The effect of occupational licensing deregulation on migrants in the German skilled crafts sector

Abstract

Occupational licensing on the national level reduces labor market prospects of individuals with a low likelihood of fulfilling the licensing requirements. Such regulation has the potential to adversely affect the labor market integration of foreign-born citizens and can be an obstacle to the free movement of labor toward its most productive uses. Before the backdrop of increased levels of migration into Germany, and the discussion about harmonizing labor standards in Europe, this paper empirically examines the effects of the deregulation of occupational licensing in the German crafts sector on the proportion of migrants working in this sector. The results suggest that the deregulation has increased the proportion of migrants among self-employed as well as employed craftsmen in the fully deregulated trades.

Appendix: Classification of crafts trades

The following procedure was used to identify individuals who are working in the crafts sector by using the microcensus occupation codes (KldB1992). In a first step, information was gathered on all training occupations and their classification codes (KldB 1992) Training occupations are different from occupations but are nevertheless associated with a particular crafts trade. This was achieved by consulting the official classifications of the ZDH and the Federal Institute for Vocational Education and Training and included present as well as predecessor occupations (Bundesinstitut für Berufsbildung, BiBB 2012).

In a second step, I used data provided online by BiBB concerning the information about how many apprentices within one occupational field are trained either within crafts companies or non-crafts (mainly industrial) companies. Subsequently, I computed a proportion of crafts apprentices within each occupational code. To exclude occupation codes with a high proportion of non-crafts workers, I used the information on the proportion on crafts trainees and dropped codes if this proportion was less than 60%. Lowering or increasing this cut-off point by up to 20% hardly affects the classification as most occupations contain either a very low or a high proportion of craftsmen. Observations were also removed if occupations could not be clearly marked as either an A or B occupation.

This method is not error-proof as it assumes that the proportion of crafts trainees strongly correlates with the proportion of crafts employees. However, this method removes occupation codes from the analysis that most probably contain very low proportions of crafts workers. For example, while the KldB code 141 (“Chemiebetriebswerker”, chemical plant employee) may seem a good proxy for the B-trade of “Wachszieher” (candle maker), according to my results less than 1% of individuals in the occupation of chemical plant employee are actually craftsmen. The classification scheme implies that most of the individuals in that occupation are industrial workers such as chemical production specialists, chemical technicians or pharmaceutical technicians.

In a last step, I scrutinized the occupation of building cleaners (KldB code: 934). The occupation comprises about 45% of all individuals in the deregulated B-trades in the microcensus dataset. Owing to its large size, it potentially biases any general conclusions about B-trades.

After a thorough inspection, it is doubtful if the occupational group of cleaners in the microcensus data perfectly matches the TCC trade of cleaners. For example, while official company registration data by the Federal Statistics office of Germany points to a sharp increase in market entry in that trade after 2004 (Mueller 2006), no such trend can be established in the microcensus data. The proportion of self-employed cleaners in the microcensus only increases from 1.6 (2004) to 2.3% (2011). Upon request, employees of the Research Data Centers of the German States confirmed our suspicion and suggested several other classification codes under which cleaners might be found, none of which can be identified as crafts trades based upon our classification scheme.

According to the documentation for an older occupation classification system (KldB1975), there are about seven activity profiles coded as 933 or 934 (cleaners). The classification scheme in the microcensus (KldB1992) merges these codes into one code (934). According to the crafts classification scheme recently developed by the Federal Employment Agency (BAA 2014), only three of these seven occupations belong to the crafts sector. I therefore do not include cleaners in the analysis. Doing so, however, increases the effects size as well as the statistical significance of our results, suggesting that the share of migrants rose more strongly amongst cleaners (see Table 12).

Table 12 Classification of crafts occupations by Runst et al. (KldB1992 titles)