line of fit, larger coefficients, and a greater R-squared value ( Fig. 8 b). The strong relationship between these factors of social disadvantage and economic outcomes in COVID-19 might inform post-COVID recovery intervention strategies to reduce COVID-19-related economic vulnerability burdens. For example, in the light of findings on socio-economic and demographic subpopulations at a higher risk for economic damages, prioritization of economic relief distribution might be based on community disadvantage status targeting individuals from areas with existing inequalities to increase economic resilience of marginalized communities.

Unemployment and Social disadvantage: (a) pre-COVID (averaged August 2019–January 2020); (b) during COVID (November 2020).

5. Discussion

Current studies on the impacts of COVID-19 tend to focus on medical aspects while non-medical urban research mostly analyzes the role of environmental quality. To better understand the full effects of pandemics on communities and minimize the various impacts as well as to improved response, other aspects need to be examined. This includes studying less researched themes including socio-economic impacts consisting of both social impacts and social factors making individuals and communities less resilient and more vulnerable to the effects of the COVID. Additionally, economic impacts of the pandemic-caused recession so far remain relatively underexplored and need to be investigated ( Sharifi and Khavarian-Garmsir, 2020 ).

Communities are often severely segregated along wealth and social lines in developing and developed world ( Wilkinson et al., 2020 ). We study the role of social factors and the impact of the COVID on labor market conditions in Tennessee. Specifically, we studied the impacts of social environment on employment and unemployment through the concept of a multi-dimensional social disadvantage by using geospatial science.

A recent study identified factors which can make a community more vulnerable to the pandemic’s effects using as a case study the province of Silesia in Poland, one of the largest industrial and mining regions in Europe. Specialized functions such as mining-oriented industries, large care centers, polycentricity, and urban shrinkage make communities most at risk due to very negative impacts on urban economy ( Krzysztofik et al., 2020 ). Since vulnerability is always very context-specific, we found a combination of different causal factors of social disadvantage captured by a composite variable making communities most at risk during the COVID reflected in broader social and economic outcomes. In creating a composite variable to capture social disadvantage logically and meaningfully, the following variables were used: % African American, % Hispanic, % below 100% federal poverty level, % population with less than high school diploma (an indicator of poverty), % multi-generation households (an indicator of crowdedness), % estimated obese adults reporting to be obese with the BMI 30 or greater, % unemployed. The proposed method can be generalized beyond the study area and used as a tool by policy makers using consistent criteria for the delineation of areas carrying a greater risk for the more severe impact by the pandemic due to co-existence and co-location of the multi-dimensional social disadvantage factors which are more likely to experience further socio-economic disruptions.

Current urban research on COVID economic impacts found that some cities are more vulnerable than others and are most at risk. Cities with an undiversified economic structure with industries where a large number of workers are shoulder-to-shoulder share cramped spaces for a prolonged time and where social distancing is challenging (e.g., meat-packing and poultry processing plants), cities relying on tourism as well as cities that have large care centers, polycentric cities, and shrinking cities are the most vulnerable to negative impacts on urban economy. The urban hotel market, city tax revenues, citizens' income, tourism and hospitality, small- and medium sized firms, urban food supply chain, and migrant workers are all impacted ( Krzysztofik et al., 2020 ). Other recent studies similarly concluded that the COVID has revealed the extreme vulnerability of cities and urban areas disrupting tourism and affecting supply chains in cities ( Batty, 2020 ; Gössling et al., 2020 ). We support this statement but also find that rural areas can experience a broad range of social and economic damages related to COVID.

Before and during the COVID-19 period, money laundering, limitations of economic development, environmental pollution and uncontrolled deforestation, population displacement, institutional incompetence, and corruption of political elites have been debated including corruption and conflagration in Bucharest before the pandemic ( Creţan & O’Brien, 2020 ), as well as other contestations on selling masks and different medical products highlighted in different countries during the pandemic period. Following catalytic events, the affected community may respond to long-held concerns with demands to address these problems bringing about important changes to the systems. Marginalized stigmatized minorities may effectively overcome discriminatory laws, higher poverty and other constraints and influence public opinion and politics in their favor through collective action via various strategies including protests against corruption and the inaction of the political leaders in Romania in 2015 forcing the resignation of the Government, and protests in the US in the aftermath of police violence against black people have been documented ( Creţan & O’Brien, 2020 ; Fryer, 2019 ). During the COVID-19, the non-payment of wages and poor working and living conditions caused seasonal workers in Germany to protest against this unfair treatment, however, generating low coverage in the national press ( Mayer-Ahuja, 2020 ).

6. Conclusions

Some socio-economic and demographic conditions consistently and significantly impact some communities more often than others, particularly based on ethnic minority status, low income, and rural location. The conditions include systemic issues such as fragmented health care system (within which some individuals do not get health care in a timely fashion), racism and structural disparities in education, income, wealth, a consistent lack of economic opportunity, environmental factors, transportation and housing ( Petterson et al., 2020 ). These factors interact in complex ways resulting in persisting social environment-driven health and other inequalities which if left unaddressed will only increase.

Respectively, among policies goals across the Global North enhancing wellbeing and social mobility for disadvantaged and marginalized families, creating socially mixed, heterogeneous neighborhoods (that is, desegregation) is promoted to avoid spatial segregation based on racial and ethnic membership and class while supporting social cohesion ( Méreiné-Berki et al., 2021 ). Importantly, a marginalized community is not a homogeneous group as the lived experience of disadvantage within the communities is variegated: respectively, policies to improve socio-spatial integration and addressing the various causes of extreme poverty including social, economic, and cultural that improve social equity have been suggested since desegregation on its own is insufficient (( Méreiné-Berki et al., 2021 ). Sustainable planning may mitigate consequences of urban sprawl noted in the urban studies literature including urban blight which is the greatest in poorest areas entrapping the low-income residents in the inner city where they have only limited regional mobility and access to job opportunities at the urban edge. Understanding the links between a development of a metropolitan-wide blight remediation strategy toward a sustainable urban form and welfare enhancing among the disadvantaged populations needs to be further investigated.

During public health crises, the importance of the central role of the community has been highlighted especially when some state-based social services may be less available due to lockdown. Rather than inventing new solutions, voluntary informal social networks that have been generated by communities utilize local assets and resources ( Bear et al., 2020 ). Community-based initiatives may rely on the voluntary sector, faith- and charities-based organizations, and social enterprises for various services including help with visiting housebound people, or using them as a distribution hub for food distribution to families in need.

In conclusion, in this study, we situated the research on economic impacts of the COVID in the broader context of social disadvantage with findings both domestically and from other countries in line with those in our study. The earlier misleading view of the global epidemic representing a systematic disadvantage that may affect and limit everyone’s economic activity, with any socioeconomic status or from any geographic location, was rejected. Our finding indicates that certain factors may increase people's vulnerability to the financial stress related to COVID-19. We find support that the social distribution of economic vulnerability is magnified in regions with pre-existing social disparities, creating new forms of disparity ( Qian and Fan, 2020 ).

This work was supported by the UTHSC/UofM SARS-CoV-2/COVID-19 Research CORNET (Collaboration Research Network) Award.

CRediT authorship contribution statement

Anzhelika Antipova: Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing.

Declaration of competing interest

The author declares no conflict of interest.

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Unemployment Rate And Its Effect On The S&P 500: It's Less Intuitive Than You Think

Economic event releases are curveballs to unprepared and inexperienced traders, resulting in emotional and frantic trading that lead to great losses. So how can we avoid this? Read on to find out how you can prepare for the next macroeconomic event and maximize your profits.

Economic event releases are curveballs to unprepared and inexperienced traders, resulting in emotional and frantic trading that lead to great losses. So how can we ... avoid this? Read on to find out how you can prepare for the next macroeconomic event and maximize your profits.

11 times Unemployment was higher than expected

3 years time-frame of our analysis

Macroeconomic events can have big impacts on markets. As traders, we need to be prepared to react to any changes in the economy. Fig. 1 (click to enlarge the image) shows the unemployment rate in the United States over the last 70 years. We see that the unemployment rate has been falling in recent years, but that spells a future trend of rising unemployment as it will never fall to 0%.

The Problem

With big impact often comes big confusion. The release of the unemployment rate causes a lot of volatility in the markets, and it is often unclear which markets are most affected by its release. Even the most experienced trader can find themselves confused. One key example of this is how traders react to a surprising rate of unemployment, and its predicted impact on the S&P 500.

Take a negative event, a release of a higher than expected unemployment rate, for instance. When the unemployment rate is surprisingly higher than expected, fewer people are employed, so the overall income consumers receive will be lower. Logically, with less income, people will spend less money, and thus the demand for products will fall. In general, this causes stock prices to fall, although the extent of this fall is industry-dependent. The expectation of most traders then is that, with the release of higher than expected unemployment, the S&P500 will go down.

But here’s the kicker: the S&P500 is inversely related to the unemployment rate, and thus the market actually goes up as a response to a release of a higher than expected unemployment rate. This may seem illogical conceptually, but historical analysis and statistics show that it is true.

In the last 3 years, the unemployment rate in the United States has been surprisingly higher than expected 11 times. The result? The S&P500 went up 80% of those times within a time-frame of 90 minutes (see Fig. 2, click to enlarge the image).

But why? Well, it is likely that the market reacts to the higher than expected unemployment rate by cutting back on spending. This encourages looser monetary policies in the form of low interest rates by the Central Bank. Low interest rates then create an inflow of money into higher-risk assets like the stock market.

So what? How can I profit from this? With adequate preparation, traders can profit from such events. Buying after the release and holding for 30-90 minutes can lead to gains (see Fig. 3, click to enlarge the image).

What we can take away from this example is that there are various possible impacts that an economic event can have on a market. As traders, our job is to discern which impact is the most plausible based on the current narrative or economic outlook.

As humans, it is near impossible to recall what we ate for lunch two weeks ago, let alone recalling historical economic events from years ago to aid our present judgment.

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The BackTesting Solution

Keeping up with economic events in and of itself is hard enough, and it is harder still to predict their impacts on markets. As humans, it is near impossible to recall what we ate for lunch two weeks ago, let alone recalling historical economic events from years ago to aid our present judgment. That is why we created our BackTester tool.

BackTesting is generally defined as the process of applying a trading strategy or analytical method to historical data to see how accurately the strategy or method predicts actual results. The tool employs artificial intelligence to generate trade analysis based on similar historical events. It prepares you well before an event release and provides a snapshot of the market reaction in similar situations. Using our BackTester will give you the edge you need to trade economic events and price actions.

The Results

With the statistical analysis our backtester provides, you will be able to recognize trading opportunities and discern which trades should be avoided long before the release of the economic event..

Traders that used our BackTester saw immediate results:

• 22% average reduction in losses.
• 7 minutes average fall in reaction time.

Our BackTester not only lowered the loss rate of its users, but it also helped our clients work smart and win hard, resulting in:

• 37% average increase in success rate.
• 164% average increase in the ability to identify profitable trading opportunities.

The BackTester is a powerful tool for any trader who is looking to develop and refine their trading strategy, as well as for traders who want to be able to predict how the market will react to specific economic events. Armed with the unparalleled analytics it provides, you can be confident trading economic events and price actions. The BackTester works hard for you, so you can work smart and come out successful.

About USD Unemployment Rate

The percentage of the total labor force that is unemployed but actively seeking employment and willing to work. The unemployment rate is considered a lagging indicator, confirming but not foreshadowing long-term market trends.

Type: Economic Event

Frequency of releases: Monthly

Location: United States

Analysis tool: The BackTester

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Race and underemployment in the US labor market

Subscribe to the economic studies bulletin, ryan nunn , ryan nunn assistant vice president for applied research in community development - federal reserve bank of minneapolis jana parsons , and jana parsons former research analyst - the hamilton project jay shambaugh jay shambaugh under secretary for international affairs - u.s. department of the treasury.

August 1, 2019

Each month a new reading of the unemployment rate helps us assess the health of the labor market. However, as many have pointed out , the unemployment rate is in some ways a narrow measure of the labor market that misses important aspects of labor market distress. A broader indicator of labor market weakness called the under employment rate—and in Bureau of Labor Statistics jargon referred to as the U-6 unemployment rate—takes into account some of this additional distress. Examining both unemployment and underemployment is useful for analyzing different aspects of the labor market, and, as shown below, it can reveal dramatic racial disparities. 

In addition to the number of unemployed (those without a job and actively seeking work), the underemployment rate captures the number of people who work part time but would rather have a full-time job (called “part time for economic reasons”) and those who want and can take a job but have not looked for work in the past four weeks (called “marginally attached”). These groups make sense to include in a measure of underemployment because while they are not unemployed in the formal sense, they would work  more  if the option presented itself. While the unemployed are often those most ready to take new jobs, workers who are marginally attached and part time for economic reasons also stand ready to take full-time employment when employers are hiring.

At its peak in the wake of the Great Recession, the underemployment rate was 17.1 percent in October 2009, indicating that more than one in six people were experiencing some sort of labor market hardship (see figure 1). This was far above the 10.0 percent unemployment rate at the time and demonstrates the wide swath of individuals who were in labor market distress in the aftermath of the Great Recession. Since then the underemployment rate has steadily declined, and is now below its prerecession low, but it did not fall below its prerecession low for nearly a year after the unemployment rate did. In addition, at 7.2 percent in June 2019, the underemployment rate is nearly double the June 2019 unemployment rate of 3.7 percent. This makes clear that while a relatively small percentage of people are both out of work and currently searching for a job, there is still a considerable amount of underutilized labor and many people for whom the labor market is not providing adequate opportunities.

In 2010, after the Great Recession had officially ended, the average annual unemployment rate for black workers was 16.0 percent, compared to 12.5 and 8.7 percent for Hispanics and whites, respectively. The black unemployment rate is typically about twice as high as the white unemployment rate. Economists Cajner, Radler, Ratner, and Vidangos  find  that observable characteristics such as education, age, and marital status explain relatively little of the gap. Instead, black workers have a much higher risk of losing their jobs, accounting for much of the unemployment rate gap.

While unemployment rates have fallen substantially since 2010, racial disparities still exist today: in the first half of 2019 the  unemployment rate  was 6.6 percent for blacks, 4.4 percent for Hispanics, and 3.3 percent for whites. Moreover, as Andre Perry  notes , national estimates of the unemployment rate mask the wide geographic differences in the labor market experiences of minorities.

Like the standard unemployment rate, the underemployment rate reveals very different labor market outcomes for black, Hispanic, and white workers (see figure 2). Below, we highlight a few of the distinct insights the underemployment rate provides about the varied labor market experiences of American workers.

• White workers have lower underemployment rates than black or Hispanic workers at all points in the business cycle. In fact, the peak white underemployment rate in the wake of the Great Recession was only slightly higher than the prerecession low for black underemployment.
• For black workers, the underemployment rate is strikingly high: black underemployment reached 24.9 percent in April 2011, well after the peak of the black unemployment rate at 16.8 percent in March 2010. That is, for more than a year after the black unemployment rate began to fall, the underemployment rate continued to rise.
• The Hispanic underemployment rate rose 12 percentage points from December 2007 to December 2009, compared to 10 percentage points for blacks and just under 7 percentage points for whites.
• This was predominantly driven by increases in unemployment and the number of those working part time for economic reasons (i.e., those who would like full-time jobs but cannot find them) rather than changes in the number of marginally attached workers.
• Much of the gap between white and Hispanic involuntary part-time work can be  explained  by observable characteristics like education, industry, and occupation.

The large gaps in underemployment between white and non-white workers seen in figure 2 are driven by differing within-race gender gaps, as shown in figure 3. In December 2018, at 6.3 percentage points, the gap between black and white male underemployment is substantially larger than the 4.6 percentage point black–white gap for women. But the white–Hispanic gap is based on the opposite pattern: the underemployment gap for women (4.7 percentage points) is larger than the gap for men (2.8 percentage points).

The unemployment rate is an incredibly valuable summary statistic for the labor market. That statistic is effective in capturing changes in labor market conditions, making it a  valuable basis  for assessing cyclical patterns—particularly at the start of downturns. But as shown above, the unemployment rate misses the full extent of labor market distress. This omission is especially important during economic downturns and for black and Hispanic workers. The underemployment rate is also helpful for determining the amount of labor market slack that remains once the unemployment rate has fallen to a low level.

A focus on the unemployment rate alone would have suggested that the labor market experience of black workers was improving in 2010 when the underemployment measure showed deterioration, and an exclusive focus on unemployment would also have missed the particularly sharp deterioration of conditions for Hispanic workers in 2007–8. Examining patterns in underemployment is a partial remedy. However, the Bureau of Labor Statistics does not release a seasonally-adjusted monthly series for individual racial groups making underemployment rates less useful for real-time analysis to understand turning points in the business cycle. Still, much can be learned from these data to better understand how well the labor market is functioning for all workers.

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UNEMPLOYMENT AND ITS EFFECTS ON ECONOMY: A CASE STUDY

This study was carried out to examine the causes of unemployment in Nigeria and its effects on the economy of the country. Data were sourced from the Central Bank of Nigeria, Statistical Bulletin (2017), National Bureau of Statistics (NBS), Internet, CIA site, Index Mundi and past studies. The study covered period of 18 years, 1999 to 2017. EViews version 7 was used to analyse the data gathered, while Ordinary Least Squares (OLS) method was adopted. This study discovered that the unemployment in Nigeria is the major contribution to the poverty level in the country. Money supply and credit to private sector were recognised to have significant effect on unemployment level in the economy. It is also discovered that gross domestic product and unemployment rate in Nigeria has positive relationship which is an indication that growth in GDP does not amount to overall development in Nigeria. This study recommend that government should make credit available to private sector, increase money in circulation and try to reduce external debt which drain away the wealth of the nation through interest payments to reduce poverty rate in Nigeria.

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Muideen Isiaka

Macroeconomic policies seek to attain optimum and sustainable growth by reducing unemployment rate of a nation. This study assessed the impact of macroeconomics of Nigeria on unemployment. Secondary data of unemployment and economic variables such as GDP, Government final consumption expenditure, Gross fixed capital information, Export and Import were collected from various issues of Central Bank of Nigeria, Statistical Bulletin, National Bureau of Statistics, National Account of Nigeria for the period of 1995 to 2015. Quantitative research methodology involving the use of statistical methods such as descriptive statistics, trend analysis and regression analysis was adopted. The result shows that the between 2003 and 2015, unemployment rate range from 11.9% to 25.3% with a low standard deviation of 5.24, which goes to show that, there was no significant variation in unemployment rate for the 13 years of study. The study further shows that unemployment is negatively related to GDP, GFCE and export, while it is positively associated with GFCF and import. The study concludes that the relationship between macroeconomic and employment needs a systematic approach of analysis in Nigeria, as a result macroeconomic policies should be reviewed urgently.

aminu balakaoje

The rate of unemployment has risen in the last decade in most of the sub-Saharan African countries. The situation in Nigeria is rapid population growth with low level of employment rate. The theoretical proposition of the Okun's law is that a negative relationship exists between unemployment rate and economic growth. This study intends to test the validity of Okun's law in Nigeria. In order to examine the relationship between unemployment rate and economic growth, Error Correction Model (ECM) and Johasen cointegration test were employed to determine both the short run and long run relationships among the variables employed in the study. Empirical findings show that there is both the short and the long run relationship between unemployment rate and output growth in Nigeria. Hence, there is need to incorporate fiscal measures and increase the attraction of foreign direct investment (FDI) to reduce the high rate of unemployment in the country. 1. Introduction One of the greatest challenges of the Sub-Saharan African economies today is the high rate of unemployment that has maintained a rising trend over the years. The problem of unemployment has been of great concern to the economists and policy makers in Nigeria since early 1980s. The effect of financial crisis on public and private sectors has led to renew attention on the phenomenon. It is a widely accepted view in economics that the growth rate of the Gross Domestic Product (GDP) of an economy increases employment and reduces unemployment. The three most significant elements for the economy overall are productivity, income distribution and unemployment. This theoretical proposition relating output and unemployment has been proposed by Okun (1962). This relationship is among the most famous in macroeconomics theory and has been found to hold for several countries and regions mainly, in developed countries (Christopoulos, 2004; Daniels and Ejara, 2009). Okun's (1962) postulates a negative relationship between movements of the unemployment rate and the real gross domestic product (GDP) by focussing on the empirical relationship between unemployment and GDP variations. He emphasised that as a result of changes in aggregate demand, industry changes their production pattern which leads to changes in demand for labour which alter the unemployment rates. This empirical relationship is a major part of every traditional macro-model as the aggregate supply curve is derived by combining Okun's law with the Phillips curve. Moreover, this relationship has also important implications for macroeconomic policy. It is simply very interesting to know the growth rate necessary to reduce unemployment (if this is even possible). Furthermore, the effectiveness of disinflation policy depends on the responsiveness of unemployment on the output growth rate (sacrifice ratio). Unemployment problem in Nigeria has different dimensions. There are underemployment cases in which people receive incomes that are inadequate to support their basic needs, in terms of food, clothing and shelter. There are also cases of disguised unemployment where people take up jobs that are below their educational attainment and experience. The worst case of all is that of people seeking for job opportunities but who cannot find any either in the public or the private sector. Some people are willing and ready to set up enterprises themselves and engage in one type of economic activity or the other but are constrained by the prevailing poor macroeconomic environment. All these have contributed significantly to the high level of unemployment and poverty in Africa (Oni, 2006). Another dimension of unemployment problem in Africa is the differentials in its manifestation by sector, sex and educational level. For instance, in Nigeria, available data from National Bureau of Statistics show that as the incidence of poverty is higher in rural than urban area, so also is the rate of unemployment, particularly in the late 1990s and 2000s. The incidence of poverty is higher in Nigeria among those who have little or no education than the other categories. In the same vein, the proportion of employment persons with little or no education is higher than all other categories of people with different levels of education. The objective of this study is to test the validity of Okun's law for Nigeria through. Specifically, the study intends to examine the impact of unemployment on the Nigerian Economic growth and testing the sensitivity of output to change in unemployment rates in both short run and long run.

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Ibrahim Inuwa

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Ogungbenle Sola

Oluchukwu Anowor

The relationship between unemployment and poverty has been of interest to many a scholar with interest in development economics and social sciences. This paper is an addition to the empirical attempts to re-examine the relationship between unemployment rate and poverty incidence in Nigeria using secondary data sourced from relevant institutions to obtain major Social and Economic indicators spanning within 1980-2015. The study used Trend graph analysis, Correlation coefficient analysis and Granger causality tests in its analyses. As shown from the results, there is a positive-significant correlation between unemployment and poverty in Nigeria. More so, this was corroborated by the Trend graph analysis. It also established that unemployment granger causes poverty in Nigeria as suggests from the Granger causality tests. The economic implication of this result is that poverty is an increasing function of unemployment; and the Error Correction Mechanism (ECM) pointed that short run disequilibrium in the economy can be returned to equilibrium in the long run with a poor speed of adjustment of 6 %. In the light of these findings, this study recommends that efforts should be intensified in Nigeria towards implementation of unemployment reduction policies as this will significantly reduce poverty incidence.

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This study investigates the impact of unemployment on economic growth in Nigeria using the OLS multiple regression analytical method in analyzing annual time series secondary data obtained from the Central Bank of Nigeria, statistical abstract from National Bureau of Statistics, as well as the World Development Indicators from the period 1980 – 2016. This study established that unemployment, population and labour force have significant impact on Nigeria's economic growth, while minimum wage does not have a significant impact on the country's economic growth. The underlying principle for such a result is rooted in the Keynesian theory of unemployment which is applicable to the Nigerian economy that is trying to come out from the economic recession. Based on this, the following recommendations were proffered: the government should ensure there is job creation in the economy especially in the real sector; private sector employers should be given subsidies so as to encourage them to employ more people; and the labour market should be deregulated.

abubakar sule

Unemployment has become a global concern with dire consequence to sustainable growth and development. This paper examines the paradox of high unemployment amidst economic growth: an investigation of the Nigerian situation (1980-2013). The paper used Descriptive Statistics, Cointegration technique, vector error correction model (VECM), and granger causality test. The results showed that 1% increase in unemployment rate (UMPL) decreases Economic Growth (GDP) by 34% of that unit change in the long-run while in the short-run by 1.34%. The study reaffirms the negative relationship between unemployment and economic growth as postulated by Okun's Law and the consequence of under-utilization of resources enshrined on Production Possibility Curve (PPC). The result of the trend analysis employed in the study also shows that from 2003 till date, economic growth rate has not gone below 6.0 percent, however, unemployment has kept increasing even to a peak of 23.5% in 2012_ thus the paradox. The study therefore concludes that the existing high unemployment amidst economic growth clearly proves that the economy is growing below her full potentials. It thus recommends the need for government to make concerted efforts to fully utilised Nigeria's economic resources by increasing domestic investment and putting policies that will attract foreign direct investment especially in human capital development.

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Cosmopolitan Civil Societies: An Interdisciplinary Journal

Vol. 13, No. 2 2021

Article (Refereed)

Covid-19 Pandemic and Unemployment in Malaysia: A Case Study from Sabah

Janice L. H. Nga, Wijaya Kamal Ramlan, Shafinaz Naim

Universiti Malaysia Sabah

Corresponding author: Janice Nga, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia. [email protected]

DOI: http://dx.doi.org/10.5130/ccs.v13.i2.7591

Article History: Received 06/02/2021; Revised 13/05/2021; Accepted 15/06/2021; Published 19/07/2021

Covid-19 not only exposed the vulnerability of most industries especially industries that relies on air travel and tourism but resulted in the exponential increase of unemployment in Malaysia. At the same time, online business or trade and ‘GIG’ economy increased exponentially. The sudden and unexpected loss of jobs had dire consequences for many people. This paper examines how policies enacted during the Covid-19 pandemic affected unemployment in Malaysia by focussing on the situation in Sabah, one of the three remaining partners in the formation of Malaysia. It draws on data from the Family, Women and Youth Survey conducted online towards the end of 2020, as well as secondary data. The study shows that hardship has been faced by many people, especially those previously in professional roles, and those who are younger. The widespread damage to the economy, and to social cohesion, will require significant collaboration between business and industry, the government and the people.

Covid-19; Employment; Unemployment; Malaysia; Sabah

Introduction

Covid-19 was initially a localised health crisis, but it expanded into a global health crisis with severe economic impact, with the speed and scale of which the world has never seen before. Economists have yet to comprehensively measure the effects of the pandemic since the chain of reaction is still progressing and spreading around the world in the form of 2 nd and 3 rd waves ( Chan & King 2020 ) or even 4 th waves ( Ravindran 2021 ; Whyte 2021 ). Covid-19 not only exposed the fundamental flaws in all industries in Malaysia, with the exception of industries in glove-making and pharmaceuticals, but also exposed the fragility of employment. Many companies and industry players have adopted unpaid leave and layoffs to reduce their operational costs so that the company could have sufficient liquidity to tide over this difficulty. Some of these former employees managed to survive in the challenging situation. However, some of them could not withstand the loss of their jobs and choose to commit suicide instead. Blustein et al. claim that unemployment has damaging effects at both individual and community levels, in the aspects of psychological, economic, and social well-being ( Blustein et al. 2020 ; Ferreira et al. 2015 ). The lockdown or Movement Control Orders (MCO) initiated by the government to flatten the infection rate also added to the depression of being retrenched as feelings of isolation also accompanied the lockdown.

The initial shock from the pandemic distinguished the Covid-19 recession as one of the worst ever pandemics ( Ferguson et al. 2020 ). While previous economic hard times, like recessions, were mainly caused by a series of unfortunate economic or financial policies, by 2020, the adversely impacted labour market was severely disrupted, triggered by the emergence of a new virus, worldwide. By the second quarter of 2020, millions of employees were moved into a temporary job loss category. The record-high in temporary unemployment percentage dwarfed previous recessions, which typically begin with an increase in permanent layoffs ( Elsby et al. 2010 ). The path of job vacancies has also been unusual: while vacancies fell throughout the first half of 2020, the drop was much smaller than is typical observed in most recessions due to the ever-increasing popularity of the ‘Gig’ economy ( Umar et al. 2020 ; Lim 2021 ). In fact, vacancies at the lowest point were similar in 2015, when the labour market shrank ( Altig et al. 2020 ). While the Beveridge curve – the negative relationship between vacancies and unemployment – usually ‘loops around’ during and after a recession, the rise in the unemployment rate was much greater than the related decrease in work vacancies in the early months of the Covid-19 recession.

Pandemic and Unemployment

Economic hard times caused by historical pandemics will enable us to establish a long-term impact on health derived directly from the current pandemic’s mortality trends. But what about the ramifications due to the disruption of everyday economic life when people become unemployed? The rapid, severe, and widespread economic disruption caused by the implementation of the MCO to reduce the daily infection spike caused by the spread of the Covid-19 virus is one of its most prominent features in this pandemic. This pandemic may have less in common with other pandemics such as H1N1 swine flu 2009-2010, or the Spanish Flu 1918 in this regard ( Newman 2020 ). For example, the 1918 pandemic’s immediate economic damage pales in comparison to the damage triggered by Covid-19 currently. While this pandemic can provide an insight into long-term social and psychological health effects (direct effect), we need to look elsewhere to consider the long-term consequences of pandemics because of economic downturns, particularly for employment ( Blustein et al. 2020 ; Su et al. 2021 ).

According to the definition of the International Labour Office (ILO), employment comprises of “work performed for others in exchange for pay or profit” ( ILO 2013 ). With that, “employed persons include those who, in their present job, were ‘not at work’ for a short duration but maintained a job attachment during their absence” ( OECD 2020a ). The ILO has reported that there are trends of income inequalities in terms of gender and age. However, different regions may vary in the level of differences for these criteria in the access to employment. For instance, the female labour force participation rate globally is 47% as compared to 74% for their male counterparts ( ILO 2020 ). It should also be pointed out, that the youth face challenges in the labour market such as the growing trend of temporary employment. Unemployment involves individuals with the expected total duration of absence of more than three months or where there is no or unknown expected date for returning to work, and these individuals do not receive any portion of salary or wage from their employers ( OECD 2020a ).

Theoretical modelling of the interaction between pandemics and economic dynamics has been examined by Eichenbaum et al. (2020) . This theoretical model suggests that reductions in consumption and work will slow down the spread of the Covid-19. Unfortunately, these measures also increase the severity of economic recession. It is reported that offline consumption in China dropped by an average of 32% hitting hard across the board ( Chen et al. 2020 ). This reduction in offline consumption cost China over 1.2%of its 2019 GDP in the twelve-week period of initial lockdown, giving a significant estimate of the impact and magnitude faced. Other countries have reported experiencing similar situations ( Chen et al. 2020 ).

As Covid-19 continues to spread across the globe, the expected decline in consumption has since become a reality with unemployment reaching nearly 10 million in March 2020 ( Wolfers 2020 ). Martin et al. (2020) claimed that three months of lockdown during the pandemic recorded an increase of about 17.1 to 25.9% in the poverty rate in San Francisco Bay Area with significant decreases in household saving and consumption. Long recovery times have further exacerbated the slowdown of overall economic activity by low household consumption due to changes in consumption behaviours. Sobieralski (2020) projected that job loss in the airline industry during times of air travel instability, due to most countries restricting or closing their borders to curb the spread, was approximately 7%of the airline workforce. Although air travel was not completely halted, the Covid-19 pandemic caused most airlines to ground their planes and retrench a major portion of their workforce.

The Covid-19 pandemic also impacted other economic activities around the world. De Vito and Gómez (2020) reported the fear of lockdown resulted in a change in consumer demand for products and services. This situation has affected the production and service supply chain at domestic and international levels. The pandemic has caused job losses that led to a reduction in consumer demand and resulted in a global economic recession ( OECD 2020b ). Investment activities were also affected as investors tried to discount the liquidity risk in stock prices. In this regard, the liquidity of listed firms across 26 countries showed a drop of 50%in sales for a full operating firm, with a partial operating firm facing up to a 75% drop in sales ( De Vito & Gómez 2020 ). It was suggested that government fiscal policies, tax deferrals and bridge loans are the most practical policies to overcome the issue of a massive problem with cash flow, a cash crunch.

Pandemic, Unemployment and Mental Health

Unemployment may be generally viewed as an economic issue, but its impacts are actually greater than that as it affects mental health, stress levels and consequently an individual’s quality of life, as well as having an impact on community. As it is, staying employed during economic hard times is one of the most challenging things in life, and when unemployment is prevalent, finding work is doubly difficult. Unemployment is regarded as one of the major discrete and objective life events that necessitate some social or psychological adjustment on the part of the individual in the stress literature (Wheaton 2009). The stress process model ( Pearlin 1981 , 1989 ) is a common explanation for the relationship between unemployment and mental health that is significant in the present scenario.

Although the theoretical model in its original study was based on the effects of stress by people with Parkinson’s disease, its findings are useful and applicable in explaining the stress caused by unemployment due to the Covid-19 pandemic. Pearlin claims that being unemployed increases the likelihood of experiencing stress-inducing factors such as a lack of resources, limited opportunities, and low self-esteem, as well as limiting access to privileges and security ( Pearlin 1989 ). That is, unemployment is likely to result in a variety of losses, including social ties and economic stability, and these losses may be stressful in and of themselves ( Pearlin 1981 ). As a result, unemployment raises the risk of poor mental health. Achdut and Refaeli (2020) through a hierarchical linear model demonstrate that contextual stressors related with the Covid-19 outbreak such as worries and fears of family members being infected, and financial strain might further reinforce the strain caused by unemployment. Additional stress would be in the form of lack of social services for mental health that most government in third world countries are hard pressed to provide. This situation compounds the problem.

Methodology

Primary and secondary data were utilised in this paper. Primary data were collected through two separate sets of questionnaires conducted by the Family, Women and Youth Survey (see Tey 2020 ). The survey was conducted online between 13 November and 5 December 2020. Data were collected from 2503 respondents from all districts in Sabah. The data were collected using stratified sampling method to achieve a representative sample. In that, the number of respondents for each district was pre-determined proportional to the population size, and stratified according to gender, ethnicity, and age of the respondents. Secondary data were mainly sourced from various official agencies and organisations especially online publications and reports.

This paper is structured with an introduction of the background concerning the issue and relates it to theoretical discourses. It continues with methodological and limitations issues, and a discussion on employment in the airlines industry as the most vulnerable industry under threat. While the airline and hotel industries which makes up the bulk of the hospitality part in the tourism sector, were directly affected in this pandemic, many other downstream industries felt the brunt as well. Thus, the general employment situation needs to be examined to assess the impact of the pandemic. For that purpose, this paper includes a discussion about employment in Malaysia as a whole, utilising secondary data. It then scales the discussion to the employment situation in Sabah based on findings from the primary data collected and corroborated to the secondary data. Sabah is selected as case study because of the importance of the tourism sector in its economy, ranking third in the hierarchy of contributing sectors to the economy of Sabah.

Limitations

While this paper attempts to make a first inventory about the Covid-19 pandemic effects on the employment sector, it does have its limitations. Arguably, it is still relatively early to make any determination about long term effects, whether from national government policies or from global contributions. Currently as the pandemic rages on throughout the world, many companies are trying various attempts and measures to survive, with or without laying off staff. Some experts have warned that with the emergence of variants, Covid-19 may become endemic. However, it is hoped that the vaccination process currently underway, worldwide, will put a halt to the spread and in due time, reverse the damage done. Nevertheless, this paper will be a valuable contribution if and when we find ourselves in a post-pandemic era.

The Vulnerable Airline Industry

According to Sobieralski (2020) , the travel restrictions put in place by governments have greatly affected the movement of peoples around the world, while having a negative impact on numerous downstream industries such as the tourism, hospitality and the transportation industries. The issue of flight cancellations and capacity reductions have made the airline industry fight for survival. Some locally grown airlines, for example Malindo Air, had thrown in the towel due to various cash flow problems over the one-year lockdown period, resulting in the majority of its staff losing their jobs, with only 1000 staff remaining employed ( Ram 2020 ; Birruntha 2020 ). For airlines, a reduction in the workforce was unavoidable. It was estimated that nearly 7% of the airlines’ workforce was trimmed with an upper limit of over 13% with major airline workers being most impacted while regional and low-cost workers were the least affected ( Sobieralski 2020 ). The work a person didalso plays a part here. An airline employee who handled flight operations and passengers was the most affected while management employees were relatively little impacted during this period. It is projected that the recovery of this industry will take around 4 to 6 years to return to pre-Covid-19 situation provided government support via plans and financial assistance are forthcoming.

As airlines are the most directly and immediately affected by the travel restrictions put in place by governments, employment in the airline industry is the focus of discussion here. At the beginning of the pandemic, airlines management opted for unpaid leave and pay cuts for its employees. Unpaid leave was seen as a temporary measure to help airlines reduce the cost burden, but it could not reduce the pressure for the potential risk of unemployment to employees. During the period of unpaid leave, the most affected employees were aircrew members, who were not able to work from home like other employees because the nature of their jobs has been serving passengers on flight or in the airport. During the MCO, they had to survive with their existing savings, and they may have needed to find other ways of making ends meet.

As the airline industry could not operate normally during this period, some aircrew members who had been flying for many years chose to start their own businesses to earn an income to supplement their daily needs. The first MCO in Malaysia was imposed from 18 March 2020. According to a report in Malay Mail , a pilot named Johan MD Rosnan took the unpaid leave program from the beginning of the MCO ( Zikri 2020 ). Since the international borders of most countries and regions closed, this pilot, who flew international flights, has not had any job assigned to him. It is fortunate that Johan, his wife, and his friends had started a plan prior the announcement of the MCO to sell their homemade rainbow bread and coffee bread. As there had been an increase in sales orders for the bread, he decided to open a physical store before MCO in Johor to earn income during the pandemic period. This bakery business not only helped him significantly financially during the pandemic, but it has also helped him psychologically. Since the MCO brought a lot of financial pressure to everyone, his business has helped to keep him busy.

Similar situations were found in Sabah as well. An example is that of stewardess Wendy Radin who had her last flight in April 2020, and is now a full-time financial planning consultant in unit trust sales ( Inus 2021 ). She shared her experience and recruited others to the industry to earn some income to support their family, including those who could not afford to invest themselves. Investment in unit trusts has been encouraging when the government announced the i-sinar programme. This programme was aimed at those who were affected during the MCO and it allowed eligible depositors of the Employees’ Provident Fund (EPF) to withdraw a certain amount from their respective Account 1 with a ceiling of RM10,000 for those depositors with RM100,000 or below; and maximum 10% or RM60,000 for those have more than RM100,000 in their Account 1 ( Azman 2021 ). There were success stories for some but not for all.

The AirAsia Group Berhad has gone through several cycles of layoff. The first cycle of layoffs in Air Asia involved more than 333 employees to reduce operational cost under difficult circumstances ( Aroff 2020 ; Bernama 2020 ). The 333 comprised 111 cabin crew members, 172 pilots and 50 engineers across Malaysia. From that number, it was reported 50 of the laid off staff were based in Sabah ( Micheal 2020 ). The majority of those terminated in Sabah has been working for 10 to 15 years in Air Asia. It was claimed that those involved had not received any information about the layoffs prior to the announcement. The sudden news of layoffs had caused the affected employees to panic and feel helpless. This number was a significant proportion because it represented about one sixth of the total laid off staff. Hence, it reflected the importance of Air Asia’s presence in the air travel industry, especially in Sabah. With tourism being one of the dominant sectors in Sabah, the pandemic has severely affected the Sabah economy. Tourism is the third largest contributor to the economic sector in Sabah prior to the pandemic, representing 23% of its total labour force with RM8 billion (USD 1.85 billion) generated in 2018 ( Chan & King 2020 ). Thus, the repercussion to be felt as the pandemic progressed throughout the year 2020 were severe.

Retrenchment brings people into financial hardship, especially those with families to support. As the breadwinner of the family, the loss of a job means the loss of one’s self-identity, besides losing the only source of income for the family, resulting in mental and psychological stress. According to another news report, the stress of losing a job had forced a former pilot to jump to his death from an apartment in Kuala Lumpur ( Bernama 2020 ). This 36-year-old man, a former pilot with local airline, had taken such drastic action due to the pressure of unemployment.

One might ask, in relation to the retrenchment exercise, whether, prior to the airlines laying off its employees, they conducted an exit interview with the personnel involved. If the action of exit interviews were undertaken, the airlines could identify vulnerable employees and taken immediate steps to remediate the situation. In general, companies need to address some or all of the problems and elements related to layoff. As companies plan and communicate the layoffs, it would be useful to maintain a record of the actions taken to help the employee adjust to the new situation. The elements involved in planning layoff can be divided into three sections which are layoff pre-planning, layoff notification planning, and post-layoff notification planning. In the context of Covid-19, there are questions over whether these processes were followed. Were the unfortunate employees prepared mentally and emotionally to face this new challenge in their life? For instance, what kind of package or program would be offered to the laid off employees or to employees offered early-retirement? This kind of package or program offered may include subsistence e.g. their daily needs like money, financial advice, but the most important issues were emotional and mental support. Did the airline offer any kind of psychological need for their employees? These processes would help the airline determine if the employee had enough resources to face retrenchment. Unfortunately, the evidence is that these processes were lacking as many terminated staff claimed that they were given short notices i.e. 2 days, and offered unfair compensation ( Micheal 2020 ).

There is evidence from several sources that there was a significant need for psychological support for people facing hardship at this time. According to the police records, from 18 March 2020 (when the MCO started) to 9 June 2020 there were 78 suicides across the country that reflects a spike as compared to 64 suicides reported in the same period for the previous year 2019 ( Astro Ulagam 2020 ). A study conducted by the Malaysian think tank centre in April also found that 45% of 1084 Malaysians surveyed experienced anxiety and depression during the partial lockdown ( Astro Ulagam 2020 ). Referring to the Department of Statistic Malaysia (DOSM), the unemployment rate in Malaysia for May rose to 5.3%, and 826,100 Malaysians were unemployed that month. Losing a job can easily make people lose the will to live. Although unemployment can lead people to a dead end, it may also bring a new opportunity to learn new things such as new business ventures.

The National Health and Morbidity Survey 2019 found that the prevalence of depression in Sabah was 4.0%, higher than the national prevalence of depression at 2.3%. It also revealed that Bumiputera Sabah, the indigenous people of Sabah, ranked highest among all ethnicities with score of 5.2% for depression ( IPH 2020 ). Thus, the consequences of mental threat due to the Covid-19 pandemic posed a significant threat to people in Sabah especially the indigenous people. Recognising the potential threats and risks, mental health psychosocial support teams were established in every district throughout Sabah at the early stages of the pandemic. However, information about accessing this support was somewhat lacking.

The survival of airlines is dependent on travelling passengers. As such, tourist arrivals is an important factor to examine. While there are other means to reach Sabah such as by land (from other parts of Borneo Island) and sea (from West Malaysia), air travel remains the popular method of travel, especially for international tourists, that accounted for 95% of the total arrivals ( Chan & King 2020 ). Thus, it is a relevant indicator and a useful reference point to explain the situation in Sabah and its relationship to the employment situation in Sabah. In other words, employment is explained through the data of visitor arrivals that is directly indicative of the vitality on the airlines and hospitality industry, subsectors of the tourism sector that forms a major part of the Sabah economy, and other industries that service this sector indirectly.

In Sabah, the total tourist arrivals experienced a drastic reduction for the year 2020 due to the Covid-19 pandemic and its corresponding measures to curb the spread such as border closure and movement control orders (MCO) of which there were various kinds: Conditional Movement Control Order (CMCO), Recovery Movement Control Order (RMCO), Enhanced Movement Control Order (EMCO), Targeted Enhanced Movement Control Order (TEMCO), Administrative Enhanced Movement Control Order (AEMCO), and Full Movement Control Order (FMCO) or ‘Total Lockdown’ that each has a separate set of standard operating procedures ( Povera & Harun 2020 ). Currently, the FMCO has completed its first phase 1st -14th of June 2021 and has been continued with its second phase of implementation from the 15th-28th of June 2021 that may be further extended if necessary.

As early as January 2020, Sabah was the first state in the country to ban all flights from Wuhan as part of its preventive measures ( Bernama 2020 ). A few days after this ban was imposed, the Government of Sabah took another bold step to ban all China-Sabah flights on 30 January 2020, a decision that was opposed by several quarters (FMT 2020; MATTA 2020 ). The concern raised may have been due to the fact that tourists from China comprised 42.3% of the total foreign tourists in 2019. The decision was a serious blow to revenue from the tourism sector and its related subsectors. The ban on China-Sabah flights has affected 127 weekly flights involving airlines including AirAsia (60), China Southern Airlines (21), Malindo Air (20), Malaysia Airlines (9), Xiamen Air (7), Shanghai Airlines (7) and Loong Air (3). It is clear that Air Asia has played a dominant role in Sabah tourism, being the main air carrier for Chinese tourists.

As a result, Sabah had managed to remain free from the Covid-19 while other places in Malaysia started to record cases of Covid-19. Sabah maintained its record at zero cases until 12 March 2020 when its first case of the Covid-19 was reported ( Kee 2020 ). The patient zero for Sabah had attended a religious gathering at Sri Petaling, Kuala Lumpur. Over time, further restrictions were imposed as the situation changed. By the middle of March 2020, special approval from the Government of Sabah was required for non-Sabahan Malaysians to enter Sabah, with the provision of medical certificates and polymerase chain reaction (PCR) swab test results, while all foreign tourists and visitors were banned from entering Sabah ( Geraldine 2020 ).

Source: Sabah Tourism Board (STB), 2021

By comparing the data available from the year 2019, the total tourist arrivals in Sabah decreased 76.7% from 4.2 million in 2019 to less than a million in 2020 i.e. from 4,195,903 (2019) and 977,460 (2020) respectively ( Sabah Tourism Board 2021 ). Total international tourist arrivals observed a greater decline than domestic arrivals. There was a reduction of 87.7% for international tourist arrivals from 1,469,475 in 2019 to 180,284 in 2020; while domestic arrivals decreased 70.8% from 2,726,428 to 797,176 for the same period. Prior to the pandemic, total tourist arrivals in Sabah have been rising steadily over time ( Table 1 ). When MCO was declared on the 18 th of March 2020, the decline became noticeable. For the year 2020, tourist arrivals from January to March represented 71.3% of total visitors for the year while April to December the proportion recorded was 28.7%. It was particularly obvious for international tourists with 97.5% of them visiting Sabah during the period from January to March 2020 while only 2.5% of the total international visitors arrived during the remaining months of the year ( Sabah Tourism Board 2021 ). This indicates a vast shrinkage in foreign arrivals, bordering on an almost total absence in Sabah from April 2020 onwards. While we understand the situation of the tourism sector in Sabah, we must now explore this situation with regards to the general employment situation in Malaysia.

Employment Situation in Malaysia

In Malaysia, the Covid-19 pandemic has affected the labour market. Job losses and reduction in working hours implemented by employers as cost-cutting measures were observed in almost all industries affected by unemployment for the year 2020 at 711,000, increased from 508,200 for year 2019 or unemployment rate increases to 4.5% (2020) from 3.3% (2019) as reported by the Department of Statistics Malaysia (2020).

It has been observed that the ratio of labour force participation for male is higher than female for all ranges of age. While these data might be a decade old as the census in Malaysia is taken on a 10-year cycle (Census 2010, as shown in Table 2 ) they are useful as a benchmark.

Source: Department of Statistics Malaysia (DOSM)

The major motivations for people to work are ranked in order as follow: to sustain cost of living, seeking attention, addressing pressure from one’s surrounding peers, family and relatives, making one feeling comfortable and having peace of mind by working. For youth aged 20-29 years old, the main factor for them to work is to sustain their cost of living or cover their daily expenses (39%), follow by seeking attention (26%), addressing pressure from their peers, family and relatives (22%), feeling comfortable (8%) and obtain peace of mind (5%). This ranking order of factors are consistent with other age groups, as well as for both genders and regardless of whether one is with or without dependent (see Table 3 ).

Source: Credit Counselling & Debt Management Agency (AKPK)

Adapted by: Institute of Youth Research Malaysia (IYRES)

The year 2020 was a challenging year for most countries around the world. Besides the airline industry, many industries in Malaysia were also affected by the pandemic. Figure 1 shows the loss of employment (LOE) in Malaysia for the year 2020. LOE in Malaysia refers to individuals who are insured with the Employment Insurance System (EIS), who had lost their job, but it does not include compulsory retirement, voluntary resignation, expiry of a fixed-term contract, and retrenchment due to misconduct. While it does not include all individuals affected by the pandemic such as those that did not sign up for the EIS, it is a significant point of reference to examine the impact in due course. The implementation of the Movement Control Order (since the18 March 2020) has resulted the number of LOE to drastically increasing in tandem, peaking in June and July 2020 as can be seen in Figure 1 .

Figure 1. Monthly Loss of Employment in Malaysia, 2018 – 2020

Source: EIAS, 2020

The data also indicated that most industries were affected by the MCO, with the top three most affected industries being the manufacturing industry, followed by the wholesale and retail industries, and finally the accommodation and food and beverage sectors. Many other industries underwent a similar retrenchment process but to a lesser extent as shown in Figure 2 . The main causes for these losses were downsizing, constituting 28% of total LOE, followed by business closure (25%), Voluntary/Mutual Separation Schemes (23%) (VSS/MSS), company financial problems (15%) and other issues (9%) as shown in Figure 2 .

Figure 2. Loss of Employment in Malaysia by Industry and Causes, 2020

In terms of occupations affected by the LOE, professionals top the list, follow by technicians and associate professionals, various categories of managers, service and sales workers, plant & machine operators and assemblers, and finally clerical support workers and others (see Figure 3 ).

Figure 3. Loss of Employment in Malaysia by Major Occupation Category, 2020

It is important to note that most LOE involved those earning a relatively lower income. It shows a tendency for lower salary earners to face a greater risk of having their employment terminated in a challenging economy. As such 40.1% were those earning below RM2,000, while 62.2% of the LOEs have an income less than RM3,000 or 78.1% were those below RM4,000. Those with relatively higher incomes were not spared from being laid off. Table 4 demonstrates the income levels of LOE in the year 2020 that may well reflect the income disparity and further increase the gap between the rich and the poor, worsened over time by the Covid-19 pandemic.

Figure 4. Loss of Employment by Wage Level in Malaysia, 2020

Employment Situation in Sabah

The case study for Sabah was examined and compared with the situation at the national and international levels. Further data requested from Social Security Organization (SOCSO) in Malaysia, revealed that 74.6% of those suffering loss of employment (LOE) in Sabah are workers earning below RM2,000 ( SOCSO 2021 ). While the trend is consistent with the LOE trend in Malaysia that lower income earners are the major casualties in employment, the situation in Sabah is relatively more severe than the situation in Malaysia as a whole (40.1%, refer Table 4 above). LOE over time in Sabah was found consistent with the trend of LOE in West Malaysia, peaking in June and July 2020 (see Figure 1 and Figure 5 ).

Figure 5. Monthly Loss of Employment in Sabah, 2019 – 2020

Source: SOCSO, 2021

The main reasons for job losses in Sabah were due to business closure (17.9%), financial problems of companies (17.4%), downsizing (15.2%), other issues (10.2%), partial closure (6.4%), VSS/MSS (5.5%), and several other categories that were below 4% ( SOCSO 2021 ). In comparison with the overall Malaysian situation, the businesses were hard hit and relatively more difficult to sustain under the circumstances due to the fact that many companies in Sabah were relatively more engaged in economic activities pertaining to the tourism sector or have a higher business overhead cost in Sabah. With the implementation of MCOs to curb the spread of the disease, this overhead cost became unmanageable ( Idris 2021 ). It was also interesting to note that the LOE on VSS/MSS in Sabah was relatively much lower in Sabah as compared to Malaysia as a whole (23%). This disparity may well reflect that fewer people have the option to ‘select’ or ‘choose’ the circumstances under which they would become unemployed, or rather this was not an option available to them. Although these may be unsubstantiated claims, this situation is well known anecdotally. As such, it would be interesting to establish its extent via further investigation into the wellbeing of workers as well as industry players in due course.

On the type of industries that top the list for LOE in Sabah was accommodation and food and beverages (21.6%), wholesale and retail (17.2%), administrative and support service (16.3%), construction (15.1%), manufacturing (4.6%), real estate (4.5%) while other industries accounted for less than 4% in each of the categories ( SOCSO 2021 ). The industry based LOE composition in Sabah was very different from the West Malaysia situation where manufacturing sector had the highest LOE. This corroborates the fact that tourism plays a dominant role in the economy of Sabah.

In following section, some relevant primary data from the Family, Women and Youth Survey, conducted online between 13 November and 5 December 2020, were analysed. While these data did not specify the tourism sector, it is useful considering the main sector affected by loss of employment were mainly those related to tourism directly or indirectly as discussed above. Table 5 shows the distribution of respondents under unmarried and married sample categories that cover all districts in Sabah.

Source: Tey et al. 2020

From the data collected from 2503 respondents at the end of 2020, 779 respondents (31%) reported that they were forced out of their jobs, comprising 438 female respondents and 341 male respondents. In terms of marital status, 443 unmarried respondents and 336 married respondents had lost employment due to the implementation of MCO (see Table 6 ). The findings demonstrated that more unmarried respondents had their jobs terminated as compared to married respondents. Unmarried respondents are relatively younger that married respondents on average. Hence, this is also in line with the claim that younger workers are facing greater risks of losing their job than more experienced workers. Besides, it may well be that marital status has been one of the factors considered by employer in retrenchment. However, this claim needs further verification.

The survey also investigated the loss of businesses as a consequence of implementing the MCO. A total of 582 respondents or about one quarter of respondents reported that they have loss businesses. Detailed figures on loss of businesses due to MCO are shown in Table 7 . These primary data were consistent with the analysis of the secondary data presented in earlier section.

With the imposition of MCO, it is noticed that majority of the respondents or more than two-third of respondents experienced a reduction in income (see Table 8 ). Thus, we can extrapolate that whether it is the loss of businesses or employment, most respondents were impacted financially under MCO. This claim is consistent when comparing the loss of employment for year 2019 and 2020 in Sabah, a significant increase that LOE in 2020 has doubled from year 2019 or 104.2%, i.e. from 2123 cases to 4344 that was recorded by SOCSO ( SOCSO 2021 ).

The pandemic of Covid-19 has affected working hours and the earnings of workers around the world. The International Labour Organization (ILO) has compiled the list of industries affected and urgent government policy is needed to address the situation. This crisis is expected to affect 6.7% of working hours around the world, which is equivalent to 195 million full-time workers ( ILO 2020 ). Around 81% of the global workforce of 3.3 billion was exposed partially or fully by this pandemic. Thus, comprehensive policies are needed to focus on, but not limited to, the following four aspects: supporting enterprises, employment, and incomes; protecting workers in the workplace; stimulating the economy and jobs; and using social dialogue between government, workers, and employers to find the solutions.

As Malaysia enters a new phase of the Covid-19 wave, it is unknown where the peak is as new highs are reported daily. Thus, it is important to prepare the community in facing the new challenges, financially and psychologically by evaluating and revisiting the strategies employed over the last one year. If the pandemic is prolonged and continue to impact the bread and butter of the people, fatigue will set in, resulting in the Malaysian society putting aside the current Standard Operating Procedures (SOP) in the Covid-19 prevention strategies or measures. We have seen this happen in countries like Indonesia and India, where livelihood has trumped Covid 19 prevention SOPs.

Indecisiveness in policy, political uncertainty and politicians behaving as if they are above the law in complying with SOP, will result in the erosion of the people’s trust as the situation of Covid-19 and economy continues to worsen over time. With uncertainty ahead and the declaration of emergency, the ruling PN-government may have been able to avoid a ‘vote of confidence’ in parliament, however, the ruling government cannot prevent the declining of ‘voice of confidence’ in the government.

With the news of the availability of vaccines for the COVID19 after successfully passing the mandatory clinical trials, being made available in Malaysia, the situation is looking favourable. However, effective measures to address the economic issues are still lacking. It is generally accepted that the people’s confidence in the government is closely related to the well-being of the economy. As shown in the survey conducted in Sabah and in the secondary data obtained from government agencies at the national level in Malaysia, the findings pointed to one common ground i.e. the hardship faced by the majority of Malaysians in the time of this pandemic Covid-19. Government roles and engagement with various communities and industries are essential especially when it involves retrenchment, loss of employment and/or loss of businesses in due course. Supports and measures for employers and employees need to be emphasised to overcome the challenges in this difficult time for all.

Acknowledgments

The support of the Research Management Centre of Universiti Malaysia Sabah and GRRI-HOSEI (Grant No. GKP 0020-2018) is gratefully acknowledged. The authors would like to thank SOCSO for additional data provision and UNFPA through IF066-2019 (UMS-TLK2019) for sharing primary data.

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Gendered Impact on Unemployment: A Case Study of India during the COVID-19 Pandemic

India witnessed one of the worst coronavirus crises in the world. The pandemic induced sharp contraction in economic activity that caused unemployment to rise, upheaving the existing gender divides in the country. Using monthly data from the Centre for Monitoring Indian Economy on subnational economies of India from January 2019 to May 2021, we find that a) unemployment gender gap narrowed during the COVID-19 pandemic in comparison to the pre-pandemic era, largely driven by male unemployment dynamics, b) the recovery in the post-lockdown periods had spillover effects on the unemployment gender gap in rural regions, and c) the unemployment gender gap during the national lockdown period was narrower than the second wave.

Introduction

The coronavirus disease (COVID-19) has adversely impacted labour markets all around the world. According to the International Labour Organization, the working hours lost in 2020 were equal to 255 million full-time jobs, which translated into labour income losses worth US$3.7 trillion (International Labour Organization 2021). Due to the existing gender inequalities, women were more vulnerable to the economic impact of COVID-19 (Madgavkar et al. 2020). The sudden closure of schools and daycare centres due to the Great Lockdown exacerbated the burden of unpaid care on women (Collins et al. 2020; Power 2020; Czymara et al. 2020; Seck et al. 2021). Women also disproportionately represented the accommodation, food services, and retail and wholesale trade sectors, which were worst-hit by the COVID-19 pandemic (Alon et al. 2020; Adams-Prassl et al. 2020; Bonacini et al. 2021). In most countries, women often work in these sectors without any work protection or job guarantee (United Nations Women 2020), leading them to loose their livelihoods faster than men while also dealing with their deteriorating mental health. India is an interesting case study with one of the lowest female labour force participation rates (LFPRs) globally to analyse how the COVID-19 pandemic exacerbated the pre-existing gender disparities in unemployment. According to the World Bank data, India’s female LFPRs was approximately 21% in 2019, the lowest among the BRICS nations (Brazil, Russia, India, China, and South Africa) and 26 percentage points lower than the global average. An even more troubling fact is that women’s LFPRs has been falling since the mid-2000s (Ghai 2018; Andres et al. 2017; Sarkar et al. 2019). Since the onset of the pandemic, women in India have been increasingly dropping out of the labour force. As seen in Figure 1, the greater female labour force, which comprises unemployed females who are active and inactive job seekers, has been lower than the pre-pandemic average since April 2020. The number of unemployed women actively looking for jobs has also been lower than the pre-pandemic average barring the months of April, May, and December in 2020. On the contrary, the number of women who are unemployed but inactive in their job search has risen drastically, albeit with minor fluctuations, during this period (Figure 2). A recent survey by Deloitte (2021) identified that the burden of household chores and responsibility for childcare and family dependents increased exponentially for women worldwide and more so in India due to the pandemic. The surveyed women mentioned increase in work and caregiving responsibilities as the main reasons for considering leaving the workforce.

Figure 1 : Percent Change in Female Greater Labour Force and Unemployed Active Job Seekers Compared to the Pre-pandemic Average

Source: Centre for Monitoring Indian Economy April 2020 - May 2021

Figure 2: Percent Change in Female Unemployed and Inactive Job Seekers Compared to the Pre-pandemic Average

Figure 3: Unemployment Rate in India (Percent)

Source: Centre for Monitoring Indian Economy Jan 2020 - May 2021  

This study analyses the effect of the COVID-19 pandemic on the gender unemployment gap from its onset until the second wave using the subnational-level monthly data from the Centre for Monitoring Indian Economy (CMIE). The gender unemployment gap is defined as the difference between male and female unemployment rates  ( Albanesi and Şahin 2018 ). We assess the gender unemployment gap during the COVID-19 pandemic compared to the pre-pandemic era using a difference-in-differences (DID) model. A preliminary investigation of the gender unemployment gap based on the raw data reveals that the gap declined in the lockdown period compared to the pre-lockdown period (Figure 3). We find the gender gap to widen during the second wave, albeit smaller than the pre-pandemic level.

Although a large number of national-level studies were conducted on the impact of the COVID-19 pandemic on unemployment (Estupinan and Sharma 2020; Estupinan et al. 2020; Bhalotia et al. 2020; Chiplunkar et al. 2020; Afridi et al. 2021; Deshpande 2020; Desai et al. 2021), this study is among the very first to assess the impact of the second wave of COVID-19 on the unemployment gender gap in India. A previous study found the rise in male unemployment during the lockdown period contributing to a smaller gender gap (Zhang et al. 2021). In this study, we take one step further to assess the effect of the second COVID-19 wave on the unemployment gender gap in India.

The remainder of the article is organised as follows. In Sections 2 and 3, we present the data sources and some facts on the unemployment trend in India. The effects of first and second COVID-19 waves on unemployment disaggregated by gender are discussed in Section 4. Section 5 delves into the gendered impact on unemployment dynamics across urban and rural regions. The concluding remarks are presented in Section 6.

Data and Methodology

In this study, we use the subnational-level monthly employment data from the CMIE from the period of

January 2019 to May 2021 . Starting from January 2016, the CMIE has been conducting household surveys in India on a triennial basis, covering the periods of January to April, May to August, and September to December. This is the only nationally representative employment data in the absence of official government data (Abraham and Shrivastava 2019) and has been used by several employment studies on India (Beyer et al. 2020; Deshpande 2020; Deshpande and Ramachandran 2020).

The employment data are classified into three categories—the number of persons employed, the number of persons unemployed and actively seeking jobs, and the number of persons unemployed and not actively seeking jobs. The sum of these three categories constitutes the greater labour force. The data are also disaggregated by gender (male and female) and residence (rural and urban).[1]   For the analysis, we focus on five time periods as indicated in Table 1.

Table 1: Time Periods

For state[2] i at time t, we construct the unemployment rate as given below:

Unemployment rate = Number of persons unemployed and seeking jobs/Greater labour force                                                                                                    (1)

Stylised Facts on Unemployment

This section describes some stylised facts based on the subnational unemployment data from February 2019 to May 2021. To this end, we estimate the regression model below:

where Unemp it is the unemployment rate of state i in time t . To see the unemployment dynamics over the period of study, we use a binary variable Month s that takes the value one for month s and 0, otherwise. The model takes into consideration the impact of past unemployment rates, represented by  Unemp it −1. Additionally, the state fixed effects  δ i  are included to account for unobserved, time-invariant state-level characteristics that may potentially confound our estimates.

Figure 4: Trends in Unemployment Rate

Our coefficient of interest is β 1 s which depicts the time trend in unemployment. The results from the model estimation are shown in Figure 4, in which we can see the dynamics of aggregate unemployment in India from February 2019 to May 2021. The vertical axis pertains to coefficient β 1 s , and the horizontal axis corresponds to the respective months. In Figure 4, the aggregate unemployment rate is found to be relatively stable during the pre-pandemic era. This trend faces an overhaul during the national lockdown (April–May 2020) with a structural upward shift in the unemployment rate. The shock to the unemployment rate does not persist as economic recovery during the post-lockdown period enables unemployment to fall steadily from June 2020 onwards. The unemployment rate becomes stable from January to March 2020 as the country returned to a sense of normalcy with the continued resumption of economic activity.[3]   However, the economic impact from the onset of the second wave of the COVID-19 pandemic caused the unemployment rate to rise again in April and May 2021.

Next, we estimate Equation (3) separately for the female and male unemployment rates to assess the gender differential impacts of the COVID-19 pandemic on unemployment in India.[4]

where binary variable Quarter s  takes the value one for quarter s in the time period of our sample. The model also accounts for lagged unemployment effects through Unemp it −1.

Figure 5: Trends in Unemployment Rate by Gender

Figure 5 shows that a stark gender gap in the unemployment rate (distance between the red and blue lines) exists in the pre-pandemic era as the male unemployment rate is consistently lower than that of the female. Figure 5 also shows that the gender gap dynamics are primarily driven by male unemployment. The sharp rise in male unemployment during the national lockdown causes the gender gap to close in Q2 2020. The post-lockdown recovery (Q3–Q4 2020) is found to have a favourable impact on male unemployment, causing gender gap to revert to the pre-pandemic levels. Although both males and females lost jobs during the onset of the second wave (Q2 2021), the gender gap narrowed as males are found to lose more jobs in absolute terms.

Figure 6: Trends in Urban and Rural Unemployment Rate by Gender

Figure 6 shows the estimates of β 1 s  (see Equation [3]) for urban and rural unemployment in Panels (a) and (b), respectively. During the national lockdown, the sharp rise in male unemployment is more evident in urban areas than rural. In fact, the national lockdown period dynamics in aggregate male and female unemployment in Figure 5 largely resemble the effects seen in the urban region (see Figure 6, Panel [a]). The post-lockdown recovery suits male unemployment, both in rural and urban areas. Female unemployment remains stable in rural areas during the pandemic.

Figure 7: Trends in Regional Unemployment Rate by Gender

7 c                                                                                                                                                                         

The subsample regression estimates of β 1 s  pertaining to the north, east, west and south regions are shown in Figure 7. All regions witnessed a rise in male unemployment during the national lockdown period. On the contrary, the female unemployment dynamics differ between regions. During the national lockdown period, female unemployment rose in the west and south regions (Panels [c] and [d] in Figure 7). The north region shows an interesting anomaly (Panel [a] in Figure 7). Contrary to other regions, female unemployment dipped steeply in the north during the national lockdown period. East region alone did not 

experience any strong movements in female unemployment throughout the pandemic (Panel [b] in Figure 7).

Impact of COVID-19 on Unemployment

Section 3 discussed how the overall unemployment and unemployment gender gap witnessed structural breaks during the COVID-19 pandemic. To further investigate the gender aspect of the COVID-19 unemployment dynamics in India, we begin our empirical exercise by examining the unemployment changes during the COVID-19 pandemic compared to the pre-pandemic era. We use the following model:

where Period 1 , Period 2 , Period 3 , and Period 4  pertain to lockdown, post-lockdown, post-lockdown normalcy, and second wave time periods, respectively. Besides the overall unemployment, we also estimate Equation (4) for male and female unemployment separately. The results are shown in Table 2. We can see from Column (1) of Table 2 that the overall unemployment rate ( β 11 ) witnessed an increase of 0.066 (statistically significant at one percent level) during the lockdown period in comparison to the pre-pandemic period. This effect was primarily driven by the rise in the male unemployment that shot up by 0.082 during the lockdown period (Column [3]).

The uneven distributional effects of the post-lockdown recovery are seen from β 12 estimates. Male unemployment rose by 0.01, while female unemployment fell by 0.036 in comparison to the pre-pandemic era. The fall in female unemployment does not necessarily indicate that the overall labour conditions improved for women during this period. Equation (1) shows that the unemployment rate is driven by two components. Figure 1 validates that the female unemployment rate fell over time due to the decline in the number of unemployed females actively seeking jobs being higher than the decline in the female labour force.[5]

β 14 estimate in Column (1) indicates that the total unemployment rose by 0.019 (statistically significant at 10 percent level) during the second wave compared to the pre-pandemic period. A comparison between β 14 and β 11 estimates reveals an interesting policy highlight that the second wave’s impact on unemployment was smaller than the nationwide lockdown. Finally, the rise in unemployment during the second wave is primarily driven by male unemployment.

Table 2: Impact of COVID-19 on Unemployment

Note: *** p<0.01, ** p<0.05, and * p<0.1. The robust standard errors are in parentheses.

Unemployment Gender Gap in Urban and Rural Regions

This section delves further into the gendered impact of lockdown on the unemployment dynamics across urban and rural regions. As defined in Section 1, the unemployment gender gap measures the difference between female and male unemployment rates. To identify the effect of the first and second COVID-19 waves on the unemployment gender gap, we estimate the regression model below:

where Female is a binary variable that takes the value 1 for female unemployment and 0, otherwise.

Table 3 shows the estimation results of Equation (5). We discuss the coefficient estimates that are found to be significant. The significant β 1 coefficient reiterates that the unemployment gender gap was an existential problem in India even before the COVID-19 pandemic. The β 31 estimates reveal that the urban region dynamics drove the narrow unemployment gender gap during the lockdown period. Although the magnitude of the narrowing gap during the lockdown did not persist to the post-lockdown period ( β 32 ), rural regions experienced a narrow unemployment gender gap (marginally significant at 10%). This trend continues even in the post-lockdown normalcy period ( β 33 ) as the unemployment gender gap is narrower than the pre-pandemic level by 0.047 in the rural region. This highlights the possibility that the post-lockdown recovery process had a spillover effect on the unemployment gender gap in rural regions. Finally, β 34 estimates show that the narrowing gender gap trend persists only in the urban region during the second wave.

Table 3: Impact of COVID-19 on Unemployment across Urban and Rural Regions during the post-lockdown and post-lockdown normalcy periods.

This article analyses the impact of the COVID-19 pandemic vis-à-vis the pre-pandemic period on the gender unemployment gap. Our findings indicate that the gender gap in unemployment narrowed during the COVID-19 pandemic, primarily driven by male unemployment dynamics. Interestingly, we find that female unemployment declined during the post-lockdown period. Such a decline was likely driven by women dropping out of the labour force rather than a dip in the absolute number of unemployed persons. Further, the region-wide subsample analysis finds the unemployment gender gap in urban regions to narrow across all periods of the COVID-19 era. In contrast, the rural regions witness narrowing gender gap during the post-lockdown normalcy. This indicates that the rural regions’ unemployment gender gap witnessed spillover effects from recovery associated with the economic reopening. Finally, the narrow gender gap (compared to the pre-pandemic level) is smaller during the second wave.

There is a looming uncertainty whether the impending third wave will further narrow the gender unemployment gap at the expense of increasing male unemployment and females being pushed out of the workforce. Further research is required with a more extended period of assessment and focussed on household-level data to understand the difference in the impact of COVID-19 on the gender unemployment gap across the different parts of the country and income strata.

The authors thank Paul Cheung and the anonymous referee for their valuable comments and feedback. They also thank Rohanshi Vaid for her excellent research assistance.

[1] The data are not available for Jammu and Kashmir, Andaman and Nicobar Islands, Arunachal Pradesh, Dadra and Nagar Haveli, Daman and Diu, Lakshadweep, Manipur, Mizoram, Nagaland, and Sikkim. Hence, the main analysis focuses on only 26 subnational economies.

[2] The terms “state” and “subnational economy” are used interchangeably throughout the article.

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Constructing a Novel Network Structure Weighting Technique into the ANP Decision Support System for Optimal Alternative Evaluation: A Case Study on Crowdfunding Tokenization for Startup Financing

• Research Article
• Open access
• Published: 26 August 2024
• Volume 17 , article number  222 , ( 2024 )

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• Chun-Yueh Lin 1  

This study constructed a novel decision-making framework for startup companies to evaluate token financing options. A Network structure weighting (NSW) technique was developed and integrated with the analytic network process (ANP) to create a comprehensive assessment model. This innovative approach addressed the limitations of traditional multi-criteria decision-making methods by effectively capturing the complex interdependencies between factors influencing token financing decisions. The proposed model comprises three main steps: (1) utilizing a modified Delphi method to identify key factors affecting token financing, (2) developing the NSW technique to determine the network structure of these factors, and (3) integrating the NSW results into the ANP model to evaluate and rank the critical factors and alternatives. This study applied this framework to assess three token financing alternatives: Initial Coin Offerings (ICO), Initial Exchange Offerings (IEO), and Security Token Offerings (STO). The results indicate that STO is the optimal financing alternative for the analyzed startup scenario in token financing, followed by Initial Exchange Offerings and Initial Coin Offerings. The model identified platform fees, issuance costs, and financing success rate as the three most critical factors influencing the decision. This study contributes to both methodology and practice in FinTech decision-making. The NSW-ANP framework offers a more robust approach to modeling complex financial decisions, while the application to token financing provides valuable insights for startup companies navigating this emerging funding landscape. The proposed framework lays the groundwork for more informed and structured decision-making in the rapidly evolving field of cryptocurrency-based financing.

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1 Introduction

Due to the rise and development of Financial Technology (FinTech), as well as the enactment of the Jumpstart Our Business Startups (JOBS) in the U.S. [ 1 ], crowdfunding has become the newest financing means for enterprises in need of external funds [ 2 , 3 ]. In 2014, the total amount of funds raised through crowdfunding reached USD 16.2 billion, which was 167% higher than that of 2013 [ 4 ]. In addition, according to the statistical results of Statista Inc. (2020) [ 5 ], the total amount of alternative financing in 2020 was USD 6.1 billion, among which crowdfunding accounted for the largest market share. For this reason, it could be said that the development scale of crowdfunding in the global financial market has been rocketing.

Crowdfunding involves a number of different forms. The first form is donation-based crowdfunding, which mainly means to raise charity funds for the implementation of programs and projects. The second form is rewards-based crowdfunding, in which the investor can receive non-monetary rewards because of capital contributions. The third form is debt-based crowdfunding, in which the relevant interest arrangements between the investor and the fundraiser are determined in line with credit contracts. The fourth form is equity-based crowdfunding, in which the fundraiser uses the equities of the target company to exchange funds from the investor, while the investor receives such equities and therefore is entitled to that company’s revenues or dividends [ 6 , 7 , 8 ]. Estrin et al. [ 9 ] pointed out that equity-based crowdfunding depends mainly on the Internet or social network platforms. This fund-raising method not only reduces the transaction cost but also stands for a new business pattern under which startup companies can establish their own goodwill and provide investors with opportunities for investment. Although crowdfunding has many advantages for startup companies, risks do exist, including uncertainty of equity ownership, lack of liquidity, and damage to stockholder equity [ 10 , 11 , 12 ]. For this reason, past studies suggested that startup companies might obtain funds by offering tokens on the basis of distributed ledger technology and the immutability of blockchains. This not only could reduce the potential risks of traditional fundraising platforms but also could promote the transparency level of the relevant transactions [ 12 , 13 , 14 ]. Howell et al. [ 15 ] indicated that token financing has become one of the important sources for enterprises to raise funds through digital platforms. Presently, the development of crowdfunding tokenization mainly involves three patterns: (1) initial coin offerings (ICO), (2) initial exchange offerings (IEO), and (3) security token offerings (STO). ICO has the advantages of low cost and high speed. However, the risks of theft and fraud exist [ 15 , 16 , 17 ]. The advantages of IEO include having the business reputation of a third-party platform as a guarantee and handling the relevant transactions directly on the transaction platform. However, the possibility of the token price being manipulated cannot be ruled out [ 17 , 18 ]. The last pattern, STO, has the advantages of the highest level of safety and of being protected by the rules and regulations of regional governments. However, the high complexity of examination and verification as well as excessively low liquidity are problems that cannot be avoided [ 17 , 19 ]. The research results of past literature also show that for startup companies, the efficiency of token financing is higher than that of equity financing [ 20 ]. Furthermore, Chod et al. [ 14 ] pointed out that enterprises may take advantage of the decentralization features of token financing to make it more convenient for token investors in their project investments and reduce the cost of encouraging token investors to join the investment platforms. In this way, it is easier for entrepreneurs in raising funds.

For this reason, the utilization of token financing for the purpose of raising operation efficiency has become an important business strategy. The aforesaid three patterns of crowdfunding tokenization have their respective advantages and disadvantages, as well as potential risks. If startup companies intend to raise funds through virtual currencies, the alternatives of financing in cryptocurrency will affect the financing efficiency and lead to the capital turnover issue. Previous studies on token financing focused more on risk-return analysis [ 21 , 22 , 23 , 24 ], token rules and regulations [ 25 , 26 , 27 ], hedging of tokens [ 28 , 29 , 30 , 31 ], and prediction of price in tokens [ 32 , 33 , 34 , 35 ]. However, there is scarce evidence and a lack of applicable measurement tools in regard to assessing the optimal solution for the token financing of startup companies. Hence, algorithms for multiple criteria decision-making can be utilized for the construction of assessment models, so that the optimal solution for assessment can be reached [ 36 , 37 , 38 ]. Past studies also suggested that the optimal solution can be solved using the analytic hierarchy process (AHP) [ 38 , 39 , 40 , 41 , 42 ]. Although AHP can be used to assess the optimal solutions in different fields, it is unsuitable to use traditional AHP methods for decision-making problems in real situations. AHP is characterized by a hierarchical structure and based upon the presumption that the variables or criteria are independent from each other. Numerous problems relating to the assessment of optimal solutions and the relevant variables are correlated to or dependent on each other; as a result, complicated internal relationships cannot be solved through hierarchical or independent methods [ 43 , 44 ]. To solve this problem, Saaty [ 45 ] proposed the analytic network process (ANP), which added a feedback mechanism and interdependency to the AHP method to solve the problems of a lack of correlation and interdependency. ANP does not require the linear relationship of traditional AHP methods, which is top-down, and can establish an assessment pattern of networked relationships. Past literature has applied ANP models in the assessment of different industries, such as traffic problems [ 46 , 47 ], environment and energy assessment [ 48 , 49 , 50 ], filtration and selection of suppliers [ 51 , 52 , 53 ], and assessment of risk factors [ 54 , 55 , 56 ]. Thus, it can be seen that the problem of correlation or interdependency between criteria or variables cannot be solved effectively through AHP during decision-making, while ANP can effectively solve this problem. Although ANP can overcome the difficulties related to the presumption of independence in AHP, the ANP algorithm cannot ascertain the strength of the dependence and relationships between variables needed to generate a network structure. Previous studies addressing the network structure issue have applied deep machine learning concepts, as demonstrated by Moghaddasi et al., Gharehchopogh et al., and subsequent works by Moghaddasi et al. [ 57 , 58 , 59 , 60 , 61 ]. However, these studies primarily focused on the relationship in the Internet of Things, implicitly highlighting the challenges in applying such approaches to multi-criteria decision-making (MCDM) problems. Additionally, several studies employed the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to resolve network structures among criteria [ 62 , 63 , 64 , 65 , 66 ]. This approach offers an alternative perspective on capturing complex interrelationships within decision-making frameworks. However, the DEMATEL method has several limitations. First, the relationships derived through DEMATEL may be biased or misleading [ 67 , 68 ]. Additionally, the method faces convergence issues, as it cannot determine relationships between criteria when the data fail to converge [ 69 ]. As evident from Table  1 , there are two primary gaps in the existing literature. First, in terms of network structure methodology, while ANP, DEMATEL, and other decision-making frameworks have been proposed, they each have limitations. Second, regarding the research problem, while many studies have examined different aspects of token financing, there is a notable absence of comprehensive, quantitative decision-making frameworks specifically designed for startup companies evaluating token financing alternatives. In view of the above, this study developed a new network structure weighting (NSW) model, and then integrated NSW into ANP to remedy ANP’s shortcoming of being unable to determine the network structure. Finally, case studies were carried out to assess the optimal solution for startup companies engaging in token financing.

For the proposed NSW-ANP model, the modified Delphi method was utilized to determine the clusters and factors influencing startup companies engaging in token financing. Then, the network structure of these clusters and factors was determined based on the NSW method. Finally, the ANP model was utilized to calculate the weights of various factors and financing schemes for startup companies engaging in token financing and then sequence them to determine the optimal token financing schemes and their key factors. While ANP has been applied in various fields, this study proposed the first application of an enhanced ANP approach (integrated with NSW) to evaluate the token financing options for startups. This novel application demonstrates the versatility and effectiveness of our integrated approach in addressing complex FinTech decision-making scenarios.

This study makes significant contributions to the existing literature in both methodological innovations and novel applications. In terms of methodological advancements, we introduce a novel NSW technique that quantifies the strength of relationships between decision factors in a network structure. Furthermore, we develop an integrated NSW-ANP framework that enhances the capabilities of the traditional ANP by incorporating a more robust method for determining network relationships. With regard to novel applications, this study breaks new ground in two key areas. Firstly, we apply this integrated NSW-ANP framework to evaluate token financing options for startup companies, an area that has not been addressed using such a comprehensive decision-making approach. Secondly, this study provides the first systematic evaluation of ICO, IEO, and STO using a multi-criteria decision-making framework. This framework resolves the complex interdependencies between various factors, offering a more nuanced understanding of these emerging financing mechanisms. By combining methodological innovation with practical application in an emerging field, this study not only advances the theoretical understanding of multi-criteria decision-making processes but also provides valuable insights for practitioners in cryptocurrency-based startup financing. Academically, the new NSW-ANP model put forward in this study could be used for determining the network relationship of a research structure, and be integrated into the ANP to remedy the ANP’s shortcomings. The new integrated decision-making pattern put forward in this study also could provide valuable references for the measurement of the interdependency and correlation among variables in the assessment of the optimal solution of token financing for startup companies. Practically, the proposed framework could provide startup companies with a measurement tool containing a network structure and is valuable, so as to determine the optimal solution of token financing for startup companies introducing token financing to their businesses.

The remainder of this paper is organized as follows: Sect.  1 is the introduction, Sect.  2 describes the research method, Sect.  3 presents the case study, and Sect.  4 offers the conclusions.

2 Methodology

In this study, the clusters and factors were acquired through collecting experts’ opinions and literature reviews via modified Delphi method (MDM) as a first step. Next, the network structure of the clusters and factors was determined on the basis of the network structure weighting (NSW) method. Finally, the analytic network process (ANP) model was utilized to calculate and sequence the weightings of the various factors and financing schemes of startup companies engaging in token financing so that the most suitable token financing scheme and the key factors could be determined. The research method is presented in the following sections.

The Delphi method is an anonymous technique of decision-making by a group of experts. To solve a certain problem or find a solution for a particular future event, these experts are treated as the appraisal targets. For the final goal of reaching a stable group consensus among the experts, the group members are anonymous to each other, and particular procedures and repetitive steps are employed. The Delphi method attempts to combine the knowledge, opinions, and speculative abilities of experts in the field in an interruption-free environment. The Delphi method can be used to deduce what will happen in the future, effectively predict future trends, or reach a consensus over a certain issue [ 70 , 71 ]. This method is based upon the judgment of experts, and multiple rounds of opinion feedback are utilized to solve complicated decision-making problems. The traditional Delphi method emphasizes the following five basic principles [ 72 , 73 ]:

The principle of anonymity: All experts voice their opinions as individuals, and they remain anonymous when doing so.

Iteration: The questionnaire issuer gathers up the experts’ opinions and sends them to other experts. This step is carried out repeatedly.

Controlled feedback: In each round, the experts are required to answer pre-designed questionnaires, and the results are served as references for the next appraisal.

Statistical group responses: Comprehensive judgments are made only after the statistics of all the experts’ opinions are conducted.

Expert consensus: The ultimate goal is to reach a consensus after the experts’ opinions are consolidated.

The procedures of the Delphi method are as follows [ 74 ]:

Select the anonymous experts.

Carry out the first round of the questionnaire survey.

Carry out the second round of the questionnaire survey.

Carry out the third round of the questionnaire survey.

Consolidate the experts’ opinions and reach a consensus.

According to the modified Delphi method, Steps C and D are carried out repeatedly until a consensus is reached among the experts, and the number of experts should be between five and nine [ 75 , 76 ].

In this study, the experts’ opinions were gathered through the Delphi method and the relevant literature was discussed, so that the clusters and factors influencing startup companies engaging in token financing could be obtained.

2.2 NSW Model

This study utilized the Delphi method to collect the clusters and factors that could influence startup companies engaging in token financing schemes. In order to effectively carry out the calculation and assessment of ANP, the network structure of these clusters and factors need to be determined as a prerequisite for the subsequent filtration and selection of the optimal token financing scheme. Therefore, this study put forward the NSW method in order to acquire the relationships and the structure chart between clusters and factors. The NSW procedure is as follows:

Step 1: Collect and confirm the decision factors

The collection and confirmation of the decision factors can be realized through common tools such as literature reviews, the Delphi method, focus group interviews, and brainstorming. When decision-makers or experts need to determine n assessment factors that are consistent with the decision-making issues, the n assessment factors may be defined as \(\{ C_{1} ,C_{2} , \ldots ,C_{n} \}\) .

Step 2: Design the questionnaire

As far as the n factors determined by the decision makers or experts in Step 1 are concerned, a nine-point Likert scale can be utilized to ascertain the correlation and correlation strength between the factors. In the event of n factors, n ( n  − 1) comparisons in line with the scale need to be carried out.

Step 3: Calculate the weight of the network structure

Each expert compares and scores the decision factors. After that, all the comparison scores of the experts are used in the matrix construction and weighted calculation. The procedure is as follows:

2.2.1 Establish the Matrix of the Network Correlation and the Correlation Diagram

The correlation matrix is established as M , while \(\{ C_{1} ,C_{2} , \ldots ,C_{n} \}\) are the decision factors. If C i is influenced by C j , \(m_{ij}\) will be the scores of a quantitative judgment given by experts. On the contrary, if \(m_{ij} = 0\) , C i is not influenced by C j . The results can be shown in matrix M ( n  ×  n ) as follows:

The column aggregation and row aggregation of matrix M are:

\({\text{Column}}_{j}\) and \({\text{row}}_{i}\) , respectively, give the scores of factor j , which affects other factors, or factor j , which is influenced by other factors.

2.2.2 Define the Transition Probability Matrix

If transition matrix A is defined by the features of the Markov chain, A  = ( a ij ), as shown in Eq. ( 2 ). A is a regular Markov matrix, and the existence of stationary distribution \(x = \left( {x_{1} ,x_{2} , \ldots ,x_{N} } \right)^{T}\) satisfies Ax  =  x and \(\sum\nolimits_{i} {x_{i} = 1}\) . The characteristic value of 1 can be acquired through the characteristic vector corresponding to the characteristic value of Matrix A , or through the iteration method \(x^{0}\) , where \(x^{k + 1} = Ax^{k}\) , to obtain the characteristic value. x stands for the distribution of probabilities of the various factors being influenced when the transition number approaches infinity, and \(x_{i}\) stands for the network node score of the i th factor.

2.2.3 Calculate the Weightings of the Network Structure

According to the results described in II above, the network node score of each factor is distributed to the correlation diagram of each expert ( n experts have n correlation diagrams). Afterwards, based on the node score of factor i , the strength score of each expert’s factor i influencing other factor j goes through a standardized distribution using the correlation diagram to obtain each expert’s weighted value of the network structure, R, as shown in Eq. ( 3 ). In the end, the \(R(C_{i} ,C_{j} )\) of n experts is averaged and standardized, as shown in Eq. ( 4 ) and Eq. ( 5 ). The standardized results can then be integrated into the ANP model to assess the optimal token financing scheme for startup companies.

Saaty put forward ANP in 1996. This method is rendered through a network structure and derived from an ANP. Practically, there are many questions about decision-making assessment that are not limited to expressing their complex interrelated properties in a hierarchical and independent manner, and they are not of purely linear relationships either. Rather, these questions have a network-like structure [ 45 , 77 , 78 , 79 ]. Based on the original presumption and prerequisite of the analytic hierarchy process (AHP), Saaty [ 45 ] integrated relationship and feedback mechanisms into the AHP model to solve the problem of correlation between different principles.

Saaty pointed out that the relationships of interactive influence between clusters and elements can be analyzed in a graphic manner. Such relationships and interactive influence can be demonstrated through arrow lines [ 45 , 80 ], as shown in Fig.  1 . This network structure is crucial for understanding the fundamental difference between hierarchical and network-based decision-making models. Unlike traditional hierarchical structures, this network allows for complex interdependencies between different elements of the decision-making process. In Fig.  1 , the bidirectional arrows indicate that influence can flow both ways between clusters, reflecting real-world complexities where factors can mutually affect each other.

Source : Ref. [ 45 ]

The network structure.

According to the relationships and strengths of different factors in the aforesaid models and structure charts of ANP, a supermatrix is utilized for demonstration, as shown in Fig.  2 . This matrix is a critical component of the ANP, allowing for the quantification of relationships between all elements in the network. It is formed when the various clusters and respective factors contained in such clusters are listed on the left side and upper part of the matrix in an orderly manner. The supermatrix consists of a number of sub-matrices, which are formulated based on the eigenvectors after the comparison of different factors. In Fig.  2 , \(W_{11} ,W_{kk} , \ldots ,W_{nn}\) are the values of the eigenvectors after the comparisons and calculations.

Source : Refs. [ 45 ] [ 80 ]

The supermatrix of a network.

ANP is an algorithm based on AHP and can be divided into four steps. In Step 1, the structures are formed step by step. In Step 2, the questions are raised. In Step 3, comparisons of interdependent clusters are made in pairs and a supermatrix is formed. In Step 4, the ultimate choice and optimal scheme are selected [ 45 , 79 ].

This study apples the ANP as the foundation of our approach due to several key advantages it offers in the context of complex decision-making scenarios. First, it is well-suited for this application because it allows for the consideration of interdependencies and feedback relationships between decision factors, which is crucial in the dynamic and interconnected world of FinTech and token financing. Furthermore, it provides a structured approach to incorporating both qualitative and quantitative factors into the decision-making process. This is particularly beneficial when evaluating token financing options, as it allows us to consider both qualitative and quantitative data. Finally, it is able to prioritize alternatives based on a comprehensive set of criteria and sub-criteria. This is especially valuable when comparing different alternatives, each of which has its own unique set of characteristics and implications. ANP allows for a more comprehensive comparison than simpler decision-making tools. Among various MCDM techniques, the ANP has a superior capacity to model complex systems with intricate interdependencies. While other MCDM techniques, such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, offer effective means for ranking alternatives, they exhibit limitations in accounting for the multifaceted interrelationships among criteria.

Consequently, this study employs the ANP method as the foundation for constructing an integrated decision-making model. A brief introduction of the construction program of the network process pattern is as follows:

Step 1: Confirm the research problems and network structure

Determine the targets according to features of the problems and search for decision-making clusters, as well as the factors contained in the various clusters by employing the proposed NSW method to acquire the influencing strength of the various factors; finally, draw the network structure models of the decision-making problems according to the results of NSW.

Step 2: Create pair-wise comparison matrices and priority vectors

Compare the factors in pairs. This step has two parts: the comparison of clusters (in pairs) and the comparison of factors within clusters (in pairs). The comparison of factors within clusters (in pairs) can be divided into the comparison within a particular group and comparisons among different clusters. The assessment scale of the comparison is similar to that of AHP. In addition, the eigenvectors, which are reached through the various comparison matrices, serve as the values of the supermatrix, which can be used to illustrate the interdependency and relative significance among the clusters. Equation ( 6 ) can be utilized to calculate the scores of relative significance in regard to the various clusters and factors. As for the strength of the interdependency among the clusters and among the factors, NSW can be utilized to determine the network structure (as described in Sect.  2.2 .)

Step 3: Construct the supermatrix

The supermatrix can effectively solve problems related to the interdependency among the various clusters and factors within the system (as shown in Fig.  2 ). The values of the supermatrix consist of small matrices, which include the comparison of different factors (in pairs) and the comparison of interdependent factors (in pairs). The numerical values of clusters or factors without the influence of feedback are 0, as shown in Eq. ( 7 ). In this study, it was suggested that the overall network structure could be confirmed by NSW. For this reason, the NSW results were integrated into the supermatrix for subsequent assessment and to determine the strength of the interdependency in the supermatrix, as shown in Eq. ( 8 ).

The ANP calculation process includes three matrices: the unweighted supermatrix, the weighted supermatrix, and the limit supermatrix. The unweighted supermatrix stands for the weightings of the original results of the comparison in pairs. In the weighted supermatrix, the weighted values of a particular element within an unweighted matrix are multiplied by the weighted values of the relevant clusters. In the limit supermatrix, the weighted matrix multiplies itself repeatedly until a stable state is attained. According to ANP, if supermatrix W is in an irreducible state of stability, all columns in the supermatrix will have similar vectors, indicating convergence can be attained. The ultimate weighted values of each cluster, factor, and scheme can be calculated through Eq. ( 9 ) during the convergence process.

Step 4: Evaluate the optimal alternative

Through the ANP framework and the calculations of the unweighted supermatrix, weighted supermatrix, and limit supermatrix, all the alternative schemes, as well as the ultimate values of the groups and factors, can be attained in the limit supermatrix. The ultimate results of the weighted values are then ranked to determine the optimal scheme.

3 Case Study

This study aimed to establish the network structure weighting (NSW) model by integrating NSW into the analytic network process (ANP) and establishing an assessment pattern to analyze the optimal scheme of token financing for startup companies, as well as the weighted values of clusters and factors. The consolidation-type diagram of the analytical process is shown in Fig.  3 . This integrated framework is a key innovation, that employs the Modified Delphi Method to identify relevant factors, and applies the NSW technique to determine the network structure. The results are then integrated into the ANP model for final calculations and analysis. This integrated approach addresses the limitations of traditional ANP by providing a more robust and objective method for determining network relationships. It combines the strengths of expert knowledge (through the Delphi method), systematic relationship quantification (via NSW), and comprehensive decision analysis (through ANP), resulting in a more reliable and nuanced decision-making tool for token financing. First, the modified Delphi method was utilized to calculate the clusters and factors influencing startup companies engaging in token financing. Second, the network structure of the clusters and factors was determined on the basis of the NSW method put forward in this study. Finally, the weighted values of the network structure of NSW were integrated into the ANP model to calculate the weighted values for the various factors and various financing schemes of startup companies engaging in token financing. These weighted values were then sequenced to obtain the optimal scheme and key factors of token financing. Figure  4 presents the integrated framework for evaluating token financing options. This model incorporates five main clusters: Finance, Laws and Regulations, Risk, Investor, and Online Community, each containing several specific factors. The model also includes three token financing alternatives: ICO, IEO, and STO. This structure allows for a comprehensive evaluation of token financing alternatives, considering a wide range of relevant factors. By inclusion of diverse clusters including financial considerations, as well as legal, risk-related, investor-focused, and community aspects, the proposed framework allows startup companies to make well-informed decisions based on a thorough analysis of all relevant factors.

The integration processes

The research model

Step 1: Research the problem and confirm the decision factors

Past literature has pointed out that a research framework can be established only after experts reach a consensus on the factors [ 81 , 82 ]. Regarding the assessment of multiple principals, the number of selected experts should be between five and nine [ 76 ]. Therefore, this study included three scholars and four business starters, totaling seven experts. The goal of this study was to construct a consolidation-type pattern for the optimal scheme of token financing. Taking startup companies as examples, through a literature review and utilization of the Delphi method, a total of 17 factors, five clusters, and three token financing schemes were obtained, as shown in Fig.  4 . Relevant materials of each cluster and factors are shown as follows:

The definitions and illustrations of the clusters, factors, and token financing schemes in this study are as follows:

Finance: This includes issuance costs, platform fees, and transaction costs.

Issuance costs (C1) [ 83 , 84 ]: The costs of issuing tokens in different token financing schemes (for instance, Mint), which can vary.

Platform fees (C2) [ 83 ]: The costs for different token financing schemes to be launched on platforms (for instance, the costs for the schemes to be launched in Finance).

Transaction costs (C3) [ 83 ]: The transaction costs of different token financing schemes, which can vary (for instance, service charges).

Laws and regulations: This includes the place of issuance, government policy, token security regulations, and information disclosure transparency.

Place of issuance (C4): The laws, regulations, and rules of different countries and regions, as far as the issuance of tokens is concerned.

Policies (C5): The degree of support from government authorities on token financing.

Token security regulations (C6) [ 84 ]: The relevant policies on token security.

Information disclosure transparency (C7) [ 85 ]: Policies regarding the information disclosure of enterprises that issue tokens.

Risk: This includes financing schedules, token price fluctuations, reputation, shareholding proportion, and financing success rates.

Financing schedule (C8): The length of the financing scheme. For instance, Initial Coin Offerings (ICO) take a relatively long time, while Security Token Offerings (STO) take a relatively short time.

Token price fluctuations (C9) [ 83 ]: The price fluctuations of token transactions are obvious and influence relevant financing efficiency.

Reputation (C10) [ 86 ]: The degree of the token financing scheme’s requirements for the business reputation of the enterprises. For instance, ICO requires relatively less on the business reputation of the enterprises.

Shareholding proportion (C11): The proportion of shares corresponding to the tokens, which are held by the investors.

Financing success rates (C12) [ 87 ]: The success rates of different token financing schemes for enterprises.

The investor aspect: This includes the financing objects and financing thresholds.

Financing objects (C13): The investors being sought out by enterprises engaging in token financing. For instance, ICO and Initial Exchange Offerings (IEO) focus more on private investors, while STO focuses more on professional investors.

Financing thresholds (C14): The thresholds for enterprises to engage in token financing. For instance, the threshold of STO is relatively high.

The online community aspect: This includes the online sharing of voice, online public sentiment, and online trends.

Online sharing of voice (C15) [ 88 ]: The degree of influence of investors’ preferences of network volume in different financing platforms.

Online public sentiment (C16): The degree of influence of investor sentiment in the social network platforms of different financing platforms.

Online trends (C17): The degree of influence of the tendencies on the investors in the overall environment of token financing.

Token financing schemes: These include ICO, IEO, and STO.

ICO: The development, maintenance, and exchange for the purpose of financing, using blockchain technologies and virtual tokens.

IEO: The issuance and sales of tokens through the endorsement of exchanges. It also refers to the rules under which the exchanges are responsible for knowing your customer (KYC) compliance and anti-money laundering (AML).

STO: ICO is supervised by the government. It refers to the practice of linking the assets of enterprises to tokens through securitization, as well as the sales of such assets.

Step 2: Develop the network structure models through NSW

The results acquired in Step 1 were integrated into the NSW models suggested by this study, so as to determine the network structure. The relevant procedures are as follows:

Step 2.1: Design the questionnaire

In regards to the five clusters and 17 factors obtained by the experts in Step 1, a nine-point Likert scale was utilized to determine the strength of correlation between different factors. In the event of n factors, n ( n  − 1) comparisons of the scale were carried out. Because this study referred to seven experts for the development of the network structure model, the data involved were quite complicated. The NSW procedures were illustrated in accordance with the finance clusters, as well as the three factors of issuance costs, platform fees, and transaction costs. The questionnaire design for the finance clusters is shown in Table  2 , in which 0 indicates no influence was observed, while 9 indicates the influence was of the highest level. The strength of correlation among the three factors of finance obtained through the questionnaires of the seven experts is shown in Fig.  5 . Each expert’s assessment is represented in a separate diagram, allowing for a comparison of individual perspectives. The differences in experts’ opinions highlight the subjective nature of these assessments and underscore the importance of aggregating their opinions. The generally strong correlations between factors, particularly between issuance costs and platform fees, suggest that these financial aspects are closely interrelated in token financing decisions. This visualization is crucial for understanding the foundation of our network structure, as it forms the basis for our NSW calculations.

The strength of correlation among the three factors of finance obtained through the questionnaires of the seven experts

Step 2.2: Calculate the weight of the network structure

Each expert compared the factors and scored them in terms of strength. After that, the comparison scores provided by the experts were used in the construction of the matrices and weighted calculations. First, the correlation matrices of the finance clusters, M 1 to M 7 , were established on the basis of Eq. ( 1 ) and the scores of the strength given by the seven experts, as shown below. Second, correlation matrix M was transformed into probability matrices A 1 to A 7 through Eq. ( 2 ), as shown below, and the iteration method was used n times to obtain the characteristic values (eigenvalues) of each questionnaire and factor. Third, this study calculated the weighted values of the correlation among C 1 , C 2 , and C 3 , as well as R ( C i , C j ) 1 to R ( C i , C j ) 7 , through Eq. ( 3 ), as shown in Fig.  6 . This visualization is crucial for understanding how individual expert opinions contribute to the overall network structure. The variation in weights across experts highlights the subjective nature of these assessments and the necessity to aggregate multiple expert opinions. Notably, most experts consistently assign higher weights to the relationships between issuance costs ( C 1 ) and platform fees ( C 2 ), indicating a strong perceived connection between these two factors. In the end, the ultimate weighted values of the network structure (the scores of the correlation degree) were calculated using Eq. ( 4 ) and Eq. ( 5 ). The weighted values of the network structure of the various clusters and factors are shown in Fig.  7 . Figure  7 illustrates the final network structure weights for all five clusters and their respective factors, which is the foundation for our subsequent ANP analysis. These network structure weights provide a comprehensive understanding of the relative importance and interconnectedness of various factors in token financing decisions. They serve as a crucial input for our ANP model, ensuring that the final decision-making process accurately reflects the complex realities of token financing.

The network structure weights of finance cluster’s factors by 7 experts

The network structure weights of five cluster’s factors

Upon completing the calculations, the results of the weighted values for the network structure were integrated into the ANP models to establish the comparison matrices and calculate the eigenvectors.

Step 3: Perform pair-wise comparisons of the matrices and priority vectors

The eigenvectors of the clusters and factors were calculated through the AHP processes and pairwise comparison of features of matrices. The eigenvectors of the degree of correlation between different clusters and factors were calculated through NSW. The cases in this study involved five clusters (finance, laws and regulations, risk, investor, and online community), 17 factors (issuance costs, platform fees, transaction costs, place of issuance, government policy, token security regulations, information disclosure transparency, financing schedules, token price fluctuations, reputation, shareholding proportion, financing success rates, financing objects, financing thresholds, online share of voice, online public sentiment, and online trends), as well as three schemes.

The comparison matrices (in pairs) and the geometric method were utilized to calculate the eigenvectors, while the eigenvectors for the network structure of the correlation strength scores were obtained on the basis of NSW. The eigenvectors obtained for the various comparison matrices, as well as the eigenvectors related to the correlation strength of the factors, served as the values of the supermatrix, which was used to illustrate the correlation strength and the relative importance of different clusters. The clusters might confirm the eigenvectors of the network structure through NSW, and the scores of the relative importance were calculated using Eq. ( 6 ). The results of the eigenvectors for the network structure of the various factors are shown in Step 2.2, and the comparison matrices (in pairs) and the weighted values of the five clusters are shown in Table  3 . Table 4 contains the scores for the relative importance of the various factors against the alternative schemes. In this study, Super Decision V2.0 (software) was utilized for the subsequent assessment of the ANP models. The eigenvectors of the network structure obtained through the NSW were inputted into Super Decision V2.0 to integrate NSW and ANP and assess the optimal scheme and the key factors.

Step 4: Construct the supermatrix

The eigenvectors of the relationships among the factors, as well as the eigenvectors regarding the weights of the factors to the schemes, were determined according to the results of Step 3. In Step 4, a supermatrix is established on the basis of the eigenvectors obtained in Step 3, so that the optimal scheme for startup companies engaging in token financing could be measured. During the ANP process, the ultimate weighted values of the various factors and schemes were calculated through the unweighted supermatrix, the weighted supermatrix, and the limit supermatrix. First, the calculated eigenvectors of the NSW model for the factors and pair-wise comparison matrices were utilized to establish the unweighted supermatrix. Second, the unweighted supermatrix was multiplied by the reciprocals of the weighted values of the relevant clusters to generate the weighted supermatrix. Finally, the results of the weighted supermatrix were multiplied by themselves repeatedly until a stable probability distribution was realized. This probability distribution reflected the ultimate weighted values to be reached. The various supermatrices are shown in Tables 5 , 6 , and 7 .

Step 5: Evaluate the optimal alternative

Through the supermatrix mentioned in Step 4, as well as the operation of Super Decision, the ultimate weighted values of the various factors and schemes under the consolidated NSW network structure could be obtained, as shown in Table  8 .

This study suggested the establishment of a set of network assessment procedures integrating the new NSW technique with the ANP model, in order to analyze the optimal scheme for startup companies engaging in token financing. The findings indicated a number of results. The sequence of the weighted values for the five clusters was as follows: finance (0.307) > risk (0.294) > laws and regulations (0.211) > investors (0.106) > online community (0.082). In addition, the sequence of the weighted values for the factors was as follows: platform fees (0.083) > issuance costs (0.078) > financing success rate (0.053) > government policy (0.0049) = financing schedule (0.049) > transaction costs (0.044) > financing threshold (0.040) > information disclosure transparency (0.039) > token price fluctuations (0.032) = shareholding proportion (0.032) > financing object (0.031) > reputation (0.030) > place of issuance (0.027) > token security regulations (0.026) > online share of voice (0.022) > online public sentiment (0.019) > online trend (0.014). Finally, the sequence of the optimal scheme for startup companies engaging in token financing is as follows: ICO (0.057) > IEO (0.101) > STO (0.175). STO is the optimal scheme for startup companies to engage in token financing.

4 Conclusion and Future Work

4.1 conclusion.

The rapid development of FinTech has become one of the goals of inclusive financing. Fintech, which depends on information technology to find solutions in the financial field, is becoming the mainstream future trend in the financial industry, especially in the development of new business patterns. Startup companies might find it difficult to borrow money from traditional financial institutions due to their business operation features and financial structures. For this reason, alternative financing has gradually become an important channel for startup companies to acquire financing. Token financing is a relatively new business pattern in the field of alternative financing, and it can avoid the shortcomings and problems of crowdfunding.

However, the development history of token financing is diversified and complicated. Previous studies in this field focused more on the analysis of the values of virtual currencies. Generally speaking, when startup companies are faced with the option of token financing, which is a new business pattern, they have relatively little information available for business assessments and decision making. When startup companies assess the optimal scheme for token financing, they often use multi-principle decision-making models, which can solve the problems of filtration and selection in token financing. However, multi-principle decision-making models depend heavily on the presumption that the variables (or criteria) are independent from each other. Therefore, such models might not be suitable for the assessment of decision-making problems in the real world.

ANP can be used to solve the problem of independence assumption in traditional multi-principle decision-making models. Although ANP can overcome the problem of independence assumption, it is still unable to ascertain the strength of the dependence and relationships between variables before producing a network structure. In this study, a new model, NSW, was put forward. This new model could be used to calculate the correlation between variables and generate the network structure. In addition, NSW could be integrated into ANP to generate the network structure. In the end, the assessment of the optimal scheme for startup companies engaging in token financing served as the case study. The results of this study show that finance is the most critical cluster in the assessment aspect. In other words, when startup companies intend to engage in token financing, financial issue is the first aspect to be considered. Token financing is the most up-to-date financing method in the era of FinTech, and capital turnover and financial structure are key issues during the development of startup companies. The sequence of key factors are platform fees, issuance costs, and financing success rate. Moreover, this sequence suggests that when startup companies intend to engage in token financing, the key factors are the aspect of costs and the success rate of financing. Finally, the optimal scheme for startup companies engaging in token financing is STO. After considering financial issues, costs, and relevant risks, startup companies should, based on the cost assessment and the success rate of financing, adopt STO for token financing to promote the financial efficiency of such companies.

This study proposed the NSW technique as a novel tool for validating network structures in decision-making processes and integrated NSW into the ANP model to develop a comprehensive framework for evaluating optimal token financing strategies. The contributions of this study in token-based financing include both methodological advancement and practical application. In terms of methodology, this study integrated the NSW technique with the ANP to enhance the robustness of existing frameworks in capturing complex interrelationships within decision-making processes. This innovative approach addresses limitations in traditional methods by providing a more comprehensive quantification of the strength and directionality of relationships between decision factors. As for practical application, this study presents the first comprehensive evaluation of token financing options for startup companies utilizing this advanced decision-making approach. The integrated NSW-ANP framework can be applied to ICO, IEO, and STO, thus offering valuable options for cryptocurrency-based startup financing. This systematic evaluation considers the intricate interdependencies among various factors influencing the selection of optimal financing strategies. By bridging the gap between theoretical innovation and practical implementation, this study not only advances the field of multi-criteria decision-making but also provides startup entrepreneurs and investors with a sophisticated tool for token-based financing options. Academically, this study provided a new NSW technique, as well as the application procedures to integrate NSW into ANP. This study also presented a case study of the assessment of the optimal scheme for startup companies engaging in token financing. Practically, this new framework could provide entrepreneurs of startup companies with valuable measurement tools for promoting their company’s capital turnover rate through token financing under the rapid development of FinTech.

4.2 Limitation and Future Research

While acknowledging the substantial advantages offered by our integrated framework, it is imperative to recognize its inherent limitations. The following constraints warrant further investigation and potential mitigation in future research:

The potential complexity and mathematical technique of the proposed model, which might make it challenging to implement for organizations.

The static nature of the model, which may not fully capture the decision risks of uncertainty in the cryptocurrency and token financing landscape.

At the current stage of development, the model may not comprehensively capture the effects of factor weight variations on the rankings of alternatives.

After discussing these limitations, we will outline potential directions for future research. This section will propose several avenues for extending and refining our work:

Expanding the application of the NSW-ANP method to other areas of FinTech decision-making beyond token financing.

Integration of fuzzy set theory into the NSW-ANP model to address decision uncertainty risks.

A sensitivity analysis was conducted to ascertain the effects of factor weight variations on the rankings of alternatives.

Data Availability

Not applicable.

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Lin, CY. Constructing a Novel Network Structure Weighting Technique into the ANP Decision Support System for Optimal Alternative Evaluation: A Case Study on Crowdfunding Tokenization for Startup Financing. Int J Comput Intell Syst 17 , 222 (2024). https://doi.org/10.1007/s44196-024-00643-0

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Received : 19 March 2024

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Published : 26 August 2024

DOI : https://doi.org/10.1007/s44196-024-00643-0

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