We present the differences in the social and financial status of the populations of four disadvantaged rural micro-regions through three indicators: educational attainment, labour market position, and – material well-being. (All three are suitable for measuring important resource surpluses or deficits of the studied spatial categories)61.
Figure 33. shows the composition of respondents living in the studied villages and towns by educational attainment. In addition to the fact that the population’s educational attainment closely follows the municipal downward slope, we should pay attention to percentages. In terms of percentages, 9% of people living in dis-advantaged rural peripheral micro-regions failed to even complete their primary education. This is about four times the amount measured in metropolitan suburban areas. The prevalence of being unschooled is especially high in underdeveloped villages located in disadvantaged micro-regions (at 16%)62, it is more than three times the value measured in underdeveloped settlements located in the vicinity of major cities, and more than 1.5 times the value for developed villages in the same peripheral micro-regions.
We should add that this is mainly due to the great proportion of uneducated old-aged inhabitants: in metropolitan suburban zones among those failing to complete even the primary level of education, the proportion of people aged less than 50 was only 4%. The same index was 9.3% in the lagging villages of disadvan-taged micro-regions, which is significantly higher, but here the representation of the elderly generation among the leastedu -ca ted population was also over 90%.
We have not seen such a big difference in the proportion of uni-versity graduates living in the different metropolitan areas: one quarter of metropolitan city centre residents and 13% of residents of peripheral micro-regional centres has tertiary education. The presence of highly qualified residents in the underdeveloped
61We did not consider the income indicators reliable enough so we will present the latter through the survey subjects’ own opinions (“How adequate or inadequate is your payment?”), and through two variables suitable for the comparative analysis of monetary deprivation.
62Most of these villages are lagging behind and are often (but not always) small towns undergoing ghettoization.
localities of metropolitan suburban areas shows only a smaller scale majority, as it is ‘only’ twofold ratio (13%) as compared to the lagging settlements of peripheral rural micro-regions (6%) but if the presence of people with higher education is considered as human (intellectual) resource, we may clearly speak of a deficit in the latter spatial category.
Today public discourses intensively deal with the role of public employment in the life of people permanently excluded from labour market and from the benefits of regular income. While the majority of social scientists strongly criticise certain aspects of public employment, the opinion of the mayors questioned about it was rather positive than negative. We understand this attitude in regions where large masses of the working-age members of families must face years of joblessness.
Public works offer an extremely low-wage employment opportu-nity mainly for men: in the backward villages of rural micro-regions, 15.6% of women, and 21.9% of men were employed in public employment schemes in February 2014, while in backward villages only 17.7% of women and 16.8% of men could be employed in the business and the public/local governmental sector.
In developed settlements of the studied rural regions some more and in small urban centres much more jobs are available for both sexes, and the same gradualism starting from higher level Figure 33: The composition of population by educational attainment in the investi-gated spatial categories (%, 2014)
Source: The authors’ edition based on data of TÁMOP social survey
0% 20% 40% 60% 80% 100%
Underdeveloped villages of rural micro-regions
Developed localities of rural micro-regions Centres of rural micro-regions Underdeveloped metropolitan suburban
zones
Developed metropolitan suburban zones Peripheral rural areas total Metropolitan areas total
Lower than primary education Primary education Vocational school Secondary grammar school College or university degree
characterizes the employment chances of people living in metro-politan areas.
The extent of the deficit in job opportunities provided by the state/local governmental and business sectors and its inverse relationship with public employment can clearly be demonstrated, which of course, is correlating with the centralization of institu-tions valid until the recent past and with the incompleteness or absence of economic regeneration processes following the politi-cal changes of the 1990s.
Finally, material well-being or its absence, as an outstanding aspect of objective well-being, will be presented. The indicators of material well-being or the absence thereof are as follows: 1) households that do not have savings, 2) households that have serious difficulties paying the bills and 3) households that cannot make ends meet on their available income, or where people live in hardship. In the light of educational attainment and labour mar-ket characteristics it is not surprising that examining the spatial representation of materially deprived households we meet almost the same slope formula which is tightly determined by settlement type and development degree (see Figure 35.).
All the three indicators take up their highest value in underde-veloped villages of rural micro-regions, where 1) 92% of the respondents’ households do not have savings (this is by 20% high-Figure 34: Access to various forms of employment in the surveyed spatial categories (%, 2014)
Source: The authors’ edition based on data of TÁMOP social survey 0%
Accessible welfare jobs (public works) (for female) Accessible welfare jobs (public works) (for male)
State/local government and business sector jobs (for female) State/local government and business sector jobs (for male)
er than in the case of localities classified as undeveloped around big cities); 2) 42% had a problem in paying the bills, or has not paid the overheads (the ratio of the size of the affected house-holds with payment difficulties is just half in underdeveloped metropolitan areas); and 3) 10.3% of the respondents live in dep-rivation (as compared to 3.4% of the respondents representing underdeveloped metropolitan suburban areas).
Regarding the ratio of population lacking savings, there is a big gap between those living in rural micro-regions and residents of metropolitan suburban areas but the difference between the vari -ous metropolitan area categories is also significant. However, for the other two indicators (ability of covering monthly bills and making ends meet) there is a sharp dividing line between dwellers of the developed localities and lagging villages of peripheral rural micro-regions for the detriment of the latter against which the dif-ferences between additional spatial categories may be regarded as minimal. In the lagging villages of peripheral rural micro-regions thus the severe deprivation in terms of financial abilities of households is not simply evident resulting in deeper and more overall poverty than in the more developed localities of the same micro-regions; the gap has widened recently to a dramatic extent.
Figure 35: The representation of households deprived in material terms in the surveyed spatial categories (%, 2014)
Source: The authors’ edition based on data of TÁMOP social survey
0% 20% 40% 60% 80% 100%
Underdeveloped villages of rural micro-regions Developed localities of rural micro-regions Centres of rural micro-regions Underdeveloped metropolitan suburban zones Developed metropolitan suburban zones
Households lacking all forms of savings Households having problems in paying their bills Households living in hardship
The difference is significantly smaller between spatial categories for the two indicators of subjective well-being, as shown in the following table. According to the table, just like in other research-es (Lengyel–Janky, 2003),happiness in this study also had higher scores than satisfaction in all spatial categories; in case of satis-faction a settlement slope is seen again, i.e. the large cities and their developed and undeveloped suburban areas have happier people than settlements of the respective peripheral type. Of all categories respondents of the backward villages of peripheral rural micro-regions have the biggest ‘satisfaction deficit’ in com-parison with those interrogated in other spatial categories; the average degree of happiness indicated in scores is the smallest in lagging villages, and the difference here is the largest in compari-son with the advanced areas of the same district63.
In the following part we present happiness and life satisfaction scores (of 11-grade scales) grouped by districts (see Figure 36).
There are significant differences within each category: the average life satisfaction value of the respondents in the sample taken from the district of Sarkad shows a very low value (5.21 points). There is a relatively big difference between the most satisfied and the
63And although this aspect of analysis goes beyond the scope of this sub-chapter, there seems to be equality between the average score of happiness of people living in large cities and their advanced metropolitan areas, thus settlement slope model cannot be clearly applied at this point.
Table 8: The average scores of life satisfaction and happiness by spatial categories (N = 6619)
Source: The authors’ edition based on data of TÁMOP social survey
Spatial categories Satisfaction Happiness
Small-town centre 6,37 6,77
Peripheral developed area 6,34 6,56
Peripheral underdeveloped area 5,64 5,93
Developed metropolitan area 6,89 7,24
Underdeveloped metropolitan area 6,68 7,22
least satisfied groups of respondents of the underdeveloped vil-lages in the districts of Fehérgyarmat and Sarkad (6.29 and 5.12 points). Paradoxically, people in the district of Sárbogárd are only a little, in the district of Sarkad are much more satisfied in the more developed localities than those living in district centres.
‘Normal’ correlation between life satisfaction and happiness is seen only in the district of Sásd, the score of happiness is the low-est in the town of Sásd (5.65 points). In the district of Sarkad the residents of developed villages assessed their degree of happiness higher than those living in the town of Sarkad. The result is the same in the case of life satisfaction, but the difference is much smaller. The average score of happiness measured in the lagging villages of the district of Fehérgyarmat – just like of satisfaction – is higher than those in the settlements of developed rural areas.
Presumably, this is due to the relative success of public employ-ment programs, to the spread of small churches and to cross-border smuggling.
Figure 36: The average scores of life satisfaction and happiness by spatial cate-gories and districts (0 =the lest happy/satisfied, 10 = the most happy/satisfied)
Source: The authors’ edition based on data of TÁMOP social survey
0 1 2 3 4 5 6 7 8
&ĞŚĠƌŐLJĂƌŵĂƚ
^ĄƌďŽŐĄƌĚ Sarkad
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&ĞŚĠƌŐLJĂƌŵĂƚ
^ĄƌďŽŐĄƌĚ Sarkad
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&ĞŚĠƌŐLJĂƌŵĂƚ
^ĄƌďŽŐĄƌĚ Sarkad
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District centres Developed villages of peripheral rural zone Underdevelop ed villages of peripheral rural zone
Level of happiness Level of life satisfaction