Informality, regulation and productivity: do small firms escape EPL through shadow employment?

Informal firms have a limited access to capital markets (Cull et al. 2007), to market support institutions and law enforcement (Fajnzylber 2007). Moreover, they tend to invest less both in physical and human capital (Gandelman and Rasteletti 2017). In addition, informality constraints firm size because of concealment efforts (OECD 2004). For a discussion and empirical evidence on the relationship between informality and poverty, see Canelas (2018).

Informal employment is one of the main aspects of the shadow economy (Packard et al. 2012); even in the more advanced countries, where the shadow economy mostly assumes the form of underreporting of income to the tax authorities, it involves a large use of undeclared (informal) labour (OECD 2004, 2009). In a study on firms in Southern Europe, Putniņš and Sauka (2015) document that underreporting employees’ wages and hours worked is the most widespread form of undeclared practice.

Informality reflects individuals’ incentives to conceal their economic activities. This may take various forms, ranging from full participation in the underground sector (e.g. working and producing exclusively in the shadow labour market) to practices that are just partially informal (e.g. misreporting income, wages and employment). See ILO (2011) for a discussion. In this paper we indicate as informal employment those workers who are engaged in the production of legal goods and services and are either totally or partially undeclared (as, for instance, a full-time employee officially registered as a part-time one), are excluded from social security benefits and the protection afforded by formal labour market contracts.

Although we acknowledge that there are industries/sectors that are essentially formal (such as the public sector), we follow Bosch and Esteban-Pretel (2012) and argue that firms’ decisions on adjusting on the formal and informal sectors concern jobs with the same characteristics. In our analysis, we consider only formal firms and claim that part of their workers may not be legally registered. Informal workers are unregulated workers (fully or partially) who coexist with formal workers in a single labour market, as opposed to assuming segmented labour markets (Maloney 2004).

Within the theoretical literature, a number to studies use the search and matching framework to address the impact of tax policies, labour regulations and enforcement on informal work (Kolm and Larsen 2003; Fugazza and Jacques 2004; Albrecht et al. 2009; Bosch and Esteban-Pretel 2012; Basu et al. 2014; Di Porto et al. 2017). From the empirical side, Loayza (1996), Friedman et al. (2000) and Schneider et al. (2011) provide evidence at macro level that tax policies and the social security burden are among the main determinants of the shadow economy, followed by tax morale and the quality of state institutions.

See among the others Loayza (1996), Dabla Norris et al. (2008), La Porta and Shleifer (2008) and McKenzie and Sakho (2010).

As already mentioned, with informal labour, we refer to both the intensive margin (number of irregular workers) and the extensive margin (number of undeclared work hours).

Hijzen et al. (2017) show that strict EPL produces substitution between permanent contracts and temporary contracts with the perverse effect of reducing rather than increasing worker security on average. Analogously, Cappellari et al. (2012) point out that changes in protection rules for temporary employment lead to substitution between different types of temporary contracts.

On the one hand, in a standard search and matching model, the presence of dismissal costs will reduce the productivity threshold at which workers and firms decide to terminate their relationship, and this causes a decrease in firms’ average productivity. On the other, given that the worker-vacancy match implies the presence of quasi-rents, which are typically allocated between workers and firms through a Nash bargaining mechanism, an increase in firing costs reduces firms’ outside options. This induces a rise in the reservation productivity (below which firms do not hire) and potentially increases firm’s average productivity since less productive matches are not realized (Lagos 2006; Autor et al. 2007). There are other channels through which the presence of dismissal costs can impact firms’ productivity. When firing is costly, the firm has a lower incentive to undertake risky investments with high returns and high risk of failure (Saint-Paul 1993; Bartelsman and Hinloopen 2005). Moreover, EPL strengthens workers’ bargaining power and exacerbate hold-up problems related to the investment activity (Bertola 1994; Garibaldi and Violante 2005). Finally, dismissal costs influence productivity since it affects employees’ behavior and incentives. Belot and Van Ours (2007) show that an increase in the stability of the employment relationship induces to invest in productivity-enhancing human capital. Conversely, by using a standard model of efficiency wages Ichino and Riphahn (2005) claim that when firing become costlier for the firm, workers tend to exert less effort since there is less threat of layoff in response to shirking.

Other studies also model the impact of overregulation on informality using different approaches. Using a moral hazard framework with credit rationing, Straub (2005) demonstrates that labour market rigidities push firms to remain informal and reduce employment in the formal sector.

Tax morale can be defined as a social norm for tax compliance. In presence of tax morale, the cost incurred by evaders includes not only the fines payable upon detection, but also other nonpecuniary considerations (Bethencourt and Kunze 2019). See also Luttmer and Singhal (2014) for an insightful discussion on the determinants of tax morale and its effects on individuals’ tax compliance.

Another way of looking at the relationship between informality and productivity is to analyze the effect of going informal on the compliant firms; based on a sample of 1430 businesses in South Eastern Europe, Williams and Bezeredi (2019) report that businesses who assert that their competitors always or in most cases participate in the informal economy have significantly lower productivity growth rates compared with those who assert that their competitors do not participate in the informal economy.

From the empirical side, studies on the effects of EPL on productivity are scarce This is partly explained by the challenging identification issues faced when using aggregate country or sector-level data, and by the lack of accurate data at firm level. In this respect, empirical evidence on the effect of firing costs on firms’ productivity is provided by Autor et al. (2007), who exploit US cross-state variation in the adoption of wrongful-discharge protection norms and find evidence of a negative impact on total factor productivity (TFP). Cingano et al. (2010) use a large panel of European firms and find a negative effect of EPL on productivity and capital deepening. Focusing on Italy, Hijzen et al. (2017), Gianfreda and Vallanti (2017) and Cingano et al. (2014) analyse how different EPL provisions for firms above and below the 15-employee threshold impact on firms’ productivity Our study builds on these prior works in one major respect. Differently from the papers mentioned above, we focus on the interplay between labour regulation costs and informality costs, by exploiting both the discontinuity of EPL at the 15-employees threshold as well as the geographical variation in the costs of entry into informality.

When the layoff is ruled fair, a common practice in Italy is that the labour union pays all the legal costs.

The recent reforms in the Italian labour market, (the “Fornero reform” in 2012 and the Jobs Act in 2014 and 2015), have changed some of the rules related to the termination of the employment relationship. According to the new rules, also for firms with more than 15-employees, it has been restricted reinstatement to certain specific cases of unfair or unjustified dismissal and introduced an upper limit up to 24 months’ salary to the compensation a firm must pay in case of unfair dismissal. The change in legislation does not affect our estimates, since our data covers the period 2007–2010.

Boeri and Jimeno (2005) study the effect of employment protection on lay-off probabilities by comparing small and large firms. Garibaldi et al. (2004), and Schivardi and Torrini (2008) assess the effects of employment protection on the size distribution of Italian firms, by looking at the probability of firm size adjustments around the 15-employees threshold. Similarly, Hijzen et al. (2017) analyze the effect of different EPL provisions on the composition of workforce, worker turnover and productivity of firms above and below the 15-employee threshold. All these papers identify the effect of employment protection by exploiting the fact that Italian firms with fewer than 15 employees are subject to lower dismissal costs than firms with more than 15 employees. Other studies exploit both the discontinuity in EPL at the 15-employee threshold as well as the temporal variation in the legislation, in order to assess the effect of reforms on job flows (Kugler and Pica 2008), wages (Leonardi and Pica 2013), productivity and capital deepening (Cingano et al. 2014). Gianfreda and Vallanti (2017 and 2020) show that stricter EPL due to labour trial delays reduces job reallocation rates and productivity for firms above the 15 workers threshold and affect the composition of overall employment as well.

The result that EPL provisions do not affect firm’s propensity to grow is not new in the empirical literature. See among the others, Cingano et al. (2014), Leonardi and Pica (2013) and Schivardi and Torrini (2008) which examine the effect of EPL on firms’ size distribution below and above the 15-employee threshold in Italy.

This graphical evidence is confirmed in Sect. 7.1 where we check the robustness of our main results by using alternative cut-off thresholds obtained by defining the control group as firms below 13 employees and the treatment group as firms above 17 employees. Firms in the two groups are sufficiently above and below the threshold to avoid the problem of measurement error in the estimation of the size cut-off as well as the problem of non-random sorting above and below the threshold. In addition, the employment growth regressions reported in Sect. 7.2 show that the cost of informality does not affect firms’ propensity to grow around the threshold lending further support to our identification strategy.

Provincial elections have been abolished since 2014. In the transitory regime (2012–2014), the scheduled provincial elections were not held.

From the administrative point of view, the Italian territory is organized in regions, provinces and municipalities. Provinces have administrative tasks in province areas and in inter municipality territory; they have coordination tasks and oversee the implementation of public works in various sectors, including the economic, productive, trade sectors; they carry out programming activity for the provincial territory.

As we will discuss in the following session, provinces (NUTS 3) are also the finest geographical entities at which data on the informal economy are available.

The participation of the municipalities in the tax assessment is encouraged through the recognition of a 30% share of the amount of extra taxes collected, following the intervention of the municipality (Law 248/2005; Provvedimento AE 3.12.2007).

In the period under study the legislation regulating the election and the tasks of province level representative was the Testo Unico, G.U. n. 227 del 28 settembre 2000, s.o. n. 162/L. The legislation was reformed in 2014. Between 2007 and 2010 elections occurred before the end of the natural term only in 13 provinces. In most cases anticipation was due to the resignation of the President motivated by the choice to campaign in the Parliament election.

Since 1991, in Italy a municipal government can be dissolved by the national government and replaced by a group of three commissioners when there is the presumption of ties between local politicians and organized crime. Commissioners govern the municipality for the next 12–24 months, after which a new local government is elected and takes office (law no. 164/1991).

The sectors are (1) agriculture, forestry and fishing; (2) mining and quarrying; (3) food, beverages and tobacco; (4) textiles; (5) wood products; (6) paper products, publishing and printing; (7) refined petroleum, nuclear fuel and chemical products; (8) rubber and plastic products; (9) other non-metallic products; (10) basic metals and fabricated metal products; (11) machinery and equipment; (12) electrical and optical equipment; (13) transport equipment; (14) other manufacturing sectors; (15) electricity, gas and water supply; (16) construction; (17) wholesale and retail trade, repairs; (18) hotels and restaurants; (19) transport and communications; (20) other services. The financial and public sectors are excluded from the analysis.

A growing literature about employer-employee collusion to evade taxes in registered firms show how in presence of both labour and corporate taxes, a profit maximizing firm chooses optimally the amount of unreported labour and revenues (Kolm and Nielsen 2008; Barth and Ognedal 2018).

The NUTS classification from EUROSTAT lists 20 regions at NUTS 2 level and 107 provinces at NUTS 3 level in Italy.

Our indicator of tax evasion at province level can be interpreted as an indicator of firms’ local exposure to informality. We claim that it is negatively related to the expected costs of going informal (both from the workers’ and firms’ side).

This measure is the share of undeclared working hours (in FTEUs) and accounts for both totally irregular workers and partially undeclared employment (i.e. false part-time). Full time equivalent units are obtained as the ratio between the total amount of (undeclared) hours actually worked and the average number of hours worked by a full-time job. It is provided by ISTAT and is available at a region (NUTS 2) and macro-industry level (Agriculture and Fishing, Manufacturing, Construction and Services). Descriptive statistics on the share of irregular workers at region-macro industry level are reported in Table 12 in Appendix 1.

The Stata module xtivreg2 (Schaffer 2010) is used for all instrumental variable regressions in this paper.

The inclusion of province-by-year dummies allows to control for all province-specific time-varying characteristics (for example, the quality of local infrastructure) which have the same effects across firms. Notice that this set of dummies absorbs the main effect of tax gap, as this variable only varies by district and time.

Our results can also be read as the effect of informality on firms’ outcome (job turnover and productivity) in different firing costs regimes. As we discussed in the previous paragraph, we do not observe informality at firm level. Nevertheless, we expect the index of tax compliance at the local level to be negatively correlated with the likelihood of using informal labour. Therefore, we can interpret the coefficient of taxgap β₁ as the average effect of informality exposure on firms’ turnover and productivity while the interaction term taxgap x size captures the differential effect of informality induced by the stricter EPL regimes. As a robustness check, we also report the results obtained by regressing the average turnover rate and productivity both at the province level on the province taxgap on. Such results are consistent with those found using firm level data.

As in Schivardi and Torrini (2008), Garibaldi et al. (2004) and Gianfreda and Vallanti (2017) our measure of firm size does not distinguish between full and part-time workers. However, the problem of misclassification due to part-time workers have been shown to be relatively negligible if the interval in firm size is not too short, ranging from 2.7 to 5.8% from 5 to 25 to 11–20 firms’ size (Hijzen et al. 2017).

Notice that in this set of regressions, firms fixed effect “absorb” the main effect of the different time-invariant EPL regime that applies to firms above and below the 15-employees threshold.

The result that EPL provisions do not affect firms’ propensity to grow is not new in the empirical literature. See among the others, Cingano et al. (2015), Leonardi and Pica (2013) and Schivardi and Torrini (2008) which examine the effect of EPL on firms’ size distribution below and above the 15-employee threshold in Italy.

This result is in line with that in other empirical studies for Italy on the effect of EPL discontinuity at the 15-employee threshold (see for example Schivardi and Torrini 2008).

Results are qualitatively similar if we use the continuous values of the presample capital intensity rather than the dummy. We obtain the same results using the presample productivity level.

The attenuation bias is due to the loss of information from aggregating micro data. It is defined as the deviation of the macro parameters form the average of the corresponding micro parameters. See, among others, Theil (1954). Notice that the attenuation bias should work against our results because it should bias our coefficients of interest towards zero. Also, if we assume that measurement error is firm specific (and time invariant) the attenuation bias is eliminated by including firms fixed effects in the specification in which firms’ threshold is kept time invariant (as for example in Table 8, column 8).