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.
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.
Male | Female | Total | |
---|---|---|---|
10-14 | 7.4% | 5.4% | 6.4% |
15-19 | 21.1% | 15.7% | 18.4% |
20-24 | 73.3% | 57.8% | 65.6% |
25-29 | 84.3% | 62.7% | 73.7% |
30-34 | 90.0% | 56.9% | 73.7% |
35-39 | 92.3% | 55.8% | 74.0% |
40-44 | 92.6% | 52.8% | 73.8% |
45-49 | 93.5% | 45.8% | 71.3% |
50-54 | 90.0% | 43.4% | 68.5% |
55-59 | 76.3% | 39.0% | 58.8% |
60-64 | 64.2% | 33.8% | 50.8% |
65-69 | 53.3% | 38.1% | 45.5% |
70-74 | 49.6% | 30.8% | 39.4% |
75+ | 47.9% | 26.6% | 37.6% |
Total | 49.6% | 31.7% | 40.9% |
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
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.
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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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.
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.
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)
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.
[3] According to the official data, power consumption grew by 10.2% in January 2021; the highest growth rate in three months, which was indicative of higher commercial and industrial demand (Press Trust of India 2021).
[4] In order to obtain the unemployment dynamics on a quarterly basis, Equation (2) is revised to Equation (3) with dummies pertaining to quarter instead of month.
[5] This reason is also validated by CMIE who found the female labour participation in urban regions to fall to 7.2% in October 2020, the lowest since the organisation started measuring this indicator in 2016 (Centre for Monitoring Indian Economy 2020).
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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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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.
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.
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:
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.
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.
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.
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.
4.1 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.
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.
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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This paper presents a simple methodology for real-time unemployment rate projections. at this approach pe. formed considerably better in 2020 at the onset of theCOVID-19 rece. sion. We then provide unemployment projections and an alternative scenari. analysis for 2021 based on the methodology we build using real-time data.
In the U.S., we're seeing an increasing number of calls to increase the national minimum wage to $15/hour. Many states and municipalities have already passed minimum wage hikes in the last ...
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 ...
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.
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 ...
Read Case Study. 11 times ... 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 ...
The unemployment rate is a measure of hardship for Americans families. While the rate fell to 11.1% in June, unemployment remains worse than at any other time since the Great Depression.
Following the Box-Jenkins method, the formulated model for forecasting the unemployment rate is SARIMA (6, 1, 5) × (0, 1, 1)4 with a coefficient of determination of 0.79.
Moreover, this effect is larger the lower the local unemployment rate was at the time of past unemployment episodes, confirming the stigmatization effect on US workers. ... The expected r varies from − 0.004 to − 0.056 in the case of studies focused on employment outcomes, and from − 0.006 to − 0.071 when the labor market outcome ...
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 ...
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.
Automatic time series modelling of the unemployment rate using leading economic indicators (LEI) Another widely studied time series is the (US) unemployment rate. Montgomery, Zarnowitz, Tsay and Tiao (Montgomery et al., Citation 1998) modelled the quarterly unemployment rate for the 1948 to 1993 period and reached several very interesting ...
It is a persistent problem that has various. social and economic consequences, including poverty, crime, and inequality. According to the latest available data from the National Statistical Office ...
Unemployment rates in South Africa are high, with a level of 32.7% being registered in the last quarter of 2022 (Statistics South Africa, 2022). This means that many families face adversity. ... The researchers applied a multiple case study cross-analysis and audit, as outlined by Yin ...
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. ... The case study for Sabah was examined and compared with the situation at the national and international levels.
Unemployment is today considered perhaps the most serious of the problems afflicting less developed countries and one that is believed to be steadily worsening as the gap between the ... A Case Study of a Poor District of Tehran ... or from 4-5 per cent to 4-9 per cent. Rates for the urban areas of Chile in 1968, Panama in 1963-4 ...
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 ...
violent crime rates for each U.S. state (n=50).A one percent increase in the unemployment rate will increase the vio. nt crime rate by 14.3 per 100,000 inhabitants. A one percent increase in the high school graduation rate will increase the vio.
The surprising link between unemployment and recessions. The U.S. labor market is having a strong start to 2023, adding 504,000 nonfarm payrolls in January, and 311,000 in February. Both figures surpassed analyst expectations by a wide margin, and in January, the unemployment rate hit a 53-year low of 3.4%. With the recent release of February ...
CASE STUDY . The Process: Unemployment Insurance Applications ... Although rates of application and recipiency increased during the pandemic, in 2022 the majority of individuals
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 ...