Measuring the Level of the Youth Informal Economy in Lithuania in 2004–2020

Abstract

This paper investigates the development of the youth informal economy in Lithuania in 2004–2020. Specific youth-tailored multiple indicators multiple causes (MIMIC) model has been derived in order to estimate the level of the youth informal economy. In total, 173 direct phone interviews with company managers were conducted in order to acquire information for the MIMIC model calibration. It was revealed that during the investigated period, the youth informal economy fluctuated between 38.7% and 46.1% and was significantly higher compared with the overall informal economy in Lithuania. Although showing a strong correlation level (0.742) the overall informal economy within the country and the youth informal economy show slightly different developmental paths, the latter being more prone to higher increases during a crisis period.

1. Introduction

Scientific focus on informal economies spans more than three decades (Hart 1985; Sassen 1993). The importance of the informal economy research is acknowledged by scholars aiming for a more accurate assessment of the economic performance of the country (Elbahnasawy et al. 2016), investigating possibilities for a more thorough taxation regime (Meagher 2018), combating corruption (Choi and Thum 2005; Hoinaru et al. 2020) or even focused on tackling terrorist activities (Schneider 2019). The answer to these scientific problems mainly presumes a more accurate estimation of the level of the informal economy within the state, not paying significant attention to its particular constituent sectors. Even though there is another significant scientific stream, which considers informal economies as an effect of governments’ inadequate social policy measures, rather than a deliberate intention of a person to break existing legislation (McKeever 1998; Eijdenberg and Borner 2017). In this case, informal economic activities are treated as transient measures undertaken by individuals in order to satisfy their basic needs before the same aim can be achieved through the official economic means. This perspective utilizes a more detailed approach aimed at identifying the level of the informal economy in various sectors of the state’s economy (Schneider et al. 2022), not only in order to identify the sectors where informal economic activities are more intense, but also to recognize the most socially vulnerable parts of society. Such an approach enabled estimates of the levels of the informal economy among the immigrant (Camacho et al. 2017), refugee (Oka 2011) and depraved women (Kabeer 2014) communities, although there are additional socially vulnerable groups within society which may display informal economy levels significantly above the country average. One of them is youth, which is characterized by the highest unemployment levels among European Union (EU) employees (Maguire et al. 2013; Liotti 2020), high social insecurity (Melrose 2012; Banks 2016), perceived social injustice (Carlo et al. 2022), etc. Keeping in mind the aforementioned scientific evidence, we presume that the level of the youth informal economy may differ from the level of the overall informal economy in the country, and could display a different nature or causal factors compared to the overall informal economy within the state. This paper aims to reveal the real extent of the youth informal economy, and compare its development with the development of the overall informal economy within the country.

In our investigation the definition of the informal economy proposed by Putniņš and Sauka (2015): informal economy consists of the labour and income which are legal by nature, although were not officially declared for various reasons. Thus, it excludes the activities which are illegal in general, such as drug dealing, smuggling, etc. We have selected Lithuania as a basis for our research due to the possibility to acquire primary data required for the calibration procedure of the MIMIC model. Another reason for a such selection is that youth socioeconomic problems in Central and Eastern Europe (CEE) remained in a relative scientific shadow until now (Bugajski 2020), and additional research on the development of the youth informal economy in one of the CEE countries may help to fulfill this scientific vacuum.

The paper is structured as follows. A theoretical section provides a brief literature review on possible causes and indicators of the youth informal economy. The methodological section introduces the two distinct approaches used for the measurement of the youth informal economy. The results and discussion section presents the main results and juxtaposes them with existing theoretical streams. The conclusions section generalizes the research, acknowledges limitations of the study, and introduces possible future research directions.

2. Literature Review and Hypothesis Setting

There is an extensive debate about the pros (Dube et al. 2007; Lemos 2009; Belman and Wolfson 2014; Bossler and Schenck 2022) and cons (Neumark et al. 2004; Whiting et al. 2019; Manning 2021) of the Government set minimum wage. Lemos (2008) reveals that benefits of the increases in minimum wage experienced by low income workers significantly overcome the small drawbacks (in particular, only marginal increases in retail prices) it produces. Boeri (2012) argues that the minimum wage set by governments will always be lower compared to the minimum wage set by an agreement among labour unions. Harasztosi and Lindner (2019) argue that costs of increased minimum wage are largely bared by the consumers. Meer and West (2016) prove that an increase in wage floors negatively mirrors in an employment situation, although this effect manifests not immediately, but after a significant time lag. Caliendo et al. (2019) report that after the introduction of a minimum wage, employers started to declare less hours worked. This finding allows one to presume that some underreported work hours were conducted in the informal economy. Neumark and Shirley (2022) found that an increase in the minimum wage negatively affects youth and low-skilled workers’ employment situation the most. Keeping in mind the aforementioned scientific evidence, we formulated our first hypothesis (cause 1):

Hypothesis 1 (H1). 

Increases in minimum monthly salary within the country increases youth informal economy.

The impact of the increased average salary on the informal economic activities is ambiguous. One of the prevailing scientific streams argues that an increase in the average salary/income of a person decreases the shadow/informal economy (Williams and Schneider 2013; Galdino et al. 2018; Charmes 2020). This happens due to the fact that the more person earns, the less motivation he/she has to engage into informal economic activities, although contradicting evidence also persists (Neef 2018; Webb et al. 2020). It is assumed that not all companies are able to retain their competitive advantage after an increase in labour price (Liu et al. 2021). Striving to remain in the market, these companies engage into some informal economy activities, such as underreported working hours (Franic 2020), envelope wages (Di Nola et al. 2019; Franic and Cichocki 2021), unaccounted income (Putniņš and Sauka 2015), etc. Sometimes employees are forced to accept informal contracts/partially declared working hours in order to preserve their jobs (Horodnic and Williams 2021). Low-skilled/inexperienced employees are among the first to experience pressure from the employer to accept disadvantageous contract terms due to weak bargaining power (Moon 2018). As young employees are typically less qualified compared to their older counterparts, we presume, that aforementioned factors may also affect the youth informal economy. This leads to our second hypothesis (cause 2):

Hypothesis 2 (H2). 

Increases in average wage in the country increases youth informal economy.

The tax burden is widely seen as one of the main drivers of the informal economy around the world (Schneider and Enste 2013; Williams 2017; Němec et al. 2021; Remeikienė et al. 2022). It manifests its influence both to the overall economy (Medina et al. 2017) and to the particular economic sectors investigated (Chancellor and Abbott 2015; Kelmanson et al. 2019; Schneider et al. 2022). Due to such a focal position of the tax burden in the formation of the informal economy (Altındağ et al. 2020), we presume it will also affect the development of the youth informal economy. Thus, we formulate our third hypothesis (cause 3):

Hypothesis 3 (H3). 

Increase in tax burden within the country increases youth informal economy.

There is extensive literature about the relationship between trust in the government and citizens’ economic behavior (Tonkiss 2009; Nunkoo and Smith 2013; Morkūnas 2022). Trust in the government/public institutions also affects undeclared economic activities (D’Hernoncourt and Méon 2012; Teobaldelli and Schneider 2013; Arsić et al. 2015; Popescu et al. 2018; Ginevicius et al. 2020). Although the scientific evidence for the youth socio-economic behavior suggests that the aforementioned trust in the government—informal economy relationship might not be impregnable. Kitanova (2020) reports, that while increasing, youth political participation in the EU is still significantly lower compared to the older persons’ political activism. Esau et al. (2019) show that youth display significantly lower trust in government compared to their parents. Youth economic behavior is determined more by intrinsic motivation than by social norms (Browman et al. 2019). These scientific findings motivated us to test our fourth hypothesis (cause 4):

Hypothesis 4 (H4). 

The increase in trust in Government within the country decreases youth informal.

Common Agricultural Policy (CAP) has constantly consumed the biggest part of the EU budget since its creation (Matthews 2018). Although highly criticized for the ambiguity of its objectives (Heinemann and Weiss 2018; Bachtler and Mendez 2020), CAP is also acknowledged for maintaining rural sustainability (Volkov et al. 2019; Balezentis et al. 2020). One of the main functions of the CAP in the assurance of rural sustainability is the provision of decent jobs in the EU periphery (Žičkienė et al. 2022). Karamessini et al. (2019) show that the highest job insecurity among youth in the EU is observed in its rural regions. This forces internal migration of youth (Salamońska and Czeranowska 2019) and encourages informal economy activities (Ilieva-Trichkova and Boyadjieva 2020). Synthesizing the aforeementioned scientific evidence, we presume that more intensive financial support for the development of rural regions should positively affect the youth informal economy. This allowed us to formulate our fifth hypothesis (cause 5):

Hypothesis 5 (H5). 

Increases in funding for the regional development decreases youth informal economy.

The relationship between corruption and informal (Smith and Thomas 2015; Ouédraogo 2017; Baklouti and Boujelbene 2020; Elbahnasawy 2021) as well as shadow (Dreher and Schneider 2010; Borlea et al. 2017; Dell’Anno and Teobaldelli 2015; Chen et al. 2018) economies is very well discussed. Although these studies were made using aggregate country/county level data, and did not take into account the age factor as a possible moderating variable of the development of the informal economy. It might be considered as an overlooked but important determinant of the youth informal economy, as it is prove that youth and older adults differ significantly r in their economic behavior (Kautonen et al. 2015; Loichinger et al. 2017). In order to test if the level of corruption influences the level of youth informal economy, we formulated our sixth hypothesis (cause 6):

Hypothesis 6 (H6). 

The higher is the level of corruption within the country, the higher is the level of the youth informal economy.

Dell’Anno and Davidescu (2019) suggest that one of the most reliable indicators in measuring informal economy is labour participation rate. The higher the undeclared economic activities, the lower the labour participation rate. This can be explained by the simple fact that all labour force consists of formal and informal employment. It is obvious that if some of the formal labour force migrates to the informal sector, the level of the official labour force decreases (Chen et al. 2020). Williams (2015) support these arguments for developed nations, and offer to use this indicator in a quantitative research (Williams et al. 2015). Based on the above-mentioned, we formulated our seventh hypothesis (indicator 1):

Hypothesis 7 (H7). 

An increase in the level of youth informal economy decreases the youth labour participation rate.

Berdiev and Saunoris (2019) report that the informal economy is one of the main drivers of income inequality. It can be easily explained: it is an obvious fact that persons involved in informal economic activities are not officially declaring at least a part of their income. The more person is engaged in informal economic activities, the higher is discrepancy between his/her official and real income (Huynh and Nguyen 2020b). If the informal economy involves a significant share of a population, it starts to be reflected in an increasing official income inequality within the country (Chen et al. 2020). We believe this is also true with the youth, so we formulated our last hypothesis (Indicator 2):

Hypothesis 8 (H8). 

An increase in youth informal economy level increases income inequality.

The relationship between researched hypotheses and its predicted theoretical signs are presented in a Figure 1 below:

3. Materials and Data

In our research, we combined two of the most frequently used techniques for the estimation of the informal economy: MIMIC and a direct survey method.

3.1. MIMIC

We employed a MIMIC (Multiple indicators, multiple causes) approach as a main research tool. It is a widely accepted research technique for the estimation of the informal economy (Dell’Anno and Adu 2020; Fredström et al. 2021; Schneider et al. 2022).

MIMIC models are of the type of structural equation models developed for confirmation of the impact of a set of exogenous causal variables on the latent variable, and the effect of the latent variable on observed indicator variables as well. Basically, the MIMIC technique investigates the interface between the latent variable and observable variables. For the sake of the simplicity, it could be stated that MIMIC model employs latent variable, determinants (causes), and indicators. Typically, the MIMIC approach consists of two general equations:

y = λYIE + ε;

YIE = γ′x + υ,

where the youth informal economy (YIE) is the unobservable latent variable, y′ = (y1, …, yp) is a vector of indicators reflecting the size of the informal economy, x′ = (x1, …, xq) is a vector of causes of the informal economy, λ and γ are the (p × 1) and (q × 1) vectors of parameters, and ε and υ are the (p × 1) and scalar errors.

Generally, the first equation relates the researched youth informal economy to its observable indicators, while the second one associates the informal economy with observable causes. For the data and errors, normally distributed and showing significant correlation with var(υ) = συ² and cov(ε) = Θε, the general equations can be rewritten to create the following equation:

y = πx + μ

where π = λγ′, μ = λυ + ε and cov(μ) = λλ′σ²_υ + Θε.

Since y and x are scalar vectors, the obtained equation can be solved by a maximum likelihood approach using the restrictions implied in both the coefficient matrix π and the covariance matrix of the errors μ.

A JASP (University of Amsterdam, Amsterdam, The Netherlands) software was used for the MIMIC estimations.

Regarding the model proposed in the current paper, the latent variable is Informal Economy, which is affected by the set of observed causes and has an impact on observed indicators as presented in a Figure 1.

For the minimum monthly salary, we used a Government set minimum net wage for full time (40 h per week) monthly work. For average salary, we took a mean net salary for each calendar year calculated on the 31 December of each year. For total tax burden, we took a sum of all direct taxes, indirect taxes, and social security contributions within the country for a given year. Trust in government was represented by the trust in government index, which is an approved measure for measuring citizens’ trust in their respective governments (Lee 2021). As it is available only from 2010, values for 2004–2009 were back extrapolated. Funding for regional development covers all direct payments and financial mechanisms for rural development under all three pillars of the CAP. Youth labour participation rate is a percentage of active employees under 30 years old. Such a limit was set because the Ministry for Labour and Social Security of Lithuania (2021) determines youth as persons between 16 and 29 years old. Corruption refers to a Transparency International Corruption perception index. In order to represent income inequality within the country, we referred to a Gini index.

3.2. Direct Survey

As MIMIC estimations provide only relative values in order to calculate the level of informal economy among the youth, so a calibration procedure is required (Schneider et al. 2022). As we could not obtain retrospective data about the level of the youth informal economy which could be used as a starting value for the calibration procedure, a direct survey of company managers was chosen as an alternative. The start year is 2020, as the survey was conducted in October—December 2021, and the managers were asked to think about the previous year when answering questions. This approach of acquiring primary data about the extent of informal economy was proposed by Putniņš and Sauka (2015) and is verified in the Baltic States. Its main strength compared to the traditional surveys lies in the fact that during this survey, respondents are asked not to report the amount of informal economy in their enterprise (avoiding the bias of self-reporting), but to think about the level of youth informal economy in the economic sector their company belongs to. We applied the same questionnaire form as Putniņš and Sauka (2015), so it can be said that our questionnaire is validated, thus suitable for this research. In total, we conducted 173 direct phone surveys with company managers who employ young employees. Surveys were conducted in 7 (Alytus, Panevėžys, Utena, Tauragė, Šiauliai, Marijampolė, Vilnius) out of 10 counties in Lithuania. Therefore, it can be said that our survey represents the whole picture of the country, as it covers not only the capital region (Vilnius) and major cities (Šiauliai and Panevėžys), but also rural counties such as Tauragė or Utena.

After the collection of a primary data, a three step procedure was undertaken to calculate the level of the youth informal economy in Lithuania in 2020 (Putniņš and Sauka 2015):

UR^er = 1 − (1 − UR^s)(1 − UR^emp);

where: UR^er—underreported young employee remuneration comprised of underreported part or whole salary (UR^s) and underreported number of young employees (UR^emp).

Later, the informal proportion (IP) of company’s income, consisting of underreported employee remuneration (UR^er) and underreported operating income (UR^oi), which was acquired during the interview.

IP = PS ∗ UR^er + (1 − PS)UR^oi;

where: PS—predicted salary of a young (≤29 y.o.) employee in 2020 in Lithuania.

As Statistics Lithuania does not provide information about the average salary of persons between 16 and 29 years old, we took a mean of the officially set minimum wage in Lithuania in 2020 (607 EUR) and an average salary for the quarter IV 2020 in Lithuania (1524.2 EUR). This is based on an assumption that young person entering the labour market typically earn significantly less than an average salary; (Peiró et al. 2010; Wu et al. 2021) found that before their thirties, employees increase their remuneration due to increased qualification and experience, and their salaries come closer to the country’s mean (Zaharee et al. 2018). So, in our case, PS is calculated as follows:

PS = (1524.2 + 607)/2

During the third step the relative share (IE^y) of the informal economy among the youth is calculated:

$$I{E}^y={{\displaystyle \sum }}_{i=1}^N{w}_{i}IP$$

The weights, w_i, are the relative contribution of each firm to the youth informal economy, which is approximated by the relative amount of wages paid by the respective firm (Putniņš and Sauka 2015).

4. Results and Discussion

All our hypotheses were confirmed, and indicators and causal variables show the expected theoretical signs (

Table 1. Results of MIMIC estimations.

Model; Latent Variable—Youth Informal Economy	6-1-2	5-1-2
Causal variables
Minimum monthly salary in the country	0.5024 ***	0.5469 ***
Average wage in the country	0.2589 ***	0.2711 ***
Total tax burden in the country	0.3012 ***	0.2887 ***
Trust in Government	−0.0106 *	−0.012 **
Funding for regional development	−0.0178 **	−0.0211 **
Level of Corruption	0.4187 **
Indicator variables
Youth labour participation rate	−0.4338 ***	−0.4981 ***
Income inequality	0.0573 **	0.0711 **
Statistical tests
RMSEA	0.059	0.037
CFI	0.914	0.963
SRMR	0.041	0.024

Significant at: *—0.05; **—0.01; ***—0.001.

).

Our estimations provided two models (6-1-2 and 5-1-2) of sufficient statistical reliability (p > 0.05; root mean square error of approximation (RMSEA) < 0.08; comparative fit index (CFI) > 0.9; standardized root mean squared error (SRMR) < 0.05). We decided to use both models in calculating the level of youth informal economy in Lithuania in 2004–2020. The levels obtained using each of the models were considered of an equal importance thus the final level of youth IE in Lithuanian is an arithmetic mean of the figures obtained by the model 6-1-2 and 5-1-2. The level of the youth informal economy during the researched period is provided in a

Table 2. The level of the youth informal economy in Lithuania in 2004–2020.

Year	Average	Minimum Value	Maximum Value	2004	2005	2006	2007	2008	2009	2010	2011
Youth IE in Lithuania	41.5%	38.7%	46.1%	46.1%	44.2%	41.7%	39.6%	39.0%	42.5%	44.1%	44.7%
IE level in Lithuania (Schneider 2021)	27.4%	21.9%	31.7%	31.7%	31.1%	30.6%	29.7%	29.1%	29.6%	29.7%	29.0%
Year	2012	2013	2014	2015	2016	2017	2018	2019	2020	Correlation
Youth IE in Lithuania	44.6%	42.1%	41.6%	39.4%	39.7%	39.3%	38.9%	38.7%	39.0%	0.742
IE level in Lithuania (Schneider 2021)	28.5%	28.0%	27.1%	25.8%	24.9%	23.8%	23.0%	21.9%	23.0%

below:

Our calculations show that during the research period, the level of the youth informal economy in Lithuania fluctuated between 38.7% and 46.1%, and shows a clear decreasing trend. This could be attributed to the followings: after the accession to the EU, Lithuania became eligible for the EU funding aimed at regional development and cohesion within the EU (Di Cataldo 2017). As our research shows, the funding for the regional development has a strong positive effect on the decreasing trend of the youth informal economy. Secondly, the tax morale in the New EU Member States has sufficiently improved after the accession to the EU (Horodnic 2018). Another significant factor is the increased trust in government which, in turn, is the direct result of the more efficient and transparent public institutions (Šiugždinienė et al. 2019), and decreased perception of the corruption within the country (Toleikienė et al. 2020). Broadly speaking, it can be said that as a youth informal economy is a part of the overall informal economy within the country, it should follow the same path as the overall informal economy in the country. To test if this is really true, we have checked if there is a correlation between the development of the youth informal economy in Lithuania in 2004–2020 and the development of the whole informal economy in Lithuania during the same period. The research revealed a strong, although not a very strong, positive convergence between the two. Such a result may be caused by the fact that bys being a part of the overall informal economy, the youth informal economy is being determined by slightly different factors than the overall informal economy within the country. These findings suggest that in order to tackle the youth informal economy, government measures must be tailored to better reflect the nature and uniqueness of the youth informal economy.

Analyzing Figure 2 shows that the youth informal economy is prone to higher fluctuations compared to the overall informal economy within the country. This trend manifested itself during the financial crisis (2008–2010) even more. It may be explained by the fact that younger persons have less bargaining power, as well as lower qualifications compared to their older counterparts in the labour market. Hence, if there is a competitive pressure for the employer and he/she chooses to get involved ininformal activities in order to remain in the market, the youth become the first to be pushed behind the informality threshold. It not only compromises their career path and social security benefits (pensions, sick leave payments, etc.), but also leave them in a significantly less secure position, as the informal contracts are honored remarkably more rarely compared to official ones. Since 2016, the development paths of the youth informal economy and the overall informal economy within the country started to diverge. The gap between the youth informal economy and the overall informal economy within the country reached 16.8 percent points in 2019. The average difference during the researched period is 14 percent points. The overall informal economy within the country continued to decrease since 2016, although the youth informal economy remained relatively stable, displaying only a minor tendency to decrease. This is an interesting finding, as from the 2016, the Lithuanian government is undertaking intensified actions towards tackling the informal economy within the country. This once again confirms a slightly different nature of the factors causing the informal economy within the whole country and among the youth. This example confirms that actions which are relatively successful in combating the informal economy within the whole country may not be suitable in some particular sectors of the economy. It echoes Pasovic and Efendic’s (2018) arguments about the impossibility to derive universal measures for tackling the informal economy in all sectors of the country’s economy.

5. Conclusions

We found that the youth informal economy in Lithuania have steadily decreased during the 2004–2020 period. The highest youth informal economy in Lithuania was in 2004, while the lowest was in 2019. The slight increase in 2020 can be attributed to the COVID-19 pandemic, which contributed to the increase in the informal economy in various countries (Elgin et al. 2022). Our research confirmed that informal economy among the youth is significantly higher compared to the overall economy, and it also increases remarkably more during various economic crises. This reflects the relatively insecure position of young employees in the labour market compared to their older counterparts. Such a situation may compromise government efforts aimed at achieving social cohesion, which is one of the main preconditions for the creation of a welfare state (Andreotti et al. 2012; Koehler 2021). The youth informal economy appeared to be more immune to government initiatives directed at tackling the overall informal economy within the state. This not only substantiates arguments about the differences in factors causing youth informal economy and the overall informal economy, but also echoes the opinion about the necessity to tailor measures created for combating informal economy to the characteristics of various sectors of the economy.

Although our MIMIC estimations derived statistically reliable and robust results, our newly created models for the investigation of the youth informal economy should be treated with some caution, as they were validated only in one country—Lithuania. To be considered universal, our 6-1-2 or 5-1-2 models should be verified by studying additional countries. Another limitation to our study lies in the approach to acquiring the starting values for the model calibration procedure. Even though scientifically validated (Putniņš and Sauka 2015), the method of measuring the informal economy using a survey technique sometimes receive critique (Wyżnikiewicz 2019; Lichard et al. 2021). Thus, our findings should be considered as a first step in studying the youth informal economy phenomenon, but in order to investigate it in detail and more exactly, a lot of work must be done.

Our findings benefit quite a broad range of various groups of interest. First of all, it is the state tax inspectorate. As we proved that the youth informal economy is significantly higher compared to the overall informal economy within the country, additional attention should be paid to the companies employing a high share of young persons or operating in an economic sector focused on the youth. It would help to better assure tax compliance within the country. The less secure position of the youth in the labour market and their susceptibility to possible unfair manifestations of employers’ bargaining power suggests the necessity of additional improvements in Lithuanian labour regulation and legislation aimed at improving the job security of the young employees. Although requiring additional verification, our findings about the importance of the funding for regional development on decreasing the youth informal economy within the country (presumably—in its rural regions) may also have a far reaching implications. It would not only strengthen arguments about the CAP role in ensuring social development of the EU rural regions (Volkov et al. 2019), but could also help to dispel doubts about CAP of being over-financed (Buckwell et al. 2019), as additional arguments about the CAP’s part in assuring social cohesion may be raised (Mikuš et al. 2019).

One of the prospective research directions in the youth informal economy could be associated with obtaining more accurate and indisputable starting values required for the calibration of the estimations obtained by the MIMIC structural equation modeling. Another promising research avenue could be the comprehensive and multifaceted investigation of the real antecedents of the differences in development paths of the youth informal economy and the overall informal economy within the state. These findings would be very helpful in deriving effective measures for tackling the youth informal economy, which appeared more resilient to the existing government actions aimed at the decreasing of the informal economy in Lithuania The age of a young person’s involvement in informal economy activities, place of residence, whether she/he unemployed for some period of time or not, and the duration of unemployment period may also contribute to a young person’s willingness to engage in informal economic activities. A more thorough investigation of these factors would also help to increase the knowledge of the youth informal economy phenomenon.