‘Genuine’ or ‘Quasi’ Self-Employment: Who Can Tell?

In many industrialised countries, including the Netherlands, the share of solo self-employed workers has strongly increased in recent years. This development is subject to a lot of public debate as it is feared that this increase is caused by ‘quasi’ self-employment. There still seems to be little consensus, however, on what constitutes ‘genuine’ self-employment and what not. In this article we present a theoretical framework for ‘quasi’ solo self-employment and discuss how the various indicators for ‘quasi’ self-employment that are used in the literature fit in this framework. We then compare the outcomes of different indicators by applying them to solo self-employed workers in the Netherlands. The data used for the analysis are taken from the Dutch Labour Force Survey (NL-LFS) 2017 complemented with the European Labour Force Survey (EU-LFS) ad hoc module 2017 on self-employment. Our results show that about 7% of the solo self-employed workers is dependent on one client. Furthermore, almost 20% of all solo self-employed had an involuntary start. The correspondence between dependency and involuntariness is very low: less than 2% of the solo self-employed workers are both dependent and involuntary. Both dependency and voluntariness are related to the fiscal and legal status of the solo self-employed workers and to the type of work activities. Solo self-employed workers that own their own business and who mainly sell products are less likely to be dependent and/or involuntary self-employed compared to those who do not own a business and/or offer services. Dependency is hardly related to the unfavourable outcomes of solo self-employment. Involuntariness, on the contrary, seems to have some impact on outcomes. Those who became self-employed because they couldn’t find a job as an employee have a higher probability to be unsatisfied with their job, to have financial problems or problems due to a lack of work or a low income. Nevertheless even among the involuntary solo self-employed workers, the majority does not report negative outcomes.

Introduction
In many European countries the share of solo self-employed workers has strongly increased in recent years (Kösters, 2017). In the Netherlands currently 12 per cent of the workers is solo self-employed, an increase of almost 4 percentage points since 2007. This development is subject to a lot of public debate. Some state that the increase of solo self-employment is not a sign of more entrepreneurial activity but that, on the contrary, many employees are pushed into self-employment because of a lack in alternative employment options. According to this view employers want to save on labour costs and social security contributions (Kautonen et al., 2010) by offering workers self-employed arrangements instead of regular employment contracts. This type of solo self-employed is being supervised by the firm which makes it comparable to a hierarchical employer-employee working relationship (Thörnquist, 2015), but without all social protection and labour law rights for employees. Others point to the fiscal facilities that favour self-employment (Kunda et al., 2002). These facilities do not stimulate 'genuine' entrepreneurship but stimulate employees to accept or even demand solo self-employed arrangements instead of regular employment contracts because these arrangements seem to lead to higher incomes, at least in the short run. But since these arrangements do not provide any social protection (such as disability or pension benefits) future incomes are more uncertain.
The public debate about these workers in the grey area between employment and selfemployment is not limited to the Netherlands. Also, in other countries there is concern about this supposedly growing group of vulnerable solo self-employed workers. In the UK this debate dates back to the 1980s, a period that witnessed a considerable growth in selfemployment (Smeaton, 2003). Although this growth was welcomed by the British government as a positive development contributing to employment growth, other observers feared that the rise in self-employment was due to a decline in wage-employment opportunities (Bögenhold & Staber, 1991) pushing vulnerable workers towards self-employment. Bögenhold and Staber (1991) found indeed some evidence that self-employment has risen with unemployment. Moreover Hakim (1988) showed that about a third of the self-employed had involuntarily entered self-employment. The debate on rising self-employment initially focused on the question whether the rise in self-employment was a sign of more entrepreneurial activity or instead a rise in false self-employment (Smeaton, 2003). In the 2000's the debate in the UK shifted to the question whether the new solo self-employed were the successful workers who were pulled towards self-employment because of the associated benefits or the marginal workers who were pushed towards self-employment because of a lack of alternative employment options. Böheim and Muehlberger (2009) find some evidence that in the UK it is indeed the most vulnerable workers who are pushed into (dependent) self-employment. Other European countries where 'quasi' solo self-employment is subject of debate are Germany (Dietrich & Patzina, 2018), Austria (Heineck et al., 2004;Muehlberger & Bertolini, 2008), Italy (Muehlberger & Pasqua, 2006), Finland (Kautonen et al., 2009(Kautonen et al., , 2010, the Czech Republic and Spain (Eurofound, 2017). Research for all these countries focuses on identifying the vulnerable self-employed workers by making a hard distinction between 'genuine' self-employed workers and the 'quasi' and vulnerable self-employed.
Although there is a lot of debate on what is alternately called 'quasi', 'bogus', 'false' or 'fake' self-employment there is no generally recognized definition of 'quasi' self-employment. On the one hand there are researchers who consider dependency on a single principal as 'quasi' self-employment and on the other hand are those for whom the involuntariness of the decision is an indicator of 'quasi' self-employment. Furthermore, regardless of the criteria chosen for 'quasi' self-employment much of the literature on 'quasi' self-employment implicitly assumes that 'quasi' self-employment leads to unfavourable socio-economic outcomes for workers considered. In this article we present a theoretical framework for 'quasi' solo self-employment and discuss how the various indicators for 'quasi' self-employment that are used in the literature fit within this framework. We then compare the outcomes of different indicators by applying them to solo self-employed workers in the Netherlands. Our contribution to the literature is twofold. Firstly, by empirically comparing those indicators we provide evidence of their consistency. In other words, to what extent do the groups of workers labelled as 'quasi' self-employed by the different indicators overlap? Secondly, we explore to what extent self-employed workers who satisfy some or all of the proposed criteria for 'quasi' self-employment indeed experience less favourable social and financial outcomes of self-employment.
Our research is related to the literature on atypical work and dual labour markets. Due to amongst others, increased international competition and rapid technical change, firms are confronted with more fluctuations in the demand for their products and need to be able to quickly adjust the size of their workforce (Kalleberg, 2003(Kalleberg, , 2009). Many firms have responded to these developments by segmenting their workforce in a core of permanent (regular) workers and a periphery of workers in non-standard employment arrangements. 'Quasi' solo self-employment is one of many non-standard employment arrangements next to, for example, temporary agency hires, on call work, direct hire (short) fixed term contracts and the use of independent contractors (solo self-employed workers). While the core workers have secure and well paid jobs, the work at the periphery is characterised by uncertainty, low pay, few training possibilities and unfavourable working conditions. This dualization between the core and the periphery of the workforce has far reaching consequences, not only for individuals it concerns, but also for society as a whole as it is expected to have a negative impact on the sustainability of the social security system (Rueda, 2014, Häusermann & Schwander, 2012. Another strand of the literature that is related to our research is the literature on undeclared work and the informal economy. As mentioned above, firms might prefer to use solo self-employment arrangements instead of regular employment contracts to save on labour costs and social security contributions. By exiting the formal economy altogether and imposing informal employment relationships they can save on taxes as well (Pfau-Effinger, 2009;Williams, 2013;Williams & Horodnic, 2015;Ødegård et al., 2012;Thörnquist, 2013). In both cases it is expected that the most vulnerable workers are affected most by such practises (Williams et al., 2013).
The next section presents a theoretical framework and discusses the various criteria applied in the literature to distinguish 'genuine' self-employment from 'quasi' selfemployment. Section 3 discusses the data and methodology. Section 4 presents the results and Sect. 5 concludes.

Self-Employed or Not?
Although there is much debate on the topic of so called 'quasi', 'fake', 'false' or 'bogus' self-employment there still seems to be little consensus on what constitutes genuine self-employment and what not. Self-employed workers are often defined as "individuals who earn no wage or salary but who derive their income by exercising their profession 1 3 or business on their own account and at their own risk" (Parker, 2004, p8). The literature on self-employment provides several criteria that are used to distinguish 'quasi' selfemployment from 'genuine' self-employment (see for example Szaban & Skrezek-Lubasínska, 2018) but none of these criteria seems to be comprehensive. The criteria used can be arranged in two dimensions: (1) the degree of dependence of the self-employed workers on one or on a small number of principals and (2) the degree of involuntariness of the decision to start as a solo self-employed worker. Before we discuss dependency and involuntariness in more detail it is useful to place these two dimensions in a simple framework for analysing 'quasi' solo self-employment (Fig. 1). The archetype of the 'quasi' selfemployed worker prominent in the public debate is the employee who was forced by his former employer to accept a self-employed arrangement, performing the same activities as before, still in a hierarchical subordinate working relationship but now lacking the social protection and labour law rights for employees (Kösters & Smits, 2020). This archetype of the 'quasi' solo self-employed worker, complies with both sets of criteria of 'quasi' selfemployment, this self-employed worker is both dependent on a single client and had an involuntary start (dependent & involuntary self-employed workers, type 1 in Fig. 1). At the other end of the spectrum we have the solo self-employed workers who do not comply with any of the criteria for 'quasi' solo self-employment: they are not dependent on a single client and had a voluntary start (independent & voluntary, type 4). In between these two extremes are the solo self-employed workers who are dependent on a single client but had a voluntary start (dependent & voluntary, type 2) and self-employed workers who are not dependent but had an involuntary start (independent & involuntary, type 3).
Dependency on a single principal is a widely used criterion for 'quasi' self-employment (see Muehlberger, 2007;Böheim & Muehlberger, 2009;ILO, 2003). The idea behind this criterion is that firms have an incentive to contract out work previously performed by employees to self-employed workers to save on labour costs and social security contributions (Kautonen et al., 2010). If these self-employed workers perform tasks within the hierarchy of the firm, this work arrangement is comparable to a hierarchical employeremployee working relationship, in which the self-employed worker depends on the firm for his work and income. Usually two forms of dependency are distinguished, organizational dependency and economical dependency. Organizational dependency refers to the situation in which the worker is highly integrated in the principal's organization. It is the principal who decides on how, where and when the work has to be performed. Economical dependency occurs when all or most of the self-employed worker's income stems from one client (Kautonen et al., 2010).
The prevalence of dependent self-employment varies considerably between countries (see also Table 6 in the appendix). Estimates based on the European Working Conditions Survey (EWCS) 2015 range from 11% of the self-employed (including self-employed with employees) in Denmark to 63% of the self-employed in Romania (Williams & Lapeyre, 2017). Among the western European countries, Austria and the UK stand out with an estimated 43% of all self-employed being economically and organizationally dependent. The estimate for the Netherlands is 23% of all self-employed.
Although dependency is clearly an indication of the economic vulnerability of the selfemployed workers, it is questionable whether dependency is a sufficient criterion to determine whether the work arrangement is indeed a 'quasi' self-employed arrangement. 'Genuine' self-employed, even those who have employees, might also be economically dependent on a single client (Kautonen et al., 2010). Furthermore, economical dependency may be a temporary situation, when starting up a business it may take some time to increase the customer base. Also organizational dependency will probably depend to a large extent on the type of work that needs to be done and as such not only 'false' self-employed workers but also 'genuine' self-employed workers with or without employees may engage in inter-firm cooperation that involve some extent of organizational dependency on other firms (Kautonen et al., 2010). For example, franchise-arrangements in which the franchisor dictates how to operate the business. Or the outsourcing of facility management services like cleaning and catering to contractors. As these services need to be provided on site of the client, the contractor is restricted how, when and where the work is performed.
So, dependency may occur even among 'genuine' self-employed workers. The reverse is also true, the absence of economical or organizational dependency does not necessarily imply that a worker is 'genuinely' self-employed. There are many examples of work previously done by employees now performed by solo self-employed workers, notably in the publishing and creative industry (Smeaton, 2003;Stanworth & Stanworth, 1997), that are not characterized by organizational and economical dependency. Digitalization decreases the need to perform tasks like writing articles, graphic design within the hierarchy of the firm, pushing workers in those industries often reluctantly to self-employment (Stanworth & Stanworth, 1997).
Finally it is worth noting that there is some evidence that part of the dependent solo self-employed workers are workers who possess highly demanded scarce skill. Those workers have a preference to work as solo self-employed workers even though the firm would like to hire them as employees. As solo self-employed workers they can bargain a higher numeration then as employees (Kösters & Smits, 2020). The latter might happen if there are sectoral agreements or firm-level rules with respect to maximum wages for specific types of employees (Houseman et al., 2003). Clearly these are not the vulnerable workers referred to in the public debate.
A second set of criteria to categorize 'quasi' and 'genuine' self-employed workers is therefore the involuntariness of their employment situation. Involuntariness with respect to the decision to become self-employed implies a lack of alternative employment options (Kautonen et al., 2009(Kautonen et al., , 2010Szaban & Skrzek-Lubansinka, 2018). For example, because workers could only continue to work for their former employer by accepting a self-employment arrangement instead of a regular employment contract or because they were unemployed and did not succeed to find a job as an employee. Although there is a lot of public debate on the former, there is little empirical evidence that 'forced' self-employment is indeed very common. Román et al. (2011) used the European Community Household Panel (ECHP) for the period 1994-2001 and found that only 0.5% of all employed workers in their sample switched from salary employment to self-employment while continuing to work for the same employer. Kautonen et al. (2009) studied involuntary selfemployment in Finland and found that in 2006 5% of the solo self-employed workers were pushed into self-employment by their former employer ( Table 6 in the appendix). Kösters and Smits (2017) found that in 2016 in the Netherlands less than 3% of the solo selfemployed workers who recently started their own business had been pushed by their former employer. For the UK it is estimated that in 2000 7% of the solo self-employed working men became self-employed upon request of their former employer, for women this was even 0% (Smeaton, 2003). However, starting a business because of unemployment seems to be much more common. For the UK this concerned 26% of the self-employed men and 10% of the self-employed women in 2000, for the Netherlands 21% in 2016 (Kösters & Smits, 2017;Smeaton, 2003). However, even if the decision to become self-employed was dictated by negative circumstances the business may still be successful. These successful solo self-employed workers might prefer to remain self-employed even if they had the possibility to obtain a job as an employee. Therefore, it is important not only to consider involuntariness at the start of the career as a solo self-employed worker but also later in the career. Research for the UK (Smeaton, 2003) shows that the vast majority (70%) of the solo self-employed workers prefers to remain self-employed. Figures from Eurostat (2018) show that for all self-employed (including self-employed with employees) in the EU-28 an average of 2% would prefer to work as an employee. In countries like Greece, Italy and Romania this percentage is higher (ranging from 9 to 5%) (see also Table 6 in the appendix).
The incidence of dependent and/or involuntary solo self-employment is likely to be related to the legal and fiscal status of the solo self-employed worker. In the Netherlands, for example, a distinction can be made between self-employed entrepreneurs and 'other' self-employed. The former declares income from profits to the tax authorities and is usually also registered at the Chamber of Commerce. To qualify for profit declaration one should work at least 1225 hours per year in their self-employed job. The latter doesn't own a registered business but works as a freelancer declaring result from labour other than profit or wage employment to the tax authorities. These are mostly small self-employed jobs. Legally and fiscally solo self-employed who own a business thus have more rules to comply with to show their self-employment, than a solo self-employed who doesn't own a business. We therefore expect that: Solo self-employed who do not own a business ('other' self-employed) are more likely to be dependent and/or involuntary (type 1, 2 or 3) whereas solo self-employed who do own a business (entrepreneurs) are more likely to be independent and voluntary (type 4) (Hypothesis 1a).
The type of activities that need to be performed by the solo self-employed worker is another aspect to consider. An important distinction in this respect is between service activities and activities that involve the production and/or sale of (material) products (Kösters & Smits, 2020). Solo self-employed in service activities are more likely to be hiring their services to organizations and maybe even for just one organization or unwillingly for the employer they used to be working as an employee whereas solo self-employed who sell products usually sell to multiple customers. It is therefore to be expected that: Solo self-employed who offer services or their own labour are more likely to be dependent and/ or involuntary (type 1, 2 or 3) whereas solo self-employed selling products are more likely to be independent and voluntary (type 3) (Hypothesis 1b).
Both sets of criteria for 'quasi' self-employment point to a certain vulnerability of solo self-employed workers. Workers who depend on a single client presumably face more economic uncertainty than solo self-employed workers with a larger client base, since the loss of that single client implies a loss of all their income generated from self-employment. The same is true for involuntariness, being pushed toward self-employment by a lack of other employment options certainly points to a weak labour market position. It is unclear, however, to what extent 'quasi' solo self-employment is related to worse social and financial outcomes compared to 'genuine' self-employed workers (Szaban & Skrezek-Lubansinka, 2018).
Research shows that in many Western countries, compared to employees, self-employed workers have a higher risk on a low income and poverty but also report a higher job satisfaction than employees (Hyytinen et al., 2013;Hamilton, 2000;Hundley, 2001;Cueto & Pruneda, 2017). It may well be that self-employed workers trade off a lower income for a higher job satisfaction, for example due to more autonomy in their work (Cueto & Pruneda, 2017;Josten & Vlasblom, 2017). There is far less evidence on the social and financial outcomes of 'quasi' solo self-employed workers compared to 'genuine' self-employed workers. Dirven et al. (2017) show that workers who are pushed into self-employment both report less job satisfaction and have lower incomes than those who became self-employed for the perceived benefits but the differences are small.
As explained above both dependency on a single client and an involuntary start as a solo self-employed worker point to a certain vulnerability of solo self-employed workers. For that reason we expect that: Dependent & involuntary solo self-employed (type 1) will experience the worst socio-economic outcomes while independent & voluntary solo selfemployed workers (type 4) will experience the best outcomes (Hypothesis 2a).
Furthermore involuntariness is thought to be a better indication of vulnerability than dependency, as there is evidence that some of the (voluntary) dependent solo self-employed workers are workers who possess highly demanded scarce skills which allows them to bargain profitable self-employment arrangements (Kösters & Smits, 2020

Dutch Labour Force Survey (NL-LFS)
The NL-LFS is a household survey in which information is collected on the labour market situation of the Dutch population aged 15 years and older. The survey is conducted using a rotating panel with five waves. Respondents are asked about their labour market situation five times in a time period of one year, having approximately three months between the subsequent waves. Yearly approximately 3% of all households are in the sample, this comes down to around 200 thousand households. The response rate is almost 60%.
The NL-LFS contains information on the (self-reported) employment status. Respondents who are employed are asked whether they are employees or self-employed. Those who report to be self-employed are asked whether they employ employees or not. The solo self-employed workers are the self-employed workers without employees. As discussed in Sect. 2 the legal and fiscal status as well as the type of activities performed by solo self-employed workers are expected to have an impact on the incidence of 'quasi' solo self-employment. Within the group of solo self-employed workers, we therefore distinguish solo self-employed workers who have their own business or private practice (solo self-employed entrepreneurs) and 'other' solo self-employed workers such as independent professionals or freelancers. In general, the solo self-employed entrepreneurs are registered at the Chamber of Commerce while the 'other' solo selfemployed very often are not. The former usually declare income from profits while the latter declare labour income other than salary, tips or business profits. Within the group of solo self-employed entrepreneurs, a further distinction can be made between those who mainly sell products (solo self-employed entrepreneur-products) and those who mainly sell their own labour/services (solo self-employed entrepreneur-labour). Of all solo self-employed workers 70% is a solo self-employed entrepreneur-labour, 22% a solo self-employed entrepreneur-products and 8% an 'other' solo self-employed worker.
There are considerable differences in occupations between solo self-employed entrepreneurs-labour, solo self-employed entrepreneurs-products and 'other' solo selfemployed workers. Solo self-employed entrepreneurs-labour often work in technical or business and administrative occupations while the majority of the solo self-employed entrepreneurs-products work in sales and public relations (PR) occupations or agricultural occupations. The 'other' solo self-employed workers are relatively often found in teaching and health and welfare occupations.

European Labour Force Survey (EU-LFS) Adhoc Module 2017
Information on dependency, the voluntariness of the decision to become self-employed and a number of subjective socio-economic outcomes come from the EU-LFS ad hoc module 2017. This is a questionnaire module with a yearly changing topic in the European Labour Force Survey (EU-LFS), which in 2017 addressed the self-employed (Eurostat, 2018). In the NL-LFS this information is collected in the second wave.

System of Social Statistical Datasets (SSD)
Finally administrative data on incomes from the System of Social Statistical Datasets (SSD) is used to construct a number of objective socio-economic indicators. The SSD is a system of interlinked and standardized registers and surveys that contains information on persons, households, jobs, benefits and more (Bakker, Van Rooijen, & Van Toor, 2014). The (integral) income information available in the SSD comes from the Dutch Tax Administration.

Indicators of Quasi Self-Employment: Dependency and Involuntariness
The EU-LFS ad hoc module 2017 contains information on both economical and organizational dependency and involuntariness. A solo self-employed worker is considered to be economically dependent on a single client if during the last 12 months he or she has been working for only one client or obtained at least 75% of his or her revenue from one client. Organizational dependency refers to the situation in which it is not the solo self-employed worker who decides about his working hours but the client (or another party). The two dependency indicators available in the LFS only partly overlap with the three indicators available in the European Working Conditions Surveys (EWCS) to which we referred in the previous section (see also Table 6 in the appendix). Dependency on a single client is an indicator for economic dependency in both surveys. In the LFS, however, those who have more than one client but obtain more than 75% of their revenue on a single client are considered economically dependent while in the EWCS only the number of clients matters (Eurofound, 2013). Furthermore the EWCS uses two different indicators for organizational dependency: (1) not being able to hire employees and (2) not being able to make the most important decisions about how to run their business. Those indicators, apart from the fact that they are not available in the EU-LFS, are to our opinion less appropriate for our research as we also include the so called 'other' solo self-employed, many of which do not legally have a business, as discussed above. Finally, while we consider those who are both economically and organisationally dependent as the dependent self-employed, Williams and Lapeyre (2017) using the EWCS to study dependent self-employment, classify those who meet at least one of the three criteria for dependency as dependent self-employed. 1 Indicators for involuntariness are based on questions regarding the reasons someone had to become self-employed. Being 'forced' by the former employer to the lack of alternative employment options are reasons that indicate involuntariness. As discussed above, involuntariness at the start of self-employment does not necessarily imply that the current selfemployed situation is still involuntary. Therefore, we also consider the involuntariness of the current situation. Self-employed workers are asked whether they have a preference to remain self-employed or would rather become an employee. The latter case is an indication for current involuntariness of the self-employment situation. As the current involuntariness is endogenous, it may also depend on the socio-economic outcomes experienced by the self-employed worker, we will not treat current involuntariness as an indicator for 'quasi' self-employment but as a (negative) outcome from self-employment.

Socio-Economic Outcomes
As discussed above 'quasi' self-employed are expected to be more vulnerable than 'real' self-employed. A common finding in the literature is that self-employed workers, compared to employees, have a higher risk to experience poverty but report higher job satisfaction. In this article we focus on poverty and job satisfaction as the main socio-economic outcomes as well. With respect to poverty we consider both subjective and objective indicators. The subjective indicators are based on questions in the EU-LFS ad hoc module 2017 regarding difficulties experienced by the self-employed workers. More specifically we consider two subjective indicators for the risk on poverty (1) whether the solo self-employed worker reports "times with little money to live on" as a problem and (2) whether the solo selfemployed worker reports "times when there is no work to do".
The objective poverty indicators are based on income information from the SSD. These concern: (1) earning a low personal income from work and (2) living in a low income household. A low personal income is defined as a net personal income from work below the Dutch social benefit level for a single person (CBS Statline, 2019a). The low household income threshold represents the same purchasing power for all households and is adjusted annually for price changes using the consumer price index (CPI). The level is based on the welfare benefit level for a single person in 1979 (CBS Statline, 2019b).
Job satisfaction in the EU-LFS is measured on a four-point scale. We consider those who report to be satisfied to some or a large extent as satisfied with their job as a solo selfemployed worker and those who report to be not satisfied at all or to be satisfied to a small extent as not satisfied.

Control Variables
As the possibilities to be 'genuine' or 'quasi' self-employed might depend on personal and job characteristics we also added control variables to our model. It is for example known that women more often than men choose to become self-employed to improve the balance between work and family life (Dirven et al., 2017). We therefore added sex as a control variable. As older people have more experience on the labour market, they are more likely to choose to become self-employed than younger people, age is therefore also a control variable. Field of occupation is added as a control variable, as occupations differ in the type of interaction that is needed between the solo self-employed worker and the client which in turn might have an impact on the degree of dependency. Other control variables are level of education and duration of the self-employed job. Table 1 provides the descriptives of all variables used in the analysis.

Method
For both sets of criteria for 'quasi' self-employment discussed above (dependency and involuntariness) we first determine the proportion of solo self-employed workers for which these criteria apply. Next we examine to what extent both sets of criteria overlap and classify the solo self-employed workers in our data set in 4 groups distinguished in Fig. 1: solo self-employed workers that are 'quasi' self-employed according to both the dependency and involuntariness criteria (type 1: dependent & involuntary), solo self-employed workers who only fulfill the dependency criteria (type 2: dependent & voluntary), solo self-employed workers who only fulfil the involuntariness criteria (type 3: independent & involuntary) and solo self-employed workers who fulfill neither set of criteria (type 4: independent & voluntary). Subsequently, to analyse how this classification is related to a certain type of self-employed (an entrepreneur-labour, an entrepreneur-products or an 'other' self-employed) we estimate a multinomial logit model for the probability to belong to each of the four groups (hypothesis 1a and 1b). The results of this regression are presented in the form of average marginal effects. An average marginal effect is the average absolute change in the probability to belong to a certain group due to a 1-unit increase of the independent variable (see also Breen et al., 2018), or in short the probability to be in a certain group relative to all other groups. The marginal effects are weighted by population proportion weights. Finally, we investigate to what extent self-employed workers who satisfy either one or both sets of the criteria for 'quasi' self-employment indeed experience less favourable social and financial outcomes of self-employment (hypothesis 2a and 2b). For each type of socio-economic outcome, we estimate a logit model in which the probability to experience such an outcome is explained by the four categories of 'quasi' self-employment and personal and job characteristics.

Incidence of 'Quasi' Self-Employment: Dependency and Involuntariness
About 7% of all solo self-employed workers in the Netherlands reports to have only one client. Another 13% has more than one client but generates at least 75% of their income from the main client. So, one fifth of the solo self-employed workers in the Netherlands is economically dependent on one client. Furthermore, nearly one third of the solo self-employed workers is organizationally dependent on their clients. About 7% of the solo self-employed workers is both economically and organizationally dependent on their client. This figure is higher than the EU-28 average of 4% of the self-employed (Eurostat, 2018).
Very few solo self-employed workers report to have started their self-employment career because their previous employer wanted them to do so (2.5%). A lack of alternative employment options is mentioned far more often: 17% of the solo self-employed workers says to have started because they couldn't find a job as an employee. All in all, about 19% started their career involuntary.
Dependency and involuntariness hardly overlap. Less than 2% (1.65%) of the solo selfemployed workers is both (economically and organizationally) dependent on a single client and had an involuntary start of the self-employed career (dependent & involuntary).
As expected (Hypothesis 1a and 1b) dependency and involuntariness are indeed related to the type of solo self-employment (see Table 2). Compared to solo self-employed entrepreneurs who mainly offer their own labour, self-employed entrepreneurs who mainly sell products have a lower probability of being both dependent & involuntary self-employed (a decrease of 1.4 percentage points) and also a lower probability of being dependent& voluntary self-employed (a decrease of 2.8 percentage points). The 'other' solo self-employed workers, have a higher chance on the other hand to be dependent & voluntary solo selfemployed workers (an increase of 7.8 percentage points) and less often as independent & involuntary self-employed workers (decrease of 5.9 percentage points). The differences in 'quasi' self-employment between these types of solo self-employed are hardly related to differences in personal or occupational characteristics. The exception is the lower probability of solo self-employed entrepreneurs-products to be dependent & voluntary selfemployed, which can be traced back to the lower incidence of these workers in ICT occupations. In ICT occupations we see a lot of dependent solo self-employed workers but this concerns mostly entrepreneurs who offer their own labour.
Being both a dependent and involuntary solo self-employed worker, is related to the occupational field of the solo self-employed worker. Especially solo self-employed workers in teacher occupations, sales and PR occupations, business and administrative occupations Table 2 Marginal effects of the probability on a (in)dependent and/or (in)voluntary self-employment (multinomial logit). Source: and technical occupations have a higher probability to be both dependent and involuntary self-employed. Those working in agricultural occupations have a lower probability to be involuntary self-employed. Finally, solo self-employed workers in transport occupations have a lower probability to be both independent and voluntary self-employed. Age seems to have little impact on the probability to be 'quasi' self-employed, although we see that workers between 55 and 65 years have a higher incidence of involuntary selfemployment. These are the workers who, in the Netherlands, after losing their job as an employee, have the lowest probability to find another employee job.
Surprisingly, educational level doesn't seem to matter for 'quasi' self-employment. Woman have a higher probability of involuntary solo self-employment. The number of years since the start of the self-employment career is related to involuntariness. Those that started more than two years before the interview date are less likely to be involuntary selfemployed. This may be a selection effect, however, as those who made an involuntary start are probably more likely to stop shortly after the start (because of other opportunities).

Outcomes of Solo Self-Employment
As mentioned before, involuntariness at the start of the solo self-employed career does not necessarily imply that the current situation is still involuntary, but our results show that these two types of involuntariness are strongly related. Nearly 9% of the solo selfemployed workers say to prefer a job as an employee. For those who had an involuntary start and are also dependent on one client, this probability is 10 percentage points higher than for solo self-employed who are not 'quasi' self-employed by either criterion (Table 3). For those who had an involuntary start but are not dependent this is nearly 8 percentage points higher. Dependency does not seem to be related with current involuntariness.
It is mostly the 'other' solo self-employed workers who prefer to work as an employee. Furthermore, women and solo self-employed workers younger than 25 have a higher probability to prefer working as an employee. Not surprisingly, solo self-employed workers in agricultural occupations less often report a preference to be an employee.
Job (un)satisfaction shows the same pattern as current involuntariness. The overall probability of being not satisfied with the job is quite low, 1.7%, but this probability is 3 percentage points higher among those who started involuntary. There are however, no differences in job (un)satisfaction between the different groups of solo self-employed workers, neither is job (un)satisfaction related to any of the personal and job characteristics included in the model.
Although job satisfaction is high, a relatively large part of the solo self-employed report financial problems (19%) or periods without clients (21%) (see Table 4). Again, the probabilities to experience any of these problems are much higher among those who had an involuntary start. Without controls for the type of solo self-employment or personal and job characteristics we find marginal effects for the probabilities of experiencing financial problems and periods without work of respectively 15 and 11 percentage points. The type of solo self-employment does matter, however. Compared to entrepreneurs who offer their own labour, entrepreneurs who sell products have a higher probability to report financial problems but a lower probability to report periods without work (about 5 percentage points in either direction). Periods without work seem to be related to the personal and job characteristics of the entrepreneurs who sell products. They are working in agricultural occupations more often. Solo self-employed workers in public administration, protective services and legal occupations, agriculture occupations, health and welfare occupations and Table 3 Marginal effects of the probability on current involuntariness and job satisfaction (logit). Source:  Table 4 Marginal effects of the probability on financial problems or lack of work (logit). Source:  transport and logistics occupations have a lower probability to report periods without clients. For the 'other' solo self-employed workers both the probability of experiencing financial problems and the probability to experience periods without work is lower than for the entrepreneurs-labour. The probability to report financial problems is slightly higher for those between 45 and 65 years old compared to other age categories and also for those working in artistic occupations (and to a lesser extent in agricultural occupations). Finally, we see that selfemployed workers who started more than two years ago, have a lower probability to experience periods without work. Again, this may be the result of a selection process as those who have periods without work more often are likely to have lower survival rates as solo self-employed. Nearly 32% of the solo self-employed workers has a low personal income and 7.5% is member of a low income household. Again involuntariness seems to be the determining factor (see Table 5). Furthermore 'other' solo self-employed workers more often have a low personal income while entrepreneurs-products have a higher risk to be part of a low income household. Female solo self-employed workers have a higher risk on a low personal income. The same is true for young solo self-employed workers and those who started their business less than two years ago. Surprisingly education and occupation do not seem to matter. The risk of being part of a low income household does not depend on any personal characteristics.
These results only partially support our expectations. We expected that dependent and involuntary solo self-employed workers would experience the worst socio-economic outcomes while the independent and voluntary self-employed workers would have the best outcomes (Hypothesis 2a). Furthermore we expected that the negative impact on socioeconomic outcomes of involuntariness would be larger than the negative impact of dependency (Hypothesis 2b). We find that the independent & involuntary workers experience the worst outcomes. This suggests that involuntariness is indeed related to more unfavourable socio-economic conditions while dependency is not.

Conclusions and Recommendations
This paper considers two types of criteria used in the literature to distinguish 'quasi' from 'genuine' solo self-employment: (1) dependency on a single client and (2) involuntariness of the decision to start solo self-employment. It is shown that these criteria have a very low correspondence, that is the percentage of workers that might be classified as 'quasi' self-employed according to both sets of criteria is quite low, less than 2% of the Dutch solo self-employed workers.
We expected that dependency and involuntariness are related to the type of solo selfemployment (hypothesis 1a and 1b). Compared to solo self-employed entrepreneurs who mainly offer their own labour, self-employed entrepreneurs who mainly sell products have a lower probability of being both dependent & involuntary self-employed and also a lower probability of being dependent and voluntary self-employed. The 'other' solo selfemployed workers, have a higher chance on the other hand to be dependent and voluntary solo self-employed workers and less often as independent and involuntary self-employed workers. We also expected that dependent and involuntary solo self-employed workers would experience the worst socio-economic outcomes while the independent and voluntary self-employed workers would have the best outcomes (Hypothesis 2a). Furthermore we expected that the negative impact of involuntariness would be larger than the negative impact of dependency (Hypothesis 2b). Our expectations are not fully confirmed. We only found evidence for hypothesis 2a: involuntariness is related to more unfavourable socioeconomic conditions while dependency is not. Self-employed workers who started involuntarily have a higher probability to report financial problems or periods without work and also have a higher risk on a low personal income or being a member of a low income household. Nevertheless even among the solo self-employed workers who started involuntarily the majority seems to be satisfied with their job and don't have a preference to work as an employee.
Given these results, an interesting question for future research is whether starting selfemployment when there are no other employment options is actually a rewarding strategy. Compared to remain unemployed, starting as a self-employed worker prevents skills obsolescence and offers the possibility to gain new experiences. Of course, as mentioned before, the involuntary solo self-employed workers in our data set might be a selective group, as those who started a business but failed are not in our sample. Future research on involuntary self-employment should, therefore, take a longitudinal perspective. Comparing the careers of involuntary starters with the careers of voluntary starters. Additionally a comparison should be made with the careers of workers who after job loss, didn't start solo self-employment but remained unemployed for some time.
Our results suggest that the distinction between 'quasi' and 'genuine' solo selfemployed workers is not particularly useful if one is interested in identifying the marginal, vulnerable solo self-employed workers. Policy makers concerned with marginal workers could therefore better directly target the marginal self-employed workers, that is the selfemployed workers who have difficulties to survive and show unfavourable socio-economic outcomes. The number of socio-economic outcomes considered in this study is limited, however. Further research should consider a wider range of outcomes, measuring precariousness including access to social security and health outcomes.

Table 6
Prevalence ( (1) (2) (1) (2) Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.


extfieldsofstudy:
Computer Science
Economics

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From banks to DeFi: the evolution of the lending market

The Internet of Value (IOV) with its distributed ledger technology (DLT) underpinning has created new forms of lending markets. As an integral part of the decentralised finance (DeFi) ecosystem, lending protocols are gaining tremendous traction, holding an aggregate liquidity supply of over $40 billion at the time of writing. In this paper, we enumerate the challenges of traditional money markets led by banks and lending platforms, and present advantageous characteristics of DeFi lending protocols that might help resolve deep-rooted issues in the conventional lending environment. With the examples of Maker, Compound and Aave, we describe in detail the mechanism of DeFi lending protocols. We discuss the persisting reliance of DeFi lending on the traditional financial system, and conclude with the outlook of the lending market in the IOV era.

Introduction
Lending and credit have been an integral part of human society for thousands of years, with the first evidence of loans in human history taking place in Mesopotamia approximately 5000 years ago (Freas, 2018). Since then, lending markets have evolved to assume many different forms including consumer lending, student loans, mortgages, corporate debt and government bonds.
Fundamentally, lending is intricately related to the concept of trust and the promise of repayment. The act of lending is to lend money, to earn interest, and to be paid back. The term "credit" itself comes from the mid-16th century French, meaning to believe and trust.
Lending has now become one of the most important financial activities in society. It fuels economic growth and cultivates forward-looking commercial activities. The size of the world's debt markets as of 2019 was estimated to be more than $255 trillion or nearly $32,500 for each of the world's 7.7 billion people, and more than three times the world's annual output (Jones, 2019).
In recent times, technology's impact on lending markets has shown significant progress in solving many problems in relatively inefficient markets. For example, artificial intelligence and alternative data are making breakthroughs for financial inclusion through alternative credit scoring. This paper focuses on how blockchain technology and its role in the Internet of Value are helping create more efficient lending markets. In particular, we address consumer, business and more recent crypto-asset lending, but the principles discussed can, of course, apply to the broader unsecured and secured lending markets (FCA, 2021).
The rest of this paper is structured as follows: in Section 2 we review conventional lending in society, fuelled by commercial banks as well as new marketplaces and lending businesses; in Section 3 we raise challenges currently faced in lending markets; in Section 4 we elaborate on new lending protocols in the decentralised finance (DeFi) space, and argue how blockchain technology can empower lending markets for the age of the Internet of Value; in Section 5 we discuss DeFi lending protocols' status quo and their coevolution with centralised financial (CeFi) platforms; in Section 6 we conclude with an examination on how lending markets may evolve and transform in the future.

Lending in the conventional financial market
Lending requirements of consumers and businesses are primarily met by two major types of organisation: (i) banks and (ii) new lending marketplaces and specialised companies. In this section, we discuss the principal lending mechanics of these organisations.

Banks
In the lending market, banks hold a unique position as that of a "money creator" (R. A. Werner, 2014). When providing a loan, a bank simultaneously extends its balance sheet by increasing its assets (loans receivable) and its liability (borrower's deposit) (Lindner, 2015). The amount that commercial banks can lend is not as simple as a multiplier effect of the reserves they hold at the central bank; instead, it depends on various factors to do with market forces, risk, interest rates, borrower behaviour and regulatory policy. The majority of broad money in economies (with particular reference to the UK) are bank deposits, accounting for 97% of the total amount of money in circulation, created via loans (McLeay et al., 2014). Let us look at Central banks and Commercial banks in more detail below.
Central banks A new loan issued by a central bank can increase the central bank's outstanding money supply. Thus, a central bank is the "lender of last resort" as it is technically capable of issuing large-scale loans commonly used as a rescue plan to "bail out" commercial banks in a financial crisis. This practice has been occasionally employed since the global financial crisis of 2007-2009 in the form of Quantitative Easing (QE), where central bank reserves are created to purchase financial assets, mainly from non-bank financial companies (McLeay et al., 2014). Naturally, a central bank's excessive loan issuance inflates monetary supply, which potentially leads to currency depreciation. Bailout plans of this sort-where central banks "print" fresh money to ensure market liquidity-have historically received criticism as the action dilutes the money's value.
Commercial banks For commercial banks, newly created deposits through loan issuance can also be used as money in the sense that they can be accepted as a form of payment by non-banks. In effect, a deposit at a commercial bank represents the bank's promise to pay central bank money. Failure to collect a sufficient amount of outstanding loans as a result of e.g. borrowers not being able to repay, thus induces a breach of promise on the part of the commercial bank, potentially resulting in bankruptcy and, more severely, mistrust in the overall banking system. This was precisely the story of the global financial crisis, where banks collapsed due to the high rate of default in the secondary mortgage market.
In summary, loans from banks (central or commercial) generate deposits, fundamentally different from the deposit-first operation of lending companies, which we discuss below.

Lending companies and marketplaces
Distinct from banks, ordinary lending companies function only as an intermediary of loanable funds; they cannot create forms of money. In other words, a lending company must absorb liquidity from lenders first in order to provide its borrowing clients' loans.
Indeed, it is mechanically feasible for lending companies to also operate as commercial banks: issuing a loan by creating a new deposit in the form of "promise to pay central bank money", in which case deposits would not need to fully cover loans. Critically, however, the "promise" from lending companies-unlike commercial banks-can not be used as legal tender. Consequently, borrowers would find it difficult to use this newly created deposit as a form of payment anywhere outside the lending platform. As Minsky, 1986 put it: "everyone can make money; the problem is to get it accepted". Nevertheless, there are many other derivatives such as collateralised debt obligations (CDOs) that can be fungible in capital markets. It is also possible to transfer plain vanilla loans to those who might accept it as a substitute for fiat funds.
Generally speaking, there exist three mechanisms within non-bank lending.
Quote driven market In a quote-driven market, the lending platform attracts both lenders and borrowers at the same time. Lenders on these types of platforms can be either institutional wealth managers or retail investors, as seen on UK-based Funding Circle and US-based LendingClub. The market trend has been that more and more institutional funding is being drawn to these platforms, and preferred by the platforms due to the economies of scale when dealing with larger investors (Ziegler et al., 2020). The borrowers are often individuals or small and medium enterprises, who apply for a loan by supplying relevant information such as loan principal required, borrowing period and credit history. While acting as an agent without taking any credit risk, a lending platform usually performs some degree of risk assessment of borrowers based on their profile to ensure the minimum quality of listed loans. 1 Rather than linking individual quotes from lenders and borrowers, most platforms nowadays pull funds and manage a diverse set of borrowers to better diversify risks for their pool of investors. Loans are fractionalised such that lenders can invest in a large number of these smaller loan chunks to reduce idiosyncratic risks, and can achieve exposure based on their risk appetite.
Order driven exchange In an order-driven exchange platform, orders to borrow and lend with various price levels (in the form of e.g. interest rate per annum) are cleared automatically through an order book. To address default risk, borrowers often need to provide sufficient collateral before placing an order. This mechanism is typically adopted for trading in global debt markets.
Over-the-counter An over-the-counter model refers to a purely bilateral lending model. Similar to how quote driven markets operate, firstly a borrower submits a request to the platform enclosing personal information, credit history, etc. The platform then evaluates the request and assigns an interest rate suitable for the borrower's particular request and credibility. The request is subsequently posted on the platform where lenders can choose to fulfil individual requests. Peer-to-peer lending platforms, such as the China-based PPDai, adopt this model. Earlier lending platforms typically engaged in this type of model before moving to a quote-driven market model.

Challenges with the mainstream lending market
The current mainstream lending market, led by banking institutions, is fraught with issues.
Financial inclusion After the global financial crisis, more stringent lending rules and underwriting models were applied to banks. As a consequence, the threshold of credit rating was increased to lower the default risk. Clients with thin credit files, such as small and medium enterprises (SME) and retail borrowers (particularly immigrants and women) have become the most negatively impacted, facing increased difficulty to borrow from banks. The particular role that SMEs play in most major economies, especially developing economies, cannot be underestimated. Unfortunately, they are significantly affected by financial inclusion issues in lending, and lack access to alternative lending channels such as international capital markets. SMEs represent 90% of businesses and more than 50% of employment worldwide. The International Finance Corporation estimates that 65 million firms have unmet financing needs of $5.2 trillion each year. 2 Passthrough issues Owing to the inefficiency of information flow and the tardiness in policy execution, government lending schemes often do not reach end clients fast enough. This was particularly evident during the Coronavirus pandemic where commercial banks responsible for distributing loans to SMEs in the UK have been not been efficiently passing them along (Barrett et al., 2020). The passthrough of changes in the interest rates were also delayed, resulting in a high lending rate in a low savings interest environment, which is detrimental to borrowers. For example, in the UK, the Bank of England's rate cuts are often not passed on to lower mortgage costs for borrowers (Manas Pratap Singh, 2020).
Intermediary cost Regulatory obstacles keep the barrier of entry for lending entities high, leading to an oligopoly market with imperfect competition. The centralisation of lending services enables high intermediary costs, and the resultant market friction further contributes to the inefficient usage of market liquidity, leading to the non-maximisation of utilitarian societal welfare. For example, in 2016, the UK government set out to challenge high street banks' oligopoly by encouraging the alternative lending sector's growth. To date, these new players have only managed to capture 20% of the market (Prill, 2020).
Liquidity inefficiency The existing lending mechanisms in the mainstream lending market produces sub-optimal liquidity outcomes. Both the supply and demand sides of liquidity are crudely diverged into siloed submarkets based on factors such as the lending period, interest rate, credit rating, etc., even within the same lending platform. The oversupply of liquidity in one submarket cannot be promptly transferred to serve the demand from another submarket.
Subprime problems The challenges in securing a loan with a bank or any other major financial institutions leave many potential borrowers unserved. This prompts the rise of alternative lending entities, including peer-to-peer lending markets, that cater to the cohort unable to obtain financing from banks. Those lending entities typically charge borrowers a premium for securing funding, understanding that the borrowers have already been rejected by other sources and are left with no other options. Unfortunately, fraudulent activities and high default rates permeate these less strict lending markets, stifling their growth.
Legacy infrastructure The dated information technology infrastructure used by mainstream lending entities is a crucial impediment to efficiency gains. Due to a lack of data exchange between financial institutions and the absence of a functional tracking system, clients' credit history is fragmented and opaque, exacerbating financial exclusion and fraud. Even with the entrance of new lending platforms with substantially lower operating costs (at levels half that of commercial banks), their ability to compete in this domain is outweighed by commercial banks' far cheaper funding costs (Deloitte UK, 2016 These new solutions present themselves in the form of protocols (Gudgeon et al., 2020), sets of rules determining how a lending market operates. The DeFi market picked up momentum in 2020 and as of January 2021, the DeFi market's size, measured by the Total Value Locked, was estimated to be approximately $44 billion. 3 DeFi projects include decentralised lending platforms, decentralised exchanges, derivatives, payments, and assets.

Key components of DeFi lending protocols
Despite their respective particularities, most DeFi lending protocols share two features: (i) they deviate from existing subjective frameworks of centralised credit assessment to codified collateral evaluation; (ii) they employ smart contracts to manage crypto-assets (Bartoletti et al., 2021). A smart contract is a program deployed on a distributed ledger, commonly the Ethereum blockchain, that can perform bookkeeping and calculations, receive and hold digital assets, and execute transactions automatically when triggered by predefined events.
Value locked Value locked originates from a users' deposits in a protocol's smart contract(s). The locked value forms a reserve to pay back depositors in redemption, and can also be used as collateral.
IOU token Lending protocols issue users IOU tokens against their deposits. Only those IOU tokens can be used to redeem deposits at a later stage, and they are also transferable and usually tradeable in exchanges.
Collateral A loan's collateral comprises the entirety or part of the borrower's deposit. The collateral value, together with the underlying asset's maximum loan-to-value ratio determines how much crypto-assets a user is allowed to borrow.
Liquidation The liquidatable status of collateral is triggered automatically by a smart contract. When a loan's loan-to-value ratio exceeds a critical thresholdtypically referred to as "liquidation threshold"-due to interest accrued or market movements, any network participant can compete to liquidate the collateral. The market price information of locked and borrowed assets are supplied to smart contracts through external data feeds providers called "price oracles".
Interest rate Borrowing and lending interest rates are computed and adjusted by smart contracts according to the supply-borrow dynamics, based on protocol-specific interest rate models.
Governance token Certain DeFi lending protocols distribute to users governance tokens that allow them to propose and vote on protocol changes, such as modification of interest rate models. Governance tokens are often used as a reward scheme to incentivise participation, from both borrowing and lending sides, in a protocol.

Maker
Maker protocol revolves around DAI, a stablecoin whose value is soft-pegged to the US dollar. Users who wish to borrow DAI must first lock collateral in a smart contract named the Maker collateral vault. The collateral can be ETH (the native coin on Ethereum) or certain ERC20 tokens (digital assets on Ethereum). The collateral can constitute one single asset or multiple assets.
The smart contract calculates the collateral value based on its quantity and its market price, where the market price is input through an oracle, i.e. an external data feed provider. The borrower can then request the smart contract to issue him some DAI amount below a predetermined threshold fraction of the collateral value. This threshold fraction determines when the collateral will liquidate. Borrowers are thus advised to request the issuing amount of DAI to be moderately below the threshold, such that the slight downward price movement of the collateral against DAI will not render the collateral available for liquidation. There is no margin call with the Marker protocol. It is the borrower's responsibility to keep the borrow position overcollateralised by topping up collateral or repaying DAI in case of unfavourable price change. As soon as a borrower's loan-to-value ratio exceeds the liquidation threshold, any network participant is entitled to bid for the collateral by repaying part of the loan, thus liquidating the position.
To redeem the collateral from the vault, the borrower needs to repay their DAI loan and the loan interest, termed the "stability fee". The stability fee accrues over time. Its value is dynamically adjusted. When the stability fee is high, the borrower is incentivised to pay back some DAI, which is subsequently burned by the smart contract. Thus, this stability fee serves as a mechanism to steer the circulating supply of DAI, ensuring that the currency value does not deviate too much from its peg. There is no fixed loan period; DAI can be repaid partially or entirely at any time.

Compound
Users of the Compound protocol can supply and borrow ETH, as well as an array of ERC-20 tokens. Users who deposit crypto-assets into the protocol's smart contract will receive some cToken (e.g. aETH, cDAI) of an equivalent value, which can be used in exchange for the deposited asset plus interest in the future. A cToken is an interest-bearing token, whose exchange rate against the deposited asset increases over time (Leshner & Hayes, 2019).
To borrow from the protocol, users first have to deposit funds, which are used as collateral for the borrow position. In that sense, a borrower must first and foremost also be a depositor. The Compound protocol computes and updates borrowing and lending interest rates for each asset automatically and continuously, based on the amount deposited and locked, as well as the amount borrowed (Perez et al., 2021).
The funds borrowed thus accrue interest in a time-variant manner. There is a maximum amount of funds that the user can borrow against his collateral. The market value of assets is determined through the protocol's price oracle. As both borrowed assets and collateralised assets experience price movements and accrue interest, the collateralisation ratio of a borrower's position changes continuously. Despite the absence of a fixed loan term, the borrower needs to ensure the overcollateralisation of his borrow position, such that the collateral does not become available to be liquidated by other network participants.

Aave
With the Aave protocol, formerly "ETHLend", liquidity suppliers deposit funds in a smart contract and receive the corresponding aToken (e.g. aETH, aDAI), representing a deposit certificate. An aToken is an interest-bearing token whose value is one-to-one pegged to the deposited asset. For example, if a user deposits 12 ETH, they will receive 12 aETH as proof of deposit. The balance of aETH automatically increases over time, mirroring the interest payment against the ETH deposit (wow@aave.com, 2020). aToken holders can always redeem the underlying asset with a 1:1 exchange rate by sending aToken to the smart contract and receiving an equivalent amount of the underlying asset from the smart contract.
As with Compound, Aave users' borrow positions must be collateralised by their supplied funds and face liquidation risk once the position becomes undercollateralised. With Aave, borrowers can choose to use part of or the entirety of their deposited assets as collateral, and may switch between a variable interest rate and stable interest rate at any time.
Aave also supports "flash loans". Wolff, 2018 introduced flash loans as a type of loan where a user borrows and repays funds within one transaction without collateral. Flash loans are often used to take arbitrage opportunities (see Flash loan 1), and to liquidate insufficiently collateralised borrow positions on lending protocols (see Flash loan 2).
Flash loan 1 Arbitrage 1: Borrow crypto-asset XYZ 2: Sell XYZ on exchange A at P A 3: Buy XYZ on exchange B at P B with P B < P A 4: Repay XYZ Flash loan 2 Liquidation 1: Borrow x 1 crypto-asset XYZ 2: Liquidate a borrow position by paying x 1 XYZ to receive the collateral ABC with a bonus 3: Swap received ABC for x 2 XYZ with x 2 > x 1 4: Repay x 1 XYZ Users themselves are responsible for customising each specific flash loan contract for a determined cycle of operations. A flash loan transaction is atomic: if the loan is not paid back, the entire transaction is reverted, while transaction fees still incur.

Current use cases
As of today, borrowers have been using DeFi lending protocols for two main purposes.

Earning rewards
At the moment, the primary motivation for ordinary retail users of DeFi lending protocols has been to receive participation rewards in the form of e.g. valuable, tradable governance tokens (see Section 4.1).
In the extreme, borrowers would repetitively (i) borrow, (ii) re-deposit borrowed funds as collateral, (iii) borrow again. As such, a "borrow spiral" is formed to maximize the amount of reward tokens that a user can receive .

Leveraged trading
Leveraged trading is commonly seen among more sophisticated investors as well as institutional investors such as hedge funds. For example, an investor bullish about ETH may borrow, say, DAI to buy some ETH. In expectation of a price increase of ETH, investor would swap borrowed DAI for ETH on an exchange, hoping that the purchased ETH can be worth more DAI in the future to such an extent that it exceeds the loan amount and leaves the investor some profit.
Similar to the borrow spiral discussed above, an investor can repetitively (i) borrow DAI, (ii) swap DAI for ETH, (iii) re-deposit ETH as collateral, (iv) borrow more DAI. As such, a "leveraging spiral" is formed (Perez et al., 2021) to maximise the investor's long exposure to a crypto-asset that is expected to appreciate.

Advantages of DeFi lending protocols
DeFi lending protocols exhibit apparent advantages over conventional lending schemes, broken down as follows.
Transparency Deployed on a public blockchain, the exact content of smart contracts according to each DeFi lending protocol is freely available and auditable. In addition, users' historical interactions with protocols and their lent and borrow positions are transparently recorded on the blockchain. Market information is public for everybody.
Democracy Absent a central authority, users vote on amendments of a protocol. In particular, governance tokens that are issued by many of these protocols to users give proportionate voting rights to those who have economic stakes in these platforms.
Liquidity Funds supplied to a lending protocol are pooled together and can be utilised efficiently. Thanks to smart contracts and blockchain, lending, borrowing and arbitraging can all be performed inexpensively and almost instantaneously. By guaranteeing IOU tokens' redeemability to fund suppliers, DeFi lending protocols also ensure full transferability and exchangeability of debt holdings.
Agility DeFi lending protocols can automatically update interest rates and ceaselessly reflect the latest supply-borrow balance of the market.
Trustlessness Lenders do not need to trust borrowers' solvency anymore, as ingeniously designed smart contracts enforce liquidation when default risk is present.

Status quo of DeFi lending protocols
The pace of innovation of DeFi lending protocols is astonishing, with new projects emerging and rapid gaining attention, being copied and improved with new ideas quickly integrated into existing protocols. Nonetheless, the decentralised lending market is still in its infancy and at an experimental stage.
In particular, the growth of today's DeFi lending space is still mainly propelled by short-term oriented, "financialised" borrowing (see Section 4.3). Nevertheless, the development of the entire DeFi ecosystem coupled with the increasing adoption of crypto-assets will encourage more dominated genuine, utilitydriven borrowing activities with DeFi lending protocols.
Given that everybody must "play by the code" in the decentralised environment, security requirement for protocols is exceptionally high. Unfortunately, there persist vulnerabilities and loopholes in smart contracts which attackersrightfully albeit not righteously-exploit and profit from (Qin et al., 2021). In particular, malicious users are seen to abuse certain features of DeFi lending protocols to perform e.g. flash-loans-funded price oracle attacks . The pseudonymous nature of blockchain, plus the absence of central authority, makes it challenging for attack victims to claim damage. As it stands, the community still resorts to regulations to deter malicious activities. In the long run, an improved protocol governance will lead to system resilience, and a dependence on law enforcement external to the decentralised ecosystem will be reduced.

Coevolution of DeFi and CeFi
DeFi still heavily relies on the long-established banking system. Notably, the value of crypto-assets on DeFi are still primarily gauged and acknowledged in fiat values. Among the most widely used crypto-assets are stablecoins (e.g. DAI), whose value is anchored to fiat currencies. As discussed at the beginning of this article, only central banks can, by definition, issue central bank money. DeFi's reliance on fiat denomination makes central banks irreplaceable, at least in the near future.
Centralised finance (CeFi) platforms for lending such as Salt, Celsius and YouHodler serve as bridges between the conventional monetary market and crypto-asset market. With those platforms, one can borrow fiat money directly (instead of fiat-pegged stablecoins), with their crypto holdings serving as collateral. Those platforms are operated by registered companies that act as counterparties to both their depositing and borrowing users. For that reason, those companies are also called crypto banks (see Figure 1).
6 Outlook of the lending market -the path to the Internet of Value The emergence of the alternative finance sector focussed on lending has not led to the anticipated change and disruption to mainstream lending markets. The majority of these companies, reflected in the bottom-left part of Figure 1, are yet to make a profit. While many have floated on international stock exchanges, their share prices have tumbled since their Initial Public Offerings (Sanborn, 2019). Their problem has not only been the high costs of funding versus the incumbent commercial banks. Their technology advantage and abandonment of legacy infrastructure have not sufficed to outplay the competition. Furthermore, consolidation is occurring in the traditional part of the sector (Megaw, 2020). Commercial banks are buying marketplace lenders who are in trouble and combining the latter's technological advantage with their own lower funding costs. Hopefully, this can only be good news for borrowers. The Internet of Value, with a full spectrum of DeFi lending enabled, will mean that borrowers and lenders can seamlessly find yield and maximise their economic returns with whatever tokens and assets they wish to hold, without needing to go through any centralised intermediaries.
Expectably, the current DeFi markets' exceptional double-digit yields will fall as it is unlikely these yields will be sustainable. However, already they are beginning to attract interest from institutional players who have only invested in the traditional financial markets (Shen, 2021). Although there are many more esoteric notions with blockchain technology powering the Internet of Value, such as asset tokenisation, the concept of yield is much more familiar to financial services professionals. Therefore, traditional financial services companies may seek to incorporate DeFi into their offerings sooner than expected.
As things stand, the traditional money market underpinned by central banks will not disappear. DeFi still faces regulatory and technology maturity issues, and it remains to be seen what will happen when these systems scale. However, with increased interest in Central Bank Digital Currency (CBDC) development, the conventional money market and DeFi will converge in the foreseeable future.


Research on the Related Issues of Rural Land Circulation in Escape Agriculture—Taking Village D in S City as an Example

Affected by the acceleration of urbanization, a large number of rural labour forces have flowed out. The "empty villages" are accompanied by the idleness of rural land and obstacles to improving land utilization. This article uses a semi-structured interview method to take village D in S city as an example. Based on the social exchange theory, this paper studies the rural land transfer issues that affect the order and stability of rural development. The research finds that rural land transfers have low participation and transfer behavior in rural land transfers. Lack of standardization and other issues, and on this basis, put forward corresponding suggestions, which have important theoretical value and practical significance for the analysis of rural land transfer issues.

RAISING THE PROBLEM
The problems of agriculture, rural areas, and peasants have always been fundamental issues related to my country's national economy and people's livelihood. At present, with the acceleration of urbanization, a large number of rural laborers have migrated to work, and the problem of "empty villages" in rural areas has become more and more serious. At the same time, a large number of farmers have fled the fields, resulting in large tracts of idle arable land in rural areas. As a result, the land utilization rate is greatly reduced. The No. 1 Central Document issued by the government in 2017 clearly pointed out that in the transfer of rural land, it supports the coexistence of management rights transfer, shareholding system cooperation, substitute farming and planting, and land trusteeship, so as to accelerate the development of multiple forms of large-scale operations [1] . Although the government has issued a series of preferential policies on rural land transfer and contracting, it has not effectively alleviated the increasing trend of rural idle land. On this basis, it is of great practical significance to explore the problems in the process of rural land transfer through semi-structured interviews, and to put forward corresponding countermeasures, which is of positive significance to the stable development of rural areas.

Research on the issue of rural land transfer
In the past, scholars have conducted research on rural land transfer issues from different angles. In view of the previous literature on this aspect, this article mainly summarizes the following two aspects from the factors affecting rural land transfer and the impact of rural land transfer: First, the research on the factors affecting rural land transfer. Scholars have conducted in-depth research based on different entry points. Scholar Gong Binbin and others pointed out that an important measure to revitalize the rural economy is land circulation, land rent collection is an important variable for rural economic development, and food prices are the main reason for the rigid increase in land circulation prices. [2]. From the connotation of urban-rural integration and rural land transfer, scholar Cheng Xuejuan pointed out that it has affected rural land transfer [3]. Scholar Sun Mingyang believes that affected by factors such as the differentiation of labor capacity, the increase in agricultural costs and difficulty, and the decrease in agricultural income, farmers generally have the willingness to transfer land [4]. Second, the research on the impact of rural land transfer. The scholar Zhang Nailian analyzed the impact of rural land transfer on the agricultural economy. Its advantages are to increase villagers' income, promote the modernization of rural land, transfer rural labor, and bring better economic benefits to rural development [5]. The transfer of rural land has increased the income level of farmers, promoted the diversified development of agriculture, and promoted the construction of a new type of agriculture. It also promotes the concept of agricultural operation and management, broadens the development channels of the agricultural industry, and promotes the modernization of agriculture in my country [6]. On the whole, although many results have been achieved in our own research, in the new era, the problem of rural land transfer presents a new situation, and the past research results are insufficient to explain the new phenomenon. Scholars' analysis of rural land transfer lacks relevant theoretical support, and some studies have strong homogeneity, and it is difficult to analyze the problems existing in rural land transfer in accordance with local conditions. Based on the current situation of rural land transfer in Village D in S city, this article analyzes its existing problems in depth, and puts forward corresponding countermeasures and suggestions in a targeted manner, hoping to help the process of rural land transfer.

Theoretical basis
The theoretical basis of this research is social exchange theory. The well-known American sociologist George Homans founded the theory of social exchange. He studied social exchange behavior from the micro level of the individual. He believed that social exchange is based on certain social relationships and exchanges in terms of social psychology and behavior. The objects include material and non-material rewards, and each exchange object is a rational economic man, pursuing the maximization of his own interests [7]. The reason why this study chooses the theory of social exchange as the theoretical basis is that the two parties involved in the rural land transfer are based on the existence of a transaction relationship to make behaviors or exchanges of material and non-material rewards.

Overview of Village D in S City
D Village in S City has rich product information and a long history and cultural tradition. It is located in the Huanghuaihai Plain. The villagers in this village are mainly engaged in crop planting, with wheat and corn wheels as the main products. Tree planting has a long history and planting area is relatively wide. The village covers an area of 1.8 square kilometers, a population of 1,237 people, and 4807 acres of arable land in the village, of which 1,576 acres are involved in the process of land transfer, and the level of economic development is relatively backward. Most young men and women go to work in Jiangsu and Zhejiang. In recent years, due to the influence of migrant workers, the village has gradually shown a trend of "empty village", with a large outflow of labor force and the phenomenon of unused land becoming more and more common. The current land transfer in the village mainly includes land lease, land subcontracting, and land exchange. The legal and orderly land transfer in the county where Village D is located is about 120,000 acres. The county government has also issued a series of preferential policies to guide farmers in the transfer of land, with the aim of transforming agricultural development towards intensification.

Overview of interviewees
This research adopts a semi-structured interview method. According to the interview outline designed in advance, the interviewees are selected by snowball sampling. This interview selected 10 interviewees, aged between 27 and 75 years old, of which 5 male and 5 females; the details are as follows:

PROBLEMS IN THE LAND TRANSFER OF VILLAGE D IN S CITY
Through a series of interviews with the interviewees, the following issues are found in the current land circulation in D Village, S City:

Low participation of farmers in land transfer
In recent years, affected by urban integration, the existing population is mainly composed of the elderly and children. The labor force required for agricultural production is squeezed by the tide of migrant workers, showing a downward trend. Affected by the shortage of labor supply

Advances in Social Science, Education and Humanities Research, volume 598
and demand, a large amount of the original cultivated land is left idle. These large amounts of idle arable land did not participate in the village's land transfer. First of all, because most of the people who invest in the existing farm work are middle-aged and elderly people. Secondly, many people have no time to understand land transfer policies and matters. Finally, Village D lacks typical contractors or tenants, and it is not attractive to villagers to participate in the transfer. At present, the village's participation in the transfer of land accounts for less than a quarter of the total cultivated land area, and villagers' participation in the transfer of land is low. "Our family's land is under management, and they are getting older, and my aunt comes to help when the farming is busy. The remaining acres are idle. Now who is still pointing to farming to make money. Anyway, I will not stay at home to grow. Locally, those large contractors also have a certain funding base to manage them." (Interviewee

Lack of normative transfer behaviour
The existing land transfer information in Village D of S City is low and the transfer behavior lacks standardization. Many villagers believe that the existing land transfer process is relatively cumbersome. Generally, to save time and effort for both parties, the transfer is usually carried out in the form of an oral agreement. Often the two parties have not signed a transfer contract. Due to the prevalence of simple contracts, the parties to the contract have neglected many accountability issues, such as the lack of clarity of the rights, obligations, and liabilities of the parties to the contract, and the lack of transfer filing intentions for the subletter. Due to the lack of standardization of the transfer behavior, the transferor's interests are often lost, and it is at a disadvantage in the entire land transfer process. "My land is leased to several houses, and they are all in the same village. Everyone knows them and no one can run away. So I don't think there is much risk. The neighbors just promised them. The neighbors have been so many years, so I can't believe it. (Interviewee A6) "In my village, it is not uncommon for the two parties to have conflicts in the future due to the conditions listed in the contract. I also posted precautions about land transfer on the publicity boards in the village before, but how many people actually saw it?" (Interviewee A10)

Land transfer impacts the local characteristic planting industry
The village is located in the pear tree planting area with national characteristics. However, due to the saturation of the pear market in the past few years, the development of the pear industry has been affected by price shocks, and it is difficult to sell in the saturated market. Even if these farmers choose to go out to work and idle the orchard, they are unwilling to participate in the management of pear tree planting. Affected by the loss of pear tree planting, some villagers chose to cut down pear trees to convert them into conventional arable land for land transfer. The characteristic planting industry supported by the state has also been hit by the tide of land transfer, which is not conducive to this support. The progress of the work. "My family used to have two acres of pear trees south of the railway, but later they were cut down without making money. I think pears are the cheapest fruit, a few cents, and a lot of money." (Interviewee A2) "Even if you have contracted so many pear orchards, it is not as simple as you think. The national support policy is limited in funding and limited in scope." (Interviewee A5)

COUNTERMEASURES AND SUGGESTIONS FOR LAND TRANSFER
In response to the problems in the above research, the countermeasures are as follows:

Strengthen policy propaganda and guidance, and play an exemplary leading role
The publicity, interpretation and guidance of the rural land transfer policy is slightly insufficient, and the village leading cadres should further implement the interpretation. Through vigorously cultivating and propagating transfer models, we will dispel the worries of the majority of farmers, change farmers' traditional concept of land, and earnestly realize the benefits of land transfer. Actively transfer and move land that is unwilling or unable to cultivate in the family, actively participate in the transfer of rural land, and form a diversified and intensive pattern of land use.

Relevant departments strengthen the standardization of circulation behaviour
In response to the lack of standardization of current land transfer behavior, relevant departments need to establish a simple and clear land use or transfer system, make full use of land transfer service platforms, integrate transfer supply and demand information, provide information channels for them, and guide farmers to sign long-term standardized Advances in Social Science, Education and Humanities Research, volume 598 transfer contracts , And do a good job of reporting and filing. Village organizations encourage farmers to cancel land transfers that use the transfer verbal promises under the social network of rural acquaintances, and provide normative guidance for the transfer contracts declared to reduce the probability of future conflicts.

Strengthen the transfer of local characteristic planting industry to benefit the people
Some existing land transfers have already impacted the local characteristic planting industry supported by the state. Therefore, in the specific land transfer support policies, corresponding subsidies will be given to the large-scale operation entities of the characteristic planting industry with a larger transfer area, and free technology will be provided. Training, provide specific support. Improve the planting management confidence of contractors. Increase the coverage area of Pear tree's characteristic tourism industry and apply for it to be included in the Liyuan Tourism Economic Circle.

CONCLUSION
In summary, the road to intensive and large-scale rural land transfer is a long way to go. Farmers should take an active participatory attitude in the process of land transfer. Both parties to the circulation shall carry out planned behavioral operations based on fairness and justice when signing the circulation contract. Village organizations should focus on promoting policy guidance and transfer support work, so that the transfer of rural land can be truly implemented. Only in the future can the circulation of idle land in rural areas reach a new level.