Note: Persons who were not in the labour force at the time of the census are omitted.
In general terms, it is clear that those from a Lithuanian-speaking background are nearly twice as likely to be found in a so-called’ white-collar occupation (i.e. the top four categories in the table) as those from a Polish or Belarussian speaking background. While 33% of Lithuanian native speakers are to be found in these occupations, only 18% of Polish native speakers are in these groups. Native Russian speakers (26%), and native speakers of ‘other languages (28%) are much closer to the Lithuanian percentage.
By contrast, native Polish speakers are more likely (than native Lithuanian speakers) to be found in agricultural, craft/trade, machine operations or elementary occupations. Native Russian speakers are similarly more likely to be found in craft and trade occupations, or as plant and machinery operators. They are not, however, strongly represented among agricultural or elementary occupations. Native Belarussian speakers are closer to the Russian pattern than they are to the Polish.
The final point of note concerns the different rates of unemployment. Irrespective of their occupational structure, native speakers of all minority languages are far more likely than native speakers of Lithuanian to have been unemployed at the time of the census in 2001. The differences here are substantial; 19.7% compared to an average of about 25%. As minority groups differ considerably in their relationship to the labour market, the fact that all experience similarly high rates of unemployment suggests that the problem is not due to deficiencies in qualifications or experience, but may instead be due to the language related requirements of the market.
Because, as already noted, ‘other’ or second languages are learned in the school rather than the home, rates of acquisition are even more likely to reflect the perceived ‘market’ value of the language in question. Therefore, one would expect the distribution of abilities to speak a second or third to be related the language requirements of different sectors of the labour market.
Table 12 below demonstrates this to be the case. Because respondents could nominate more than one ‘other ‘ language, the percentages here do not total 100. The figures may be read as follows. Of those in the occupation category ‘legislators, senior officers and managers’, 9.6% spoke Lithuanian as a second language, 91% spoke Russian, 16% spoke Polish, etc.
Table 12: Percentages of each Socio-economic group who claim to be able to ‘read/write/speak’ selected ‘Other Languages’
Source: Lithuanian Statistics Department. Special tabulations
There are three features of Table 12 which merit comment. First, the figures for Lithuanian and Russian are mostly the inverse of the distribution of native speakers of those languages. That is to say, it is mostly Russian speakers who learn Lithuanian as a second language, and mostly Lithuanian speakers who have learned Russian. When the figures for both native and second language speakers are combined, it appears that over 90% of those in all occupations, except agriculture and fishing75, can speak both Russian and Lithuanian as either a first or second language. In the top three occupational groups the percentage claiming these abilities is, in fact, over 98%.
Secondly, Polish and Belarussian appear to have a completely different status in the labour market. Generally, only about 10-15% of those in workplace have learned Polish as a second language, and the percentages of any occupational group who have learned Belarussian as a second language are under 1%.
Finally, the distribution of speaking abilities in the two ‘foreign’ languages in the table – English and German – is clearly class related. These abilities are claimed, in the case of English, by about 40% of the top two occupational groups and by less than 10% of the four lowest groups. The pattern for German is similar, although overall percentages are generally lower. (The Armed Forces form a special category in this regard as the figures combine both high-ranking and lower-ranking officers)
These two tables are based on census totals and therefore, reflect the operational characteristics of the national labour market. But as Pierre Bourdieu has pointed out, local or internal labour markets may have their own particular features76, which ‘disregard the conventions and proprieties of the dominant (linguistic) market’.
The survey The Adaptation of Ethnic Groups in Lithuania: Context and Process77 (2000-2001) reveals that ‘nearly half of Russian and Polish respondents (44—45%) work in ethnically homogeneous environment, among Jews this accounts for 30%, Tartars - 23% (in most cases within the same ethnic group). The impact of ethnic relations in work relations is universally suppressed, though presumably significant. The majority of mono-ethnic work relations are observed in small businesses, such as shops, barber's shops, repair shops, garages, taxi companies, etc. In most cases these enterprises are organised on the basis of family or primary relations. Mono-ethnic environments at the place of work are mostly found in areas where population of respective nationalities is concentrated: Russians and Tartars in Vilnius and Visaginas; Poles in Salcininkai and Jews in Vilnius and Klaipeda.
Communication and relations with Lithuanians in business is closely related to the status in the case of Jews and Russians, i.e. the higher the status, the more relations with Lithuanians respondents maintain. This also suggests that groups with a higher social status include higher proportions of Lithuanians. As far as Russians are concerned, education plays an important role; It is important to note that, according to the research data, in business and professional environment open and ethnically diverse relations prevail’.