SOCIO-ECONOMIC DEVELOPMENT OF AGRICULTURAL PROBLEM AREAS IN POLAND

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1 Iwona Pomanek 218 Iwona Pomanek, Soco-economc Development of Agrcultural Problem Areas n Poland,, pp DOI: / X.2014/7-2/18 Iwona Pomanek PhD Department of European Polcy Publc Fnance and Marketng Faculty of Economc Scences Warsaw Unversty of Lfe Scences SGGW Warsaw, Poland E-mal: wona_pomanek@sggw.pl Receved: March, st Revson: Aprl, 2014 Accepted: May, 2014 DOI: / X.2014/7-2/18 SOCIO-ECONOMIC DEVELOPMENT OF AGRICULTURAL PROBLEM AREAS IN POLAND ABSTRACT. Ths artcle attempts to dentfy a relaton between several local development factors and localzaton of agrcultural problem areas n Poland, whch nclude areas wth lmted potental for proper agrcultural producton,.e., adverse sol and clmatc condtons, severe sol degradaton processes and fragmented structure of land. Such condtons consequently lead to low compettveness of farms located there as well as bad demographc structure, depopulaton and perpheral developmental poston of the gmnas. To assess levels of development, 6 detaled measures are constructed, usng Hellwg s synthetc measure. The fnal measure, groupng 17 varables, presents spatal dstrbuton of the level of soco-economc development of selected 73 most problem gmnas n Poland. JEL Classfcaton: J10, O18, P48, R15, R23, R51 Keywords: rural areas, local development, agrcultural producton, demography, entrepreneurshp, Poland. Introducton Agrcultural problem areas nclude areas wth lmted potental for proper agrcultural producton,.e., adverse sol and clmatc condtons, severe sol degradaton processes and fragmented structure of land, whch consequently leads to low compettveness of farms located there. In addton, these areas are exposed to margnalzaton and dsturbance of socal processes ( access: 03/22/2014). On the one hand, modernzng agrculture reduces demand for labour and consequently leads to ether reducton n employment or overgrowth (Rosner, 2012, p. 14). On the other hand, areas wth low compettveness of agrculture tend to depopulaton they have been left especally by young and educated people. There s also a preponderance of men over women (see: Rakowska, 2011a, p 9). The observed low values of femnzaton ndces do not help natural ncrease and mprovement of the demographc structure of these areas. An mportant role should be played by local authortes, creatng some ncentves for potental entrepreneurs and mmgrant populaton (see: Dzemanowcz, Swanewcz, 2007; Msterek, 2011). Accordng to the Insttute of Sol Scence and Plant Cultvaton (IUNG) research ( access: 03/22/2014), agrcultural problem areas

2 Iwona Pomanek 219 nclude areas meetng at least one of the followng condtons (factors lmtng proper agrcultural producton): fragmentaton of farms (average farm area 1-10 ha, more than 4 plots, average plot area smaller than 2.5 ha), ncluded n LFA area: lowland (2 nd type), of specfc dffcultes or mountanous, low content of organc matter n sols (less than 1.3%), potental water eroson rsk (medum and hgh level), sol acdfcaton (ph < 4.5), sol polluton wth trace elements. In fact, maxmum number of ponts reached by a gmna amounts to 4. The analyss covered 73 the most problem gmnas all gmnas wth 3 or 4 factors ncludng 14 wth 4 factors and 59 wth 3 factors lmtng proper agrcultural producton (see Fg. 1). Fg. 1. Problem areas of agrculture regardng number of factors lmtng producton Source: Author s elaboraton based on of the Insttute of Sol Scence and Plant Cultvaton (IUNG) nformaton. The man objectve of the study s to assess soco-economc development of the most problem areas of agrculture n Poland. Method The multdmensonal nature of the soco-economc development entals use of synthetc ndcators for ts level measurng. Consderable Polsh lterature shows dversty of avalable development methods and measures. The most popular taxonomc analyses of socal or economc development are as followng:

3 Iwona Pomanek 220 Hellwg s measure (Gralak, 2005; Feltynowsk, 2009; Pomanek, 2010; Czapewsk, 2010; Tarka, 2013; Zmny, 2013); method of standardzed sum or Perkal s ndex (Babuchowska, Ksel, 2006; Feltynowsk, 2009; Brodznsk, 2011); method of rank sum (Rakowska, 2009, 2011b); k-means method (Petrzykowsk, 2009; Chrzanowska, Drejerska, Pomanek, 2013; Tarka, 2013); Ward s cluster analyss (Surówka, 2007; Tarka, 2013); non-pattern synthetc measure (Krakowak-Bal, 2005; Ossowska, 2008); factor analyss (Tarka, 2013); Czekanowsk s dstance tables (Górka, Chmurska, 2004) etc. The analyss bases on constructon of seven smlar measures (and rankngs), usng Hellwg s synthetc measure (Hellwg, 1968, 1981; Nowak, 1990; Panek, 2009). Sx of them are detaled and concern dfferent aspects of local development (Table 1): demographc development, socal development, local government potental, local fnances structure, entrepreneurshp development and techncal nfrastructure development. The fnal measure s an aggregated form of the other sx measures and presents a complex vew of soco-economc level of the most problem areas of agrculture. Table 1. Varables accepted n the analyss Symbol Varables Notes Measure 1 demographc development X1.1 populaton densty (populaton per 1 square klometre) stmulant X1.2 brth rate (balance of brths and deaths per 1,000 populaton) stmulant X1.3 demographc burden (populaton of post workng-age per 100 populaton of workng-age) destmulant Measure 2 socal development X2.1 proporton of regstered unemployed n the workng-age populaton destmulant X2.2 total balance of mgraton stmulant X2.3 proporton of chldren aged 3-5 partcpatng n preschool educaton stmulant Measure 3 local government potental X3.1 proporton of councllors wth unversty degrees stmulant X3.2 proporton of councllors wth hgh professonal qualfcatons stmulant Measure 4 local fnances structure X4.1 gmna s own ncome per capta stmulant X4.2 gmna s ncome for the EU projects realzaton per capta stmulant X4.3 gmna s property nvestment expendture per capta stmulant Measure 5 entrepreneurshp development X5.1 natonal economy enttes regstered n REGON per 10,000 populaton stmulant X5.2 natural person runnng economc actvty per 100 workng-age populaton stmulant X5.3 foundatons, assocatons and socal organzatons per 10,000 populaton stmulant X5.4 newly regstered enttes per 10,000 workng-age populaton stmulant Measure 6 techncal nfrastructure development X6.1 proporton of populaton wth water supply connecton stmulant X6.2 proporton of populaton wth sewerage connecton stmulant Source: Author s complaton based on CSO LDB nformaton.

4 Iwona Pomanek 221 The varables enable to dvde gmnas nto three classes, regardng ther levels of development by Hellwg s synthetc measure. Hellwg s synthetc measure of development groups nformaton from a set of dagnostc features and assgns a sngle (aggregate) measure. The measure s calculated as a synthetc ndcator of taxonomc dstance of the object from the theoretcal pattern of development. The method allows to organze a set of objects (gmnas) P (where: = 1, 2,..., n), when each of them s descrbed n a set of m dagnostc features: stmulants or destmulants. Descrpton of a set of objects can be presented n the form of the X matrx as followng: x11 x12... x1 m x21 x22... x2m X (1) xn1 xn2... xnm where: x j means the j-th characterstcs of the -th object ( = 1, 2,..., n; j = 1, 2,..., m). In order to unfy the varables, normalzaton of the characterstcs s made by ther standardzaton accordng to the formula: z j ( x x j S j j ), (j=1,2,,m) (2) As a result, a transformaton matrx (Z) of standardzed values of the characterstcs s obtaned: z11 z12... z1 m z21 z22... z2m Z (3) zn1 zn2... znm where: z j s a standardzed value of x j. The matrx s used to determne the pattern of development,.e. an abstract object (gmna) P 0 wth standardzed coordnates z 01, z 02,..., z 0j, where z 0j = max {z j }, f Z j s a stmulant or z 0j = mn {z j }, f Z j s destmulant. It follows that the pattern s a hypothetcal gmna wth the best values of the observed varables. Then, dstance from the pattern for each P object (gmna) s determned accordng to the formula: where: D o o m j1 o d D o 1 ( = 1,2,,n) (4) Do 2 ( z z ) (5) (dstance of -th object from P o object) D D 2S (6) j o oj

5 Iwona Pomanek 222 D n 1 o n D o 1 (7) n 1 2 S o n ( Do Do ) (8) 1 In ths way, synthetc ratngs are determned for each gmna. The taxonomc measure (d ) takes values from the nterval [0, 1]. However, f varables values are outstandng n relaton to a whole populaton, they do not belong to the typcal range of varaton (mean - standard devaton, mean + standard devaton) and the measure s value can be negatve.the more the characterstcs of a gmna are smlar to the pattern, the level of ts development s hgher. The classfcaton of gmnas accordng to a level of development uses two parameters: arthmetc mean and standard devaton. Dvson nto the followng class ntervals s made: Class 1 (hgh level of development) d d s, groupng gmnas whch dstances from the pattern exceed the value of d s d, Class 2 (medum level of development) d sd d d s whch dstances from the pattern ranges n d s, d s, Class 3 (low level of development) d from the pattern does not exceed the value of where: d Hellwg s synthetc measure value; d d d s d d d d, groupng gmnas, groupng gmnas whch dstances d, s d d arthmetc mean of d ; s d standard devaton of d. The analyss requres adopton of data at the LAU-2 level, whch s at a communal level n Poland (n lterature: gmnas, gmnas, muncpaltes). Unfortunately, a great problem s data avalablty, because the Central Statstcal Offce does not collect some potentally sgnfcant data for LAU 2 level. These are avalable only at the LAU-1 level (powats) or even the NUTS-3 level of vovodshps (n lterature also: provnces, regons), so t would be dffcult or even mpossble to adopt them to dfferentate the level of a partcular phenomenon n gmnas. The analyss uses data as of (Local Data Bank, Central Statstcal Offce, Poland, avalable at: access: 03/23/2014). Research results The frst measure was constructed usng three varables: populaton densty, brth rate and demographc burden. These are fundamental demographc ndcators, havng mpact on a local development level. On the one hand, number of populaton, related to the nhabted area, requres adequate amount of communal servces and fnancng (.e. schools, transport, safety etc.). On the other hand, the populaton partcpates n local budget drectly by payng local taxes and fees and ndrectly, n the form of some share of PIT (Personal Income Tax) and CIT (Corporate Income Tax) transferred from the natonal level. The brth rate, as a result of brths and deaths as well as populaton of post workng-age per 100 populaton of workngage, nform of postve or negatve tendences n local socety. The Class 1 (Table 2, Fg. 2) s domnated by gmnas from Małopolske Vovodshp: Tymbark, Nawojowa, Olkusz, Bały Dunajec, Kamonka Welka, Lmanowa and Raba

6 Iwona Pomanek 223 Wyżna. Two other gmnas (Węgerska Górka, Glowce) are stuated n Śląske Vovodshp. On the other hand, n the Class 3, three gmnas from Mazowecke Vovodshp have been ncluded: Garbatka-Letnsko, Grabów nad Plcą, Odrzywół. Moreover, there are two Śląske gmnas (Rajcza, Ujsoły) as well as Csna from Podkarpacke Vovodshp, Kełczygłów from Łódzke Vovodshp and Kleszczele from Podlaske Vovodshp. Table 2. Measure 1 extreme classes of demographc development Rank Gmna Value Rank Gmna Value Class 1 Class 3 1 Tymbark (r) Csna (r) Nawojowa (r) Garbatka-Letnsko (r) Olkusz (u-r) Grabów nad Plcą (r) Glowce (r) Rajcza (r) Bały Dunajec (r) Ujsoły (r) Kamonka Welka (r) Kełczygłów (r) Lmanowa (r) Odrzywół (r) Raba Wyżna (r) Kleszczele (u-r) Węgerska Górka (r) Note: r rural gmna, u-r urban-rural gmna Source: Author s calculaton based on CSO LDB nformaton. Although, as noted above, the Hellwg s measure takes values from 0 to 1, the value calculated for Kleszczele s negatve. It s a symptom of sgnfcant standng out n terms of the analysed characterstcs compared to other unts. The value of X1.2 varable s the lowest, whereas the value of and X1.3 s the hghest n the studed group of gmnas. The outstandng values do not belong to the typcal range of varaton (mean - standard devaton, mean + standard devaton). In 2012, the populaton densty amounted to 19 people per square klometre, whereas the mean for the gmnas amounted to as much as 108 people (medan 101). Negatve natural ncrease stood at -13 (mean 1.8, medan 2.1) and the proporton of populaton of over workng age per 100 populaton of workng age amounted to 59.8% (mean 25.5%, medan 24%). The Measure 2 combnes three varables: proporton of regstered unemployed n the workng-age populaton, total balance of mgraton and proporton of chldren aged 3-5 partcpatng n preschool educaton. There s no nformaton (for the LAU-2 level) on unemployment rate, so the best avalable characterstcs of local labour market concerns number of unemployed nhabtants n relaton to all nhabtants of workng-age. The varables requre specal actveness of local government takng care for unemployed, facltatng and promotng self-employment, and fnally promotng the gmna as an attractve area of lvng, rasng chldren and runnng economc actvty.

7 Iwona Pomanek 224 Fg. 2. Measure 1 classes of demographc development Source: Author s calculaton based on CSO LDB nformaton. The Class 1 ncludes as many as 13 gmnas: 5 from Śląske Vovodshp (Koszarawa, Czernchów, Ślemeń, Śwnna, Radzechowy-Weprz), 6 from Małopolske Vovodshp (Łapsze Nżne, Trzebna, Wśnowa, Czarny Dunajec, Pcm, Lubeń) and 2 from Łódzke Vovodshp (Brąszewce, Galewce). Another 8 gmnas have been classfed nto the Class 3: 3 from Małopolske Vovodshp (Gródek nad Dunajcem, Lmanowa, Olkusz), 4 from Podkarpacke Vovodshp (Czarna, Krempna, Nozdrzec, Domaradz) as well as Odrzywół (Mazowecke Vovodshp). Next 5 gmnas stuated on the bottom of the rankng have been charactersed by partcularly low values of the varables (Table 3). Ths tme, as many as 3 gmnas have receved negatve values of Hellwg s measure. In 2012, Olkusz was charactersed by the lowest mgraton balance, amountng to 195 people (mean -2, medan -1). In Domaradz the rank has been a result of the hghest proporton of regstered unemployed n the workng-age populaton, whch amounted to 19.7% (mean 10%, medan 0.3%). Fnally, Nozdrzec owes ts measure s value to relatvely hgh proporton of unemployed (17.3%) and low proporton of chldren aged 3-5 partcpatng n pre-school educaton (36.8% compared to mean: 57.8% and medan: 60.3%). Table 3. Measure 2 extreme classes of socal development Rank Gmna Value Rank Gmna Value Class 1 Class 3 1 Koszarawa (r) Gródek nad Dunajcem (r) Czernchów (r) Odrzywół (r) Trzebna (u-r) Lmanowa (r) Ślemeń (r) Czarna (r) Wśnowa (r) Krempna (r) 0.002

8 Iwona Pomanek Brąszewce (r) Nozdrzec (r) Czarny Dunajec (r) Domaradz (r) Pcm (r) Olkusz (r) Galewce (r) Lubeń (r) Radzechowy-Weprz (r) Łapsze Nżne (r) Śwnna (r) Note: r rural gmna, u-r urban-rural gmna Source: Author s calculaton based on CSO LDB nformaton. The spatal dstrbuton of development classes by the Measure 2 s presented n Fg.3. The next measure (3) presents level of local government potental, basng on two characterstcs: proporton of councllors wth unversty degrees and proporton of councllors wth hgh professonal qualfcatons. Moreover, the varables nform of socal nvolvement n local ssues (see: Heffner, Rosner, 2002, pp ). Accordng to studes conducted by A. Rosner and M. Stanny (2007, p. 141) and I. Pomanek (2010), n gmnas of low level of soco-economc development hgh level of educaton of canddates for councllors may not be a decsve factor for the choce. However, t may suggest also less nterest of local populaton n ssues of the gmna, reluctance of better-educated people to st on the board or just a lower level of educaton of people lvng there. Fg. 3. Measure 2 classes of socal development Source: Author s calculaton based on CSO LDB nformaton. The Class 1 ncludes 11 gmnas. 7 gmnas lay n Małopolske Vovodshp (Olkusz, Kroścenko nad Dunajcem, Tymbark, Muszyna, Krynca-Zdrój, Rabka-Zdrój, Gródek nad Dunajcem). Other gmnas belong to followng vovodshps: Podkarpacke (Jasenca Roselna,

9 Iwona Pomanek 226 Zagórz), Śląske (Węgerska Górka) and Mazowecke (Garbatka-Letnsko). On the other hand, the Class 3 ncludes addtonal 7 gmnas from Małopolske Vovodshp (Bukowna Tatrzańska, Zawoja, Czorsztyn, Tokarna, Nawojowa, Spytkowce, Czarny Dunajec). Three more gmnas of low potental of local government lay n followng vovodshps: Mazowecke (Grabów nad Plcą), Łódzke (Kełczygłów) and Podkarpacke (Czarna) see Table 4. Table 4. Measure 3 extreme classes of government potental Rank Gmna Value Rank Gmna Value Class 1 Class 3 1 Olkusz (u-r) Bukowna Tatrzańska (r) Węgerska Górka (r) Zawoja (r) Kroścenko nad Dunajcem (r) Czorsztyn (r) Tymbark (r) Tokarna (r) Muszyna (u-r) Grabów nad Plcą (r) Krynca-Zdrój (u-r) Nawojowa (r) Rabka-Zdrój (u-r) Spytkowce (r) Jasenca Roselna (r) Czarny Dunajec (r) Garbatka-Letnsko (r) Kełczygłów (r) Gródek nad Dunajcem (r) Czarna (r) Zagórz (u-r) Note: r rural gmna, u-r urban-rural gmna Source: Author s calculaton based on CSO LDB nformaton. The spatal dstrbuton of development classes by the Measure 3 s presented n Fg. 4. Fg. 4. Measure 3 classes of government potental Source: Author s calculaton based on CSO LDB nformaton.

10 Iwona Pomanek 227 Important aspect of soco-economc development concerns local fnances, ts dfferentaton, avalablty as well as autonomy of spendng. Therefore the Measure 4 combnes three characterstcs of a gmna: ts own ncome per capta, ts ncome for the EU projects realzaton per capta and ts property nvestment expendture per capta. They llustrate both economc stuaton of a gmna and level of actveness of local government. As the analyss shows, 10 gmnas have been found n the class of hgh development level, ncludng four from Małopolske Vovodshp (Muszyna, Pwnczna-Zdrój, Krynca- Zdrój, Łapsze Nżne), 5 from Śląske Vovodshp (Łękawca, Ślemeń, Czernchów, Negowa, Koszarawa) and Csna from Podkarpacke Vovodshp. The Class 3 ncluded only 5 gmnas: four from Małopolske Vovodshp (Budzów, Lubeń, Kamonka Welka, Raba Wyżna) and Krzywda from Lubelske Vovodshp (Table 5). Table 5. Measure 4 extreme classes of local fnances structure Rank Gmna Value Rank Gmna Value Class 1 Class 3 1 Muszyna (u-r) Budzów (r) Pwnczna-Zdrój (u-r) Lubeń (r) Łękawca (r) Kamonka Welka (r) Ślemeń (r) Raba Wyżna (r) Krynca-Zdrój (u-r) Krzywda (r) Czernchów (r) Csna (r) Negowa (r) Łapsze Nżne (r) Koszarawa (r) Note: r rural gmna, u-r urban-rural gmna Source: Author s calculaton based on CSO LDB nformaton. The spatal dstrbuton of development classes by the Measure 4 s presented n Fg.5. The Measure 5 can be nterpreted as a manfestaton of actvty of both communtes (takng up self-employment, runnng socal actvty) and local authortes (ncentves for foregn nvestors and local entrepreneurs). It aggregates 4 varables: number of natonal economy enttes regstered n REGON per 10,000 populaton, number of natural people runnng economc actvty per 100 workng-age populaton, number of foundatons, assocatons and socal organzatons per 10,000 populaton and fnally number of newly regstered enttes per 10,000 workng-age populaton.

11 Iwona Pomanek 228 Fg. 5. Measure 4 classes of local fnances structure Source: Author s calculaton based on CSO LDB nformaton. The top ten, creatng the Class 1, ncludes two gmnas from Podkarpacke Vovodshp (Csna, Czarna) and as many as 8 gmnas from Małopolske Vovodshp: Krynca-Zdrój, Czorsztyn, Poronn, Kroścenko nad Dunajcem, Koścelsko, Rabka-Zdrój, Ropa, Muszyna (Table 6). Most of them are very popular wth toursts and spas vstors. Table 6. Measure 5 extreme classes of entrepreneurshp development Rank Gmna Value Rank Gmna Value Class 1 Class 3 1 Csna (r) Spytkowce (r) Krynca-Zdrój (u-r) Kluk (r) Czorsztyn (r) Czarny Dunajec (r) Poronn (r) Lpnca Welka (r) Kroścenko nad Dunajcem (r) Krzywda (r) Koścelsko (r) Nozdrzec (r) Rabka-Zdrój (u-r) Łapsze Nżne (r) Ropa (r) Domaradz (r) Czarna (r) Muszyna (u-r) Note: r rural gmna, u-r urban-rural gmna Source: Author s calculaton based on CSO LDB nformaton. On the contrary, the Class 3 groups four gmnas from Małopolske Vovodshp (Spytkowce, Czarny Dunajec, Lpnca Welka, Łapsze Nżne), two from Podkarpacke Vovodshp (Nozdrzec, Domaradz) as well as Kluk (Łódzke) and Krzywda (Lubelske).

12 Iwona Pomanek 229 The spatal dstrbuton of development classes by the Measure 5 s presented n Fg.6. Fg. 6. Measure 5 classes of entrepreneurshp development Source: Author s calculaton based on CSO LDB nformaton. Last measure the Measure 6, nforms about level of techncal nfrastructure development. It concerns two major techncal aspects: avalablty of water supply network and sewerage network. Lterature shows that other characterstcs that can be used n constructon of development rankngs regard qualty of local roads and access to gas supply. However, there s no current data on qualty of local roads avalable for the LAU-2 level. Wth regard to gas supply the network has been stll hghly underdeveloped n rural areas and comparng the small proportons (even fractons) does not make sense. The Class 1 ncludes 6 gmnas from Małopolske Vovodshp (Trzebna, Krynca- Zdrój, Olkusz, Czorsztyn, Muszyna, Poronn), 3 from Śląske Vovodshp (Radzechowy- Weprz, Węgerska Górka, Łękawca) and Garbatka-Letnsko from Mazowecke Vovodshp. On the other hand, the class of low development level covers Glowce (Śląske) and as many as 8 gmnas from Małopolske Vovodshp: Raba Wyżna, Bystra-Sdzna, Zawoja, Pcm, Tokarna, Łabowa, Ropa, Budzów (Table 7). The measure values for Ropa and Budzów have been negatve. In Budzów, the water supply system was used by only 0.6% of the populaton, whle the sewerage system 0.2%. In Ropa these percentages amounted respectvely to 5.3% and 1.1%. In comparson, the average proporton of populaton wth water supply network n all surveyed gmnas amounted to 46.3% (medan 43.3%). Moreover, 35.3% of the populaton had connecton to the sewerage system (medan 33.2%).

13 Iwona Pomanek 230 Table 7. Measure 6 extreme classes of techncal nfrastructure development Rank Gmna Value Rank Gmna Value Class 1 Class 3 1 Trzebna (u-r) Glowce (r) Krynca-Zdrój (u-r) Raba Wyżna (r) Olkusz (u-r) Bystra-Sdzna (r) Czorsztyn (r) Zawoja (r) Radzechowy-Weprz (r) Pcm (r) Węgerska Górka (r) Tokarna (r) Garbatka-Letnsko (r) Łabowa (r) Muszyna (u-r) Ropa (r) Poronn (r) Budzów (r) Łękawca (r) Note: r rural gmna, u-r urban-rural gmna Source: Author s calculaton based on CSO LDB nformaton. The spatal dstrbuton of development classes by the Measure 6 s presented n Fg.7. Fg. 7. Measure 6 classes of techncal nfrastructure development Source: Author s calculaton based on CSO LDB nformaton. Soco-economc development level of agrcultural most problem areas The class of hgh level of development has grouped 9 gmnas from Małopolske Vovodshp (Krynca-Zdrój, Muszyna, Rabka-Zdrój, Trzebna, Czorsztyn, Kroścenko nad Dunajcem, Pwnczna-Zdrój, Tymbark, Koścelsko), 2 from Śląske Vovodshp (Czernchów, Węgerska Górka) and Csna (Podkarpacke).

14 Iwona Pomanek 231 On the other hand, the class of low level of development ncludes 3 Podkarpacke gmnas (Krempna, Nozdrzec, Domaradz), 2 Mazowecke gmnas (Odrzywół, Grabów nad Plcą), 2 Łódzke gmnas (Kluk, Kełczygłów) as well as Krzywda (Lubleske) and Kleszczele (Podlaske). Table 8. Extreme classes of soco-economc development of most problem agrcultural areas Rank Gmna Value Rank Gmna Value Class 1 Class 3 1 Krynca-Zdrój (u-r) Odrzywół (r) Muszyna (u-r) Krzywda (r) Csna (r) Kluk (r) Czernchów (r) Krempna (r) Rabka-Zdrój (u-r) Kełczygłów (r) Węgerska Górka (r) Grabów nad Plcą (r) Trzebna (u-r) Nozdrzec (r) Czorsztyn (r) Kleszczele (u-r) Kroścenko nad Dunajcem (r) Domaradz (r) Pwnczna-Zdrój (u-r) Tymbark (r) Koścelsko (r) Note: r rural gmna, u-r urban-rural gmna Source: Author s calculaton based on the CSO LDB nformaton. Krynca-Zdrój and Muszyna, whch took the two frst places n the fnal rankng, have been ncluded n the classes of hgh development level n four detaled rankngs: 3, 4, 5 and 6. Csna, located on the thrd place, was classfed as the Class 1 twce (Measures 4 and 5), but t also occurred once n the Class 3 (Measure 1). The class of hgh level of development ncluded n at least two rankngs followng gmnas: Czernchów (Measures 2 and 4) Rabka-Zdrój (Measures 3 and 5), Węgerska Górka (Measures 1, 3 and 6), Trzebna (Measures 2 and 6 ), Kroścenko nad Dunajcem (Measures 3 and 5) and Tymbark (Measures 1 and 3). Pwnczna-Zdrój took the 2 nd place n the rankng by the Measure 4 and Koścelsko 6 th place by Measure 5. On the other hand, Czorsztyn occurred twce n the Class 1 (Measures 5 and 6), but once n the Class 3 (Measure 3). Even one presence n the Class 3 has been enough for gmnas: Kluk (Measure 5), Krempna (Measure 2), Kleszczele (Measure 1), to be placed fnally n the class of low level of development. Other gmnas appear n Class 3 twce: Odrzywół Measures 1 and 2; Krzywda Measures 4 and 5; Kełczygłów as well as Grabów nad Plcą Measures 1 and 3; Nozdrzec and Domaradz Measures 2 and 5. The spatal dstrbuton of development classes by the fnal, aggregated measure s presented n Fg. 8.

15 Iwona Pomanek 232 Fg. 8. Classes of soco-economc development of most problem agrcultural areas Source: Author s calculaton based on CSO LDB nformaton. Comparson of varables average values shows that there s a great gap between the hgh-developed gmnas and the low developed ones, especally concernng populaton densty, brth rate, local government potental and fnances as well as entrepreneurshp development (Table 9). Table 9. Comparson of varables average values n the extreme classes of soco-economc development of most problem agrcultural areas Varables Average values Class 1 Class 3 X X X X X X X X X X X X X X X X X Source: Author s calculaton based on CSO LDB nformaton.

16 Iwona Pomanek 233 Conclusons The specfcty of mountan areas affects to a greater extent a declne n farms productvty, reducng ther compettveness sgnfcantly related to freedom of choce of more proftable lnes of producton (Baran, 2008). In such areas wth lmted potental for proper agrcultural producton an mportant role should be played by non-agrcultural functons. The analyss shows that the relatvely hgher level of development s observed n areas whch are attractve for toursts mountanous parts of Śląske, Małopolske and Podkarpacke Vovodshps. Specfcty of Polsh mountan toursm enables recevng toursts throughout the year (see: Ostrowska, 2012; Musał, 2008). It has been benefcal for development of local entrepreneurshp, local employment and keepng young and educated people, whch has a postve effect on rates of mgraton, populaton growth and favourable relaton of populaton of workng-age to populaton of post workng-age. References Babuchowska, K., Ksel, R. (2006), Wpływ samorządu lokalnego na lokalny rozwój gospodarczy na przykładze gmn województwa warmńsko-mazurskego, Acta Sc. Pol. Oeconoma 5(2), Warszawa. Baran, E. (2008), Uwarunkowana przeman gospodark górskej w śwetle procesów ntegracyjnych z UE, Problemy Zagospodarowana Zem Górskch, Issue 55, KZZG PAN. Chrzanowska, M., Drejerska, N., Pomanek, I. (2013), Analza porównawcza pozomu rozwoju gmn wejskch mejsko-wejskch województwa mazoweckego w latach z wykorzystanem metody k-średnch, [n:] Problemy społeczno-ekonomczne w uwarunkowanach ryzyka statystycznej neokreślonośc. Metody modele w rozwoju regonów. Red. Włodzmerz Szkutnk Wydawnctwo Unwersytetu Ekonomcznego w Katowcach, Katowce, pp Czapewsk, K. Ł. (2010), Koncepcja wejskch obszarów sukcesu społeczno-gospodarczego ch rozpoznane w województwe mazoweckm, Studa Obszarów Wejskch, Vol. XXII, IGPZ PAN, Warszawa. Dzemanowcz, W., Swanewcz, P. (ed.) (2007), Gmna pasywna, KPZK PAN, Warszawa. Feltynowsk, M. (2009), Rankng potencjału nnowacyjnego polskch regonów z wykorzystanem mar syntetycznych [n:] Nowakowska A. (ed.): Zdolnośc nnowacyjne polskch regonów. Wydawnctwo Unwersytetu Łódzkego, Łódź. Górka, J., Chmurska, M. (2004), Wykorzystane wybranej metody taksonomcznej do klasyfkacj powatów województwa welkopolskego na podstawe wybranych cech turystyczno-rekreacyjnych, Rocznk Naukowe AWF W Poznanu, Issue 53. Gralak, A. (2005), Pozom konkurencyjnośc wschodnch regonów Polsk na tle zróżncowań mędzyregonalnych, Acta Sc. Pol. Oeconoma, 4(1), Warszawa. Heffner, K., Rosner, A. (2002), Czynnk specyfczne wywerające wpływ na potencjał rozwojowy obszarów wejskch, [n:] Rosner A. (ed.): Wejske obszary kumulacj barer rozwojowych, IRWR PAN, Warszawa. Hellwg, Z. (1968), Zastosowane metody taksonomcznej do typologcznego podzału krajów ze względu na pozom rozwoju oraz zasoby strukturę wykwalfkowanych kadr, Przegląd Statystyczny, No. 4, pp Hellwg, Z. (1981), Welowymarowa analza porównawcza jej zastosowane w badanach welocechowych obektów gospodarczych, [w:] Metody modele matematycznoekonomczne w doskonalenu zarządzana gospodarką socjalstyczną, red. W. Welfe, PWE, Warszawa.

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18 Iwona Pomanek 235 Tarka, D. (2013), Wpływ metody doboru cech dagnostycznych na wynk klasyfkacj obektów na przykładze danych dotyczących ochrony środowska, Prace Naukowe Unwersytetu Ekonomcznego we Wrocławu, No. 279, pp Zmny, A. (2013), Rozwój społeczno-gospodarczy gmn podregonu konńskego [n:] Fmńska-Banaszyk R., Zmny A. (ed.), Doskonalene zarządzana w samorządach lokalnych, Wydawnctwo PWSZ w Konne, Konn, pp